A transcranial magnetic stimulation individualized target point determination system

Abstract

The application belongs to the technical field of disease treatment devices, and relates to a transcranial magnetic stimulation individualized target point determination system, which comprises an individualized brain network construction module, a neural circuit selection module, a comparison module and a target point determination module; the individualized brain network construction module is used for establishing individualized brain networks of patients according to brain electrical data of the patients; the neural circuit selection module is used for selecting a neural circuit from the individualized brain networks of the patients; the comparison module is used for comparing the neural circuit with a normal value range to determine abnormal points; and the target point determination module is used for corresponding the abnormal points to cerebral cortex target points and determining individualized target point positions. The individualized algorithm is adopted, individualized differences of the patients are fully considered, and target point selection is not limited to the dorsolateral prefrontal cortex, so that more possibilities are provided for clinical treatment.

Description

Technical Field

[0001] The present invention relates to a transcranial magnetic stimulation (rTMS) individualized target determination system, belonging to the technical field of disease treatment devices. Background Art

[0002] Alzheimer's disease (AD) is the most common neurocognitive disorder. It affects 50% to 75% of patients with dementia and is characterized by progressive cognitive decline that impairs behavior and function. Its etiology remains uncertain, and treatments have limited efficacy. Repetitive transcranial magnetic stimulation (rTMS) has been used as an alternative treatment strategy. rTMS is a non-invasive neuromodulation technique that has emerged in recent years and can increase neural activity, connectivity, and synaptic plasticity in brain regions associated with memory and cognition. Most studies have shown modest benefits of rTMS in mild to moderate AD, with some studies showing modest benefits in normal aging and mild cognitive impairment. Improvements are more pronounced with high-frequency rTMS compared to low-frequency rTMS. In past clinical studies, the traditional target of rTMS has been the dorsolateral prefrontal cortex (DLPFC), either left, right, or bilaterally.

[0003] However, the effectiveness of this approach remains controversial. Observed differences in efficacy may be attributed to inaccurate target localization. Recent studies have shown that the effectiveness of rTMS depends on the functional connectivity between deep subcortical regions and superficial target areas. A systematic review of 33 studies on fMRI resting-state functional connectivity baseline and post-rTMS measurements found that rTMS can induce significant changes in brain connectivity that propagate within and between functional brain networks. Given these observed effects on functional connectivity, recent studies are attempting to indirectly target distal brain regions by measuring their functional connectivity with accessible proximal cortical regions during resting state. In addition, existing algorithms for determining targets are primarily based on MRI data, which is difficult to acquire in real time in clinical practice and cannot meet clinical needs. Summary of the Invention

[0004] In response to the above problems, the purpose of the present invention is to provide a transcranial magnetic stimulation (rTMS) personalized target determination system, which adopts an individualized algorithm to fully consider the individual differences of patients, and the target selection is not limited to the dorsolateral prefrontal cortex (DLPFC), providing more possibilities for clinical treatment.

[0005] To achieve the above-mentioned objectives, the present invention proposes the following technical solutions: a transcranial magnetic stimulation personalized target determination system, comprising: a personalized brain network construction module, a neural circuit selection module, a comparison module and a target determination module; the personalized brain network construction module is used to establish a patient's personalized brain network according to the patient's EEG data; the neural circuit selection module is used to select a certain neural circuit from the patient's personalized brain network; the comparison module is used to compare the neural circuit with the normal value range to determine the abnormal point; the target determination module is used to correspond the abnormal point to the cerebral cortex target and determine the personalized target position.

[0006] Furthermore, the method for establishing an individualized brain network in the individualized brain network construction module is: collecting EEG data, preprocessing the EEG data, and extracting an individualized alpha frequency band from the processed EEG data; performing source analysis on the extracted individualized alpha frequency band to obtain source data of the region of interest; and establishing an individualized brain network based on the source data.

[0007] Furthermore, the preprocessing includes: removing bad segments, interpolating bad leads, filtering, and using independent component analysis to remove artifact interference from electrooculogram, electromyography and electrocardiogram signals.

[0008] Furthermore, the method for extracting personalized alpha bands is: using broadband data to create a first channel co-equation matrix R; within the frequency range, using narrowband data to create a second channel co-equation matrix S; decomposing the generalized characteristics of the first channel co-equation matrix R and the second channel co-equation matrix S, separating the first channel co-equation matrix R and the second channel co-equation matrix S to the greatest extent, and generating a maximum separated eigenvector; calculating the pairwise square correlation between the eigenvectors of all frequencies, and storing them in an eigenvector similarity matrix, performing cluster analysis on the similarity matrix, and generating personalized alpha bands.

[0009] Furthermore, the method for establishing the personalized brain network based on the source data is as follows: constructing the personalized brain network based on the source data using a phase-locked value, wherein the calculation formula of the phase-locked value is:

[0010]

[0011] Among them, PLV XY is the phase-lock value, φ rel (t n ) is the phase difference between X(t) and Y(t), N is φ rel (t n ) length.

[0012] Furthermore, the phase-locking value is the distribution of the phase difference time series on the unit circle. If the phase-locking value is large, it means that the phase difference time series occupies a small part of the unit circle. If the phase-locking value is equal to 1, it means that the phase difference time series is constant within the entire time series range; if the phase-locking value is less than 1, it means that the phase difference is evenly distributed within the range of the unit circle.

[0013] Furthermore, the individualized brain network is characterized by a connection difference map of different head circumferences.

[0014] Furthermore, the neural circuit is compared with the normal value range to find the connection with the largest difference in the connection difference map of different head circumferences, and the cortical brain area corresponding to the connection is the treatment target.

[0015] Furthermore, if the differential connectivity is smaller than the normal value range, high-frequency transcranial magnetic stimulation is used; if the differential connectivity is larger than the normal value range, low-frequency transcranial magnetic stimulation is used.

[0016] Furthermore, the neural circuit includes a memory circuit, a situational memory circuit and an emotion circuit.

[0017] The present invention has the following advantages due to the adoption of the above technical solution:

[0018] 1. The present invention solves the problem of summarizing individual differences in the transcranial magnetic stimulation process: it adopts an individualized brain network, fully taking into account the individual differences of patients; and the target selection is not limited to the dorsolateral prefrontal cortex (DLPFC), providing more possibilities for clinical treatment.

[0019] 2. The present invention solves the problems that the existing algorithms for determining targets are mainly based on MRI data, MRI data acquisition is difficult in real time in clinical practice, and cannot meet clinical needs. EEG signals are used for analysis. EEG has the advantages of high time resolution, simple equipment and ease of use. It can not only objectively reflect the state of the brain, but also has simpler implementation conditions than MRI, making it easier to promote in clinical environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 2 is a schematic diagram of a transcranial magnetic stimulation (rTMS) individualized target determination system according to an embodiment of the present invention;

[0021] Figure 2 4 is a flow chart of a process for establishing an individualized brain network in one embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be described in detail through specific embodiments. However, it should be understood that the specific embodiments are provided only for a better understanding of the present invention and should not be construed as limiting the present invention. In the description of the present invention, it should be understood that the terms used are for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0023] In order to solve the problems that the traditional target of rTMS has always been the dorsolateral prefrontal cortex (DLPFC) in the prior art, whether on the left, right or bilateral side, it cannot be personalized according to the actual situation of the patient, and the existing algorithm for determining the target is mainly based on nuclear magnetic resonance data, and nuclear magnetic resonance data acquisition is difficult in real time in clinical practice and cannot meet clinical needs. The present invention proposes a transcranial magnetic stimulation rTMS personalized target determination system, which solves the problem that the traditional target has always been the dorsolateral prefrontal cortex (DLPFC) and cannot be accurately personalized by establishing an individualized brain network building module. Moreover, the individualized brain network in the present invention processes electroencephalogram (EEG) signals instead of traditional magnetic resonance data, so it is easy to obtain, can monitor the patient's intracranial state in real time, can adjust transcranial magnetic stimulation according to real-time EEG signals, and can locate the target position more accurately. Below, in conjunction with the accompanying drawings, the present invention is described in detail through examples.

[0024] Example 1:

[0025] This embodiment discloses a transcranial magnetic stimulation individualized target determination system. Figure 1 As shown, it includes: individualized brain network construction module, neural circuit selection module, comparison module and target determination module;

[0026] The personalized brain network construction module is used to establish a patient-specific brain network based on the patient's EEG data. The patient's EEG data is collected by collecting 64 channels of resting EEG for 8 minutes, and the 3-minute data with the strongest alpha energy in the occipital region is extracted as the final selected EEG data;

[0027] A neural circuit selection module is used to select a neural circuit from the patient's individualized brain network, where the neural circuit includes at least a memory circuit, an episodic memory circuit, and an emotion circuit;

[0028] A comparison module is used to compare the neural circuit with the normal value range to identify abnormal points;

[0029] The target determination module is used to map abnormal points to cerebral cortex targets and determine the individualized target locations.

[0030] On the one hand, the embodiment adopts individualized brain network, fully considering the individualized differences of patients; and the target selection method is more diverse and more accurate, providing more possibilities for clinical treatment. On the other hand, the embodiment adopts electroencephalogram signals, so that the time resolution of the system in the embodiment is higher, the device is simpler and convenient for practical application.

[0031] As shown in Figure 2 , the method for establishing an individualized brain network in the individualized brain network construction module in the embodiment is as follows:

[0032] Collecting electroencephalogram data, preprocessing the electroencephalogram data, and extracting individualized alpha band from the processed electroencephalogram data; wherein the preprocessing at least includes: removing bad segments, interpolating bad leads, filtering, and using independent component analysis (ICA) to remove the artifact interference of electrooculogram, electromyogram and electrocardiogram signals. In the embodiment, the filtering includes 1Hz high-pass filtering, 70Hz low-pass filtering and 50Hz notch filtering. The electroencephalogram data can also select other preprocessing methods according to actual needs, which is not limited by the disclosure in the embodiment.

[0033] Extracting individualized alpha band from the processed clean electroencephalogram data. In the prior art, alpha is a fixed frequency boundary (8-13HZ), however, in actual application, the fixed boundary prevents more detailed and potential information analysis on how these boundaries are different due to population (such as patient population or genetic background) or other individual factors (such as age, personality, task performance, etc.). Therefore, the embodiment adopts the gedBounds algorithm to extract individualized alpha band, and the specific process is as follows:

[0034] Using wideband data (non-time filtering) to create a first channel equation matrix R;

[0035] Within a frequency range, for example, 2-100Hz is selected in the embodiment, at each frequency, using narrowband data to create a second channel equation matrix S, and the second channel equation matrix S is a signal matrix;

[0036] Decomposing the generalized characteristics of the first channel equation matrix R and the second channel equation matrix S, the decomposition process identifies a spatial filter, i.e. a set of weights for all channels, which maximally separates the first channel equation matrix R and the second channel equation matrix S, while suppressing the data features represented in the two matrices, generates a maximum separation eigenvector, and stores the maximum separation eigenvector;

[0037] The pairwise squared correlations between the eigenvectors of all frequencies are calculated and stored in the eigenvector similarity matrix. Cluster analysis is performed on the similarity matrix to form highly similar "clusters" on the diagonal. These clusters are frequency ranges, and their edges are frequency boundaries. The cluster with the lowest frequency boundary closest to 10 Hz is selected and defined as the individualized alpha band range to generate the individualized alpha band.

[0038] The extracted individualized alpha frequency bands were traced back and analyzed to obtain the source data of the region of interest. The traced map used the brain network group map, and the brain network group map used functional connectivity to divide the brain areas in order to better fit the transcranial magnetic stimulation (rTMS) treatment mechanism.

[0039] Based on the source data, an individualized brain network is established, and the individualized brain network is represented by a connection difference map of different head circumferences, such as Figure 2 shown.

[0040] In this embodiment, the phase-locked value is used to construct an individualized brain network based on the source data. The calculation formula of the phase-locked value is:

[0041]

[0042] Among them, PLV XY is the phase-lock value, φ rel (t n ) is the phase difference between X(t) and Y(t), N is φ rel (t n ) length.

[0043] The phase-locking value evaluates the distribution of the phase difference time series in the unit circle [0, 2π). If the phase-locking value is large, it means that the phase difference time series occupies a smaller part of the unit circle [0, 2π). The phase-locking value range is 0-1. If the phase-locking value is equal to 1, it means that the phase difference time series is constant in the entire time series range; if the phase-locking value is less than 1, it means that the phase difference is evenly distributed in the range of the unit circle [0, 2π).

[0044] By comparing the neural circuits with the normal range, the connection with the largest difference in the connectivity difference map for different head circumferences is identified. The corresponding cortical brain area is designated as the treatment target. If the difference in connectivity is less than the normal range, high-frequency transcranial magnetic stimulation is used; if the difference in connectivity is greater than the normal range, low-frequency transcranial magnetic stimulation is used.

[0045] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0046] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0047] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0048] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific embodiments of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be included within the scope of protection of the claims of the present invention. The above content is only a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included within the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A transcranial magnetic stimulation individualized target determination system, characterized in that: include: Individualized brain network construction module, neural circuit selection module, comparison module and target identification module; The individualized brain network building module is used to establish individualized brain networks for each patient based on the patient's EEG data; The neural circuit selection module is used to select a neural circuit from the patient's individualized brain network; The comparison module is used to compare the neural circuit with the normal value range to determine abnormal points; The target point determination module is used to map the abnormal point to a cerebral cortex target point and determine the individualized target point position; The method for establishing an individualized brain network in the individualized brain network building module is: collecting EEG data, preprocessing the EEG data, and extracting individualized alpha frequency bands from the processed EEG data; Performing source analysis on the extracted individualized alpha frequency band to obtain source data of the region of interest; establishing a personalized brain network based on the source data; The method for extracting the individualized alpha band is: Create the first channel coequation matrix R using the broadband data; In the frequency range, the second channel coequation matrix S is created using the narrowband data; Decomposing the generalized features of the first channel co-equation matrix R and the second channel co-equation matrix S to separate the first channel co-equation matrix R and the second channel co-equation matrix S to the greatest extent possible, and generating a maximum separation feature vector; Pairwise squared correlations between the eigenvectors of all frequencies are calculated and stored in an eigenvector similarity matrix, and cluster analysis is performed on the similarity matrix to generate individualized alpha bands.

2. The transcranial magnetic stimulation individualized target determination system according to claim 1, wherein: The preprocessing includes: removing bad segments, interpolating bad leads, filtering, and using independent component analysis to remove artifact interference from electrooculogram, electromyography and electrocardiogram signals.

3. The transcranial magnetic stimulation individualized target determination system according to claim 1, wherein: The method for establishing an individualized brain network according to the source data is as follows: constructing the individualized brain network according to the source data using a phase-locked value, wherein the calculation formula of the phase-locked value is: in, is the phase lock value, is X( ) and Y( ) phase difference, yes length.

4. The transcranial magnetic stimulation individualized target determination system according to claim 3, wherein: The phase-locking value is the distribution of the phase difference time series on the unit circle. If the phase-locking value is large, it means that the phase difference time series occupies a small part of the unit circle. If the phase-locking value is equal to 1, it means that the phase difference time series is constant within the entire time series range; if the phase-locking value is less than 1, it means that the phase difference is evenly distributed within the range of the unit circle.

5. The transcranial magnetic stimulation individualized target determination system according to claim 1, wherein: The individualized brain network is characterized by a connectivity difference map with different head circumferences.

6. The transcranial magnetic stimulation individualized target determination system according to claim 5, wherein: The neural circuit is compared with the normal value range to find the connection with the largest difference in the connection difference map of different head circumferences, and the cortical brain area corresponding to the connection is the treatment target.

7. The transcranial magnetic stimulation individualized target determination system according to claim 6, wherein: If the connection with the largest difference is smaller than the normal value range, high-frequency transcranial magnetic stimulation is used; if the connection with the largest difference is larger than the normal value range, low-frequency transcranial magnetic stimulation is used.

8. The transcranial magnetic stimulation individualized target determination system according to any one of claims 1 to 7, wherein: The neural circuits include a memory circuit, a situational memory circuit, and an emotion circuit.

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