Brain navigation method and device

Abstract

The present application proposes a system for differential recording in real-time navigation, connectable to an electrical lead having at least two electrodes, comprising: at least one electrical lead having a longitudinal axis and a distal end and at least two electrodes for recording electrical signals; a memory configured for storing a differential signal between the at least two electrodes and a reference indication of electrical signals associated with a neural tissue; a processing circuit, wherein the processing circuit: during advancement of the at least one electrical lead, computes in real-time a differential signal between electrical signals recorded from the at least two electrodes, processes the differential signal and the reference indication of electrical signals associated with the neural tissue; and computes an anatomical position of the electrical lead based on the processing, wherein the computing an anatomical position comprises estimating a proximity between at least one of the electrodes or the distal end of the electrical lead and a boundary between anatomical regions.

Description

[0001] This application is a divisional application of the invention patent application filed on July 7, 2017, with application number 2017800456406.

[0002] Cross-citation of related applications

[0003] This application claims priority to U.S. Provisional Patent Application No. 62 / 359,615, filed July 7, 2016; U.S. Provisional Patent Application No. 62 / 370,806, filed August 4, 2016; U.S. Provisional Patent Application No. 62 / 459,415, filed February 15, 2017; and U.S. Provisional Patent Application No. 62 / 459,422, filed February 15, 2017, the contents of which are incorporated herein by reference in their entirety.

[0004] Furthermore, this application claims priority to patent application No. PCT / IL2017 / 050328 filed by the same applicant on March 14, 2017.

[0005] The contents of the above application are incorporated herein by reference in their entirety, as if fully described herein.

[0006] Technical Field and Background Technology

[0007] In some embodiments of the invention, the invention relates to the navigation of electrical leads, and more specifically, but not exclusively, to navigating electrical leads to brain targets.

[0008] US7941202B2 discloses that "simultaneous sampling recordings can be used to improve the speed and accuracy of data acquisition. Electrode arrays capable of simultaneously sampling from the same neuronal region may also detect regions of statistically independent background noise and / or artifacts. Using advanced signal processing techniques such as independent component analysis, these unwanted signals can be identified and removed, thereby improving the signal-to-noise ratio and further enhancing neuronal spike differentiation. This technique can also reveal previously hidden signals within background noise."

[0009] US8532757 discloses that "in some examples, during a programming session following the implantation of IMD16 and leads 20A, 20B in a patient 12, a combination of stimulation electrodes can be selected. For example, during the programming session, bio-computer signals can be sensed within the brain 28 via one or more of electrodes 24, 26. Each sensing electrode combination may include a different subset of one or more electrodes 24, 26. The frequency domain characteristics of each sensed bio-computer signal can be compared with each other and one or more stimulation electrode combinations can be selected based on the comparison. Examples of frequency domain characteristics may include power levels (or energy levels) within a specific frequency band. The power level can be determined based, for example, spectral analysis of the bio-computer signal. Spectral analysis can indicate the frequency distribution of the power contained in the signal based on a limited dataset."

[0010] US8538513 discloses that "a combination of multiple sensing electrodes can be used to sense bioelectrical signals in the brain of a patient. A combination of stimulation electrodes can be selected based on the frequency domain characteristics of the sensed bioelectrical signals to deliver stimulation to the patient to manage the patient's condition. In some examples, the combination of stimulation electrodes is selected based on determining which sensing electrodes are closest to the target tissue site (as indicated by one or more sensing electrodes sensing a biocomputation signal having a relatively highest value in the frequency domain). In some examples, determining which sensing electrodes are closest to the target tissue site may include performing an algorithm using relative values ​​of the frequency domain characteristics." Summary of the Invention

[0011] The present invention aims to provide an automated system for navigating tools to target regions in the brain.

[0012] This application proposes a system for differential recording in real-time navigation, which can be connected to an electrical lead having at least two electrodes, including:

[0013] It has at least one electrical lead with a longitudinal axis and a distal end, and at least two electrodes for recording electrical signals;

[0014] A memory is configured to store reference indications of differential signals between the at least two electrodes and electrical signals associated with neural tissue;

[0015] Processing circuit, wherein the processing circuit:

[0016] During the advance of the at least one electrical lead, the differential signal between the electrical signals recorded from the at least two electrodes is calculated in real time.

[0017] The reference indication for processing the differential signal and the electrical signal associated with the neural tissue; and

[0018] The anatomical location of the electrical conductor is calculated based on the processing, wherein the calculation of the anatomical location includes estimating the proximity between at least one of the electrodes or the distal end of the electrical conduction and the boundary between the anatomical region.

[0019] Preferably, the memory stores an algorithm including at least one of a classifier and a predictor, and wherein the processing circuitry uses the algorithm to analyze the stored differential signal and estimates the proximity based on the results of the analysis.

[0020] Preferably, the at least two electrodes include at least one macroelectrode and / or at least one microelectrode.

[0021] Preferably, it includes an amplifier capable of being electrically connected to the at least one electrical conduction, wherein the at least one amplifier generates the differential signal.

[0022] Preferably, the at least one amplifier generates the differential signal by subtracting the signal recorded by the other of the at least two electrodes from the signal recorded by one of the at least two electrodes.

[0023] Preferably, the system includes a module for processing the differential signal, wherein the processing includes generating the differential signal by subtracting a signal measured by the other electrode of the at least two electrodes from a signal measured by one of the at least two electrodes via the module.

[0024] Preferably, the processing circuitry estimates proximity by estimating the proximity between the distal end of the electrical lead and the selected anatomical target.

[0025] Preferably, the boundary includes one or more of the following: the dorsal boundary of the subthalamic nucleus, the ventral boundary of the subthalamic nucleus, the boundary between the subthalamic nucleus and the substantia nigra reticularis, the boundary between the striatum and the outer part of the globus pallidus, the boundary between the outer part of the globus pallidus and the inner part of the globus pallidus, the boundary between the subdomains of the subthalamic nucleus, or the ventral boundary of the inner part of the globus pallidus.

[0026] Preferably, the electrical signal includes a local field potential, and the differential signal includes a differential LFP.

[0027] Preferably, the processing circuit calculates at least one of the root mean square (RMS), normalized RMS (NRMS), and power spectral density (PSD) values ​​from the differential signal.

[0028] Preferably, it includes:

[0029] User interface circuitry

[0030] The processing circuitry sends a signal to the user interface circuitry to generate a user-detectable signal based on the estimated proximity.

[0031] Preferably, the at least two electrodes are axially and / or angularly separated on the circumference of at least one electrical conduction for measuring signals from different directions and / or different depths.

[0032] Preferably, the electrical signal is recorded as the at least one electrical lead propagates through the nerve tissue.

[0033] Preferably, the stored electrical signal includes a differential LFP signal and / or a MER signal.

[0034] Preferably, the processing circuit is configured to calculate the β-band power oscillation and estimate the proximity based on the calculation result.

[0035] Preferably, the processing circuit is configured to calculate the power band in the frequency range of 5-300Hz, and estimate the proximity based on the calculation results.

[0036] Preferably, the memory stores propulsion parameters, and the system comprises:

[0037] An electric motor is functionally connected to the leads and the processing circuitry; wherein the control circuitry is configured to calculate a desired propulsion parameter value based on the estimated proximity and using the stored propulsion parameters, and to signal the electric motor to propel the electric leads according to the desired propulsion parameter value.

[0038] Preferably, the processing circuit modifies the recording rate of the electrical signal based on the estimated proximity.

[0039] Preferably, the processing circuit increases the recording rate as it approaches the boundary.

[0040] Preferably, the processing circuit estimates the proximity online, and the online estimation includes providing an estimate of the time taken for the lead to advance to a maximum distance of 20 micrometers.

[0041] Preferably, the memory circuit stores at least one functional tissue map, the functional tissue map including anatomical data and reference indications of electrical signals associated with the anatomical data, and wherein the processing circuit estimates the proximity based on a comparison between the recorded electrical signals and the functional tissue map.

[0042] Preferably, the electrical leads include deep brain stimulation (DBS) leads, and wherein the at least two electrodes are axially separated on the at least one electrical lead and configured to deliver DBS therapy.

[0043] This application also proposes a system for differential recording in real-time navigation, which can be connected to an electrical lead having at least two electrodes, including:

[0044] It has at least one electrical lead with a longitudinal axis and a distal end, and at least two electrodes for recording electrical signals;

[0045] A memory is configured to store reference indications of differential signals between the at least two electrodes and electrical signals associated with neural tissue;

[0046] Processing circuit, wherein the processing circuit:

[0047] During the advance of the at least one electrical lead, the differential signal between the electrical signals recorded from the at least two electrodes is calculated in real time.

[0048] The reference indication for processing the differential signal and the electrical signal associated with the neural tissue; and

[0049] The anatomical location of the electrical leads is calculated based on the processing, wherein the calculation of the anatomical location includes estimating the proximity between the distal end of the electrical leads and a selected anatomical target.

[0050] This application also proposes a system for navigating electrical leads to selected brain targets, comprising:

[0051] The electrical leads include at least two electrodes, wherein the shape and size of the electrical leads are adapted to be inserted through brain tissue along a selected insertion trajectory;

[0052] A memory circuit, wherein the memory circuit stores propulsion parameters and electrical signals recorded by the at least two electrodes;

[0053] An electric motor, which is functionally connected to the lead;

[0054] A processing circuit, electrically connected to the motor, wherein the processing circuit is configured to estimate the location of the electrical leads within the brain tissue online, calculate a desired propulsion parameter value using the stored propulsion parameters, and signal the electric motor to propel the electrical leads according to the desired propulsion parameter value.

[0055] This application also proposes a method for estimating the position of electrical leads along a selected insertion trajectory, comprising:

[0056] By applying machine learning algorithms to the stored electrical signals, anatomical regions are associated with the stored electrical signals;

[0057] A functional organization map is generated based on the results of the application.

[0058] Select an insertion trajectory that passes through the anatomical region;

[0059] The functional tissue map is matched to the selected trajectory by matching the anatomical regions of the functional tissue map to the anatomical regions along the insertion trajectory;

[0060] The position of the electrical leads along the insertion trajectory is estimated using the electrical signals recorded by the leads and the functional organization map.

[0061] This application also proposes a method for estimating the proximity between an electrical lead having at least two electrodes and a selected brain target while advancing the electrical lead toward the selected brain target, comprising:

[0062] Electrical signals are recorded by the at least two electrodes during the propulsion process;

[0063] The recorded signal is analyzed along the insertion trajectory using a reference indicator of the stored electrical signals associated with the tissue;

[0064] Based on the results of the analysis, the proximity between the distal end of the electrical lead and the selected brain target is estimated.

[0065] This application also proposes a method for analyzing electrical signals recorded by electrical leads while continuously advancing the leads to a selected brain target along a selected insertion trajectory, comprising:

[0066] During the continuous propulsion, electrical signals are recorded by the at least two electrodes;

[0067] The recorded electrical signals are analyzed while the leads continue to advance toward the selected brain target.

[0068] This application also proposes a method for generating a functional organization map for navigation to brain targets, comprising:

[0069] Provides an initial map indicating the anatomical features of the brain;

[0070] Data is collected from external resources, wherein the data includes electrical signals;

[0071] At least one machine learning algorithm and the collected data are applied to the initial map;

[0072] A predictive functional organization map is generated based on the results of the application.

[0073] The predicted functional tissue map includes reference indicators of electrical signals associated with anatomical brain regions.

[0074] This application also proposes a method for detecting human consciousness during navigation of an electrical lead, including at least one electrode, through brain tissue along a selected insertion trajectory to a selected brain target in the human brain, comprising:

[0075] During the propulsion process, electrical signals are recorded by the at least one electrode;

[0076] The recorded signals are analyzed using a reference indicator of the storage of electrical signals associated with at least one state of consciousness of the person;

[0077] The state of consciousness of the person is detected based on the results of the analysis.

[0078] The following are some examples of embodiments of the present invention:

[0079] Example 1. A system for differential recording, connectable to an electrical lead having at least two electrodes, comprising:

[0080] The lead has a distal end;

[0081] At least one amplifier electrically connected to the at least two electrodes, wherein the at least one amplifier subtracts the signal recorded by the other of the at least two electrodes from the signal recorded by one of the at least two electrodes to generate a differential signal;

[0082] A memory configured to store the differential signals and reference indications of electrical signals associated with neural tissue;

[0083] A processing circuit for detecting anatomical location, wherein the processing circuit calculates the anatomical location of the electrical leads based on processing of the differential signal and the reference indication of the electrical signal associated with the neural tissue.

[0084] Example 2. The system according to Example 1, wherein the memory stores an algorithm including at least one of a classifier and a predictor, and wherein the processing circuitry uses the algorithm to analyze the stored differential signal and calculates the anatomical location of the electrical leads based on the results of the analysis.

[0085] Example 3. The system according to Example 1, wherein the at least two electrodes include at least one macroelectrode.

[0086] Example 4. The system according to Example 1, wherein the at least two electrodes include at least one microelectrode.

[0087] Example 5. The system according to Example 1, wherein the processing circuitry calculates the anatomical location by calculating whether the distal end of the electrical lead has crossed the boundary between the two anatomical regions.

[0088] Example 6. The system according to Example 1, wherein the processing circuitry calculates the anatomical location by estimating the proximity between the distal end of the electrical leads and the selected anatomical target.

[0089] Example 7. The system according to Example 1, wherein the processing circuitry calculates the anatomical location by estimating the proximity between at least one of the electrodes or the distal end of the electrical leads and the boundary between the anatomical region.

[0090] Example 8. The system according to Example 1, wherein the electrical signal includes a local field potential (LFP) and the differential signal includes a differential LFP.

[0091] Example 9. The system according to Example 1, wherein the processing circuit calculates at least one of the root mean square (RMS), normalized RMS (NRMS), and power spectral density (PSD) values ​​from the differential signal.

[0092] Example 10. The system according to Example 1 includes:

[0093] User interface circuitry

[0094] The processing circuitry signals the user interface circuitry to generate a user-detectable signal upon detection of the anatomical location.

[0095] Example 11. The system according to any one of Examples 1 to 10, wherein the neural tissue includes brain tissue or spinal cord tissue.

[0096] Example 12. The system according to Example 1 includes a module for processing the reference indication of electrical signals associated with neural tissue.

[0097] Example 13. A method for estimating the location of electrical leads along a selected insertion trajectory, comprising:

[0098] By applying machine learning algorithms to the stored electrical signals, anatomical regions are associated with the stored electrical signals;

[0099] A functional organization map is generated based on the results of the application.

[0100] Select an insertion trajectory that passes through the anatomical region;

[0101] The functional tissue map is matched to the selected trajectory by matching the anatomical regions of the functional tissue map to the anatomical regions along the insertion trajectory;

[0102] The position of the electrical leads along the insertion trajectory is estimated using the electrical signals recorded by the electrical leads and the functional organization map.

[0103] Example 14. A method for delivering electrical stimulation therapy to a selected target, comprising:

[0104] The electrical conduction, comprising at least two electrodes, is advanced through the tissue to the selected target;

[0105] During the propulsion process, electrical signals from the tissue are recorded by the at least two electrodes;

[0106] The recorded electrical signal is used to determine whether the electrical lead has reached the selected target;

[0107] Electrical stimulation therapy is delivered to the selected target via at least one of the at least two electrodes of the electrical leads.

[0108] Example 15. The method according to Example 14, wherein the electrical stimulation therapy is chronic electrical stimulation therapy.

[0109] Example 16. The method according to Example 14, wherein the at least two electrodes comprise at least one microelectrode or at least one macroelectrode.

[0110] Example 17. The method according to Example 14, wherein the recorded electrical signal is a differential LFP signal and / or a MER signal.

[0111] Example 18. The method according to Example 17, comprising: calculating an RMS value and / or power spectral density from the recorded electrical signal, wherein the determination includes determining, based on the result of the calculation, that the electrical conduction reaches the selected target.

[0112] Example 19. The method according to Example 17 includes calculating a ratio between one or more power bands below 50 Hz and one or more power bands above 75 Hz from the recorded electrical signal, and wherein the determination includes determining, based on the result of the calculation, that the electrical conduction reaches the selected target.

[0113] Example 20. The method according to Example 17 includes calculating a power band in a frequency range of 5-300 Hz, and wherein the determination includes determining, based on the result of the calculation, that the electrical conduction reaches the selected target.

[0114] Example 21. The method according to Example 14 or 15, wherein the selected target includes at least one of the subthalamic nucleus (STN), the globus pallidus interior (GPi), the globus pallidus exterior (GPe), the ventral medial thalamus (VIM) nucleus, the thalamus, the basal ganglia nucleus, the hippocampal fornix, and the pontine nucleus (PPN).

[0115] Example 22. A method for navigating electrical leads toward brain regions, comprising:

[0116] The electrical leads, comprising at least two electrodes, are advanced through brain tissue.

[0117] Electrical signals are recorded by the at least two electrodes during the propulsion process;

[0118] The boundary transition between two anatomical regions is detected based on the recorded electrical signals.

[0119] Example 23. The method according to Example 22, wherein the at least two electrodes comprise at least one microelectrode or at least one macroelectrode.

[0120] Example 24. The method according to Example 22, wherein the recorded electrical signal is a differential LFP signal and / or a MER signal.

[0121] Example 25. The method according to Example 24 includes calculating an RMS value and / or power spectral density based on the recorded electrical signal, and wherein the detection includes detecting the boundary transition between two regions based on the result of the calculation.

[0122] Example 26. The method according to Example 24 includes calculating a ratio between one or more power bands below 50 Hz and one or more power bands above 75 Hz from the MER signal, and wherein the detection includes detecting the boundary transition between the two regions based on the result of the calculation.

[0123] Example 27. The method according to Example 24 includes calculating a power band in a frequency range of 5-300 Hz, and wherein the detection includes detecting the boundary transition between two regions based on the result of the calculation.

[0124] Example 28. The method according to Example 22 or 23, wherein the detection includes detecting crossing of the ventral boundary of the STN or the boundary between the STN and the SNr.

[0125] Example 29. The method according to Example 22 or 23, wherein the detection includes detecting crossing of the boundary between the striatum and Gpe or the boundary between Gpe and Gpi.

[0126] Example 30. The method according to Example 22 or 23 includes:

[0127] When the boundary transition is detected, a user-detectable indication is delivered.

[0128] Example 31. A method for navigating an electrical lead having at least two electrodes to a selected brain target, comprising:

[0129] The insertion is advanced through brain tissue along a selected insertion trajectory, including electrical leads with at least two electrodes.

[0130] Electrical signals are recorded by the at least two electrodes during the propulsion process;

[0131] The recorded signals are analyzed along the insertion trajectory using a reference indicator of stored electrical signals associated with the tissue;

[0132] Based on the results of the analysis, the proximity between the distal end of the electrical lead and the selected brain target is estimated.

[0133] Example 32. The method according to Example 31, wherein the at least two electrodes include at least one microelectrode.

[0134] Example 33. The method according to Example 31, wherein the at least two electrodes include at least one macroelectrode.

[0135] Example 34. The method according to Example 31 or 32, wherein the recorded electrical signal includes LFP and / or MER.

[0136] Example 35. The method according to Example 31 or 32 includes:

[0137] The propulsion parameters are adjusted based on the estimated proximity.

[0138] Example 36. A system for navigating electrical leads to selected brain targets, comprising:

[0139] The electrical leads include at least two electrodes, wherein the shape and size of the electrical leads are adapted to be inserted through brain tissue along a selected insertion trajectory;

[0140] A memory circuit, wherein the memory circuit stores propulsion parameters and electrical signals recorded by the at least two electrodes;

[0141] An electric motor is functionally connected to the aforementioned conductor;

[0142] A processing circuit, electrically connected to the motor, wherein the processing circuit is configured to estimate the location of the electrical leads within the brain tissue online, calculate a desired propulsion parameter value using the stored propulsion parameters, and signal the electric motor to propel the electrical leads according to the desired propulsion parameter value.

[0143] Example 37. The system according to Example 36, wherein the online estimation includes providing an estimate of the time taken for the lead to advance to a maximum distance of 20 μm.

[0144] Example 38. The system according to Example 36, wherein the memory circuitry stores at least one functional tissue map, the functional tissue map including anatomical data and reference indications of electrical signals associated with the anatomical data, and wherein the processing circuitry controls the advancement of the leads based on a comparison between the recorded electrical signals and the functional tissue map.

[0145] Example 39. The system according to Example 36, wherein the propulsion parameters include at least one of propulsion speed, propulsion duration, propulsion step length, and propulsion number.

[0146] Example 40. The system according to Example 36, wherein the processing circuit controls the motor to continuously advance the lead along the selected insertion trajectory, the motor having a maximum delay of 10 seconds.

[0147] Example 41. The system according to Example 36, wherein the memory circuit stores a predicted functional organization map, and wherein the processing circuit adjusts the advance of the leads based on the stored functional organization map.

[0148] Example 42. The system according to Example 41, wherein the at least two electrodes record electrical signals of brain tissue, and wherein the processing circuitry adjusts the advancement of the leads based on a comparison between the recorded electrical signals and the predicted functional tissue map.

[0149] Example 43. The system according to Example 42, wherein if the position of the lead is not along the selected insertion trajectory, the processing circuit signals the motor to stop the advance of the lead.

[0150] Example 44. The system according to Example 42, wherein if the lead passes through the selected brain target, the processing circuit signals the motor to retract the lead.

[0151] Example 45. The system according to Example 42, wherein if the lead has reached the selected brain target, the processing circuit signals the motor to stop the advance of the lead.

[0152] Example 46. The system according to Example 36, wherein when the lead enters the selected brain target, the processing circuit signals the motor to adjust the advance speed of the lead.

[0153] Example 47. The system according to Example 46, wherein when the lead leaves the selected brain target, the processing circuit signals the motor to change the propulsion direction.

[0154] Example 48. The system according to Example 46, the system including a sensor for measuring the value of at least one propulsion parameter of the lead.

[0155] Example 49. The system according to Example 48, wherein the memory stores a desired range of propulsion parameter values, and wherein if the measured value is not within the range of propulsion parameter values, the processing circuit signals the motor to stop the propulsion of the lead.

[0156] Example 50. A method for navigating electrical leads to selected brain targets, comprising:

[0157] Advance at least two electrical leads along substantially parallel insertion trajectories, each lead comprising at least two electrodes;

[0158] Electrical signals are recorded through the at least two electrodes;

[0159] The transition between the two brain regions is determined based on the recorded electrical signals.

[0160] Example 51. According to the method of Example 50, the distance between the substantially parallel insertion trajectories is at least 0.5 mm.

[0161] Example 52. The method according to Example 50, wherein the at least two electrodes comprise at least one microelectrode or at least one macroelectrode.

[0162] Example 53. The method according to Example 50, wherein the at least two electrodes comprise at least two macroelectrodes.

[0163] Example 54. The method according to Example 52 or 53, wherein the recorded electrical signal includes a MER signal and / or an LFP signal.

[0164] Example 55. A method for analyzing electrical signals recorded by electrical leads while simultaneously advancing the leads to selected brain targets, comprising:

[0165] The electrical leads, including at least two electrodes, are continuously advanced to the selected brain target along the selected insertion trajectory;

[0166] During the continuous propulsion, electrical signals are recorded by the at least two electrodes;

[0167] The recorded electrical signals are analyzed while the leads continue to advance toward the selected brain target.

[0168] Example 56. The method according to Example 55, wherein the lead is continuously advanced by constantly activating a motor connected to the lead.

[0169] Example 57. The method according to Example 55, wherein continuous advancement comprises continuously advancing the lead by gradually moving the lead with a motor until explicitly stopped by a user or computer command.

[0170] Example 58. The method according to Example 55, wherein the analysis includes analyzing the recorded electrical signal with a delay, allowing the electrical leads to advance to a maximum distance of 20 μm before generating the analysis results.

[0171] Example 59. A method for navigating electrical leads along a selected trajectory, comprising:

[0172] Provides a state transition map adjusted to the selected trajectory, including a reference indication of the stored electrical signals associated with each state along the selected trajectory;

[0173] The electrical leads are advanced along the selected trajectory;

[0174] During the propulsion process, electrical signals are recorded through at least one electrode of the electrical leads;

[0175] The location of the distal end of the electrical lead is estimated using the state transition map;

[0176] Instructions are delivered to the user based on the results of the estimation.

[0177] Example 60. The method according to Example 59, wherein the state transition map includes a reference indication of an electrical signal associated with a boundary between two adjacent states along the selected trajectory, and wherein the estimation includes using the state transition map to estimate the boundary crossing of the electrical leads between the two adjacent states.

[0178] Example 61. A method for generating a functional organization map for navigation to brain targets, comprising:

[0179] Provides an initial map indicating the anatomical features of the brain;

[0180] Data is collected from external resources, wherein the data includes electrical signals;

[0181] At least one machine learning algorithm and the collected data are applied to the initial map;

[0182] A predictive functional organization map is generated based on the results of the application.

[0183] The predicted functional tissue map includes reference indicators of electrical signals associated with anatomical brain regions.

[0184] Example 62. The method according to Example 61, wherein the collected data includes expert-labeled data.

[0185] Example 63. The method according to Example 61 or 62, wherein the at least one machine learning algorithm comprises at least one of a dynamic Bayesian network, an artificial neural network, a deep learning network, a structured support vector machine, a gradient boosting decision tree, and a long short-term memory (LSTM) network.

[0186] Example 64. The method according to Example 61 includes:

[0187] During the navigation, the predicted functional organization map is updated based on the electrical signals recorded by the electrical leads during the navigation of the electrical leads.

[0188] Example 65. A method for detecting human consciousness during electrical lead navigation to a selected brain target in the human brain, comprising:

[0189] The insertion trajectory is used to advance through brain tissue, including electrical leads with at least one electrode.

[0190] During the propulsion process, electrical signals are recorded by the at least one electrode;

[0191] The recorded signals are analyzed using a reference indicator of the storage of electrical signals associated with at least one state of consciousness of the person;

[0192] The state of consciousness of the person is detected based on the results of the analysis.

[0193] Example 66. The method according to Example 65, wherein the electrical signal includes an LFP and / or a MER signal.

[0194] Example 67. The method according to Example 65 includes calculating the spectral power density from the electrical signal and analyzing the calculated spectral power density using a stored spectral power density associated with at least one state of consciousness.

[0195] Example 68. The method according to Example 65, wherein the analysis includes analyzing the signal of the record using an algorithm that includes at least one of a classifier and a predictor.

[0196] Example 69. The method according to Example 65, wherein the at least one electrode comprises at least one macroelectrode.

[0197] Example 70. The method according to Example 65, wherein the at least one electrode comprises at least one microelectrode.

[0198] According to one embodiment of the present invention, a method for real-time mapping during surgery for the transition between the subthalamic nucleus (STN) and different regions in the brain, the method comprising the steps of: (i) inserting one or more electrodes into the brain according to a predetermined insertion trajectory; (ii) recording readings of the one or more electrodes; (iii) calculating multiple characteristics of the readings recorded along at least a portion of the insertion trajectory; and (iv) using an algorithm based on at least a portion of the readings of the one or more electrodes and based on the calculated characteristics to detect the transition between the STN and different regions in the brain.

[0199] Preferably, the characteristics include at least one of power spectral density analysis values ​​and root mean square (RMS) values. More preferably, the algorithm is a hidden Markov model (HMM).

[0200] Preferably, power spectrum analysis is performed in the 100-150 Hz frequency band. Furthermore, power spectrum analysis is performed in the 5-25 Hz frequency band. Additionally, power spectrum analysis is performed in both the 5-25 Hz and 100-150 Hz frequency bands.

[0201] According to an embodiment of the invention, the method for real-time mapping during surgery for the transition between the subthalamic nucleus (STN) and different regions in the brain further includes the step of calculating the ratio of high-frequency power to low-frequency power for detecting the transition between the STN and different regions in the brain.

[0202] Preferably, high-frequency power is measured in the 100-150Hz frequency band, and low-frequency power is measured in the 5-25Hz frequency band.

[0203] Preferably, the algorithm is executed to detect a direct transition from STN to SNr or a transition between STN and white matter (WM).

[0204] According to one embodiment of the invention, the method for real-time mapping during surgery for the transition between the subthalamic nucleus (STN) and different regions in the brain further includes a support vector machine (SVM) analysis step for detecting the transition between the STN and different regions in the brain.

[0205] The following are some additional examples of some embodiments of the present invention:

[0206] Example 1. A method for real-time navigation of an electroencephalogram (EEG) lead, comprising: transmitting an electrical lead to the brain, the electrical lead including at least two macroelectrodes having a predetermined axial spacing between the at least two macroelectrodes; advancing the electrical lead to an estimated location in the brain toward a target region; and during the advancement: obtaining a differential local field potential (LFP) between any pair of at least two macroelectrodes; and determining the boundary location of the target region relative to the at least two macroelectrodes based on the difference and the predetermined axial spacing.

[0207] Example 2. The method according to Example 1, wherein the at least two macroelectrodes are characterized by having a diameter greater than about 10 μm. 2 The contact area.

[0208] Example 3. The method according to any one of Examples 1-2 further includes stimulating the brain using at least one of the at least two macroelectrodes.

[0209] Example 4. The method according to any one of Examples 1-3, wherein an EEG lead is used for implantation.

[0210] Example 5. The method according to any one of Examples 1-4, wherein the target area is the subthalamic nucleus.

[0211] Example 6. The method according to any one of Examples 1-4, wherein the target area is a pale sphere.

[0212] Example 7. The method according to any one of Examples 1-4, wherein the target area is the dorsolateral oscillatory region (DLOR) of the subthalamic nucleus.

[0213] Example 8. The method according to any one of Examples 1-4, wherein the target area is the thalamus.

[0214] Example 9. The method according to any one of Examples 1-8, wherein the determination includes calculating the root mean square value of the differential LFP.

[0215] Example 10. The method according to any one of Examples 1-9, wherein the determination includes calculating the power spectral density value of the differential LFP.

[0216] Example 11. The method according to any one of Examples 1-10, wherein the record is used as a biological marker of pathological brain function.

[0217] Example 12. The method according to any one of Examples 1-11, wherein the advancement is performed automatically.

[0218] Example 13. The method according to any one of Examples 1-12, wherein obtaining and determining are performed automatically.

[0219] Example 14. The method according to any one of Examples 12-13, wherein when determining the boundary transition, the advance step size is reduced by at least 10%.

[0220] Example 15. The method according to any one of Examples 12-13, wherein the speed of advancement is reduced by at least 10% when the boundary transition is determined.

[0221] Example 16. The method according to any one of Examples 1-15, wherein the boundary is determined when at least two macroelectrodes transition into the target region.

[0222] Example 17. The method according to any one of Examples 1-16, wherein the boundary is determined when at least two macroelectrodes transition out of the target region.

[0223] Example 18. The method according to any one of Examples 1-17 further includes repositioning the electrical leads in the target region such that at least two macroelectrodes are inside the target region.

[0224] Example 19. The method according to any one of Examples 1-17 further includes: repositioning the electrical leads in the target region such that at least two macroelectrodes are located inside the target region and at least two macroelectrodes are located outside the target region.

[0225] Example 20. The method according to any one of Examples 1-17 further includes: repositioning the electrical leads in the target region such that at least one macroelectrode is inside the target region and at least one macroelectrode is outside the target region.

[0226] Example 21. The method according to any one of Examples 1-17 further includes: repositioning the electrical leads in the target region such that at least one macroelectrode is located on the dorsal side outside the target region and at least one macroelectrode is located on the ventral side outside the target region.

[0227] Example 22. The method according to any one of Examples 1-21, wherein the differential LFP is derived by subtracting the unipolar signal.

[0228] Example 23. The method according to any one of Examples 1-21, wherein the differential LFP is derived by sensing a bipolar signal.

[0229] Example 24. The method according to any one of Examples 1-23 further includes calibrating a predefined axial interval to detect different local electrical activities and associated remote electrical activities.

[0230] Example 25. A system for real-time navigation of an EEG lead, comprising: an electrical lead including at least two macroelectrodes having a predefined space between the at least two macroelectrodes; an amplifier for recording EEG activity detected by the at least two macroelectrodes; a memory circuit configured to record a differential electric field generated between the at least two macroelectrodes to obtain a difference in local field potentials; and a processing circuit having instructions for determining the boundary position of a target brain region relative to the at least two macroelectrodes based on the difference and the predefined space.

[0231] Example 26. The system according to Example 25 further includes a stimulator for transmitting an electric field to at least one of the at least two macroelectrodes.

[0232] Example 27. The system according to Example 26, wherein at least one of the two macroelectrodes includes a ring.

[0233] Example 28. The system according to any one of Examples 25-27, wherein at least one of the two macroelectrodes includes at least one ring segment.

[0234] Example 29. A system according to any one of Examples 25-28, wherein the leads comprise at least four macroelectrodes, wherein at least two macroelectrodes have a predefined space between them.

[0235] Example 30. The system according to any one of Examples 25-28, wherein the leads comprise at least eight macroelectrodes, wherein at least two macroelectrodes have a predefined space between them.

[0236] Example 31. The system according to any one of Examples 25-28, wherein the leads comprise at least 32 macroelectrodes, wherein at least two macroelectrodes have a predefined space between them.

[0237] Example 32. The system according to any one of Examples 25-31 further includes a reference electrode, and wherein a differential electric field is provided by calculating the difference between at least two unipolar electric fields.

[0238] Example 33. The system according to any one of Examples 25-32 further includes a motor configured to automatically propel the electrical conduction.

[0239] Example 34. The system according to any one of Examples 25-33, wherein the processing circuitry further includes instructions for automatically determining the boundary position.

[0240] Example 35. The system according to Example 34, wherein the processing circuitry is operatively connected to the motor.

[0241] Example 36. The system according to Example 35, wherein the processing circuitry is configured to stop the motor when a boundary position is determined.

[0242] Example 37. The system according to Example 35, wherein the processing circuitry is configured to instruct the motor to advance the lead a predetermined distance when a boundary position is determined.

[0243] Example 38. The system according to Example 35, wherein the processing circuitry is configured to instruct the motor to backtrack the lead a predetermined distance when a boundary position is determined.

[0244] Example 39. A method for automatically guiding a probe to a region of interest in a subject's brain, comprising:

[0245] a. The probe is provided having multiple macro contacts;

[0246] b. Based on the predetermined insertion trajectory, position the probe in the region of interest;

[0247] c. Move the probe to the region of interest;

[0248] d. Record neurophysiological responses by using a probe along a predetermined insertion trajectory;

[0249] e. Calculate multiple predetermined observation elements based on probe-recorded neurophysiological responses;

[0250] f. Implant the probe into the region of interest.

[0251] Unless otherwise defined, all technical and / or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. While similar or equivalent methods and materials to those described herein may be used to practice or test embodiments of the invention, exemplary methods and / or materials are described below. In case of conflict, the patent specification, including definitions, shall prevail. Furthermore, materials, methods, and examples are illustrative only and are not intended to be limiting.

[0252] As those skilled in the art will understand, some embodiments of the present invention can be embodied as systems, methods, or computer program products. Therefore, some embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments (including firmware, resident software, microcode, etc.), or embodiments combining software and hardware aspects, all of which are generally referred to herein as “circuit,” “module,” or “system.” Furthermore, some embodiments of the present invention can take the form of computer program products embodied in one or more computer-readable media having computer-readable program code contained thereon. Implementations of methods and / or systems of some embodiments of the present invention can involve manually, automatically, or a combination thereof performing and / or completing selected tasks. Furthermore, in practical instruments and apparatus according to some embodiments of the methods and / or systems of the present invention, several selected tasks can be implemented by hardware, software, or firmware and / or a combination thereof (e.g., using an operating system).

[0253] For example, hardware for performing a selected task according to some embodiments of the present invention can be implemented as a chip or circuit. As software, the selected task according to some embodiments of the present invention can be implemented as a plurality of software instructions executed by a computer using any suitable operating system. In exemplary embodiments of the present invention, one or more tasks according to some exemplary embodiments of the methods and / or systems described herein are performed by a data processor, such as a computing platform for executing a plurality of instructions. Optionally, the data processor includes volatile memory for storing instructions and / or data and / or non-volatile memory, such as a magnetic hard disk and / or removable media for storing instructions and / or data. Optionally, a network connection is also provided. A display and / or a user input device, such as a keyboard or mouse, are also optionally provided.

[0254] Any combination of one or more computer-readable media can be used in some embodiments of the present invention. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any suitable combination thereof. More specific examples (not an exhaustive list) of computer-readable storage media will include the following: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable optical disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the context of this document, a computer-readable storage medium can be any tangible medium that can contain or store programs for use by or in connection with an instruction execution system, apparatus, or device.

[0255] Computer-readable signal media may include propagated data signals containing computer-readable program code, for example, in baseband or as part of a carrier wave. Such propagated signals may take any of a variety of forms, including, but not limited to, electromagnetic, optical, or any suitable combination thereof. A computer-readable signal medium may be any computer-readable medium that is not a computer-readable storage medium and may communicate, propagate, or transmit programs for use by or in connection with an instruction execution system, apparatus, or device.

[0256] The program code contained on the computer-readable medium and / or the data used therefrom may be transmitted using any suitable medium, including but not limited to wireless, wired, fiber optic cable, RF, or any suitable combination thereof.

[0257] Computer program code used to perform operations of some embodiments of the present invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" programming language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, to use an internet service provider via the internet).

[0258] Some embodiments of the present invention will now be described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to generate a machine, such that the instructions are executed via the processor of the computer or other programmable data processing apparatus to create means for implementing the functions / actions specified in the flowchart illustrations and / or block diagram blocks.

[0259] These computer program instructions may also be stored in a computer-readable medium and may instruct a computer, other programmable data processing apparatus or other device to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of writing including instructions that implement the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0260] Computer program instructions may also be loaded onto a computer, other programmable data processing apparatus or other equipment to cause a series of operational steps to be performed on the computer, other programmable apparatus or other equipment to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide for implementing the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0261] Some of the methods described in this article are typically designed for computers only and may be infeasible or impractical for human experts to perform purely manually. Human experts who wish to perform similar tasks manually (e.g., determining the location of electrical leads in the brain based on recorded electrical signals) may expect to use entirely different methods, such as leveraging expert knowledge and / or the pattern recognition capabilities of the human brain, which are more efficient than manually completing the steps of the methods described herein. Attached Figure Description

[0262] Some embodiments of the invention have been described herein by way of example only, with reference to the accompanying drawings. Reference will now be made in detail to the drawings, and it is emphasized that the details shown are by way of example and for the purpose of illustrative discussion of embodiments of the invention. In this regard, the description taken in conjunction with the drawings will make it clear to those skilled in the art how to practice embodiments of the invention.

[0263] In the attached diagram:

[0264] Figure 1A This is a general flowchart of a navigation process according to some embodiments of the present invention;

[0265] Figure 1B This is a flowchart of a real-time navigation process according to some embodiments of the present invention;

[0266] Figure 1C This is a graph of the average β power peak activity recorded by microelectrode, unipolar macroelectrode peak activity and bipolar macroelectrode peak activity according to some embodiments of the present invention;

[0267] Figure 2 This is an exemplary use of electroencephalogram (EEG) leads according to some embodiments of the present invention;

[0268] Figure 3A -H is an exemplary electrode configuration on a lead according to some embodiments of the present invention; wherein, respectively, Figure 3A -D shows a side view of the leads with alternative macroelectrode configurations, and Figure 3E -H indicates that it has Figure 3A Top view of the leads with alternative macroelectrode configurations for -D;

[0269] Figure 4A -F illustrates exemplary navigation and / or relocation in a target area according to some embodiments of the present invention. Figure 4A , Figure 4B , Figure 4C , Figure 4D , Figure 4E and Figure 4F Examples showing different orientations of the macroelectrode relative to the target boundary;

[0270] Figure 5 This is a block diagram of a system for manual real-time navigation according to some embodiments of the present invention;

[0271] Figure 6A This is a block diagram of a system for automatic real-time navigation according to some embodiments of the present invention;

[0272] Figure 6B This is a block diagram of a processing circuit according to some embodiments of the present invention;

[0273] Figure 7 This is a flowchart of an exemplary processing circuit decision algorithm for automatic navigation according to some embodiments of the present invention;

[0274] Figure 8 This is a flowchart of an exemplary difference calculation algorithm according to some embodiments of the present invention;

[0275] Figure 9A -F is an exemplary graphical representation of two tripolar neural probe recordings according to some embodiments of the present invention, wherein Figure 9A An example illustrates the normalized root mean square. Figure 9B An example is given of the spectrum of peak activity. Figure 9C An example is given of the LFP spectrum. Figure 9D An example is given of the spectrum of peak activity. Figure 9E Examples illustrate LFP and Figure 9F An example illustrates the spectrum recorded by the LFP differential bipolar macroelectrode;

[0276] Figure 10 These are exemplary power spectral density (PSD) along the trajectory and their average spectrum outside and inside the STN, according to some embodiments of the present invention;

[0277] Figure 11 These are exemplary average power (4-35Hz) microelectrode spike activity and differential macroelectrode LFP along the trajectory according to some embodiments of the present invention;

[0278] Figure 12 This is an exemplary population coherence between two parallel recording electrodes according to some embodiments of the present invention;

[0279] Figure 13 These are exemplary predicted internal and external correlation values ​​based on some embodiments of the present invention;

[0280] Figure 14A and 14B These are exemplary normalized root mean square (RMS) and variance ratios of common and independent activities inside and outside the STN, according to some embodiments of the present invention;

[0281] Figure 14C This is a flowchart of a process for detecting the ventral boundary of an STN according to some embodiments of the present invention;

[0282] Figure 15A This is a simplified schematic diagram of a typical trajectory of an electrode targeting the STN during a DBS process according to some embodiments of the present invention;

[0283] Figure 15B This is a simplified illustration of the MER signal along the electrode insertion trajectory according to some embodiments of the present invention;

[0284] Figure 15C This is a simplified state model representing the anatomical structures encountered during microelectrode recording in STN detection according to some embodiments of the present invention;

[0285] Figure 16A This is a simplified illustration of the STN-WM transition in the subthalamic nucleus (STN) of three different patients, based on normalized root mean square (NRMS) analysis and spectral power distribution (PSD) analysis, according to some embodiments of the present invention.

[0286] Figure 16B A simplified illustration showing the STN-SNr transition in three different patients according to NRMS and PSD analysis based on some embodiments of the present invention;

[0287] Figure 17A This is a simplified illustration of the distribution of NRMS in different regions of the brain according to some embodiments of the present invention;

[0288] Figure 17B This is a simplified illustration of PSD as a function of frequency according to some embodiments of the present invention, having linear and logarithmic scale plots in different regions of the brain;

[0289] Figure 17C This is a simplified illustration of the power ratio distribution in different regions of the brain according to some embodiments of the present invention;

[0290] Figure 18 This is a simplified illustration of a linear support vector machine according to some embodiments of the present invention, which defines the decision boundary as a function of two features: NRMS and the power ratio between the STN and SNr regions.

[0291] Figure 19A This is a simplified illustration of typical electrode trajectory NRMS analysis according to some embodiments of the present invention;

[0292] Figure 19B This is a simplified illustration of typical electrode trajectory PSD analysis as a function of the estimated distance to the target (EDT) according to some embodiments of the present invention;

[0293] Figure 19C This is a simplified illustration of the power ratio in a typical electrode trajectory as a function of the estimated distance to the target (EDT) according to some embodiments of the present invention;

[0294] Figure 20 This is a flowchart of a process for generating an updated model for online mapping using a machine learning algorithm, according to some embodiments of the present invention.

[0295] Figure 21-25AThis is a schematic diagram of a lead for differential mapping with different electrode contact rearrangements according to some embodiments of the present invention;

[0296] Figure 25B This is a flowchart of a process for adjusting the electrical lead shift parameters based on a recorded MER / LFP signal, according to some embodiments of the present invention;

[0297] Figure 26 This is a state diagram of the transitional states between different brain regions according to some embodiments of the present invention;

[0298] Figure 27 This is a flowchart of an automatic navigation process performed by a trained system according to some embodiments of the present invention; and

[0299] Figure 28 This is a flowchart of a process for estimating the location of electrical leads in the brain based on stored information, according to some embodiments of the present invention. Detailed Implementation

[0300] In some embodiments of the present invention, the present invention relates to a brain navigation lead, and more specifically, but not exclusively, to a brain navigation lead including macroelectrode contacts and / or a method for analyzing such a brain navigation lead.

[0301] One aspect of some embodiments involves navigating electrical leads to a desired target using differential recording (e.g., bipolar recording or any type of differential recording). In some embodiments, the leads are navigated through neural tissue (e.g., through brain or spinal cord tissue). In some embodiments, differential recording is used to record MER and / or LFP signals. In some embodiments, the leads include two or more electrodes or electrode contacts, such as microelectrodes, macroelectrodes, or any combination of microelectrodes and macroelectrodes. In some embodiments, signals recorded by two or more electrodes are combined by using one electrode as a reference electrode to another electrode. Optionally, when the leads include more than two electrodes, several electrodes are used as references to at least one different electrode. In some embodiments, the reference electrode is the electrode whose recorded electrical signal serves as a baseline for other electrodes. In some embodiments, bipolar or any type of differential recording includes recording MER, LFP, and / or differential LFP signals through two or more electrodes.

[0302] According to some embodiments, two or more electrodes are positioned on the outer surface of the distal end of the leads. Optionally, the leads are also used for stimulation, such as DBS stimulation upon reaching a desired target. In some embodiments, the electrodes have the same axial position on the outer surface of the leads and different angular positions on the circumference of the leads. Alternatively, the electrodes have the same angular position but different axial positions along the circumference of the leads. In some embodiments, in this electrode arrangement, the electrodes face the same angular direction but are located at different distances from the end of the leads. In some embodiments, the electrodes are positioned at different axial and different angular positions on the probe circumference. In some embodiments, the electrodes are positioned with different geometric arrangements on the lead circumference.

[0303] According to some exemplary embodiments, two or more electrodes are connected to one or more differential amplifiers, for example, to allow bipolar recording or other types of differential recording. In some embodiments, the one or more differential amplifiers are used to amplify the differential signal between the digitized signals of the two or more electrodes. In some embodiments, the differential amplifier subtracts from and amplifies a reference signal recorded by at least one electrode on the electrical leads from the signal recorded by the other electrodes. In some embodiments, subtracting the reference signal allows for the reduction of noise from the other recorded signals.

[0304] According to some embodiments, amplification is performed on an analog signal. In some embodiments, digitization is performed after amplification. In some embodiments, the signal is subtracted before digitization, or digitized and then subtracted.

[0305] According to some embodiments, one or more differential amplifiers are electrically connected to at least two electrodes by inserting a plug connected to the proximal end of an electrode wire into the input socket of at least one differential amplifier. In some embodiments, at least two electrodes are connected to a single differential amplifier, each electrode being connected to a different input socket.

[0306] According to some embodiments, the differential amplifier is located within the lead base. In some embodiments, the differential amplifier is a separate box, optionally permanently attached to the leads. In some embodiments, the connection between the differential amplifiers is via a plug-and-receptacle connection, such as a multi-branch plug or a single-electrode plug.

[0307] According to some embodiments, the leads are connected to the system via a cable having a connector at the distal end compatible with the proximal end of the leads and a connector at the proximal end compatible with the system. Alternatively, the cable is permanently connected to the system and has a connector only at the proximal end of the leads.

[0308] According to some embodiments, several types of connectors exist for leads: 1- Simple pins on the lead end, connecting to a socket connector on the cable. 2- "In-line" connectors, where the lead has a conductive ring on its proximal end, and the proximal end is surrounded by a connector with compatible conductive segments (pins / rings) such that the connector conductor contacts the lead when surrounded. 3- Multiplexing circuits, where the number of physical wires leading from the lead to the system is less than the number of channels being recorded. Multiplexing uses the same physical wires for more than one channel by switching between channels that transmit signals on the wires in a predetermined manner.

[0309] According to some embodiments, the first amplification stage, such as a preamplifier or head stage, is located as close as possible to the electrode connections and optionally has a cable length of 10-30 cm to reduce electromagnetic noise accumulated on the cable. In some embodiments, the cable has electromagnetic shielding, such as a "Faraday cage," to reduce the effects of electromagnetic noise. In some embodiments, the signal is further filtered and amplified after the first amplification stage and before sampling. In some embodiments, it is advantageous if all analog processing is located near the electrodes to reduce noise, and from there the signal is transmitted to further processing via digital communication.

[0310] According to some embodiments, the navigation system compares signals recorded by the electrodes of the electrical leads with indications or reference indications of electrical signals stored in memory to determine the anatomical location of the leads. In some embodiments, the anatomical location is a description of any region or part of the body. In some embodiments, the indication includes electrical signals, processed electrical signals, electrical signal values, characteristics of the electrical signals, signal sequences, signal values ​​as a function of depth, electrode contact orientation, relationships between different contacts, and one or more of model parameters as a function of depth.

[0311] According to some embodiments, the navigation system is calibrated based on the axial and / or angular distance between the electrodes of the electrical leads. In some embodiments, the navigation system measures the distance between two or more electrodes. Optionally, the navigation system measures the distance between the farthest electrode on the lead and a more proximal electrode.

[0312] One aspect of some embodiments involves using machine learning algorithms to train a learning machine, such as the processing circuitry of a computer or navigation system, to distinguish different brain regions and / or to navigate using such a trained machine. In some embodiments, machine learning is used to generate a brain model, and optionally, predictions are generated based on the model. In some embodiments, the predictions are arranged as a map, such as a predicted functional organization map, which is optionally used by the learning machine for a desired target during an automated navigation process. Optionally, the functional organization map is a state transition map. In some embodiments, the learning machine uses the functional organization map to determine the location of electrical leads and / or to determine whether the location of electrical leads is a desired location. In some embodiments, the machine learning algorithms include dynamic Bayesian networks, artificial neural networks, deep learning networks, structured support vector machines, gradient-boosting decision trees, and long short-term memory (LSTM) networks.

[0313] According to some embodiments, machine learning algorithms are used to modify parameters of existing functional tissue models. In some embodiments, the model includes anatomical information of different anatomical regions or different anatomical regions along a specific insertion trajectory in different anatomical regions. In some embodiments, the algorithm modifies the parameters of the existing model based on expert-labeled data collected from the surgical procedure. Alternatively or additionally, the algorithm uses anatomical and / or physiological and / or optionally any other relevant data stored in a database to modify the existing model.

[0314] According to some embodiments, the functional tissue map includes different anatomical regions and optionally includes the geometric relationships between the anatomical regions. In some embodiments, anatomical regions in the functional tissue map are selected based on a selected insertion trajectory. Furthermore, the functional tissue map includes electrical signals, statistical data, and indications predicting measurements to be taken at the selected anatomical regions. In some embodiments, the functional tissue map is provided as a classifier and / or predictor, optionally for each anatomical region or sub-region, such as proximal regions, intermediate regions, and / or boundary regions.

[0315] According to some embodiments, a functional organization map includes a collection of data associations between recorded signals (e.g., physiological signals or signal characteristics) and anatomical locations, such as regions or subdomains. In some embodiments, a functional organization map includes indications predicting electrical signals to be measured at specific anatomical locations.

[0316] According to some embodiments, a functional organization map allows the transformation of an electrical signal measured by one electrode type into an electrical signal predicted to be measured by electrodes of different electrode types or with different electrode geometric rearrangements (e.g., electrodes with different diameters, different electrode or electrode contact sizes, different relative geometries). In some embodiments, the functional organization map includes recorded correlations between signals or signal features and boundaries between regions or subdomains. In some embodiments, the functional organization map is adjusted to a specific lead type or a specific lead model. In some embodiments, the functional organization map is adjusted to a specific electrode arrangement and / or a specific number and / or electrode type on the outer surface of the leads.

[0317] According to some embodiments, the processing circuitry compares the recorded signal with at least one stored functional organization map to determine the location of the distal end of the electrical lead. Alternatively or additionally, the learning machine compares the recorded signal with at least one stored functional organization map to detect crossings of boundaries between anatomical regions or subdomains. In some embodiments, the functional organization map is updated online during lead advancement.

[0318] According to some embodiments, when the lead position is fixed in the desired target and used to deliver long-term stimulation, a functional tissue map is used to detect any movement of the leads. In some embodiments, the long-term stimulation (such as that provided to the implant) is stimulation provided over a long period of time, such as chronic long-term stimulation therapy for therapeutic purposes, while short-term stimulation (e.g., provided to the electrodes during navigation surgery) may optionally be used for diagnostic purposes.

[0319] In some embodiments, lead movement is detected by comparing recorded signals with a functional tissue map after and / or during stimulation. In some embodiments, if the position of the lead changes, an indication is provided to the user and / or an expert (e.g., a physician). Alternatively or additionally, different electrodes or groups of electrodes on the leads are used to deliver prolonged stimulation.

[0320] One aspect of some embodiments involves using the same electrical leads for both navigation and long-term stimulation therapy. In some embodiments, the same electrodes are used for both navigation and long-term stimulation therapy. In some embodiments, electrical leads comprising at least two macroelectrodes or at least two microelectrodes are used for both navigation and long-term stimulation, for example, for DBS therapy. Optionally, electrical leads comprising a combination of one or more macroelectrodes and one or more microelectrodes are used for both navigation and long-term stimulation.

[0321] According to some embodiments, a first electrode assembly is used for navigation, and a second electrode assembly is used for applying long-term stimulation. Optionally, some electrodes are used for both navigation and stimulation. Alternatively, the same electrode assembly is used for both navigation and the application of long-term stimulation.

[0322] According to some embodiments, the electrical leads are part of an automated or semi-automated system for both navigating to and stimulating a desired brain region. In some embodiments, the electrical leads are connected to a signal recording module and a pulse generator, configured to generate long-term stimulation. In some embodiments, once the desired brain target is reached, the processing circuitry automatically switches from the signal recording module to the pulse generator to allow, for example, long-term stimulation therapy to be delivered to the desired brain target. Alternatively, the system switches to the pulse generator and / or provides long-term stimulation therapy upon receiving a signal from the system user. In some embodiments, the processing circuitry of the navigation system delivers a human-detectable instruction upon reaching the desired brain target for long-term stimulation therapy. In some embodiments, upon receiving an instruction, the electrical leads disconnect from the navigation system and connect to a pulse generator, such as an implanted pulse generator (IPG), for delivering long-term stimulation therapy.

[0323] One aspect of some embodiments involves analyzing MER and / or LFP signals during the navigation process of the leads entering the brain. In some embodiments, MER and / or LFP signals are analyzed online as the leads enter the brain. Alternatively, when the advancement of the leads stops, the MER and / or LFP signals may be analyzed at selected locations along the advancement trajectory of the leads.

[0324] According to some embodiments, MER and / or LFP signals are analyzed to determine the location of the distal end of the electrical lead in the brain. Additionally or alternatively, MER and / or LFP signals are analyzed to determine whether the lead crosses a boundary between two brain regions. In some embodiments, MER and / or LFP signals are analyzed to estimate the proximity between the distal end of the electrical lead or the electrode at the distal end and at least one selected brain region or sub-region and / or the boundary between the regions.

[0325] According to some embodiments, the MER signal is analyzed to detect one or more power spectral bands. Optionally, the MER signal is analyzed to detect power spectral bands in a frequency range of 5-300 Hz. In some embodiments, the MER signal is analyzed to detect power bands in low frequencies of 5-25 Hz and / or high frequencies of 100-150 Hz. Optionally, the MER signal is analyzed to determine the ratio between the power of a higher frequency band and the power of a lower frequency band, or between the power of a selected band.

[0326] According to some exemplary embodiments, the location of electrical leads is estimated by using stored electrical signals and / or ratios determined by stored ratio analysis associated with anatomical regions and / or sub-regions.

[0327] According to some exemplary embodiments, the LFP signal is analyzed by subtracting the signal or signal features recorded by the first electrode from the signal or signal features recorded by the second electrode, for example, to reduce noise.

[0328] One aspect of some embodiments involves navigating electrical leads by detecting transitions between brain regions. In some embodiments, transitions are detected based on analysis of recorded LFP and / or MER signals. In some embodiments, electrical leads are navigated by comparing online transitions with planned transitions. Alternatively, transitions may be detected automatically, for example, by a learning machine.

[0329] According to some embodiments, when a target brain region is determined, an electrical lead insertion trajectory is selected. In some embodiments, a brain transition map is prepared from the electrode insertion site to the desired brain target or a desired subdomain within said target. In some embodiments, each of the 1, 2, 3, or more transitions in the map is associated with a specific value of MER and / or LFP signal parameters stored in memory. In some embodiments, during the navigation process, the measured signal parameter values ​​are compared with the stored values ​​to detect transitions between two regions.

[0330] According to some embodiments, if the lead crosses an undesirable boundary, the lead can optionally be retracted to the desired position. Alternatively, the electrode can be retracted from the brain, and an alternative insertion trajectory can be selected. In some embodiments, the transition map is adjusted to match a specific insertion trajectory.

[0331] According to some embodiments, when a transition between areas is detected or predicted, a user of the navigation system controlling and / or monitoring the navigation process receives a human-detectable transition indication. Alternatively or additionally, indications may be received based on recorded signals or predictions of recorded signals, upon reaching desired recorded parameter values ​​and / or upon entering an undesired area. In some embodiments, such as in an automated navigation system, the transition indication is passed to processing circuitry. In some embodiments, the processing circuitry automatically controls the advancement of the electrical leads based on the transition indication, for example, by decreasing or increasing the advancement speed of the electrical leads. Optionally, the transition indication is visualized graphically, such as on a map, e.g., an anatomical map or a graphical indicator.

[0332] One aspect of some embodiments involves navigating electrodes by estimating from any selected location in a selected brain target or tissue. In some embodiments, the distance to the target is determined by comparing a recorded signal or characteristics of a recorded signal with stored signal characteristics. In some embodiments, stored signal characteristics are simulated based on a specific insertion trajectory. In some embodiments, proximity to the desired target region can be estimated by estimating the location of the electrical leads and knowing the simulated signal characteristics along the insertion trajectory.

[0333] According to some embodiments, the distance to a desired brain target is monitored during lead advancement into the brain. In some embodiments, an indication is transmitted to the user based on the distance to the desired target. In some embodiments, an indication is transmitted to processing circuitry based on changes in the distance to the target, which may optionally adjust advancement parameter indications, such as advance parameters of the leads, advance parameter values, such as advancement speed. In some embodiments, the advancement speed decreases as the leads get closer to the desired target.

[0334] According to some embodiments, the sampling and / or recording rate is modified based on proximity to a desired target. In some embodiments, the analysis rate, analysis method, and / or analysis type are varied based on proximity to the desired target. For example, the signal sampling rate increases as the target brain is closer. Alternatively or additionally, the signal sampling rate is modified based on distance to a selected brain region.

[0335] One aspect of some embodiments of the present invention relates to real-time determination of transitions into and / or out of target brain regions by sensing differential macroelectrodes having predetermined axial spacing. In some embodiments, the predetermined spacing is based on the distribution of electrical activity typically detected by macroelectrodes at each location, e.g., local and / or distal activity typically sensed by each macroelectrode. Alternatively or additionally, the axial spacing is defined by the size of the target region. Optionally, the axial spacing is selected according to the target size region. In some embodiments, the macroelectrodes comprise a size larger than a typical neuron (e.g., having 10 μm). 2 and 20μm 2 The contact area between the projected areas of a typical neuron cell (excluding its axonal portion) is considered. In some embodiments, the target brain region includes areas in the brain that control movement, optionally the thalamus and / or the subthalamic nucleus (STN) and / or the globus pallidus and / or the dorsolateral oscillating region (DLOR) of the STN. In some embodiments, the transition into and / or out of the target brain region includes determining the boundary location relative to the macroelectrode.

[0336] Potentially, a predetermined axial spacing between at least two macroelectrodes provides differential sensing that can be used to identify the boundaries of target brain regions. In some embodiments, the spacing between the two electrodes is configured to be sufficiently large (e.g., greater than 0.1 mm) to provide differential recording. In some embodiments, the spacing between the two electrodes is configured to be sufficiently small (e.g., less than 1.2 mm) to detect mutual background, such as to detect the same distal electrical activity. For example, 1.1, 1, 0.5 mm, or any intermediate or smaller value.

[0337] In some embodiments, macroelectrode recording is configured to detect aggregated activity of a population of neurons in the electrode contact region. For example, aggregated activity may include a combination of far-field activity, optional neuronal volume conductance and local field activity, and optional local field potentials (LFPs). In some embodiments, neuronal volume conductance is derived from cortical spherical shell dipoles, which are generated by organized and / or synchronized activity in the cortex. In some embodiments, LFPs are extracellular recorded potentials with a low frequency range (e.g., 0.1–70 Hz) that may represent subthreshold activity, such as synaptic activity and / or information flowing to neurons. In some embodiments, simultaneous recording includes recording within a timeframe smaller than the typical rate of change of the signal measured by brain volume conductance.

[0338] In some embodiments, simultaneous unipolar macroelectrode recording can produce differential bipolar recording. For example, bipolar recordings can be analyzed by subtracting the signal recorded from the distal electrode (i.e., the electrode closer to the lead) from the signal recorded from the proximal electrode (i.e., the electrode farther from the lead), or vice versa. Alternatively or additionally, differential recordings can be provided by direct bipolar sensing between any pair of macroelectrodes. Optionally, unipolar macroelectrode recording can produce differential bipolar recordings, for example, if examining the statistical properties of the signal, such as the average power level of the power band. A potential advantage of sensing macroelectrodes unipolarly with a reference and then subtracting the recording to provide differential calculations is the flexibility in a selected number of recording macroelectrodes.

[0339] Differential recording can eliminate similar signals between macroelectrode recording sites, which are likely to represent far-field activity. In some embodiments, differential macroelectrode recording eliminates cortical activity over a relatively long range, such as 0.1-5 mm in a horizontal plane and / or up to 70 mm in a vertical plane.

[0340] In some embodiments, differential recording between macroelectrodes is used to identify locally generated neuronal activity. For example, a potential advantage of simultaneously sensing multiple macroelectrodes using real-time and / or online recording is the possibility of simultaneously recording neuronal volume conductance at multiple locations. In some embodiments, alternatively, by deriving differential sensing to identify and eliminate mutual volume conductance signals from each electrode recording, it may be possible to extract only locally generated neuronal activity.

[0341] In some embodiments, navigation is automated by automatically advancing brain navigation leads with macroelectrodes and automatically identifying brain region transitions. Optionally, differential electrophysiological detection of the macroelectrodes is recorded and target verification is automatically analyzed. In some embodiments, the ΔLFP between the signals of the macroelectrodes is used as a marker and / or signature of the transition into and / or out of the target brain region.

[0342] In some embodiments, differential LFP recording is used to detect β-oscillatory activity generated primarily within the dorsolateral portion of the STN. A correspondence between the dorsolateral oscillatory region (DLOR) and the sensorimotor region of the STN is disclosed in U.S. Patent No. 8,792,972 (incorporated herein by reference), and β-oscillatory activity may predict effective contact with deep brain stimulation (DBS) of the STN. In some embodiments, STN boundaries are also determined by identifying locally increased oscillatory activity that may be present in patients with Parkinson's disease (PD).

[0343] In some embodiments, differential recording is used to determine entry sites into a target brain region (e.g., the STN). Alternatively or additionally, differential recording is used to determine exit sites from the target brain region (e.g., the STN). A potential advantage of identifying transitions from brain region exits is to avoid over-penetration into brain regions that are not intended for stimulation. Alternatively or additionally, differential recording is used to identify transitions between subdomains of the target brain region, such as motor subdomains entering and / or exiting the STN.

[0344] Potentially, real-time differential recording, including online detection and / or computation, could reduce operation time, save costs such as operating room and / or medical staff availability, and potentially reduce patient discomfort, as patients may wake up during surgery.

[0345] In some embodiments, differential sensing is used to identify at least four stimulation points. Optionally, at least one stimulation point is located outside the STN, and at least three stimulation points are located inside the STN. Optionally, at least two stimulation points within the STN are located within a motion subdomain.

[0346] Optionally, a stimulation test is provided after determining the boundaries and / or interior location of the target area. In some embodiments, a sensor is positioned on the patient's body to optionally obtain a physiological response to the stimulation test. In some embodiments, the stimulation is detected automatically and optionally analyzed by processing circuitry.

[0347] One aspect of several embodiments of the present invention relates to a computer lead having a distal end for access to the brain and a proximal end for user operation, and having at least four macroelectrode contacts. In some embodiments, a predetermined interval is provided between two distal macroelectrodes, optionally with a resolution suitable for detecting the boundary of a target area. Alternatively or additionally, a predetermined interval is provided between two proximal macroelectrodes, optionally with a resolution suitable for detecting the boundary of a target area, which may be the same as or different from the distal distance. Optionally, a pair of distal macroelectrodes and a pair of proximal macroelectrodes are separated by a predetermined interval suitable for the stimulation procedure.

[0348] One aspect of some embodiments relates to a navigation system that automatically adjusts lead propulsion parameters. Optionally, the navigation system adjusts the propulsion parameters while continuously propulsing the leads to a desired target area. In some embodiments, the navigation system adjusts the lead propulsion parameters with a delay of less than 0.04 seconds (e.g., 0.03, 0.02, 0.01, or any intermediate or smaller value in seconds). In some embodiments, the lead propulsion parameters are adjusted based on recorded signals (e.g., signals recorded based on MER, LFP, or differential LFP). Alternatively or additionally, the lead propulsion parameters are adjusted according to an electrical guidance navigation plan (optionally a simulation plan). For example, the navigation plan determines how to adjust the settings or rule displays for each zone to be based on location or detected changes in settings. In some embodiments, the lead propulsion parameters include propulsion direction, propulsion speed, propulsion duration, propulsion steps, duration of each step, and / or the speed and / or the duration of the interval between steps.

[0349] According to some embodiments, lead propulsion is modified, for example, by slowing down, as the lead approaches the vicinity of a desired target. In some embodiments, lead propulsion is modified when fine-tuning the lead's positioning, such as when positioning the lead in a desired sub-region. In some embodiments, the lead propulsion speed is reduced when higher mapping is required. In some embodiments, lead propulsion is modified when the processing speed is slower than desired. In some embodiments, the need for higher mapping resolution is predicted, for example, based on proximity to a selected region.

[0350] According to some embodiments, the leaded navigation plan includes a selected insertion trajectory optionally chosen by an expert, and leaded propulsion parameter values ​​matching the insertion trajectory. In some embodiments, the leaded navigation plan is stored in the memory circuitry of the navigation system. In some embodiments, processing circuitry or control circuitry controls the propulsion of the leaded system by controlling a motor connected to the leaded system. Optionally, the motor is connected to the leaded system via a driver (e.g., a micro-driver). In some embodiments, the processing circuitry controls the rotational speed, time, and / or direction of the motor.

[0351] According to some exemplary embodiments, processing receives signals from at least one sensor configured to monitor the advancement of an electrical lead. In some embodiments, the at least one sensor senses the velocity, acceleration, duration of movement, and / or direction of the electrical lead. Additionally or alternatively, the at least one sensor senses the insertion depth of the electrical lead. In some embodiments, the sensor is mounted on the lead or connected to a driver of the lead.

[0352] In some embodiments, the navigation system automatically stops the advancement of the electrical leads. In some embodiments, the navigation system stops the electrical leads upon reaching the desired target. Alternatively or additionally, the navigation system stops the advancement of the electrical leads if at least one parameter value associated with the advancement of the electrical leads is not based on a desired value or is outside the desired range. For example, at least one parameter includes the advancement speed. In some embodiments, if the electrical leads advance too quickly or at an unexpected speed, the navigation system automatically stops the leads, for example by stopping the motor and / or drive.

[0353] According to some embodiments, the navigation system automatically stops the electrical lead propulsion based on signals recorded during the propulsion process. Alternatively, the navigation system automatically stops the electrical lead propulsion based on a simulation of the inserted trajectory. In some embodiments, the navigation system stops the electrical lead propulsion when a safety limit for the propulsion parameters is crossed.

[0354] According to some embodiments, the electrical lead is advanced toward a target area while recording electrical signals. In some embodiments, the lead is advanced by a motor or actuator connected to the lead. In some embodiments, during advancement, the navigation system analyzes the recorded signals to optionally use one or more algorithms to detect proximity to a boundary or boundary crossing. In some embodiments, processing circuitry controls the advancement of the lead based on the analysis results. In some embodiments, the lead is advanced during signal processing.

[0355] According to some embodiments, if there is a delay in generating analytical results, such as a delay when the lead advances to an allowed distance of 2 μm, 5 μm, 10 μm, 20 μm, or 50 μm without receiving analytical results, then lead advancement is stopped. Alternatively, the lead advancement rate is reduced, for example, by at least 1%, such as 1%, 5%, 10%, 50%, or any intermediate or larger value. In some embodiments, the allowed distance is determined based on proximity to the desired target or based on the lead's insertion trajectory. In some embodiments, the advancement rate is adjusted to minimize friction in the anatomical region along the insertion trajectory.

[0356] One aspect of some embodiments involves navigating to a desired brain target by inserting at least two electrical leads. In some embodiments, each of the at least two electrical leads includes at least one microelectrode and / or at least one macroelectrode contact. In some embodiments, the at least two electrical leads record MER, LFP, and / or differential LFP signals. In some embodiments, the distance between the at least two electrical leads is in the range of 0.5-5 mm, such as 0.5 mm, 1 mm, 2 mm, 3 mm, 4 mm, 5 mm, or any intermediate distance.

[0357] According to some embodiments, when at least two leads reach the desired brain target, they are used to deliver long-term stimulation, such as DBS stimulation. Alternatively, stimulation leads may be used instead of leads. In some embodiments, the at least two leads are used to deliver long-term stimulation to different targets simultaneously or via continuous pulses, optionally with a pulse delay between 0 and 100 μs.

[0358] One aspect of some embodiments involves using a state transition map to navigate electrical leads to a selected brain target. In some embodiments, the state transition map is tuned to a specific insertion trajectory. In some embodiments, the state transition map includes anatomical information, such as a list of anatomical regions along the insertion trajectory, and predicted electrical signals measured at the boundaries between the anatomical regions and / or adjacent anatomical regions. In some embodiments, the state transition map is an example of a functional organization map used in some embodiments of the invention.

[0359] According to some embodiments, electrodes on the leads record electrical signals from brain tissue during navigation along a selected trajectory. In some embodiments, the recorded brain signals are compared with a state transition map, for example, to determine the anatomical location of the leads. In some embodiments, based on the comparison with the state transition map, the navigation system detects a transition between two anatomical regions and optionally generates instructions to the user. Alternatively or additionally, based on the comparison with the state transition map, the navigation system determines whether the leads are entering or leaving a selected brain target.

[0360] In some embodiments, during the navigation process, for example, the recorded electrical signals are analyzed, and the state transition map is updated using the analyzed signals. In some embodiments, the insertion trajectory is selected based on the state transition map associated with the insertion trajectory. For example, the user can select an insertion trajectory that predicts the minimum noise signal recorded by the electrode.

[0361] One aspect of some embodiments involves estimating the proximity between an electrical lead and a boundary between two anatomical regions. In some embodiments, the proximity between the lateral side of the distal end of the lead and the boundary is estimated. In some embodiments, the proximity is estimated based on MER and / or differential LFP signals recorded by at least two electrodes located at the distal end of the lead.

[0362] In some embodiments, proximity to boundaries is estimated by analyzing recorded electrical signals using a functional tissue map, which includes reference indicators of electrical signals associated with anatomical regions. In some embodiments, electrical signals are recorded as described in patent application IL2017 / 050328, which is incorporated herein by reference.

[0363] According to some embodiments of the invention, the methods and apparatus described herein are used to navigate at least one electrical lead to one or more potential targets for DBS stimulation. In some embodiments, DBS stimulation may optionally be used to treat movement disorders, such as PD, dystonia, and / or essential tremor. Long-term stimulation, such as DBS stimulation for treating movement disorders, may optionally be delivered to the subthalamic nucleus (STN), globus pallidus (GPi), lateral globus pallidus (GPe), ventral medial thalamus (VIM) nucleus, thalamus, basal ganglia nuclei, hippocampal fornix, and / or pontine nuclei (PPN) or any other potential brain target.

[0364] According to some embodiments, an automated process is used to locate the STN exit region and facilitate the detection of the transition from the STN to the SNr. In some embodiments, an automated method using RMS values ​​successfully identifies the STN-white matter (STN-WM) transition. In some embodiments, MER along a pre-planned trajectory is used to confirm the STN region during DBS surgery for Parkinson's disease. Optionally, MER allows for separation between the STN exit point and the SNr entry point. In some embodiments, fewer kinesthetic neurons are present in the ventral region of the STN, and STN VMNR neurons are characterized by consistently reduced β-band and increased γ (30-100 Hz) activity.

[0365] According to some embodiments, ideal isolation of a single cell requires electrode steps of 5-10 μm and is very time-consuming. Alternatively, the normalized root mean square (NRMS) value based on unclassified multi-cell activity is readily measurable. In some embodiments, STN-entry and STN-exit are typically labeled as sharp increases and decreases in NRMS, respectively. Alternatively, NRMS is used in conjunction with the spectral characteristics of the analog signal, which is computationally calculated.

[0366] According to some exemplary embodiments, several methods exist for distinguishing STN from SNr using NRMS and features derived from the power spectrum, employing automated detection methods. Some studies have proposed rule-based detection methods; however, these rule-based systems cannot detect direct STN-SNr transitions.

[0367] According to some embodiments, accurate differentiation between the STN and SNr is important for achieving optimal therapeutic benefits while avoiding psychotic complications during DBS surgery for Parkinson's disease (PD). In some embodiments, the beneficial effects of bilateral STN DBS on motor symptoms and quality of life have been demonstrated in patients with advanced PD; however, STN DBS has also been reported to cause psychotic complications. In some patients with PD who have impulse control disorders, aberrant behavior may be optionally triggered by ventral contact in the DBS lead and suppressed by closing that contact. In some embodiments, stimulation of active contacts located in the SNr induces manic and depressive symptoms. Alternatively, the SNr is assumed to be specifically involved in balance control during gait. Therefore, combined stimulation of the SNr and STN improves axial symptoms (including gait, balance, and postural freezing) compared to standard STN stimulation.

[0368] According to some examples, surgical treatment for advanced Parkinson's disease (PD) includes high-frequency deep brain stimulation (DBS) of the subthalamic nucleus (STN), which has been shown to be safe and beneficial surgically over time.

[0369] In some embodiments, microelectrode recording (MER) along a pre-planned trajectory is often used to improve the delineation of STN location during DBS surgery for Parkinson's disease. In some embodiments, the detection of changes in electrical activity in the dorsolateral region of the STN is evident: a sharp increase in the total power of the MER, measured by root mean square, RMS, and β-oscillatory activity (13–30 Hz).

[0370] Conversely, in some embodiments, several factors can make the electrophysiological determination of the ventral STN boundary more difficult, especially the uninterrupted STN-SNr transition, as activity (and RMS) does not decrease sharply. Furthermore, cells in the ventral STN region exhibit SNr-like excitation properties (reduced β-band and tremor frequency oscillations).

[0371] In some embodiments, the electrophysiological determination of the STN exit can be challenging because white matter interstitial spaces within the STN may lead to early detection of the STN exit. Therefore, the electrophysiological determination of the ventral boundary of the STN can be fuzzy and occasionally difficult to define.

[0372] Although recent imaging studies have improved the distinction between STN and SNr in some embodiments, electrophysiology is still needed to identify and verify the STN-SNr transition intraoperatively.

[0373] In some embodiments, it should be understood that an automated process is desired for locating the STN exit region and facilitating the detection of the transition from STN to SNr. In some embodiments, existing automated methods using RMS values ​​have been successful in identifying STN-white matter (STN-WM) transitions but unsuccessful in identifying STN-SNr transitions.

[0374] According to some embodiments, MER along a pre-planned trajectory is commonly used to identify STN regions during DBS surgery for Parkinson's disease; however, there is no consensus on whether MER allows for reliable separation between the STN exit point and the SNr entry point. In some embodiments, fewer kinesthetoid neurons are present in the ventral region of the STN, such as STN VMNR neurons, characterized by persistently reduced β bands and increased γ (30-100 Hz) activity.

[0375] In some embodiments, similarly, the firing patterns of neurons in SNr (below the STN target) lack β-band and tremor frequency oscillations, while exhibiting increased γ activity. Furthermore, cell islands possessing firing characteristics of both SNr and STN cells have been observed. Therefore, in some embodiments, the electrophysiological determination of the transition from STN to SNr is ambiguous and difficult to assess.

[0376] According to some embodiments, some studies have developed automated detection and visualization of STNs based on objective and quantitative MER features, as well as automated detection and visualization of SNr. Optionally, some of these studies used features that require spike detection algorithms to identify firing patterns. While these features can help detect the ventral boundary of the STN near SNr, computing neuronal spike features in a real-time intraoperative setting remains computationally challenging.

[0377] Furthermore, in some embodiments, ideal isolation of a single cell requires electrode steps of 5–10 μm and is very time-consuming. Conversely, normalized root mean square (NRMS) values ​​based on unclassified multi-cell activity are readily measurable. In some embodiments, STN-in and STN-out are typically labeled as sharp increases and decreases in NRMS, respectively. Some studies use NRMS along with spectral features of analog signals, which are computationally calculated. However, in some embodiments, these spectral features do not allow for reliable and robust identification of the transition between STN and SNr.

[0378] According to some embodiments, several methods exist for distinguishing STN from SNr using NRMS and features derived from the power spectrum, employing automated detection methods. Some studies have optionally proposed rule-based detection methods; however, these rule-based systems cannot detect direct STN-SNr transitions.

[0379] According to some embodiments, accurate differentiation between the STN and SNr is crucial for achieving optimal therapeutic benefits while avoiding psychotic complications of the PDBS procedure. In some embodiments, the beneficial effects of bilateral STN DBS on motor symptoms and quality of life have been demonstrated in patients with advanced PD; however, psychotic complications arising from STN DBS have also been reported. In some embodiments, in some patients with PD exhibiting impulse control disorders, abnormal behavior can be suppressed by stimulating the ventral contact of the DBS lead and closing that contact. Alternatively or additionally, manic and depressive symptoms have also been reported to be induced by stimulating active contacts located in the SNr. On the other hand, in some embodiments, the SNr is presumed to be particularly involved in balance control during gait. Thus, combined stimulation of the SNr and STN has been reported to improve axial symptoms (including gait, balance, and postural freezing) compared to standard STN stimulation. In summary, in some embodiments, automated and reliable localization of the STN-SNr transition and STN lower boundary detection can lead to improved localization of the DBS lead and better clinical outcomes of DBS.

[0380] According to some embodiments of the invention, the electrode is delivered through openings in a sheath, lead, or catheter, and optionally has exposed electrode contacts facing the tissue.

[0381] A broad aspect of some embodiments of the present invention relates to detecting STN boundaries using differential LFP recording. In some embodiments, the electrical leads include electrodes having at least two macro contacts, optionally used for detecting entry points in the STN. In some embodiments, the electrical leads are used to detect entry points into the Gpi and other anatomical regions.

[0382] A broad aspect of some embodiments of the present invention relates to detecting the exit from the STN to the SNR or white matter. In some embodiments, for detecting the exit from the STN, the electrical conduction includes at least one microelectrode.

[0383] In some embodiments, the electrode probe is an example of electrical conduction.

[0384] According to some embodiments, at least one microelectrode on the electrical leads records MER and / or LFP for detecting the boundary between proximity, anatomical regions, and / or boundary crossings.

[0385] Before explaining at least one embodiment of the invention in detail, it should be understood that the invention is not necessarily limited to its application to the construction details and arrangements of components and / or methods set forth in the following description and / or illustrated in the drawings and / or examples. The invention can have other embodiments or can be practiced or implemented in various ways.

[0386] Exemplary lead insertion and navigation

[0387] According to some exemplary embodiments, electrical leads are inserted into the brain. In some embodiments, electrodes are inserted to identify desired brain targets. Optionally, the desired brain targets are selected for delivering therapy, such as deep brain stimulation (DBS) therapy. In some embodiments, the same electrodes used for mapping and / or detecting the desired brain targets are also used to stimulate the desired brain targets.

[0388] Now for reference Figure 1A It describes a general process for inserting electrical leads into the brain and navigating electrodes to desired brain targets according to some embodiments of the invention.

[0389] According to some exemplary embodiments, at box 101, an expert (e.g., a physician) determines to insert electrodes into the brain of a subject (e.g., a patient). In some embodiments, the physician determines to insert electrodes into the brain based on diagnostic results. Optionally, the diagnosis is based on the results of an imaging technique (e.g., MRI, CT, PET-CT, or any other imaging technique). In some embodiments, the brain target for the electrical leads is selected based on the results of the imaging technique. In some embodiments, an insertion trajectory is selected after the brain target is selected. Optionally, at least one alternative insertion trajectory is also selected. In some embodiments, the brain target includes the subthalamic nucleus (STN) and / or the globus pallidus and / or the motor subdomain, which is estimated as the dorsolateral oscillatory region (DLOR) of the STN.

[0390] According to some exemplary embodiments, the patient's skull is opened at box 103. In some embodiments, an entry point for electrical leads is opened within the skull. Optionally, the entry point is opened based on a selected insertion trajectory and / or at least one alternative insertion trajectory.

[0391] According to some exemplary embodiments, an electrical lead is inserted and advanced into the brain at block 105. In some embodiments, the electrical lead includes at least two macroelectrode contacts positioned on the outer surface of the lead. In some embodiments, the macroelectrode includes a ring electrode or a segmented electrode. Alternatively, the electrical lead includes at least two microelectrodes or microelectrode contacts located on the outer surface of the lead and / or at the distal end of the lead as a guiding tip when the lead is advanced into the brain. Optionally, the electrical lead includes at least one microelectrode contact and at least one macroelectrode contact. In some embodiments, the electrical lead includes lead 200 or lead 504, such as... Figure 3A -H、 Figures 4A-4F and Figure 5 As shown.

[0392] According to some exemplary embodiments, the electrical lead includes at least two electrodes. In some embodiments, one of the at least two electrodes, such as a ring macroelectrode or a segmented macroelectrode, is located on the circumference of the electrical lead. In some embodiments, a second electrode, such as a microelectrode, extends from the lead lumen through an opening on the circumference of the lead. In some embodiments, the opening is located at the distal end of the electrical lead. Alternatively or additionally, the opening is located on the side of the electrical lead.

[0393] According to some exemplary embodiments, the electrode contacts, such as microelectrode contacts and / or macroelectrode contacts, have the same axial position on the leads but different angular positions on the circumference of the leads. Alternatively, the electrode contacts have the same angular position but different axial positions along the outer surface of the leads.

[0394] According to some exemplary embodiments, while recording the electrical activity of the surrounding brain tissue, after a first centimeter, the leads are inserted into the brain in a continuous or near-continuous manner. Alternatively, the leads are inserted into the brain in predetermined steps. In some embodiments, while the lead position is fixed, the leads record the electrical activity of the surrounding brain tissue between these predetermined steps. In some embodiments, the insertion speed of the electrodes may optionally be varied based on the recording results and / or trajectory.

[0395] According to some exemplary embodiments, the electrical connection is achieved by continuously activating the motor (e.g., Figure 6A The motor 602 shown, which is functionally connected to the lead, continuously propels the motor toward the selected target. Alternatively or additionally, this can be achieved by continuously activating the drive (e.g., Figure 6A The micro-actuator or driver 603 shown continuously advances the electrical leads toward a selected target. In some embodiments, the motor continuously advances the leads incrementally until the advancement is explicitly stopped by a user or computer command.

[0396] According to some exemplary embodiments, at block 107, the electrical lead records MER or LFP. In some embodiments, the electrical lead continuously records MER or LFP as it advances into the brain. Alternatively, MER or LFP is recorded between steps of the electrical lead's movement. In some embodiments, the signal is processed as a continuous signal. Alternatively, the signal is processed in segments, wherein each segment includes the signal recorded within a specific time window. In some embodiments, MER refers to microelectrode recordings, which are divided into...

[0397] 1- Single Unit Activity (SUA) - Essentially records the potentials of a single neuron or a small number of neurons (e.g., a maximum of 10 neurons, such as 10, 9, 5, or any intermediate or fewer neurons), which are optionally high-frequency signals (approximately 300-6000 Hz).

[0398] 2-Multi-unit activity (MUA) - Records indistinguishable potentials of multiple neurons (e.g., at least 50 neurons, such as 50, 60, 70 neurons or any intermediate or larger number of neurons), optionally having frequency characteristics similar to SUA.

[0399] 3-Local field potentials (LFP) - Recordings from populations that cannot distinguish individuals, such as potentials from large populations of neurons, optionally by analyzing low-frequency content (<300 Hz).

[0400] According to some exemplary embodiments, the recorded MER or LFP is analyzed at block 109. In some embodiments, the analysis includes calculating different characteristics of the recorded signal, such as calculating a root mean square (RMS) estimate based on the signal recorded at each electrode depth or a selected electrode depth. Optionally, the RMS is normalized, for example, to the white matter RMS or the RMS of any defined region used as a baseline, to produce a normalized RMS (NRMS). In some embodiments, the analysis includes generating a power spectrum or an average power spectrum of one or more bands.

[0401] According to some exemplary embodiments, during the insertion of an electrical lead, at block 111, the system determines whether the electrical lead or at least one electrode contact crosses a boundary between brain regions. Optionally, the system determines whether the electrode crosses a boundary, e.g., a dorsal boundary entering a desired brain target or a desired sub-region. In some embodiments, the transition between two brain regions is based on recordings of neuronal activity in at least one brain region. In some embodiments, the transition between two brain regions is determined based on recordings of differential local field potentials (LFPs), e.g., based on extracted root mean square (RMS) values ​​and a normalized RMS calculated from the differential LFP signal. Alternatively or additionally, power spectral analysis is performed, e.g., by calculating (optionally normalized) power spectral analysis density (PSD) values ​​to record neurophysiological activity along the insertion trajectory. Alternatively or additionally, statistical analysis is performed on the analysis results, e.g., median and standard error of the median. Alternatively or additionally, power in different frequency domains is calculated, e.g., alpha power, beta power, etc. In some embodiments, a dynamic Bayesian network, such as a Hidden Markov Model (HMM) based on power spectral analysis values ​​computed partially and / or entirely along the insertion trajectory, is computed, optionally assigning the region with the highest probability value among multiple regions along the insertion trajectory for each selected point. In some embodiments, the lower boundary of the STN is detected during the insertion of the electrical lead. Optionally, the transition between the STN and the SNr region is detected.

[0402] According to some exemplary embodiments, at block 113, during the insertion of the electrical lead, the system determines whether the electrical lead or at least one electrode contact is close to a desired target. In some embodiments, the system determines whether the electrical lead or at least one electrode contact is close to the desired target in a manner similar to the process described in blocks 109 and 111.

[0403] According to some exemplary embodiments, at block 115, the system determines whether an electrical lead or at least one electrode contact on the probe is located within or at a desired relative position to the desired brain target. In some embodiments, the boundaries of the target region are determined, for example, to determine whether the electrical lead is located within or adjacent to the target region. Alternatively or additionally, the system determines whether the electrode is detached from the desired brain target or sub-region. In some embodiments, the desired brain target is a selected brain target for treatment, optionally with DBS treatment performed at block 101.

[0404] According to some exemplary embodiments, the system uses a process similar to that described in blocks 109 and 111 to determine whether the electrical leads are at the desired brain target and / or whether the electrical leads have not left the desired brain target.

[0405] According to some exemplary embodiments, if the electrical leads are located at the desired brain target, a positioning fine-tuning is performed at box 117. In some embodiments, positioning fine-tuning is performed by slowly moving the electrical leads to a specific location within the desired brain target, optionally by advancing or retracting the probe in small steps of 0.1-5 mm (e.g., 0.5, 1, 2 mm, or any intermediate distance). Optionally, the electrical leads are rotated, for example, to reach a desired angular position between at least one electrode and the selected target.

[0406] According to some exemplary embodiments, the electrical leads at block 119 are replaced with stimulation leads. In some embodiments, the stimulation leads are positioned at the desired brain target based on recording previously performed by the recording leads. Alternatively, the leads used for recording are also used to deliver stimulation, such as DBS, to the desired brain region. In some embodiments, DBS is delivered via electrodes different from those used for MER and / or LFP recording. Alternatively, one or more of the same electrodes used for MER and / or LFP recording are used to deliver DBS.

[0407] According to some exemplary embodiments, once the positions of the electrical leads used for stimulation are fixed, the skull is closed at 121.

[0408] According to some exemplary embodiments, if it is determined at 115 that the lead is not at the desired target location, the lead is retracted at 123. In some embodiments, the electrode is removed from the brain. Alternatively, the electrode is retracted back to a selected brain region.

[0409] According to some exemplary embodiments, leads at 125 locations or different leads are inserted and advanced along alternative insertion trajectories. In some embodiments, if the leads retract into a selected brain region, the electrodes are advanced along different trajectories to a selected brain target.

[0410] An exemplary real-time navigation process using differential LFP

[0411] In some embodiments, navigation is performed by determining the transition into or out of a target brain region by deriving differential bipolar sensing of the macroelectrodes. In some embodiments, the transition between adjacent anatomical regions located along the insertion trajectory of the leads is determined. In some embodiments, differential bipolar sensing is derived directly from sensing between any pair of macroelectrodes. Alternatively or additionally, differential bipolar sensing is derived by subtracting measurements of unipolar sensing between at least two macroelectrodes and a reference. A potential advantage of using unipolar sensing is greater flexibility in the number and configuration of macroelectrodes.

[0412] In some embodiments, prior to surgery, magnetic resonance imaging (MRI) and / or computed tomography (CT) scans are used to estimate the target's location within the brain. Optionally, the estimated location is used to calculate the estimated insertion trajectory.

[0413] Now refer to the attached diagram, Figure 1B A flowchart of a real-time navigation process according to some embodiments of the present invention is shown. In some embodiments, navigation begins at step 102, where an EEG lead is connected at box 104, for example, by delivering an electrode probe having at least two macroelectrodes into the brain. Optionally, at box 106, the lead is advanced toward an estimated target location, for example toward the subthalamic nucleus (STN) and / or the globus pallidus and / or the motor subregion of the dorsolateral oscillatory region (DLOR) estimated to be the STN. The estimated trajectory may optionally be based on pre-acquired imaging, such as CT and / or MRI scans.

[0414] In some embodiments, the lead is manually advanced by the user. Alternatively or additionally, the lead is automatically advanced by a motor and control circuitry. Alternatively or additionally, the lead is semi-automatically advanced by a user-controlled motor. In some embodiments, the lead is advanced continuously. Alternatively or additionally, the lead is advanced gradually. In some embodiments, once a boundary transition is determined, the lead advancement speed and / or step size is reduced.

[0415] In some embodiments, differential local field potentials (LFPs) are derived from macroelectrode sensing at block 108. In some embodiments, differential LFPs can be obtained at block 108 by directly measuring bipolar sensing between any pair of macroelectrodes. Alternatively or additionally, unipolar macroelectrode sensing is simultaneously recorded at block 108. In some embodiments, simultaneous recording is provided within a timeframe of changes in volumetric conductance smaller than that of the brain. As used herein, volumetric conductance refers to computer activity originating from a region relatively far from the area being examined, for example, activity originating from a horizontal distance greater than 1 mm, or greater than 3 mm, or greater than 5 mm, or any horizontal distance between these ranges, or activity vertically at least 2 mm, or 5 mm, or 10 mm from the area being examined. As used herein, horizontal is defined as substantially perpendicular to the longitudinal axis of the leads, and vertical is defined as substantially parallel to the longitudinal axis of the leads.

[0416] In some embodiments, the differential local field potential (LFP) is calculated at block 110 by subtracting the sensing signals from any pair of macroelectrodes. Optionally, the differential LFP is derived by subtracting the signal recorded from the distal electrode (i.e., the electrode closer to the lead end) from the signal recorded from the proximal electrode (i.e., the electrode farther from the lead end). A potential advantage of subtracting the unipolar signal derived from at least two macroelectrodes lies in the composition of the signal picked up by each macroelectrode. Potentially, each macroelectrode senses both locally generated activity and far-field activity, for example, for volumetric conductance. In some embodiments, far-field activity is activity in high-volume regions, and optionally, far-field activity measurement allows for a global view of brain regions. The axial spacing or distance between macroelectrodes can be optionally selected such that local activity may differ from that of each macroelectrode, but far-field activity may be similar across all macroelectrodes. In some embodiments, the selection is made by selecting a lead or by selecting which one or more electrodes on a lead are used for recording.

[0417] Then, at box 110, the calculated LFP is optionally used for further analysis to determine the target boundary. In some embodiments, the boundary is an entrance into the target region. Alternatively or additionally, the boundary is an exit from the target region. Alternatively or additionally, the boundary is a transition between subdomains of the target region. In some embodiments, a subdomain, also referred to as a subregion in some implementations, is a region within a larger anatomical region. In some embodiments, the target boundary is determined once differential bipolar sensing is derived from at least two macroelectrodes transitioning at the boundary.

[0418] According to some exemplary embodiments, target boundaries, such as boundaries or target regions, or selected subdomains containing electrical leads, can be optionally determined during real-time or online brain navigation based on the calculated LFP. In some embodiments, the target boundaries are determined based on the axial spacing distance and / or angular spacing between the recorded LFP signals and the electrodes recording the signals.

[0419] In some embodiments, once the boundary is determined, the lead is moved back at box 120; for example, once the exit from the STN is determined, the lead is moved back into the STN. A potential advantage of determining the exit boundary and moving back step by step is to verify brain regions that should not be stimulated, such as the reticular formation in the substantia nigra.

[0420] Alternatively or additionally, lead movement may be stopped at box 140 once the boundaries have been determined. Optionally, lead movement may be stopped once the entry point into the target area has been determined. For example, after the entry point has been determined, stimulation of the lead may optionally be provided to further determine the lead's location.

[0421] Alternatively or additionally, once the boundary is determined, the lead is further advanced at box 160. In some embodiments, the lead is advanced at a decreasing rate and / or step size. Optionally, after determining the inlet boundary, the lead is further advanced to explore the outlet boundary. Alternatively or additionally, the lead is further advanced to determine the subdomain boundary.

[0422] The boundary is determined, for example, based on the axial and / or angular spacing of the electrodes.

[0423] According to some exemplary embodiments, such as those discussed in block 100, by understanding the axial and / or angular distribution of the electrodes on the electrode probe, it can be determined whether the electrode probe crosses the boundary between two regions. Referring now to... Figure 1C It depicts, according to some embodiments of the invention, the average β power (12-35Hz) microelectrode spike activity along the STN trajectory, unipolar macroelectrode LFP, and bipolar macroelectrode LFP.

[0424] According to some exemplary embodiments, the location of the determined or detected boundary depends on or the detection resolution depends on the size of the electrode, such as the size of the macroelectrode or the size of its tissue-facing outer surface and optionally their distribution on the outer surface of the electrode probe.

[0425] Now for reference Figure 1C It depicts the average β power (12-35Hz) microelectrode spike activity, unipolar macroelectrode LFP, and bipolar macroelectrode LFP along the insertion trajectory according to some embodiments of the present invention.

[0426] According to some exemplary embodiments, curve 170 represents the average peak activity recorded by the bipolar macroelectrode LFP, curve 172 represents the average peak activity recorded by the microelectrode SPK, and curve 174 represents the average peak activity recorded by the unipolar macroelectrode LFP. In some embodiments, the macroelectrode recording curves 170 are all 0.5 mm wide and spaced 0.5 mm apart. In some embodiments, the y-axis value indicates the power in the β band (expressed as a z-score), normalized to the power in the 4-200 Hz band. In some embodiments, 0 on the x-axis represents the entry point from the furthest macroelectrode to the STN, as determined by a conventional STN detection algorithm based on microelectrode peaks.

[0427] According to some exemplary embodiments, the bipolar LFP β-band power, as shown by curve 170, begins to rise slightly before the distal macro contact enters the STN and continues to increase until the second macro contact is completely within the boundary, after 1.5 mm – the distance between the distal edges of the macro electrodes. It then begins to decrease, possibly because the distal contact is moving out of the dominant β-oscillation region (DLOR). In some embodiments, the boundary location can be derived by finding the depth of the peak power of the bipolar LFP β-band, or the depth at which the power stops rising, and subtracting the distance between the distal edges of the macro electrodes.

[0428] According to some exemplary embodiments, if the size of the macroelectrode is large, for example, about 1.5 mm wide as is common in implanted DBS electrodes, the distance between the distal edges of the macroelectrode is about 3.5 mm, and the distal macroelectrode may leave the DLOR many times before the proximal macroelectrode is completely inside the DLOR, and therefore the detection boundary is not allowed.

[0429] According to some exemplary embodiments, the boundary is determined by knowing the angular spacing between the electrodes. In some embodiments, the maximum relative β-band power is recorded when both contacts are within the DLOR. In some embodiments, the boundary is determined by knowing the angular geometry and the location of the peak signal along the rotation axis.

[0430] According to some exemplary embodiments, using multiple pairs of macroelectrodes, the relative differential β-band power between different pairs can be compared to find the pair with the maximum power and use that number as the peak value. Alternatively, the peak value can be defined using interpolation of multiple bipolar measurements, and the boundary location can be found from these by subtracting the distance between the distal ends of the electrode pairs.

[0431] Exemplary use of real-time navigation

[0432] Now for reference Figure 2This illustrates exemplary use of an EEG lead according to some embodiments of the invention. In some embodiments, the patient's brain 220 is explored to identify a target region 224, optionally identifying a target region boundary 222. In some embodiments, the brain 220 is explored via a navigation lead 200. Optionally, the lead 200 has a distal end 201 for delivery into the brain 220. The proximal end 202 is optionally included within a needle insert 240 and / or any electrode holder, such as a Ben-Gun electrode holder.

[0433] In some embodiments, lead 200 includes at least two insulated wires, optionally relatively thin, each wire having at least one macroelectrode contact. Optionally, commercially available lead 200 is used, such as Medtronic DBS lead 3387, and / or 3389, and / or St. Jude Medical “Infinity,” and / or Boston Scientific “Vercise,” and / or PINSModel G101 lead, and / or Adtech Depth Electrode. In some embodiments, lead 200 is inserted into and / or implanted into the brain through a small opening in the skull. The distal portion of the lead optionally includes macroelectrode contacts and is optionally navigated to be positioned within a target brain region. Alternatively or additionally, it is navigated to be positioned anterior to the target brain region. Alternatively or additionally, it is navigated to be positioned posterior to the target brain region. In some embodiments, a ground electrode 250 is provided, optionally for sensing a monopolar signal through the lead macroelectrode. Optionally, the monopolar signal is processed to provide a differential signal.

[0434] A potential advantage of using a suitable implantable lead for navigation is that once the target area is identified, there is no need to replace the navigation lead with a stimulation lead, which may speed up the process and / or reduce patient discomfort and / or the probability of errors.

[0435] In some embodiments, an extension cable 240 passes under the skin of the head and / or neck and / or shoulder, connecting leads to a stimulator 280. In some embodiments, leads 200 are optionally delivered an electric shock by the stimulator 280 via the cable 240. Optionally, commercially available stimulants such as Medtronic Activa and / or St Jude Medical Brio, and / or Boston Scientific Vercise IPG, and / or PINS Model G101 IPG are used. In some embodiments, the stimulator 280 is configured to generate a sensing electric field through leads 200. Alternatively or additionally, the stimulator 280 is configured to generate a stimulating electric field through leads 200. Alternatively or additionally, the stimulator 280 is configured to generate electrical pulses that interfere with and / or block (optionally pathological) electrical signals generated in the brain through leads 200. In some embodiments, the pathological function includes a neurodegenerative disease (e.g., Parkinson's disease), and the implanted stimulator 280 is used for deep brain stimulation (DBS).

[0436] In some embodiments, recordings made by the macroelectrode serve as biomarkers, potentially for diagnosing pathological brain function. In some embodiments, the stimulator 280 is optionally implanted under the skin for therapeutic purposes, optionally near the clavicle, and / or under the skin below the chest and / or above the abdomen.

[0437] In some embodiments, more than one trajectory may be explored simultaneously using more than one lead 200. Potentially, adding more leads increases the chance that the trajectory will pass through the optimal target location. On the other hand, adding more leads increases the chance of causing damage along the trajectory, for example, through small blood vessels. Optionally, leads of ranges 1 and 5 may be used.

[0438] Potentially, the majority of the distance traversed by the electrodes before reaching the STN is white matter. A potential advantage of navigating through white matter regions is that, unlike the horizontal plane in the cortex, somatic activity that deactivates cortical dipoles may be less. Another potential advantage is that white matter may be a better conductive tissue due to the presence of myelin and pronounced fiber orientation. Alternatively or additionally, signals recorded from white matter allow for normalization, for example, of signals recorded from other regions of the brain. In some embodiments, signals recorded from white matter are used for correlation analysis as described in the "Exemplary Correlation Signals of Two Electrodes" section below.

[0439] Alternatively, navigation leads consisting only of macroelectrodes are used as navigation tools to access the brain, without microelectrodes capable of detecting individual cell spikes.

[0440] Exemplary macro electrode configuration

[0441] Now for reference Figure 3A -H illustrates exemplary electrode configurations on leads according to some embodiments of the present invention, wherein Figure 3A -D shows a top view of the leads with an alternative macroelectrode configuration, and Figure 3E -H respectively show Figure 3A -D Top view of the alternative macroelectrode shown.

[0442] In some embodiments, lead 200 includes at least two macroelectrode contacts, such as macroelectrodes 302 and / or 304 and / or 306. As used herein, a macroelectrode contact or macroelectrode is defined as having, for example, a specific size of about 10-20 μm. 2 A sensing surface larger than a typical neuron cell. In some embodiments, the maximum size of the neuron cell is approximately 10-20 μm. In some embodiments, the lead has a macroelectrode with a contact area of ​​approximately 20 μm. 2 Approximately 50 μm 2 and / or 50μm 2 Approximately 100 μm 2 and / or 100μm 2 Approximately 500 μm 2 or any smaller, larger, or intermediate range. In some embodiments, the lead has a macroelectrode having a diameter greater than 500 μm. 2 Contact area, for example, 500 μm 2 1000μm 2 2000μm 2 Or any intermediate or larger contact area. In some embodiments, lead 200 is a navigation lead. In some embodiments, such as... Figure 1C As described, in order to detect the boundary between two regions via bipolar recording, two axially spaced electrodes on a lead need to be located in the same region. In some embodiments, the boundary is detected when the proximal electrode is fully inside the region while the distal electrode remains fully within the same region. Therefore, in some embodiments, when using electrodes with large contact areas, one electrode may not be entirely located in the same region as the second electrode.

[0443] In some embodiments, the two macroelectrodes have a predefined axial spacing 310. Optionally, the spacing length 310 is determined by a trade-off between detecting distinct local signals in each of the two separate macroelectrodes and detecting similar far signals in each of the two separate macroelectrodes. The axial spacing or distance between the macroelectrodes is chosen such that local activity may differ for each macroelectrode, but far activity may be similar across all macroelectrodes. Such a distance may be in the range of about 0.1 mm to about 1.2 mm, for example, 0.1 mm to about 0.2 mm, and / or about 0.2 mm to about 0.4 mm, and / or about 0.3 mm to about 0.5 mm, and / or 0.5 mm to about 0.7 mm, and / or about 0.7 mm to about 1 mm, and / or about 1 mm to about 1.2 mm, or any smaller, intermediate, or larger range.

[0444] In some embodiments, the macroelectrode is ring-shaped, such as 302a, 302b, 304a-d, 308a, and 308d, etc. Figure 3A As shown in Figures B, D, E, F, and H. Alternatively or additionally, the macroelectrode may be in the form of a ring segment, optionally divided into two segments (such as 306a and 306b), as... Figure 3C and 3G As shown, and / or a ring divided into three segments (such as 308b and 308c), as Figure 3D and 3H As shown. In some embodiments, one or more segments are shaped into rectangles, squares, circles, triangles, or different geometries.

[0445] In some embodiments, the lead includes at least two macroelectrodes. Alternatively or additionally, the lead includes at least four macroelectrode contacts, optionally as two loops, each loop being divided into two segments. Alternatively or additionally, the lead includes at least eight macroelectrodes, optionally as... Figure 3D and 3H As shown, it has two rings and two segmented rings, each ring having three segments. Alternatively or additionally, the leads include 32 macroelectrode contacts, optionally at least some in the form of ring segments.

[0446] In some embodiments, the macroelectrode has an axial spacing between two ring electrodes (A), or between two proximal ring electrodes in four ring electrodes (B), or between two central ring electrodes in four ring electrodes, or between two distal ring electrodes in four ring electrodes, or between two pairs of segmented electrodes, each pair of segmented electrodes having similar axial positions (E), or between a proximal ring electrode and a more distal segmented electrode (f), or between two segmented electrodes (g), or between a proximal segmented electrode and a distal ring electrode (H).

[0447] In some embodiments, the axial spacing is predefined such that any pair of macroelectrodes will have sensed joint distal activity but sense distinct local activity. Alternatively or additionally, the distance of the axial spacing can be predefined according to the desired navigation resolution, optionally based on the target area size, for example, for navigating within a region and detecting transitions between sub-regions; the axial spacing is predefined as a minimum. Alternatively or additionally, the axial spacing is predefined once its positioning is determined based on the stimulation that the macroelectrodes are expected to provide; for example, if stimulation is delivered to a large volume region, the axial spacing should be larger compared to delivering stimulation to a region with a smaller volume (e.g., for a specific sub-region located within a large region).

[0448] In some embodiments, the axial spacing between the multiple macroelectrode pairs is equidistant. A potential advantage of equidistant spacing is that it may be easier to navigate and / or locate and / or reposition when the distance between the pairs is the same. Alternatively or additionally, the axial spacing between the multiple macroelectrode pairs is not equidistant. A potential advantage is that one spacing distance can be used for navigation, and another spacing distance can be used for stimulation without changing the leads.

[0449] Exemplary macroelectrode navigation and repositioning in the target area

[0450] Exemplary aspects of some embodiments of the present invention relate to macroelectrode navigation and / or repositioning in a target region. In some embodiments, electrical leads including at least two macroelectrode contacts are navigated and / or positioned in the target brain region. In some embodiments, optionally, with respect to the macroelectrode location, it is desirable to determine the location of several boundaries of the target region. Optionally, once the boundaries of the target region are identified, the leads are repositioned relative to the target region. In some embodiments, navigation is performed using leads having macroelectrodes, which are also used for stimulation. Optionally, the macroelectrode includes macroelectrode contacts.

[0451] Now for reference Figure 4A -F illustrates an exemplary positioning and / or repositioning of the macroelectrode 306 relative to the target region 224. Figure 4A Lead 200 is shown, which has four macroelectrode segments 306 with axial spacing, wherein each pair of segmented electrodes has the same axial position on lead (A) and approaches target region 224 in the direction of boundary 222a. Figure 4B The lead 200 is shown after advancement into the target region 224 and after the transition of the first set of macroelectrode segments 306a across boundary 222a.

[0452] Figure 4C The diagram shows the lead 200 after propulsion, such that the second set of macroelectrode segments 306b transitions across boundary 222a. In some embodiments, the boundary location is determined once the two axially spaced macroelectrode contacts have transitioned across the boundary.

[0453] Figure 4D Lead 200 is shown for further advancement within the target region. In some embodiments, lead 200 is used to determine the boundaries of subdomains within the target region.

[0454] Figure 4E The lead 200 is shown after being pushed beyond the target region 224, and the first set of macroelectrodes 306a transitions at the boundary 222b. Figure 4F The lead 200 is shown once the second set of macroelectrodes 306b has transitioned across boundary 222b. In some embodiments, the boundary is identified once both sets 306a and 306b have transitioned across boundary 222b.

[0455] In some embodiments, a predetermined axial interval A is used to determine the position of the boundary relative to the macroelectrode. Alternatively or additionally, when the macroelectrodes are repositioned, for example from their position... Figure 4F Return to their positions in Figure 4D When positioning the target area 224, axial spacing A is used. Alternatively or additionally, axial spacing A is predefined based on the desired resolution of the target area 224 boundary. Alternatively or additionally, axial spacing A is predefined based on stimulus requirements.

[0456] Exemplary Conductor-Coordinated Navigation System

[0457] Exemplary aspects of some embodiments of the present invention relate to a system for real-time brain navigation using macroelectrodes. Electroencephalogram (EEG) leads having at least two macroelectrodes are delivered to the patient's brain, optionally for targeting brain regions in real-time (i.e., while delivering the leads) to determine their location. Alternatively or additionally, the leads are delivered to establish the boundaries of the target brain region in real-time. Alternatively or additionally, the leads are delivered to provide stimulation to the target brain region.

[0458] Now for reference Figure 5The diagram illustrates a block diagram of a system according to some embodiments of the invention, such as system 501 for real-time navigation. In some embodiments, the real-time navigation system records the electrical activity of surrounding tissues as a recording probe is advanced into the brain. In some embodiments, the system records continuously while advancing the probe. Alternatively, the recording probe records when the probe position is fixed. For example, if the recording probe is advanced stepwise, recording is performed between these steps. Electrode probes, such as electrical leads 504 having at least two macroelectrode contacts (e.g., macroelectrode 540), are used to deliver signals into the patient's brain. Optionally, lead 504 is used for navigation and to provide short-term and / or long-term stimulation. Alternatively, lead 504 may be used only for navigation or only for navigation and short-term stimulation, and not for long-term stimulation, optionally replaced by a stimulation element.

[0459] According to some exemplary embodiments, short-term stimulation refers to stimulation used during the navigation phase, for example, to observe the response to stimulation at a specific location in the brain (the response may be a clinical symptom, such as tremor, rigidity, or a physiological symptom, such as beta band oscillations). In some embodiments, short-term stimulation occurs at the specific location for several seconds. According to some exemplary embodiments, long-term stimulation refers to therapeutic DBS designed to alleviate patient symptoms over a period of one year or more.

[0460] Optionally, lead 504 includes at least one microelectrode, such as microelectrode 539, or microelectrode contact. In some embodiments, the microelectrode contact is located at the distal end of lead 504. In some embodiments, the distal end of the lead serves as a guiding tip as the lead is advanced into the brain. In some embodiments, lead 504 includes at least one microelectrode 539 and at least one macroelectrode 540.

[0461] In some embodiments, lead 504 is connected to driver 505, which is configured to precisely drive electrode probes, such as lead 504, into or out of the brain. In some embodiments, driver 505 is manually activated by rotating a knob to control the user's movement of lead 504.

[0462] In some embodiments, lead 504 is operatively connected to stimulator 502, which transmits electrical signals, optionally for sensing. In some embodiments, the signals sensed by lead 504 are recorded in circuitry 560 having memory circuitry 564. Optionally, the signals recorded by memory circuitry 564 are further analyzed and / or processed in real time (i.e., during guided propulsion) by processing circuitry 562. In some embodiments, the analysis is performed to determine the transition into and / or out of the target brain region.

[0463] In some embodiments, a display 508 is provided, optionally graphically representing the advancement of leads 504 in the brain. Optionally, imaging data (e.g., CT and / or MRI scans) is used to provide a navigation map, optionally visually illustrating the estimated trajectory. In some embodiments, once processing circuitry 562 detects a transition into and / or out of a brain region, the display 508 is configured to signal to the user, optionally in the form of a graphical visualization on a map, and / or a text message on the display, and / or sound and / or acoustic signals. Alternatively or additionally, an external alarm 510 is provided, optionally in the form of a user indicator light and / or a buzzer sound, and / or a vibration alarm.

[0464] According to some exemplary embodiments, system 501 includes at least one sensor, such as sensor 541, for sensing parameters related to the movement of lead 504. In some embodiments, sensor 541 includes a precision sensor for monitoring drive acceleration, velocity, or position, for example, allowing monitoring of the insertion depth of lead 504. In some embodiments, processing circuitry 562 receives signals from sensor 541 during the advancement of lead 504 into the brain or at a predetermined time. Optionally, sensor 541 monitors the position of lead 504 at selected time points, and / or a selected range of movement of lead 504 or driver 505.

[0465] Now for reference Figure 6A It illustrates a block diagram of a system for automatic and / or semi-automatic real-time navigation according to some embodiments of the present invention, wherein the same reference numerals denote... Figure 5 The same components described in [the document / document].

[0466] In some embodiments, an automatic navigation system, such as system 601, is provided, optionally including a motor 602 for automatically propelling leads 504 toward a brain target. In some embodiments, the motor is connected to a driver 603, which is configured to precisely drive electrode probes, such as those entering or leaving leads in the brain. Optionally, driver 603 includes one or more micro-actuators. Alternatively or additionally, a user interface, such as in the form of a display 508, is provided, configured to enable control of input from the user to reach motor 602, optionally via processing circuitry 562, and to operate motor 602 in a semi-automatic manner. Alternatively or additionally, a remote control 604 is provided. In some embodiments, display 508 and / or remote control 604 include a trigger button that must be pressed to automatically navigate the leads.

[0467] In some embodiments, motor 602 advances lead 504 along a pre-estimated trajectory, optionally derived from pre-acquired imaging, optionally calculated automatically. In some embodiments, motor 602 is a stepper motor. In some embodiments, motor 602 is configured to advance lead 504 with equal step sizes, optionally ranging from about 200 μm to about 400 μm, and / or from about 300 μm to about 500 μm, and / or from 100 μm to about 300 μm. Alternatively or additionally, motor 602 is configured to advance lead 504 with unequal step sizes. Optionally, once at least one boundary has been identified as having been crossed by lead 504, the step size of motor 602 is reduced, optionally having a range of about 50 μm to about 100 μm. In some embodiments, the step size of motor 602 is reduced when lead 504 is within a desired brain target. In some embodiments, the motor step size is reduced by at least 10%. Alternatively, the motor step size is reduced by at least 20%. Alternatively, the motor step size can be reduced by at least 30%. Alternatively, the motor step size can be reduced by at least 40%. Alternatively, the motor step size can be reduced by at least 50%.

[0468] In some embodiments, motor 602 advances lead 504 in a stepwise manner. Alternatively or additionally, motor 602 advances lead 504 continuously. In some embodiments, motor 602 advances lead 504 continuously at a fixed speed. Alternatively, motor 602 advances lead 504 continuously at a variable speed, for example, 100 μm / s. Optionally, once at least one boundary has been identified as having been transitioned by lead 504, the speed of motor 602 is reduced. In some embodiments, the motor speed is reduced by at least 10%. Alternatively, the motor speed is reduced by at least 20%. Alternatively, the motor speed is reduced by at least 30%. Alternatively, the motor speed is reduced by at least 40%. Alternatively, the motor speed is reduced by at least 50%.

[0469] According to some exemplary embodiments, system 601 includes, for example, at least one sensor 605 for determining the position of lead 504 within the brain. In some embodiments, sensor 605 monitors movement of lead 504, for example by monitoring the acceleration, velocity, or position of lead 504. Alternatively or additionally, sensor 605 monitors the acceleration and / or velocity of actuator 603. In some embodiments, sensor 603 monitors movement of motor 602, for example, the rotational speed and / or rotational time of motor 602.

[0470] Exemplary control circuit

[0471] Now for reference Figure 6B It describes a module of a processing circuit according to some embodiments of the present invention.

[0472] According to some exemplary embodiments, the control circuit (e.g., control circuit 562) includes at least one signal receiving module, such as signal receiving module 620. In some embodiments, the signal receiving module receives signals from at least one macroelectrode and / or at least one microelectrode located on an electrode probe, such as EEG lead 504, etc. Figure 5 and 6A As shown. In some embodiments, the signal receiving module 620 receives a signal from at least one electrode located at a distance from the electrode probe. In some embodiments, the signal receiving module receives a signal, for example, such as... Figure 1A Box 107 and / or shown Figure 14C As shown in block 954. In some embodiments, the signal receiving module receives a MER signal and / or an LFP signal, such as a differential LFP signal.

[0473] According to some exemplary embodiments, the control circuit (e.g., control circuit 562) includes at least one LFP analysis module 622. In some embodiments, the LFP analysis module 622 analyzes LFP signals and / or differential LFP signals received by the signal receiving module 620. In some embodiments, the LFP analysis module 622 analyzes LFP and / or different LFP signals by filtering the signals. Alternatively or additionally, the LFP analysis module analyzes the differential LFP signal by subtracting one or more signals or signal features from different signals or different signal features. In some embodiments, the LFP analysis module analyzes LFP and / or differential LFP, for example as... Figure 1A Box 109 and / or shown Figure 8 Boxes 804, 806, 808 and / or shown Figure 14C As described in box 954 shown.

[0474] According to some exemplary embodiments, the control circuit (e.g., control circuit 562) includes at least one MER analysis module 624. In some embodiments, the MER analysis module analyzes the MER signal received by the signal receiving module 620. In some embodiments, the analysis of the MER signal performed by the MER analysis module 624 includes filtering the received MER signal. In some embodiments, the MER analysis module analyzes the received MER signal, for example, as... Figure 1A As shown in box 109.

[0475] According to some exemplary embodiments, the control circuit (e.g., control circuit 562) includes at least one boundary crossing measurement module 626. In some embodiments, the boundary crossing module 626 receives signals analyzed by the LFP analysis module 622 and / or the MER analysis module 624. In some embodiments, the boundary crossing measurement module analyzes the received signals and measures whether the boundary between two regions is crossed, for example, as... Figure 1A Box 11 and / or shown Figure 8 The box 810 shown describes, and / or Figure 14C As shown in boxes 956-960.

[0476] According to some exemplary embodiments, the control circuit (e.g., control circuit 562) includes at least one distance measurement module 628. In some embodiments, the distance measurement module 628 measures the distance between an electrode probe or at least one electrode located on the probe and a selected boundary or region. In some embodiments, the distance measurement module measures the distance based on analysis signals received from the LFP analysis module 622 and / or the MER analysis module 624.

[0477] According to some exemplary embodiments, the control circuit (e.g., control circuit 562) includes at least one positioning determination module 630. In some embodiments, the positioning determination module analyzes signals received from the LFP analysis module 622 and / or from the MER analysis module 624, for example, to determine the location of an electrode probe or at least one electrode of an electrode probe. In some embodiments, the positioning determination module 630 determines whether the electrode probe or the electrode of the electrode probe is located in a desired brain region target or in an adjacent target.

[0478] According to some exemplary embodiments, the control circuit, such as control circuit 562, includes at least one motor control module 632, for example, for controlling the movement of an electrode probe connected to a motor.

[0479] Exemplary automatic navigation algorithm

[0480] Now for reference Figure 7 The diagram illustrates a flowchart of an exemplary processing circuit decision algorithm for automated navigation according to some embodiments of the present invention. A potential advantage of automated navigation is that it reduces reliance on the subjective judgment of the user and / or the caregiver performing the navigation, and potentially overcomes the limitations of individual expertise.

[0481] In some embodiments, the processing circuitry has pre-acquired imaging, such as CT and / or MRI, and is optionally configured to estimate the target location at box 702. Alternatively or additionally, the location is manually identified and input into the processing circuitry using a user interface. In some embodiments, the transfer start point at box 704 is manually entered, or alternatively or additionally, it is automatically identified and marked.

[0482] In some embodiments, the processing circuitry is configured to simulate the estimated trajectory and / or a guided path from the transfer point to the estimated target location at block 706. Optionally, navigation begins only after the user provides a start command at block 708. In some embodiments, the start command may be a dedicated button and / or switch. Alternatively or additionally, the start command may be a verification module in the user interface.

[0483] Once the automatic process begins, a signal is sent at block 710 to the motor to advance the leads, optionally along an estimated trajectory. Optionally, the motor is signaled to advance the leads at a selected speed. In some embodiments, the differential LFP is calculated in real-time by processing circuitry at block 712 (optionally while advancing the leads). Optionally, the processing unit determines the boundary in real-time or online at block 740. In some embodiments, as long as no boundary transition is identified, signaling to the motor to advance the leads continues at block 710, and stimulation of the leads continues at block 712 to generate the differential LFP.

[0484] In some embodiments, once a transition to a brain region (e.g., reaching a boundary) is detected at box 740, a signal is sent at box 714 to instruct the motor to reduce its step size and / or speed. In some embodiments, the potential advantage of reaching the target brain region and reducing the lead advance rate once a transition is detected is a reduction in the likelihood of causing damage and / or over-penetration.

[0485] Optionally, after the first boundary is identified at block 740, the motor is still signaled at block 716 to advance the lead, and a differential LFP is derived in real time at block 718 while the lead is advancing. In some embodiments, the motor is still signaled to advance as long as the second transition is not identified at block 760. Optionally, the motor is stopped at block 720 once the second boundary is identified at block 760. Alternatively, the motor advances the lead, optionally by a predetermined distance. Alternatively, the motor may optionally retract the lead by a predetermined distance.

[0486] Exemplary difference calculation method

[0487] Now for reference Figure 8The diagram illustrates a flowchart of an exemplary differential calculation algorithm according to some embodiments of the invention. Optionally, the differential calculation algorithm is calculated in real time during lead advancement into the brain and is designed to provide transitions into and / or out of the target brain region in real time. In some embodiments, real time means once a transition is identified and when at least two macroelectrodes are transitioned into and / or out of the brain region. In some embodiments, real time means once a macroelectrode has partially transitioned into or out of the target region, where a portion is at least 0.5% of the transition, such as 0.5%, 10%, 25%, 50%, or any intermediate or larger value, up to a maximum transition of 100%. In some embodiments, partial transition of an electrode means that a portion of the outer surface of the macroelectrode facing the tissue transitions into or out of the target region. In some embodiments, the algorithm is used to calculate differential recordings between at least two electrodes (e.g., electrode contacts, one of which serves as a reference to the other). Alternatively or additionally, the algorithm is used to calculate differential recordings when using at least one external electrode contact not located on the electrode probe. In some embodiments, when a reference metal object in the brain (e.g., an insertable cannula) records signals from two electrodes on a probe, the signals are subtracted to calculate a differential signal.

[0488] In some embodiments, neural electrical activity is recorded at block 802 from each macroelectrode or from selected macroelectrodes. Optionally, the recorded data is filtered and / or purged at block 804, optionally defined by a signal greater than a predetermined threshold. In some embodiments, the signal is subtracted at block 806 to obtain a difference calculation, optionally removing similar inputs that may originate from relatively distant activities. Alternatively or additionally, the neural electrical activity at block 802 is directly recorded as a bipolar difference, which proceeds directly to further analysis 808.

[0489] In some embodiments, the differential LFP value is further calculated at block 808. In some embodiments, a 1 / F correction is applied to the differential LFP value. In some embodiments, the root mean square (RMS) value is calculated. Alternatively or additionally, a normalized root mean square (NRMS) value is calculated. Alternatively or additionally, a power spectral analysis is performed, for example by calculating a power spectral density (PSD) value, optionally normalized, for recording neurophysiological activity along the insertion trajectory. Alternatively or additionally, statistical analyses are derived, such as the median and the standard error of the median. Alternatively or additionally, the power in different frequency domains, such as alpha power, beta power, etc., is calculated.

[0490] In some embodiments, a dynamic Bayesian network, such as a hidden Markov model (HMM) based on partial and / or complete power spectral analysis values ​​calculated along the insertion trajectory, is computed and optionally assigned along the insertion trajectory to each selected point, representing a region within a plurality of regions with the highest probability value. In some embodiments, the points are selected by a user or processing circuitry. At block 810, the potential outcome is to identify points of one or more electrodes within a target region.

[0491] In some embodiments, average coherence is calculated between at least two macroelectrode leads in the same STN trajectory, optionally separated by a 2 mm horizontal distance. Potentially, coherence reflects the common input of electrodes containing far-field and shared-field activities, optionally including the same local activities. In some embodiments, coherence analysis is used to understand the contributing factors of activity recorded in white matter before entering the STN (white matter) and / or within the STN (gray matter).

[0492] Exemplary correlation signals of two electrodes

[0493] The following embodiments, together with the above description, illustrate some embodiments of the invention in a non-limiting manner.

[0494] In some embodiments, the related signals of two electrodes that share a common input signal and have independent activity are defined as follows:

[0495]

[0496] C represents the common input of the electrodes. Optionally, the common input may include multiple sources, such as volume-conducted cortical dipoles, and / or STN dipoles and / or the shared cross field of the electrodes.

[0497] In some embodiments, Ind1 and Ind2 represent local independent inputs of electrode number 1 and number 2, respectively.

[0498] In some embodiments, by definition, Ind1 and Ind2 are uncorrelated (independent); therefore, the covariance of Ind1 and nd2 is zero.

[0499] In some embodiments, it is assumed that C and Ind are uncorrelated, therefore Var(C+Ind) = Var(C) + Var(Ind)

[0500] Alternatively, it is assumed that when both electrodes are outside the STN and / or both electrodes are inside the STN, the variance of Ind1 is the same as the variance of Ind2 because they are recorded in the same brain tissue: Var(Ind1) = Var(Ind2) = Var(Ind). Therefore, in some embodiments, the relevant equations can be written as follows:

[0501]

[0502] In some embodiments, the relevant equation (2) can be used twice: first when both electrodes are outside the STN, and second when both electrodes are inside the STN, using Ind outside and Ind inside This represents the local activity inside and outside the STN. Alternatively, it can be represented by assuming Var(C outside ) = Var(C inside = Var(C), when one electrode is outside the STN and the second electrode is inside the STN (Ind) outside ≠Ind inside When inside the STN, neglecting the contribution of the STN dipole can further simplify the model. This is likely correct because most common activity is conducted from the cortical (EEG) volume, and the small difference in common signal intensity can be ignored over the small distance used here (6 mm movement, approximately 80-90 mm from the cortex in a typical STN trajectory path). In some embodiments, the internal-external correlation equation can be written as follows:

[0503]

[0504] Alternatively or optionally, Equation (2) can be used to predict the correlation between the case of one electrode outside the STN and the case of the two electrodes inside the STN in Equation (3).

[0505]

[0506] In some embodiments, the prediction (e.g., Equation 4) is used both for coherence (correlation as a function of frequency) and for the time-domain cross-correlation function at hysteresis zero (clz). Optionally, the correspondence between observed data and individual predicted clz values ​​is quantified. The quality of fit between the observed “inside / outside” and predicted “inside / outside” clz values ​​can optionally be assessed by calculating their Pearson product-moment correlation coefficient, denoted as cc.

[0507] In some embodiments, the coherence values ​​and clz values ​​are in the range of 0 to 1. Optionally, to overcome the distortion of this cutoff range, the correlation values ​​are transformed by Fisher's Z-transform (Equation 5), and / or the population statistic is calculated, and / or the population transformed values ​​are transformed back to values ​​in the range of 0 to 1 by the inverse Fisher's Z-transform (Equation 6) based on Sokal and Rohlf, 1995 (the entire contents of which are incorporated herein by reference).

[0508]

[0509]

[0510] ln(°) is the natural logarithm.

[0511] In some embodiments, an estimate of the ratio of the common signal to the local signal of each record configuration record can be derived from Equation 2:

[0512]

[0513] Optionally, Equation 7 can be used twice: first, when both electrodes are outside the STN, and second, when both electrodes are inside the STN. In some embodiments, dividing by the ratio described above (two electrodes outside the STN, two electrodes inside the STN) can derive Var(Ind) inside ) / Var(Ind outside The ratio of ).

[0514] Exemplary three-pole navigation

[0515] Now for reference Figure 9A -F depicts two tripolar neural probe recordings according to some embodiments of the present invention. Figure 9A -F describes an example of a trajectory of two sets of tripolar neural probe electrodes simultaneously recorded along a dorsolateral-ventromedial axis according to some embodiments of the invention. Some of the images and details discussed herein are described in “Local vs. volumeconductance activity of field potentials in the human subthalamic nucleus”, Marmor O. 2017, which is incorporated herein by reference.

[0516] In some embodiments, the electrodes are horizontally separated by 2 mm: optionally, the left column is the data recorded by the first electrode; alternatively or additionally, the right column is the data recorded by the second electrode (2 mm in front of the first electrode). Optionally, the depth indicates the position on the dorsolateral-ventromedial axis. Alternatively or additionally, the red line (902) marks the STN inlet. Figure 9A The normalized root mean square (RMS) of spike activity from microelectrode recordings is illustrated according to some embodiments of the invention. The X-axis is the position of the dorsolateral-ventromedial axis starting 10 mm anterior to the STN center and is given as the estimated distance to the target (EDT). The red line (902) automatically detects the STN entry point based on the spike activity markers from the microelectrode recordings. Figure 9BAn example is shown of the spectrum of peak activity recorded from the microelectrode after full-wave rectification. In some embodiments, the power is normalized to an average power of 4–200 Hz. Figure 9C Example of a spectrum recorded by an LFP microelectrode after 1 / F (α = 1) correction. Power is on a 10log10 scale. Figure 9D An example is provided of the spectrum of peak activity recorded by the macroelectrode after full-wave rectification. The power is on a 10log10 scale. According to some embodiments, the red line (902) marks the STN inlet of the distal macroelectrode contact defined 3 mm after entering the microelectrode. Figure 9E Example of a spectrum recorded from an LFP macroelectrode at the distal contact after 1 / F correction. Power is on a 10log10 scale. Figure 9F An example is shown of the spectrum recorded by the LFP differential bipolar macroelectrode after 1 / F correction. The power is on a 10log10 scale. The red line (902) marks the STN inlet of the distal macroelectrode contact, defined 3 mm after entering the microelectrode.

[0517] Exemplary power spectral density spectrum

[0518] Now for reference Figure 10 This illustrates the power spectral density (PSD) along the trajectory and its average spectrum outside and inside the STN according to some embodiments of the invention. (The above figure...) Figure 10 In Figure A and B): the median population spectral plot is used as a function of depth (position on the dorsolateral-ventromedial axis). Depth "0" indicates the STN entry point on the dorsolateral-ventromedial axis. (See figure below) Figure 10 C, D, and E in the figure): Average power spectrum in white matter 1006 (WM) outside STN (shaded blue line, average ± SEM) and inside STN 1004 (shaded red line, average ± SEM). A. Microelectrode peak activity (n=56) after full-wave rectification as a function of position on the dorsolateral-ventromedial axis (3 mm before and after entering STN), normalized to power by average power of 4-200 Hz. B. Macroelectrode peak activity (n=48), codified as AC monopolar microelectrode LFP (n=56), spectrum (above figure) Figure 10 In the figure, A and B are 1 / F (α = 1) corrections and expressed as 10log10. The power recorded by the LFP is not normalized by the average power. D. Monopolar macroelectrode LFP (n = 48). Average spectrum (see figure below) Figure 10 C, D, and E in the image were only captured from a depth of 1–2.5 mm after entering the STN. E. Bipolar-macroelectrode LFP recording (n = 11). Average spectrum (see figure below). Figure 10 C, D and E in the image are only taken from a depth of 1-2.5mm after entering the STN.

[0519] An exemplary comparison between microelectrode spike activity along the trajectory and bipolar macroelectrode LFP activity.

[0520] Now for reference Figure 11 This example illustrates the average power (4-35 Hz) microelectrode spike activity and bipolar macroelectrode LFP along a trajectory according to some embodiments of the invention. The mean Z-score of the 4-35 Hz power is calculated based on the activity at the recording location before entering the STN (3 mm to 1 mm prior). The shaded light blue (1104) and purple (1102) lines represent the median ± standard deviation (n = 11) of the microelectrode spike (SPK) 1104 activity and the bipolar macroelectrode LFP 1102 activity, respectively. The bipolar macroelectrode LFP power is normalized by the average power over the 4-200 Hz range to match the analysis of the microelectrode spike activity. For this analysis, the macroelectrode LFP signal is filtered over the 3-200 Hz range. “0” indicates the STN entry point automatically detected from the microelectrode spike activity.

[0521] Exemplary population coherence between two parallel recording electrodes

[0522] Now for reference Figure 12 This illustrates the population coherence between two parallel recording electrodes according to some embodiments of the invention. The average coherence (with a 2mm horizontal distance between them) is calculated between the electrode pairs when both electrodes are in the white matter (WM) outside STN 1202 (blue); when one electrode is in the WM outside STN and the second electrode is inside STN (green) 1204; and when both electrodes are inside STN (red) 1206. The black dashed line 1202 is a prediction of the input-output configuration (derived from Equation 4 in the Method section). The solid line and light dots represent mean coherence ± SEM, respectively. According to some embodiments, the coherence values ​​are averaged before the Fisher Z-transform and inverse Z-transform back. Anomalous electrode pairs with artifacts are excluded. The number of trajectories for the paired electrodes is given in each subplot. A. Spike coherence of microelectrode recording; B. LFP coherence of microelectrode recording; C. Spike coherence of macroelectrode recording; D. LFP coherence of macroelectrode recording.

[0523] An exemplary comparison between predicted and actual intrinsic and extrinsic correlation values.

[0524] Now for reference Figure 13This illustrates the predicted versus actual intrinsic and extrinsic correlation values ​​according to some embodiments of the invention. Each correlation value is the average cross-correlation at a hysteresis zero (clz) value. This includes the recording location along the trajectory when one of the parallel electrodes is located in white matter outside the STN (WM) and the other inside the STN (STN). According to some embodiments, the correlation coefficient (cc) value and the slope represented are calculated after the Fisher Z-transform. A red dashed line 1302 is plotted to allow comparison of the regression line slope with the slope = 1 line. A. Microelectrode spike activity. B. Macroelectrode spike activity. C. Microelectrode LFP. D. Macroelectrode LFP. In the inset, values ​​are represented by the Fisher Z-transform to better stretch the values ​​(because the range of the values ​​is distorted by the truncation). The number of trajectories for the paired electrodes is given in each subplot. Anomalous electrode pairs with artifacts are excluded.

[0525] Exemplary calculations of common and independent activities inside and outside the STN

[0526] Now for reference Figure 14A Examples of normalized root mean square (RMS) and variance ratios of common and independent activities inside and outside the STN according to some embodiments of the invention. A. Normalized root mean square (nRMS) for different recording configurations. Normalization is based on the average RMS from 3 mm to 1 mm before entering the STN. Red line 1402 marks the STN entry point. B. Variance ratios of common inputs (Var(C)), local independent activities outside the STN (Var(Lout)), and local independent activities inside the STN (Var(Lin)) in different recording configurations. In some embodiments, the variance ratios are calculated from the clz value and derived from Equation 2 in the Method section.

[0527] Estimating proximity to the boundary between anatomical regions as an example

[0528] According to some exemplary embodiments, the proximity between the distal end of an electrical lead and the boundary of an anatomical region is estimated based on electrical signals recorded by the leads. In some embodiments, the proximity to the boundary is estimated by detecting changes in the recorded electrical signals. In some embodiments, these signal changes indicate proximity to the boundary.

[0529] According to some exemplary embodiments, the functional organization map used during navigation, optionally continuously and / or automatically, includes changes in electrical signals associated with boundaries between proximal anatomical regions. In some embodiments, the functional organization map is used to analyze recorded electrical signals to estimate proximity. Optionally, different signal changes are associated with different distances from boundaries and / or the proximity of electrical leads to different boundaries.

[0530] According to some exemplary embodiments, spike activity, such as the number of spikes or the power and / or intensity of spikes, changes as the electrical conduction approaches its boundary. According to some exemplary embodiments, such as... Figure 9A As shown, the number of spikes changes in regions 910 and 912 before the STN inlet boundary, marked by line 902. In some embodiments, such as... Figure 9B As shown, the spectrum of peak activity also reveals the variation of peak activity in regions 912 and 914 near the STN inlet boundary.

[0531] According to some exemplary embodiments, variations in peak activity are noticeable at specific frequencies of the recorded signal. In some embodiments, such as... Figure 10 As shown in A, the change in spike activity marked by line 1002 before entering through the STN inlet boundary is evident at high frequencies (e.g., frequencies above 20 Hz), as seen in region 1010.

[0532] According to some exemplary embodiments, the navigation system adjusts the propulsion speed of the electrical leads based on estimated proximity. Furthermore, the navigation system communicates and indicates to the user that the electrical leads are getting closer to the boundary.

[0533] Detection / transition of exemplary subthalamic nucleus boundary between STN and SNr was detected.

[0534] One aspect of some embodiments relates to automated real-time electrophysiological detection of the lower boundary of the subthalamic nucleus (STN).

[0535] In some embodiments, the transition between STN and SNr regions in the brain is detected in order to navigate the tool to a region of interest in the brain for the treatment of Parkinson's disease.

[0536] According to some embodiments, high-precision methods, optionally based on computational analysis procedures, are provided for distinguishing STN and SNr regions of the brain. In some embodiments, the method uses several features of the power spectrum from microelectrode recordings (MER). Optionally, the method is used in real time during deep brain stimulation (DBS) surgery, for example to allow computer-assisted MER navigation.

[0537] According to some exemplary embodiments, a machine learning process is utilized to accurately distinguish between STN and SNr. In some embodiments, this process utilizes the MER power spectrum. In some embodiments, a support vector machine (SVM) classifier is used to confirm that the MER power spectrum features can provide robust differentiation between SNr and STN groups, optionally as a first step in the process. In some embodiments, a hidden Markov model (HMM) process is then performed, using MER features and trajectory history to detect STN exits, or to detect (white matter) WM or SNr. In some embodiments, a machine learning algorithm, such as the one described herein, is used to identify the lower boundary of the STN and / or the transition between the STN and SNr.

[0538] Optionally, at least one additional step, as described in detail below, is performed to provide automated real-time electrophysiological detection of the lower boundary of the subthalamic nucleus (STN).

[0539] Exemplary procedure for detecting STN exit point / ventral boundary

[0540] According to some embodiments, when manipulating the brain and inserting electrodes into the STN, when the STN is the target of the brain, the electrode probes must be kept within the STN boundary and not cross the ventral boundary of the STN into the SNr.

[0541] Now for reference Figure 14C It describes a process for detecting the ventral boundary of the STN according to some exemplary embodiments of the present invention.

[0542] According to some exemplary embodiments, an electrode probe is inserted and advanced into the brain at block 950. In some embodiments, the electrode probe includes at least two macroelectrode contacts located on the outer surface of the electrode probe. In some embodiments, the macroelectrode includes a ring electrode or a segmented electrode. Alternatively, the electrode probe includes at least two microelectrodes or microelectrode contacts located on the outer surface of the electrode probe and / or on the distal end of the electrode probe, which serves as the tip of the electrode probe when it is advanced into the brain. Optionally, the electrode probe includes at least one microelectrode contact and at least one macroelectrode contact. In some embodiments, the electrode probe includes lead 200 or lead 504, such as... Figure 3A -H、 Figures 4A-4F and Figure 5 As shown respectively.

[0543] According to some exemplary embodiments, at block 952, the electrode probe records MER or LFP. In some embodiments, the electrode probe continuously records MER or LFP as the lead is advanced into the brain. Alternatively, MER or LFP is recorded between steps of electrode probe movement.

[0544] According to some exemplary embodiments, the recorded MER or LFP is analyzed at block 954. In some embodiments, the analysis includes calculating different characteristics of the recorded signal, such as calculating a root mean square (RMS) estimate based on the recorded signal at each electrode depth or a selected electrode depth. Optionally, the RMS is normalized, for example, normalized to the white matter RMS or RMS of any defined region, to generate a normalized RMS (NRMS). In some embodiments, the analysis includes generating a power spectrum or average power spectrum based on the RMS or NRMS.

[0545] According to some exemplary embodiments, the ratio between the high-frequency power spectrum and the lower-frequency power is calculated at block 956. In some embodiments, the ratio is calculated between frequencies in the 5-300 Hz range of the power spectrum, such as 5-25 Hz, 5-30 Hz, 5-50 Hz, 50-300 Hz, 100-150 Hz, 120-250 Hz, or any other mid-frequency or frequency range. In some embodiments, the ratio is calculated between the power spectrum or average power spectrum at 100-150 Hz and the power spectrum or average power spectrum at 5-25 Hz. Optionally, the ratio is calculated between power spectra or average power spectra above 80 Hz and between power spectra or average power spectra below 50 Hz.

[0546] According to some exemplary embodiments, STN and / or STN boundaries, such as the STN ventral boundary, are detected at block 958. In some embodiments, the detection is based on calculated RMS, NRMS, power spectrum, and / or average power spectrum. In some embodiments, the detection is based on the ratio between high-frequency power and low-frequency power calculated at block 956, such as the 100-150Hz / 5-25Hz power ratio.

[0547] According to some exemplary embodiments, if the electrode probe crosses the ventral boundary, optionally crossing the SNr at block 960, the electrode probe is retracted at block 966. In some embodiments, the electrode is retracted to a position known to indicate the last STN. Alternatively, the electrode is retracted in a predetermined step. Optionally, the predetermined step size is in the range of 0.1-5 mm, such as 0.1, 0.5, 1 mm, or any intermediate or larger step size. In some embodiments, after the electrode is retracted at block 966, MER and / or LFP are recorded at block 952, optionally to determine or verify the current position of the electrode probe.

[0548] According to some exemplary embodiments, if the electrode does not cross the ventral boundary of the STN, the system determines at block 962 whether the electrode is in the desired target. In some embodiments, if the electrode is in the desired target, the movement of the electrode stops at block 964. Alternatively, if the electrode is not in the desired target, the electrode probe is further advanced into the brain at block 950.

[0549] microelectrode recording

[0550] Now for reference Figures 15A-15C This illustrates an overview of STN targeting according to some embodiments of the invention. Some of the images and details discussed herein are described in “Stop! border ahead: Automatic detection of subthalamic exit during deep brain stimulation surgery”, Valsky D. 2017, which is incorporated herein by reference.

[0551] Figure 15A The diagram illustrates a typical trajectory of two parallel microelectrodes displaying subcortical structures. In some embodiments, the structures include the STN (subthalamic nucleus), SNr (substantia nigra reticularis), and ZI (zone of indeterminates). Figure 15B Overall, one-second raw signal traces recorded at different depths (in descending order) along trajectories from Parkinson's disease patients are shown. In some embodiments, the traces indicate regions of the inner capsule (white matter); the dorsal oscillating region (DLOR) STN; the ventromedial non-oscillating region (VMNR) STN; and the white matter between the STN and the substantia nigra reticular formation (SNr). Figure 15C A functional state model representing anatomical structures is shown, which are optionally encountered sequentially during microelectrode recordings detected by STN. Arrows between states indicate possible state transitions.

[0552] According to some exemplary embodiments, optionally, one or two parallel microelectrodes are inserted for the left and right hemispheres, such as... Figure 15A As shown, the recording begins 10 mm above the calculated target area. In some embodiments, a specific trajectory is adjusted for each patient. Alternatively, two or more microelectrodes may be inserted. In some embodiments, the recording begins at a distance between 1 mm and 20 mm from the target area, such as distances 1, 3, 5, or any intermediate or greater distance from the target area. In some embodiments, the microelectrode is a microelectrode contact located on an electrode probe or lead, such as lead 200 or lead 504, as... Figures 3A-3H 4A-4F and Figure 5 Figures 6 and 7 are shown respectively. In some embodiments, recording is performed using a combination of microelectrode contacts and macroelectrode contacts.

[0553] In some embodiments, two microelectrodes 1100 and 1102 are used, for example as follows: Figure 15A As shown: Figure 15AThe diagram illustrates optional positioning. In some embodiments, the "central" electrode may optionally point towards the center of the dorsolateral STN target, depending on the imaging results. In some embodiments, the "central" electrode traverses the STN and optionally enters the SNr without penetrating the white matter. In some embodiments, the "anterior" electrode advances 2 mm anterior to the central electrode (in the parasagittal plane), thus traversing the STN-SNr region in a more ventral plane. In some embodiments, the "anterior" electrode advances between 0.5 and 5 mm anterior to the central electrode (e.g., 0.5 mm, 1 mm, 2 mm, or any intermediate or greater distance (in the parasagittal plane)). In some embodiments, a posterior electrode, a lateral electrode, or a central electrode, or any combination of electrodes, is used. In some embodiments, a central, anterior, and / or lateral electrode, or any combination of these electrodes, is used. Optionally, the anterior electrode, unlike the central electrode, penetrates the white matter before entering the SNr.

[0554] Exemplary neural database

[0555] According to some embodiments, the neuronal database is divided into two parts. In some embodiments, the training dataset has multiple trajectories obtained from multiple patients containing multiple stable MERs recorded in multiple brain regions (i.e., white matter before the STN, dorsolateral oscillating region of the STN (DLOR), ventral non-oscillating region of the STN (VMNR), and white matter after the STN and SNr).

[0556] Optionally, a subset of this dataset, comprising multiple MERs from the dorsal and ventral STNs and SNr, is used for a support vector machine (SVM). In some embodiments, the training dataset of multiple trajectories is also used to find the optimal parameters of the hidden Markov model (HMM). Optionally, additional trajectories recorded from other patients are used to test the robustness of the HMM detection.

[0557] According to some embodiments, in a subsequent step, a root mean square (RMS) estimate is calculated based on multi-cell activity recorded by the microelectrode at each electrode depth. In some embodiments, since the RMS value is susceptible to electrode characteristics (e.g., electrode impedance), the RMS is optionally normalized using the pre-STN (white matter) baseline RMS, resulting in a normalized root mean square (NRMS).

[0558] According to some embodiments, visual inspection of the average STN and SNr power spectra reveals significant differences in the 5-300 Hz domain. In some embodiments, to identify the frequency band containing the largest difference between the STN and SNr, the 5-300 Hz range of the power spectrum is divided into several approximately logarithmically spaced bands, such as ten approximately logarithmically spaced bands. In some embodiments, for each band, the average power of each MER is calculated, and optionally, the difference between the mean power of the STN and SNr is then evaluated. In some embodiments, the results are normalized by the square root of the sum of the variances of the STN and SNr.

[0559] In some embodiments, when using the method, the frequency band containing the greatest difference between STN and SNr is identified. In some embodiments, the back-side boundary is detected by identifying the rise in RMS (NRMS) and β-band power.

[0560] Support Vector Machine (SVM) distinguishes between STN and SNrMER

[0561] According to some exemplary embodiments, a linear SVM with a linear kernel algorithm is used to provide high-performance differentiation between the STN and SNr populations. In some embodiments, the SVM is a classification method that finds a linear boundary that maximizes the interval between two classes (e.g., STN and SNr). In some embodiments, the SVM linear boundary is computed only from those MERs closest to the interface between the two groups of interest, e.g., as... Figure 18 As shown.

[0562] According to some exemplary embodiments, such as for SVM analysis, measurements in the time and frequency domains (which may be based on the NRMS and power spectrum of the MER) are used as features for SVM classification. In some embodiments, the classification process uses the NRMS and the “100-150Hz / 5-25Hz power ratio” feature for each MER in the training dataset, and optionally uses their class labels STN or SNr.

[0563] According to some exemplary embodiments, firstly, the MERs from the entire training dataset are randomly divided into a training subset (90% of the MERs) and a test subset (10% of the MERs). In some embodiments, in a second step, the model is trained by finding the optimal separating boundary based on features from the training MERs. In some embodiments, in a third step, an SVM is used to predict the class labels of the test subset, and the predictions are compared with known values ​​to evaluate accuracy. In some embodiments, this process is repeated multiple times, optionally ten times, using different and non-overlapping 10% of the MERs for testing in each repetition, and the remaining 90% of the MERs for training in that repetition. In some embodiments, multiple results are averaged to produce a performance estimate.

[0564] Using Hidden Markov Models for STN Ventral Boundary Detection as an Example

[0565] According to some exemplary embodiments, the HMM process is used to estimate the state of the electrode at each depth along the trajectory based on MER-based NRMS and power spectrum characteristics.

[0566] In some embodiments, the HMM procedure is used to distinguish STN from white matter. According to some embodiments of the invention, the HMM procedure is designed to improve the ability to detect STN exits by depicting the boundary between STN and SNr, optionally even for cases where there is a lack of white matter (WM) gaps between STN and SNr.

[0567] According to some exemplary embodiments, the input data for the HMM process consists of a single value sequence of MER-based features. In some embodiments, the features used are typically NRMS from the PSD, β power (13-30 Hz), and the “100-150 Hz / 5-25 Hz power ratio” used in SVM. Optionally, to assess accuracy, the HMM predictions are compared with an electrophysiologist’s determination of the location of the ventral boundary of the STN (STN exit).

[0568] According to some exemplary embodiments, as a result of the previously described steps (including microelectrode recording, neuron database processing, generation of support vector machine (SVM) for STN and SNr MER differentiation, and HMM process), differentiation between STN and SNr recordings is performed.

[0569] The ratio between MER high-frequency power (100-150Hz) and low-frequency power (5-25Hz) is used as an example for STN exit point detection.

[0570] According to some exemplary embodiments, power spectrum characteristics help distinguish between STN and SNr recordings. In some embodiments, the ratio between high-frequency power (e.g., 100-150 Hz or greater than 70 Hz) and lower-frequency power (e.g., 5-25 Hz or less than 50 Hz) is calculated to detect exit points from the STN to different regions of the brain (e.g., SNr or WM). Optionally, the STN exit point is detected by calculating the ratio.

[0571] Now for reference Figure 16A and 16B It illustrates the relationship between STN-white matter transition and STN-SNr transition detected according to some embodiments of the present invention. Figure 16AThe STN-WM transitions from three exemplary trajectories from three patients are illustrated according to some embodiments of the present invention. The first three plots represent the Normalized Root Mean Square (NRMS) analysis as a function of EDT. The bottom three plots represent the spectral power distribution (PSD) spectra of the data in relation to EDT on the x-axis. Figure 16B Similar data are shown according to some embodiments of the invention, but for STN-SNr transition. Notably, the estimated distance to the target (EDT) is defined as the STN center based on preoperative imaging.

[0572] In some embodiments, the NRMS value calculated from MER is effective in detecting STN boundaries with white matter. In some embodiments, for example, such as Figure 16A As seen in the three examples, the top panel, STN inlet, and STN outlet boundaries are marked as sharp increases and decreases in NRMS, respectively.

[0573] In some embodiments, in these cases presented with three top panels, the electrode traverses the STN and enters the SNr after passing through the white matter (WM). In some embodiments, for example, as Figure 16A As seen in the three bottom panels, the power spectrum of SNr depicts a unique feature – dark vertical lines, indicating a decrease in relative power at lower frequencies.

[0574] According to some embodiments, such as... Figure 16B As shown, some trajectories lack a well-defined STN exit. In some embodiments, these are cases where there is no obvious instantaneous reduction in the NRMS (NRMS gap), most likely because the electrode traverses the STN and enters the SNr without passing through the white matter after the STN.

[0575] In some embodiments, such as in these cases, for example Figure 16B As shown, SNR cannot be identified by NRMS, but SNr (between 0mm and -2mm of the estimated distance to the target) can be identified by electrophysiologists and can be detected by NRMS. Figure 16B The dark vertical line in the bottom panel is identifiable in the power spectrum.

[0576] In some embodiments, for example, such as from Figure 16B As seen in the example shown in the bottom panel, characteristics from the power spectrum can be used to assist in the detection of the STN outlet, especially in cases where there is a lack of STN-WM transition and NRMS gap.

[0577] Now refer to another source Figures 17A-17C The distribution of MER characteristics shows that, according to some embodiments of the invention, the “100-150Hz / 5-25Hz power ratio” separates STN and SNr better than NRMS. Figure 17AThe diagram shown on the left illustrates the NRMS distribution of the dorsal STN, ventral STN, SNr, pre-STN white matter, and post-STN white matter according to some embodiments of the present invention. The diagram shown on the right... Figure 17A The graphs show the same data, but with three subcortical structures superimposed on the x-axis, demonstrating the overlap of the NRMS distributions of STN and SNr according to some embodiments of the invention.

[0578] Shown on the left Figure 17B The graphs illustrate the power spectral density as a function of frequency according to some embodiments of the invention, where linear scaling plots are present in DLOR STN, VMNR STN, and SNr. The graphs shown on the right... Figure 17B The graph shows the same data according to some embodiments of the present invention, but with a logarithmic scale on the x-axis.

[0579] Shown on the left Figure 17C The graph illustrates the "100-150Hz / 5-25Hz power ratio" distribution in five regions according to some embodiments of the present invention. The right side shows... Figure 17C The graph shows the same data according to some embodiments of the present invention, but the three subcortical structures are superimposed on the x-axis.

[0580] According to some exemplary embodiments, in order to evaluate the ability of NRMS to distinguish STN from SNr, the distribution of their NRMS values ​​is calculated. Figure 17A The overlap of the NRMS distributions of 660 MERs in STN DLOR, 990 MERs in STN VMNR, and 155 MERs in SNr (training dataset) is shown. In some embodiments, such as... Figure 17A As shown, there is significant overlap between the different distributions, so there is no obvious separation between STN and SNr using NRMS.

[0581] According to some exemplary embodiments, such as Figure 17B As shown, the mean PSD of STN and SNr records is illustrated, and features derived from the PSD are used to distinguish STN and SNr. Optionally, with Figure 16A and 16B The feature labels for STN and SNr in the spectrum diagrams shown are consistent. In some embodiments, the average PSDs of the two STN domains and SNr exhibit distinct and non-overlapping characteristics. In some embodiments, it is shown as Figure 17BThe mean SNr PSD of the brightest line (thus indicated by reference numeral 1200) indicates reduced activity in the 5-25 Hz band compared to the mean PSD of STN DLOR represented by the line specified by reference numeral 1210 and VMNR represented by the line specified by reference numeral 1220. In some embodiments, the mean PSD in SNr shows increased activity in the 85-300 Hz band.

[0582] According to some embodiments, in order to quantitatively determine which part of the power spectrum allows the highest or best distinction between STN and SNr, multiple bands with approximate logarithmic distributions along the frequency axis in the power spectrum are examined.

[0583] According to some embodiments of the invention, the average power in two different frequency bands: high frequency (100-150Hz) and low frequency (5-25Hz) provides the greatest distinction between STN and SNr.

[0584] Now for reference Figure 17C It describes the power ratio between 100-150Hz and 5-25Hz according to some embodiments of the present invention.

[0585] According to some exemplary embodiments, the power ratio of the two frequency bands mentioned above is calculated, and this feature is further referred to as "100-150Hz / 5-25Hz power ratio". In some embodiments, for example, such as Figure 17C As shown, the overlap in the distributions of the STN and SNr power ratios is very small.

[0586] According to some exemplary embodiments, such as Figure 18 As shown, the utility of the power ratio for STN-SNr differentiation was confirmed in the support vector machine (SVM) analysis.

[0587] According to some exemplary embodiments, an SVM classifier is used to examine the ability of the "100-150Hz / 5-25Hz power ratio" to provide robust differentiation between SNr and STN. Now refer to Figure 18 The diagram illustrates the results of an SVM classifier according to some embodiments, which is trained and tested using multiple randomly selected samples from STN and SNr. In some embodiments, a linear kernel decision boundary is used to classify the training set as SNr (hollow squares) and STN (hollow triangles); then new data points are classified as either SNr (solid squares) or STN (solid triangles). Circles represent support vectors that define the decision boundary between STN and SNr samples.

[0588] According to some exemplary embodiments, there is a lack of correlation between NRMS and the "100-150Hz / 5-25Hz power ratio", for example, Figure 12As shown. In some embodiments, both features enhance the utility of the power ratio feature as an additional attribute for classifying MER. In some embodiments, the overall classification accuracy is approximately 98%.

[0589] According to some exemplary embodiments, Hidden Markov Model (HMM) analysis enables reliable detection of STN outlets. In some embodiments, the HMM process uses MER features and trajectory history to make real-time decisions regarding electrode placement, whether manual or automated using a drive mechanism. Optionally, in addition to MER features, the use of trajectory history allows the HMM process to ignore recorded faults that would be misclassified by classification methods (e.g., SVM).

[0590] According to some exemplary embodiments, the HMM process used in this invention is adapted to distinguish between STN and SNr using a "100-150Hz / 5-25Hz power ratio" and NRMS characteristics, as well as the depth of the trajectory (i.e., the estimated distance to the target).

[0591] Now for reference Figures 19A-19C It shows two examples of NRMS for typical trajectories, specifically shown in Figure 19A And in the PSD, specifically shown in Figure 19B The "100-150Hz / 5-25Hz power ratio" characteristic, as a function of the estimated distance to the target (EDT), is specifically as follows: Figure 19C As shown.

[0592] According to some exemplary embodiments, such as those shown in these two examples, the sharp increase in the "100-150Hz / 5-25Hz power ratio" is consistent with the decisions of human experts regarding the STN-SNr transition, which is determined by... Figure 19A The lines shown are indicated here by reference numeral 1900.

[0593] According to some exemplary embodiments, two measurements are used to evaluate the performance of the HMM. In some embodiments, one is the mean OUT position error. In some embodiments, the mean OUT position error is defined as the difference between the position defined by a human expert (which is the transition position defined by a neurophysiologist) and the position (HMM) inferred as the transition position, both being estimated distances to the target measured in mm. Optionally, the second measurement is the OUT transition error, defined as an OUT position error greater than 1 mm. In some embodiments, the hit count is the number of correctly detected OUT transitions. Furthermore, the miss count is the number of OUT transitions not detected by the HMM process, based on the decision of the human expert.

[0594] In some embodiments, STN-SNr and STN-WM exhibit better mean and standard deviation for OUT location errors than previously known methods. The performance on the training dataset for OUT location errors has a 97% hit rate.

[0595] According to some exemplary embodiments, while performing the above-described process steps, precise automated real-time electrophysiological detection of the ventral STN boundary can be performed. In some embodiments, a computational machine learning process with novel characteristics of the ratio of high-frequency (100-150Hz) power to low-frequency (5-25Hz) power allows for high-precision differentiation between the STN and SNr.

[0596] In some embodiments, such as those described above, the SVM process is used to verify that the "100-150Hz / 5-25Hz power ratio" is a reliable feature for distinguishing between STN and SNr populations. In some embodiments, the MER feature, along with trajectory history, is used to leverage the HMM process to detect STN exits of white matter (WM) or SNr. Optionally, the SVM process is followed by the HMM process.

[0597] In some embodiments, algorithms such as multi-class SVMs, decision trees, and augmented decision stumps can be used to perform initial clustering of the data. Furthermore, gradient-augmented decision trees and Long Short-Term Memory (LSTM) networks can be used for STN boundary discrimination.

[0598] In some embodiments, MER data from multiple centers can be combined to test the broad applicability of the algorithm for automatic navigation and differentiation between different anatomical structures in DBS surgery.

[0599] Models that use machine learning algorithms to generate functional organizational maps

[0600] According to some exemplary embodiments, the model for functional tissue mapping is used by the computer of the navigation system to map brain tissue online during surgery. In some embodiments, an existing model is updated using a machine learning algorithm to generate a trained model before electrode probes are inserted into the brain. Reference now Figure 20 It describes the process of generating a trained model for functional organization mapping of brain tissue according to some embodiments of the present invention.

[0601] According to some exemplary embodiments, a model for functional organization mapping is provided at 2002. In some embodiments, the model includes possible different states along a trajectory during a particular type of surgery, such as... Figure 15C The STN model shown or as Figure 26The GP model is shown. In some embodiments, each state is represented by a "balloon," and arrows linking states indicate possible transitions between states. Furthermore, the model includes observed features such as RMS, β-band power, high-frequency / low-frequency ratio, or any other features of the recorded signal. In some embodiments, the model includes a set of states, possible transitions, and observations. In some embodiments, when a machine learning method is applied to the model, the algorithm alters the model's "internal parameters," such as the relationship between observed features and probabilities at each state or experiencing transitions between states. In some embodiments, after training the system with a machine learning algorithm, the system learns at each step-n to indicate the most probable sequence of steps 1, 2, ..., n, for example, what the most probable state sequence S1, S2, ..., Sn is at time 1, 2, ..., n.

[0602] According to some exemplary embodiments, expert-labeled data is collected at box 2004. In some embodiments, expert-labeled data is collected from the surgical procedure. In some embodiments, the expert identifies different states based on experience and optionally on various features he observes, including observed features of the model or any other features. In some embodiments, one or more human experts analyze data from the surgical procedure to identify and label various regions, such as regions A, B, etc. In some embodiments, the labeled regions are fed as input into a machine learning algorithm, which modifies the model's "internal parameters" based on some similarity measure, making the system's state labels similar to the expert's labels. Optionally, human experts may base their labels on other observed features not given to the system. For example, an expert may identify a specific single neuron spike shape found in region A rather than region B, and therefore he may determine that it is region A—however, this may not be a good feature of the system because it is relatively rarely observed in practice.

[0603] According to some exemplary embodiments, a machine learning algorithm is applied at block 2006 to modify model parameters. In some embodiments, the machine learning algorithm includes a dynamic Bayesian network, an artificial neural network, a deep learning network, a structured support vector machine, a gradient boosting decision tree, and a long short-term memory (LSTM) network. In some embodiments, applying a machine learning algorithm to modify an existing model allows, for example, the generation of a trained model. Optionally, the machine learning algorithm is used to train and / or modify other machine learning algorithms.

[0604] According to some exemplary embodiments, a model is trained at box 2008 for online mapping during surgical procedures. In some embodiments, the trained model is used during the advancement of the electrode probe through the brain. Furthermore, the trained model is used to optionally determine online whether the electrode probe crosses the boundary between two regions, and / or whether the probe has reached the desired target region. Alternatively or additionally, the trained model is used to optionally determine online whether the electrode probe crosses the ventral boundary of the desired target region.

[0605] Exemplary machine learning algorithms

[0606] According to some exemplary embodiments, a "machine learning" algorithm is used to train a "learning machine" computer to perform the task of distinguishing two or more tissue regions or subregions within an anatomical environment of a target region. In some embodiments, the target regions, as optional DBS target regions, include the subthalamic nucleus (STN), the internal globus pallidus (GPi), the external globus pallidus (GPe), and / or the ventral medial thalamus (VIM) nucleus. Additionally, the thalamus and / or basal ganglia nuclei are targeted. Optionally, other regions are targeted, such as the hippocampus fornix and the pontine nuclei (PPN).

[0607] According to some exemplary embodiments, machine algorithms, and particularly supervised machine learning algorithms, are methods for modifying parameters in a computational model based on a database of examples. Optionally, these examples are in the form of input-output pairs, each pair associating a set of input data with the correct output.

[0608] According to some exemplary embodiments, the mapping algorithm includes one or more of the following: dynamic Bayesian networks, artificial neural networks, deep learning networks, structured support vector machines, gradient-boosting decision trees, and long short-term memory (LSTM) networks. The method described in WO2016182997 is a generalization of hidden Markov models (HMMs) and serves as another example of how to leverage a trained system during the mapping process.

[0609] According to some exemplary embodiments, the set of input data includes features of electrophysiological signals recorded from the brain via probes, such as electrodes recording extracellular potentials. In some embodiments, these features in the signals may be, for example, root mean square or normalized root mean square (NRMS), power spectral density at a specific frequency, or power in a specific frequency band, correlation or coherence between simultaneously recorded signals, or a combination of any of these features. Alternatively or additionally, the features include peak rate, correlation with signals recorded by other means (e.g., electromuscular activity or electroencephalography (EEG) or any combination thereof, surface electromyography (EMG) recordings). In some embodiments, the electrophysiological signals are MER and / or LFP signals, such as... Figure 14C ,1A As described in 1B.

[0610] According to some exemplary embodiments, RMS and NRMS signal values ​​change significantly as the recording point moves from the neuronal white matter to the gray matter nucleus (e.g., STN, SNr, or GPi). In some embodiments, the power spectral density (PSD) of the β band (i.e., 12-30 Hz) has been found to represent the DLOR of the STN in Parkinson's disease patients, while the ratio of the average PSD at high frequencies (e.g., 100-150 Hz) to low frequencies (e.g., 5-25 Hz) is shown to distinguish STN and SNr structures. Optionally, correlation is used to measure the relationship between two simultaneously measured signals and also indicates the distance to the signal source.

[0611] According to some exemplary embodiments, coherence measurements are similar to correlation measurements, but provide more detail about the frequencies of correlated components in a signal. In some embodiments, signals recorded on a probe emitted from a high-amplitude source may be correlated when the distance between probes is small relative to the distance to that source. Conversely, in some embodiments, signals emitted from weaker and more localized sources are less likely to introduce correlation in signals recorded by both probes. Therefore, when recording is made from more than one probe, correlation and coherence measurements provide informative characteristics indicating whether two or more signals share a common, relatively distant source or two or more localized sources.

[0612] In some embodiments, when applied to components in a signal, i.e., when high coherence is observed at specific frequencies or time periods of high correlation, indications can be found for these components with respect to common or different, possibly independent, sources.

[0613] According to some exemplary embodiments, the spiking rate is a measurement of neuronal firing over time, with the spike being a typical biphasic feature in the voltage signal recorded by a probe in the extracellular media near the neuron experiencing the action potential. In some embodiments, the spiking rate indicates high neuronal activity, and oscillations in the spiking rate can indicate a disease state, such as oscillations in the β band of the spiking rate in STN neurons in Parkinson's disease. In some embodiments, when the spiking rate is found to correlate with, for example, EMG recordings, it indicates that the spiking neuron or several neurons are part of a motor control system. Optionally, this can indicate that they are located in areas where treatments such as DBS for tremor, dystonia, or other movement disorders can be beneficial in alleviating such symptoms related to the motor system.

[0614] According to some exemplary embodiments, the training system associates the values ​​of input observation features (e.g., one or more values ​​of NRMS, β-band PSD, coherence and / or peak rate measured at one or more touches on one or more probes) with the output, i.e., a state externally defined by an experienced user. In some embodiments, after applying the training algorithm to a given surgical database, it is provided with complete or partial input observation features and output states. The trained system can predict the output states of new observation groups and optionally create maps of tissues based on the records.

[0615] According to some exemplary embodiments, the spectral power density within the envelope of a high-pass filtered “spik” signal (a single neuronal firing signal) is particularly useful for detecting the neural relevance of movement disorder symptoms and has been found to indicate different subregions of the target DBS region, thus supporting a clinically significant mapping of neural tissue within and around the target region. In some embodiments, the output in this case would be the brain region where the signal was recorded, and / or the subject’s state of consciousness, and / or the relationship between neural activity in a specific brain location and disease symptoms.

[0616] According to some exemplary embodiments, in some models or algorithms, such as structured support vector machines and / or dynamic Bayesian networks, it is possible for outputs to be linked through sequential structures (i.e., some state transitions or sequences of states), while it is not possible in others. In some models, some sequences of states have higher probabilities than other states, and these probabilities may depend on the observations or inputs.

[0617] According to some exemplary embodiments, such structured model algorithms are clearly advantageous in utilizing the fact that anatomical structures are generally known, and despite patient-to-patient variability, the trajectory of the navigation probe is likely to traverse different regions in one of several possible sequences, namely white matter-striatum-GPe-GP boundary white matter-Gpi-optic region, as a possible trajectory for probes targeting GPi. In some embodiments, for probes targeting STN-DLOR (dorsal oscillatory region), possible sequences would be white matter-STN-DLOR-STN-VMNR (ventromedial non-oscillatory region)-white matter or white matter-STN-DLOR-STN-VMNR-substantia nigra reticular formation (SNr). However, this does not preclude machine learning methods that do not rely on internal structures from performing such tasks and may be advantageous in other respects.

[0618] According to some exemplary embodiments, machine learning algorithms that can be used to train a learning machine to perform labeling or region discrimination tasks may include dynamic Bayesian networks, artificial neural networks, deep learning networks, structured support vector machines, gradient boosting decision trees, and / or long short-term memory (LSTM) networks. Alternatively, other algorithms that may be used in the discrimination task or in the preprocessing stage of preparing data for improving training performance may include multi-class SVMs, decision trees, augmented decision stumps, principal component analysis, and independent component analysis.

[0619] Bipolar-based navigation

[0620] According to some exemplary embodiments, the implanted electrode for delivering long-term DBS therapy has two or more macro contacts disposed on the distal end of the lead, such as... Figures 3A-3H As shown in 4A-4F. In some embodiments, examples of such DBS electrodes are Medtronic 3789 and 3787, Boston Scientific Vercise, PINS G101, and St. Jude Medical Infinity electrodes, each having at least four macro contacts along an axial dimension of approximately 1.5 mm. Furthermore, they extend horizontally, either along a full circumference of approximately 4 mm or approximately one-quarter of a circumference, i.e., along a curve approximately 1 mm long. These macro contacts are generally unsuitable for reliably recording the firing of a single neuron, or the indistinguishable firing of a group of neurons, also known as multi-unit activity (MUA). This is primarily because the macro contacts are large, i.e., larger than approximately 50 μm in diameter or length, and the potential is averaged across the electrode surface, resulting in the relatively rapid disappearance of high-frequency and low-correlation spikes. In some embodiments, these macro contacts are suitable for recording LFP signals, which optionally represent the average of low-frequency signals from a large number of neurons or even several neuronal groups.

[0621] According to some exemplary embodiments, leads having at least two or more contacts (e.g., micro-contacts or macro-contacts, or any combination thereof) are used as mapping probes. In some embodiments, signals from at least two contacts are combined by using one as a reference to the other, optionally resulting in bipolar or differential recording. In some embodiments, this is useful when at least two contacts are positioned on a lead at a close distance from each other (e.g., a distance between 0.05 mm and 15 mm, such as 0.05 mm, 0.1 mm, 0.15 mm, or any intermediate or greater distance). In some embodiments, depending on the application, the distance between at least two contacts is considered close. Optionally, the advantage here is "common-mode rejection," i.e., "noise" signals arriving from relatively distant sources have a similar effect on both contacts, and these signals are attenuated when one signal is subtracted from the other in differential recording.

[0622] According to some exemplary embodiments, bipolar or differential recording is achieved using analog instruments (e.g., differential amplifiers), where the difference between signals from at least two contacts is amplified before digitization. Now refer to Figure 21 It depicts probes for connecting to a differential recorder of a differential amplifier according to some embodiments of the invention.

[0623] According to some exemplary embodiments, the probe 2102 includes at least two electrode contacts, such as electrode contacts 2106, on its circumference. In some embodiments, the minimum circumferential and / or axial distance between the two electrode contacts 2106 is at least 0.05 mm, such as 0.05, 0.1, 0.15, or any intermediate or greater distance.

[0624] According to some exemplary embodiments, at least two electrode contacts, such as electrode contact 2106, are connected via wires, such as wires 2108 and 2110, to a single differential amplifier, such as differential amplifier 2112. In some embodiments, differential amplifier 2112 uses one of the recorded signals from at least one of the contacts as a reference signal, which optionally indicates a "noise signal". In some embodiments, differential amplifier 2112 subtracts the reference signal from the signal recorded by the other electrodes to generate a processed signal that more accurately reflects neural tissue activity. In some embodiments, differential amplifier 2112 transmits the processed signal to acquisition system 2114 via wire 2113.

[0625] Alternatively, in some embodiments, differential recording is implemented digitally: the signal is recorded as a unipolar signal, i.e., the potential or signal from each contact is measured with reference to a distant common reference, and one digitized signal is subtracted from another digitized signal by software.

[0626] According to some exemplary embodiments, leads with at least two or more contacts are acute-only probes that perform a similar function to acute-only microelectrode recording (MER) probes, such as the Alpha Omega Neuroprobe electrode, commonly used in DBS electrode implantation. In some embodiments, these probes are inserted into the brain and advanced along one or more trajectories toward the implantation target while recording electrophysiological signals at various depths, for example to help select the optimal trajectory and depth for implantation.

[0627] According to some exemplary embodiments, after mapping, the probe is removed from the brain and a chronic lead capable of delivering long-term stimulating currents is implanted. In some embodiments, the probe has microelectrodes, such as those for sensing single-cell spikes or multi-unit activity to support tissue mapping, and a separate single macroelectrode primarily for stimulating the tissue. Optionally, the macroelectrode is also used to observe whether symptom relief is satisfactory and without undesirable side effects.

[0628] According to some exemplary embodiments, an acute differential LFP probe includes two or more macroelectrodes for recording and / or calculating differential LFP signals, which will be used, for example, to map tissue using an automated navigation algorithm, and optionally to stimulate tissue to observe symptom relief or side effects. In some embodiments, the LFP probe is then removed and a long-term DBS electrode is implanted. Optionally, LFP probes are generally simpler and less expensive to manufacture than long-term implantable electrodes because they do not require different stiffness modes, long-term biocompatibility, and evaluation of performance and safety as an implant for many years of use.

[0629] According to some exemplary embodiments, differential LFP probes are chronically implanted, for example for DBS stimulation therapy purposes, such as Medtronic 3789 and 3787, Boston Scientific Vercise, PINS G101, and St. Jude Medical Infinity electrodes. In some embodiments, these devices are made of highly biocompatible materials, can remain in vivo for years without causing immune or inflammatory responses, and optionally include an endometrium for receiving a core needle to alter electrode stiffness and are qualified to retain function for many years. Optionally, in this case, the probe is attached to an IPG to deliver DBS stimulation.

[0630] According to some exemplary embodiments, at least two or more contacts on a lead have the same axial position and are arranged at different angular positions along the circumference of the lead. In some embodiments, positioning the electrode contacts at different angular positions with similar axial positions allows, for example, better recording of the sensitivity of the arriving signal. Alternatively, at least two or more contacts on a lead have the same angular position but are axially offset from each other. Optionally, at least two or more contacts on a lead have different axial positions and different angular positions on the lead surface.

[0631] In some embodiments, two or more contacts have the same shape, such as a ring, or a portion or segment of a ring. Alternatively, each electrode contact may have a different shape than the other electrode contacts on the lead.

[0632] Now for reference Figure 22This document depicts probes for differential recording connected to two differential amplifiers according to some embodiments of the invention. According to some exemplary embodiments, probe 2202 includes at least three electrode contacts 2203, 2206, and 2207 located on the circumference of probe 2202. In some embodiments, electrode contacts 2203, 2206, and 2207 have the same axial position along probe 2202, but different angular positions on the circumference of probe 2202. In some embodiments, electrode contacts 2203, 2206, and 2207 are positioned at a minimum axial distance of at least 1 mm, for example, 1, 2, 5, 10 mm from the probe tip 2204, or any intermediate or greater distance. In some embodiments, the minimum angular distance 2205 between two proximal electrode contacts is at least 0.05 mm, for example, 0.05 mm, 1 mm, 2 mm, or any intermediate or greater distance. In some embodiments, at least three electrode contacts 2203, 2206, and 2207 are connected to two differential amplifiers, differential amplifier 2208 and differential amplifier 2210. In some embodiments, the wires connecting the electrode contacts are interconnected into a single conductor entering the differential amplifier. Alternatively, the wire from each electrode is connected to a different connector in the differential amplifier. In some embodiments, signals recorded by at least two electrode contacts are combined in the differential amplifier.

[0633] According to some exemplary embodiments, the output from differential amplifier 2208 is a bipolar LFP signal obtained by subtracting the LFP of macro contact 2203 from the LFP of macro contact 2207. The output of differential amplifier 2 is a bipolar LFP signal obtained by subtracting the LFP of macro contact 2206 from the LFP of macro contact 2203. Alternatively, similar results can be obtained by recording and digitizing the LFP of each electrode contact with reference to the common ground electrode, and then calculating the signal obtained by subtracting the macro contact 2203 from macro contact 2207, and the signal obtained by subtracting the macro contact 2206 from macro contact 2203.

[0634] Now for reference Figure 23 The document describes an additional exemplary probe for differential recording having at least three electrode contacts according to some embodiments of the invention. According to some exemplary embodiments, probe 2302 includes at least two electrode contacts, such as electrode contacts 2303, 2306, and 2307, which have the same angular position on the probe circumference but different axial positions on the probe circumference. In some embodiments, electrode contacts 2303, 2306, and 2307 are electrically connected to two differential amplifiers 2308 and 2310.

[0635] According to some exemplary embodiments, the output from differential amplifier 2308 is a bipolar LFP signal obtained by subtracting the LFP of macro contact 2303 from the LFP of macro contact 2307. In some embodiments, the output of differential amplifier 2310 is a bipolar LFP signal obtained by subtracting the LFP of macro contact 2306 from the LFP of macro contact 2303. In some embodiments, a similar result can be obtained by recording and digitizing the LFP of each contact with reference to a common ground electrode, and then by calculating the signal of macro contact 2307 minus the signal of macro contact 2303, and the signal of macro contact 2306 minus the signal of macro contact 2303.

[0636] Now for reference Figure 24 This describes an exemplary probe for differential recording having at least three annular electrode contacts according to some embodiments of the invention. According to some exemplary embodiments, probe 2402 includes at least three annular electrode contacts, such as electrode contacts 2403, 2406, and 2407 with different axial positions on the probe circumference. In some embodiments, electrode contacts 2403, 2406, and 2407 are electrically connected to two differential amplifiers 2408 and 2410.

[0637] According to some exemplary embodiments, the output from differential amplifier 2408 is a bipolar LFP signal obtained by subtracting the LFP of macro contact 2403 from the LFP of macro contact 2407. In some embodiments, the output of differential amplifier 2410 is a bipolar LFP signal obtained by subtracting the LFP of macro contact 2406 from the LFP of macro contact 2403. In some embodiments, similar results can be obtained by recording and digitizing the LFP of each contact with reference to a common ground electrode, and then calculating the signal of macro contact 2403 minus the signal of macro contact 2407, and the signal of macro contact 2406 minus the signal of macro contact 2403.

[0638] According to some exemplary embodiments, the differential signal recorded and used by the automatic navigation system can also be a multi-pole signal, i.e., derived from a combination of three or more electrode contacts. Referring now to FIG25, another example of a probe for differential recording according to some embodiments of the present invention is depicted.

[0639] According to some exemplary embodiments, four electrode contacts of probe 2502 are used, optionally in the following configuration: electrode contact 2504 is an annular electrode contact located at a first longitudinal position along the lead axis. In some embodiments, electrode contact 2506 records signal s1. Furthermore, electrode contacts 2503, 2507, and 2508 are optionally electrode contacts located at substantially similar axial positions on the circumference of probe 2502, which can be considered a single second longitudinal position. In some embodiments, electrode contacts 2503, 2507, and 2508 record signals s2a, s2b, and s2c, respectively. The signal recorded from an annular electrode contact 2504 is subtracted from the sum of the signals recorded by 2503, 2507, and 2508, such that Sd = s1 - (s2a + s2b + s2c). In some embodiments, the differential signal Sd carries information about the amplification of local signals over long-range noise via common-mode rejection. Optionally, the sign of the signal can be changed by calculating the differential signal Sd = (s2a + s2b + s2c) - s1.

[0640] Exemplary continuous movement and movement adjustment

[0641] According to some exemplary embodiments, the electrical leads are continuously advanced through the brain. In some embodiments, the probe's movement parameter values ​​are modified as the leads traverse different brain regions. In some embodiments, the modification is based on the probe's position within the brain.

[0642] Now for reference Figure 25B It describes a process for continuous movement of a probe according to some exemplary embodiments.

[0643] According to some exemplary embodiments, when it is determined to insert an electrical lead into the brain, for example... Figure 1A As described in section 101, a trajectory is selected at 2540 to reach a desired brain target, such as the STN or Gpi. Optionally, a trajectory is selected to reach a specific subregion. In some embodiments, the trajectory is selected based on the results of various neurophysiological and / or imaging techniques as described above.

[0644] According to some exemplary embodiments, a movement parameter value or range of values ​​for the electrical lead is determined at 2542. In some embodiments, the movement parameter value is determined based on a selected trajectory. Optionally, the movement parameter value is determined based on the type of electrical lead, motor, and / or actuator. In some embodiments, the movement parameters include velocity, acceleration, and / or movement duration and / or movement steps. In some embodiments, the movement parameter value is determined based on the brain region along the selected trajectory.

[0645] According to some exemplary embodiments, the electrical leads are inserted and advanced through the brain at 2544. In some embodiments, the electrodes are advanced along a selected trajectory and / or using determined movement parameter values.

[0646] According to some exemplary embodiments, MER and / or LFP signals are recorded at 2546. In some embodiments, signals are recorded continuously as the electrodes propagate through the brain. Alternatively, signals are recorded at selected locations and / or at selected time points as the leads propagate through the brain continuously.

[0647] According to some exemplary embodiments, (2548) lead movement is determined. In some embodiments, lead movement parameters are measured as the leads are continuously advanced through the brain. Alternatively, movement parameters are measured at selected probe locations and / or at selected time points. In some embodiments, movement parameters are determined using sensors or by measuring motor activity.

[0648] According to some exemplary embodiments, the electrical lead location is determined at 2550. In some embodiments, the electrical lead location is determined based on the analysis of recorded MER and / or LFP signals. In some embodiments, the probe location may optionally be determined by one or more methods described in Figures 1, 2, 7, 8, and 14C of this application.

[0649] According to some exemplary embodiments, the relationship between the measured lead movement parameters and the lead position is determined at 2552. In some embodiments, if the electrode movement parameter value measured at 2548 is based on the lead position, it is determined whether the lead is at the desired brain target, as described at 133 in FIG1. ​​In some embodiments, if the electrode movement parameter value is not based on the determined position, the movement parameter value is adjusted at 2554. In some embodiments, once the movement parameter value is adjusted, the lead is continuously advanced into the brain at 2544.

[0650] Exemplary continuous mobile applications and drivers

[0651] According to some exemplary embodiments, when the electrical lead, such as lead 504, is located in the brain, MER and / or LFP are recorded, for example, as... Figure 1A and 14C As described in [the text]. Optionally, MER and / or LFP are recorded as leads that propagate through brain tissue.

[0652] According to some exemplary embodiments, the driver, for example Figure 5The actuator 505 shown in Figure 5 or the actuator 603 shown in Figure 6 is responsible for accurately driving leads, such as lead 504, into or out of the brain. In some embodiments, the micro-actuator is manually activated by rotating a knob to control the user's movement, or the micro-actuator is activated automatically. In some embodiments, the recorded signal is typically unusable during movement due to movement-related noise and / or because of changes in depth during movement. Optionally, the computer-controlled actuator typically moves in small steps, such as 0.1-1 mm, and records a signal at each "stop depth" for display and any further analysis.

[0653] According to some exemplary embodiments, the continuous motion application combines a micro-driver for controlling continuous motion, and hardware and software for reducing recorded noise during continuous motion.

[0654] Exemplary microdriver

[0655] According to some exemplary embodiments, the microactuator is adapted to control continuous movement. In some embodiments, the movement of the actuator in response to a command voltage or current is predictable and repeatable, i.e., a velocity profile is defined, and the actual depth at each moment can be reliably predicted. Alternatively, precise sensors exist, such as... Figure 5 The sensor 541 of system 501 shown in Figure 5 or the sensor 605 of system 601 shown in Figure 6 is used to monitor drive acceleration, velocity or position, so that depth can be reliably monitored at each moment.

[0656] According to some exemplary embodiments, a sensor that can be used to monitor the speed of the drive is an encoder that monitors the angular velocity of the motor rotation, and the linear velocity of the drive can be correlated with knowledge of the screw along which the drive is propelled. In some embodiments, a sensor for measuring the linear position of the drive is a potentiometer that changes its resistance according to the length of the travel distance. Alternatively, the drive position velocity or acceleration can be evaluated by combining feedback from multiple sensors or from an optical encoder.

[0657] Exemplary hardware and software for continuous movement

[0658] According to some exemplary embodiments, the hardware and / or software reduce signal noise during movement and optionally enable continuous control. In some embodiments, the acquisition of signals from the drive position / velocity sensor is at the same rate as the acquisition of electrophysiological signals, optionally allowing each sample to be registered to a specific depth in the tissue. In some embodiments, the hardware is adapted to respond to control signals and optionally regulate the control voltage and / or current delivered to the drive during its movement. Optionally, the regulation of the voltage and / or current has a sufficiently short delay that is negligible compared to the drive speed and the associated tissue geometry. In some embodiments, if the drive moves at about 0.5 mm / s, the control loop has a delay of, for example, 0.01 s, such that the distance traveled before a response is about 5 μm, which is negligible for the purpose of precise navigation. Optionally, for the purpose of precise navigation, a delay resulting in no response during travel between 5 and 20 μm is considered to be tolerable.

[0659] According to some exemplary embodiments, when, for example, importing and / or navigating via GPi, a potential feature is the continuous variation of speed to optimize the balance between precise mapping and the duration of the mapping process. Optionally, this optimization is performed by a closed-loop control design, implemented in hardware, software, firmware, or any combination thereof, wherein the controller circuitry (e.g., Figure 5 The processing circuit 562 shown receives a processing signal recorded from the tissue as feedback and responds by modifying a command to the driver. In some embodiments, such modification of the command includes, for example, changing the current or voltage to increase or decrease the drive speed, or stopping the driver, or reversing the speed of the driver and moving it in the opposite direction.

[0660] According to some exemplary embodiments, due to the large and sparse nature of the GPi structure (compared to the STN structure), the controller is programmed to command high speed to cover a defined distance when the characteristics of the processed signal are stable and unchanging. Alternatively, the controller is programmed to command a lower speed when a change in signal characteristics is detected. In some embodiments, this allows for, for example, less mapping time per distance for mapping relatively uniform portions of the structure, and more time per distance when the signal suggests a possible transition between regions.

[0661] According to some exemplary embodiments, the controller is programmed to be sensitive to well-defined individual cell spike patterns, such that a high speed is used when no individual cell is detected, but a lower speed is used when an individual cell signal is detected. In some embodiments, this allows, for example, more time to be devoted to individual cell patterns that convey information about electrode location. An example is the “boundary cell” (the boundary is also called the inner medulla) often found in the boundary band between GPe and GPi neurons, which has a typical spike feature, distinct from GPe or GPi neurons, and represents recordings from the boundary band.

[0662] According to some exemplary embodiments, the controller is programmed to apply a small velocity, such as from the ventral exit of the STN or GPi, as the electrode approaches the ventral (deep) boundary of the target region. In this way, unintended insertion into neural structures (e.g., SNr and optic tract) further ventrally into the target region is less likely to occur when the actuator advances slowly, tissue mapping is more accurate, and the boundary can be detected and responded to by stopping the actuator, optionally with a shorter delay.

[0663] Exemplary software applications and algorithms

[0664] According to some exemplary embodiments, software applications and algorithms are used to map tissue, for example, to utilize signals acquired from continuously varying depths. In some embodiments, mapping includes processing signals from driving monitoring sensors such that each signal sample is correlated with the depth at which it was acquired. In some embodiments, this also includes, for example, applying a “window” (e.g., a “moving window”) to the data when calculating RMS features or NRMS features for each depth, which requires calculating the RMS based on a series of signal values. For example, for each depth d on which the RMS value is calculated, a “window” is defined that includes signals acquired from d-Δd to d+Δd, on which the RMS is calculated. Optionally, the same window or windows of different sizes are used to calculate the power spectral density (PSD) value for each depth.

[0665] In some embodiments, the window is defined based on samples rather than depth, such that for each sample *s* for which feature values ​​are computed, the feature is computed from a window comprising samples from *s-Δs* to *s+Δs*. Optionally, the window size varies depending on the drive speed and / or on location or "state" to maintain a balance between precisely computed features (which typically require more samples) and a high-resolution mapping of the tissue (which typically uses samples from a smaller region).

[0666] According to some embodiments, as an alternative to using Fourier transforms (including fast Fourier transforms) to calculate PSD values, a more time-efficient implementation, such as an IIR (infinite impulse response) passband filter, is used to calculate the power of a specific band (e.g., β-band or γ-band), optionally coupled to a rectifier and a summerizer. In some embodiments, in this way, a small number of samples can be processed with short latency to calculate the power at a specific frequency band. In some embodiments, several features are computed in parallel using an architecture capable of implementing massively parallel computing, such as an FPGA, to reduce control latency and / or to feed the processed signal to a navigation algorithm for detecting the location in tissue.

[0667] In some embodiments, the potential advantages of continuous mobile applications include one or more of the following:

[0668] 1. Less damage to tissues, which may be due to the relatively large forces during acceleration and deceleration.

[0669] 2. It takes less time to complete tissue mapping, improve economic efficiency, and reduce the risk of infection for patients.

[0670] 3. Improved ability to detect and measure sparsely distributed neural signal sources in tissues, such as “tremor cells”—cells associated with tremor symptoms that “fire” tremor-related patterns. These cells are distributed throughout the tissue, making them more difficult to locate when the tissue is sampled in discrete steps, and also indicating sub-boundaries of DBS targets (e.g., GPi). In some embodiments, the detection and identification of these sources can be incorporated into a mapping algorithm to indicate a higher or lower probability of recordings originating from specific functional neural structures.

[0671] Exemplary transitions between GP layers when navigating to GP

[0672] According to some embodiments of the present invention, Globus Pallidus (GP) is another deep nucleus frequently treated with DBS in Parkinson's disease, dystonia, and other conditions. In some embodiments, treatment of GPi involves implanting a DBS electrode that delivers an electric current to the implantation site and / or lesion site in the GPi, causing permanent damage to the tissue, which helps alleviate disease symptoms.

[0673] According to some exemplary embodiments, an automated system for automatically recording data as it moves into the brain (e.g., as described herein) includes algorithms and applications for targeting the internal portion of the GP, the GPi. Reference is now made to... Figure 26 It depicts the transition between different anatomical states when navigating to GPi according to some embodiments of the invention.

[0674] According to some exemplary embodiments, typical states inferred when targeting GPi will be “white matter” 2602, “striatum” 2604, “striatum-Gpe boundary band” 2606 (or outer medulla) “outer part of GP” (GPe) 2608, “GPe-GPiBorder” (or inner medulla) 2610, “GPi” 2612, “GPi-optic tract boundary band” 2614 and “optic tract” 2616, and other regions, which can be stated in the model.

[0675] According to some exemplary embodiments, the navigation system (e.g., an automated system) may optionally be a trained automated system. In some embodiments, the navigation system uses functions to organize the map, such as... Figure 20The functional organization map described herein is used to navigate leads to desired brain targets. In some embodiments, the navigation system's processing circuitry compares electrical signals recorded by the electrodes of the leads with a stored functional map or stored indications to determine the position of the leads.

[0676] According to some exemplary embodiments, as the leads are navigated through the striatum 2604, the navigation system determines whether the leads have entered GPe 2608 or whether the leads are advancing toward the striatum-Gpe boundary. In some embodiments, as the leads advance within GPe, the navigation system determines whether the leads are now located at GPi 2612, or whether the leads are toward or already positioned within the Gpe-Gpi boundary band. In some embodiments, the navigation system may optionally provide the system user with indications as the leads approach the boundary between regions and / or as they enter a region.

[0677] According to some embodiments, upon exiting the striatum 2604 (e.g., by detecting its boundary 2605), the next region is the striatum-GPe boundary band 2606 or Gpe 2608. In some embodiments, if electrical conduction is within the striatum-GPe boundary line, the next region is GPe 2608 when that region ends. In some embodiments, upon exiting GPe 2608, for example by detecting its boundary 2609, the next region may be the GPe-GPi boundary band 2610 or GPi 2612. In some embodiments, the region ends at the optic beam 2616 or the GPi optic beam boundary band 2614, which ends within or outside the optic beam 2616 and is followed by the optic beam 2616.

[0678] In some embodiments, the learning machine (e.g., computer circuitry) uses input to train an existing model to distinguish different brain regions when navigating to a GPi. Figure 20 An example of the training process is described in the document.

[0679] According to some exemplary embodiments, the computational features used as input to the learning machine to be trained and subsequently used to perform a distinguishing task are features in the recorded signal, such as MER and / or LFP signals, such as root mean square, power density at a specific frequency, power in a specific frequency band, correlation or coherence between simultaneously recorded signals, or any combination of these features.

[0680] According to some exemplary embodiments, the power at the β band (12-30 Hz) is used as a marker for potential optimal implantation sites. In some embodiments, the power at higher frequencies (e.g., 30-50 Hz) is correlated with recordings from the striatum and therefore with important features for machine learning algorithms. Alternatively or additionally, other features, such as spike rate, are used to correlate with signals recorded by other means (e.g., surface electromyography (EMG) recordings of electroencephalography (EEG) or any combination of features). Optionally, the spectral power density in the envelope of the high-pass filtered “spike” signal is used.

[0681] Exemplary automatic and continuous navigation processes

[0682] According to some exemplary embodiments, electrical leads (e.g., electrode probes including macroelectrode contacts and / or microelectrode contacts) are automatically advanced to selected target brain regions. In some embodiments, the electrode probes are automatically navigated, optionally by a learning machine (e.g., a computer or processing circuitry) continuously moved to the desired target. Referring now... Figure 27 It describes an automated process for navigating electrodes to a desired brain target via a brain navigation system, according to some embodiments of the invention.

[0683] According to some exemplary embodiments, a brain navigation system, such as system 601 shown in FIG. 6, is trained using a machine learning algorithm at 2702. In some embodiments, such as Figure 20 Brain navigation is trained as described herein. In some embodiments, machine learning algorithms are used, such as dynamic Bayesian networks, artificial neural networks, deep learning networks, structured support vector machines, gradient boosting decision trees, and long short-term memory (LSTM) networks, or any combination of these algorithms. In some embodiments, machine learning algorithms are applied to modify an existing model or existing model parameters and / or parameter values ​​to optionally generate a trained model.

[0684] According to some exemplary embodiments, at point 2704, an electrode probe, such as an electrical lead comprising at least two electrodes or electrode contacts, is delivered into the brain. In some embodiments, the electrode probe is delivered into the brain according to a selected trajectory and a selected entry site.

[0685] According to some exemplary embodiments, at 2706, leads (e.g., lead 504 shown in FIG. 6) are continuously advanced to selected brain targets while differential LFP and / or MER are recorded. In some embodiments, the recorded differential LFP and / or MER signals are analyzed to extract different signal features, such as... Figure 1A 109 Figure 1B And as described throughout the application.

[0686] According to some exemplary embodiments, at 2708, the trained system identifies the entry point of the target region based on the analyzed differential LFP and / or MER signals.

[0687] According to some exemplary embodiments, at 2710, the trained system identifies subdomains in the target region based on the analyzed differential LFP and / or MER signals.

[0688] According to some exemplary embodiments, at 2712, the trained system identifies the exit from the target region and optionally indicates whether the electrode probe has entered the SNr region based on the analyzed differential LFP and / or MER signals. Optionally, the trained system identifies the transition between STN and SNr based on the ratio between the high-frequency power spectral band and the low-frequency power spectral band, for example... Figure 14C As shown. In some embodiments, if the electrode probe leaves the STN, the trained system retracts the electrode probe back into the STN.

[0689] According to some exemplary embodiments, if the electrode probe is positioned within the desired brain target, at 2714, the trained system stops moving the electrode probe. In some embodiments, the trained system fixes the probe's position and optionally records the fixed position.

[0690] According to some exemplary embodiments, at 2716, the trained system recommends the optimal location for permanent implantation of the DBS lead.

[0691] According to some exemplary embodiments, at 2718, the electrode leads used for recording differential LFP and / or MER are replaced with DBS leads at the recommended location determined at 2716. Alternatively, the electrode leads used for recording differential LFP and / or MER are used to deliver DBS at the recommended location.

[0692] Exemplary process for estimating electrical lead location

[0693] According to some exemplary embodiments, the electrical leads may optionally be navigated to selected brain targets, such as those used for long-term stimulation therapy, along the insertion trajectory. In some embodiments, electrical signals are recorded by at least two electrodes of the probe during lead advancement. In some embodiments, the recorded electrical signals and stored electrophysiological information associated with anatomical data are used to estimate the location of the electrical leads. Referring now to... Figure 28 It describes the process of estimating the location of electrical leads using stored information according to some embodiments of the present invention.

[0694] According to some exemplary embodiments, anatomical data is provided at block 2802. In some embodiments, the anatomical data is provided to processing circuitry or any type of processing device, such as a computer. In some embodiments, the anatomical data is stored in memory connected to the processing circuitry or processing device. Optionally, the memory is part of the processing device. In some embodiments, the anatomical data includes anatomical data associated with anatomical regions and / or subregions in the brain. Additionally or optionally, the anatomical data includes anatomical data associated with boundary regions between anatomical regions or subregions in the brain.

[0695] According to some exemplary embodiments, electrophysiological data is collected at box 2804. In some embodiments, the electrophysiological data includes electrical signals recorded from brain tissue or indications of electrical signals. In some embodiments, the electrophysiological data is collected from experts and / or databases. Optionally, the electrophysiological data is collected concurrently with surgical procedures (e.g., neurosurgery).

[0696] According to some exemplary embodiments, at block 2806, anatomical data is correlated with collected electrophysiological data. In some embodiments, the correlation is performed manually or using machine learning algorithms, such as... Figure 20 As described in box 2006. Optionally, additional information, such as clinical information, is associated with anatomical and / or electrophysiological data.

[0697] According to some exemplary embodiments, an algorithm, such as a predictor and / or classifier, is generated at box 2808. In some embodiments, the algorithm is based on the correlation between anatomical data and electrophysiological data. In some embodiments, the algorithm classifies a set of electrical signals into a specific anatomical region or a specific anatomical subregion. Optionally, the algorithm classifies the electrical signals into specific states, such as... Figure 26 The state described in the text.

[0698] According to some exemplary embodiments, the location of the electrical leads is estimated in block 2810, optionally during lead navigation to a selected brain target. In some embodiments, the location of the leads is estimated based on electrical signals recorded by electrodes on the leads during navigation and an algorithm. In some embodiments, the algorithm generates an estimated anatomical location output based on the input of the recorded electrical signals. In some embodiments, a classifier classifies the recorded electrical signals into anatomical regions, states, and / or sub-regions. In some embodiments, a predictor predicts the association between the recorded electrical signals and the anatomical regions, states, and / or sub-regions.

[0699] According to some exemplary embodiments, an insertion trajectory is determined, optionally for navigating electrical leads to the brain of a specific patient at box 2812. In some embodiments, the insertion trajectory, and the insertion point in the brain, are determined by selecting a brain target suitable for the application of long-term stimulation therapy (e.g., DBS therapy). Optionally, at least one alternative trajectory is determined to reach the selected brain target. In some embodiments, the insertion trajectory includes a set of anatomical regions and / or subregions along the path of the insertion trajectory.

[0700] According to some exemplary embodiments, a specific functional tissue map is generated for a selected trajectory at box 2814. In some embodiments, the functional tissue map is generated by combining relevant electrophysiological data from box 2806 with anatomical regions along the selected insertion trajectory.

[0701] According to some exemplary embodiments, the location of the electrical leads is estimated at box 2810 based on the recording signal and functional organization map generated at box 2814.

[0702] According to some exemplary embodiments, after generating the association between anatomical and physiological data at box 2806, multiple functional tissue maps for a set of universal insertion trajectories are generated at box 2816. In some embodiments, the universal insertion trajectory is an insertion trajectory that is non-specific to a particular patient and / or not designed for treating that particular patient based on the patient's anatomical and / or clinical data. In some embodiments, functional tissue maps for universal insertion trajectories are generated as described in box 2814.

[0703] According to some exemplary embodiments, a specific insertion trajectory is selected from a set of insertion trajectories at box 2818. In some embodiments, a specific insertion trajectory is selected to navigate electrical leads in the brain of a specific patient. In some embodiments, a specific insertion trajectory is selected from a set of generic insertion trajectories by an automated system or manually by a physician, by specifying a desired brain target and a desired lead insertion point. Alternatively, a specific insertion trajectory is selected by filtering a functional organization map associated with the insertion trajectory. In some embodiments, the functional organization map is filtered to identify insertion trajectories that allow recording electrical signals with minimal noise.

[0704] According to some exemplary embodiments, the location of the electrical leads is estimated at box 2810 based on the recorded electrical signals and the functional organization map of the selected insertion trajectory.

[0705] Exemplary sleep / consciousness assessment based on local field potential (LFP) recordings from probes

[0706] According to some exemplary embodiments, the patient may fall asleep during the navigation process, which may optionally cause changes in the recorded electrical signals. In some embodiments, the changes in the recorded electrical signals may indicate incorrect anatomical areas and / or affect the navigation process. Therefore, detecting the patient's state of consciousness is important, for example, to maintain an accurate navigation process.

[0707] According to some exemplary embodiments, LFP is recorded from a probe microelectrode or macroelectrode. In some embodiments, the LFP signal is sensitive to sources remote from the measurement location. In some embodiments, cortical sources (e.g., at a distance of approximately >30 mm from the lead when approaching the DBS target), as well as other distant sources at a distance of approximately >5 mm from the lead and local sources at a distance of approximately <5 mm from the lead, can indicate the patient's state of consciousness.

[0708] According to some exemplary embodiments, a patient's consciousness or state of consciousness is estimated based on LFP and / or MER records.

[0709] According to some exemplary embodiments, when a patient is asleep, such as occasionally during awake DBS surgery, these signals are used to detect this shift in consciousness and optionally indicate the depth of sleep. In some embodiments, this is important because physiological signals measured from awake and sleeping patients differ significantly, and optionally, these differences can automatically or non-automatically affect the interpretation of the signals. In some embodiments, when using an automated navigation system, it is important that the system be able to detect unwanted changes in the state of consciousness and thus perform one or more of the following:

[0710] 1. Remind the user to bring the patient back to the desired state of consciousness so that the procedure can continue.

[0711] 2. Continue the process while taking into account states of consciousness by modifying the movement control parameters, i.e., moving with smaller steps, staying at each site for longer periods, etc., and / or modifying model parameters, for example, the probabilities coupled to each observation state in the case of a Hidden Markov Model.

[0712] According to some exemplary embodiments, stimulation is provided during sleep, as optionally indicated in the stimulation protocol. Alternatively, stimulation is provided when the patient is not asleep. According to some embodiments, it is sometimes desirable to operate on a patient under light anesthesia, where the patient is unconscious but physiological recordings are not altered in a way that renders them useless. In some embodiments, it is important to monitor the level of consciousness during the procedure to ensure that the patient remains unconscious but has not reached a level of deep anesthesia. Optionally, this applies to the cognitive or physiological state of patients for whom awake brain surgery is difficult or impossible.

[0713] According to some embodiments, monitoring the patient's awareness of the leads themselves is advantageous compared to other methods described elsewhere (e.g., EEG recording). The advantage is that no additional equipment, additional setup time / personnel, etc., are required.

[0714] According to some exemplary embodiments, the functional organization map used by the navigation system includes electrical signals associated with anatomical regions and different physiological states, such as sleep / consciousness. In some embodiments, the navigation system detects a patient's physiological condition by analyzing signals recorded by electrodes on electrical leads using the functional organization map.

[0715] Alternatively or additionally, electrical records may be collected during different physiological conditions, such as... Figure 28 As described in box 2804. In some embodiments, these electrical symptoms are associated with anatomical regions. In some embodiments, classifiers and / or predictors generated based on the association between electrical recordings and anatomical data, such as those described in box 2806, allow for the detection of physiological states, such as sleep / consciousness.

[0716] In some embodiments, the functional tissue map, classifier, and / or predictor can optionally allow continued navigation of electrical leads toward the desired brain target by predicting the electrical signals to be recorded during the physiological state of the anatomical region along the insertion trajectory, even when the patient's physiological state changes during navigation.

[0717] According to some exemplary embodiments, the functional organization map used during navigation includes reference indicators of electrical signals associated with anatomical regions and states of consciousness. In some embodiments, the reference indicators include electrical signal values, processing results of the electrical signals, electrical signal characteristics, such as RMS, NRMS, PSD, or values ​​of different calculations performed on the electrical signals. In some embodiments, during navigation, the navigation system uses the functional organization map to analyze recorded MER and / or LFP signals to determine the patient's state of consciousness, for example, to determine whether the patient is asleep. In some embodiments, if the patient is asleep, the navigation system uses electrical signals associated with a sleep state to analyze the recorded signals, instead of using electrical signals associated with an awake patient.

[0718] Exemplary directional navigation / mapping

[0719] According to some exemplary embodiments, mapping is performed in several angular directions, for example, to detect boundaries or regions around electrical leads. According to some exemplary embodiments, the mapping algorithm is simultaneously applied to multiple electrodes deposited on the same probe. In some embodiments, this results in mapping based on neural signals originating from sources located in different tissue orientations and / or at different depths. In some embodiments, these signals may be:

[0720] 1. Signal from the microelectrode on the probe

[0721] 2. Signal from the macroelectrode on the probe

[0722] 3. Signals originating from bipolar or differential macroelectrode LFP signals.

[0723] 4. Signals originating from bipolar or differential microelectrode LFP signals.

[0724] According to some exemplary embodiments, firstly, the mapping algorithm is applied separately to each signal, producing multiple mapping results, thereby generating a more detailed map and better supporting the user's decision regarding the stimulation / implantation target. Additionally or alternatively, the maps obtained from the various signals should produce a coherent "big picture": for example, overlapping areas or boundaries between volumes sensed by different electrodes should have similar characteristics and exhibit fairly smooth spatial variations, and signals originating from locations at the same angular position but longitudinally displaced on the probe should be quite similar. In some embodiments, the degree of consistency among the various maps serves as a tool for the user to assess the reliability of the map in a particular patient and to consider in the user's decision-making process.

[0725] According to some exemplary embodiments, while generating the map, signals are combined using, for example, a framework similar to that of machine learning algorithms, while simultaneously considering inputs from different signal sources. In some embodiments, this results in a more reliable map and / or a map that can be generated more quickly because multiple signals measured in a short time replace the longer measurement time of a single signal.

[0726] According to some exemplary embodiments, a "second" trajectory is selected based on a mapping obtained from "orientation" signals (i.e., signals recorded by microelectrodes oriented in a specific "horizontal" (i.e., perpendicular to the axial direction), macroelectrodes oriented in a specific direction, or bipolar signals between these microelectrodes or macroelectrodes). In some embodiments, these signals reflect neuronal activity signals originating from a specific direction—LFP or multi-unit activity (MUA) signals.

[0727] In some embodiments, these directional signals may indicate to the user that a “second” trajectory, different from the trajectory of the inserted probe, is better suited for delivering effective DBS treatment, and indicate the spatial orientation of the second trajectory.

[0728] According to some exemplary embodiments, users can analyze these signals themselves, or automatic or semi-automatic algorithms can analyze them to indicate a better second trajectory. In some embodiments, this can be achieved by finding that the mapping of directions in the second trajectory is better correlated with finding the mapping that is optimal for patient outcomes.

[0729] In some embodiments, the purpose-specific signal may be a signal that is generally more sensitive to sources >0.2 mm from the recording contact, such as 0.5 mm or more. LFPs and bipolar / differential LFPs recorded from microelectrodes or macroelectrodes are sensitive to neuronal sources at this distance or longer, in addition to their sensitivity to immediately adjacent signals. In some embodiments, changing the trajectory in small steps of less than 0.2 mm is impractical, so a “better” trajectory closer to the first trajectory is less useful. Alternatively, changing the trajectory in small steps of less than 1 mm is more practical but still challenging and difficult, and the second trajectory is ≥1 mm from the first trajectory, for example, about >=0.5 mm from the contact on the lead circumference; specific values ​​may be used to indicate such an optimal second trajectory.

[0730] According to some exemplary embodiments, physiological mapping and anatomical information are used simultaneously: In some embodiments of the invention, in addition to physiological mapping based on electrical recordings, users can also access anatomical maps and / or statistical anatomical atlases based on certain imaging modalities. In some embodiments, the anatomical map can be derived directly from imaging of a specific patient's brain (e.g., MRI, CT, PET, SPECT, or a combination thereof). Optionally, the anatomical map can also be based on a "global" atlas of human brain anatomy, which is composed by combining data from multiple human subjects, such as imaging data obtained through dissection or postmortem anatomical data. In some embodiments, the map can also consist of a patient-specific adaptation of the anatomical atlas: based on brain imaging data of a specific patient, the global atlas undergoes a processing step that deforms the map to fit the image of the specific patient. The anatomical map is then used in conjunction with the physiological atlas in one of the following ways:

[0731] According to some exemplary embodiments, an automated algorithm-based physiological map is displayed on an anatomical map, such that the two maps are displayed overlapping to help the user understand the mapped area and make decisions about the optimal implantation location.

[0732] According to some exemplary embodiments, physiological mapping is used to modify anatomical maps online during surgery. In some embodiments, anatomical maps are often no longer accurate due to changes in intracranial pressure caused by alterations in known anatomical structures after skull opening. In some embodiments, automated electrophysiological mapping is used as input to an anatomical map deformation algorithm that modifies the anatomical map to be consistent with the result of physiological mapping. Optionally, this deformation algorithm can account for known effects such as gravity due to fibers with specific orientations, different tissue densities, and tissue anisotropy. Thus, an updated anatomical map is displayed to the user, optionally with physiological map overlap.

[0733] According to some exemplary embodiments, anatomical maps are used as input to physiological maps. In some embodiments, information from the anatomical maps can be used to modify physiological mapping algorithms, in a sense, taking the anatomical maps into account when labeling specific tissue locations with physiological labels. For example, in statistical physiological mapping algorithms, where labeling is based on finding the most likely label based on recorded signals and possibly on previous labeling decisions in the trajectory, the anatomical maps can be used to update the probabilities assigned to different labels at different depths. For example, they can be used as input as prior probability distributions to methods that include prior and posterior probability distributions.

[0734] According to some exemplary embodiments, the above-described method of combining physiological and anatomical maps is applicable both to probes having multiple contacts on their surface and to probes having one or more recording contacts on their surface. In some embodiments, the physiological map combined with the anatomical map is thus derived from multiple signals recorded from the same probe device or from multiple probe devices.

[0735] Exemplary optional features

[0736] According to some embodiments, the present invention generally relates to using electrophysiology to navigate tools to regions in the brain, and more specifically to a real-time method and system for navigating tools to specific regions in the brain during surgery using a computational approach based on machine learning algorithms.

[0737] According to some embodiments, this disclosure relates to an automated brain probe guidance system. In some embodiments, specifically, this disclosure relates to a real-time method and system for guiding a probe to a brain region or cell nucleus of a subject in need using closed-loop electrophysiological feedback.

[0738] In some embodiments, deep brain stimulation (DBS) is a surgical procedure involving the implantation of a medical device called a macroelectrode (also known as a "lead," "brain pacemaker," "electrode," or "chronic electrode") that delivers electrical impulses to specific areas of the brain. In some embodiments, DBS in selected brain regions has provided significant therapeutic benefits for other treatments of motor and infectious disorders, such as chronic pain, Parkinson's disease (PD), tremor, dystonia, and depression. Currently, in some embodiments, the method is used only for patients whose symptoms cannot be adequately controlled with medication. In some embodiments, DBS directly alters brain activity in a controlled manner, and its effects are reversible (unlike lesioning techniques).

[0739] According to some embodiments, DBS uses a surgically implanted, battery-operated medical neurostimulator, also known as an implantable pulse generator (IPG), to deliver electrical stimulation to target areas in the brain. In some embodiments, brain regions that control movement can be targeted, for example, to block abnormal neural signals that cause tremors and PD symptoms.

[0740] In some embodiments, prior to surgery, neurosurgeons use magnetic resonance imaging (MRI) or computed tomography (CT) scans to identify and locate precise targets within the brain. Optionally, in order to treat movement disorders, these targets are areas that control movement, such as the thalamus, subthalamic nucleus, and globus pallidus, where electrical nerve signals produce undesirable symptoms.

[0741] According to some embodiments, a DBS system typically consists of three components: a macroelectrode, an extension, and a neurostimulator. In some embodiments, the macroelectrode (a thin, insulated wire) is inserted into a small opening in the skull and implanted in the brain. Optionally, the tip of the electrode is located within a target brain region.

[0742] According to some embodiments, the extension is an insulated wire that can then pass under the skin of the head, neck, and shoulders, optionally connecting leads to a neurostimulator. In some embodiments, the neurostimulator (“battery pack”) is a third component and is typically implanted under the skin near the collarbone. Optionally, in some cases, it can be implanted under the skin below the chest or above the abdomen.

[0743] In some embodiments, once the system is in place, electrical pulses are sent upwards from the neurostimulator along extension lines and leads into the brain. Optionally, these pulses interfere with and block electrical signals that cause undesirable symptoms. In some embodiments, the person can turn off DBS if desired.

[0744] According to some embodiments, accurate and rapid guidance of macroelectrodes is crucial to improving the effectiveness of the implanted macroelectrodes. Therefore, in some embodiments, there is a need in the art to accurately guide macroelectrodes to target regions in the most precise manner available. A prior invention (incorporated herein by reference) discloses a system in which probes are used to perform automated and closed-loop navigation in brain targets (WO 2016 / 182997). In the invention disclosed below, in some embodiments, we illustrate how automated brain navigation can be improved in terms of reliability, accuracy, patient safety, and reduced time required using one or a combination of several techniques.

[0745] It is anticipated that many related macroelectrodes will be developed during the lifetime of the patents derived from this application, and the scope of the term macroelectrode is intended to a priori include all of these new technologies.

[0746] As used in this article, the term “about” means ±25%.

[0747] The terms “including,” “containing,” “comprising,” “having,” and their suffixes mean “including but not limited to.”

[0748] The term "composed of" means "including but not limited to".

[0749] The term "consistently made up of" means that a composition, method, or structure may include other ingredients, steps, and / or portions, but only if the additional ingredients, steps, and / or portions do not substantially alter the essential and novel characteristics of the claimed composition, method, or structure.

[0750] As used in this article, the singular forms “a,” “one,” and “the” include plural references unless the context clearly indicates otherwise.

[0751] Throughout this application, various embodiments of the invention may be presented in a scope format. It should be understood that the scope format is merely for convenience and brevity and should not be construed as an inflexible limitation of the scope of the invention. Therefore, the scope description should be considered to specifically disclose all possible sub-scopes and the individual numerical values ​​within those scopes. For example, a description of a scope such as 1 to 6 should be considered to have particularly disclosed sub-scopes, such as from 1 to 3, 1 to 4, 1 to 5, 2 to 4, 2 to 6, 3 to 6, and individual numbers within those scopes, such as 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the scope.

[0752] Whenever a range of numbers is indicated herein, it is intended to include any referenced numbers (fractions or integrals) within the indicated range. The phrases “range / range between the first indicated number and the second indicated number” and “range / range from the first indicated number to the second indicated number” are used interchangeably herein and mean to include the first indicated number and the second indicated number, as well as all fractional and integer numbers in between.

[0753] As used herein, the term "method" refers to the manner, means, techniques, and procedures used to accomplish a given task, including but not limited to those manner, means, techniques, and procedures known to practitioners in the fields of chemistry, pharmacology, biology, biochemistry, and medicine, or those manner, means, techniques, and procedures developed directly from those known manner, means, techniques, and procedures.

[0754] As used herein, the term “treatment” includes eliminating, substantially inhibiting, slowing or reversing the progression of a disease, substantially improving the clinical or aesthetic symptoms of a disease, or substantially preventing the occurrence of the clinical or aesthetic symptoms of a disease.

[0755] It should be understood that, for clarity, certain features of the invention described in the context of a single embodiment may also be provided in combination in a single embodiment. Conversely, for brevity, various features of the invention described in the context of a single embodiment may also be provided individually or in any suitable sub-combination or adapted to be provided in any other described embodiment of the invention. Certain features described in the context of various embodiments are not considered essential features of those embodiments unless the embodiment does not function without these elements.

[0756] The various embodiments and aspects of the invention claimed as described above and in the following claims section are supported by experimental and computational evidence in the following examples.

[0757] Although the invention has been described in conjunction with specific embodiments thereof, it will be apparent to those skilled in the art that many alternatives, modifications, and variations will be apparent. Therefore, it is intended to cover all such alternatives, modifications, and variations falling within the spirit and broad scope of the appended claims.

[0758] All publications, patents, and patent applications mentioned in this specification are incorporated herein by reference in their entirety, to the extent that each individual publication, patent, or patent application is specifically and individually indicated to be incorporated herein by reference. Furthermore, any reference or designation of any reference in this application should not be construed as an admission that such reference is prior art to the invention. Within the scope of the use of section headings, they should not be construed as necessarily limiting.

Claims

1. A system for differential recording in real-time navigation, connectable to an electrical lead having at least two electrodes, comprising: a. At least one electrical lead having a longitudinal axis, a distal end, and at least two electrodes located on the same at least one electrical lead; b. A memory configured to store a reference indication of the differential signal between the at least two electrodes and an electrical signal associated with the neural tissue; and c. The processing circuit, wherein the processing circuit is: i. During the advance of the at least one electrical lead, calculate in real time the differential signal between the electrical signals recorded from the at least two electrodes; ii. The reference indication for processing the differential signal and the electrical signal associated with the neural tissue; and iii. Calculate the anatomical location of the electrical leads based on the aforementioned processing; The calculation of the anatomical location of the leads includes estimating the proximity between at least one of the electrodes or the distal end of the leads and the boundary between the anatomical region.

2. The system of claim 1, wherein the memory stores an algorithm comprising at least one of a classifier and a predictor, and wherein the processing circuitry uses the algorithm to analyze the stored differential signal and estimates the proximity based on the results of the analysis.

3. The system of claim 1, wherein the at least two electrodes comprise at least one macroelectrode and / or at least one microelectrode.

4. The system of claim 1, comprising an amplifier electrically connectable to the at least one electrical conduction, wherein the at least one amplifier generates the differential signal.

5. The system of claim 1, wherein the at least one amplifier generates the differential signal by subtracting the signal recorded by the other electrode from the signal recorded by one of the at least two electrodes.

6. The system of claim 1, further comprising a module for the processing of the differential signal, wherein the processing includes: The differential signal is generated by subtracting the signal measured by the other electrode from the signal measured by one of the at least two electrodes using the module.

7. The system of claim 1, wherein the processing circuitry estimates the proximity by estimating the proximity between the distal end of the electrical lead and the selected anatomical target.

8. The system of claim 1, wherein the boundary comprises one or more of the following: the dorsal boundary of the subthalamic nucleus, the ventral boundary of the subthalamic nucleus, the boundary between the subthalamic nucleus and the substantia nigra reticularis, the boundary between the striatum and the outer part of the globus pallidus, the boundary between the outer part of the globus pallidus and the inner part of the globus pallidus, the boundary between the subdomains of the subthalamic nucleus, or the ventral boundary of the inner part of the globus pallidus.

9. The system of claim 1, wherein the electrical signal includes a local field potential, and the differential signal includes a differential local field potential.

10. The system of claim 1, wherein the processing circuit calculates at least one of the root mean square (RMS), normalized RMS (NRMS), and power spectral density (PSD) values ​​from the differential signal.

11. The system according to claim 1, comprising: User interface circuitry The processing circuitry signals the user interface circuitry to generate a user-detectable signal based on the estimated proximity.

12. The system of claim 1, wherein the at least two electrodes are axially and / or angularly separated on the circumference of at least one electrical conduction for measuring signals from different directions and / or different depths.

13. The system of claim 1, wherein the electrical signal is recorded during the propagation of the at least one electrical lead through the neural tissue.

14. The system of claim 1, wherein the stored electrical signal comprises a differential LFP signal and / or a MER signal.

15. The system of claim 1, wherein the processing circuitry is configured to calculate the β-band power oscillation and estimate the proximity based on the result of the calculation.

16. The system of claim 1, wherein the processing circuitry is configured to calculate a power band in a frequency range of 5-300 Hz and to estimate the proximity based on the result of the calculation.

17. The system of claim 1, wherein the memory stores propulsion parameters, and wherein the system comprises: An electric motor, functionally connected to the electrical leads and the processing circuit; The processing circuitry is configured to calculate a desired propulsion parameter value based on the estimated proximity and using the stored propulsion parameters, and to signal the electric motor to propel the electrical conductor according to the desired propulsion parameter value.

18. The system of claim 1, wherein the processing circuitry modifies the recording rate of the electrical signal based on the estimated proximity.

19. The system of claim 18, wherein the processing circuit increases the recording rate as it approaches the boundary.

20. The system of claim 1, wherein the processing circuitry estimates the proximity online, and wherein the online estimation includes providing an estimate of the time taken for the electrical leads to advance to a maximum distance of 20 μm.

21. The system of claim 1, wherein the memory stores at least one functional tissue map, the functional tissue map including anatomical data and reference indications of electrical signals associated with the anatomical data, and wherein the processing circuitry estimates the proximity based on a comparison between the recorded electrical signals and the functional tissue map.

22. The system according to any one of claims 1 to 21, wherein the electrical leads include deep brain stimulation leads, and wherein the at least two electrodes are axially separated on the at least one electrical lead and configured to deliver deep brain stimulation therapy.

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