Likelihood determination of stimulation-induced side-effect regions in deep brain stimulation

Abstract

Methods and systems are described for stimulation parameter programming to assist in deep brain stimulation (DBS) of a subject patient. The cumulative database includes data from a plurality of historical adaptation / programming sessions. The data may include various sets of stimulation parameters attempted for the patient in a database; data relating to the position of the electrode lead relative to an anatomical feature of the patient's brain, e.g., alignment with respect to its subthalamic nucleus (STN); a score of the stimulation parameter indicative of therapeutic effectiveness of the stimulation parameter and / or causing a side effect in the patient; and data relating to a stimulation field model (SFM) of the various sets of stimulation parameters. The database may be used to predict sets of stimulation parameters that may be effective or cause side effects to the treatment of the subject patient.

Description

Technical Field

[0001] This application relates to deep brain stimulation (DBS), and more specifically, to methods and systems for optimizing DBS. Background Technology

[0002] Implantable neurostimulatory devices are devices that generate electrical stimulation and deliver it to the body's nerves and tissues for the treatment of various biological disorders, such as pacemakers for treating arrhythmias, defibrillators for treating cardiac fibrillation, cochlear stimulators for treating deafness, retinal stimulators for treating blindness, muscle stimulators for generating coordinated limb movements, spinal cord stimulators for treating chronic pain, cortical and deep brain stimulators for treating motor and mental disorders, and other neurostimulators for treating urinary incontinence, sleep apnea, shoulder subluxation, etc. The following description will focus primarily on the use of the invention in the context of deep brain stimulation (DBS). DBS has been used therapeutically to treat neurological disorders, including Parkinson's disease, essential tremor, dystonia, and epilepsy, to name a few. Further details discussing the use of DBS in treating diseases are disclosed in U.S. Patent Nos. 6,845,267 and 6,950,707.

[0003] Each of these neurostimulation systems (whether implantable or external) typically includes one or more stimulation leads carrying electrodes, which are implanted at the desired stimulation site; and a neurostimulator, which is used externally or implanted remotely from the stimulation site, but is directly coupled to one or more neurostimulation leads or indirectly coupled to one or more neurostimulation leads via lead extensions. The neurostimulation system may also include a handheld external control device to remotely instruct the neurostimulator to generate electrical stimulation pulses according to selected stimulation parameters. Typically, the stimulation parameters programmed into the neurostimulator can be adjusted by manipulating controls on the external control device to modify the electrical stimulation provided to the patient by the neurostimulator system.

[0004] Therefore, based on stimulation parameters programmed by an external control device, electrical pulses can be delivered from a neurostimulator to one or more stimulating electrodes to stimulate or activate a specific volume of tissue according to a set of stimulation parameters, providing the desired therapeutic effect to the patient. The optimal set of stimulation parameters typically delivers stimulation energy to the tissue volume that must be stimulated to provide therapeutic benefit (e.g., treating movement disorders) while minimizing the volume of non-target tissue stimulated. A typical set of stimulation parameters may include the electrode acting as the anode or cathode, and the amplitude, duration, and rate of the stimulation pulse.

[0005] Non-optimal electrode placement and stimulation parameter selection can lead to: excessive energy expenditure due to excessively high amplitude, excessively wide pulse duration, or excessively high frequency settings; inadequate or marginalized treatment due to excessively low amplitude, excessively narrow pulse duration, or excessively slow frequency settings; or stimulation of adjacent cell populations that may cause undesirable side effects. For example, bilateral deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been shown to provide an effective therapy for improving major motor indications in advanced Parkinson's disease, and although bilateral DBS of the STN is considered safe, a new concern is its potential negative impact on cognitive function and overall quality of life (see AMM Frankemolle et al., Reversing Cognitive-Motor Impairments in Parkinson's Disease Patients Using a Computational Modelling Approach to Deep Brain Stimulation Programming, Brain 2010; pp. 1-16). This phenomenon is largely due to the small size of the STN. Even if the electrodes are primarily located within the sensorimotor region, the electric field generated by DBS will be applied indiscriminately to all neural elements surrounding the electrodes, causing current to propagate to neural elements affecting cognition. Therefore, during STN stimulation, cognitive decline may occur due to the non-selective activation of non-motor pathways within or around the STN.

[0006] The availability of a large number of electrodes, combined with the ability to generate a variety of complex stimulation pulses, presents clinicians or patients with a wide range of stimulation parameter sets to choose from. In the context of DBS, neural stimulation leads with complex electrode arrangements can be used, where the electrodes are distributed not only along the lead axis but also as segmented electrodes distributed circumferentially around the neural stimulation lead.

[0007] To facilitate such selections, clinicians typically program external control devices and, where applicable, neurostimulators via computer programming systems. This programming system can be a self-contained hardware / software system or primarily software-defined, running on a standard personal computer (PC) or mobile platform. The PC or custom hardware can actively control the characteristics of the electrical stimulation generated by the neurostimulator, allowing optimal stimulation parameters to be determined based on patient feedback, which are then used to program the external control devices.

[0008] When electrical leads are implanted in a patient, a computer programming system can be used to instruct the neurostimulator to apply electrical stimulation to test the placement of the leads and / or electrodes, thereby ensuring that the leads and / or electrodes are implanted in the patient's effective position. The system can also instruct the user on how to improve lead positioning or confirm when the leads are properly positioned. Once the leads are correctly positioned, the computerized programming system can be used to program external control devices and, where applicable, the neurostimulator, executing an adaptation procedure using a set of stimulation parameters optimally suited to one or more neurological disorders; this procedure may be referred to as a navigation session. Methods and systems are needed to help clinicians determine optimized stimulation parameters for treating patients. Summary of the Invention

[0009] This document discloses a method for programming electrical stimulation parameters to provide deep brain stimulation (DBS) to a subject patient, wherein the subject patient is implanted with an implantable medical device comprising: an implantable pulse generator (IPG) connected to one or more electrode leads implanted in the subject patient's brain, wherein each electrode lead includes multiple electrodes; the method includes: receiving imaging data of the subject patient; using the imaging data to determine the position of at least one electrode lead relative to at least one anatomical feature of the subject patient's brain; receiving accumulated data from a database, wherein the accumulated data includes data from previous patients, which correlate the position of the electrode lead relative to the anatomical feature of the previous patient's brain with stimulation parameters that induced adverse effects in the previous patient; and using the accumulated data and the imaging data to determine the stimulation parameters for the subject patient. According to some embodiments, the at least one anatomical feature includes the subthalamic nucleus (STN). According to some embodiments, the imaging data of the subject patient includes preoperative magnetic resonance imaging (MRI) data and postoperative computed tomography (CT) and / or MRI data. According to some embodiments, at least one anatomical feature of the STN of the test patient includes one or more of the following: medial / lateral axis, anterior / posterior axis, medial border, lateral border, anterior border, and posterior border. According to some embodiments, using imaging data to determine the position of at least one electrode lead relative to at least one anatomical feature of the STN of the test patient includes: using the imaging data to determine a 3-D model of the patient's STN and voxelizing the 3-D model. According to some embodiments, the method further includes using the 3-D model to determine one or more axes of the STN of the test patient. According to some embodiments, principal component analysis is used to determine one or more axes. According to some embodiments, the accumulated data includes: an indication of the radius of a stimulation field model (SFM) corresponding to stimulation parameters that have provided therapeutic benefit in at least some prior patients. According to some embodiments, determining the stimulation parameters of the test patient includes: receiving information indicating a set of experimental stimulation parameters for the test patient, determining the SFM for the experimental stimulation parameter set, determining the SFM radius for the experimental stimulation parameter set, and comparing the SFM radius for the experimental stimulation parameter set with the SFM radius from the accumulated data. According to some embodiments, a stimulus optimization algorithm is used to automatically suggest experimental stimulus parameter sets.According to some embodiments, determining stimulation parameters for a subject using accumulated data and imaging data includes: displaying on a graphical user interface (GUI) a representation of a search space indicating potential sets of experimental stimulation parameters, and one or more likelihood plots derived from the accumulated data, wherein the one or more likelihood plots indicate sets of experimental stimulation parameters within the search space that may induce side effects in the subject. According to some embodiments, the one or more likelihood plots indicate the SFM radius corresponding to the sets of experimental stimulation parameters that may induce side effects in the subject.

[0010] This document also discloses a system for programming electrical stimulation parameters to provide deep brain stimulation (DBS) to a subject patient, wherein the subject patient is implanted with an implantable medical device comprising: an implantable pulse generator (IPG) connected to one or more electrode leads implanted in the brain of the subject patient, wherein each electrode lead includes multiple electrodes; the system includes: an external computing device including control circuitry configured to execute a method comprising: receiving imaging data of the subject patient; using the imaging data to determine the position of at least one electrode lead relative to at least one anatomical feature of the subthalamic nucleus (STN) of the subject patient; receiving accumulated data from a database, wherein the accumulated data includes data from previous patients relating the position of the electrode lead relative to the anatomical feature of the STN of a previous patient to stimulation parameters that induced side effects in the previous patient; and using the accumulated data and the imaging data to determine the stimulation parameters of the subject patient. According to some embodiments, the imaging data of the subject patient includes preoperative magnetic resonance imaging (MRI) data and postoperative computed tomography (CT) and / or MRI data. According to some embodiments, at least one anatomical feature of the STN of the test patient includes one or more of the following: medial / lateral axis, anterior / posterior axis, medial border, lateral border, anterior border, and posterior border. According to some embodiments, determining the position of at least one electrode lead relative to at least one anatomical feature of the STN of the test patient using imaging data includes: determining a 3-D model of the patient's STN using the imaging data and voxelizing the 3-D model. According to some embodiments, the method further includes determining one or more axes of the STN of the test patient using the 3-D model. According to some embodiments, principal component analysis is used to determine one or more axes. According to some embodiments, the accumulated data includes: an indication of the radius of a stimulation field model (SFM) corresponding to stimulation parameters that have provided therapeutic benefit in at least some prior patients. According to some embodiments, determining the stimulation parameters of the test patient includes: receiving information indicating a set of experimental stimulation parameters for the test patient, determining the SFM for the set of experimental stimulation parameters, determining the SFM radius for the set of experimental stimulation parameters, and comparing the SFM radius for the set of experimental stimulation parameters with the SFM radius from the accumulated data. According to some embodiments, a stimulation optimization algorithm is used to automatically suggest a set of experimental stimulation parameters. According to some embodiments, determining stimulation parameters for a subject using accumulated data and imaging data includes: displaying on a graphical user interface (GUI) a representation of a search space indicating potential sets of experimental stimulation parameters, and one or more likelihood plots derived from the accumulated data, wherein the one or more likelihood plots indicate sets of experimental stimulation parameters within the search space that are likely to induce side effects in the subject. According to some embodiments, the one or more likelihood plots indicate the SFM radius corresponding to the sets of experimental stimulation parameters that are likely to induce side effects in the subject.

[0011] This document also discloses a method for programming electrical stimulation parameters to provide deep brain stimulation (DBS) to a subject patient, wherein the subject patient is implanted with an implantable medical device including an implantable pulse generator (IPG) connected to one or more electrode leads implanted in the subject patient's brain, wherein each electrode lead includes multiple electrodes. The method includes: receiving accumulated data from a database, wherein the accumulated data includes data from previous patients relating stimulation field model (SFM) information compiled from previous patients for multiple experimental stimulation parameter sets to stimulation parameters that induced side effects in previous patients, and using the accumulated data to determine stimulation parameters for the subject patient. According to some embodiments, the accumulated data includes: an indication of the radius of the stimulation field model (SFM) corresponding to stimulation parameters that provided therapeutic benefit in at least some previous patients. According to some embodiments, determining the stimulation parameters for the subject patient includes: receiving information indicating experimental stimulation parameter sets for the subject patient, determining the SFM for the experimental stimulation parameter sets, determining the SFM radius for the experimental stimulation parameter sets, and comparing the SFM radius for the experimental stimulation parameter sets with the SFM radius from the accumulated data. According to some embodiments, experimental stimulation parameter sets are automatically suggested using a stimulation optimization algorithm. According to some embodiments, determining stimulation parameters for a subject using accumulated data and imaging data includes: displaying on a graphical user interface (GUI) a representation of a search space indicating potential sets of experimental stimulation parameters, and one or more likelihood plots derived from the accumulated data, wherein the one or more likelihood plots indicate sets of experimental stimulation parameters within the search space that are likely to induce side effects in the subject. According to some embodiments, the one or more likelihood plots indicate the SFM radius corresponding to the sets of experimental stimulation parameters that are likely to induce side effects in the subject.

[0012] This document also discloses a system for programming electrical stimulation parameters to provide deep brain stimulation (DBS) to a subject patient, wherein the subject patient is implanted with an implantable medical device including an implantable pulse generator (IPG) connected to one or more electrode leads implanted in the subject patient's brain, wherein each electrode lead includes multiple electrodes. The system includes: an external computing device including control circuitry configured to perform a method comprising: receiving accumulated data from a database, wherein the accumulated data includes data from previous patients relating stimulation field model (SFM) information compiled from previous patients for multiple experimental stimulation parameter sets to stimulation parameters that induced side effects in previous patients, and using the accumulated data to determine stimulation parameters for the subject patient. According to some embodiments, the accumulated data includes: an indication of the radius of the stimulation field model (SFM) corresponding to stimulation parameters that provided therapeutic benefit in at least some previous patients. According to some embodiments, determining the stimulation parameters for the subject patient includes: receiving information indicating experimental stimulation parameter sets for the subject patient, determining the SFM for the experimental stimulation parameter sets, determining the SFM radius for the experimental stimulation parameter sets, and comparing the SFM radius for the experimental stimulation parameter sets with the SFM radius from the accumulated data. According to some embodiments, a stimulus optimization algorithm is used to automatically suggest experimental stimulus parameter sets. According to some embodiments, determining stimulus parameters for the subject using accumulated data and imaging data includes: displaying on a graphical user interface (GUI) a representation of a search space indicating potential experimental stimulus parameter sets, and one or more likelihood plots derived from the accumulated data, wherein the one or more likelihood plots indicate experimental stimulus parameter sets within the search space that are likely to induce side effects in the subject. According to some embodiments, the one or more likelihood plots indicate the SFM radius corresponding to the experimental stimulus parameter sets that are likely to induce side effects in the subject.

[0013] This document also discloses a method for programming electrical stimulation parameters to provide deep brain stimulation (DBS) to a subject patient, wherein the subject patient is implanted with an implantable medical device including an implantable pulse generator (IPG) connected to one or more electrode leads implanted in the subject patient's brain, wherein each electrode lead includes multiple electrodes. The method includes: receiving accumulated data from a database, wherein the accumulated data includes: a set of experimental stimulation parameters provided to previous patients, stimulation field model (SFM) information for the experimental stimulation parameter set, and information indicating the therapeutic effectiveness of the experimental stimulation parameter set for previous patients; and using the accumulated data to predict stimulation parameters that may have a therapeutic effect on the subject patient. According to some embodiments, the method further includes updating the accumulated database based on the therapeutic effectiveness of the predicted stimulation parameters.

[0014] This document also discloses a non-volatile computer-readable medium comprising instructions for programming electrical stimulation parameters to provide deep brain stimulation (DBS) to a subject patient, wherein the subject patient is implanted with an implantable medical device including an implantable pulse generator (IPG) connected to one or more electrode leads implanted in the subject patient's brain, wherein each electrode lead includes multiple electrodes, wherein the non-volatile computer-readable medium includes instructions that, when executed on a computing device, configure the computing device to perform a method comprising the following steps: receiving imaging data of the subject patient; using the imaging data to determine the location of at least one electrode lead relative to at least one anatomical feature of the subthalamic nucleus (STN) of the subject patient; receiving accumulated data from a database, wherein the accumulated data includes data from previous patients relating the location of the electrode lead relative to the STN of the previous patient to stimulation parameters that induced side effects in the previous patient; and using the accumulated data and the imaging data to determine stimulation parameters for the subject patient. According to some embodiments, the imaging data of the subject patient includes preoperative magnetic resonance imaging (MRI) data and postoperative computed tomography (CT) and / or MRI data. According to some embodiments, at least one anatomical feature of the STN of the test patient includes one or more of the following: medial / lateral axis, anterior / posterior axis, medial border, lateral border, anterior border, and posterior border. According to some embodiments, using imaging data to determine the position of at least one electrode lead relative to at least one anatomical feature of the STN of the test patient includes using the imaging data to determine a 3-D model of the patient's STN and voxelizing the 3-D model. According to some embodiments, the method further includes using the 3-D model to determine one or more axes of the STN of the test patient. According to some embodiments, principal component analysis is used to determine one or more axes. According to some embodiments, the accumulated data includes an indication of the radius of a stimulation field model (SFM) corresponding to stimulation parameters that have provided therapeutic benefit in at least some prior patients. According to some embodiments, determining the stimulation parameters of the test patient includes receiving information indicating a set of experimental stimulation parameters for the test patient, determining the SFM for the set of experimental stimulation parameters, determining the SFM radius for the set of experimental stimulation parameters, and comparing the SFM radius of the set of experimental stimulation parameters with the SFM radius from the accumulated data. According to some embodiments, a stimulation optimization algorithm is used to automatically suggest a set of experimental stimulation parameters. According to some embodiments, determining stimulation parameters for a subject using accumulated data and imaging data includes: displaying on a graphical user interface (GUI) a representation of a search space indicating potential sets of experimental stimulation parameters, and one or more likelihood plots derived from the accumulated data, wherein the one or more likelihood plots indicate sets of experimental stimulation parameters within the search space that are likely to induce side effects in the subject. According to some embodiments, the one or more likelihood plots indicate the SFM radius corresponding to the sets of experimental stimulation parameters that are likely to induce side effects in the subject.

[0015] The invention may also reside in the form of a programmable external device, such as a clinician programmer or other computing device (via its control circuitry) for performing the methods described above, a programmable implantable pulse generator (IPG) or external experimental stimulator (ETS) (via its control circuitry) for performing the methods described above, a system comprising a programmable external device and an IPG or ETS for performing the methods described above, or as a computer-readable medium stored in an external device, IPG, or ETS for performing the methods described above. The invention may also reside in one or more non-transitory computer-readable media comprising instructions that, when executed by a machine's processor, configure the machine to perform any of the methods described above. Attached Figure Description

[0016] Figure 1A An implantable pulse generator (IPG) is shown.

[0017] Figure 1B A percutaneous lead with a split-ring electrode is shown.

[0018] Figure 2A and Figure 2B Examples of stimulation pulses (waveforms) that can be generated by an IPG or by an external experimental stimulator (ETS) are shown.

[0019] Figure 3 An example of a stimulation circuit available in an IPG or ETS is shown.

[0020] Figure 4 An ETS environment that can be used to provide stimulation prior to IPG implantation is shown.

[0021] Figure 5 Various external devices capable of communicating with the IPG or ETS and programming stimuli in the IPG or ETS are shown.

[0022] Figure 6 An example of a user interface (UI) for programming stimuli is shown.

[0023] Figure 7 An embodiment of a system for optimizing stimuli for DBS is shown.

[0024] Figure 8 A method for optimizing stimuli for DBS is shown.

[0025] Figure 9 The workflow for determining the orientation of electrode leads relative to the anatomical features of the patient's STN is shown.

[0026] Figure 10 The cross-section of the electrode lead is shown.

[0027] Figure 11Aand Figure 11B An example is shown for determining the distance between the electrode leads and the anatomical features of the patient's STN.

[0028] Figure 12A and Figure 12B The determined distance between the electrode leads and the anatomical features of the patient's STN is shown.

[0029] Figure 13 An example of a database is shown, which catalogs the distances between electrode leads and anatomical features of the patient's STN for multiple patients.

[0030] Figure 14 The location of the stimulation field model (SFM) relative to the anatomical features of the patient's STN is shown.

[0031] Figure 15 A GUI for optimizing stimulation parameters for patient DBS is shown, including a likelihood model.

[0032] Figure 16A and Figure 16B The radii of the SFM for the parameter groups with therapeutic effects and those causing side effects are shown separately.

[0033] Figure 17 The target area and avoidance area are shown.

[0034] Figure 18 The GUI shows an overlay of avoidance areas. Detailed Implementation

[0035] A typical DBS system includes Figure 1A The implantable pulse generator (IPG) 10 is shown. The IPG 10 includes a biocompatible device housing 12 that houses circuitry and a battery 14 to provide power for IPG operation. The IPG 10 is coupled to tissue stimulation electrodes 16 via one or more electrode leads forming an electrode array 17. For example, one or more electrode leads 15 may be used, having a ring electrode 16 supported on a flexible body 18.

[0036] exist Figure 1BIn yet another example shown, electrode lead 33 may include one or more split-ring electrodes. In this example, eight electrodes 16 (E1-E8) are shown. Electrode E1 at the distal end of the lead and electrode E8 at the proximal end of the lead comprise a ring electrode spanning 360 degrees around the central axis of lead 33. In some embodiments, electrode E1 may be a “bullet-tip” electrode, meaning it may cover the tip of the electrode lead. Electrodes E2, E3, and E4 consist of split-ring electrodes, each located at the same longitudinal position along the central axis 31, but each spanning less than 360 degrees around the axis. For example, each of electrodes E2, E3, and E4 may span 90 degrees around the axis 31, with each separated from the others by a 30-degree gap. Electrodes E5, E6, and E7 also consist of split-ring electrodes, but located at a different longitudinal position along the central axis 31 than the split-ring electrodes E4, E2, and E3. As shown in the figure, the split-ring electrodes E2-E4 and E5-E7 can be located longitudinally along axis 31 between the ring electrodes E1 and E8. However, this is just one example of a lead 33 with split-ring electrodes. In other designs, all electrodes can be split-ring electrodes, or there can be a different number of split-ring electrodes at each longitudinal position (i.e., more or less than three), or ring and split-ring electrodes can appear at different or random longitudinal positions, etc.

[0037] Lead wires 20 within the lead wire are coupled to electrodes 16 and proximal contacts 21, which can be inserted into lead connectors 22 in headers 23 fixed to IPG 10. These headers may be made of, for example, epoxy resin. Once inserted, proximal contacts 21 connect to header contacts 24 within the lead connector 22, which are then coupled to stimulation circuitry 28 within housing 12 via feedthrough pins 25 passing through housing feedthrough 26. Stimulation circuitry 28 will be described below.

[0038] exist Figure 1A In the IPG 10 shown, there are 32 electrodes (E1 to E32) split between four percutaneous leads 15, and therefore the connector 23 may include an eight-electrode lead connector 22 in a 2×2 array. However, the type and number of leads and the number of electrodes in the IPG are application-specific and may therefore vary. The conductive housing 12 may also include electrodes (Ec).

[0039] In DBS applications, such as those used to treat tremors in Parkinson's disease, the IPG 10 is typically implanted below the patient's clavicle (cervical bone). Lead wires 20 are tunneled through the neck and scalp, and electrode leads 15 (or 33) are implanted through holes drilled into the skull and, for example, in the subthalamic nucleus (STN).

[0040] IPG 10 may include an antenna 27a that allows it to communicate bidirectionally with several external devices, as discussed later. As shown, antenna 27a includes a conductive coil within housing 12, although this coil antenna 27a may also appear in connector 23. When antenna 27a is configured as a coil, near-field magnetic induction is preferably used for communication with external devices. IPG 10 may also include a radio-frequency (RF) antenna 27b. Figure 1A In the diagram, the RF antenna 27b is shown within the connector 23, but it may also be within the housing 12. The RF antenna 27b may include a patch, slot, or wire, and may operate as a monopole or dipole antenna. The RF antenna 27b preferably uses far-field electromagnetic waves for communication and can operate according to any number of known RF communication standards, such as Bluetooth, Bluetooth Low Energy (BLE) (as described in U.S. Patent Publication 2019 / 0209851), Zigbee, WiFi, MICS, and the like.

[0041] The stimulation in IPG 10 is typically provided by pulses, each pulse may include multiple phases, such as 30a and 30b, as... Figure 2A An example is shown. In the example shown, such stimulation is unipolar, meaning that current is provided between at least one selected lead-based electrode (e.g., E1) and the housing electrode Ec 12. Stimulation parameters typically include amplitude (current I, although voltage amplitude V may also be used); frequency (f); pulse width (PW) of the pulse or its various phases such as 30a and 30b; the selected electrode 16 to provide stimulation; and the polarity of such selected electrodes, i.e., whether they act as anodes to source current into the tissue or as cathodes to sink current into the tissue. These, along with other possible stimulation parameters, together constitute a stimulation program that the stimulation circuitry 28 in the IPG 10 can execute to provide therapeutic stimulation to the patient.

[0042] exist Figure 2A In the example, electrode E1 has been selected as the cathode (during its first phase 30a) and thus provides a pulse of negative current with an amplitude of -I from the tissue. The housing electrode Ec has been selected as the anode (again during the first phase 30a) and thus provides a pulse of corresponding positive current with an amplitude of +I pulling into the tissue. Note that at any given time, the current from the tissue (e.g., -I at E1 during phase 30a) is equal to the current pulling into the tissue (e.g., +I at Ec during phase 30a) to ensure that the net current injected into the tissue is 0. The polarity of the currents at these electrodes can be changed: Ec can be selected as the cathode, and E1 can be selected as the anode, and so on.

[0043] As mentioned above, IPG 10 includes stimulation circuitry 28 to generate a prescribed stimulus at the patient's tissue. Figure 3 An example of a stimulation circuit 28 is shown, which includes one or more current-pulling circuits 40. i and one or more current sinking circuits 42 i Pull circuit 40 i And the 42-cell circuit i It can include digital-to-analog converters (DACs), and can be referred to as PDAC 40 based on the positive (pull-in, anode) current and negative (sink, cathode) current they emit respectively. i and NDAC42 i In the example shown, NDAC / PDAC 40 i / 42 i For a specific electrode node Ei 39 (hardwired to), each electrode node Ei 39 is connected to electrode Ei 16 via a DC blocking capacitor Ci 38 for reasons explained below. PDAC40 i and NDAC 42 i It may also include a voltage source.

[0044] For PDAC 40 i and NDAC 42 i Proper control allows either electrode 16 or housing electrode Ec 12 to act as an anode or cathode to create a current through the patient tissue R, with the desired therapeutic effect. In the example shown, and with... Figure 2A The first pulse phase 30a is consistent, electrode E1 has been selected as the cathode electrode to infuse current into tissue R, and the shell electrode Ec has been selected as the anode electrode to draw current into tissue R. Therefore, PDAC 40 C The NDAC 421 is activated and digitally programmed to generate the desired current I with the correct timing (e.g., according to the specified frequency F and pulse width PW). As further described in detail in U.S. Patent Application Publication 2013 / 0289665, the power for the stimulation circuit 28 is provided by the compliance voltage VH.

[0045] Other stimulation circuits 28 can also be used in IPG 10. In an example not shown, a switching matrix can be located between one or more PDAC 40s. i Between electrode node ei 39 and one or more NDAC 42 iBetween the electrode nodes. The switching matrix allows one or more PDACs or one or more NDACs to be connected to one or more electrode nodes at a given time. Various examples of stimulation circuits can be found in USP 6,181,969, USP 8,606,362, USP 8,620,436 and U.S. Patent Application Publications 2018 / 0071520 and 2019 / 0083796. The stimulation circuits described herein provide multiple independent current control (MICC) (or multiple independent voltage control) to guide the estimation of current subdivision between multiple electrodes and to estimate the total amplitude providing the desired intensity. In other words, the total anode (or cathode) current can be split between two or more electrodes, and / or the total cathode current can be split between two or more electrodes, thereby allowing adjustment of the stimulation location and the shape of the generated field. For example, by subdividing the current between two electrodes, a “virtual electrode” can be created at a location between two physical electrodes.

[0046] As described in U.S. patent applications published in 2012 / 0095529, 2012 / 0092031, and 2012 / 0095519, Figure 3 Many stimulation circuits 28 (including PDAC 40) i and NDAC 42 i The switch matrix (if present) and electrode nodes (ei 39) can be integrated on one or more application-specific integrated circuits (ASICs). As explained in these references, the ASIC may also contain other circuitry useful in IPG 10, such as telemetry circuitry (for off-chip interfacing with telemetry antennas 27a and / or 27b), circuitry for generating compliant voltage VH, various measurement circuitry, etc.

[0047] Figure 3 The diagram also shows DC blocking capacitors Ci 38, which are placed in series in the electrode current path between electrode node ei 39 and each of electrodes Ei 16 (including housing electrode Ec 12). DC blocking capacitors 38 act as a safety measure to prevent DC current from being injected into the patient, for example, in the event of a circuit failure in the stimulation circuit 28. DC blocking capacitors 38 are typically located off-chip (outside the ASIC), but can also be located on or within a circuit board in the IPG 10 used to integrate its various components, as explained in U.S. Patent Application Publication 2015 / 0157861.

[0048] Refer again Figure 2AThe stimulation pulses shown are biphasic, with each pulse comprising a first phase 30a followed by a second phase 30b of opposite polarity. Biphasic pulses facilitate the active recovery of any charge that may be stored on capacitive elements (such as DC blocking capacitor 38) in the electrode current path. Reference Figure 2A and Figure 2B Charge recovery is illustrated. During the first pulse phase 30a, charge accumulates across the DC blocking capacitors C1 and Cc associated with the electrodes E1 and Ec used to generate the current, thereby causing voltages Vc1 and Vcc to decrease according to the amplitude of the current and the capacitance of capacitor 38 (dV / dt = I / C). During the second pulse phase 30b, when the polarity of the current I reverses at the selected electrodes E1 and Ec, the charge stored on capacitors C1 and Cc is actively recovered, and therefore voltages Vc1 and Vcc increase and return to 0V at the end of the second pulse phase 30b.

[0049] To restore all charge (Vc1=Vcc=0V) at the end of the second pulse phase 30b of each pulse, the first phase 30a and the second phase 30b are charged in a balanced manner at each electrode, wherein the first pulse phase 30a provides a charge of -Q (-I * PW) at electrode E1, and the second pulse phase 30b provides a charge of +Q (+I * PW) at electrode E1, and wherein the first pulse phase 30a provides a charge of +Q at the housing electrode Ec, and the second pulse phase 30b provides a charge of -Q at the housing electrode Ec. In the example shown, this charge balance is achieved by using the same pulse width (PW) and the same amplitude (Vc1=Vcc=0V) for each of the pulse phases 30a and 30b of opposite polarities. This is achieved by [the method described in the original text]. However, if the product of the amplitude and pulse width of the two phases 30a and 30b is equal, or if the area under each phase is equal, then the pulse phases 30a and 30b can also be charged in balance at each electrode, as is known.

[0050] Figure 3 The stimulation circuit 28 is shown to include a passive recovery switch 41i, which is further described in U.S. Patent Application Publications 2018 / 0071527 and 2018 / 0140831. The passive recovery switch 41i can be attached to each of the electrode nodes ei 39 and is used to passively recover any remaining charge on the DC blocking capacitor Ci 38 after the second pulse phase 30b is emitted; that is, to recover charge without actively driving current using the DAC circuitry. Passive charge recovery can be cautious because non-ideals in the stimulation circuit 28 may result in incomplete charge balance between pulse phases 30a and 30b.

[0051] Therefore, as Figure 2AAs shown, passive charge recovery typically occurs after the second pulse phase 30b is emitted, for example, during at least a portion 30c of the silence period between pulses, by closing the passive recovery switch 41. i Proceed. For example... Figure 3 As shown, switch 41 is not coupled to electrode node ei 39. i The other end is connected to a common reference voltage, which in this example includes the voltage Vbat of battery 14, but another reference voltage can also be used. As explained in the references above, passive charge recovery tends to balance the charge on the DC blocking capacitor 38 by placing the capacitor in parallel between the reference voltage (Vbat) and the patient tissue. Note that in Figure 2A In the figure, passive charge recovery during 30c is shown as a small exponential decay curve, which can be positive or negative depending on whether pulse phase 30a or 30b has a charge advantage on a given electrode.

[0052] Passive charge recovery 30c can reduce the need for charge recovery using biphasic pulses, especially in DBS backgrounds where current amplitudes may be low, making charge recovery less critical. For example, although Figure 2A Not shown, but the pulses supplied to the tissue may be monophase, consisting only of the first pulse phase 30a. Thereafter, passive charge recovery 30c can be performed to eliminate any charge buildup that occurred during the single pulse 30a.

[0053] Figure 4 An external test stimulation environment is illustrated prior to IPG 10 implantation in a patient, such as during test stimulation and lead placement confirmation in an operating room. During external test stimulation, stimulation can be attempted on the implantation patient to assess the side effect threshold and confirm that the lead is not too close to structures that could cause side effects. Similar to IPG 10, the external test stimulator (ETS) 50 may include one or more antennas to enable bidirectional communication with external devices, such as… Figure 5 As shown above, such antennas may include near-field magnetic induction coil antenna 56a and / or far-field RF antenna 56b. The ETS 50 may also include stimulation circuitry capable of generating stimulation according to a stimulation program, which may be similar to or include the same stimulation circuitry 28 present in the IPG 10. Figure 3 The ETS 50 may also include a battery (not shown) for operating power. Since the IPG may include housing electrodes, the ETS can provide one or more connections to establish a similar circuit; for example, using patch electrodes. Similarly, the ETS can communicate with a clinician programmer (CP) so that the CP can process data as described below.

[0054] Figure 5Various external devices that can wirelessly transmit data to the IPG 10 or ETS 50 are shown, including a patient-handheld external controller 60 and a clinician programmer (CP) 70. Both devices 60 and 70 can be used to wirelessly transmit stimulation programs to the IPG 10 or ETS 50—that is, to program its stimulation circuitry to produce stimulations with the desired amplitude and timing described above. Both devices 60 and 70 can also be used to adjust one or more stimulation parameters of the stimulation program currently being executed by the IPG 10. Both devices 60 and 70 can also wirelessly receive information (such as various status information) from the IPG 10 or ETS 50.

[0055] For example, external controller 60 may be as described in U.S. Patent Application Publication 2015 / 0080982 and may include a controller specifically designed for use with IPG 10 or ETS 50. External controller 60 may also include a general-purpose mobile electronic device (such as a mobile phone) programmed with a Medical Device Application (MDA) to allow it to function as a wireless controller for IPG 10 or ETS, as described in U.S. Patent Application Publication 2015 / 0231402. External controller 60 includes a user interface, preferably including devices for inputting instructions (e.g., buttons or selectable graphical elements) and a display 62. The user interface of external controller 60 allows the patient to adjust stimulation parameters, although it may be limited in functionality compared to the more powerful clinician programmer 70 described below.

[0056] The external controller 60 may have one or more antennas capable of communicating with the IPG 10. For example, the external controller 60 may have a near-field magnetic induction coil antenna 64a capable of wirelessly communicating with coil antennas 27a or 56a in the IPG 10 or ETS 50. The external controller 60 may also have a far-field RF antenna 64b capable of wirelessly communicating with RF antennas 27b or 56b in the IPG 10 or ETS 50.

[0057] The clinician programmer 70 is further described in U.S. Patent Application Publication 2015 / 0360038 and may include a computing device 72, such as a desktop computer, laptop computer or notebook computer, tablet computer, mobile smartphone, personal data assistant (PDA) type mobile computing device, etc. Figure 5 In the image, computing device 72 is shown as a laptop computer, which includes typical computer user interface devices such as screen 74, mouse, keyboard, speakers, stylus, printer, etc. For convenience, not all of these devices are shown. Figure 5Also shown is an auxiliary device for the clinician programmer 70, which is generally specific to its operation as a stimulus controller, such as a communication "wand" 76 that can be coupled to a suitable port (such as USB port 79) on the computing device 72.

[0058] The antenna in the Clinician Programmer 70 used for communication with the IPG 10 or ETS 50 may vary depending on the type of antenna included in these devices. If the patient's IPG 10 or ETS 50 includes coil antennas 27a or 56a, the stick 76 may also include coil antenna 80a to establish near-field magnetic induction communication over short distances. In this case, the stick 76 may be attached near the patient, such as by placing the stick 76 in a patient-wearable strap or holster and close to the patient's IPG 10 or ETS 50. If the IPG 10 or ETS 50 includes RF antennas 27b or 56b, the stick 76, computing device 72, or both may also include RF antenna 80b to establish communication over greater distances. The Clinician Programmer 70 may also communicate wirelessly or via a wired link provided at an Ethernet or network port with other devices and networks, such as the Internet.

[0059] To program the stimulation procedures or parameters of IPG 10 or ETS 50, the clinician interfaces with a clinician programmer graphical user interface (GUI) 100 provided on the display 74 of the computing device 72. As will be understood by those skilled in the art, the GUI 100 can be presented by executing clinician programmer software 84 stored in the computing device 72, which may be stored in the device's non-volatile memory 86. Execution of the clinician programmer software 84 in the computing device 72 can be driven by control circuitry 88, such as one or more microprocessors, microcomputers, FPGAs, DSPs, other digital logic structures, etc., which are capable of executing programs in the computing device and may include their own memory. For example, the control circuitry 88 may include an i5 processor manufactured by Intel Corporation, as described at https: / / www.intel.com / content / www / us / en / products / processors / core / i5-processors.html. In addition to executing the clinician programmer software 84 and presenting the GUI 100, this control circuit 88 can also communicate via antenna 80a or 80b to communicate stimulation parameters selected through the GUI 100 to the patient's IPG 10.

[0060] The user interface of the external controller 60 can provide similar functionality because the external controller 60 can include hardware and software programming similar to that of a clinician programmer. For example, the external controller 60 includes control circuitry 66 similar to control circuitry 88 in a clinician programmer 70, and can be similarly programmed using external controller software stored in the device memory.

[0061] Especially in the context of DBS, providing clinicians with visual indications of how the stimulation selected for the patient will interact with the tissue of the implanted electrode can be useful. Figure 6 As shown, this figure illustrates a graphical user interface (GUI) 100 that can operate on an external device capable of communicating with the IPG 10 or ETS 50. Typically, as assumed in the description below, the GUI 100 will be presented on the clinician programmer 70. Figure 5 This programmer can be used during surgical implantation of the lead, and also after implantation when selecting therapeutically useful stimulation programs for the patient. However, the GUI 100 can be used with the external programmer 60 ( Figure 5 It can be displayed on any other external device that can communicate with IPG 10 or ETS 50.

[0062] GUI 100 allows clinicians (or patients) to select the stimulation program that IPG 110 or ETS 50 will provide, and offers options for controlling the sensing of innate or evoked responses, as described below. In this respect, GUI 100 may include a stimulation parameter interface 104 in which aspects of the stimulation program can be selected or adjusted. For example, interface 104 allows the user to select the amplitude (e.g., current I) used for stimulation; the frequency (f) of the stimulation pulse; and the pulse width (PW) of the stimulation pulse. Stimulation parameter interface 104 can be significantly more complex, particularly if IPG 10 or ETS 50 supports providing stimulation more complex than repetitive pulse sequences. See, for example, U.S. Patent Application Publication 2018 / 0071513. Nevertheless, for simplicity, Figure 7 Interface 104 is simply illustrated, allowing only amplitude, frequency, and pulse width adjustments. Stimulation parameter interface 104 may include inputs to allow the user to select whether to use biphasic stimulation (…). Figure 2A The stimulation is provided by either a monophasic pulse or a passive charge recovery, although these details are not shown again for simplicity.

[0063] The stimulation parameter interface 104 can also allow the user to select the activation electrode, i.e., the electrode that will receive the specified pulse. The selection of the activation electrode can occur in conjunction with the lead interface 102, which can include images 103 of one or more leads that have been implanted in the patient. Although not shown, the lead interface 102 may include a selection of a library of relevant images 103 of lead types that may be implanted in different patients.

[0064] exist Figure 6 In the example shown, lead interface 102 shows an image 103 of a single split-loop lead 33, as previously discussed regarding Figure 1B The lead interface 102 may include a cursor 101 that a user can move (e.g., using a mouse connected to the clinician programmer 70) to select the indicated electrodes 16 (e.g., E1-E8 or housing electrodes Ec). Once an electrode is selected, the stimulation parameter interface 104 can be used to designate the selected electrode as an anode that will draw current into the tissue, or as a cathode that will perfuse current into the tissue. Furthermore, the stimulation parameter interface 104 allows specifying, in percentage X, the amount of total anodic or cathode current +I or -I that each selected electrode will receive. For example, in... Figure 6 In this configuration, the housing electrode 12Ec is designated to receive X = 100% of the current I as the anode current +I. The corresponding cathode current -I is split among electrodes E5 (0.18*-I), E7 (0.52*-I), E2 (0.08*-I), and E4 (0.22*-I). Therefore, two or more electrodes can be selected at a given time using the MICC (as described above) to act as either anode or cathode, thereby shaping the electric field in the tissue. The current specified at the selected electrode can be provided during the first pulse phase (if a biphasic pulse is used) or only during the pulse phase (if a monophasic pulse is used).

[0065] GUI 100 may also include a visualization interface 106 that allows the user to view indications of the effects of stimulation, such as a stimulation field model (SFM) 112 (also referred to herein as the volume of tissue activated (VTA)) formed using selected stimulation parameters. SFM 112 is formed, for example, through field modeling in a clinician programmer 70. The illustrated embodiment of GUI 99 includes selection options 125 for initiating such modeling. For simplicity, only one lead is shown in visualization interface 106, although a given patient may be re-implanted with more than one lead. Visualization interface 106 provides images 111 of one or more leads, which may be three-dimensional.

[0066] The visualization interface 106 preferably (but not necessarily) also includes tissue imaging information 114 obtained from the patient, in Figure 6 The images are denoted as 114a, 114b, and 114c, representing three different tissue structures of the patient in question. These tissue structures may include different regions of the brain. This tissue imaging information may include magnetic resonance imaging (MRI), computed tomography (CT) images, or other types of images. Typically, one or more images (such as MRI, CT, and / or brain atlases) are scaled and combined in a single image model. Those skilled in the art will understand that, because one or more leads are implanted using a stereotactic frame (not shown), the location of one or more leads can be precisely referenced to tissue structure 114i. This allows a clinician programmer 70, on which a GUI 100 is presented, to overlay lead images 111 and SFM 112 with the tissue imaging information in a visualization interface 106, making it possible to visualize the location of SFM 112 relative to the various tissue structures 114i. In some embodiments, images of the patient's tissue may also be obtained after implantation of one or more leads, or the tissue imaging information may include generic images pulled from a library that is not specific to the patient in question.

[0067] The various images shown in the visualization interface 106 (i.e., lead image 111, SFM 112, and tissue structure 114i) can be three-dimensional in nature, and therefore can be presented on the visualization interface 106 in a way that allows the user to better understand this three-dimensionality, such as by shading or coloring the images. Furthermore, the view adjustment interface 107, for example, can allow the user to move or rotate the images using the cursor 101.

[0068] GUI 100 may also include a cross-sectional interface 108 to allow viewing various images in a two-dimensional cross-section. Specifically, the cross-sectional interface 108 shows a specific cross-section 109 obtained perpendicular to the lead image 111 and passing through the split-ring electrodes E5, E6, and E7. This cross-section 109 may also be shown in a visualization interface 106, and a view adjustment interface 107 may include controls to allow the user to specify the plane of the cross-section 109 (e.g., in the XY, XZ, or YZ plane) and move its position in the image. Once the position and orientation of the cross-section 109 are defined, the cross-sectional interface 108 can show additional details. For example, SFM 112 can allow the user to sense the intensity and arrival of the stimulus at different locations. Although GUI 100 includes stimulus definitions (102, 104) and imaging (108, 106) in a single screen of the GUI, these aspects can also be separated as part of GUI 100 and accessed through various menu selections, etc.

[0069] Especially in DBS applications, determining the correct stimulation parameters for a given patient is crucial. Inappropriate stimulation parameters may fail to effectively alleviate the patient's symptoms or may lead to unnecessary side effects. To determine the appropriate stimulation, clinicians typically use GUIs (such as GUI 100) to try different combinations of stimulation parameters. This may occur at least in part during the DBS procedure when the lead is implanted. This determination of intraoperative stimulation parameters can be used to determine the overall efficacy of the DBS therapy. However, the final stimulation parameters suitable for a given DBS patient usually occur post-operatively after the patient has had a chance of recovery and after the lead's position in the patient's body has stabilized. Therefore, the patient will typically appear in the clinician's office during a programming session to determine (or further optimize) the optimal stimulation parameters.

[0070] Measuring the effectiveness of a given set of stimulus parameters typically involves programming IPG 10 with that set of parameters and then reviewing the resulting therapeutic effectiveness and side effects. Therapeutic effectiveness and side effects are typically assessed using one or more different scores (S) for one or more different clinical responses, which are entered into the GUI 99 of the clinician programmer 70, where they are stored along with the set of stimulus parameters being evaluated. These scores, based on patient or clinician observations, can be subjective in nature. For example, bradykinesia (slow movement), stiffness, tremor, or other symptoms or side effects can be rated by the patient or clinician while observing or questioning the patient. In one example, such scores could range from 0 (best) to 4 (worst).

[0071] Scores can be objective in nature, based on measurements of a patient's symptoms or side effects. For example, a Parkinson's patient might be equipped with wearable sensors that measure tremors, such as by measuring the frequency and amplitude of such tremors. The wearable sensors can then feed these measurements back to the GUI 99 and convert them into scores if necessary. U.S. Patent Application Publication 2021 / 0196956 (the entire contents of which are incorporated herein by reference) discusses determining which symptoms and / or side effects are most sensitive to scores for a given patient.

[0072] This disclosure relates to methods and systems for optimizing stimulation parameters for patients with deep brain stimulation (DBS). In embodiments, quantitative, objective information related to the position of electrode leads relative to the anatomical features of the patient's STN is used to predict stimulation parameters that should be effective for the patient. Figure 7 A simplified overview 700 of a system for performing various aspects of the disclosed methods is shown. The system shown includes a clinician programmer (CP) 770, which may include the components described above regarding CP 70 (… Figure 5The features described herein. CP 770 may include control circuitry configured to perform aspects of the disclosed methods. According to some embodiments, CP 770 includes non-transitory computer-readable computer code that, when executed by CP 770, configures CP to perform aspects of the disclosed methods. CP 770 may be configured to communicate with IPG 710, which may include the features described above regarding IPG 10. Figure 1A and Figure 5 CP 770 can also be configured to access database 720. The characteristics of database 720 will be apparent from the following discussion. Database 720 can be included within CP 770, for example, in computer-readable storage. Alternatively (or additionally), portions of database 720 can be configured externally to CP 770, for example, on a local or remote external server. In this case, CP 770 can communicate with database 720, for example, via an Internet connection.

[0073] Figure 8 A workflow 800 according to some aspects of this disclosure is illustrated. It is assumed that one or more electrode leads have been implanted into a patient's brain. For example, prior to performing workflow 800, the electrode leads may be implanted in or near a target brain structure, such as the patient's STN. Other target brain structures for implantation, as known in the art, may include the medial part of the globus pallidus, the ventral intermediate nucleus, or any other target. Step 802 involves receiving imaging data from the subject patient. According to some embodiments, the imaging data may include preoperative MRI data and postoperative CT scans. Step 804 involves determining the location of one or more electrode leads relative to anatomical features of the patient's STN. According to some embodiments, anatomical features may include various axes and / or various borders of the STN. For example, anatomical features may include the medial border, lateral border, medial / lateral axis, anterior border, posterior border, anterior / posterior axis, superior border, inferior border, and / or inferior / superior axis, etc.

[0074] Figure 9 An example of workflow 900 for determining relevant anatomical features of a patient's STN and determining the position of electrode leads relative to these anatomical features is shown. Step 902 involves processing image data to determine the surface of relevant brain structures such as the STN, red nucleus (RN), internal capsule, substantia nigra, etc. According to some embodiments, three-dimensional models of relevant structures can be generated from the fusion of MRI and CT images using automated segmentation algorithms known in the art using personalized segmentation software. Example algorithms are included in commercial segmentation software, such as Brainlab Elements from Brainlab, Germany. TM In step 904, the relevant 3D model is voxelized, that is, the three-dimensional structure is divided into volume elements, i.e., voxels.

[0075] At step 906, a voxelized model of the STN can be used to determine the STN axes. According to some embodiments, the determination of the STN axes mentioned in the next paragraph can also be accomplished using the coordinates of the surface vertices of the brain object. According to some embodiments, principal component analysis (PCA) is applied to the voxels to determine the medial / lateral axis, anterior / posterior axis, and / or superior / inferior axis of the STN. For example, a first PCA component may reflect the anterior / posterior axis, a second component may reflect the superior / inferior axis, and a third component may reflect the medial / lateral axis. Step 908 involves using the aforementioned voxelization and PCA to determine the relevant borders of the patient's STN.

[0076] Step 910 of workflow 900 involves aligning the determined STN features relative to the electrode leads. Aligning the STN features relative to the electrode leads involves determining the relationship between the orientation of the STN within the patient body (i.e., "patient space") and the orientation of the STN features relative to the electrode leads. Specifically, it may be desirable to quantify how the STN features (such as axes) are angularly aligned with the stimulation field that the electrode leads can generate.

[0077] Figure 10 The central axis 31 along the lead with the split-ring electrode is shown (see example). Figure 1B The image shows a cross-section of the electrode lead 33 viewed downwards. In some embodiments, the electrode lead 33 may have an orientation mark 1000 that informs the rotational positioning of the lead. For example, the electrode lead may be implanted such that the orientation mark 1000 is pointed in front of the patient's head. As described above, the split-loop (i.e., directional) electrode can be used to form a stimulation field emanating from the lead at different angles. However, in some embodiments, the resolution of the stimulation angle is limited. In the illustrated embodiment, the controllable angular resolution of the stimulation is limited to 30º, as shown by the dashed line 1002. Therefore, the stimulation field can be controlled along the 30º line, and the angle can be measured relative to the orientation mark 1000. Thus, the workflow 900 ( Figure 9 One aspect of step 910 involves orienting or transforming the anatomical features of the STN, which may be located in the “patient space,” relative to the orientation marker 1000 and the resolution line 1002 of the electrode. This can be considered as orienting the anatomical features of the STN relative to the “leader space.”

[0078] Figure 11AThis illustration shows an embodiment where the medial / lateral axis and anterior / posterior axis are aligned with the electrode lead (located at point 1101) within the patient's STN 1102. In the illustration, the thick dashed line 1002 represents the highest resolution where angular stimulation can be controlled, as described above (30º in this example). In this example, upsampling can be used to improve the resolution of PCA for axis determination. The light dashed line 1106 shows polarity upsampling, providing 15º resolution for axis determination. Other forms of upsampling, such as Cartesian upsampling, can also be used. Note that in Figure 11A In this diagram, line 1106a corresponds to the longest component determined using polarity upsampling. Therefore, this line represents the best estimate of the anterior / posterior axis. However, it should be noted that line 1106a lies between the angles at which the electrode lead can be angularly discriminated against the stimulus. The closest angularly discriminated line is 1002a. According to some embodiments, line 1002a can be simply considered as the anterior / posterior axis. According to other embodiments, the alignment can be refined by projecting the components of the “more realistic axis” 1106a onto the discriminated line 1002a to provide a more accurate estimate of the aligned distances of the electrode lead to the anterior and posterior edges. The same process can be used to align the electrode lead to the medial / lateral axis and estimate the distances of the electrode lead to the medial and lateral edges.

[0079] Figure 11B One embodiment is shown in which the electrode lead (position 1101) is implanted near, but not within, the STN 1002. Similar techniques described above can be used to radially determine or project a relevant axis relative to the electrode lead alignment.

[0080] Refer again Figure 9 Step 912 involves determining the distance from the electrode leads to relevant anatomical structures of the STN, such as the axis and edge of the STN. Figure 12A The calculated distances from electrode lead 33 to inner edge 1202, outer edge 1204 and inner / outer axis 1206 are shown. Figure 12B The calculated distances from electrode lead 33 to leading edge 1208, trailing edge 1210 and front / rear axis 1212 are shown.

[0081] Once the positions of the electrode leads relative to the anatomical features of the STN for the test patients are determined, these determined distances can be recorded in the database. Figure 13An example of database 1300 is shown, populated with a large amount of accumulated patient data. It is assumed that each of these patients underwent an adaptation procedure, thereby optimizing the stimulus for each of them. For each of these patients, stimulus parameters have been identified that have been shown to have therapeutic effects and / or not produce side effects. Then, each patient's orientation / distance information can be correlated with the effective stimulus parameters and used as a predictive tool for optimizing stimuli for future subjects. In other words, orientation / distance measurements can be used as a quantitative and objective standard for predicting stimulus parameters that may have a therapeutic effect on future subjects. Therefore, refer again... Figure 8 Step 806 involves comparing the patient's location / distance information with a database that correlates accumulated orientation / location information with therapeutically effective stimulation parameters to predict stimulation parameters for the patient.

[0082] According to some embodiments, stimulation parameters can be optimized based on the radius of the stimulation field generated by the stimulus. Figure 14 The electrode leads 33 shown are associated with the inner and outer edges of the STN and the inner / outer axis (similar to...). Figure 12A For a given set of stimulus parameters (e.g., amplitude, pulse width, frequency, duty cycle, etc.), stimulus field modeling can be used to predict the radius of the SFM. Figure 14 The modeled SFM radius at a distance d (elect.) from electrode 33 is shown. Since the distance between the electrode and the lateral border is known, the SFM radius and the distance d (lat. border) from the lateral border can be calculated for a specific set of stimulation parameters. It is assumed that for a given patient, the following parameters are generated... Figure 14 The stimulation parameters with the SFM radius shown provide an effective therapy without side effects. Other stimulation parameters that produce a smaller SFM radius (e.g., stimulation parameters with smaller amplitudes) may not provide an effective therapy. Similarly, stimulation parameters with a larger SFM radius (e.g., stimulation parameters with larger amplitudes) may cause side effects.

[0083] According to some embodiments, databases (such as database 1300) Figure 13This may include information relating orientation / distance information of each of multiple patients to effective stimulation parameters. Stimulation parameters may be expressed in terms of SFM radius, as discussed herein. Alternatively, other stimulation parameter values, such as amplitude, stimulation field energy, frequency, pulse width, duty cycle, or electrode configuration, may be compiled in a database. As is known in the art, stimulation based on each set of experimental parameters can be scored based on its effectiveness for the patient, using the patient's response to the stimulus. According to some embodiments, patient responses may include one or more of the following: speech, tremor, rigidity, finger tapping, toe tapping, bradykinesia, hypokinesia, agility posture, gaze, postural stability, etc.

[0084] According to some embodiments, when comparing electrode lead locations of a patient relative to the anatomical features of their STN with values ​​for multiple patients included in database 1300, it may be desirable to normalize the distance relative to the size of the STN for each individual. This is because the STN may be of different sizes in different patients. According to some embodiments, each set of data is normalized relative to the maximum width of the STN (see...). Figure 14 The normalization is performed. As another example, normalization can also be based on the length or height of the STN (i.e., the front-to-back axis or the ventral-to-dorsal axis, respectively).

[0085] Refer again Figure 8 Step 808 involves optimizing the stimulation for the subject using predicted stimulation parameters from a database. According to some embodiments, the database may simply indicate specific stimulation parameters to try based on determined orientation / distance information for the subject. According to other embodiments, the database may suggest a range of stimulation parameters that may be beneficial to the patient.

[0086] Figure 15 A GUI 1500 is illustrated, configured to assist a user in optimizing stimulation parameters for a subject patient using information from a database that correlates the position of electrode leads relative to the anatomical features of the STN with stimulation parameters that may provide effective therapy. As described above, the database includes historical data collected from numerous patients, which correlates position / distance information for those patients with stimulation parameters that have been found to be effective. According to some embodiments, GUI 1500 may be incorporated into sub-features of the aforementioned GUI 100 ( Figure 6 The GUI 1500 includes an implantable electrode lead 33 and a representation of the patient's STN 1102.

[0087] In the example shown, it is assumed that the stimulus parameters are optimized based on the location of the SFM radius of the experimental stimulus parameter set. According to other embodiments, other aspects of the experimental stimulus parameter set, such as individual parameter values ​​(e.g., amplitude, frequency, pulse width, etc.), can be used. The GUI shown includes edge 1502, which defines a “search space” for the experimental SFM radius. In other words, the clinician will try various experimental stimulus parameter sets to produce an SFM radius that falls within the space defined by edge 1502. It should be noted here that optimization can be performed in a “brute-force” manner, whereby the clinician simply decides which parameter sets they wish to try within the search space. Alternatively, one or more optimization algorithms can be used to suggest parameter sets within the search space for trying. For example, U.S. Patent Application No. 2022 / 0257950 (the entire contents of which are incorporated herein by reference) describes a DBS optimization algorithm configured to suggest experimental stimulus parameters within the search space.

[0088] GUI 1500 is characterized by a "likelihood map" compiled from historical data in a database. The likelihood map provides an indication of where effective stimulation parameters might be found or where stimulation might lead to side effects. In the case of a side effect likelihood map (or model), these likelihood maps can be generated based on the cumulative likelihood of the SFM radius leading to a side effect (traveling from the electrode lead in a direction increasing in distance from the electrode lead). This likelihood can be estimated, for example, by a bivariate histogram that considers the number of SFM radii falling within a combination of SFM location and SFM radius along the lead axis. An example of a likelihood map is represented by the dashed line 1504. Based on the position of the electrode lead relative to the anatomical STN features of the subject, stimulation parameters resulting in SFM radii within the space contained within 1504 are likely beneficial to the subject. This likelihood map is derived by comparing the subject's lead orientation with lead orientations of multiple patients reflected in the database (typically after normalizing the STN size, as described above). The illustrated GUI 1500 also features a heatmap 1506, which reflects the locations in the search space where effective stimulus parameters may be found. The heatmap may be based on a histogram derived from a historical patient database. In the illustrated embodiment, darker portions of the heatmap represent a higher probability of success. For example, data from the database might indicate that more patients respond well to stimulation when the stimulus parameters provide an SFM radius within the darker portions of the heatmap.

[0089] Likelihood models / graphs (e.g., 1504 and 1506) effectively reduce the search space of parameters to be challenged during the optimization process. If a clinician is using a "brute-force" approach, a likelihood graph can indicate which set of parameters should be focused on. Similarly, if an optimization algorithm is being used (such as the one described in the patent application incorporated above), the search space available to the algorithm can be limited to the space indicated by the likelihood graph. Alternatively, the sets of parameters falling within the likelihood graph can simply be weighted more heavily in the optimization algorithm. In either case, the likelihood graph reduces the number of sets of parameters to be challenged, thereby reducing the amount of time required to optimize the patient stimulus. See again Figure 8 Step 810 involves programming the patient’s IPG with optimized stimulation parameters as determined herein.

[0090] Figure 14 and Figure 15 The above discussion explains how a database containing SFM-related information for stimulation parameter sets used for multiple patients can be used to predict stimulation parameters that may provide therapeutic benefit to a patient. Similarly, a database containing SFM-related information for stimulation parameter sets used for multiple historical patients can be used to predict stimulation parameters that may cause side effects. According to such embodiments, a database can be constructed for multiple historical patients who have undergone an adaptation procedure. During the adaptation procedure, various experimental stimulation parameter sets are applied to the patient. The SFM can be determined for each experimental stimulation parameter set. The experimental stimulation parameter sets and their corresponding SFM data can be cataloged in the database. Specifically, experimental stimulation parameter sets that have caused side effects can be noted and associated with their SFM data, such as SFM radius information (or other features of the SFM). When determining stimulation parameters for a patient, such a side effect database can be used to predict stimulation parameters that may cause side effects to the patient. In other words, such a database can be used to predict “avoidance zones” that include a parameter space defining stimulation parameters that may cause side effects in patients.

[0091] Figure 16A and Figure 16B The database contains data including those proven to have therapeutic effects. Figure 16A ) or has been shown to cause side effects ( Figure 16B The charts show the SFM (Self-Factor Radius) for multiple patients across cumulative stimulation parameter groups. Specifically, these charts correlate the SFM radius with treatment outcomes for various trial parameter groups. Figure 16A In the diagram, each circle represents the SFM radius of a stimulation parameter in the database that has been proven to have a therapeutic effect on a given patient. Figure 16B In the diagram, each "×" represents the SFM radius of the stimulation parameter in the database that has been proven to cause side effects in patients.

[0092] Figure 17 It shows how to use Figure 16A and Figure 16B The data shown are used to predict stimulation parameters that are expected to have a therapeutic effect on or cause side effects in test subjects. Parameter space 1700 includes the SFM radius for various experimental parameter groups. Parameter space 1700 includes an SFM radius region 1702 corresponding to parameter groups predicted to have a therapeutic effect and not cause side effects. Region 1702 may be referred to as the "likelihood region." Parameter space 1700 also includes an SFM radius region 1704 corresponding to parameter groups predicted to cause side effects. Region 1704 may be referred to as the "avoidance region." When optimizing stimulation for test subjects, clinicians focus on experimental stimulation parameter groups whose SFM radii fall within the likelihood region, rather than the avoidance region.

[0093] It should be noted that, for reference Figure 16A , Figure 16B and Figure 17 The described database does not take into account the position of the electrode leads relative to STN anatomy features. Other embodiments may include determining the position of the electrode leads relative to STN anatomy features, as described above (e.g., see...). Figure 9 In such embodiments, the side effect database may include anatomical data, such as database 1300 (…). Figure 13 The data contained in the database can be used to correlate the SFM radius of the stimulation parameter that causes the side effects with information indicating the position of the electrode lead relative to the anatomical features of the STN (such as the various axes of the STN (e.g., medial / lateral, anterior / posterior and / or ventral / dorsal) and / or the edges of the STN (e.g., medial edge, lateral edge, anterior edge, posterior edge, ventral edge and / or dorsal edge)). Historical information from this side effect database can be projected onto a GUI (such as GUI 1500). Figure 15 (), to assist in programming the stimulation parameters of the test patients. Figure 18An embodiment of GUI 1800 is shown, which is similar to GUI 1500 discussed above. GUI 1800 includes a representation of the implanted electrode lead 33 and the patient's STN 1102. Like the embodiments of the GUI described above, GUI 1800 includes an edge 1802 that defines a "search space" for testing the SFM radius. GUI 1800 is characterized by an "avoidance diagram" compiled from historical data in a database. The avoidance diagram provides an indication of stimulation parameters that may lead to side effects. An example of a likelihood diagram is represented by a dashed line 1804. Based on the position of the electrode lead relative to the anatomical STN features of the subject, stimulation parameters of the SFM radius within the space enclosed by 1804 are considered likely to lead to side effects in the subject. This likelihood diagram is derived by comparing the subject's lead orientation with lead orientations of multiple patients reflected in a database (typically after normalization of STN size, as described above). The illustrated GUI 1800 also features a heatmap 1806, which reflects the location in the search space where stimulation parameters that may lead to side effects could occur. This heatmap can be based on a histogram derived from a historical patient side effect database. In the illustrated embodiment, darker portions of the heatmap indicate a higher likelihood of a side effect. For example, data from the database could indicate that more patients would experience a side effect when the stimulation parameter provides an SFM radius within the darker portions of the heatmap. Therefore, clinicians could try to avoid using stimulation parameters whose SFM radii fall within these regions.

[0094] Figure 15 , Figure 17 and Figure 18 An example is shown of how stimulation parameter information accumulated from multiple patients (and multiple STN hemispheres) and associated with stimulation parameters that have been shown to have therapeutic effects and / or cause side effects can be used to inform stimulation parameter programming for a subject. As mentioned above, such a database may or may not include anatomical data estimating the position of one or more electrode leads relative to the anatomical features of the patient's STN. In either case, the database information can be used to generate: a likelihood model indicating the parameter space from which therapeutic parameters may be obtained, and / or an avoidance model indicating the parameter space from which parameters that may cause side effects may occur. The foregoing example focuses primarily on using the SFM radius of parameters accumulated in a historical database as a basis for determining the likelihood space / avoidance space. Other aspects of the accumulated SFM can also be used. For example, the volume of the accumulated SFM can be used to predict the target volume and / or avoidance volume of stimulation for the subject. The target volume can predict the volume in the patient's brain from which stimulation may provide therapeutic benefits, while the avoidance volume can predict the volume from which stimulation may cause side effects.

[0095] As described above, once the accumulated database has been used to predict target / likelihood regions and / or avoidance regions, these predicted regions can be used to facilitate the programming of stimulus parameters for the subject. According to some embodiments, programming optimization algorithms (such as those described in incorporated U.S. Patent Application No. 2022 / 0257950) can be used to suggest trial stimulus parameters located within the target / likelihood region and / or avoiding the avoidance region. Alternatively, clinicians can simply use brute force to select various trial stimulus parameters based on the predicted target and avoidance regions. After applying each of the trial stimulus parameters, the effect of the stimulus on the patient can be scored.

[0096] According to some embodiments, verifying the accuracy of target and / or avoidance regions predicted based on a cumulative database may be done cautiously. According to some embodiments, the final stimulation parameters that ultimately provide optimal stimulation for the patient can be compared with the target and / or avoidance regions predicted based on the cumulative database. If the final stimulation parameters agree well with the predicted target / avoidance regions, this agreement can be used as validation of the predicted regions. If there is a discrepancy between the final stimulation parameters and the predicted regions, this discrepancy may indicate that the target / avoidance regions predicted based on the cumulative database are unreliable. In this case, one or more database entries can be marked as unreliable, and in this situation, they can be ignored or weakened in further programming sessions.

[0097] Although specific embodiments of the invention have been shown and described, it should be understood that the foregoing discussion is not intended to limit the invention to these embodiments. Those skilled in the art will recognize that various changes and modifications can be made without departing from the spirit and scope of the invention. Therefore, the invention is intended to cover alternatives, modifications, and equivalents that may fall within the spirit and scope of the invention as defined in the claims.

Claims

1. A system for programming electrical stimulation parameters to provide deep brain stimulation (DBS) to a subject patient, wherein the subject patient is implanted with an implantable medical device, the implantable medical device comprising an implantable pulse generator (IPG) connected to one or more electrode leads implanted in the brain of the subject patient, wherein each electrode lead comprises a plurality of electrodes, the system comprising: An external computing device includes control circuitry configured to execute a method comprising: Receive imaging data from the subjects. The imaging data was used to determine the location of at least one electrode lead relative to at least one anatomical feature of the subthalamic nucleus (STN) of the subject patient. Receive accumulated data from a database, wherein the accumulated data includes data from previous patients, the data relating the position of the electrode leads relative to the anatomical features of the STN in the previous patients to stimulation parameters that induced side effects in the previous patients, and The accumulated data and the imaging data are used to determine the stimulation parameters of the subject.

2. The system of claim 1, wherein the imaging data of the subject patient includes preoperative magnetic resonance imaging (MRI) data and postoperative computed tomography (CT) and / or MRI data.

3. The system according to claim 1 or 2, wherein at least one anatomical feature of the STN of the subject patient includes one or more of the following: medial / lateral axis, anterior / posterior axis, medial border, lateral border, anterior border, and posterior border.

4. The system according to any one of claims 1-3, wherein using the imaging data to determine the position of at least one electrode lead relative to at least one anatomical feature of the STN of the subject patient comprises: The imaging data was used to determine a 3D model of the patient's STN and the 3D model was voxelized.

5. The system of claim 4, wherein the method further comprises using the 3-D model to determine one or more axes of the STN of the subject patient.

6. The system of claim 5, wherein principal component analysis is used to determine the one or more axes.

7. The system according to any one of claims 1-6, wherein the accumulated data comprises: An indication of the radius of the stimulation field model (SFM) corresponding to the stimulation parameters that provide therapeutic benefits in at least some of the previous patients.

8. The system according to any one of claims 1-7, wherein determining the stimulation parameters of the subject patient comprises: Receive information indicating the test stimulus parameter set for the subject. Determine the SFM for the set of experimental stimulus parameters. Determine the SFM radius for the set of experimental stimulus parameters, and The SFM radius for the experimental stimulus parameter set is compared with the SFM radius from the accumulated data.

9. The system of claim 8, wherein the set of experimental stimulus parameters is automatically suggested using a stimulus optimization algorithm.

10. The system according to any one of claims 1-9, wherein using the accumulated data and the imaging data to determine the stimulation parameters of the subject patient comprises: The following is displayed on the graphical user interface (GUI): The representation of the search space indicating potential experimental stimulus parameter sets, and One or more likelihood plots derived from the accumulated data, wherein the one or more likelihood plots indicate a set of experimental stimulation parameters within the search space that may induce side effects in the subjects.

11. The system of claim 10, wherein the one or more likelihood plots indicate the SFM radius corresponding to a set of test stimulus parameters that may induce side effects in the test patient.

12. A non-volatile computer-readable medium comprising instructions for programming electrical stimulation parameters to provide deep brain stimulation (DBS) to a subject patient, wherein the subject patient is implanted with an implantable medical device, the implantable medical device comprising an implantable pulse generator (IPG) connected to one or more electrode leads implanted in the brain of the subject patient, wherein each electrode lead comprises a plurality of electrodes, wherein the non-volatile computer-readable medium comprises instructions that, when executed on a computing device, configure the computing device to perform a method comprising the following steps: Receive imaging data from the subjects. The imaging data is used to determine the location of at least one electrode lead relative to at least one anatomical feature of the brain structure of the subject's brain. Receive accumulated data from a database, wherein the accumulated data includes data from previous patients, the data relating the position of the electrode leads relative to anatomical features of the brain structures of the previous patients to stimulation parameters that induced side effects in the previous patients, and The accumulated data and the imaging data are used to determine the stimulation parameters of the subject.

13. The computer-readable medium of claim 12, wherein the imaging data of the subject patient includes preoperative magnetic resonance imaging (MRI) data and postoperative computed tomography (CT) and / or MRI data.

14. The computer-readable medium of claim 12 or 13, wherein at least one anatomical feature of the brain structure of the subject includes one or more of the following: medial / lateral axis, anterior / posterior axis, medial border, lateral border, anterior border, and posterior border of the brain structure.

15. The computer-readable medium according to any one of claims 12-14, wherein using the imaging data to determine the location of at least one electrode lead relative to at least one anatomical feature of the brain structure of the subject comprises: The imaging data was used to determine a 3D model of the patient's brain structure and to voxelize the 3D model.

16. The computer-readable medium of claim 15, wherein the method further comprises using principal component analysis to determine one or more axes of the brain structure of the subject using the 3-D model.

17. The computer-readable medium according to any one of claims 12-16, wherein the accumulated data comprises: An indication of the radius of the stimulation field model (SFM) corresponding to the stimulation parameters that provide therapeutic benefits in at least some of the previous patients.

18. The computer-readable medium according to any one of claims 12-17, wherein determining the stimulation parameters of the subject patient comprises: Receive information indicating the test stimulus parameter set for the subject. Determine the SFM for the set of experimental stimulus parameters. Determine the SFM radius for the set of experimental stimulus parameters, and The SFM radius for the experimental stimulus parameter set is compared with the SFM radius from the accumulated data.

19. The computer-readable medium according to any one of claims 12-18, wherein using the accumulated data and the imaging data to determine the stimulation parameters of the subject patient comprises: The following is displayed on the graphical user interface (GUI): The representation of the search space indicating potential experimental stimulus parameter sets, and One or more likelihood plots derived from the accumulated data, wherein the one or more likelihood plots indicate a set of experimental stimulation parameters within the search space that may induce side effects in the subjects.

20. The computer-readable medium of claim 19, wherein the one or more likelihood diagrams indicate the SFM radius corresponding to a set of test stimulus parameters that may induce side effects in the test patient.

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