Intervention optimization method, device and storage medium for transcranial electrical stimulation

Abstract

This invention discloses a method, device, and storage medium for optimizing transcranial electrical stimulation (TCS) intervention. The method includes: acquiring multimodal brain imaging data of a target subject; determining the brain network of the target subject based on the multimodal brain imaging data; wherein the brain network of the target subject reflects the functional and structural connectivity relationships between different brain regions of the target subject; acquiring a stimulation target for TCS of the target subject; determining multiple candidate brain regions based on the stimulation target and the brain network of the target subject; determining the fusion connectivity between the multiple candidate brain regions based on the multimodal brain imaging data; and determining a target brain region from the multiple candidate brain regions based on the fusion connectivity between the multiple candidate brain regions.

Description

Technical Field

[0001] This invention relates to the fields of brain cognitive science research and medical assistive devices, and more specifically, to a method, device, and storage medium for optimizing transcranial electrical stimulation intervention. Background Technology

[0002] Transcranial alternating current stimulation (tACS) directly affects the activity of neurons in the cerebral cortex by applying alternating current of a specific frequency and intensity to the scalp surface, thereby regulating brain activity.

[0003] Currently, traditional methods for determining transcranial electrical stimulation (TCS) target areas involve spatial localization of brain regions using group-level brain atlases and characterizing the anatomical and functional properties of connections between brain regions using single-modality brain imaging data. However, spatial localization using group-level brain atlases is prone to inaccuracies, and single-modality brain imaging data struggles to adequately characterize the anatomical and functional properties of connections between brain regions. Consequently, traditional methods for determining TCS target areas are insufficient for accurately identifying TCS target areas within individualized brain structures, thus affecting the accuracy and stability of TCS intervention effects. Summary of the Invention

[0004] One objective of this disclosure is to provide an optimized intervention scheme for transcranial electrical stimulation (TCS) to accurately acquire individualized TCS target areas in the brain, thereby improving the accuracy and stability of the TCS intervention effect.

[0005] According to a first aspect of this disclosure, an intervention optimization method for transcranial electrical stimulation is provided, the method comprising:

[0006] Acquire multimodal brain imaging data of the target object;

[0007] Based on the multimodal brain imaging data, the brain network of the target object is determined; wherein, the brain network of the target object is used to reflect the functional and structural connectivity between different brain regions of the target object;

[0008] Obtain the stimulation target for transcranial electrical stimulation of the target object;

[0009] Based on the stimulation target and the brain network of the target object, multiple candidate brain regions are determined;

[0010] Based on the multimodal brain functional imaging data, determine the fusion connectivity between the multiple candidate brain regions;

[0011] The target brain region is determined from the plurality of candidate brain regions based on the fusion connections between the plurality of candidate brain regions.

[0012] Optionally, the multimodal brain imaging data includes resting-state functional magnetic resonance imaging (fMRI) data, task-oriented fMRI data, and diffusion-weighted magnetic resonance imaging (DMRI) data. The step of determining the fusion connectivity between the plurality of candidate brain regions based on the multimodal brain imaging data includes:

[0013] Based on the resting-state functional magnetic resonance imaging (fMRI) data, determine the resting-state functional connectivity between the plurality of candidate brain regions;

[0014] Based on the task-state functional magnetic resonance imaging data, determine the task-state functional connectivity between the plurality of candidate brain regions;

[0015] Based on the diffusion magnetic resonance imaging data, the structural connections between the plurality of candidate brain regions are determined;

[0016] The fusion connectivity between the multiple candidate brain regions is determined based on the resting-state functional connectivity, the task-state functional connectivity, and the structural connectivity among the multiple candidate brain regions.

[0017] Optionally, determining the target brain region from the plurality of candidate brain regions based on the fusion connectivity between the plurality of candidate brain regions includes:

[0018] Based on the connection strength of the fusion connection between the multiple candidate brain regions, determine the two brain regions corresponding to the fusion connection with the target connection strength.

[0019] The two brain regions corresponding to the fusion connection of the target connectivity strength are taken as the target brain regions.

[0020] Optionally, determining the brain network of the target object based on the multimodal brain imaging data includes:

[0021] Acquire brain function imaging data of the population;

[0022] Based on the brain functional imaging data of the group, the group-level brain node functional signal sequence of the group was determined;

[0023] The functional signal sequences of the aforementioned group of horizontal brain nodes are decomposed to obtain a reference cortical network;

[0024] The reference cortical network is updated based on the multimodal brain imaging data to obtain the brain network of the target object.

[0025] Optionally, after determining the target brain region from the plurality of candidate brain regions, the method further includes:

[0026] Based on the target brain region, a set of candidate electrodes is determined; wherein, the set of candidate electrodes includes the position and stimulation parameters of each candidate electrode in the head coordinate system of the target object;

[0027] Multiple high-density stimulation spatial arrays are randomly selected from the candidate electrode set;

[0028] For any of the high-density stimulation spatial arrays, the brain electric field distribution corresponding to the high-density stimulation spatial array is determined based on the electrical conduction model of the high-density stimulation spatial array and the target object.

[0029] The target stimulation matrix is ​​determined based on the electric field intensity and focusing degree of the brain field distribution corresponding to each of the high-density stimulation spatial arrays in the target brain region.

[0030] Optionally, determining the candidate electrode set based on the target brain region includes:

[0031] The initial electrode position corresponding to the target brain region is determined according to the preset correspondence between stimulation electrodes and brain regions; wherein, the correspondence between stimulation electrodes and brain regions is used to reflect the correspondence between the electrodes that apply stimulation to the head and the brain regions that receive the stimulation.

[0032] A set of candidate electrodes is determined based on a preset distance interval and the initial electrode position.

[0033] Optionally, the multimodal brain imaging data includes brain structural imaging data, and after determining the target stimulus matrix, the method further includes:

[0034] Based on the brain structure imaging data of the target object, a head model of the target object is determined;

[0035] Based on the head model of the target object, an electrical stimulation positioning cap adapted to the head model is determined; wherein, the electrical stimulation positioning cap is provided with actual stimulation target points corresponding to the target stimulation matrix;

[0036] Based on the target stimulation matrix, transcranial electrical stimulation is performed on the target object through the electrical stimulation positioning cap.

[0037] Optionally, the multimodal brain imaging data includes brain structural imaging data, and determining the electrical conduction model of the target object includes:

[0038] Based on the brain structure imaging data of the target object, the brain tissue structure layer of the target object is determined;

[0039] The electrical conduction model of the target object is determined based on the conductivity value corresponding to each brain tissue structural layer.

[0040] According to a second aspect of this disclosure, an intervention optimization device for transcranial electrical stimulation is also provided, including a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions, when executed by the processor, implementing the steps of the intervention optimization method for transcranial electrical stimulation as described in the first aspect.

[0041] According to a third aspect of this disclosure, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the intervention optimization method for transcranial electrical stimulation as described in the first aspect.

[0042] One beneficial effect of this disclosure is that by acquiring multimodal brain imaging data of a target object, and determining the target object's brain network based on the multimodal brain imaging data, the target object's brain network is used to reflect the functional and structural connectivity between different brain regions of the target object's brain. This allows for the acquisition of stimulation targets for transcranial electrical stimulation (TCS) of the target object. Based on the stimulation targets and the target object's brain network, multiple candidate brain regions are determined. Based on the multimodal brain imaging data, fusion connections between the multiple candidate brain regions are determined. Based on these fusion connections, the target brain region is determined from among the multiple candidate brain regions. Constructing the target object's brain network for spatial localization of brain regions improves the accuracy of brain region localization. Furthermore, by characterizing the anatomical and functional properties of the connections between multiple candidate brain regions through fusion connections, and then determining the target brain region from among the multiple candidate brain regions based on these fusion connections, precise acquisition of TCS target areas under individualized brain differences can be achieved, improving the accuracy and stability of the intervention effect of TCS.

[0043] Other features and advantages of the embodiments of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0044] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments of the present disclosure and, together with their description, serve to explain the principles of the embodiments of the present disclosure.

[0045] Figure 1 This is a hardware configuration block diagram of an intervention optimization device for transcranial electrical stimulation that can be used to implement the embodiments of this disclosure;

[0046] Figure 2 This is a flowchart illustrating an intervention optimization method for transcranial electrical stimulation according to one embodiment;

[0047] Figure 3 This is a schematic diagram of the head model of a target object according to one embodiment;

[0048] Figure 4 This is a schematic diagram of an electrical stimulation positioning cap according to one embodiment;

[0049] Figure 5 This is a schematic diagram of a high-density transcranial electrical stimulator according to one embodiment;

[0050] Figure 6 This is a schematic diagram of the structure of an intervention optimization device for transcranial electrical stimulation according to one embodiment. Detailed Implementation

[0051] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention.

[0052] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0053] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0054] In all the examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0055] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0056] <Hardware Configuration>

[0057] Figure 1 This is a hardware configuration block diagram of an intervention optimization device for transcranial electrical stimulation that can be used to implement embodiments of the present disclosure.

[0058] like Figure 1 As shown, the device 1000 may include a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600, a speaker 1700, a microphone 1800, etc.

[0059] The processor 1100 executes computer programs, which can be written using instruction sets of architectures such as x86, Arm, RISC, MIPS, and SSE. The memory 1200 includes, for example, ROM (Read-Only Memory), RAM (Random Access Memory), and non-volatile memory such as a hard disk. The interface device 1300 includes, for example, a USB interface and a headphone jack. The communication device 1400 is capable of wired or wireless communication. The communication device 1400 may include at least one short-range communication module, such as any module for short-range wireless communication based on protocols such as Hilink, WiFi (IEEE 802.11), Mesh, Bluetooth, ZigBee, Thread, Z-Wave, NFC, UWB, and LiFi. The communication device 1400 may also include a long-range communication module, such as any module for WLAN, GPRS, or 2G / 3G / 4G / 5G long-range communication. The display device 1500 is, for example, an LCD screen or a touch screen. The input device 1600 may include, for example, a touchscreen or a keyboard. The speaker 1700 is used to output audio signals. Microphone 1800 is used to capture audio signals.

[0060] The device 1000 can be any type of electronic device with computing capabilities, without limitation. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. It can also be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc.

[0061] Those skilled in the art should understand that, although in Figure 1 The present invention illustrates multiple devices of a transcranial electrical stimulation intervention optimization device 1000. However, the transcranial electrical stimulation intervention optimization device 1000 of the present disclosure may involve only some of the devices, for example, only the processor 1100 and the memory 1200. This is well known in the art and will not be described further here.

[0062] In this embodiment, the memory 1200 stores computer program instructions that control the processor 1100 to perform an intervention optimization method for transcranial electrical stimulation according to any embodiment of this disclosure. Those skilled in the art can design these instructions based on the disclosed scheme. How these instructions control the processor 1100 to operate is well known in the art and will not be described in detail here.

[0063] <Method Implementation>

[0064] Figure 2 This is a flowchart illustrating an intervention optimization method for transcranial electrical stimulation according to one embodiment.

[0065] like Figure 2 As shown, the transcranial electrical stimulation intervention optimization method of this embodiment may include the following steps S210 to S260:

[0066] Step S210: Obtain multimodal brain imaging data of the target object.

[0067] In this embodiment, transcranial alternating current stimulation (tACS) is a widely used method for improving cognitive function and intervening in neuropsychiatric disorders. Its effectiveness depends on the dose of electrical stimulation received by the cerebral cortex. By applying alternating current of a specific frequency and intensity to the scalp, the activity of neurons in the cerebral cortex can be directly affected, thereby regulating brain activity. Simultaneously, tACS can stimulate multiple brain regions at the same time, causing neural oscillations that synchronize neural activity between different brain regions, helping to coordinate information transmission and collaborative work between brain regions. However, due to individual differences in the brain, pre-testing is required to determine the target brain region for tACS intervention before performing tACS on the target subject.

[0068] During the pre-testing of transcranial electrical stimulation, multimodal brain imaging data of the target subjects were acquired simultaneously. This multimodal brain imaging data could include magnetic resonance imaging (MRI) data, computed tomography (CT) images, electroencephalogram (EEG) data, etc.

[0069] Multimodal brain imaging data includes brain functional imaging data and brain structural imaging data.

[0070] Brain functional imaging data can be one or more of the following: functional magnetic resonance imaging (fMRI) data, electroencephalogram (EEG) data, magnetic resonance spectroscopy (MRS) data, arterial spin labeling perfusion imaging (ASL) data, and transcranial functional brain atlas (fTBA) data.

[0071] Brain structural imaging data can be one or more of the following: structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), magnetic resonance spectroscopy (MRS), magnetic resonance elastography (MRE), and high-resolution T2-weighted MRI.

[0072] In the example of multimodal brain imaging data being magnetic resonance imaging data, brain functional imaging data can be functional magnetic resonance imaging data, and brain structural imaging data can be structural magnetic resonance imaging data, diffusion tensor imaging (DTI) imaging data, etc.

[0073] Structural magnetic resonance imaging (MRI) data can provide high-resolution three-dimensional images of the brain, displaying its anatomical structures such as gray matter, white matter, and ventricles. Functional magnetic resonance imaging (fMRI) data can reflect brain activity during specific tasks or states by measuring changes in blood oxygen level-dependent (BOLD) signals. Diffusion-induced magnetic resonance imaging (DTI) data can show the course and integrity of nerve fibers in the brain, helping to understand the connections between different brain regions.

[0074] Step S220: Determine the brain network of the target object based on the multimodal brain imaging data.

[0075] In this embodiment, the brain network of the target subject can be determined based on functional magnetic resonance imaging data.

[0076] The brain network of the target subject can be used to reflect the functional connectivity between different brain regions of the target subject's brain. For example, brain regions 1 and 2 correspond to cognitive functions, while brain regions 3 and 4 correspond to motor functions.

[0077] In some embodiments, step S220, which involves determining the brain network of the target object based on the multimodal brain imaging data, includes steps S310 to S340.

[0078] Step S310: Obtain brain function imaging data of the group.

[0079] In this embodiment, the brain functional imaging data can be functional magnetic resonance imaging data, or other types of functional imaging data, without limitation. This application embodiment uses functional magnetic resonance imaging data as an example of brain functional imaging data.

[0080] Step S320: Determine the group-level brain node functional signal sequence of the group based on the brain functional imaging data of the group.

[0081] For example, given functional magnetic resonance imaging (fMRI) data of M subjects, N subjects (M > N ≥ 100) are randomly selected from the M subjects, and the time series T corresponding to V vertices in the brain of each subject is extracted. T × V constitutes the brain node functional signal sequence of a single subject. The brain node functional signal sequences of the N subjects are spliced ​​together to obtain the group-level brain node functional signal sequence, denoted as X. NT×V This set of horizontal brain node functional signal sequences serves as sample data for constructing a reference cortical network.

[0082] Step S330: Decompose the functional signal sequence of the group-level brain nodes to obtain the reference cortical network.

[0083] Continuing the example above, after obtaining the sample data, the nonnegative matrix factorization method is used to analyze the group-level brain node functional signal sequence X. NT×V The network is decomposed into a network time matrix UNT×K and a probability matrix VK×V, where K is the number of networks generated, and the probability matrix VK×V represents the probability distribution of the cortical brain regions obtained through a single calculation. Then, N subjects are randomly selected multiple times, resulting in multiple probability matrices. Finally, spectral clustering is used to obtain the final probability matrices of all brain nodes corresponding to each network from these probability matrices, thus obtaining the reference cortical network.

[0084] Reference cortical networks can be used to reflect the functional connectivity between different brain regions of a reference brain (standardized brain). Reference cortical networks belong to the standardized division of the cerebral cortex into brain regions, and are constructed based on population-based brain imaging data, reflecting typical patterns and variability in brain structure and function within the population.

[0085] Step S340: Update the reference cortical network based on the multimodal brain imaging data to obtain the brain network of the target object.

[0086] In this embodiment, a reference cortical network is used as a priori template, and then the priori template is updated based on the functional magnetic resonance imaging data in the multimodal brain imaging data of the target object to obtain the brain network of the target object.

[0087] Step S230: Obtain the stimulation target for transcranial electrical stimulation of the target object.

[0088] In this embodiment, the stimulation target can be a specific brain function that needs to be intervened by transcranial electrical stimulation. The stimulation target can be, for example, cognitive function, motor function, etc.

[0089] Step S240: Based on the stimulation target and the brain network of the target object, determine multiple candidate brain regions.

[0090] For example, if the stimulus target is motor function, based on the motor function and the brain network of the target object, the brain regions 3 and 4 corresponding to the motor function are calculated in reverse. Brain regions 3 and 4 are the candidate brain regions.

[0091] Step S250: Determine the fusion connectivity between the multiple candidate brain regions based on the multimodal brain imaging data.

[0092] In this embodiment, the fusion connection can be a connection obtained by fusing functional and structural connections, which can reflect the structural and functional interactions between brain regions. Structural connections are the physical connections between different brain regions, typically composed of white matter fiber bundles. Structural connections are the anatomical basis for signal transmission between brain regions. Functional connections are the functional interrelationships between different brain regions, reflecting the synchronicity of brain regions' activity patterns when performing specific tasks or under specific conditions.

[0093] In some embodiments, the multimodal brain imaging data includes resting-state functional magnetic resonance imaging (fMRI) data, task-based fMRI data, and diffusion magnetic resonance imaging (DMRI) data.

[0094] Resting-state functional magnetic resonance imaging (fMRI) data reflects the functional connectivity between brain regions when the brain is at rest (without external stimuli). Task-oriented fMRI data reflects the functional connectivity between brain regions when the brain is performing a specific task. Diffusion-weighted magnetic resonance imaging (DMRI) data reflects the physical connectivity between different brain regions.

[0095] In these embodiments, the step S250, which involves determining the fusion connectivity between the plurality of candidate brain regions based on the multimodal brain imaging data, includes steps S2501 to S2504.

[0096] Step S2501: Determine the resting-state functional connectivity between the plurality of candidate brain regions based on the resting-state functional magnetic resonance data.

[0097] In this embodiment, resting-state functional connectivity can be defined as the temporal correlation of neural activity among multiple candidate brain regions in a resting state. The process of determining resting-state functional connectivity between multiple candidate brain regions based on resting-state functional magnetic resonance imaging (fMRI) data typically involves: first, extracting the BOLD signals of the candidate brain regions from the resting-state fMRI data; then, using statistical methods (such as the Pearson correlation coefficient) to analyze the temporal correlation between these BOLD signals; and finally, determining whether functional connectivity exists between the candidate brain regions based on the correlation strength. If the BOLD signals of two or more candidate brain regions exhibit highly synchronous fluctuations over time, a functional connectivity exists between them, thus depicting the resting-state functional connectivity between multiple candidate brain regions.

[0098] Step S2502: Based on the task-state functional magnetic resonance imaging data, determine the task-state functional connectivity between the plurality of candidate brain regions.

[0099] In this embodiment, when two or more candidate brain regions exhibit temporal consistency in their activity patterns during task execution, task-state functional connectivity exists between them. The process of determining task-state functional connectivity between the multiple candidate brain regions based on task-state functional magnetic resonance imaging (fMRI) data typically involves: extracting BOLD signals from the candidate brain regions from the task-state fMRI data, analyzing the temporal correlation of these BOLD signals during task execution, and determining whether different candidate brain regions are coordinating their activities.

[0100] When two or more candidate brain regions exhibit temporal consistency in their activity patterns during task execution, task-state functional connectivity exists between these two or more candidate brain regions.

[0101] Step S2503: Based on the diffusion magnetic resonance imaging data, determine the structural connections between the plurality of candidate brain regions.

[0102] In this embodiment, diffusion magnetic resonance imaging data is processed using image processing techniques (such as fiber tracing algorithms) to reconstruct the paths of nerve fiber bundles. By analyzing the origin and end points of the fiber bundles, the connection relationships between different brain regions can be determined.

[0103] Step S2504: Determine the fusion connectivity between the multiple candidate brain regions based on the resting-state functional connectivity, the task-state functional connectivity, and the structural connectivity between the multiple candidate brain regions.

[0104] In this embodiment, the resting-state functional connectivity, task-state functional connectivity, and structural connectivity among multiple candidate brain regions are fused to obtain the fused connectivity among the multiple candidate brain regions.

[0105] Step S260: Determine the target brain region from the multiple candidate brain regions based on the fusion connection between the multiple candidate brain regions.

[0106] In this embodiment, the target brain region can be selected from the candidate brain regions based on the connection strength of the fused connections. For example, at least two candidate brain regions corresponding to a specific connection strength can be selected as the target brain region.

[0107] In some embodiments, step S260, which involves determining the target brain region from the plurality of candidate brain regions based on the fusion connection between the plurality of candidate brain regions, includes steps S2601 to S2602.

[0108] Step S2601: Based on the connection strength of the fusion connection between the multiple candidate brain regions, determine the two brain regions corresponding to the fusion connection with the target connection strength.

[0109] In this embodiment, the target connection strength can be the highest connection strength, or a specific connection strength, etc.

[0110] Step S2602: The two brain regions corresponding to the fusion connection of the target connection strength are taken as the target brain regions.

[0111] Taking the target connection strength as the highest connection strength as an example, the edge with the highest fusion connection strength among multiple candidate brain regions is usually considered to be the edge most associated with the stimulus target. The two candidate brain regions connected by this strongest edge are the target brain regions. By modulating this strongest edge, the connection between the two target brain regions can be strengthened or weakened, thereby modulating the stimulus target.

[0112] According to the embodiments of this application, by constructing a brain network of the target object for spatial localization of brain regions, the accuracy of brain region localization can be improved. Furthermore, by fusion connections between multiple candidate brain regions to characterize the anatomical and functional properties of the connections between brain regions, and then determining the target brain region from multiple candidate brain regions based on the fusion connections between multiple candidate brain regions, it is possible to accurately obtain the transcranial alternating current stimulation target area under individual differences in the brain, thereby improving the accuracy and stability of the intervention effect of transcranial alternating current stimulation.

[0113] After identifying the target brain region, it is necessary to map the target brain region onto the scalp of the target subject, that is, to determine the target stimulation matrix corresponding to the target brain region. In related technologies, after identifying the target brain region in transcranial alternating current stimulation (TCD), conventional or experience-based stimulation parameter settings can be used to apply stimulation to the target brain region. However, this method lacks precise simulation of the electrical stimulation conduction path within the target subject's cranial brain, making it difficult to accurately estimate the actual stimulation dose received by the target subject's cerebral cortex, thus affecting the accuracy and stability of the TCD intervention effect. Therefore, this application also provides a method for determining the target stimulation matrix corresponding to the target brain region by simulating the propagation path of electrical stimulation in the target subject's brain. The method is as follows:

[0114] In some embodiments, after determining the target brain region from the plurality of candidate brain regions in step S260, the method further includes steps S410 to S440.

[0115] Step S410: Determine the candidate electrode set based on the target brain region.

[0116] In this embodiment, multiple candidate electrodes corresponding to the target brain region can be determined based on the target brain region and the 10-20 system, forming a candidate electrode set. Alternatively, multiple candidate electrodes can be set independently based on the target brain region to form a candidate electrode set; this is not limited here.

[0117] The candidate electrode set includes the position and stimulation parameters of each candidate electrode in the head coordinate system of the target object.

[0118] In some embodiments, step S410, which determines a set of candidate electrodes based on the target brain region, includes steps S4101 and S4102.

[0119] Step S4101: Determine the initial electrode position corresponding to the target brain region according to the preset electrode-brain region correspondence.

[0120] In this embodiment, the electrode-brain region correspondence can be used to reflect the correspondence between the electrodes that apply stimulation to the head and the brain regions that receive the stimulation.

[0121] In one example, the electrode-brain region correspondence can be the electrode-brain region correspondence of the International 10-20 system.

[0122] For example, the electrode-brain partition correspondence in the 10-20 system can include the correspondence between electrode 1 and electrode 3 and brain partition 1. If the target brain partition is 1, the initial electrode positions 1 and 3 can be determined through the electrode-brain partition correspondence.

[0123] Step S4102: Determine the candidate electrode set based on the preset distance interval and the initial electrode position.

[0124] Continuing the example above, the initial electrode positions are electrode 1 and electrode 3. A high-density array of electrodes can be added between electrode 1 and electrode 3 at certain intervals (e.g., 2, 5, 8, etc.). This high-density array of electrodes, together with the candidate electrodes in the 10-20 system, constitutes a candidate electrode set. Generally, the smaller the interval between electrodes, i.e., the greater the electrode density, the stronger the convergence of current in the target brain region.

[0125] According to an embodiment of this application, the initial electrode position corresponding to the target brain region is determined based on a preset electrode-brain region correspondence, and a candidate electrode set is determined based on a preset distance interval and the initial electrode position. Thus, by adding high-density electrodes at the initial electrode position, the focusing of electrical stimulation on the target brain region can be increased, while the focusing on non-target brain regions can be reduced, facilitating the determination of the target stimulation matrix for the target brain region.

[0126] After determining the candidate electrode set through step S410, steps S420 to S440 are executed to perform electrical stimulation simulation to obtain the target stimulation matrix.

[0127] Step S420: Randomly select multiple high-density stimulation spatial arrays from the candidate electrode set.

[0128] In this embodiment, a high-density stimulation spatial array is randomly selected from the candidate electrode set. Each time a high-density stimulation spatial array is selected, an electrical stimulation simulation is performed. This process continues until every candidate electrode in the candidate electrode set has been traversed.

[0129] Step S430: For any of the high-density stimulation spatial arrays, determine the brain electric field distribution corresponding to the high-density stimulation spatial arrays based on the electrical conduction model of the high-density stimulation spatial arrays and the target object.

[0130] In this embodiment, for any high-density stimulation spatial array, the brain electric field distribution of the high-density stimulation spatial array in the brain of the target object is simulated based on the electrical conduction model of the high-density stimulation spatial array and the target object. That is, the electrical conduction path of the electrical stimulation corresponding to the high-density stimulation spatial array in the brain of the target object.

[0131] The electrical conduction model of the target object can be used to reflect the propagation and distribution characteristics of current in the target object's brain.

[0132] In some embodiments, the multimodal brain imaging data includes brain structural imaging data, and determining the electrical conduction model of the target object includes steps SA1 to SA2.

[0133] Step SA1: Determine the brain tissue structure layer of the target object based on the brain structure imaging data of the target object.

[0134] In this embodiment, the brain structural imaging data can be T1 structural images. Image recognition, boundary segmentation, and layering are performed on the T1 structural images to obtain the brain tissue structural layers of the target object.

[0135] A passive approach can be used for the identification, segmentation, and stratification of T1 structural images.

[0136] Passive methods refer to automated approaches. For example, using tools such as FreeSurfer, Brainstorm3, and SimNIBS4 to perform T1 structured image recognition, segmentation, and hierarchical analysis.

[0137] In some examples, an active approach can also be used for the identification, segmentation, and stratification of T1 structural images. The active approach refers to a manual method where professionals manually stratify T1 structural images based on anatomical knowledge; this method may place greater emphasis on the accuracy of stratification.

[0138] T1 structural imaging can divide the brain structure of the target subject into 5 to 7 layers (including skin, skull, cerebrospinal fluid, etc.).

[0139] Step SA2: Determine the electrical conduction model of the target object based on the electrical conductivity value corresponding to each brain tissue structural layer.

[0140] In this embodiment, an electrical conductivity value can be set for each brain tissue structural layer, and the electrical conduction model of the target object can be determined based on the electrical conductivity value corresponding to each brain tissue structural layer.

[0141] Step S440: Determine the target stimulation matrix based on the electric field intensity and focusing degree generated by the brain electric field distribution corresponding to each of the high-density stimulation spatial arrays in the target brain region.

[0142] In this embodiment, after performing electrical conduction simulation using multiple high-density stimulation spatial arrays, a corresponding number of brain electric field distributions can be obtained. Among these multiple brain electric field distributions, the high-density stimulation spatial array with the maximum electric field strength and maximum focusing degree corresponding to the target brain region is selected as the target stimulation matrix. Alternatively, the high-density stimulation spatial array with the maximum electric field strength and maximum focusing degree corresponding to the target brain region within the range of human tolerance is selected as the target stimulation matrix.

[0143] After determining the target stimulation matrix, considering that the electrode positions of the target stimulation matrix may not necessarily correspond to the electrode positions of the 10-20 system, in order to facilitate the application of transcranial electrical stimulation to the target object through the target stimulation matrix, in some embodiments, the multimodal brain imaging data includes brain structural imaging data. After determining the target stimulation matrix in step S440, the method further includes steps S510 to S530.

[0144] Step S510: Determine the head model of the target object based on the brain structure imaging data of the target object.

[0145] In this embodiment, the brain structure imaging data of the target object can be a T1 structural image. Based on the T1 structural image of the target object, a head model of the target object is determined, and this head model can be as follows: Figure 3 The head model shown.

[0146] Step S520: Determine an electrical stimulation positioning cap that is compatible with the head model of the target object.

[0147] In this embodiment, NMR-compatible non-magnetic materials and 3D printing technology are used to print an electrical stimulation positioning cap that has a good fit to the head shape of the target object, based on the head model of the target object. This electrical stimulation positioning cap is as follows: Figure 4 As shown.

[0148] The electrical stimulation positioning cap is equipped with actual stimulation target points corresponding to the target stimulation matrix. Specifically, after determining the target stimulation matrix, the actual stimulation target points for transcranial electrical stimulation of the target object can be obtained using 3D printing technology, and holes are punched below the positions of the actual stimulation target points. For example, a swimming cap or similar device can be worn on the target object's head for positioning, and then holes can be punched to obtain the actual stimulation target points for the target object.

[0149] According to the embodiments of this application, by custom-printing an electrical stimulation positioning cap, and by setting actual stimulation target points corresponding to the target stimulation matrix, it is convenient to perform transcranial electrical stimulation on the target object and improve the targeting of transcranial electrical stimulation.

[0150] Step S530: According to the target stimulation matrix, transcranial electrical stimulation is performed on the target object through the electrical stimulation positioning cap.

[0151] In some examples, a high-density transcranial electrical stimulator can be mounted on the electrical stimulation positioning cap, such as... Figure 5As shown, when the target subject wears this electrical stimulation positioning cap, electrical stimulation can be applied to the target subject by encoding and controlling the electrode positions corresponding to the target stimulation matrix, thereby maximizing the electric field strength in the target brain region and improving the modulatory effect. Furthermore, brain functional imaging data can be acquired simultaneously with transcranial electrical stimulation to optimize the intervention effect.

[0152] In other examples, the electrical stimulation positioning cap may not have electrode positions corresponding to the target stimulation matrix. When the target subject wears the electrical stimulation positioning cap, the electrode pads can be delivered to the electrode positions corresponding to the target stimulation matrix by a robotic arm to form the maximum electric field strength in the target brain region and improve the modulation effect.

[0153] According to the embodiments of this application, by customizing the electrical stimulation positioning cap for the target object, multi-channel electrical stimulation electrodes and recording sensors such as EEG can be personalized after the spatial mapping coordinate transformation of the "cerebral cortex-scalp" coordinates, facilitating electrical stimulation of the target object with different stimulation matrices. Furthermore, by setting individualized actual stimulation target points on the electrical stimulation positioning cap, compared to the traditional 10-20 system electrical stimulation target points, it is easier to achieve individualized transcranial electrical stimulation, expanding the applicability of the electrical stimulation positioning cap.

[0154] <Device Embodiment 1>

[0155] Figure 6 The transcranial electrical stimulation intervention optimization device 600 according to one embodiment includes a processor 610 and a memory 620, wherein the memory 620 stores programs or instructions that can run on the processor 610, and when the programs or instructions are executed by the processor 610, implement the steps of the transcranial electrical stimulation intervention optimization method as described in any of the above embodiments.

[0156] <Media Example>

[0157] In this embodiment of the disclosure, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the transcranial electrical stimulation intervention optimization method as described in any of the above embodiments.

[0158] The present invention may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

[0159] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0160] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0161] The computer program instructions for performing the operation of the present invention can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. The computer readable program instructions can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), is personalized by utilizing the state information of the computer readable program instructions, and the electronic circuit can execute the computer readable program instructions, thereby realizing various aspects of the present invention.

[0162] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0163] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0164] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0165] The flowcharts and block diagrams in the accompanying drawings show the possible implementation architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of an instruction, and the module, program segment or part of the instruction contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.

[0166] While various embodiments of the present invention have been described above, the foregoing description is intended to be illustrative, non-exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein. The scope of the present invention is defined by the appended claims.

Claims

1. A method for optimizing transcranial electrical stimulation intervention, characterized in that, include: Acquire multimodal brain imaging data of the target object; wherein, the multimodal brain imaging data includes resting-state functional magnetic resonance imaging data, task-based functional magnetic resonance imaging data, and diffusion magnetic resonance imaging data; Based on the multimodal brain imaging data, the brain network of the target object is determined; wherein, the brain network of the target object is used to reflect the functional and structural connectivity between different brain regions of the target object; Obtain the stimulation target for transcranial electrical stimulation of the target object; wherein, the stimulation target is a specific brain function that needs to be intervened by transcranial electrical stimulation; Based on the stimulation target and the brain network of the target object, multiple candidate brain regions are determined; Based on the resting-state functional magnetic resonance imaging (fMRI) data, determine the resting-state functional connectivity between the plurality of candidate brain regions; Based on the task-state functional magnetic resonance imaging data, determine the task-state functional connectivity between the plurality of candidate brain regions; Based on the diffusion magnetic resonance imaging data, the structural connections between the plurality of candidate brain regions are determined; Based on the resting-state functional connectivity, the task-state functional connectivity, and the structural connectivity among the multiple candidate brain regions, the fusion connectivity among the multiple candidate brain regions is determined; Based on the connection strength of the fusion connection between the multiple candidate brain regions, determine the two brain regions corresponding to the fusion connection with the target connection strength. The two brain regions corresponding to the fusion connection of the target connectivity strength are taken as the target brain regions.

2. The method according to claim 1, characterized in that, The step of determining the brain network of the target object based on the multimodal brain imaging data includes: Acquire brain function imaging data of the population; Based on the brain functional imaging data of the group, the group-level brain node functional signal sequence of the group was determined; The functional signal sequences of the aforementioned group of horizontal brain nodes are decomposed to obtain a reference cortical network; The reference cortical network is updated based on the multimodal brain imaging data to obtain the brain network of the target object.

3. The method according to claim 1, characterized in that, After identifying the target brain region, the method further includes: Based on the target brain region, a set of candidate electrodes is determined; wherein, the set of candidate electrodes includes the position and stimulation parameters of each candidate electrode in the head coordinate system of the target object; Multiple high-density stimulation spatial arrays are randomly selected from the candidate electrode set; For any of the high-density stimulation spatial arrays, the brain electric field distribution corresponding to the high-density stimulation spatial array is determined based on the electrical conduction model of the high-density stimulation spatial array and the target object. The target stimulation matrix is ​​determined based on the electric field intensity and focusing degree generated by the brain electric field distribution corresponding to each of the high-density stimulation spatial arrays in the target brain region.

4. The method according to claim 3, characterized in that, The step of determining the candidate electrode set based on the target brain region includes: Based on a preset electrode-brain region correspondence, the initial electrode position corresponding to the target brain region is determined; wherein, the electrode-brain region correspondence is used to reflect the correspondence between the electrode that applies stimulation to the head and the brain region that receives the stimulation. A set of candidate electrodes is determined based on a preset distance interval and the initial electrode position.

5. The method according to claim 3, characterized in that, The multimodal brain imaging data includes brain structural imaging data. After determining the target stimulus matrix, the method further includes: Based on the brain structure imaging data of the target object, a head model of the target object is determined; Based on the head model of the target object, an electrical stimulation positioning cap adapted to the head model is determined; wherein, the electrical stimulation positioning cap is provided with actual stimulation target points corresponding to the target stimulation matrix.

6. The method according to claim 3, characterized in that, The multimodal brain imaging data includes brain structural imaging data, and determining the electrical conduction model of the target object includes: Based on the brain structure imaging data of the target object, the brain tissue structure layer of the target object is determined; The electrical conduction model of the target object is determined based on the conductivity value corresponding to each brain tissue structural layer.

7. A transcranial electrical stimulation intervention optimization device, characterized in that, It includes a processor and a memory, the memory storing programs or instructions that can run on the processor, the programs or instructions being executed by the processor to implement the steps of the intervention optimization method for transcranial electrical stimulation as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the intervention optimization method for transcranial electrical stimulation as described in any one of claims 1-6.

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