Systems and methods for targeted neuromodulation

Abstract

Systems and methods for neuronavigation are illustrated according to embodiments of the present invention. Targeting systems and methods as described herein can generate personalized stimulation targets for the treatment of psychiatric conditions. In many embodiments, direct stimulation of the personalized stimulation targets indirectly affects brain structures that are more difficult to reach via stimulation modalities. In various embodiments, the psychiatric condition is major depressive disorder. In some embodiments, the psychiatric condition is suicidal ideation.

Description

Technical Field

[0001] (Cross - reference to related applications) This application claims the benefit and priority of U.S. Provisional Patent Application No. 63 / 090,680, filed on October 12, 2020, entitled "Systems and Methods for Neuronavigation", the disclosure of which is incorporated herein by reference in its entirety for all purposes under 35 U.S.C. § 119(e) (Section 119(e) of the United States Patent Law).

[0002] The present invention generally relates to neuromodulation therapies, and more specifically to generating personalized stimulation targets.

Background Art

[0003] Brain stimulation therapies can be delivered by several methods, including (but not limited to) transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS). Brain stimulation therapies are often delivered to or towards specific regions of a patient's brain to treat the patient's condition.

[0004] Radiographic imaging enables non - invasive scanning of internal organs. Common brain imaging techniques involve the use of magnetic resonance imaging (MRI) machines and a variant of MRI called functional MRI (fMRI) that can measure brain activity by measuring changes associated with blood flow. MRI is often referred to as "structural" in contrast to fMRI because it typically examines only the anatomical structure of the brain and not brain activity.

Summary of the Invention

Means for Solving the Problems

[0005] A system and method for targeted neuromodulation according to embodiments of the present invention are illustrated. One embodiment includes a processor and a memory containing a targeting application, the targeting application acquiring patient brain data comprising structural magnetic resonance imaging (sMRI) scans and at least one functional magnetic resonance imaging (fMRI) scans of the patient's brain, and mapping a reference region of interest (ROI) and at least one exploration ROI to the patient's brain using the sMRI scan and at least one fMRI scan, wherein the reference ROI describes a region to be indirectly affected via brain stimulation therapy, and at least one exploration ROI to be directly targeted by brain stimulation therapy. The system includes a memory for a targeted neuromodulation system that describes at least one region, derives a personalized map of ROI parcelization, the personalized map of ROI parcelization describes a criterion ROI as a plurality of criterion parcels, describes at least one exploration ROI as a plurality of candidate parcels, extracts relationships between the plurality of candidate parcels and the plurality of criterion parcels, calculates a target score for the candidate parcels in the plurality of candidate parcels based on the extracted relationships, and instructs a processor to select a target parcel from the plurality of candidate parcels and provide the target parcel based on the target score.

[0006] In another embodiment, the targeted application further instructs the processor to deliver brain stimulation therapy to a targeted parcel in order to treat the patient's mental condition.

[0007] In a further embodiment, the mental condition is major depressive disorder.

[0008] In further embodiments, the mental condition is suicidal ideation.

[0009] In further embodiments, brain stimulation therapy is selected from the group consisting of transcranial magnetic stimulation, transcranial direct current stimulation, and electrical stimulation delivered via an implantable electrical stimulator.

[0010] In another embodiment, the targeted application further instructs the processor to discard fMRI scans that deviate from the expected whole-brain network connectivity.

[0011] In further embodiments, to derive an individualized map of ROI parcelization, the targeted application further randomly subsamples voxels within a reference and at least one search ROI, clusters the voxel subsamples, records the clustering assignments, and instructs the processor to label the clusters in the clustering assignments as candidate parcels or reference parcels based on their locations.

[0012] In another additional embodiment, to derive an individualized map of ROI parcelization, the targeted application further randomly subsamples voxels in a reference and at least one exploration ROI as a first subsample of voxels, clusters the first subsample of voxels, records the first clustering assignment, randomly subsamples voxels in a reference and at least one exploration ROI as a second subsample of voxels, clusters the second subsample of voxels, records the second clustering assignment, merges the first and second clustering assignments using consensus clustering, and instructs the processor to label clusters in the merged clustering assignment as candidate parcels or reference parcels based on their locations.

[0013] In a further additional embodiment, to derive an individualized map of ROI parcelization, the targeted application further instructs the processor to partition spatially dissimilar clusters.

[0014] Again, in another embodiment, the target score is calculated based on at least one factor from the group consisting of parcel size, parcel depth, parcel shape, parcel homogeneity, functional connectivity strength to a reference ROI, and network connectivity score.

[0015] Again, in further embodiments, the network connectivity score reflects the inverse correlation between the patient's brain's default mode network and dorsal attention network.

[0016] In yet another embodiment, the targeted neuromodulation method is to acquire patient brain data, the patient brain data comprising a structural magnetic resonance imaging (sMRI) scan of the patient's brain and at least one functional magnetic resonance imaging (fMRI) scan, and to map a reference region of interest (ROI) and at least one exploration ROI to the patient's brain using the sMRI scan and at least one fMRI scan, wherein the reference ROI describes a region to be indirectly affected via brain stimulation therapy and at least one exploration ROI describes a region to be directly targeted by brain stimulation therapy. The method includes describing one region and deriving a personalized map of ROI parcelization, the personalized map of ROI parcelization describing a criterion ROI as a plurality of criterion parcels and describing at least one exploration ROI as a plurality of candidate parcels; extracting relationships between the plurality of candidate parcels and the plurality of criterion parcels; calculating a target score for the candidate parcels in the plurality of candidate parcels based on the extracted relationships; selecting a target parcel from the plurality of candidate parcels based on the target score; and providing the target parcel. In many embodiments, acquiring patient brain data may be performed by accessing patient brain data previously uploaded or transmitted to a target identification system, by requesting patient brain data from a remote agency, computer system, or database, or by accessing hardware such as an MRI or other imaging hardware to trigger the acquisition of patient brain data.

[0017] In further embodiments, the method further includes providing brain stimulation therapy to a targeted parcel in order to treat the patient's mental condition.

[0018] In yet another additional embodiment, the mental condition is major depressive disorder.

[0019] In further additional embodiments, the mental condition is suicidal ideation.

[0020] Again, in yet another embodiment, brain stimulation therapy is selected from the group consisting of transcranial magnetic stimulation, transcranial direct current stimulation, and electrical stimulation delivered via an implantable electrical stimulator.

[0021] Again, in yet further embodiments, the method further includes discarding fMRI scans that deviate from the expected whole-brain network connectivity.

[0022] In another additional embodiment, deriving an individualized map of ROI parcelization includes randomly subsampling voxels within a reference and at least one search ROI, clustering the voxel subsamples, recording the clustering assignments, and labeling the clusters in the clustering assignments as candidate parcels or reference parcels based on their locations.

[0023] In further additional embodiments, deriving an individualized map of ROI parcelization includes randomly subsampling voxels in a reference and at least one exploration ROI as a first subsample of voxels, clustering the first subsample of voxels, recording the first clustering assignment, randomly subsampling voxels in a reference and at least one exploration ROI as a second subsample of voxels, clustering the second subsample of voxels, recording the second clustering assignment, merging the first and second clustering assignments using consensus clustering, and labeling the clusters in the merged clustering assignment as candidate parcels or reference parcels based on their locations.

[0024] Again, in yet another embodiment, deriving an individualized map of ROI parcelization involves dividing spatially dissimilar clusters.

[0025] Again, and in further embodiments, the target score is calculated based on at least one factor from the group consisting of parcel size, parcel depth, parcel shape, parcel homogeneity, functional connectivity strength to a reference ROI, and network connectivity score.

[0026] Again, in another additional embodiment, the network connectivity score reflects the inverse correlation between the patient's brain's default mode network and dorsal attention network.

[0027] Again, in a further additional embodiment, a system for treating major depressive disorder includes a transcranial magnetic stimulation device, a neuronavigation device, a processor, and a memory containing a targeting application, where the targeting application acquires patient brain data, and the patient brain data includes a structural magnetic resonance imaging (sMRI) scan of the patient's brain and at least one functional magnetic resonance imaging (fMRI) scan. Using the sMRI scan and the at least one fMRI scan, a reference region of interest (ROI) and at least one search ROI are mapped to the patient's brain. The reference ROI describes the region that should be indirectly affected via the transcranial magnetic stimulation device, and the at least one search ROI describes at least one region that should be directly targeted by brain stimulation therapy. An individualized map of ROI parcelation is derived, where the individualized map of ROI parcelation describes the reference ROI as a plurality of reference parcels and the at least one search ROI as a plurality of candidate parcels. The relationship between the plurality of candidate parcels and the plurality of reference parcels is extracted, and based on the extracted relationship, a target score for each candidate parcel in the plurality of candidate parcels is calculated. Based on the target score, a target parcel is selected from the plurality of candidate parcels, and the processor is instructed to apply transcranial magnetic stimulation to the target parcel using the transcranial magnetic stimulation device and / or the neuronavigation device to treat major depressive disorder.

[0028] Again, in another additional embodiment, the target parcel is transmitted from a cloud computing platform to a neuronavigation system.

[0029] In yet another additional embodiment, a method of treating major depressive disorder comprises obtaining patient brain data, the patient brain data comprising a structural magnetic resonance imaging (sMRI) scan of the patient's brain and at least one functional magnetic resonance imaging (fMRI) scan, mapping a reference region of interest (ROI) and at least one search ROI to the patient's brain using the sMRI scan and the at least one fMRI scan, the reference ROI characterizing a region that is to be indirectly affected via brain stimulation therapy and the at least one search ROI characterizing at least one region that is to be directly targeted by brain stimulation therapy, deriving an individualized map of ROI parcellation, the individualized map of ROI parcellation characterizing the reference ROI as a plurality of reference parcels and the at least one search ROI as a plurality of candidate parcels, extracting relationships between the plurality of candidate parcels and the plurality of reference parcels, calculating a target score for a candidate parcel in the plurality of candidate parcels based on the extracted relationships, selecting a target parcel from the plurality of candidate parcels based on the target score, and treating major depressive disorder by applying transcranial magnetic stimulation to the target parcel using a transcranial magnetic stimulation device and / or a neuronavigation device.

[0030] In yet another additional embodiment, the transcranial magnetic stimulation is accelerated theta burst stimulation.

[0031] Additional embodiments and features are described, in part, in the following description and will, in part, be apparent to those of ordinary skill in the art in view of the discussion herein or can be learned by practice of the invention. A further understanding of the nature and advantages of the present invention can be realized by reference to the remaining portions of the specification and the drawings which form a part of the disclosure. The present invention provides, for example, the following: (Item 1) A targeted neuromodulation system, Processor and A memory containing a targeted application, wherein the targeted application is The method involves acquiring patient brain data, wherein the patient brain data comprises structural magnetic resonance imaging (sMRI) scans of the patient's brain and at least one functional magnetic resonance imaging (fMRI) scan. Mapping a reference region of interest (ROI) and at least one exploration ROI to the patient's brain using the sMRI scan and at least one fMRI scan, The aforementioned criteria ROI describe the areas that should be indirectly affected through brain stimulation therapy. The at least one exploration ROI describes at least one region that should be directly targeted by the brain stimulation therapy, The method involves deriving an individualized map for ROI parcelization, wherein the individualized map for ROI parcelization describes the criterion ROI as a plurality of criterion parcels, and describes the at least one search ROI as a plurality of candidate parcels. Extracting the relationship between the aforementioned multiple candidate parcels and the aforementioned multiple reference parcels, Based on the extracted relationships, the target score for each candidate parcel in the plurality of candidate parcels is calculated. Based on the target score, a target parcel is selected from the plurality of candidate parcels. To provide the aforementioned target parcel and The memory and the processor are instructed to perform the following actions. A targeted neuromodulation system equipped with the following features. (Item 2) The targeted application further instructs the processor to provide the brain stimulation therapy to the target parcel in order to treat the patient's mental condition, as described in item 1, the targeted neuromodulation system. (Item 3) The aforementioned mental condition is major depressive disorder, as described in item 2, a targeted neuromodulation system. (Item 4) The brain stimulation therapy is selected from the group consisting of transcranial magnetic stimulation, transcranial direct current stimulation, and electrical stimulation delivered via an implantable electrical stimulator, as described in item 2, for the targeted neuromodulation system. (Item 5) The targeted application further instructs the processor to discard fMRI scans that deviate from expected whole-brain network connectivity, as described in item 1. (Item 6) In order to derive the individualized map of the ROI parcelization, the targeted application further: The above criteria and randomly subsampling voxels within at least one exploration ROI, Clustering subsamples of the aforementioned voxels, Recording clustering assignments, Based on those locations, the clusters in the clustering assignment are labeled as candidate parcels or reference parcels. A targeted neuromodulation system according to item 1, which instructs the processor to perform the following: (Item 7) In order to derive the individualized map of the ROI parcelization, the targeted application further: The above criteria and randomly subsampling voxels within at least one exploration ROI as a first subsample of voxels, Clustering the first subsample of the voxels, Record the first clustering assignment, The above criteria and randomly subsampling voxels within at least one exploration ROI as a second subsample of voxels, Clustering a second subsample of the voxel, Record the second clustering assignment, Using consensus clustering, merge the first clustering assignment and the second clustering assignment, Based on those locations, the clusters in the merged clustering assignment are labeled as candidate parcels or reference parcels. A targeted neuromodulation system according to item 1, which instructs the processor to perform the following: (Item 8) To derive the individualized map of the ROI parcelization, the targeted application further instructs the processor to divide spatially dissimilar clusters, as described in item 1, the targeted neuromodulation system. (Item 9) The targeted neuromodulation system according to item 1, wherein the target score is calculated based on at least one factor from the group consisting of parcel size, parcel depth, parcel shape, parcel homogeneity, functional connectivity strength to the criterion ROI, and network connectivity score. (Item 10) The targeted neuromodulation system described in item 9, wherein the network connectivity score reflects the inverse correlation between the default mode network and the dorsal attention network in the patient's brain. (Item 11) The targeted neuromodulation system described in item 1, wherein the target parcel is transmitted from a cloud computing platform to a neuronavigation system. (Item 12) A targeted neuromodulation method, The method involves acquiring patient brain data, wherein the patient brain data comprises structural magnetic resonance imaging (sMRI) scans of the patient's brain and at least one functional magnetic resonance imaging (fMRI) scan. Mapping a reference region of interest (ROI) and at least one exploration ROI to the patient's brain using the sMRI scan and at least one fMRI scan, The aforementioned criteria ROI describe the areas that should be indirectly affected through brain stimulation therapy. The at least one exploration ROI describes at least one region that should be directly targeted by the brain stimulation therapy, The method involves deriving an individualized map for ROI parcelization, wherein the individualized map for ROI parcelization describes the criterion ROI as a plurality of criterion parcels, and describes the at least one search ROI as a plurality of candidate parcels. Extracting the relationship between the aforementioned multiple candidate parcels and the aforementioned multiple reference parcels, Based on the extracted relationships, the target score for each candidate parcel in the plurality of candidate parcels is calculated. Based on the target score, a target parcel is selected from the plurality of candidate parcels. To provide the aforementioned target parcel and A method for targeted neuromodulation, including the above. (Item 13) A targeted neuromodulation method according to item 12, further comprising providing the brain stimulation therapy to the target parcel in order to treat the mental condition of the patient. (Item 14) The aforementioned mental condition is major depressive disorder, and the targeted neuromodulation method is as described in item 13. (Item 15) The brain stimulation therapy is selected from the group consisting of transcranial magnetic stimulation, transcranial direct current stimulation, and electrical stimulation delivered via an implantable electrical stimulator, as described in item 13. (Item 16) A targeted neuromodulation method as described in item 12, further comprising discarding fMRI scans that deviate from expected whole-brain network connectivity. (Item 17) Deriving the individualized map for ROI parcelization is The above criteria and randomly subsampling voxels within at least one exploration ROI, Clustering subsamples of the aforementioned voxels, Recording clustering assignments, Based on those locations, the clusters in the clustering assignment are labeled as candidate parcels or reference parcels. A targeted neuromodulation method as described in item 12, including the method described in item 12. (Item 18) Deriving the individualized map for ROI parcelization is The above criteria and randomly subsampling voxels within at least one exploration ROI as a first subsample of voxels, Clustering the first subsample of the voxels, Record the first clustering assignment, The above criteria and randomly subsampling voxels within at least one exploration ROI as a second subsample of voxels, Clustering a second subsample of the voxel, Record the second clustering assignment, Using consensus clustering, merge the first clustering assignment and the second clustering assignment, Based on those locations, the clusters in the merged clustering assignment are labeled as candidate parcels or reference parcels. A targeted neuromodulation method as described in item 12, including the method described in item 12. (Item 19) The method for targeted neuromodulation according to item 12, wherein deriving the individualized map of the ROI parcelization includes dividing spatially dissimilar clusters. (Item 20) The targeted neuromodulation method according to item 12, wherein the target score is calculated based on at least one factor from the group consisting of parcel size, parcel depth, parcel shape, parcel homogeneity, functional connectivity strength to the criterion ROI, and network connectivity score. (Item 21) The targeted neuromodulation method according to item 12, wherein the network connectivity score reflects the inverse correlation between the default mode network and the dorsal attention network in the patient's brain. (Item 22) The targeted neuromodulation method according to item 12, wherein the target parcel is transmitted from a cloud computing platform to a neuronavigation system. [Brief explanation of the drawing]

[0032] The description and claims are presented as exemplary embodiments of the invention and should not be construed as a complete enumeration of the scope of the invention; they are better understood with reference to the following figures and data graphs.

[0033] [Figure 1] Figure 1 illustrates a targeted neuromodulation system according to one embodiment of the present invention.

[0034] [Figure 2] Figure 2 illustrates a target generator according to one embodiment of the present invention.

[0035] [Figure 3] Figure 3 is a flowchart illustrating a targeting process for generating individualized targets according to one embodiment of the present invention.

[0036] [Figure 4] Figure 4 is a flowchart illustrating a targeting process for evaluating expected network connectivity according to one embodiment of the present invention.

[0037] [Figure 5] Figure 5 is a flowchart illustrating a targeting process for deriving individualized ROI parcels according to one embodiment of the present invention.

[0038] [Figure 6] Figure 6 is a flowchart illustrating a targeting process for dividing spatially dissimilar clusters according to one embodiment of the present invention. [Modes for carrying out the invention]

[0039] Detailed explanation Mental health conditions and other neurological problems constitute a significant medical field of profound importance to both patients and society as a whole. For example, depression and suicidal ideation represent chronic public health problems. However, treatment for these conditions has traditionally relied on medications, and in some treatment-resistant cases, on surgery and / or electroconvulsive therapy (ECT). These methods can have significant side effects on both the mind and body. In contrast, a form of therapy called transcranial magnetic stimulation (TMS) has emerged as a viable non-invasive treatment option with few reported side effects.

[0040] Transcranial magnetic stimulation (TMS) involves applying a magnetic field to a specific area of ​​the brain to depolarize or hyperpolarize neurons in that area. Generally, the target area is selected by a medical professional based on its relationship to the patient's condition. For example, the dorsolateral prefrontal cortex (DLPFC) is known to be involved in major depressive disorder. However, the exact location of the DLPFC in an individual can be difficult to identify manually. Even when it can be identified, there may be specific sub-regions of the DLPFC that would actually be the most effective target for an individual patient, based on their unique brain characteristics. Furthermore, there may even be other areas in the brain that would provide a better stimulation target for the patient. Since every brain is at least slightly different, a personalized method of generating stimulation targets for an individual can provide better therapeutic outcomes.

[0041] An additional limitation of many TMS devices is the depth to which they can induce currents in a patient's brain. In many cases, TMS devices cannot target deep brain structures. However, there are numerous large networks throughout the brain that have been identified. For example, the default mode network (DMN) is a network thought to be involved in many tasks, such as rest during wakefulness. As a further example, the dorsal attention network (DAN) is thought to be important in the spontaneous orientation of visuospatial attention, and similarly, the ventral attention network (VAN) reorients attention towards prominent stimuli. Connectivity between different regions of the brain can provide an opportunity in TMS and other brain stimulation therapies for more surface brain structures that are strongly connected to deeper brain structures to be stimulated in order to bring about changes in those deeper brain regions. Furthermore, stimulation of connected networks can have a significant effect on structures within the network or otherwise connected to it. In particular, several networks, including (but not limited to) the DMN, DAN, and VAN, have specific experimentally determined relationships with major depressive disorder and suicidal ideation. Networks that relate to specific mental conditions that need treatment can be given additional priorities.

[0042] Given the complex nature of the brain, when applying neuromodulatory therapies (such as TMS), the location to which the stimulus is delivered can have a significant impact on the treatment outcome. Targeting, as discussed herein, refers to the process of identifying target structures in a patient's brain related to a stimulus in order to treat mental health conditions. While current targeting methods can yield viable targets, many conventional methods have significant drawbacks. For example, targeting is often performed using a single scan from the patient and cannot incorporate multiple scans over time. Due to fMRI scanning noise and limited test-retest reliability, deriving targets based on a single scan is more susceptible to noise and leads to a compromised level of target reliability. This reliability limitation can be even more pronounced with respect to methods employing voxel clustering for target detection, especially when the clustering procedure is highly sensitive to noise and signal loss. Furthermore, the clustering procedures used for this purpose do not necessarily consider the spatial relationships between voxels, which can lead to impractical results. Now, looking at the drawings, the systems and methods described herein seek to address these limitations and provide a more robust targeting framework for generating more effective personalized stimulation targets for more effective treatment. In many embodiments, targets generated using the systems and methods described herein are subsequently used as targets in neuromodulation therapies such as (but not limited to) TMS, transcranial direct current stimulation (tDCS), as implantation sites for one or more stimulation electrodes, and / or as targets for any number of different neuromodulation modalities as appropriate to the requirements of the specific application of embodiments of the present invention. Targeting systems according to embodiments of the present invention are discussed below.

[0043] (Targeted Neuromodulation Systems) A targeted neuromodulation system is capable of obtaining and / or accessing a scan of a patient's brain and identifying one or more personalized targets for brain stimulation therapy. In many embodiments, the targeted system may be integrated with other medical devices such as (but not limited to) a TMS device or a neuronavigation device. In various embodiments, the targeted system not only generates personalized targets but also includes, or is integrated with, a neuronavigation device and can identify where TMS coils should be positioned to correctly stimulate the targets. In many embodiments, the targeted neuromodulation system may further apply neuromodulation to the generated targets via a neuromodulation device such as (but not limited to) a TMS device, a tDCS device, an implantable neurostimulator, and / or any other neurostimulation device appropriate to the requirements of the specific application of the embodiments of the invention.

[0044] Now, looking at Figure 1, a targeted neuromodulation system according to one embodiment of the present invention is illustrated. The targeted neuromodulation system 100 includes a target generator 110. The target generator can be implemented using any number of different computing platforms, such as (but not limited to) desktop computers, laptops, server computers and / or clusters, smartphones, tablet PCs and / or any other computing platform capable of executing logical instructions appropriate to the requirements of the specific application of the embodiments of the present invention. In many embodiments, the target generator determines personalized and / or partially personalized targets in an individual's brain.

[0045] The targeted neuromodulation system 100 further includes an fMRI machine 120 and a TMS device 130. In many embodiments, the fMRI machine is capable of acquiring both structural and functional MRI images of the patient. The TMS device 130 can deliver brain stimulation therapy to a target selected by the target generator 110. However, as can be easily understood, alternative imaging modalities (e.g., computed tomography, positron emission tomography, electroencephalography, etc.) and alternative brain stimulation devices (e.g., implantable stimulators) can be used as appropriate to the requirements of the specific application of embodiments of the present invention; that is, alternatively, the targeted system 100 may not include its own imaging equipment and may receive imaging or other brain data from one or more imaging systems that are distinctly different from the neuromodulation system 100.

[0046] In many embodiments, the targeted neuromodulation system 100 includes a neuronavigation device that guides the delivery of brain stimulation therapy by the TMS device 130 to targets selected by the target generator 110. This neuronavigation device may be integrated with the targeted generator 110 or be separate from the targeted system 110 (not shown). In numerous embodiments, the neuronavigation device assists in delivering brain stimulation therapy to one or more targets generated by the targeted system, for example, by determining the rotational and translational positions of the stimulation coil and head, displaying images to guide the user to correctly position the stimulation coil, or, in addition, by using mechanical actuators such as a robotic arm to correctly position the stimulation coil. As can be easily understood, the specific functions of the neuronavigation device may vary depending on the type of neuromodulation being applied.

[0047] In many embodiments, the fMRI, TMS device, targeting system, and / or neuronavigation device are connected via network 140. The network may be a wired network, a wireless network, or any combination thereof. In fact, any number of different networks can be combined to connect the components. However, it is not a requirement that all components of the system communicate via the network. The target generator can function without operational connections between other components. In fact, a specific targeted neuromodulation system is illustrated in Figure 1, as can be easily understood, but any number of different system architectures can be used without departing from the scope or spirit of the invention. For example, in many embodiments, the targeted neuromodulation system may include different neuromodulation devices that provide different stimulation modalities.

[0048] When a targeting system is provided with patient brain data, it is possible to generate personalized targets. Turning to Figure 2, a target generator architecture according to one embodiment of the present invention is illustrated. The target generator 200 includes a processor 210. However, in many embodiments, more than one processor can be used. In various embodiments, the processor can be fabricated from any logic processing network such as (but not limited to) a central processing unit (CPU), a graphics processing unit (GPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), and / or any other circuit as appropriate to the requirements of the specific application of the embodiment of the present invention.

[0049] The target generator 200 further includes an input / output (I / O) interface 220. The I / O interface is capable of transferring data between connected components such as (but not limited to) displays, TMS devices, fMRI machines, other therapeutic devices and / or imaging devices, and / or any other computer components as appropriate to the requirements of the specific applications of the embodiments of the present invention. The target generator further includes a memory 230. The memory may be implemented using volatile memory, non-volatile memory, or any combination thereof. As can be easily understood, any machine-readable storage medium may be used as appropriate to the requirements of the specific applications of the embodiments of the present invention.

[0050] Memory 230 contains a targeting application 232, which can instruct the processor to perform various target generation processes. Memory 230 can also store patient brain data 234, which describes patient brain scans, including but not limited to structural and functional MRI scans. In many embodiments, memory 230 may further contain normative connectivity data 236, which describes expected generalized connectivity networks for a standard brain model.

[0051] While specific target generator architectures and target generators are discussed in accordance with the embodiments of the present invention described above, any number of different architectures and hardware designs can be used without departing from the scope or spirit of the invention. For example, in many embodiments, different stimulation modalities can be used. In various embodiments, transcranial direct current stimulation is used. In many embodiments, implantable electroneurostimulators are used to directly stimulate brain tissue. Target generation processes for generating individualized stimulation targets are discussed in more detail below.

[0052] (Generation of individualized stimulus targets) Some brain stimulation methods may function with some degree of efficacy without individualized, precise targeting. However, attempting to deliver stimulation to specific areas of the brain and maximize the therapeutic effect on an individual is highly beneficial. Various existing methodologies that attempt to generate individualized targets cannot fully consider existing network connectivity within the brain and / or simply cluster areas within the brain. The target identification process described herein can provide stimulation targets with greater accuracy on an individual basis, based on their personal brain network connectivity.

[0053] Now, looking to Figure 3, a flowchart of a target identification process for generating personalized stimulus targets for a patient according to one embodiment of the present invention is illustrated. Process 300 includes acquiring patient brain data (310). As noted above, patient brain data may include structural and / or functional brain scans. In many embodiments, patient brain data includes both structural and functional MRI scans. In various embodiments, multiple structural and / or functional MRI scans may be included in the patient brain data, captured at different times. MRI scans can be checked for quality. In various embodiments, scan quality is checked using commonly used fMRI quality control (QC) tools and / or by matching whole-brain connectivity structures to expected normative connectivity structures. A target identification process for performing quality control using expected normative connectivity structures is discussed in more detail in the following section with reference to Figure 4.

[0054] Process 300 further includes mapping exploration and reference areas of interest (ROIs) onto the patient's brain (320). ROIs can be any brain structure, substructure, or group of structures of interest within the brain, as determined by the user. Reference ROIs are ROIs that describe the regions in which brain stimulation therapy should indirectly affect. In contrast, exploration ROIs describe regions in which personalized brain stimulation targets may exist. Thus, applying stimulation to personalized brain stimulation targets within exploration ROIs affects the reference ROIs. ROIs can consist of one or more voxels, depending on the size of a particular ROI. In some embodiments, ROIs may overlap. In many embodiments, a brain map is used to map ROIs onto a structural scan of the patient's brain. In various embodiments, target ROIs are indicated by applying a mask to the brain structure, where the mask flags the desired target ROIs. In various embodiments, the masks may have different weighting metrics for different desired target ROIs. ROIs can also be mapped onto a functional scan. In various embodiments, a structural scan can be used as a template for aligning other functional scans. In various embodiments, multiple fMRI scans can be combined by integrating functional connectivity data to produce a “combined fMRI.” Thus, multiple fMRIs of patients acquired using similar or the same protocol can be merged to provide a more complete picture of an individual’s network connectivity.

[0055] fMRI signals (i.e., activity levels for a specific voxel or set of voxels over time) are extracted from the ROI (330). Voxels with poor signal quality can be excluded (335) and / or discarded. In many embodiments, poor signal quality may be caused by various scanning device limitations, scanning parameters, and / or movement during the scanning process. In various embodiments, poor signal quality is detected by calculating the voxel-level signal-to-noise ratio (SNR). By removing low-quality signals from consideration, targeting accuracy can be significantly increased. An individualized map of ROI parcelization is derived from the extracted fMRI signals (340). The individualized map of ROI parcelization describes multiple parcels (or groups of adjacent voxels). Candidate parcels are derived from the exploration ROI and constitute candidate targets with respect to brain stimulation therapy. Reference parcels are derived from the reference ROI and constitute the area of ​​the reference ROI that would be affected by the stimulation. A method for deriving ROI parcelization according to an embodiment of the present invention will be discussed in more detail below with reference to Figure 5.

[0056] The relationship between a potential candidate parcel and a reference parcel is extracted (350), and a target score is generated for the potential candidate parcel (360). In many embodiments, the functional connectivity between the two parcels (candidate and reference) is measured, and the target score is based on the strength of the functional connectivity. A target that has stronger functional connectivity to the reference ROI (e.g., any parcel within the reference ROI) and therefore has a stronger influence on the function of the reference may be given a higher target score. In many embodiments, other factors, including (but not limited to) parcel depth, other functions of the parcel and / or surrounding brain structures, parcel size, shape, and homogeneity, and fit to known / expected system / network level connectivity profiles, may contribute to the score, and a number of other factors may be considered appropriate for the requirements of the specific application of embodiments of the present invention. For example, a larger target may not have strong functional connectivity to the reference, but may be much larger and therefore easier to target using a specific brain stimulation device.

[0057] As an additional embodiment, a network connectivity score may be included, which incorporates network-level expectations regarding the brain region to be targeted. Where the literature suggests that a particular brain structure or network (i.e., a set of structures) is involved in a particular condition, parcels that strongly interact with that brain structure / network may be weighted more heavily as potential targets. As described above, the DLPFC is thought to be strongly associated with clinical depression and suicidal ideation, and therefore, targets that strongly interact with that region may be more desirable based on current expectations.

[0058] In many embodiments, the difference between the functional connectivity to the DAN and DMN can be calculated for each parcel. The inverse correlation between the DAN and DMN can be used as the network connectivity score, with a higher degree of inverse correlation suggesting a stronger candidate parcel. In various embodiments, the difference between the functional connectivity to the VAN and DMN is calculated and used as the network connectivity score. In some embodiments, a weighted average of the network connectivity scores for different networks can be used as the overall network connectivity score, with the weights based on the relevance of a particular network to the conditions in question. In various embodiments, functional connectivity is calculated per voxel and averaged to obtain an overall parcel score.

[0059] A personalized target parcel is then selected from a group of candidate parcels based on the target score (370). In many embodiments, the candidate parcel with the highest score is selected. In many embodiments, a center with respect to the target parcel is extracted to more precisely determine the TMS coil matching (380). In many embodiments, the center is calculated by averaging the positions of each voxel that make up the target candidate.

[0060] A specific method for generating personalized targets is illustrated in Figure 3, but as can be easily understood, any number of different modifications can be made without departing from the scope or spirit of the invention. For example, not all quality control steps need to be performed, or not all parameters need to be considered, in order to generate a network score that is appropriate to the requirements of the specific application of the embodiments of the invention. Furthermore, different weights may be given to different parameters in relation to their relative importance in calculating the target score. Additional explanations of the various steps of the above process can be found below.

[0061] (Network connectivity quality control) Patient brain data may include one or more fMRI scans, however, there is little immediate guarantee that the data will be of high quality (e.g., have a high SNR). Measurement noise and head movement are known causes of fMRI reliability limitations and are therefore assumed and partially addressed as common practices during data preprocessing. However, in some cases, poor scan quality and / or preprocessing errors may be missed, which can lead to deriving targets based on incorrect brain functional connectivity structures. Additional measures are desirable to prevent making clinical decisions based on erroneous data.

[0062] Under the highly acceptable assumption of a high probability of overall integrity in the system-level organization of the human brain, matching measured whole-brain connectivity to expected normative connectivity can reduce errors from poor scans and, in some cases, provide medical professionals with the attention of the presence of atypical brains for further manual scrutiny. In many embodiments, identified poor scans are discarded. Turning to Figure 4, a target identification process for measuring expected network connectivity according to one embodiment of the present invention is illustrated.

[0063] Process 400 involves assigning each voxel to a given network (410). Many large brain networks are known and mapped based on large samples of the population, including (but not limited to) the visual network (VIS), sensorimotor network (SMN), dorsal attention network (DAN), ventral attention network (VAN), limbic network, frontoparietal control network (FPCN), and default mode network (DMN). These networks can be overlaid on the patient's MRI so that each voxel is assigned to at least one network. For each voxel pair, a functional connectivity score (FC) can be calculated (420), which represents the strength of connectivity between voxels in fMRI (including combined fMRI). All FC values ​​relating voxels assigned to the same network are averaged (430) to obtain an "in-FC" value.

[0064] The "inter-FC" value is obtained by averaging all FC values ​​(440) that relate voxels from different networks. The inter-FC value is subtracted from the intra-FC value (450) to obtain the network fit for the voxel. Individual voxels may vary in their network associations due to expected individual differences in brain function and structure, but the average network fit across voxels (called the network quality control (QC) metric) is expected to remain positive (intra-FC > inter-FC). If the network QC metric is not significantly positive (meaning inter-FC >= intra-FC), this is an indicator that there may be a problem with either the scanning, preprocessing procedure, or a significantly atypical structural problem occurring in the patient's brain. The statistical significance of the network QC metric can be obtained by randomly rearranging the data while considering the spatial location of the voxels and repeating the network QC estimation process. In this way, the ingestion fMRI can be swept for quality. If an fMRI scan is flagged as having poor overall network fit, this allows for a more detailed examination of the data by medical professionals, preventing the deriving of targets from misinformation.

[0065] A specific method for QC control based on brain network connectivity is illustrated in Figure 4 according to one embodiment of the present invention, but network connectivity can be used as control using any of several different algorithms that are appropriate to the requirements of the specific application of the embodiment of the present invention. Ensuring high-quality data can increase the accuracy of the generated target. A discussion of how to parcel the brain into individualized ROIs is discussed further below.

[0066] (ROI parcelization) While the overall structure of the human brain is relatively conserved across individuals, it is well known that each person possesses unique brain functions and neural networks based on any number of environmental and genetic factors. Therefore, simply dividing the brain based on a standardized model can lead to inaccurate or insufficient results. Attempts to parcel the brain into ROIs have been made in the past, but the specific methodologies used often fail to robustly cluster voxels in an effective manner. Turning to Figure 5, a target identification process for deriving an individualized map of ROI parcelation according to one embodiment of the present invention is discussed.

[0067] Process 500 includes randomly subsampling a certain percentage of all voxels (510). In many embodiments, the percentage is any number greater than 80%, however, depending on the amount of available data and computation, the number may be less than 80%. The fMRI signals in the subsampled voxels are then clustered based on signal similarity (520). Any number of different clustering processes can be used, including (but not limited to) agglomerative (hierarchical) clustering, cluster identification via connectivity kernel (CLICK) clustering, k-means clustering, and / or spectral clustering. In some embodiments, clustering methods that incorporate spatial information (e.g., spatially constrained spectral clustering) can be used.

[0068] Clustering assignments are recorded (530), and new random subsamplings (510) are obtained. The process can be repeated multiple times to increase accuracy. In many embodiments, this process is repeated 100 times or more to ensure sufficient data, but fewer may be sufficient. The subsample clustering solutions are then merged (540). In many embodiments, they are merged using a consensus clustering approach. Any resulting spatially disparate clusters can then be divided into subclusters (550). The clusters (and any subclusters) are then labeled as either criterion or exploration parcels based on their location in the criterion and exploration ROI (560).

[0069] By repeatedly subsampling and clustering, noise in neural signals can be taken into account, and a more accurate representation of an individual's true brain connectivity can emerge. Furthermore, multiple fMRI scans can be performed through this process, and the resulting clusters can be integrated using consensus clustering. In this way, multiple fMRIs, including those taken on different days, can contribute to the overall dataset used for targeting. In various embodiments, spatially disparate clusters can be avoided by using a spatially constrained clustering process. However, depending on the requirements of the specific application of the embodiments of the present invention, it may be desirable to select a spatially unconstrained clustering process that can result in spatially disparate clusters. A target identification process for partitioning spatially disparate clusters according to one embodiment of the present invention is illustrated in Figure 6.

[0070] Process 600 includes recording the spatial position of each voxel in spatially disparate clusters (610). A distance matrix is ​​generated, showing the physical distance between every other voxel (620), which is then converted into a graph representation (630). Long edges in the graph (edges exceeding a given threshold) are pruned (640), resulting in a partially connected graph, which is then divided into connected subgraphs (components), where such are encountered. The set of voxels in each connected component can then be defined as a distinct cluster (650).

[0071] Thus, disparate clusters can be divided and used separately as potential candidate parcels for stimuli. In many embodiments, the problem with these disparate clusters is that the "center" of a disparate cluster may be outside any part of the disparate cluster and not anywhere in the vicinity of a viable target location.

[0072] Processes 300, 400, 500, and / or 600, and their variations, may be performed by a target identification system to provide a target parcel, which may then be archived, stored for later use, transmitted to a neuronavigation device, used in further analysis, or combined (e.g., by joining or crossing) with one or more other target parcels to result in a composite target parcel. The target identification system may be distinctly different from the neuronavigation device, separate from it, and / or integrated with it, or partially integrated with it. The target identification system may be implemented on a cloud computing platform, on a computing platform local to the clinical site, on a computing platform embedded in or part of the neuronavigation device, or on any combination of such platforms.

[0073] While specific methods of ROI parcelization are discussed above, many different methods can be implemented according to many different embodiments of the present invention, including (but not limited to) using different specific clustering processes and / or utilizing different thresholds and parameters. Therefore, it should be understood that the present invention may be practiced in ways other than those specifically described without departing from the scope and spirit of the invention. Accordingly, embodiments of the present invention should be considered illustrative and not restrictive in all respects. Thus, the scope of the present invention should be determined not by the illustrated embodiments, but by the appended claims and their equivalents.

Claims

1. A targeted neuromodulation system, wherein the targeted neuromodulation system is Processor and Memory containing targeted applications and Equipped with, The aforementioned targeting application is The method involves acquiring patient brain data, wherein the patient brain data includes structural magnetic resonance imaging (sMRI) scans and at least one functional magnetic resonance imaging (fMRI) scan of the patient's brain. Mapping a reference region of interest (ROI) and at least one exploration ROI to the patient's brain using the sMRI scan and at least one fMRI scan, wherein mapping the reference ROI and at least one exploration ROI includes using a predefined brain map to map each of the reference ROI and at least one exploration ROI onto the sMRI scan of the patient's brain, and indicating each of the reference ROI and at least one exploration ROI by applying a mask to the sMRI scan of the patient's brain, wherein the sMRI scan is used as a template for aligning the at least one fMRI scan, the reference ROI describes a region to be indirectly affected via brain stimulation therapy, and the at least one exploration ROI describes at least one region to be directly targeted by brain stimulation therapy. The method involves deriving an individualized map of ROI parcelization, which is performed by randomly subsampling voxels in the reference ROI and the at least one search ROI, clustering the voxel subsamples, recording the clustering assignments, labeling the clusters in the clustering assignments as candidate parcels or reference parcels based on their locations, and dividing spatially dissimilar clusters, thereby defining a plurality of candidate parcels and a plurality of reference parcels. By measuring the functional connectivity value obtained from at least one fMRI scan, the relationship between the plurality of candidate parcels and the plurality of reference parcels is extracted. Based on the extracted relationships, the calculation involves calculating a target score for the candidate parcels in the plurality of candidate parcels, the calculation including applying weights to a network connectivity score that reflects the inverse correlation between the default mode network of the patient's brain and the dorsal attention network of the patient's brain, and applying weights to at least one factor from the group consisting of parcel size, parcel depth, parcel shape, and parcel homogeneity. Based on the target score, a target parcel is selected from the plurality of candidate parcels. To provide the aforementioned target parcel and A targeted neuromodulation system that instructs the processor to perform the following.

2. The targeted neuromodulation system according to claim 1, wherein the targeted application further instructs the processor to deliver the brain stimulation therapy to the targeted parcel in order to treat the patient's mental condition.

3. The targeted neuromodulation system according to claim 2, wherein the aforementioned mental condition is major depressive disorder.

4. The targeted neuromodulation system according to claim 2, wherein the brain stimulation therapy is selected from the group consisting of transcranial magnetic stimulation, transcranial direct current stimulation, and electrical stimulation delivered via an implantable electrical stimulator.

5. The targeted neuromodulation system according to claim 1, wherein the targeted application further instructs the processor to discard fMRI scans that deviate from expected whole-brain network connectivity.

6. In order to derive the individualized map of the ROI parcelization, the targeting application is: Randomly subsampling voxels in the reference ROI and the at least one search ROI as a first subsample of voxels, Clustering the first subsample of the voxels, Record the first clustering assignment, Randomly subsampling voxels in the reference ROI and the at least one search ROI as a second subsample of voxels, Clustering a second subsample of the voxel, Record the second clustering assignment, Using consensus clustering, merge the first clustering assignment and the second clustering assignment, Based on those locations, the clusters in the merged clustering assignment are labeled as candidate parcels or reference parcels. The targeted neuromodulation system according to claim 1, further instructing the processor to perform the following:

7. The targeted neuromodulation system according to claim 1, wherein the target parcel is transmitted from a cloud computing platform to a neuronavigation system.

Images