Method, device, processor and storage medium for detecting abnormal neural oscillation of high-risk syndrome of mental illness based on magnetoencephalography

Abstract

The present invention relates to a method for detecting abnormal neural oscillations of high-risk syndromes of mental illness based on magnetoencephalography, the method comprising: (1) using a prodromal syndrome structured interview scale to evaluate the high-risk syndrome of mental illness on subjects in the form of face-to-face interviews; (2) obtaining magnetoencephalography data of the corresponding high-risk group of mental illness according to the evaluation results, and performing data preprocessing on the data to improve the quality of magnetoencephalography signals; (3) performing time-frequency analysis on the preprocessed magnetoencephalography data to obtain the abnormal frequency band of neural oscillations of the high-risk group of mental illness; (4) performing magnetoencephalography source positioning processing on the abnormal frequency band of neural oscillations to obtain the final magnetoencephalography detection results. The present invention also relates to a corresponding device, a processor and a computer-readable storage medium thereof. The method, device, processor and computer-readable storage medium thereof of the present invention can accurately obtain the abnormal neural oscillation conditions of the clinical high-risk group of mental illness.

Description

Technical Field

[0001] The present invention relates to the field of biomedical information technology, and in particular to the cross-technical field of imaging technology and disease diagnosis and evaluation in medical psychiatry, and specifically refers to a method, device, processor and computer-readable storage medium thereof for detecting abnormal neural oscillations of high-risk syndromes of mental illness based on magnetoencephalography. Background Art

[0002] Neural oscillations are closely related to the integration of cognitive and sensory processes by facilitating communication between groups of neurons in the brain, and play a vital role in brain function. Evidence has shown that patients with mental illnesses (such as schizophrenia) have obvious cognitive impairments and abnormal neural oscillations. However, the essential characteristics, mechanisms of occurrence, and clinical applicability of these abnormal neural oscillations remain unclear. Researchers have observed abnormal neural oscillations in patients with schizophrenia at rest, such as increased low-frequency activity and decreased high-frequency activity. In addition, it has been shown that the reduction in oscillation amplitude is accompanied by a reduction in oscillation coherence in the theta band. The resting state reflects a spontaneous, intrinsic neural activity of the brain, which can be observed when the subject remains relaxed and not engaged in any specific task during a brain imaging scan.

[0003] In recent years, clinical high-risk syndrome for psychosis (CHR-P) has played an important role in psychiatric research due to its advantages of early identification, prevention and intervention. Mental illness generally has cognitive deficits and some clinical symptoms in the early stages. Therefore, identifying abnormal brain neural activity and biomarkers in CHR-P people has become a focus of psychiatric research. However, there is limited evidence on abnormal neural oscillations in CHR-P people. Time-frequency analysis is an effective technique to describe the characteristics of neural oscillations. By comparing the time-frequency power spectrum values ​​of CHR-P people and healthy people, the frequency band of interest where abnormal neural oscillations occur can be observed. Then, the source location in the cerebral cortex corresponding to the neural oscillation signal in the abnormal frequency band caused by CHR-P people is explored. Existing studies have not yet solved this problem. Magnetoencephalography (MEG) is a functional neuroimaging technology with high temporal and spatial resolution, and the signal obtained by the sensor is almost unaffected by the interference of the skull and scalp. Since MEG signals have the advantages of direct, clean, dynamic, safe and non-invasive recording, MEG source localization methods can successfully identify the location of oscillation signals.

[0004] Therefore, the patent application of this invention is to solve the urgent problems existing in the above-mentioned prior art. At the same time, the corresponding technical solution is funded by the National Natural Science Foundation of China (81901832, 62072014), the Three-Year Action Plan to Promote Clinical Skills and Clinical Innovation Capabilities of Municipal Hospitals (SHDC2020CR4066), the Shanghai Science and Technology Commission Fund Project (19ZR1477800), and the Shanghai Mental Health Center Project (2020-FX-01). Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and to provide a method, device, processor and computer-readable storage medium for detecting abnormal neural oscillations in high-risk syndromes of psychosis based on magnetoencephalography, which can find abnormal oscillation frequency bands and their corresponding positions in the individual's cerebral cortex.

[0006] In order to achieve the above-mentioned purpose, the method, device, processor and computer-readable storage medium thereof for detecting abnormal neural oscillations of high-risk syndrome of mental illness based on magnetoencephalography of the present invention are as follows:

[0007] The method for detecting abnormal neural oscillations of high-risk syndrome of mental illness based on magnetoencephalography is mainly characterized in that the method comprises the following steps:

[0008] (1) Using the prodromal syndrome structured interview scale, the subjects were assessed for high-risk syndrome of psychosis in a face-to-face interview;

[0009] (2) Obtaining the corresponding magnetoencephalogram data of the high-risk population for mental illness based on the evaluation results, and performing data preprocessing on the data to improve the quality of the magnetoencephalogram signals;

[0010] (3) performing time-frequency analysis on the preprocessed magnetoencephalogram data to obtain abnormal frequency bands of neural oscillations in people at high risk of mental illness;

[0011] (4) performing magnetoencephalogram source localization processing on the abnormal frequency band of neural oscillation to obtain the final magnetoencephalogram detection result.

[0012] Preferably, the step (1) is specifically as follows:

[0013] (1.1) Using the prodromal syndrome structured interview scale, determine whether the subject meets the clinical high-risk status criteria through face-to-face interviews. If yes, proceed to step (1.2); otherwise, no assessment or treatment is performed;

[0014] (1.2) The subjects described herein are evaluated for high-risk syndrome of mental illness.

[0015] Preferably, the high-risk syndrome assessment for mental illness will assess four symptoms, including:

[0016] Positive symptoms were assessed using the SIPS scale P1-P5 categories;

[0017] Negative symptoms were assessed using the SIPS scale N1-N6 categories;

[0018] Disorganization symptoms, assessed using the SIPS scale D1-D4 categories; and

[0019] General symptoms were assessed using the SIPS scale G1-G4 categories.

[0020] Preferably, the step (2) is specifically as follows:

[0021] (2.1) Based on the evaluation results, continuous resting-state magnetoencephalogram data with eyes closed are obtained and processed in segments according to preset time intervals;

[0022] (2.2) Filter the segmented signal using a 0.5-60 Hz bandpass filter and a 50 Hz power frequency removal filter;

[0023] (2.3) Independent component analysis is used to remove eye movement artifacts and electrocardiogram artifacts from the filtered signal.

[0024] Preferably, the step (3) is specifically as follows:

[0025] (3.1) Using multi-window spectrum analysis time-frequency transformation based on Hanning window, a comparative time-frequency analysis was performed on high-risk syndrome and healthy people. At the same time, the energy spectrum values ​​were normalized and the same presentation threshold was set;

[0026] (3.2) By comparing the time-frequency analysis results of the two groups, the abnormal frequency bands of neural oscillations in people at high risk of mental illness were obtained.

[0027] Preferably, the step (4) is specifically as follows:

[0028] (4.1) Create an individual head model of the subject based on the acquired abnormal frequency band of neural oscillation;

[0029] (4.2) The boundary element method is used to solve the direct problem of the individual head model. The boundary element method is specifically as follows:

[0030] Calculate the potential V = TV0 of the current source in the non-uniform conductor, where T is the transfer matrix and V0 is the potential vector, expressed as:

[0031]

[0032] The position of the i-th grid point is r i 、r j The dipole current at position C j , conductivity is α;

[0033] (4.3) solving the inverse problem of the individual head model and performing estimation processing by accurate low-resolution brain electromagnetic tomography;

[0034] (4.4) Based on the processing results, the final magnetoencephalogram source is drawn and displayed.

[0035] Preferably, the step (4.1) is specifically as follows:

[0036] (4.1.1) reading the T1 image data of the subject's individual MRI magnetic resonance imaging;

[0037] (4.1.2) voxel segmentation, aligning the individual MRI with the magnetoencephalogram coordinate system, three-dimensionally reconstructing the head model, and performing voxel segmentation on the three-dimensionally reconstructed head model using a parameter of 5 mm spatial interval;

[0038] (4.1.3) Creation of head model: using a semi-realistic head model, extracting the external brain surface from the individual MRI images of the subject, and after segmenting and anatomizing the MRI images, describing the brain surface in the form of vertices and triangles, and calculating a single-shell head model.

[0039] Preferably, the step (4.3) is specifically as follows:

[0040] (4.3.1) First, the frequency band of interest is obtained from the time-frequency analysis results. For example, the abnormal oscillation frequency bands found in this technical solution are the θ frequency band (4-7 Hz) and the δ frequency band (1-3 Hz);

[0041] (4.3.2) performing bandpass filtering on the preprocessed MEG data in the current frequency bands of interest, namely theta band (4-7 Hz) and delta band (1-3 Hz);

[0042] (4.3.3) Applying principal component analysis to extract features of the result of filtering in step (4.3.2), wherein the contribution rate threshold of principal component analysis is 0.75, and performing dimensionality reduction on the magnetoencephalogram data matrix after filtering in step (4.3.2);

[0043] (4.3.4) Finally, accurate low-resolution brain electromagnetic tomography is used to accurately locate and estimate the target.

[0044] Preferably, the step (4.3.4) will calculate the brain magnetic field measurement value in the following manner:

[0045] Y = KX + ε;

[0046] Where Y = [y (1) ,y (2) ,…,y (N) ],y (i) represents the magnetoencephalogram measurement value at the i-th moment, which is specifically a vector of M×1 dimension; K represents the field gain matrix of M×J dimension; M is the number of sensors; J is the number of voxels; X represents a J×N current density matrix; ε is an M×N noise matrix.

[0047] The device for detecting abnormal neural oscillations of high-risk syndrome of mental illness based on magnetoencephalography has the following main features: the device comprises:

[0048] a processor configured to execute computer executable instructions;

[0049] A memory stores one or more computer executable instructions, which, when executed by the processor, implement the various steps of the above-mentioned method for detecting abnormal neural oscillations in high-risk syndromes of psychosis based on magnetoencephalography.

[0050] The main feature of the processor for detecting abnormal neural oscillations of high-risk syndromes of mental illness based on magnetoencephalography is that the processor is configured to execute computer-executable instructions, and when the computer-executable instructions are executed by the processor, the various steps of the method for detecting abnormal neural oscillations of high-risk syndromes of mental illness based on magnetoencephalography are implemented.

[0051] The main feature of the computer-readable storage medium is that a computer program is stored thereon, and the computer program can be executed by a processor to implement the various steps of the above-mentioned method for detecting abnormal neural oscillations in high-risk syndromes of psychosis based on magnetoencephalography.

[0052] The method, device, processor and computer-readable storage medium for detecting abnormal neural oscillations in high-risk syndrome of mental illness based on magnetoencephalography of the present invention are used, and a new technology, magnetoencephalography technology, is applied, which can record the resting neural activity of CHR-P, and introduces magnetoencephalography analysis method to describe the neural oscillation characteristics of people with high-risk syndrome of mental illness, including preprocessing, time-frequency analysis and source localization. And using this method, an abnormal oscillation frequency band and its corresponding position in the individual cerebral cortex are discovered. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 The present invention is a flow chart of magnetoencephalogram source localization processing for the method of detecting abnormal neural oscillations of high-risk syndrome of mental illness based on magnetoencephalogram.

[0054] Figure 2This is a schematic diagram of the time-frequency analysis results of the method for detecting abnormal neural oscillations in high-risk syndrome of psychosis based on magnetoencephalography of the present invention, comparing the clinical high-risk population for psychosis (CHR-P) and the healthy control population (HC).

[0055] Figure 3 A schematic diagram of the neural oscillation results in the theta frequency band (4-7 Hz) for source localization of high-risk psychotic syndromes using the method of the present invention for detecting abnormal neural oscillations of high-risk psychotic syndromes based on magnetoencephalography.

[0056] Figure 4 A schematic diagram of the neural oscillation results in the delta frequency band (1-3 Hz) for source localization of high-risk psychotic syndromes using the method of the present invention for detecting abnormal neural oscillations of high-risk psychotic syndromes based on magnetoencephalography. DETAILED DESCRIPTION

[0057] In order to more clearly describe the technical content of the present invention, further description is given below in conjunction with specific embodiments.

[0058] Before describing in detail embodiments according to the present invention, it should be noted that, hereinafter, the terms "comprises", "includes" or any other variations are intended to cover non-exclusive inclusion, whereby a process, method, article or apparatus comprising a series of elements includes not only these elements, but also other elements not explicitly listed or inherent to such process, method, article or apparatus.

[0059] The method for detecting abnormal neural oscillations of high-risk syndrome of mental illness based on magnetoencephalography, wherein the method comprises the following steps:

[0060] (1) Using the prodromal syndrome structured interview scale, the subjects were assessed for high-risk syndrome of psychosis in a face-to-face interview;

[0061] (2) Obtaining the corresponding magnetoencephalogram data of the high-risk population for mental illness based on the evaluation results, and performing data preprocessing on the data to improve the quality of the magnetoencephalogram signals;

[0062] (3) performing time-frequency analysis on the preprocessed magnetoencephalogram data to obtain abnormal frequency bands of neural oscillations in people at high risk of mental illness;

[0063] (4) performing magnetoencephalogram source localization processing on the abnormal frequency band of neural oscillation to obtain the final magnetoencephalogram detection result.

[0064] As a preferred embodiment of the present invention, the step (1) is specifically:

[0065] (1.1) Using the prodromal syndrome structured interview scale, determine whether the subject meets the clinical high-risk status criteria through face-to-face interviews. If yes, proceed to step (1.2); otherwise, no assessment or treatment is performed;

[0066] (1.2) The subjects described herein are evaluated for high-risk syndrome of mental illness.

[0067] As a preferred embodiment of the present invention, the high-risk syndrome assessment process for mental illness will assess four symptoms, including:

[0068] Positive symptoms were assessed using the SIPS scale P1-P5 categories;

[0069] Negative symptoms were assessed using the SIPS scale N1-N6 categories;

[0070] Disorganization symptoms, assessed using the SIPS scale D1-D4 categories; and

[0071] General symptoms were assessed using the SIPS scale G1-G4 categories.

[0072] As a preferred embodiment of the present invention, the step (2) is specifically as follows:

[0073] (2.1) Based on the evaluation results, continuous resting-state magnetoencephalogram data with eyes closed are obtained and processed in segments according to preset time intervals;

[0074] (2.2) Filter the segmented signal using a 0.5-60 Hz bandpass filter and a 50 Hz power frequency removal filter;

[0075] (2.3) Independent component analysis is used to remove eye movement artifacts and electrocardiogram artifacts from the filtered signal.

[0076] As a preferred embodiment of the present invention, the step (3) is specifically as follows:

[0077] (3.1) Using multi-window spectrum analysis time-frequency transformation based on Hanning window, a comparative time-frequency analysis was performed on high-risk syndrome and healthy people. At the same time, the energy spectrum values ​​were normalized and the same presentation threshold was set;

[0078] (3.2) By comparing the time-frequency analysis results of the two groups, the abnormal frequency bands of neural oscillations in people at high risk of mental illness were obtained.

[0079] As a preferred embodiment of the present invention, the step (4) is specifically as follows:

[0080] (4.1) Create an individual head model of the subject based on the acquired abnormal frequency band of neural oscillation;

[0081] (4.2) The boundary element method is used to solve the direct problem of the individual head model. The boundary element method is specifically as follows:

[0082] Calculate the potential V = TV0 of the current source in the non-uniform conductor, where T is the transfer matrix and V0 is the potential vector, expressed as:

[0083]

[0084] The position of the i-th grid point is r i 、r j The dipole current at position C j , conductivity is α;

[0085] (4.3) solving the inverse problem of the individual head model and performing estimation processing by accurate low-resolution brain electromagnetic tomography;

[0086] (4.4) Based on the processing results, the final magnetoencephalogram source is drawn and displayed.

[0087] As a preferred embodiment of the present invention, the step (4.1) is specifically as follows:

[0088] (4.1.1) reading the T1 image data of the subject's individual MRI magnetic resonance imaging;

[0089] (4.1.2) voxel segmentation, aligning the individual MRI with the magnetoencephalogram coordinate system, three-dimensionally reconstructing the head model, and performing voxel segmentation on the three-dimensionally reconstructed head model using a parameter of 5 mm spatial interval;

[0090] (4.1.3) Creation of head model: using a semi-realistic head model, extracting the external brain surface from the individual MRI images of the subject, and after segmenting and anatomizing the MRI images, describing the brain surface in the form of vertices and triangles, and calculating a single-shell head model.

[0091] As a preferred embodiment of the present invention, the step (4.3) is specifically as follows:

[0092] (4.3.1) First, the frequency band of interest is obtained from the time-frequency analysis results. For example, the abnormal oscillation frequency bands found in this method are the θ band (4-7 Hz) and the δ band (1-3 Hz).

[0093] (4.3.2) performing bandpass filtering on the preprocessed MEG data in the frequency bands of interest, such as theta band (4-7 Hz) and delta band (1-3 Hz);

[0094] (4.3.3) Applying principal component analysis to extract features of the result of filtering in step (4.3.2), wherein the contribution rate threshold of principal component analysis is 0.75, and performing dimensionality reduction on the magnetoencephalogram data matrix after filtering in step (4.3.2);

[0095] (4.3.4) Finally, accurate low-resolution brain electromagnetic tomography is used to accurately locate and estimate the target.

[0096] As a preferred embodiment of the present invention, the step (4.3.4) will calculate the brain magnetic field measurement value in the following manner:

[0097] Y = KX + ε;

[0098] Where Y = [y (1) ,y (2) ,…,y (N) ],y (i) represents the magnetoencephalogram measurement value at the i-th moment, which is specifically a vector of M×1 dimension; K represents the field gain matrix of M×J dimension; M is the number of sensors; J is the number of voxels; X represents a J×N current density matrix; ε is an M×N noise matrix.

[0099] In practical applications, this technical solution specifically includes the following four processing flows: evaluation of clinical high-risk syndromes of mental illness, magnetoencephalogram data preprocessing, time-frequency analysis, and source localization.

[0100] 1. To assess the high-risk syndrome for psychosis, the Structured Interview for Prodromal Syndrome (SIPS) scale was used to determine whether the subject met the CHR status criteria through a face-to-face interview. SIPS contains 19 items to assess four symptoms: positive symptoms (scales P1-P5), negative symptoms (scales N1-N6), disintegrative symptoms (scales D1-D4), and general symptoms (scales G1-G4).

[0101] 2. Preprocess the acquired MEG data of high-risk groups for mental illness to improve the quality of MEG signals. Divide the continuous MEG closed-eye resting state data into many segments of 10 seconds each. Filter the segmented signals using a 0.5-60Hz bandpass filter and a 50Hz power frequency removal filter. Use independent component analysis (ICA) to remove eye movement artifacts and electrocardiogram artifacts.

[0102] 3. Perform time-frequency analysis on the preprocessed MEG data. The time-frequency analysis uses multi-window spectrum analysis time-frequency transformation based on Hanning window. For the two groups of time-frequency analysis results of high-risk syndrome and healthy people, the energy spectrum value is normalized and the same presentation threshold is set. By comparing the time-frequency analysis results of the two groups, the abnormal frequency band of neural oscillation in the high-risk population is given.

[0103] 4. For abnormal frequency band signals, perform magnetoencephalography source localization. This step is the key link of the method of the present invention. The method includes the creation of an individual head model, solving the forward problem, solving the inverse problem and displaying the source. These four steps are as follows: Figure 1 As shown in Figure 2. In the head model construction, a single shell approximation of the true shape was used to construct the volume conduction model. The single MRI (magnetic resonance imaging) and digitized head shape were co-fused into the MEG coordinate system using an iterative closest point algorithm. The probabilistic organization map of the gray matter was obtained by volume segmentation of the anatomical MRI. Figure 1 The inverse solution of plays a key role in source localization and was estimated by precise low-resolution brain electromagnetic tomography (eLORETA) in this study.

[0104] In the inverse problem solution, firstly, a bandpass filtering is performed on the preprocessed MEG data using the frequency band of interest obtained from the time-frequency analysis results, for example, 1-3 Hz. Then, principal component analysis is applied to extract the features of the previous step results. The solution of the inverse problem to achieve precise localization using the eLORETA technique is described as follows.

[0105] We use the symbol Y to represent the brain magnetic field measurement value, specifically a matrix Y = [y (1) ,y (2) ,…,y (N) ] is used to represent the magnetoencephalogram. The forward solution formula of magnetoencephalogram can be described as follows:

[0106] Y=KX+ε……(1)

[0107] where y (i) represents the MEG measurement value at the i-th moment, which is a vector of M×1 dimensions; K represents the field gain matrix of M×J dimensions; M is the number of sensors; J is the number of voxels; X represents a J×N current density matrix; ε is an M×N noise matrix. Then, a linear solution of formula (1) is expressed as:

[0108]

[0109] Where α≥0 is the Tikhonov regularization factor, and W is a positive definite symmetric weight matrix. The classical weighted minimum norm solution of formula (2) is:

[0110] X=W -1 K T (KW -1 K T +αL) + Y ……(3)

[0111] Where L = I-11 T / 1 T1, I is the identity matrix, and 1 is an M×1 vector with all elements equal to 1. The key step of the eLORETA algorithm is to estimate the weights for precise positioning with zero error. The nonlinear solution of the weights is expressed as:

[0112]

[0113] where ω i i=1,…,J) represents all elements of the diagonal matrix W. Formula (4) is implemented by an iterative algorithm. By calculating the intensity of all voxels, the voxel position with the maximum intensity is the source position. The source display is performed using the ft_sourceplot function, where the method parameter is 'ortho'.

[0114] For time-frequency analysis of the magnetoencephalogram data, see Figure 2 As shown, Figure 2 The spectrum values ​​in are generated based on two steps: (1) the grand average of all channel spectrum values ​​on the segment, and (2) the average of the spectrum values ​​generated in the first step across all segments. Figure 2 In sub-graph A (CHR-P), high power spectrum values ​​are concentrated in the frequency bands of delta (1–3-Hz), theta (4–7-Hz), and alpha (8–13Hz), while these values ​​are concentrated in the alpha and delta frequency bands in sub-graph B (HC). The experimental results show that the neural oscillations of CHR-P patients are significantly abnormal in the theta frequency band (4-7-Hz) and the delta frequency band (1-3-Hz). Compared with the HC results, the power spectrum of CHR-P in the two frequency bands is significantly enhanced.

[0115] For the magnetoencephalogram source localization processing, based on the results of time-frequency analysis, we further hope to determine which areas in the MRI of individuals with high-risk syndromes for psychosis are activated based on the MEG signals of the two frequency bands of abnormal neural oscillations. Figure 3 and Figure 4 The results of source localization using the proposed method are shown. Two sources of theta band oscillations were found, namely the left occipital lobe (near cuneus) and the left temporo-occipital junction. In addition, related brain regions were found near the right dorsolateral prefrontal cortex and right orbitofrontal gyrus for delta band oscillations.

[0116] The device for detecting abnormal neural oscillations of high-risk syndrome of mental illness based on magnetoencephalography of the present invention comprises:

[0117] a processor configured to execute computer executable instructions;

[0118] A memory stores one or more computer executable instructions, which, when executed by the processor, implement the various steps of the above-mentioned method for detecting abnormal neural oscillations in high-risk syndromes of psychosis based on magnetoencephalography.

[0119] The processor for detecting abnormal neural oscillations of high-risk syndromes of psychosis based on magnetoencephalography, wherein the processor is configured to execute computer-executable instructions, and when the computer-executable instructions are executed by the processor, the various steps of the method for detecting abnormal neural oscillations of high-risk syndromes of psychosis based on magnetoencephalography are implemented.

[0120] The computer-readable storage medium stores a computer program thereon, and the computer program can be executed by a processor to implement the various steps of the above-mentioned method for detecting abnormal neural oscillations in high-risk syndromes of psychosis based on magnetoencephalography.

[0121] In this technical proposal, we applied a new technology, magnetoencephalography, to record the resting-state neural activity of CHR-P. The methods of MEG analysis were introduced in detail, including preprocessing, multi-window spectral analysis, time-frequency analysis, and eLORETA source localization. The experimental results showed that neural oscillations were significantly abnormal in the theta band (4-7-Hz) and the delta band (1-3-Hz). For the theta band and the right dorsolateral prefrontal lobe, the relevant brain areas were located at the junction of the left occipital lobe and the left temporo-occipital gyrus, and for the delta band, the relevant brain areas were located near the orbitofrontal gyrus.

[0122] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0123] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution device.

[0124] A person of ordinary skill in the art may understand that all or part of the steps of the method for implementing the above-mentioned embodiment may be completed by instructing the relevant hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0125] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0126] In the description of this specification, the description with reference to the terms "an embodiment", "some embodiments", "example", "specific example", or "embodiment" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0127] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

[0128] The method, device, processor and computer-readable storage medium for detecting abnormal neural oscillations in high-risk syndrome of mental illness based on magnetoencephalography of the present invention are used, and a new technology, magnetoencephalography technology, is applied, which can record the resting neural activity of CHR-P, and introduces magnetoencephalography analysis method to describe the neural oscillation characteristics of people with high-risk syndrome of mental illness, including preprocessing, time-frequency analysis and source localization. And using this method, an abnormal oscillation frequency band and its corresponding position in the individual cerebral cortex are discovered.

[0129] In this specification, the present invention has been described with reference to specific embodiments thereof. However, it is apparent that various modifications and variations may be made without departing from the spirit and scope of the present invention. Therefore, the specification and drawings should be regarded as illustrative rather than restrictive.

Claims

1. A device for detecting abnormal neural oscillations in high-risk syndrome of mental illness based on magnetoencephalography, characterized in that: The device comprises: a processor configured to execute computer executable instructions; A memory storing one or more computer executable instructions, wherein when the computer executable instructions are executed by the processor, the following steps of the method for detecting abnormal neural oscillations of high-risk syndrome of psychosis based on magnetoencephalography are implemented: (1) Using the prodromal syndrome structured interview scale, the subjects were assessed for high-risk syndrome of psychosis in a face-to-face interview; (2) Obtaining the corresponding magnetoencephalogram data of the high-risk population for mental illness based on the evaluation results, and performing data preprocessing on the data to improve the quality of the magnetoencephalogram signals; (3) performing time-frequency analysis on the preprocessed magnetoencephalogram data to obtain abnormal frequency bands of neural oscillations in people at high risk of mental illness; (4) performing magnetoencephalogram source localization processing on the abnormal frequency band of neural oscillation to obtain the final magnetoencephalogram detection result; The step (1) is specifically as follows: (1.1) Using the prodromal syndrome structured interview scale, determine whether the subject meets the clinical high-risk status criteria through face-to-face interviews. If yes, proceed to step (1.2); otherwise, no assessment or treatment is performed; (1.2) Evaluate and treat the subjects at high risk of mental illness syndrome as described above; The step (3) is specifically as follows: (3.1) Using multi-window spectrum analysis time-frequency transformation based on Hanning window, a comparative time-frequency analysis was performed on high-risk syndrome and healthy people. At the same time, the energy spectrum values ​​were normalized and the same presentation threshold was set; (3.2) By comparing the time-frequency analysis results of the two groups, the abnormal frequency bands of neural oscillations in the high-risk population for mental illness were obtained, including the theta band (4-7 Hz) and the delta band (1-3 Hz); The step (4) is specifically as follows: (4.1) Create an individual head model of the subject based on the acquired abnormal frequency band of neural oscillation; (4.2) The boundary element method is used to solve the direct problem of the individual head model. The boundary element method is specifically as follows: Calculate the potential V = TV0 of the current source in the non-uniform conductor, where T is the transfer matrix and V0 is the potential vector, expressed as: The position of the i-th grid point is r i 、r j The dipole current at position C j , conductivity is α; (4.3) solving the inverse problem of the individual head model and performing estimation processing by accurate low-resolution brain electromagnetic tomography; (4.4) Based on the processing results, draw and display the final MEG source; The step (4.3) is specifically: (4.3.1) First, the frequency band of interest, i.e., the abnormal frequency band of neural oscillation, is obtained from the time-frequency analysis results; (4.3.2) performing bandpass filtering on the preprocessed magnetoencephalogram data at the frequency bands of interest respectively; (4.3.3) applying principal component analysis to extract the features of the result of the filtering process in step (4.3.2), and performing dimensionality reduction on the MEG data matrix after filtering in step (4.3.2); (4.3.4) Finally, accurate low-resolution brain electromagnetic tomography is used to accurately locate and estimate the target.

2. The device for detecting abnormal neural oscillations in high-risk syndrome of mental illness based on magnetoencephalography according to claim 1, characterized in that: The psychosis high risk syndrome assessment process will assess four symptoms, including: Positive symptoms were assessed using the SIPS scale P1-P5 categories; Negative symptoms were assessed using the SIPS scale N1-N6 categories; Disorganization symptoms, assessed using the SIPS scale D1-D4 categories; and General symptoms were assessed using the SIPS scale G1-G4 categories.

3. The device for detecting abnormal neural oscillations in high-risk syndrome of mental illness based on magnetoencephalography according to claim 1, characterized in that: The step (2) is specifically as follows: (2.1) Based on the evaluation results, continuous resting-state magnetoencephalogram data with eyes closed are obtained and processed in segments according to preset time intervals; (2.2) Filter the segmented signal using a 0.5-60 Hz bandpass filter and a 50 Hz power frequency removal filter; (2.3) Independent component analysis is used to remove eye movement artifacts and electrocardiogram artifacts from the filtered signal.

4. The device for detecting abnormal neural oscillations in high-risk syndrome of mental illness based on magnetoencephalography according to claim 1, characterized in that: The step (4.1) is specifically: (4.1.1) reading the T1 image data of the subject's individual MRI magnetic resonance imaging; (4.1.2) voxel segmentation, aligning the individual MRI with the magnetoencephalogram coordinate system, three-dimensionally reconstructing the head model, and performing voxel segmentation on the three-dimensionally reconstructed head model using a parameter of 5 mm spatial interval; (4.1.3) Creation of head model: using a semi-realistic head model, extracting the external brain surface from the individual MRI images of the subject, and after segmenting and anatomizing the MRI images, describing the brain surface in the form of vertices and triangles, and calculating a single-shell head model.

5. The device for detecting abnormal neural oscillations in high-risk syndrome of mental illness based on magnetoencephalography according to claim 1, characterized in that: The step (4.3.4) will calculate the brain magnetic field measurement value in the following way: Y = KX + ε; Where Y = [y (1) ,y (2) ,…,y (N) ],y (i) represents the brain magnetic field measurement value at the i-th moment, which is specifically a vector of M×1 dimensions; K represents the field gain matrix of M×J dimensions; M is the number of sensors; J is the number of voxels; X represents a J×n current density matrix; ε is an M×N noise matrix.

6. A processor for detecting abnormal neural oscillations in high-risk syndrome of mental illness based on magnetoencephalography, characterized in that: The processor is configured to execute computer executable instructions. When the computer executable instructions are executed by the processor, the following steps of the method for detecting abnormal neural oscillations in high-risk syndrome of psychosis based on magnetoencephalography are implemented: (1) Using the prodromal syndrome structured interview scale, the subjects were assessed for high-risk syndrome of psychosis in a face-to-face interview; (2) Obtaining the corresponding magnetoencephalogram data of the high-risk population for mental illness based on the evaluation results, and performing data preprocessing on the data to improve the quality of the magnetoencephalogram signals; (3) performing time-frequency analysis on the preprocessed magnetoencephalogram data to obtain abnormal frequency bands of neural oscillations in people at high risk of mental illness; (4) performing magnetoencephalogram source localization processing on the abnormal frequency band of neural oscillation to obtain the final magnetoencephalogram detection result; The step (1) is specifically as follows: (1.1) Using the prodromal syndrome structured interview scale, determine whether the subject meets the clinical high-risk status criteria through face-to-face interviews. If yes, proceed to step (1.2); otherwise, no assessment or treatment is performed; (1.2) Evaluate and treat the subjects at high risk of mental illness syndrome as described above; The step (3) is specifically as follows: (3.1) Using multi-window spectrum analysis time-frequency transformation based on Hanning window, a comparative time-frequency analysis was performed on high-risk syndrome and healthy people. At the same time, the energy spectrum values ​​were normalized and the same presentation threshold was set; (3.2) By comparing the time-frequency analysis results of the two groups, the abnormal frequency bands of neural oscillations in the high-risk population for mental illness were obtained, including the theta band (4-7 Hz) and the delta band (1-3 Hz); The step (4) is specifically as follows: (4.1) Create an individual head model of the subject based on the acquired abnormal frequency band of neural oscillation; (4.2) The boundary element method is used to solve the direct problem of the individual head model. The boundary element method is specifically as follows: Calculate the potential V = TV0 of the current source in the non-uniform conductor, where T is the transfer matrix and V0 is the potential vector, expressed as: The position of the i-th grid point is r i 、r j The dipole current at position C j , conductivity is α; (4.3) solving the inverse problem of the individual head model and performing estimation processing by accurate low-resolution brain electromagnetic tomography; (4.4) Based on the processing results, draw and display the final MEG source; The step (4.3) is specifically: (4.3.1) First, the frequency band of interest, i.e., the abnormal frequency band of neural oscillation, is obtained from the time-frequency analysis results; (4.3.2) performing bandpass filtering on the preprocessed magnetoencephalogram data at the frequency bands of interest respectively; (4.3.3) applying principal component analysis to extract the features of the result of the filtering process in step (4.3.2), and performing dimensionality reduction on the MEG data matrix after filtering in step (4.3.2); (4.3.4) Finally, accurate low-resolution brain electromagnetic tomography is used to accurately locate and estimate the target.

7. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and the computer program can be executed by a processor to implement the following steps of a method for detecting abnormal neural oscillations of a high-risk syndrome of psychosis based on magnetoencephalography: (1) Using the prodromal syndrome structured interview scale, the subjects were assessed for high-risk syndrome of psychosis in a face-to-face interview; (2) Obtaining the corresponding magnetoencephalogram data of the high-risk population for mental illness based on the evaluation results, and performing data preprocessing on the data to improve the quality of the magnetoencephalogram signals; (3) performing time-frequency analysis on the preprocessed magnetoencephalogram data to obtain abnormal frequency bands of neural oscillations in people at high risk of mental illness; (4) performing magnetoencephalogram source localization processing on the abnormal frequency band of neural oscillation to obtain the final magnetoencephalogram detection result; The step (1) is specifically as follows: (1.1) Using the prodromal syndrome structured interview scale, determine whether the subject meets the clinical high-risk status criteria through face-to-face interviews. If yes, proceed to step (1.2); otherwise, no assessment or treatment is performed; (1.2) Evaluate and treat the subjects at high risk of mental illness syndrome as described above; The step (3) is specifically as follows: (3.1) Using multi-window spectrum analysis time-frequency transformation based on Hanning window, a comparative time-frequency analysis was performed on high-risk syndrome and healthy people. At the same time, the energy spectrum values ​​were normalized and the same presentation threshold was set; (3.2) By comparing the time-frequency analysis results of the two groups, the abnormal frequency bands of neural oscillations in the high-risk population for mental illness were obtained, including the theta band (4-7 Hz) and the delta band (1-3 Hz); The step (4) is specifically as follows: (4.1) Create an individual head model of the subject based on the acquired abnormal frequency band of neural oscillation; (4.2) The boundary element method is used to solve the direct problem of the individual head model. The boundary element method is specifically as follows: Calculate the potential V = TV0 of the current source in the non-uniform conductor, where T is the transfer matrix and V0 is the potential vector, expressed as: The position of the i-th grid point is r i 、r j The dipole current at position C j , conductivity is α; (4.3) solving the inverse problem of the individual head model and performing estimation processing by accurate low-resolution brain electromagnetic tomography; (4.4) Based on the processing results, draw and display the final MEG source; The step (4.3) is specifically: (4.3.1) First, the frequency band of interest, i.e., the abnormal frequency band of neural oscillation, is obtained from the time-frequency analysis results; (4.3.2) performing bandpass filtering on the preprocessed magnetoencephalogram data at the frequency bands of interest respectively; (4.3.3) applying principal component analysis to extract the features of the result of the filtering process in step (4.3.2), and performing dimensionality reduction on the MEG data matrix after filtering in step (4.3.2); (4.3.4) Finally, accurate low-resolution brain electromagnetic tomography is used to accurately locate and estimate the target.

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