A method for determining individual stimulation targets

Abstract

The present application discloses a method for determining individualized stimulation targets, and relates to the field of TMS target determination; the method comprises: at a set time interval, each candidate TMS stimulation target in the acquired candidate TMS stimulation target set is subjected to a set number of stimulation processes, and then all the obtained TMS-EEG synchronization signals are pre-processed and intercepted to obtain TMS evoked potential data and baseline data before TMS stimulation; the area under the signal curve data and the area under the baseline curve data of any candidate TMS stimulation target are determined, and then the comprehensive brain response index is determined; among all the comprehensive brain response indices, the comprehensive brain response index with the largest value is selected as the target response index, and the candidate TMS stimulation target corresponding to the target response index is used as the optimal individualized stimulation target. The present application can quickly screen out the best TMS stimulation target for neural regulation.

Description

Technical Field

[0001] The present application relates to the field of TMS target determination, and in particular to a method for determining an individualized stimulation target. Background Art

[0002] TMS is a non-invasive neuromodulation technology that stimulates the cerebral cortex by generating brief magnetic field pulses, thereby regulating neuronal activity. This technology has been widely used to treat depression, schizophrenia, etc. There are also many recommended TMS stimulation targets in the TMS application guidelines. However, due to large individual differences and the lack of individualized effective stimulation target selection methods, the current clinical effectiveness of TMS is generally less than 30%. Therefore, determining the best stimulation target is crucial to improving the efficacy of TMS. The TMS-EEG technology is a technology that combines TMS with EEG, which can record the brain's instantaneous response to TMS through EEG while TMS is stimulated. EEG is a method of measuring brain electrical activity with high temporal resolution, which can capture millisecond-level changes in brain activity. Therefore, by analyzing TMS-EEG data, the local and overall responses of the brain to TMS stimulation of a certain target can be captured most directly, thereby providing a basis for the selection of individualized stimulation targets.

[0003] There are many existing methods for exploring potential individualized TMS precision stimulation treatment targets, including positioning based on brain structural anatomy, or calculation based on whole-brain functional connectivity. For example, the transcranial magnetic stimulation treatment target for upper limb motor dysfunction after stroke usually selects the first motor area in the brain responsible for hand movement. It is believed that directly stimulating the somatic motor center in the brain can improve the corresponding somatic movement disorder. The method based on whole-brain functional connectivity is currently a more commonly used analysis method. This method compares the whole-brain functional connectivity of stroke patients and healthy controls to find the brain area with the most obvious differences as a potential transcranial magnetic stimulation treatment target.

[0004] Although there are many existing methods for exploring potential personalized TMS stimulation targets, most of these methods lack clinical trial verification and cannot prove their clinical effectiveness. The disadvantage of the very few personalized TMS stimulation targets that have undergone clinical trials is that the verification time is long. If the stimulation target is ineffective, another candidate target needs to be selected for verification. In addition, since the existing personalized stimulation target calculation methods are different, for the same person and the same disease, different personalized target calculation methods will be used to obtain different personalized targets, but there is currently no method to screen out the best TMS stimulation target. Summary of the invention

[0005] The purpose of this application is to provide a method for determining individualized stimulation targets, which can quickly screen out the best TMS stimulation targets to facilitate neural regulation.

[0006] To achieve the above objectives, this application provides the following solutions:

[0007] The present application provides a method for determining an individualized stimulation target, and the method for determining an individualized stimulation target includes:

[0008] Acquire a set of candidate TMS stimulation target points; the set of candidate TMS stimulation target points includes n candidate stimulation target points;

[0009] According to a set time interval, each candidate TMS stimulation target in the candidate TMS stimulation target set is stimulated a set number of times to obtain a plurality of TMS-EEG synchronization signals; the TMS-EEG synchronization signal is an EEG signal corresponding to the candidate TMS stimulation target; the EEG signal is a measurement signal for characterizing brain activity;

[0010] Preprocessing all the TMS-EEG synchronization signals to obtain TMS-EEG synchronization processing signals; the preprocessing includes: noise removal processing, segmentation processing, independent component analysis and filtering processing;

[0011] According to the signal segment of the first set range, the TMS-EEG synchronous processing signal is intercepted and processed to obtain TMS evoked potential data;

[0012] According to the signal segment of the second set range, the TMS-EEG synchronous processing signal is intercepted and processed to obtain the baseline data before TMS stimulation;

[0013] Determine the area under the signal curve data of any candidate TMS stimulation target; the area under the signal curve data includes: the area under the local signal curve data and the area under the global signal curve data; the area under the local signal curve data is determined in the nearest channel, according to the signal amplitude of the TMS evoked potential data, based on the curve constructed based on the set time interval; the nearest channel is the channel closest to the candidate TMS stimulation target among all the whole-brain channels; the area under the global signal curve data is determined based on the curve constructed based on the signal amplitude of the TMS evoked potential data and the number of whole-brain channels, based on the set time interval;

[0014] Determine the baseline area under the curve data of any candidate TMS stimulation target; the baseline area under the curve data is determined in the whole brain channel based on the baseline data before TMS stimulation and based on the curve constructed based on the set time interval;

[0015] For any candidate TMS stimulation target, determining a comprehensive brain response index according to the signal area under the curve data and the baseline area under the curve data;

[0016] Among all the comprehensive brain response indexes, the comprehensive brain response index with the largest value is selected as the target response index, and the alternative TMS stimulation target corresponding to the target response index is used as the optimal individualized stimulation target.

[0017] Optionally, preprocessing all the TMS-EEG synchronization signals to obtain TMS-EEG synchronization processing signals specifically includes:

[0018] Performing noise removal processing on the TMS-EEG synchronization signal based on a set noise threshold to obtain a removal signal;

[0019] Segmenting the rejection signal according to the set segmentation interval, and performing artifact removal and noise rejection processing on the segmented signal to obtain a segmented processed signal;

[0020] Performing independent component analysis on the segmented processed signal, and filtering it according to a set frequency range to obtain a filtered signal;

[0021] The filtered signal and the set mean of the whole brain signal are subjected to re-reference processing to obtain a TMS-EEG synchronous processing signal.

[0022] Optionally, the first setting range is 20ms to 300ms; and the second setting range is -300ms to -20ms.

[0023] Optionally, the method for determining the area under the local signal curve data specifically includes:

[0024] Based on the signal amplitude of the TMS-evoked potential data, the local mean field power of the nearest channel was determined;

[0025] constructing a local signal curve based on the set time interval according to the local average field power;

[0026] The area under the local signal curve is determined according to the local signal curve.

[0027] Optionally, the calculation formula of the local average field power is:

[0028]

[0029] Wherein, LMFP(t) is the local mean field power at the tth time point; V(t) is the signal amplitude of the TMS-evoked potential data corresponding to the tth time point; is the mean value of V(t).

[0030] Optionally, the method for determining the area under the global signal curve data specifically includes:

[0031] The global mean field power was determined based on the signal amplitude of the TMS-evoked potential data and the number of whole-brain channels;

[0032] A global signal curve constructed based on a set time interval according to the global average field power;

[0033] The area under the global signal curve data is determined according to the global signal curve.

[0034] Optionally, the calculation formula of the global average field power is:

[0035]

[0036] Where GMFP(t) is the global mean field power at the tth time point; V i (t) is the signal amplitude of the TMS-evoked potential data corresponding to the i-th whole-brain channel at the t-th time point; V i (t); m is the number of whole-brain channels.

[0037] Optionally, the method for determining the area under the baseline curve data specifically includes:

[0038] In the whole-brain channel, the baseline mean field power was determined based on the baseline data before TMS stimulation;

[0039] constructing a baseline curve based on the set time interval according to the baseline mean field power;

[0040] Baseline area under the curve data is determined based on the baseline curve.

[0041] Optionally, the calculation formula of the baseline mean field power is:

[0042]

[0043] Among them, GMFP baseline (t) is the baseline mean field power at the tth time point; V baseline (t) is the baseline data amplitude before TMS stimulation corresponding to the tth time point; V baseline The mean of (t).

[0044] Optionally, the calculation formula of the comprehensive brain response index is:

[0045]

[0046] Among them, I(X K) is the comprehensive brain response index; AUC LMFP It is the area under the local signal curve data; AUC GMFP It is the area under the global signal curve data; AUC baseline The area under the baseline curve data.

[0047] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0048] The present application discloses a method for determining individualized stimulation targets. The individualized stimulation target selection method based on TMS-EEG technology can better make up for the defects of existing methods. Using TMS-EEG synchronization technology, the candidate TMS stimulation targets obtained by each method are tested, and the response pattern of each individual brain to each stimulation target is synchronously recorded, so as to more directly, accurately and quickly screen out more effective individualized TMS stimulation targets from multiple candidate TMS targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 This is a flow chart of the method for determining individualized stimulation targets in the embodiments of the present application;

[0051] Figure 2 It is a schematic diagram of a local signal curve;

[0052] Figure 3 is a schematic diagram of the global signal curve;

[0053] Figure 4 Schematic diagram of the main workflow corresponding to the individualized stimulation target determination method in practical application. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0055] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0056] In the embodiments of the present application, Figure 1 As shown, a method for determining an individualized stimulation target point includes:

[0057] Step 100: Obtain a set of candidate TMS stimulation target points. The set of candidate TMS stimulation target points includes n candidate stimulation target points.

[0058] Step 200: According to the set time interval, each candidate TMS stimulation target in the candidate TMS stimulation target set is stimulated for a set number of times to obtain multiple TMS-EEG synchronization signals. The TMS-EEG synchronization signal is the EEG signal corresponding to the candidate TMS stimulation target; the EEG signal is a measurement signal used to characterize brain activity.

[0059] Step 300: pre-process all TMS-EEG synchronization signals to obtain TMS-EEG synchronization processing signals. The pre-processing includes: noise removal, segmentation, independent component analysis and filtering.

[0060] Step 400: According to the signal segment in the first set range, the TMS-EEG synchronous processing signal is intercepted and processed to obtain TMS evoked potential data.

[0061] Step 500: According to the signal segment in the second set range, the TMS-EEG synchronous processing signal is intercepted and processed to obtain the baseline data before TMS stimulation.

[0062] Specifically, the first setting range is 20ms to 300ms; the second setting range is -300ms to -20ms.

[0063] Step 600: Determine the area under the signal curve data of any candidate TMS stimulation target. The area under the signal curve data includes: the area under the local signal curve data and the area under the global signal curve data; the area under the local signal curve data is determined in the nearest channel, according to the signal amplitude of the TMS evoked potential data, based on the curve constructed based on the set time interval; the nearest channel is the channel closest to the candidate TMS stimulation target among all the whole brain channels; the area under the global signal curve data is determined based on the curve constructed based on the signal amplitude of the TMS evoked potential data and the number of whole brain channels, based on the set time interval.

[0064] The method for determining the area under the local signal curve specifically includes:

[0065] According to the signal amplitude of the TMS-evoked potential data, the local mean field power of the nearest channel is determined; according to the local mean field power, a local signal curve is constructed based on a set time interval; and according to the local signal curve, area data under the local signal curve is determined.

[0066] The local mean field power is calculated as:

[0067]

[0068] Wherein, LMFP(t) is the local mean field power at the tth time point; V(t) is the signal amplitude of the TMS-evoked potential data corresponding to the tth time point; is the mean value of V(t).

[0069] The method for determining the area under the global signal curve data specifically includes:

[0070] The global mean field power was determined based on the signal amplitude of the TMS-evoked potential data and the number of whole-brain channels. A global signal curve was constructed based on the set time interval according to the global mean field power. The area under the global signal curve was determined based on the global signal curve.

[0071] The global mean field power is calculated as:

[0072]

[0073] Where GMFP(t) is the global mean field power at the tth time point; V i (t) is the signal amplitude of the TMS-evoked potential data corresponding to the i-th whole-brain channel at the t-th time point; V i (t); m is the number of whole-brain channels.

[0074] Step 700: Determine the baseline area under the curve data of any candidate TMS stimulation target. The baseline area under the curve data is determined in the whole brain channel based on the baseline data before TMS stimulation and the curve constructed based on the set time interval.

[0075] The method for determining the area under the baseline curve data specifically includes:

[0076] In the whole-brain channel, the baseline mean field power is determined based on the baseline data before TMS stimulation; based on the baseline mean field power, a baseline curve is constructed based on a set time interval; and based on the baseline curve, the area under the baseline curve data is determined.

[0077] The baseline mean field power is calculated as:

[0078]

[0079] Among them, GMFP baseline (t) is the baseline mean field power at the tth time point; V baseline (t) is the baseline data amplitude before TMS stimulation corresponding to the tth time point; V baseline The mean of (t).

[0080] Step 800: For any candidate TMS stimulation target, determine a comprehensive brain response index based on the signal area under the curve data and the baseline area under the curve data.

[0081] The calculation formula of the comprehensive brain response index is:

[0082]

[0083] Among them, I(X K ) is the comprehensive brain response index; AUC LMFP It is the area under the local signal curve data; AUC GMFP It is the area under the global signal curve data; AUC baseline The area under the baseline curve data.

[0084] Step 900: Among all the comprehensive brain response indexes, the comprehensive brain response index with the largest value is selected as the target response index, and the alternative TMS stimulation target corresponding to the target response index is used as the optimal individualized stimulation target.

[0085] As an optional implementation, all TMS-EEG synchronization signals are preprocessed to obtain TMS-EEG synchronization processing signals, specifically including:

[0086] The TMS-EEG synchronization signal is subjected to noise removal processing based on the set noise threshold to obtain a removed signal.

[0087] The rejection signal is segmented according to the set segmentation interval, and the segmented signal is processed by removing artifacts and removing noise to obtain a segmented processed signal.

[0088] The segmented processed signal is subjected to independent component analysis and filtered according to a set frequency range to obtain a filtered signal.

[0089] The filtered signal and the mean of the set whole-brain signal were re-referenced to obtain the TMS-EEG synchronous processing signal.

[0090] Transcranial magnetic stimulation (TMS) is a non-invasive, reversible, non-drug neuroregulatory method that is safer than drug therapy and has great therapeutic and rehabilitation potential for a variety of neurological diseases. There are already a variety of recommended TMS stimulation regimens for some common neurological diseases such as depression, migraine, and stroke. However, the stimulation effect of TMS usually requires a period of treatment before the pros and cons of its target selection can be seen. Therefore, for individuals, there is currently a lack of a method to screen out the optimal stimulation target from multiple alternative targets.

[0091] The invention is based on the individualized stimulation target selection method of TMS-EEG synchronization technology, which fully utilizes the characteristic of TMS-EEG technology of recording brain state changes in real time through EEG during TMS stimulation, and screens out the best individualized TMS stimulation target.

[0092] like Figure 4 As shown, the main workflow is as follows:

[0093] S1. For a certain individual, n candidate TMS precise stimulation targets are sorted out from existing TMS treatment guidelines and existing individualized TMS precise stimulation target calculation methods, and are denoted as X1, X2, …, X k , …, X n .

[0094] S2. For all the alternative TMS stimulation targets in S1, the individual was given precise TMS stimulation point by point in combination with the neuronavigation system. Each target was stimulated 100 times, with a random 4-6 second interval between stimulations. At the same time, the individual was given a 64-lead EEG cap to record the EEG signals during TMS stimulation in real time.

[0095] S3. Preprocess all TMS-EEG synchronization signals. The specific process includes: (1) Eliminate EEG channels with excessive noise; (2) Segment TMS-EEG, mark 0 second with the moment of TMS stimulation, and segment the signals from -1 to 1 second; (3) Delete the 0 to 10 millisecond signal of each segment and replace it with 0 mV to remove the instantaneous high-intensity electromyographic artifacts caused by TMS stimulation; (4) Eliminate signal segments with excessive noise; (5) Perform independent component analysis on each segment of TMS-EEG signal to remove Eliminate eye movement artifacts, ECG artifacts, bad electrode artifacts, myoelectric artifacts, etc.; (6) Perform a 1-100 Hz low-pass filter and a 48-52 Hz band-pass filter on each TMS-EEG signal segment; (7) Subtract the mean of the whole brain signal from all signal segments and perform re-reference processing; (8) Cut the data of 20-300ms of the pre-processed signal segment, which is the TMS evoked potential (TEP) data; (9) Cut the data of -300 to -20ms of the pre-processed signal segment, which is the baseline data before TMS stimulation.

[0096] S4. For the kth candidate target X k , calculate the TMS-EEG local features of the stimulation target. Calculate the stimulation site X k The local mean field power (LMFP) of the nearest channel average TEP is:

[0097]

[0098] Then the area under the signal curve (AUCLMFP ). Figure 2 Schematic diagram of a local signal curve constructed based on a set time interval according to the local average field power.

[0099] S5. Calculate the stimulation target X k The global characteristics of TMS-EEG. Calculate the stimulus X k The global mean field power (GMFP) at :

[0100]

[0101] There are a total of m channels in the whole brain.

[0102] Then the area under the signal curve (AUC GMFP ). Figure 3 Schematic diagram of a global signal curve constructed based on a set time interval according to the global average field power.

[0103] S6. Extract the baseline data before TMS stimulation of all channels, average them, and then calculate the GMFP of the overall baseline data as the baseline GMFP baseline :

[0104]

[0105] Then the area under the signal curve (AUC baseline ).

[0106] S7. Calculate the stimulation target X k The comprehensive brain response index I(X k ):

[0107]

[0108] The comprehensive brain response index of all candidate stimulation targets is calculated, and the candidate target corresponding to the highest brain response index is the optimal individualized TMS stimulation target for the individual.

[0109] This application aims to solve the problem of multiple candidate target screening and proposes an optimal individualized TMS stimulation target selection method combined with TMS-EEG synchronization technology. It more comprehensively considers the instantaneous local and global responses of the brain when stimulating a selected target. It innovatively proposes a comprehensive brain response index I to more comprehensively quantify the real-time response of the brain to TMS stimulation.

[0110] The present invention combines TMS-EEG synchronization technology to screen multiple candidate stimulation targets, and uses real-time recorded EEG to directly observe the instantaneous state changes of the brain during TMS stimulation, which can very objectively and quickly screen out the TMS targets with the most significant effect on the brain. The proposed comprehensive brain response index takes into account both the local and global brain response states, evaluates the effects of the stimulation targets on the stimulation sites themselves, and the effects of whole-brain transmission from multiple angles, and more comprehensively quantifies the brain's stimulation response state to each target; it fully considers the individual differences of different individuals, and the candidate targets are not limited to the universal stimulation targets recommended by the guidelines. All individualized TMS stimulation target calculation methods can be used to generate candidate targets, and have good portability and versatility.

[0111] The present invention innovatively applies TMS-EEG synchronization technology to the selection of optimal individualized targets. The data preprocessing method, local mean field power and global mean field power calculation used are all mainstream analysis methods in the TMS-EEG field. On this basis, the present invention innovatively proposes a comprehensive brain response index to quantify the instantaneous response state of the brain to each target. After multiple data consultation and reasoning verification, the purpose of quickly screening out the best TMS stimulation targets is achieved, which is convenient for neural regulation.

[0112] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0113] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for determining an individualized stimulation target, characterized in that: The individualized stimulation target determination method comprises: Acquire a set of candidate TMS stimulation target points; the set of candidate TMS stimulation target points includes n candidate stimulation target points; According to a set time interval, each candidate TMS stimulation target in the candidate TMS stimulation target set is stimulated a set number of times to obtain a plurality of TMS-EEG synchronization signals; the TMS-EEG synchronization signal is an EEG signal corresponding to the candidate TMS stimulation target; the EEG signal is a measurement signal for characterizing brain activity; Preprocessing all the TMS-EEG synchronization signals to obtain TMS-EEG synchronization processing signals; the preprocessing includes: noise removal processing, segmentation processing, independent component analysis and filtering processing; According to the signal segment of the first set range, the TMS-EEG synchronous processing signal is intercepted and processed to obtain TMS evoked potential data; According to the signal segment of the second set range, the TMS-EEG synchronous processing signal is intercepted and processed to obtain the baseline data before TMS stimulation; Determine the area under the signal curve data of any candidate TMS stimulation target; the area under the signal curve data includes: the area under the local signal curve data and the area under the global signal curve data; the area under the local signal curve data is determined in the nearest channel, according to the signal amplitude of the TMS evoked potential data, based on the curve constructed based on the set time interval; the nearest channel is the channel closest to the candidate TMS stimulation target among all the whole-brain channels; the area under the global signal curve data is determined based on the curve constructed based on the signal amplitude of the TMS evoked potential data and the number of whole-brain channels, based on the set time interval; Determine the baseline area under the curve data of any candidate TMS stimulation target; the baseline area under the curve data is determined in the whole brain channel based on the baseline data before TMS stimulation and based on the curve constructed based on the set time interval; For any candidate TMS stimulation target, determining a comprehensive brain response index according to the signal area under the curve data and the baseline area under the curve data; Among all the comprehensive brain response indexes, the comprehensive brain response index with the largest value is selected as the target response index, and the alternative TMS stimulation target corresponding to the target response index is used as the optimal individualized stimulation target.

2. The method for determining individualized stimulation targets according to claim 1, characterized in that: Preprocessing all the TMS-EEG synchronization signals to obtain TMS-EEG synchronization processing signals specifically includes: Performing noise removal processing on the TMS-EEG synchronization signal based on a set noise threshold to obtain a removal signal; Segmenting the rejection signal according to the set segmentation interval, and performing artifact removal and noise rejection processing on the segmented signal to obtain a segmented processed signal; Performing independent component analysis on the segmented processed signal, and filtering it according to a set frequency range to obtain a filtered signal; The filtered signal and the set mean of the whole brain signal are subjected to re-reference processing to obtain a TMS-EEG synchronous processing signal.

3. The method for determining individualized stimulation targets according to claim 1, characterized in that: The first setting range is 20ms to 300ms; the second setting range is -300ms to -20ms.

4. The method for determining individualized stimulation targets according to claim 1, characterized in that: The method for determining the area under the local signal curve data specifically includes: Based on the signal amplitude of the TMS-evoked potential data, the local mean field power of the nearest channel was determined; constructing a local signal curve based on the set time interval according to the local average field power; The area under the local signal curve is determined according to the local signal curve.

5. The method for determining individualized stimulation targets according to claim 4, characterized in that: The calculation formula of the local average field power is: Wherein, LMFP(t) is the local mean field power at the tth time point; V(t) is the signal amplitude of the TMS-evoked potential data corresponding to the tth time point; is the mean value of V(t).

6. The method for determining individualized stimulation targets according to claim 1, characterized in that: The method for determining the area under the global signal curve data specifically includes: The global mean field power was determined based on the signal amplitude of the TMS-evoked potential data and the number of whole-brain channels; A global signal curve constructed based on a set time interval according to the global average field power; The area under the global signal curve data is determined according to the global signal curve.

7. The method for determining individualized stimulation targets according to claim 6, characterized in that: The calculation formula of the global average field power is: Where GMFP(t) is the global mean field power at the tth time point; V i (t) is the signal amplitude of the TMS-evoked potential data corresponding to the i-th whole-brain channel at the t-th time point; V i (t); m is the number of whole-brain channels.

8. The method for determining individualized stimulation targets according to claim 1, characterized in that: The method for determining the area under the baseline curve data specifically includes: In the whole-brain channel, the baseline mean field power was determined based on the baseline data before TMS stimulation; constructing a baseline curve based on the set time interval according to the baseline mean field power; Baseline area under the curve data is determined based on the baseline curve.

9. The method for determining individualized stimulation targets according to claim 8, characterized in that: The calculation formula of the baseline mean field power is: Among them, GMFP baseline (t) is the baseline mean field power at the tth time point; V baseline (t) is the baseline data amplitude before TMS stimulation corresponding to the tth time point; V baseline The mean of (t).

10. The method for determining individualized stimulation targets according to claim 1, characterized in that: The calculation formula of the comprehensive brain response index is: Among them, I(X K ) is the comprehensive brain response index; AUC LMFP It is the area under the local signal curve data; AUC GMFP It is the area under the global signal curve data; AUC baseline The area under the baseline curve data.

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