System and method for clinical neuronavigation

Abstract

To provide a system and method for clinical neuronavigation.SOLUTION: One embodiment includes a method for generating brain stimulation targets comprising acquiring functional magnetic resonance imaging (fMRI) image data of a patient's brain, wherein the brain imaging data describes neuronal activation in the patient's brain, mapping at least one region of interest in the patient's brain, locating a functional sub-region within the at least one region of interest based on the fMRI image data, determining a functional relationship between at least two brain regions of interest, generating a parameter for each functional sub-region, generating a target quality score for each functional sub-region based on the parameter, and selecting a brain stimulation target based on the target quality score and a neurological condition of the patient.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This invention claims priority to U.S. Provisional Patent Application No. 62 / 617,121, entitled "Systems and Methods for Personalized Clinical Applications of Accelerated Theta-Burst Stimulation," filed January 12, 2018. U.S. Provisional Patent Application No. 62 / 617,121 is incorporated herein by reference in its entirety.

[0002] The present invention relates generally to generating personalized targets for invasive and non-invasive neuromodulation and implantable stimulation. [Background technology]

[0003] Transcranial magnetic stimulation (TMS) is a noninvasive medical procedure in which a strong magnetic field is used to stimulate specific areas of a patient's brain to treat conditions such as depression and neuropathic pain. Repeated application of TMS over a short time frame is referred to as repetitive TMS (rTMS). Theta burst stimulation (TBS) is a patterned form of rTMS, typically administered as triplets of stimulation with 20 milliseconds between each stimulation in the triplet, with the triplets repeated every 200 milliseconds. When TBS is administered continuously, it results in cortical inhibition and is referred to as continuous theta burst stimulation (cTBS). When TBS is administered intermittently with an intertrain interval between triplets, it is excitatory and is referred to as intermittent theta burst stimulation (iTBS).

[0004] Functional magnetic resonance imaging (fMRI) is a non-invasive imaging technique in which neuronal activity is measured by tracking hemodynamic responses in the brain. Resting-state brain measurements are measurements of neuronal activity when the patient is not performing an explicit task. Resting-state brain measurements can be used to explore the connectivity between various structures and regions in the brain. A common example of resting-state connectivity is the default mode network (DMN). Specific resting-state connectivity between brain regions that share functional properties is called resting-state functional connectivity (RSFC). Summary of the Invention [Means for solving the problem]

[0005] Systems and methods for clinical neuronavigation are illustrated according to embodiments of the present invention. One embodiment includes a method for generating brain stimulation targets, including determining brain stimulation targets by acquiring functional magnetic resonance imaging (fMRI) image data of a patient's brain, the brain imaging data describing neuronal activation in the patient's brain, mapping at least one region of interest on the patient's brain, locating functional subregions within the at least one region of interest based on the fMRI image data, determining functional relationships between at least two brain regions of interest, generating parameters for each functional subregion, generating a target quality score for each functional subregion based on the parameters, and selecting a brain stimulation target based on the target quality score and the patient's neurological symptoms.

[0006] In another embodiment, the brain imaging data describes neuronal activity during a resting state.

[0007] In a further embodiment, the step of acquiring brain imaging data further comprises the step of pre-processing the brain imaging data.

[0008] In yet another embodiment, preprocessing the brain imaging data includes performing at least one preprocessing step selected from the group consisting of physiological noise regression, slice time correction, motion correction, co-registration, bandpass filtering, and detrending.

[0009] In still further embodiments, a brain atlas is used to map at least one region of interest onto the individual's brain anatomy.

[0010] In yet another embodiment, each functional subregion describes homogeneous brain activity.

[0011] In yet a further embodiment, functional subregions are identified and separated from one another using hierarchical agglomerative clustering.

[0012] In another additional embodiment, the subregion parameter is selected from the group consisting of the size of the functional subregion, the density of the voxels that make up the functional subregion, the correlation between the functional subregion and other functional subregions, and the accessibility of the functional subregion to a transcranial magnetic stimulation device.

[0013] In further additional embodiments, the target quality score reflects a combination of weighted parameters for each functional subregion, with higher quality scores reflecting better brain stimulation targets.

[0014] Again, in another embodiment, the surface effect for a given subregion is a voxel-number weighted combination of Spearman correlation coefficients derived from a hierarchical clustering algorithm that accounts for the correlation coefficients between the subregion on the surface of the brain and all subregions located deep within the brain.

[0015] Again, in a further embodiment, the brain stimulation target is a transcranial magnetic stimulation target.

[0016] In yet another embodiment, the method further comprises stimulating the brain stimulation target using a transcranial magnetic stimulation device according to an accelerated theta burst stimulation protocol.

[0017] In yet a further embodiment, a system for generating brain stimulation targets includes a neuronavigation computing system having at least one processor and a memory containing a neuronavigation application, the neuronavigation application instructing the processor to acquire brain imaging data from a magnetic resonance imaging machine capable of acquiring functional magnetic resonance imaging (fMRI) image data of a patient's brain, the brain imaging data describing neuronal activation in the patient's brain, map at least one region of interest to the patient's brain, locate functional subregions within the at least one region of interest based on the fMRI image data, determine functional relationships between at least two functional subregions, generate subregion parameters for each functional subregion, generate a target quality score for each functional subregion based on the subregion parameters, and select a brain stimulation target based on the target quality score and the patient's neurological symptoms.

[0018] In yet another additional embodiment, the fMRI image data describes neuronal activity during the resting state.

[0019] In still further additional embodiments, the neuronavigation application further directs the processor to preprocess the fMRI image data.

[0020] Again, in yet another embodiment, to preprocess the fMRI image data, the neuronavigation application further instructs the processor to perform at least one preprocessing step selected from the group consisting of physiological noise regression, slice time correction, motion correction, co-registration, bandpass filtering, and detrending.

[0021] Again, in still further embodiments, a brain atlas is used to map at least one subregion.

[0022] In yet another additional embodiment, each functional subregion describes homogeneous brain activity.

[0023] In yet a further additional embodiment, functional subregions are located using hierarchical agglomerative clustering.

[0024] Again, in yet another embodiment, the subregion parameters are selected from the group consisting of the size of the functional subregion, the density of the voxels that make up the functional subregion, the correlation between the functional subregion and other functional subregions, and the accessibility of the subregion from the surface of the brain.

[0025] Again, in yet a further embodiment, the target quality score reflects the surface effect for a given subregion.

[0026] Again, in another additional embodiment, the surface effect for a given subregion is the sum of a two-dimensional matrix of Spearman correlation coefficients derived from a hierarchical clustering algorithm that accounts for the correlation coefficients between the subregion on the surface of the brain and all subregions located deep within the brain.

[0027] Again, in further additional embodiments, the brain stimulation target is a transcranial magnetic stimulation target.

[0028] In yet another additional embodiment, the neuronavigation application further directs the processor to stimulate the brain stimulation target using a transcranial magnetic stimulation device according to an aTBS protocol.

[0029] Systems and methods for treating neurological symptoms using aTBS are illustrated according to embodiments of the present invention. One embodiment includes a method for treating a neurological symptom, including generating a personalized accelerated theta burst stimulation (aTBS) target representing a location in the patient's brain associated with the neurological symptom, placing a TMS device relative to the patient's brain such that a magnetic field focus is on the aTBS target, and applying TMS to the aTBS target according to an aTBS protocol to the brain using the TMS device to alleviate the neurological symptom.

[0030] In another embodiment, the aTBS protocol is an aiTBS protocol.

[0031] In a further embodiment, the aiTBS protocol comprises applying a set of pulse trains, each pulse train comprising a set of 20 Hz to 70 Hz pulses applied at 3 Hz to 7 Hz.

[0032] In yet another embodiment, the aiTBS protocol further comprises applying a set of pulse trains every 4-10 seconds for at least one 10 minute session.

[0033] In still further embodiments, the aiTBS protocol further comprises performing 3 to 15 sessions per day with 25 to 120 minutes between each session.

[0034] In yet another embodiment, the aiTBS protocol includes applying a set of 2-second pulse trains, each train comprising magnetic stimulation pulses in the form of three 50 Hz pulses at 5 Hz, the 2-second trains applied every 10 seconds for at least ten 10-minute sessions, with an inter-session interval of 50 minutes.

[0035] In still further embodiments, the aTBS protocol is an acTBS protocol.

[0036] In another additional embodiment, the acTBS protocol includes applying a set of pulse trains, each pulse at 20 Hz to 70 Hz, each pulse applied at 3 Hz to 7 Hz, and each pulse train applied every 4 to 10 seconds for at least one session lasting 40 to 44 seconds.

[0037] In further additional embodiments, the acTBS protocol further comprises applying 10 to 40 sessions per day.

[0038] Again, in another embodiment, the inter-session interval is between 10 and 50 minutes.

[0039] Again, in a further embodiment, the acTBS protocol comprises a set of 3 pulse trains, each pulse being 50 Hz applied at 5 Hz for at least one 40 second session.

[0040] In yet another embodiment, the acTBS protocol comprises a set of 3 pulse trains, each pulse at 30 Hz applied at 6 Hz for at least one 44 second session.

[0041] In still yet a further embodiment, the aTBS protocol is applied for at least two consecutive days.

[0042] In yet another additional embodiment, generating the aTBS target includes acquiring first fMRI image data describing the patient's brain resting state prior to treatment.

[0043] In yet a further additional embodiment, the method further includes acquiring second fMRI image data describing a resting state of the patient's brain after the treatment, and locating differences between the first fMRI image data and the second fMRI image data to confirm the effectiveness of the treatment.

[0044] Again, in yet another embodiment, the neurological condition is clinical depression.

[0045] Again, in a still further embodiment, the neurological symptom is suicidal ideation.

[0046] In yet another additional embodiment, a system for treating a neurological condition includes a transcranial magnetic stimulation coil directed to apply an accelerated theta burst stimulation (aTBS) protocol to an aTBS target in a patient's brain.

[0047] In still further additional embodiments, the aTBS protocol is an aiTBS protocol.

[0048] Again, in yet another embodiment, the aiTBS protocol comprises applying a set of pulse trains, each pulse train comprising a set of 20 Hz to 70 Hz pulses applied at 3 Hz to 7 Hz.

[0049] Again, in still further embodiments, the aiTBS protocol further comprises applying a set of pulse trains every 4-10 seconds for at least one 10 minute session.

[0050] Again, in another additional embodiment, the aiTBS protocol further comprises performing 3-15 sessions per day with 25-120 minutes between each session.

[0051] Again, in further additional embodiments, the aiTBS protocol includes applying a set of 2-second pulse trains, each train including magnetic stimulation pulses in the form of three 50 Hz pulses at 5 Hz, the 2-second trains applied every 10 seconds for at least ten 10-minute sessions, with an inter-session interval of 50 minutes.

[0052] In yet another additional embodiment, the aTBS protocol is an acTBS protocol.

[0053] In another embodiment, the acTBS protocol includes applying a set of pulse trains, each pulse at 20 Hz to 70 Hz, with each pulse applied at 3 Hz to 7 Hz, and each pulse train applied every 4 to 10 seconds for at least one session lasting 40 to 44 seconds.

[0054] In a further embodiment, the acTBS protocol further comprises applying 10 to 40 sessions per day.

[0055] In yet another embodiment, the inter-session interval is between 10 and 50 minutes.

[0056] In still further embodiments, the acTBS protocol comprises a set of 3 pulse trains, each pulse being 50 Hz applied at 5 Hz for at least one 40 second session.

[0057] In yet another embodiment, the acTBS protocol comprises a set of 3 pulse trains, each pulse at 30 Hz applied at 6 Hz for at least one 44 second session.

[0058] In still further embodiments, the aTBS protocol is applied for at least two consecutive days.

[0059]

[0013] Systems and methods for treating neurological conditions using implantable neurostimulators are illustrated according to embodiments of the present invention. One embodiment includes a method for treating a neurological condition, comprising determining a location on the patient's brain for implanting a neurostimulator by acquiring functional magnetic resonance imaging (fMRI) image data of a patient's brain, the fMRI image data describing neuronal activation in the patient's brain, mapping at least one region of interest on the patient's brain, locating functional subregions within the at least one region of interest based on the fMRI image data, determining a functional relationship between at least two functional subregions, generating subregion parameters for each functional subregion, generating a target quality score for each functional subregion based on the subregion parameters, and selecting a location for implanting the neurostimulator based on the target quality score and the patient's neurological condition, surgically implanting the neurostimulator at the location, and applying a stimulation protocol to the brain at the location using the neurostimulator to treat the neurological condition.

[0060] In another embodiment, the fMRI image data describes neuronal activity during the resting state.

[0061] In a further embodiment, the step of acquiring the fMRI image data further comprises pre-processing the fMRI image data.

[0062] In yet another embodiment, preprocessing the fMRI image data includes performing at least one preprocessing step selected from the group consisting of physiological noise regression, slice time correction, motion correction, co-registration, bandpass filtering, and detrending.

[0063] In still further embodiments, a brain atlas is used to map at least one subregion.

[0064] In yet another embodiment, each functional subregion describes homogeneous brain activity.

[0065] In yet a further embodiment, functional subregions are located using hierarchical agglomerative clustering.

[0066] In another additional embodiment, the subregion parameter is selected from the group consisting of the size of the functional subregion, the density of the voxels that make up the functional subregion, the correlation between the functional subregion and other functional subregions, and the accessibility of the subregion from the surface of the brain.

[0067] In yet an additional embodiment, the target quality score reflects the surface effect for a given subregion.

[0068] Again, in another embodiment, the surface effect for a given subregion is the sum of a two-dimensional matrix of Spearman correlation coefficients derived from a hierarchical clustering algorithm that accounts for the correlation coefficients between the subregion on the surface of the brain and all subregions located deep within the brain.

[0069] Again, in a further embodiment, the aTBS protocol is an aiTBS protocol.

[0070] In yet another embodiment, the aiTBS protocol comprises applying a set of pulse trains, each pulse train comprising a set of 20 Hz to 70 Hz pulses applied at 3 Hz to 7 Hz.

[0071] In still yet further embodiments, the aiTBS protocol further comprises applying a set of pulse trains every 4-10 seconds for at least one 10 minute session.

[0072] In yet another additional embodiment, the aiTBS protocol further comprises performing 3-15 sessions per day with 25-120 minutes between each session.

[0073] In yet a further additional embodiment, the aiTBS protocol includes applying a set of 2-second pulse trains, each train including magnetic stimulation pulses in the form of three 50 Hz pulses at 5 Hz, the 2-second trains applied every 10 seconds for at least ten 10-minute sessions, with an inter-session interval of 50 minutes.

[0074] Again, in yet another embodiment, the aTBS protocol is an acTBS protocol.

[0075] Again, in still further embodiments, the acTBS protocol includes applying a set of pulse trains, each pulse at 20 Hz to 70 Hz, each pulse applied at 3 Hz to 7 Hz, each pulse train applied every 4 to 10 seconds for at least one session lasting 40 to 44 seconds.

[0076] In yet another additional embodiment, the acTBS protocol further comprises administering 10 to 40 sessions per day.

[0077] In yet further additional embodiments, the inter-session interval is between 10 and 50 minutes.

[0078] Again, in yet another embodiment, the acTBS protocol comprises a set of 3 pulse trains, each pulse at 50 Hz applied at 5 Hz for at least one 40 second session.

[0079] Again, in yet a further embodiment, the acTBS protocol comprises a set of 3 pulse trains, each pulse being 30 Hz applied at 6 Hz for at least one 44 second session. Again, in another additional embodiment, the aTBS protocol is applied for at least two consecutive days.

[0080] Again, in further additional embodiments, the neurological condition is clinical depression.

[0081] In yet another additional embodiment, the neurological symptom is suicidal ideation.

[0082] In another embodiment, an implantable neurostimulator is configured to apply an aTBS protocol to the patient's brain.

[0083] In a further embodiment, the aTBS protocol is an aiTBS protocol.

[0084] In yet another embodiment, the aTBS protocol is an acTBS protocol.

[0085] In still further embodiments, the implantable neurostimulator device is further configured to apply the aTBS protocol intermittently.

[0086] In yet another embodiment, the implantable neurostimulator device is further configured to apply the aTBS protocol continuously.

[0087] In still further embodiments, the implantable neurostimulator device is further configured to apply the aTBS protocol at regular intervals.

[0088] In another additional embodiment, the implantable neurostimulator is further configured to apply the aTBS protocol based on patient feedback.

[0089] In yet additional embodiments, the parameters of the aTBS protocol are altered in response to patient feedback.

[0090] Again, in another embodiment, the patient feedback is the patient's brain activity.

[0091] Systems and methods for predicting and treating relapse for a neurological condition are illustrated according to embodiments of the present invention. One embodiment includes a method for predicting and treating clinical neurological condition relapse. The method includes selecting a threshold heart rate variability value for a patient suffering from a clinical neurological condition, monitoring the patient's heart rate variability over time using a cardiac monitor, providing an indicator that a relapse is imminent when the patient's heart rate variability falls below the threshold heart rate variability value, and treating the patient by applying an accelerated theta burst stimulation protocol using a transcranial magnetic stimulation device, the transcranial magnetic stimulation target of which is the left dorsolateral prefrontal cortex.

[0092] In a further embodiment, the threshold heart rate variability value is selected by obtaining a baseline asymptomatic heart rate variability value for the patient and a baseline symptomatic heart rate variability value for the patient, and determining a midpoint between the asymptomatic heart rate variability value for the patient and the symptomatic heart rate variability value for the patient.

[0093] In yet another embodiment, the threshold heart rate variability value is selected by calculating a population mean heart rate variability value.

[0094] In still a further embodiment, the indicator further comprises a message to a medical scheduling system instructing the medical scheduling system to schedule an appointment for treatment of the patient.

[0095] In yet another embodiment, appointment scheduling priority is based on the difference between a threshold heart rate variability value and the patient's measured heart rate variability.

[0096] In still further embodiments, the clinical neurological condition is depression.

[0097] In another additional embodiment, the indicator is provided when the patient's average heart rate variability value falls below a threshold heart rate variability value for at least three days.

[0098] In yet an additional embodiment, the cardiac monitor is a wrist-mounted heart rate variability monitor.

[0099] Again, in another embodiment, the cardiac monitor is connected to the network via a wireless connection.

[0100] Again, in a further embodiment, the indicator warns that the patient is likely to relapse.

[0101] In yet another additional embodiment, a system for predicting and treating clinical neurological symptom recurrence includes steps for a cardiac monitor configured to measure heart rate variability, a processor in communication with the cardiac monitor, and a memory in communication with the processor. The memory includes a recurrence prediction application that directs the processor to select a threshold heart rate variability value for a patient suffering from a clinical neurological symptom, monitor the patient's heart rate variability over time using the cardiac monitor, and provide an indicator that a recurrence is imminent when the patient's heart rate variability falls below the threshold heart rate variability value. The indicator indicates that the patient will require treatment using a transcranial magnetic stimulation device to apply an accelerated theta burst stimulation protocol, with the transcranial magnetic stimulation target being the left dorsolateral prefrontal cortex.

[0102] In yet another embodiment, to select the threshold heart rate variability value, the recurrence prediction application further directs the processor to obtain a baseline asymptomatic heart rate variability value for the patient and a baseline symptomatic heart rate variability value for the patient, and to determine a midpoint between the asymptomatic heart rate variability value for the patient and the symptomatic heart rate variability value for the patient.

[0103] In still yet a further embodiment, the threshold heart rate variability value is selected by calculating a population mean heart rate variability value.

[0104] In yet another additional embodiment, the indicator further includes a message to a medical scheduling system instructing the medical scheduling system to schedule an appointment for treatment of the patient.

[0105] In still further additional embodiments, appointment scheduling priority is based on the difference between a threshold heart rate variability value and the patient's measured heart rate variability.

[0106] Again, in yet another embodiment, the clinical neurological condition is depression.

[0107] Again, in still a further embodiment, the indicator is provided when the patient's average heart rate variability value falls below a threshold heart rate variability value for at least three days.

[0108] In yet another additional embodiment, the cardiac monitor is a wrist cardiac monitor.

[0109] In yet a further additional embodiment, the cardiac monitor is connected to the network via a wireless connection.

[0110] Again, in yet another embodiment, the method further comprises a step involving a transcranial magnetic stimulation device, wherein the therapy is applied to the patient.

[0111] Additional embodiments and features are set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of this specification or may be learned by practice of the invention. A further understanding of the nature and advantages of the present invention may be realized by reference to the remaining portions of the specification and drawings that form a part of this disclosure. The present invention provides, for example, the following. (Item 1) 1. A method for generating a brain stimulation target, comprising: acquiring functional magnetic resonance imaging (fMRI) image data of a brain of a patient, the brain imaging data describing neuronal activation in the brain of the patient; determining a brain stimulation target, wherein determining the brain stimulation target comprises: mapping at least one region of interest in the patient's brain; locating functional subregions within the at least one region of interest based on the fMRI image data; and Determining functional relationships between at least two brain regions of interest; generating parameters for each functional subregion; generating a target quality score for each functional subregion based on said parameters; selecting a brain stimulation target based on the target quality score and the patient's neurological symptoms; and A method comprising: (Item 2) 2. The method of claim 1, wherein the brain imaging data describes neuronal activity during a resting state. (Item 3) 2. The method of claim 1, wherein acquiring the brain imaging data further comprises preprocessing the brain imaging data. (Item 4) 4. The method of claim 3, wherein preprocessing the brain imaging data comprises performing at least one preprocessing step selected from the group consisting of physiological noise regression, slice time correction, motion correction, co-registration, bandpass filtering, and detrending. (Item 5) 2. The method of claim 1, wherein a brain atlas is used to map the at least one region of interest onto the individual's brain anatomy. (Item 6) 2. The method of claim 1, wherein each functional subregion describes homogeneous brain activity. (Item 7) 2. The method of claim 1, wherein functional subregions are identified and separated from each other using hierarchical agglomerative clustering. (Item 8) 2. The method of claim 1, wherein the subregion parameters are selected from the group consisting of the size of the functional subregion, the density of the voxels that make up the functional subregion, the correlation between the functional subregion and other functional subregions, and the accessibility of the functional subregion to a transcranial magnetic stimulation device. (Item 9) 2. The method of claim 1, wherein the target quality score reflects a combination of weighted parameters for each functional subregion, with a higher quality score reflecting a better brain stimulation target. (Item 10) 10. The method of claim 9, wherein the surface influence for a given subregion is a voxel-number weighted combination of Spearman correlation coefficients derived from a hierarchical clustering algorithm that accounts for correlation coefficients between a subregion on the surface of the brain and all subregions located deep within the brain. (Item 11) Item 10. The method of item 1, wherein the brain stimulation target is a transcranial magnetic stimulation target. (Item 12) 2. The method of claim 1, further comprising stimulating the brain stimulation target using a transcranial magnetic stimulation device according to an aTBS protocol. (Item 13) 1. A system for generating brain stimulation targets, comprising: 1. A neuro-navigation computing system, the neuro-navigation computing system comprising at least one processor and a memory containing a neuro-navigation application, the neuro-navigation application comprising: acquiring brain imaging data from a magnetic resonance imaging machine capable of acquiring functional magnetic resonance imaging (fMRI) image data of a patient's brain, the brain imaging data describing neuronal activation in the patient's brain; mapping at least one region of interest in the patient's brain; locating functional subregions within the at least one region of interest based on the fMRI image data; and determining a functional relationship between at least two functional subregions; generating subregion parameters for each functional subregion; generating a target quality score for each functional sub-region based on the sub-region parameters; selecting a brain stimulation target based on the target quality score and the patient's neurological symptoms; a neuronavigation computing system for instructing the processor to perform A system comprising: (Item 14) Item 14. The system of item 13, wherein the fMRI image data describes neuronal activity during a resting state. (Item 15) Item 14. The system of item 13, wherein the neuronavigation application further instructs the processor to preprocess the fMRI image data. (Item 16) Item 16. The system of item 15, wherein, to preprocess the fMRI image data, the neuronavigation application further instructs the processor to perform at least one preprocessing step selected from the group consisting of physiological noise regression, slice time correction, motion correction, co-registration, bandpass filtering, and detrending. (Item 17) Item 14. The system of item 13, wherein a brain atlas is used to map at least one subregion. (Item 18) Item 14. The system of item 13, wherein each functional subregion describes homogeneous brain activity. (Item 19) Item 14. The system of item 13, wherein functional subregions are located using hierarchical agglomerative clustering. (Item 20) Item 14. The system of item 13, wherein the subregion parameters are selected from the group consisting of the size of the functional subregion, the density of the voxels that make up the functional subregion, the correlation between the functional subregion and other functional subregions, and the accessibility of the subregion from the surface of the brain. (Item 21) Item 14. The system of item 13, wherein the target quality score reflects the surface effect for a given subregion. (Item 22) 22. The system of claim 21, wherein the surface influence for a given subregion is the sum of a two-dimensional matrix of Spearman correlation coefficients derived from a hierarchical clustering algorithm that accounts for correlation coefficients between a subregion on the surface of the brain and all subregions located deep within the brain. (Item 23) 22. The system of claim 21, wherein the brain stimulation target is a transcranial magnetic stimulation target. (Item 24) 22. The method of claim 21, wherein the neuronavigation application further instructs the processor to stimulate the brain stimulation target using a transcranial magnetic stimulation device according to an aTBS protocol. (Item 25) 1. A method for treating a neurological condition, comprising: generating personalized accelerated theta burst stimulation (aTBS) targets representing locations in the patient's brain associated with a neurological symptom; and placing a TMS device relative to the patient's brain such that the focus of the magnetic field is over the aTBS target; applying TMS to the aTBS target using the TMS device according to an aTBS protocol to the brain to alleviate the neurological symptoms; A method comprising: (Item 26) 26. The method of claim 25, wherein the aTBS protocol is an aiTBS protocol. (Item 27) 27. The method of claim 26, wherein the aiTBS protocol comprises applying a set of pulse trains, each pulse train comprising a set of 20 Hz to 70 Hz pulses applied at 3 Hz to 7 Hz. (Item 28) 28. The method of claim 27, wherein the aiTBS protocol further comprises applying the set of pulse trains every 4 to 10 seconds for at least one 10 minute session. (Item 29) 29. The method of claim 28, wherein the aiTBS protocol further comprises performing 3 to 15 sessions per day with 25 to 120 minutes between each session. (Item 30) 27. The method of claim 26, wherein the aiTBS protocol comprises applying a set of 2-second pulse trains, each train comprising magnetic stimulation pulses in the form of three 50 Hz pulses at 5 Hz, the 2-second trains being applied every 10 seconds for at least ten 10-minute sessions, with an inter-session interval of 50 minutes. (Item 31) 26. The method of claim 25, wherein the aTBS protocol is an acTBS protocol. (Item 32) 32. The method of claim 31, wherein the acTBS protocol comprises applying a set of pulse trains, each pulse at 20 Hz to 70 Hz, each pulse applied at 3 Hz to 7 Hz, and each pulse train applied every 4 to 10 seconds for at least one session lasting 40 to 44 seconds. (Item 33) 33. The method of claim 32, wherein the acTBS protocol further comprises administering 10 to 40 sessions per day. (Item 34) Item 34. The method according to Item 33, wherein the inter-session interval is 10 to 50 minutes. (Item 35) 32. The method of claim 31, wherein the acTBS protocol comprises a set of 3 pulse trains, each pulse being 50 Hz applied at 5 Hz for at least one 40 second session. (Item 36) 32. The method of claim 31, wherein the acTBS protocol comprises a set of three pulse trains, each pulse being 30 Hz applied at 6 Hz for at least one 44 second session. (Item 37) 26. The method of item 25, wherein the aTBS protocol is applied for at least two consecutive days. (Item 38) 26. The method of claim 25, wherein generating an aTBS target includes acquiring first fMRI image data describing the patient's brain in a resting state prior to treatment. (Item 39) acquiring second fMRI image data describing a resting state of the patient's brain after treatment; Locating differences between the first fMRI image data and the second fMRI image data to confirm the effectiveness of the treatment. Item 39. The method of item 38, further comprising: (Item 40) 26. The method of item 25, wherein the neurological condition is clinical depression. (Item 41) 26. The method of item 25, wherein the neurological symptom is suicidal ideation. (Item 42) A system for treating a neurological condition comprising a transcranial magnetic stimulation coil directed to apply an accelerated theta burst stimulation (aTBS) protocol to an aTBS target in a patient's brain. (Item 43) Item 43. The system of item 42, wherein the aTBS protocol is an aiTBS protocol. (Item 44) Item 44. The system of item 43, wherein the aiTBS protocol comprises applying a set of pulse trains, each pulse train comprising a set of 20 Hz to 70 Hz pulses applied at 3 Hz to 7 Hz. (Item 45) Item 45. The system of item 44, wherein the aiTBS protocol further comprises applying the set of pulse trains every 4 to 10 seconds for at least one 10 minute session. (Item 46) Item 46. The system of item 45, wherein the aiTBS protocol further comprises performing 3 to 15 sessions per day with 25 to 120 minutes between each session. (Item 47) Item 44. The system of item 43, wherein the aiTBS protocol comprises applying a set of 2-second pulse trains, each train comprising magnetic stimulation pulses in the form of three 50 Hz pulses at 5 Hz, the 2-second trains being applied every 10 seconds for at least ten 10-minute sessions, with an inter-session interval of 50 minutes. (Item 48) Item 43. The system of item 42, wherein the aTBS protocol is an acTBS protocol. (Item 49) Item 49. The system of item 48, wherein the acTBS protocol comprises applying a set of pulse trains, each pulse at 20 Hz to 70 Hz, each pulse applied at 3 Hz to 7 Hz, and each pulse train applied every 4 to 10 seconds for at least one session lasting 40 to 44 seconds. (Item 50) 50. The system of item 49, wherein the acTBS protocol further comprises administering 10 to 40 sessions per day. (Item 51) Item 51. The system according to item 50, wherein the inter-session interval is 10 to 50 minutes. (Item 52) Item 49. The system of item 48, wherein the acTBS protocol comprises a set of three pulse trains, each pulse being 50 Hz applied at 5 Hz for at least one 40 second session. (Item 53) Item 49. The system of item 48, wherein the acTBS protocol comprises a set of three pulse trains, each pulse being 30 Hz applied at 6 Hz for at least one 44 second session. (Item 54) Item 43. The system of item 42, wherein the aTBS protocol is applied for at least two consecutive days. (Item 55) 1. A method for treating a neurological condition, comprising: acquiring functional magnetic resonance imaging (fMRI) image data of the patient's brain, the fMRI image data describing neuronal activation in the patient's brain; determining a location on the patient's brain for implanting a neurostimulation device, wherein determining a location on the patient's brain for implanting the neurostimulation device comprises: mapping at least one region of interest in the patient's brain; locating functional subregions within the at least one region of interest based on the fMRI image data; and determining a functional relationship between at least two functional subregions; generating subregion parameters for each functional subregion; generating a target quality score for each functional sub-region based on the sub-region parameters; selecting a location for implanting a neurostimulator based on the target quality score and the patient's neurological symptoms; and surgically implanting a neurostimulation device at said location; applying a stimulation protocol to the brain at the location using the neurostimulator to treat the neurological condition; A method comprising: (Item 56) 56. The method of claim 55, wherein the fMRI image data describes neuronal activity during a resting state. (Item 57) 56. The method of claim 55, wherein acquiring the fMRI image data further comprises preprocessing the fMRI image data. (Item 58) 58. The method of claim 57, wherein preprocessing the fMRI image data comprises performing at least one preprocessing step selected from the group consisting of physiological noise regression, slice time correction, motion correction, co-registration, bandpass filtering, and detrending. (Item 59) 56. The method of claim 55, wherein each functional subregion describes homogeneous brain activity. (Item 60) 56. The method of claim 55, wherein functional subregions are located using hierarchical agglomerative clustering. (Item 61) 56. The method of claim 55, wherein the subregion parameters are selected from the group consisting of the size of the functional subregion, the density of the voxels that make up the functional subregion, the correlation between the functional subregion and other functional subregions, and the accessibility of the subregion from the surface of the brain. (Item 62) 56. The method of claim 55, wherein the target quality score reflects the surface effect for a given subregion. (Item 63) 63. The method of claim 62, wherein the surface influence for a given subregion is the sum of a two-dimensional matrix of Spearman correlation coefficients derived from a hierarchical clustering algorithm that accounts for correlation coefficients between the subregion on the surface of the brain and all subregions located deep within the brain. (Item 64) 56. The method of claim 55, wherein the aTBS protocol is an aiTBS protocol. (Item 65) 56. The method of claim 55, wherein the aTBS protocol is an acTBS protocol. (Item 66) 56. The method of item 55, wherein the neurological condition is clinical depression. (Item 67) 56. The method of item 55, wherein the neurological symptom is suicidal ideation. (Item 68) An implantable neurostimulator configured to apply an aTBS protocol to the patient's brain. (Item 69) Item 69. The implantable neurostimulator of item 68, wherein the aTBS protocol is an aiTBS protocol. (Item 70) Item 69. The implantable neurostimulation device of item 68, wherein the aTBS protocol is an acTBS protocol. (Item 71) Item 69. The implantable neurostimulation device of item 68, wherein the implantable neurostimulation device is further configured to apply the aTBS protocol intermittently. (Item 72) Item 69. The implantable neurostimulation device of item 68, wherein the implantable neurostimulation device is further configured to apply the aTBS protocol based on patient feedback. (Item 73) Item 73. The implantable neurostimulation device of item 72, wherein parameters of the aTBS protocol are altered in response to the patient feedback. (Item 74) Item 74. An implantable neurostimulation device as described in Item 73, wherein the patient feedback is the patient's brain activity. (Item 75) 1. A method for predicting and treating clinical neurological symptom recurrence, said method comprising: selecting a threshold heart rate variability value for a patient suffering from a clinical neurological condition; monitoring the patient's heart rate variability over time using a cardiac monitor; providing an indicator that a relapse is imminent when the patient's heart rate variability decreases below the threshold heart rate variability value; treating said patient by applying an accelerated theta burst stimulation protocol using a transcranial magnetic stimulation device, wherein the transcranial magnetic stimulation target is the left dorsolateral prefrontal cortex; A method comprising: (Item 76) The threshold heart rate variability value is obtaining a baseline asymptomatic heart rate variability value for the patient and a baseline symptomatic heart rate variability value for the patient; determining a midpoint between an asymptomatic heart rate variability value for the patient and a symptomatic heart rate variability value for the patient; Item 76. The method of item 75, wherein the selected (Item 77) 76. The method of claim 75, wherein the threshold heart rate variability value is selected by calculating a population mean heart rate variability value. (Item 78) 76. The method of claim 75, wherein the indicator further comprises a message to a medical scheduling system instructing the medical scheduling system to schedule an appointment for treatment of the patient. (Item 79) 79. The method of claim 78, wherein the appointment scheduling priority is based on the difference between the threshold heart rate variability value and the patient's measured heart rate variability. (Item 80) 76. The method of item 75, wherein the clinical neurological condition is depression. (Item 81) 76. The method of claim 75, wherein the indicator is provided when the patient's average heart rate variability value falls below the threshold heart rate variability value for at least three days. (Item 82) Item 76. The method of item 75, wherein the cardiac monitor is a wrist-mounted heart rate variability monitor. (Item 83) Item 76. The method of item 75, wherein the cardiac monitor is connected to a network via a wireless connection. (Item 84) 76. The method of claim 75, wherein the indicator warns that the patient is likely to have a relapse. (Item 85) 1. A system for predicting and treating clinical neurological symptom recurrence, comprising: a cardiac monitor configured to measure heart rate variability; a processor in communication with the cardiac monitor; a memory in communication with the processor and comprising a recurrence prediction application, the recurrence prediction application comprising: selecting a threshold heart rate variability value for a patient suffering from a clinical neurological condition; monitoring the patient's heart rate variability over time using the cardiac monitor; providing an indicator of an impending relapse when the patient's heart rate variability falls below the threshold heart rate variability value; the indicator indicates that the patient will require treatment using a transcranial magnetic stimulation device to apply an accelerated theta burst stimulation protocol, the transcranial magnetic stimulation target of which is the left dorsolateral prefrontal cortex; a memory for instructing the processor to A system comprising: (Item 86) To select the threshold heart rate variability value, the recurrence prediction application further comprises: obtaining a baseline asymptomatic heart rate variability value for the patient and a baseline symptomatic heart rate variability value for the patient; determining a midpoint between an asymptomatic heart rate variability value for the patient and a symptomatic heart rate variability value for the patient; Item 86. The system of item 85, wherein the system instructs the processor to: (Item 87) Item 86. The system of item 85, wherein the threshold heart rate variability value is selected by calculating a population mean heart rate variability value. (Item 88) Item 86. The system of item 85, wherein the indicator further includes a message to the medical scheduling system instructing the medical scheduling system to schedule an appointment for treatment of the patient. (Item 89) Item 89. The system of item 88, wherein the appointment scheduling priority is based on the difference between the threshold heart rate variability value and the patient's measured heart rate variability. (Item 90) Item 86. The system of item 85, wherein the clinical neurological condition is depression. (Item 91) 86. The system of claim 85, wherein the indicator is provided when the patient's average heart rate variability value falls below the threshold heart rate variability value for at least three days. (Item 92) Item 86. The system of item 85, wherein the cardiac monitor is a wrist cardiac monitor. (Item 93) Item 86. The system of item 85, wherein the cardiac monitor is connected to a network via a wireless connection. (Item 94) Item 86. The system of item 85, further comprising the transcranial magnetic stimulation device, wherein the therapy is applied to the patient. [Brief explanation of the drawings]

[0112] The description and claims will be more fully understood with reference to the following figures and data graphs, which are presented as exemplary embodiments of the invention and should not be construed as a complete recitation of the scope of the invention.

[0113] [Figure 1] FIG. 1 is a diagram illustrating an accelerated theta burst stimulation system, according to an embodiment of the present invention.

[0114] [Figure 2] FIG. 2 is a diagram illustrating an accelerated theta burst stimulation computing system according to an embodiment of the present invention.

[0115] [Figure 3] FIG. 3 is a flow chart illustrating a method for applying personalized accelerated intermittent theta burst stimulation to a patient, according to an embodiment of the present invention.

[0116] [Figure 4] FIG. 4 is a stimulation schedule for accelerated intermittent theta burst stimulation, according to an embodiment of the present invention.

[0117] [Figure 5] FIG. 5 is a stimulation schedule for accelerated continuous theta burst stimulation, according to an embodiment of the present invention.

[0118] [Figure 6] FIG. 6 is a flow chart illustrating a method for generating personalized accelerated theta burst stimulation targets, according to an embodiment of the present invention.

[0119] [Figure 7] FIG. 7 is a set of charts illustrating a hierarchical agglomerative clustering algorithm for identifying functional sub-regions within a region of interest, according to one embodiment of the present invention.

[0120] [Figure 8A] FIG. 8A is a chart of small area correlation coefficients according to one embodiment of the present invention.

[0121] [Figure 8B] FIG. 8B is a chart of ROI 1 functional subregion sizes, according to an embodiment of the present invention.

[0122] [Figure 8C] FIG. 8C is a chart of ROI 1 functional subregion voxel disaggregation, according to an embodiment of the present invention.

[0123] [Figure 8D] FIG. 8D is a chart of the net relationship between each ROI 1 sub-region and all ROI 2 sub-regions, according to an embodiment of the present invention.

[0124] [Figure 8E] FIG. 8E is a chart of small region voxel densities according to one embodiment of the present invention.

[0125] [Figure 8F] FIG. 8F is a chart of cluster quality according to one embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0126] The brain is a delicate organ, and therefore precise targeting can be extremely useful when medical professionals perform procedures, tests, or otherwise intervene in its physical structure. For example, when stimulating a specific portion of the brain to treat a patient, stimulating the wrong portion can not only result in failed or incomplete treatment, but can also cause adverse effects to the patient. Therefore, systems and methods for neuronavigation that can generate and / or utilize precise targets for stimulation can reduce failure rates and improve the quality of treatment.

[0127] Turning now to the drawings, systems and methods for personalized clinical application of accelerated intermittent theta-burst magnetic stimulation (iTBS) are illustrated, according to embodiments of the present invention. rTMS has been accepted by the U.S. Food and Drug Administration as an effective treatment for clinical depression for nearly a decade, and recently, once-daily application of iTBS (3 minutes, 600 pulses) was approved by the FDA. Recently, a new form of rTMS known as theta-burst stimulation (iTBS) has been shown to have increased efficacy over standard rTMS by shortening the amount of time the treatment needs to be applied to have a therapeutic effect in its (excitatory) intermittent form. For example, 40 minutes of TMS (approximately 3,000 pulses) has been shown to be non-inferior to 3 minutes of iTBS (approximately 600 pulses). However, conventional rTMS / TBS treatment regimens can require several weeks of sessions to achieve a stable and effective reduction in clinical symptoms. Furthermore, conventional rTMS and iTBS techniques target portions of the brain based on averaging total activity within the region. Such techniques have failed to generate targets optimized for a patient's individualized brain structure and connectivity.

[0128] Accelerated theta burst stimulation (aTBS) is a new class of rTMS variant described herein in which high-dose TBS pulses are applied over short periods to targeted locations in the brain. This can be accomplished to produce excitation (intermittent) or inhibition (continuous). Appropriate aTBS protocols, according to embodiments of the present invention, can reduce the time to clinical improvement from weeks to days compared to traditional rTMS / TBS, meaning patients can remain hospitalized for a shorter amount of time.

[0129] Additionally, methods for performing aTBS can include generating personalized aTBS targets for TBS stimulation that take into account patient characteristics. The personalized targets can be generated to maximize the efficiency and / or effectiveness of aTBS treatment for each patient. Because TMS coils are currently unable to target structures deep within the brain, personalized aTBS targets can be generated to stimulate superficial regions connected to deep regions, avoiding the need for deep brain stimulation.

[0130] The importance of proper targeting is underscored by the significant symptoms of chronic psychiatric illness. For example, in individuals suffering from severe clinical depression, neurosurgery is considered a treatment of last resort. When treatment with implanted stimulators fails, the incidence of suicide increases dramatically. By using the neuronavigation techniques described herein, proper and effective targeting can be achieved prior to surgery, increasing the success rate of the procedure in a number of ways. In many embodiments, aTBS is pilot-performed to test the effectiveness of the neural implant prior to surgery by stimulating a selected target or set of targets. In various embodiments, the target with the best trial results is selected as the target for the implantable system. If no target is found with a sufficiently high success rate for the patient and / or medical professional, invasive surgery can be avoided entirely. Because magnetic and electrical stimulation are closely related, implanted neurostimulators can be programmed to provide electrical stimulation protocols similar to aTBS magnetic protocols with similar results. Systems for implementing aTBS and neuronavigation targeting methods are discussed below.

[0131] aTBS system An aTBS system according to embodiments of the present invention can obtain neuroimaging data of a patient's brain and generate personalized aTBS targets for treatment using an aTBS device. In many embodiments, the aTBS system includes an aTBS device. A conceptual diagram of an aTBS system according to one embodiment of the present invention is shown in FIG. 1. The aTBS system 100 includes an aTBS device 110. The aTBS device can be any TMS coil capable of delivering magnetic pulses with the frequency, intensity, and duration required by aTBS. The aTBS device can be, but is not limited to, the Magventure X100 produced by MagVenture (Farum, Denmark), the Magstim coil produced by The Magstim Company Limited (Whitland, United Kingdom), the Neurosoft coil produced by Neurosoft (Utrecht, Netherlands), and the Brainsway H7-deep-TMS system or H1 Coil TMS device, both of which are produced by Brainsway Ltd. (Jerusalem, Israel). However, any TMS coil can be used as appropriate for the requirements of a particular application of embodiments of the present invention. Additionally, an aTBS system can include more than one aTBS device to better target different regions of the brain.

[0132] In many embodiments, a neuronavigation system can be used to properly position the aTBS device relative to the aTBS target. Neuronavigation systems capable of displaying the aTBS target can include the Localite TMS Navigator produced by Localite GmbH (Sankt Augustin, Germany), the visor2 system produced by ANT Neuro (Netherlands), and the BrainSite TMS Navigation produced by Rogue Solutions Ltd. (Cardiff, Wales). However, any type of neuronavigation device capable of assisting in the placement of an aTBS device can be utilized as appropriate for the requirements of a particular application of embodiments of the present invention. In many embodiments, the neuronavigation system is provided with and / or generates a target or set of targets, which are generated using the targeting process described below.

[0133] aTBS system 100 further includes an aTBS computing system 120 and a brain imaging device 130. The aTBS computing system can be implemented as one or more computing devices capable of processing brain imaging data and generating personalized aTBS targets. The brain imaging device is capable of acquiring imaging data describing a patient's brain. The brain imaging device can acquire structural, functional, and / or resting-state imaging data. In numerous embodiments, the brain imaging device is a magnetic resonance imaging (MRI) machine, a functional MRI machine (fMRI), or any other brain scanning device as appropriate for the requirements of a particular application of embodiments of the present invention. Functional imaging data can be acquired by scanning a patient using an fMRI scanner or other brain imaging device while the patient is performing a specific task and / or being provided with specific stimuli. Resting-state imaging data can be acquired by scanning a patient using an fMRI scanner or other brain imaging device while the patient is not performing any task.

[0134] In addition, aTBS system 100 includes an interface device 140. The interface device may be, but is not limited to, a computer, a smartphone, a tablet computer, a smartwatch, or any other type of computing interface device as appropriate for the requirements of a particular application of embodiments of the present invention. In many embodiments, the interface device is used to interface with a brain imaging device, an aTBS computing system, and / or an aTBS device. In many embodiments, the aTBS computing system and the interface device are implemented using the same physical device. aTBS system 100 includes a network 150 connecting aTBS device 110, aTBS computing system 120, and interface device 130. In various embodiments, the network is the Internet. However, any network, such as, but not limited to, an intranet, a local area network, a wide area network, or any other computing network capable of connecting computing devices, can be used as appropriate for the requirements of a given application.

[0135] Although an exemplary aTBS system is illustrated with respect to Figure 1, those skilled in the art will appreciate that many different configurations of an aTBS system are possible, including, but not limited to, system architectures in which different devices are not, or not all, connected by a network. The aTBS computing system is discussed below.

[0136] aTBS Computing System An aTBS computing system, according to embodiments of the present invention, can generate personalized aTBS targets for a given patient. A conceptual diagram of an aTBS computing system, according to an embodiment of the present invention, is shown in FIG. 2. The aTBS computing system 200 includes a processor 210 in communication with a communication interface 220 and a memory 230. In many embodiments, the aTBS computing system includes multiple processors, multiple memories, and / or multiple communication interfaces. In various embodiments, components of the aTBS computing system are distributed across multiple hardware platforms.

[0137] The processor 210 may be any type of computer processing unit, including, but not limited to, a microprocessor, a central processing unit, a graphical processing unit, a parallel processing engine, or any other type of processor as appropriate for the requirements of a particular application of an embodiment of the present invention. The communications interface 220 may be utilized to transmit and receive data from other aTBS computing systems, brain imaging devices, aTBS devices, and / or interface devices. The communications interface may include multiple ports and / or communications technologies for communicating with various devices as appropriate for the requirements of a particular application of an embodiment of the present invention.

[0138] Memory 230 can be implemented using any combination of volatile and / or non-volatile memory, including, but not limited to, random access memory, read-only memory, hard disk drive, solid state drive, flash memory, or any other memory format as appropriate for the requirements of a particular application of an embodiment of the present invention. In many embodiments, memory 230 stores various data, including, but not limited to, an aTBS targeting application 232 and imaging data 234. In many embodiments, the aTBS targeting application and / or imaging data are received via a communications interface. Processor 210 can be directed by the aTBS targeting application to perform various aTBS processes, including, but not limited to, processing the imaging data and generating aTBS targets.

[0139] A specific architecture for an aTBS computing system according to embodiments of the present invention is conceptually illustrated in FIG. 2 , although any of a variety of architectures, including but not limited to those utilizing different hardware capable of directing an aTBS device to perform aTBS, directing a brain imaging device to capture imaging data, and / or similarly performing the above, may also be utilized. Furthermore, an aTBS computing system may be implemented on multiple servers within at least one computing system. For example, an aTBS computing system may be implemented on various remote “cloud” computing systems as appropriate for the requirements of a specific application of embodiments of the present invention. However, those skilled in the art will understand that a “computing system” may be implemented on any suitable computing device, including but not limited to a personal computer, a server, a cluster of computing devices, and / or a computing device integrated into a medical device. In many embodiments, the aTBS computing system is implemented as part of an integrated aTBS device. A discussion of various aTBS processes is found below.

[0140] Process for conducting aTBS Traditional rTMS procedures utilize multiple pulses over a long period of time. Traditional TBS utilizes patterned pulses, which reduces the number of pulses required to achieve results similar to rTMS. However, neither method can produce clinically useful changes within one week. A process for performing aTBS according to embodiments of the present invention can include an accelerated treatment regimen compared to traditional rTMS / TBS methods.

[0141] Turning now to FIG. 3 , a process for performing aTBS is illustrated, according to an embodiment of the present invention. Process 300 includes acquiring (310) patient brain imaging data. In many embodiments, the patient brain imaging data includes anatomical imaging data, resting-state imaging data, functional imaging data, and / or any other brain imaging data as appropriate for the requirements of a particular application of an embodiment of the present invention. Anatomical imaging data may include, but is not limited to, structural MRI scan data, diffusion tensor imaging data, computed tomography scans, and / or any other structural image of the brain generated by imaging techniques. Functional imaging data is imaging data describing neuronal activation in the brain. Resting-state imaging data is imaging data describing neuronal activation in the brain during a resting state. In many embodiments, the functional imaging data and resting-state imaging data are acquired from fMRI scans.

[0142] The patient brain imaging data can be used to generate 320 a personalized aTBS target. aTBS can be applied to the patient at the personalized aTBS target. In many embodiments, the TMS coil utilized to apply the aTBS treatment is focused on the target by manipulating the coil placement and / or coil angle, and thus the orientation of the magnetic field. In many embodiments, the aTBS is applied according to a predetermined protocol. The aTBS protocol can vary depending on numerous factors, including, but not limited to, the severity of the symptoms, whether aiTBS or acTBS is used, or any other factor as appropriate for the requirements of a particular application of an embodiment of the present invention. The aTBS protocol can include a set of parameters describing the form of iTBS to be applied and a schedule describing when iTBS is applied. In many embodiments, electric field measurements are included and used to inform coil angle selection.

[0143] In many embodiments, the aiTBS schedule involves applying iTBS pulses in multiple sessions per day over several days. In various embodiments, the iTBS pulse parameters include 3 pulses at 5 Hz over a 2-second train, 50 Hz pulses, with trains every 10 seconds over a 10-minute session (1,800 total pulses per session). In many embodiments, the aiTBS schedule describes performing 10 sessions per day with a 50-minute inter-session interval over 5 consecutive days (18,000 pulses per day, 90,000 total pulses).

[0144] However, a wide range of parameters can be used, for example, iTBS pulse parameters can be at 3 Hz to 7 Hz, with any number of pulses from 20 Hz to 70 Hz, with trains every 4 seconds to 10 seconds, and with inter-session intervals of 25 minutes to 120 minutes. An exemplary schedule for treatment using aiTBS according to one embodiment of the present invention is illustrated in Figure 4.

[0145] In various embodiments, cTBS pulse parameters involve a three-pulse train with 50 Hz pulses at 5 Hz over a 40-second session (600 total pulses per session). In various embodiments, cTBS pulse parameters involve three 30 Hz pulses at 6 Hz over a 44-second session (800 total pulses per session). In many acTBS embodiments, 30 sessions are applied per day with a 15-minute inter-session interval over five consecutive days (18,000 pulses per day, 90,000 total pulses). However, a wide range of parameters can be used; for example, cTBS parameters can involve any number of pulses from 20 Hz to 70 Hz at 3 Hz to 7 Hz, with an inter-session interval of 10 to 50 minutes. An exemplary schedule for treatment using acTBS according to an embodiment of the present invention is illustrated in FIG. 5.

[0146] However, the TBS parameters and schedule for an aTBS protocol can be varied. For example, the number of pulses or frequency of sessions can be increased or decreased depending on the patient's refractoriness and / or the severity of their clinical symptoms (i.e., if the depression is less severe, fewer pulses can be effectively utilized, thereby further shortening treatment time). In many embodiments, the number of pulses per session ranges from approximately 600 to 2,400, depending on the type of aTBS being applied. In various embodiments, the number of sessions for aiTBS ranges from 3 to 15 sessions per day. In many embodiments, the number of sessions for acTBS ranges from 10 to 40 sessions per day. Nevertheless, those skilled in the art will understand that any number of pulses and length of inter-session interval can be used to suit the needs of an individual patient and as appropriate for the requirements of a specific application of an embodiment of the present invention.

[0147] A change in the patient's resting state can be measured 340 using methods similar to those described above with respect to acquiring resting-state imaging data. If there is sufficient change in resting state 350 to produce the desired clinical outcome, treatment can optionally be terminated. If there is not sufficient change in the patient's resting state 350, additional aTBS treatment sessions can be administered.

[0148] A specific process for performing aTBS according to embodiments of the present invention is described above and shown with respect to FIG. 3; however, any number of processes, including but not limited to those using alternative numbers of pulses, sessions, frequencies, imaging methods, and / or degrees of resting state variation, can be utilized as appropriate for the requirements of a specific application according to embodiments of the present invention.

[0149] Generation of aTBS targets Every person has a unique brain structure and connectivity. Averages across all brains are used to generate targets, which ignores the specifics of each patient. A process for administering aTBS, according to embodiments of the present invention, can include generating personalized aTBS targets to increase efficiency and effectiveness over standard iTBS treatment. Turning now to FIG. 6, a process for generating aTBS targets, according to an embodiment of the present invention, is illustrated.

[0150] Process 600 includes acquiring (610) brain imaging data. In many embodiments, the brain imaging data includes structural imaging data, resting-state imaging data, and / or functional imaging data similar to those described above. In many embodiments, the brain imaging data is preprocessed. Preprocessing steps may include, but are not limited to, physiological noise regression, slice-time correction, motion correction, co-registration, bandpass filtering, detrending, and / or any other preprocessing steps as appropriate for the requirements of a given application. The brain imaging data can be used to map (620) standardized regions of interest (ROIs) onto individualized anatomical structures. In many embodiments, mapping the standardized regions of interest onto individualized anatomical structures is performed by aligning the structural imaging data to a standardized brain atlas, inverting the alignment parameters, and mapping the standardized ROIs onto the individual's anatomical structures. The standardized brain atlas can define one or more brain regions that are targetable using TMS. In various embodiments, the brain atlas defines brain regions that are targetable using a specific TMS coil. In many embodiments, task-based fMRI is used to limit and / or expand the range of brain regions to be targeted with TMS.

[0151] In many embodiments, a personalized map of functional activation within the ROI is generated (630). In various embodiments, functional activation within the ROI is identified using functional imaging data and / or resting-state imaging data. The personalized map of the ROI and the resting-state imaging data can be used to generate a personalized map of functional subregions within the ROI (640). In many embodiments, a functional subregion is defined as a brain region within the ROI where overall temporal brain activity is highly correlated across the spatial extent of the subregion. In many embodiments, the resting-state imaging data can be used to generate resting-state functional connectivity data that describes functional connectivity between brain regions. In many embodiments, resting-state data extracted from the resting-state imaging data, such as, but not limited to, functional connectivity and task-based neuronal activation, can be mapped to the personalized region of interest. In many embodiments, the personalized map of task-based functional activation within the ROI can be utilized to refine or expand the functional connectivity-based personalized map of the functional subregion. Task-based functional imaging data can lead to more refined and / or higher quality analyses of functional connectivity between subregions.

[0152] In addition, resting-state functional connectivity data across ROIs can be further divided into functional subregions, with each subregion thus consisting of homogeneous brain activity measured across a series of resting-state scans. In many embodiments, the division is achieved using hierarchical clustering, such as, but not limited to, hierarchical agglomerative clustering. However, any division method can be used as appropriate for the requirements of a given embodiment. In various embodiments, the size of a subregion is determined by the number of voxels that act in a homogeneous manner. Therefore, the size of a subregion can vary across different ROIs depending on the function of the subregion, the brain structure in which the subregion is located, the characteristics of the patient's brain, and any of several other factors that affect brain connectivity and responsiveness.

[0153] The relationships between functional subregions can be determined 650 using a variety of techniques. In many embodiments, the voxel time course that most highly correlates with the median of all voxel time courses within a functional subregion can be selected as reflecting the activity of the functional subregion. In many embodiments, a simple average of all time courses for voxels within a subregion can be used, but this tends to be less robust. By reducing each functional subregion to a single time course, an accurate representation of the typical time course of brain activity occurring within a homogeneous group of voxels can be determined.

[0154] An exemplary grouping and correlation calculation according to an embodiment of the present invention is illustrated in FIG. 7. Furthermore, a single representative time course allows for the calculation of correlation coefficients between functional subregions residing within the same ROI. However, these correlation coefficients will tend to be low because groups of voxels with high correlation coefficients tend to be organized into the same subregion. The process of reducing each functional subregion identified through segmentation to a single time course also allows for the calculation of correlation coefficients between all functional subregions discovered across multiple ROIs across the brain. For example, multiple subregions within the left dorsolateral prefrontal cortex can be correlated with multiple subregions within the cingulate cortex, although any number of different subregions in any number of different brain structures can be correlated. An exemplary set of correlations across multiple ROIs according to an embodiment of the present invention is illustrated in FIGS. 8A-8F.

[0155] Process 600 further includes parameterizing (660) the functional subregions. The subregions can be assigned values ​​for various parameters, such as, but not limited to, the size of the subregion, i.e., the number of voxels or brain volume, the spatial density of the voxels, i.e., the number of voxels divided by the average Euclidean three-dimensional spatial distance between all voxels that make up the subregion, the functional relationship between a given subregion and other functional subregions (i.e., the voxel-weighted average correlation coefficient), and / or the accessibility of the subregion from the brain surface, such as, but not limited to, the depth of the subregion and / or whether the subregion is obscured by another subregion. However, any number of parameters or parameter weighting schemes can be used as appropriate for the requirements of a given application.

[0156] The functional subregion parameters can be used to calculate a target quality score for each functional subregion (670). In many embodiments, the target quality score for each subregion is a function of a weighted combination of the subregion parameters. In many embodiments, the target quality score is generated by determining the surface influence (voxel size-weighted correlation coefficient) for a set of subregions. In many embodiments, the surface influence for a given subregion is the sum of a two-dimensional matrix of Spearman or Pearson correlation coefficients derived from a hierarchical clustering algorithm that accounts for the correlation coefficients between all of the surface ROI subregions and the deep ROI subregions. In many embodiments, a first ROI is located near the brain's surface, and a second ROI is located deeper within the brain tissue. However, a difference in depth is not a requirement for ROI comparison. Surface ROI subregions that have a positive influence on deep subregions and surface ROI subregions that have a negative influence on deep subregions can be determined based on the surface influence calculation. In some embodiments, depending on the type of treatment, only the subregions that have a positive influence or only the subregions that have a negative influence may further be considered targets. In many embodiments, a surface subregion concentration can be calculated. In many embodiments, the surface subregion concentration can be calculated by dividing the number of voxels in the surface subregion by the surface subregion disaggregation as measured by the average Euclidean distance between voxels in the subregion. The surface subregion concentration can be further normalized to aid interpretation. In many embodiments, a target quality score is determined by multiplying the surface subregion concentration by the surface impact of a particular subregion. Thus, the target quality score can reflect both the potential impact of stimulating a given surface subregion on a deeper subregion and the ability and efficiency with which the subregion can be targeted by a TMS coil. However, any number of methods can be used to generate a target quality score as appropriate for the requirements of a particular application of embodiments of the present invention.

[0157] A personalized aTBS target can be generated 680 based on the target quality scores. In many embodiments, the target with the highest quality is selected as the personalized aTBS target. In various embodiments, more than one target can be selected.

[0158] A specific process for generating aTBS targets according to embodiments of the present invention is described above and shown with respect to FIG. 6; however, any number of processes can be utilized as appropriate to the requirements of a specific application according to embodiments of the present invention, including, but not limited to, those using different and / or fewer types of imaging data, different subregion parameters, different segmentation methods, and / or any other quality generation method.

[0159] Clinical treatment using aTBS Clinical evaluations have determined that aTBS can be used to treat a variety of different medical conditions, both physical and mental. For example, when administered over the left dorsolateral prefrontal cortex (L-DLPFC), aiTBS is effective in reducing suicidal ideation and alleviating depressive symptoms. Patients who receive aiTBS over the L-DLPFC treatment are often able to be discharged from hospital within 2 to 5 days. Furthermore, aiTBS over the L-DLPFC can be used to increase heart rate variability, which correlates with numerous psychiatric disorders, various types of cancer, various heart diseases, and inflammatory conditions. In various embodiments, heart rate variability is treated by targeting the L-DLPFC to produce changes in the subcallosal cingulate cortex (SCC), which in turn can produce changes in the vagus nerve that can be used to affect heart rate. Heart rate variability can be used as a treatment-responsive neurophysiological biomarker for addressing prefrontal dysregulation of sympathetic / parasympathetic balance. For example, in many embodiments, heart rate slowing is used to confirm target engagement. Similarly, ongoing recordings of heart rate deceleration can be used for ongoing target validation. Identified functional subregions can be used to find the best available targets for influencing heart rate variability. This can be done by iteratively testing each functional subregion with ROI TMS stimulation for its effect on heart rate or heart rate variability.

[0160] Embeddable aTBS In many embodiments, for severe clinical conditions, aTBS can be administered via an implanted neurostimulator. The aTBS protocol can be adapted to electrical stimulation instead of magnetic stimulation, and stimulation can be applied chronically without the need for external magnetic stimulation. Furthermore, external aTBS using an rTMS device can be administered to examine the correct targeting and therapeutic efficacy, and then a neurostimulator can be implanted, either epidurally or subdurally, over targets determined to be most likely to produce positive outcomes. In many embodiments, the neurostimulator provides stimulation and records brain activity. By providing both stimulation and recording channels, a closed-loop system can be achieved. Based on the recorded brain activity, stimulation can be modulated to either increase or decrease the amount of aTBS delivered or to change the stimulation target. Furthermore, machine learning algorithms can be used to adapt the optimal stimulation strategy for the individual. For example, if positive neurological activity is recorded, stimulation can be discontinued until abnormal neurological activity is detected. In many embodiments, recording brain activity is achieved using standard electrocorticography (ECoG) methods.

[0161] In various embodiments, multiple stimulation electrodes can be implanted across multiple targets, either for aTBS applications or otherwise. Recording activity can be used to selectively activate or deactivate different electrodes in response to different responses for safety reasons and clinical reasons related to aTBS applications. For example, if seizure activity is detected in a particular area of ​​the brain, stimulation electrodes in that region can be deactivated and / or utilized to induce normal brain activity.

[0162] Neurostimulators can also be used with open-loop parameters, where a static protocol is utilized to maintain a desired brain activation pattern. In many embodiments, the static protocol can be adapted or modified via an external controller. Many neurostimulator devices use a combination of open-loop and / or closed-loop formats as appropriate for the requirements of a given application of an embodiment of the present invention.

[0163] As described above, neurostimulators can be placed on selected targets using the neuronavigation techniques described above. However, when a surgeon is placing a neurostimulator, it may be useful to verify the placement during the procedure. In many embodiments, organ responses to brain stimulation can be used to verify placement. For example, electrodes placed on the L-DLPFC can affect heart rate, and by measuring heart rate, stimulation of the L-DLPFC can be verified. Because L-DLPFC stimulation can also affect depression, this may be a useful clinical tool for implanting neurostimulators to treat clinical depression.

[0164] Electrocorticography (epidural / subdural), EEG, and NIRS can be correlated with heart rate and heart rate variability, both of which can be used as closed-loop indicators of ongoing, effective stimulation.

[0165] Furthermore, because aTBS can be administered to have excitatory (aiTBS) or inhibitory (acTBS) effects, when the correct target is selected, various neurological changes can be implemented that may affect areas beyond the brain. For example, manipulating the hypothalamus can affect cortisol secretion by altering the hypothalamic-pituitary-adrenal axis. In addition, neurological plasticity after injury can be increased by exciting and inhibiting different neuronal connections. Similarly, aTBS can be used to facilitate learning and skill acquisition by directly increasing neurological plasticity in the targeted neuronal network or elsewhere.

[0166] While specific systems and methods for implementing aTBS are discussed above, many different systems and methods can be implemented in accordance with many different embodiments of the present invention. It should be understood, therefore, that the present invention may be practiced otherwise than as specifically described without departing from the scope and spirit of the present invention. Accordingly, the present embodiments are to be considered in all respects as illustrative and not restrictive. The scope of the present invention should, therefore, be determined not by the embodiments exemplified, but by the appended claims and their equivalents.

Claims

[Claim 1] The invention as described in the drawings of this application.

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