CN116863025B

CN116863025B - Traceability reconstruction method and device for magnetoencephalography data, electronic device and medium

Info

Publication number: CN116863025B
Application number: CN202311136376.0A
Authority: CN
Inventors: 钱浩天; 张瑜; 蒋田仔; 刘盛锋; 赵博涛; 张靖
Original assignee: Zhejiang Lab
Current assignee: Zhejiang Lab
Priority date: 2023-09-05
Filing date: 2023-09-05
Publication date: 2023-12-05
Anticipated expiration: 2043-09-05
Also published as: CN116863025A

Abstract

The application relates to a traceability reconstruction method, a traceability reconstruction device, an electronic device and a medium of magnetoencephalography data, wherein the method comprises the following steps: acquiring magnetoencephalography data to be processed of a target object; carrying out standardized pretreatment on the magnetoencephalography data to be processed and analyzing an event-related field to obtain an event-related field result; constructing a head model of the target object based on magnetic resonance structural image data of the target object, and constructing a source space based on a user instruction; calculating a forward solution based on the head model and the source space; performing traceability reconstruction based on the event-related field result and forward solution to obtain a source estimation result of a source space; and generating a brain map traceability reconstruction result based on the source estimation result of the source space and the brain map template in the standard space. The application solves the problem of low universality of the traceable reconstruction result of the magnetoencephalography data, and adds a certain commonality and comparability to the magnetoencephalography data processing and analyzing flow by means of a common brain atlas tool in the field of neuroimaging.

Description

Traceability reconstruction method and device for magnetoencephalography data, electronic device and medium

Technical Field

The application relates to the technical field of neuroimaging processing, in particular to a traceability reconstruction method, a traceability reconstruction device, an electronic device and a medium of magnetoencephalography data.

Background

A Magnetoencephalography (MEG) is a novel noninvasive brain imaging technology for reconstructing brain neuron electronic activities by measuring magnetic fields around the scalp, and is widely applied in clinic, for example, brain dynamic data acquisition and functional image reconstruction with high time resolution and higher spatial resolution can be obtained through Magnetoencephalography, and the method is used for screening brain serious diseases and reducing misdiagnosis rate.

The basic processing links of MEG data can be divided into data preprocessing, defined division of events and tests (epoch), sensor level signal analysis and traceable reconstruction analysis. Complex mathematical algorithms such as signal processing, statistical analysis, image reconstruction, etc. are involved in these links. The application of these algorithms requires highly specialized knowledge and skills, and thus it is very difficult for general scientists and medical professionals to implement MEG data processing by themselves. In addition, the current MEG data acquisition system comprises a plurality of MEG data acquisition modes, the specific processing modes of MEG data acquired by different acquisition systems are different, and the MEG data from different system sources can further increase the difficulty of non-professional staff in processing the MEG data.

Therefore, no effective solution has been proposed so far, how to increase the versatility and comparability of the processing and analysis flow of the acquired magnetoencephalography data.

Disclosure of Invention

The embodiment provides a traceability reconstruction method, a traceability reconstruction device, an electronic device and a medium of magnetoencephalography data, so as to solve the problem that the universality and comparability of the processing and analysis flow of the magnetoencephalography data acquired in the related technology are low.

In a first aspect, in this embodiment, a traceability reconstruction method of magnetoencephalography data is provided, including:

acquiring magnetoencephalography data to be processed of a target object;

carrying out standardized pretreatment on the magnetoencephalography data to be treated to obtain pretreated magnetoencephalography data;

performing event-related field analysis on the preprocessed magnetoencephalography data to obtain event-related field results, wherein the event-related field results are used for representing the activity results of neurons caused by brain activity events of the target object;

constructing a head model of the target object based on magnetic resonance structural image data of the target object, and constructing a source space based on user instructions, wherein the source space comprises a cortex source space or a voxel source space;

Calculating a forward solution based on the source space and the head model;

performing traceability reconstruction based on the event related field result and the forward solution to obtain a source estimation result of the magnetoencephalography data to be processed in the source space;

and generating a brain map traceability reconstruction result of the brain map data to be processed based on the source estimation result of the source space and a brain map template in the standard space.

In some embodiments, the acquiring the magnetoencephalography data to be processed of the target object includes:

collecting a magnetoencephalography signal of the target object;

and carrying out differential processing on the magnetoencephalography signals by acquisition equipment to obtain magnetoencephalography data to be processed.

In some of these embodiments, the acquisition device differential processing includes: at least one of signal spatial separation, gradient compensation, and channel compensation.

In some embodiments, the brain activity event of the target object includes a plurality of event types, and the performing standardized preprocessing on the magnetoencephalography data to be processed to obtain preprocessed magnetoencephalography data includes:

determining eye movement or muscle artifact components in the magnetoencephalography data to be processed;

Removing bad channels and data segments containing the eye movement or muscle artifact components to obtain removed data;

and determining test data corresponding to each event type according to the stimulation channel of the removed data to obtain the preprocessed magnetoencephalography data.

In some embodiments, the performing the event-related field analysis on the preprocessed magnetoencephalography data to obtain event-related field results includes:

determining an average value of test data of a plurality of test data corresponding to each event type;

and carrying out event-related field analysis on the average value of the test data corresponding to each event type to obtain an event-related field result.

In some of these embodiments, the computing a forward solution based on the source space and the head model includes:

determining a first conversion matrix between a coordinate system of a sensor position of the acquisition equipment of the magnetoencephalography data to be processed and a head coordinate system of the target object;

determining a second transformation matrix between the head coordinate system of the target object and the magnetic resonance structural image coordinate system of the target object;

a forward solution is calculated by assigning a preamble field to each source position associated with a sensor head position based on the first transformation matrix, the second transformation matrix, the head model, and the source space.

In some embodiments, the performing the traceable reconstruction based on the event-related field result and the forward solution to obtain a source estimation result of the magnetoencephalography data to be processed in the source space includes:

processing the line frequency domain signals of the preprocessed magnetoencephalography data to obtain a cross spectral density matrix;

determining a spatial filter based on the cross spectral density matrix and the forward solution;

and carrying out spatial filtering on the event related field result based on the spatial filter to obtain a source estimation result of the to-be-processed magnetoencephalography data in the source space.

In some embodiments, the generating a brain map traceable reconstruction result of the to-be-processed brain map data based on the source estimation result of the source space and a brain map template in a standard space includes:

performing linear transformation and nonlinear transformation on the source estimation result of the source space to obtain the source estimation result of the standard space;

mapping the source estimation result of the standard space into the brain map template of the standard space to obtain a brain map traceability reconstruction result of the brain map data to be processed.

In a second aspect, in this embodiment, a traceable reconstruction device of magnetoencephalography data is provided, including:

The acquisition module is used for acquiring the magnetoencephalography data to be processed of the target object;

the standardized preprocessing module is used for carrying out standardized preprocessing on the magnetoencephalography data to be processed to obtain preprocessed magnetoencephalography data;

the relevant field analysis module is used for carrying out event relevant field analysis on the preprocessed magnetoencephalography data to obtain event relevant field results, and the event relevant field results are used for representing the activity results of neurons caused by brain activity events of the target object;

the model construction module is used for constructing a head model of the target object based on magnetic resonance structural image data of the target object and constructing a source space based on user instructions, wherein the source space comprises a cortex source space or a voxel source space;

a calculation module for calculating a forward solution based on the source space and the head model;

the traceability reconstruction module performs traceability reconstruction based on the event-related field result and the forward solution to obtain a source estimation result of the magnetoencephalography data to be processed in the source space;

and the brain map mapping module is used for generating a brain map traceability reconstruction result of the brain map data to be processed based on the source estimation result of the source space and the brain map template in the standard space.

In a third aspect, in this embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the method for tracing and reconstructing magnetoencephalography data according to any one of the embodiments of the first aspect when the processor executes the computer program.

In a fourth aspect, in this embodiment, there is provided a storage medium having stored thereon a computer program, which when executed by a processor, implements the method for tracing reconstruction of magnetoencephalography data according to any one of the embodiments of the first aspect.

Compared with the related technology, the traceability reconstruction method of the magnetoencephalography data provided in the embodiment obtains the magnetoencephalography data to be processed of a target object through standardized preprocessing of the magnetoencephalography data to be processed after standardized preprocessing, so that bad channels and data segments in the magnetoencephalography data to be processed can be effectively removed, event-related field analysis is conducted on the magnetoencephalography data after preprocessing to obtain event-related field results, then a head model of the target object is constructed according to magnetic resonance structural image data of the target object, a source space is constructed according to user instructions, so that forward solutions are conveniently constructed according to the user instructions, further traceability reconstruction is conducted according to the event-related field results and the forward solutions, the traceability estimation result of the magnetoencephalography data to be processed of the target object in the source space is obtained, the traceability reconstruction of the magnetoencephalography data to be processed of the target object in the source space is achieved, the magnetoencephalography data can be generated in the map of the target object according to the source space and the standard space, the traceability map can be compared with the magnetoencephalography data, and the magnetoencephalography data can be compared with the map of the map can be obtained by means of the map-type.

The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the other features, objects, and advantages of the application.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:

fig. 1 is a schematic diagram of an application scenario of a traceability reconstruction method of magnetoencephalography data according to an embodiment of the present application;

FIG. 2 is a flowchart of a traceability reconstruction method of magnetoencephalography data according to an embodiment of the present application;

FIG. 3 is a flowchart of an embodiment of a method for traceable reconstruction of magnetoencephalography data according to an embodiment of the present application;

FIG. 4 is a flowchart of a standardized preprocessing of magnetoencephalography data according to an embodiment of the present application;

fig. 5 is a structural block diagram of a traceable reconstruction device for magnetoencephalography data according to an embodiment of the present application;

fig. 6 is an internal structural diagram of a computer device according to an embodiment of the present application.

Detailed Description

The present application will be described and illustrated with reference to the accompanying drawings and examples for a clearer understanding of the objects, technical solutions and advantages of the present application.

Unless defined otherwise, technical or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terms "a," "an," "the," "these" and similar terms in this application are not intended to be limiting in number, but may be singular or plural. The terms "comprising," "including," "having," and any variations thereof, as used herein, are intended to encompass non-exclusive inclusion; for example, a process, method, and system, article, or apparatus that comprises a list of steps or modules (units) is not limited to the list of steps or modules (units), but may include other steps or modules (units) not listed or inherent to such process, method, article, or apparatus. The terms "connected," "coupled," and the like in this disclosure are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The term "plurality" as used herein means two or more. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., "a and/or B" may mean: a exists alone, A and B exist together, and B exists alone. Typically, the character "/" indicates that the associated object is an "or" relationship. The terms "first," "second," "third," and the like, as referred to in this disclosure, merely distinguish similar objects and do not represent a particular ordering for objects.

A Magnetoencephalography (MEG) is a novel noninvasive brain imaging technology for reconstructing brain neuron electronic activities by measuring magnetic fields around the scalp, and is widely applied to clinic, for example, brain dynamic data acquisition and functional image reconstruction with high spatial resolution and high time resolution can be obtained through Magnetoencephalography, and the method is used for screening brain serious diseases and reducing misdiagnosis rate.

The magnetic field signals collected by the magnetoencephalography equipment are overlapped by magnetic fields generated by activities of all neurons in the brain. Complex neuron activity is abstracted into a dipole model, and the process is simulated by using a mathematical method according to the propagation rule of a magnetic field in space, namely a so-called positive problem in brain magnetic diagram research. The above process is contrary to the inverse problem in the magnetoencephalography process, i.e. the calculation of the brain internal signals by means of a correlation algorithm, which process is also called MEG traceability reconstruction.

At present, the magnetoencephalography data can be acquired through various different acquisition systems, and the data processing modes of the magnetoencephalography data acquired by the different systems are different, so that the difficulty of processing and analyzing the magnetoencephalography data by non-professional staff is increased. In addition, the common magnetoencephalography source tracing reconstruction maps the result on the vertex of the cerebral cortex or voxel space, and brain models constructed and used in different researches are different, so that a certain difficulty is caused to the repeatability and comparability of the researches.

Therefore, how to increase the versatility and the comparability of the processing and the analysis flow of the acquired magnetoencephalography data is a problem to be solved.

The traceability reconstruction method of the magnetoencephalography data provided by the embodiment of the application can be applied to an application scene shown in fig. 1, and fig. 1 is a schematic diagram of the application scene of the traceability reconstruction method of the magnetoencephalography data provided by the embodiment of the application. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be located on a cloud or other network server. The terminal 102 may be a collection device of magnetoencephalography data, such as various models of head-mounted devices. The server 104 may be implemented as a stand-alone server or as a server cluster of multiple servers.

In this embodiment, a method for tracing and reconstructing magnetoencephalography data is provided, and fig. 2 is a flowchart of a method for tracing and reconstructing magnetoencephalography data provided in the embodiment of the present application, where an execution body of the method may be an electronic device, optionally, the electronic device may be a server or a terminal, and in the embodiment of the present application, the execution body is illustrated by taking the server as an example, but the present application is not limited thereto. Specifically, as shown in fig. 2, the process includes the following steps:

Step S201, obtaining the magnetoencephalography data to be processed of the target object.

The magnetoencephalography data of the target object is collected by the magnetoencephalography data collection device and the collected magnetoencephalography data is transmitted to the electronic device, so that the electronic device can obtain magnetoencephalography data to be processed of the target object, and specifically, the magnetoencephalography data collection device can be a multichannel head-mounted wearing device, and the multichannel head-mounted wearing device can collect magnetoencephalography data to be processed by monitoring magnetic fields of cerebral neurons of the target object.

Step S202, standardized preprocessing is carried out on the magnetoencephalography data to be processed, and the preprocessed magnetoencephalography data is obtained.

Specifically, standardized preprocessing is performed on the magnetoencephalography data to be processed, so that bad segments and bad tracks in the magnetoencephalography data to be processed are removed, namely bad data in the magnetoencephalography data to be processed are removed, the preprocessed magnetoencephalography data are obtained, and accuracy of the preprocessed magnetoencephalography data is ensured.

Step S203, carrying out event-related field analysis on the preprocessed magnetoencephalography data to obtain event-related field results.

Wherein the event-related field results are used to characterize the activity results of neurons arising from brain activity events of the target subject.

Event-related fields (EventRelatedField, ERF) refer to the weak magnetic field changes in the peripheral and central nervous systems caused by the administration or withdrawal of a stimulus during the delivery of information in the course of an applied or endogenous stimulus to a portion of the human sensory system or brain to quantify brain activity associated with a given event.

For example, the preprocessed magnetoencephalography data is subjected to event-related field analysis, so as to obtain event-related field results, where an event may refer to an event related to brain activity of a target object, for example, an event such as "looking left" and "looking right". In particular, the event-related field results characterize the result of the activity of neurons arising from the event of brain activity of the target object.

Step S204, a head model of the target object is constructed based on the magnetic resonance structural image data of the target object, and a source space is constructed based on the user instruction.

Wherein the source space comprises a cortical source space or a voxel source space.

Further, magnetic resonance structural image data of the target object are acquired, and a head model of the target object is constructed according to the magnetic resonance structural image data of the target object.

Specifically, magnetic resonance structural image data of a target object are obtained through a magnetic resonance imaging scanner, cortical reconstruction is carried out on the magnetic resonance structural image data of the target object, a cortical reconstruction result is obtained, a head curved surface of the target object is created according to the cortical reconstruction result, furthermore, the head curved surface of the target object is segmented by using a watershed algorithm, an intra-brain skull curved surface, an outer skull curved surface and a scalp curved surface of the target object are obtained, further, the head of the target object is divided into three layers of a head cortex, a skull layer and a brain tissue layer according to the intra-brain skull curved surface, the outer skull curved surface and the scalp curved surface, dielectric constants and magnetic permeability are defined in each layer, and a head model of the target object is built by using a boundary element method.

And constructs a cortical (surface) or voxel (volume) based source space according to user instructions.

Specifically, the source space may include two types of cortex source space and source space, and the type of the source space is determined according to a user instruction in response to the user instruction, and further, the source space is constructed according to the determined type of the source space.

Determining the type of a source space according to a user instruction, thereby determining that the source space which is required to be constructed by a user is a cortical source space or a voxel source space, further, creating an extraction dipole grid on the white matter surface according to the determined source space type, setting the coverage range and the coverage interval of the grid, and further, constructing the source space according to the set coverage range and the coverage interval of the grid.

In step S205, a forward solution is calculated based on the source space and the head model.

Specifically, a preamble field is assigned to each source position associated with the sensor head position based on the source positions defined by the source space, thereby determining a forward solution.

And step S206, performing traceability reconstruction based on the event related field result and forward solution to obtain a source estimation result of the magnetoencephalography data to be processed in a source space.

Further, the traceable reconstruction is carried out according to the event related field result and the forward solution, so that a source estimation result of the brain magnetic map data to be processed in the source space is obtained, and the traceable reconstruction of the brain function image data in the source space is realized.

Step S207, a brain map traceability reconstruction result of the brain map data to be processed is generated based on the source estimation result of the source space and the brain map template in the standard space.

Further, the brain magnetic map data to be processed are mapped into a standard space, and a brain map template of the standard space is adopted to generate a traceable reconstruction result of the brain map.

In the implementation process, standardized pretreatment is carried out on the to-be-treated magnetoencephalogram data of the target object to obtain standardized pretreatment to-be-treated magnetoencephalogram data, so that bad tracks and bad segments in the to-be-treated magnetoencephalogram data can be effectively removed, event-related field analysis is carried out on the pretreated magnetoencephalogram data to obtain event-related field results, further, a head model of the target object is built according to magnetic resonance structural image data of the target object, a source space is built according to user instructions, so that forward solutions are conveniently built according to the user instructions, further, traceability reconstruction is carried out according to the event-related field results and forward solutions to obtain source estimation results of the to-be-treated magnetoencephalogram data of the target object in the source space, the traceability reconstruction of the to-be-treated magnetoencephalogram data of the target object in the source space is achieved, the brain map traceability reconstruction results of the to-treated magnetoencephalogram data in the standard space are generated according to the source estimation results of the source space and the brain map data in the standard space, and the brain map data of a brain map in the standard space can be represented by a brain map data of a brain researcher in the brain-magnetic domain, and the brain map data of a brain-sal-graph data of a brain-magnetic domain of a brain-domain research worker can be obtained, and the brain map data of a brain map of a brain data of a brain-research staff can be obtained in a trace-quality and a brain map data of a brain map of a professional graph can be obtained, and a brain map data of a brain map data can be obtained from a brain map data graph is obtained in a research mode. In addition, the traceability reconstruction method of the magnetoencephalography data provided by the application adds a certain commonality and comparability to the magnetoencephalography data processing and analysis flow by means of a brain atlas tool commonly used in other neuroimaging fields.

In some embodiments, acquiring the magnetoencephalography data to be processed of the target object may include the steps of:

step 1: and acquiring a magnetoencephalography signal of the target object.

Step 2: and carrying out differential processing on the magnetoencephalography signals by acquisition equipment to obtain magnetoencephalography data to be processed.

By way of example, the magnetoencephalography data to be processed can be obtained by collecting magnetoencephalography signals of the target object by different collecting devices and performing differential processing on the magnetoencephalography signals according to the different collecting devices.

Specifically, the magnetoencephalography data acquisition device may include a Neuromag/Elekta/Megin system, a CTF system, a BTI/4D system and a KIT/Yokogawa system, and data formats or data types corresponding to different acquisition systems may be different.

In the implementation process, the acquired magnetoencephalography signals are subjected to differential processing of the acquisition equipment according to different acquisition equipment types, so that the magnetoencephalography data subjected to differential processing can be subjected to subsequent processing and traceability reconstruction processes by adopting a unified method, and the magnetoencephalography data processing and traceability reconstruction efficiency is improved.

In some of these embodiments, the acquisition device diversity processing includes: at least one of signal spatial separation, gradient compensation, and channel compensation.

When the acquisition equipment of the magnetoencephalic signals is a Neuromag/Elekta/Megin system, signal space separation processing can be performed on the acquired magnetoencephalic signals, specifically, signal space separation can be performed according to the specific calibration and crosstalk files of the acquisition system, so that a signal source and environmental noise are separated, interference of the environmental noise on signals is reduced, and signal quality is improved.

When the brain magnetic signal acquisition equipment is a CTF system, gradient compensation processing can be performed on the acquired brain magnetic signals, so that the influence of magnetic field gradient non-uniformity on the brain magnetic signals is eliminated, and the accuracy and the precision of data are improved.

When the acquisition equipment of the magnetoencephalic signals is a BTI/4D system and a KIT/Yokogawa system, channel compensation can be carried out on the acquired magnetoencephalic signals, specifically, channel weight of the channel compensation is determined, noise and interference of other channels are reduced according to magnetoencephalic signal weighting in a reference channel acquired by the system, and therefore data quality is improved.

It should be noted that, in the embodiment of the present application, only one type of differential processing of the acquisition device is performed on the magnetoencephalic signal acquired by each acquisition device, and in the practical application process, one type of differential processing of the acquisition device may also be performed by using any two methods, or the differential processing of the acquisition devices may also be performed by using the three types of differential processing methods, or other differential processing schemes of the acquisition devices may also be used, which is not limited herein.

In the implementation process, the magnetoencephalography signals are subjected to differential processing by the acquisition equipment in various modes, so that the acquired magnetoencephalography data to be processed can be subjected to traceability reconstruction uniformly according to the subsequent processing flow, and the uniformity of the magnetoencephalography data analysis and traceability reconstruction flow is improved.

In some embodiments, the brain activity event of the target object includes a plurality of event types, and the standardized preprocessing is performed on the magnetoencephalography data to be processed to obtain preprocessed magnetoencephalography data, including:

step 1: eye movement or muscle artifact components in the magnetoencephalography data to be processed are determined.

Step 2: and removing bad channels and data segments containing eye movement or muscle artifact components to obtain removed data.

Step 3: and determining test data corresponding to each event type according to the stimulation channel of the removed data to obtain preprocessed magnetoencephalography data.

Illustratively, the brain activity event of the target object includes a plurality of event types, specifically, the brain activity event may include a plurality of types such as "look left", "look right", "look up" and "look down", and may also include other event types, which are not limited herein.

Further, a plurality of test data corresponding to each event type are respectively acquired, specifically, test (epoch) data under specific conditions are extracted according to the event type, and the event in the embodiment of the application can comprise two types of "looking left" and "looking right", so that the epoch data of "looking left" and the epoch data of "looking right" can be extracted, wherein the epoch data refers to data in signals of a plurality of specific time windows extracted from continuous magnetoencephalography data, and the magnetoencephalography data of each event type is stored in a corresponding data format.

Specifically, the data corresponding to eye movement or muscle artifacts in the magnetoencephalography data to be processed are determined, the eye movement artifacts are determined according to EOG channels of electrooculography, and the muscle artifacts are determined according to MEG magnetometer data filtered within the range of 110-140 Hz. And removing bad channels and data segments containing eye movement or muscle artifact components to obtain removed data, so as to remove bad track bad segments in the data, wherein the bad track bad segments refer to components containing the artifact or components which are too flat, and the bad data segments or channels are removed.

Further, test data corresponding to each event type is determined according to the stimulation channel of the removed data, so that preprocessed magnetoencephalography data is obtained.

In some embodiments, the event-related field analysis is performed on the preprocessed magnetoencephalography data to obtain event-related field results, which may include the following steps:

step 1: an average of the test data of the plurality of test data corresponding to each event type is determined.

Step 2: and carrying out event-related field analysis on the average value of the test data corresponding to each event type to obtain an event-related field result.

Further, determining an average value of test data of a plurality of test data corresponding to each event type, specifically, taking the event type as "looking left" as an example for explanation, and averaging a plurality of epoch data of "looking left" to obtain an average value of test data of "looking left".

Further, the event-related field analysis is performed on the average value of the test data of "looking left" to obtain the event-related field result of the "looking left" event, thereby realizing the determination of the event-related field result of each event type.

Similarly, by the method, the event-related field result corresponding to each event type can be determined, wherein the event-related field result can comprise event-related activation data, so that the related brain activities during data acquisition are quantized.

In the implementation process, the average value of the event-related field results of the plurality of magnetoencephalography data of each event type is subjected to event-related field analysis to obtain the event-related field result corresponding to each event type, so that the quantification of related brain activities during data acquisition is realized.

In some of these embodiments, calculating the forward solution based on the source space and the head model may include the steps of:

step 1: a first transformation matrix between a coordinate system of a sensor position of the acquisition device of the magnetoencephalography data to be processed and a head coordinate system of the target object is determined.

Step 2: a second transformation matrix between the head coordinate system of the target object and the magnetic resonance structure image coordinate system of the target object is determined.

Step 3: a forward solution is calculated by assigning a preamble field to each source location associated with the sensor head location based on the first transformation matrix, the second transformation matrix, the head model, and the source space.

The first transformation matrix between the coordinate system of the sensor position of the acquisition device of the magnetoencephalography data to be processed and the head coordinate system of the target object is determined by way of example, and in particular, the first transformation matrix can be calculated according to the MEG sensor position coordinate system provided by the magnetoencephalography data equipment manufacturer, the digital sensor position obtained when the data are acquired and the actual head coordinate system of the target object.

A second transformation matrix between the head coordinate system of the target object and the magnetic resonance structure image coordinate system of the target object is determined, specifically, the second transformation matrix may be obtained by obtaining a right-hand coordinate system (Right, anterior, superior, RAS) with the anisotropic voxel center as an origin according to the magnetic resonance (Magnetic Resonance Imaging, MRI) structure image data of the target object, fitting a reference point of the head coordinate using an iterative closest point method, and then calculating.

Further, according to the first transformation matrix, the second transformation matrix, the head model and the source space, a leading field is allocated to each source position related to the head position of the sensor in the source space to construct a forward solution, so that the forward solution corresponding to the head model is obtained.

In the implementation process, according to a first conversion matrix between the coordinate system of the acquisition equipment of the magnetoencephalography data to be processed and the head coordinate system of the target object and a second conversion matrix between the head coordinate system of the target object and the magnetic resonance structure image coordinate system of the target object, a forward solution is obtained by calculation based on a source space and a head model, and the forward solution is used in the subsequent traceability reconstruction process of the magnetoencephalography data.

In some embodiments, performing traceability reconstruction based on event-related field results and forward solution to obtain a source estimation result of magnetoencephalography data to be processed in a source space may include the following steps:

step 1: and processing the frequency domain signals of the preprocessed magnetoencephalography data to obtain a cross spectral density matrix.

Step 2: a spatial filter is determined based on the cross spectral density matrix and the forward solution.

Step 3: and carrying out spatial filtering on the event related field result based on a spatial filter to obtain a source estimation result of the magnetoencephalography data to be processed in a source space.

The frequency domain signal of the preprocessed magnetoencephalography data is processed to obtain a cross spectral density matrix, and specifically, the frequency domain signal of the standardized preprocessed epoch data is processed to obtain the cross spectral density matrix of the epoch data.

Further, a spatial filter used in the beamforming method is calculated using a beamformer algorithm based on the cross spectral density matrix of the epoch data and the forward solution.

Finally, a spatial filter is applied to the event-related field results to obtain a source estimation result. Specifically, a spatial filter is used for carrying out spatial filtering on the event-related field result, and brain activity source signals related to the event are extracted, so that a source estimation result of the brain magnetic map data to be processed in a source space is obtained.

In the embodiment of the application, the traceability reconstruction of the magnetoencephalography data is only illustrated by taking the beamforming method as an example, and in practical application, the traceability reconstruction of the magnetoencephalography data can also be performed by adopting a minimum standard method. Other methods may be used without limitation.

In the implementation process, the frequency domain signals of the preprocessed magnetoencephalography data are processed, so that a cross spectral density matrix of each event is obtained, and further, a space filter is determined according to the cross spectral density matrix and a forward solution, so that the space filter is conveniently applied to event related field results, and a source estimation result of the magnetoencephalography data to be processed in a source space is obtained.

In some embodiments, generating a brain map traceability reconstruction result of the brain map data to be processed based on the source estimation result of the source space and the brain map template in the standard space may include the following steps:

step 1: and carrying out linear transformation and nonlinear transformation on the source estimation result of the source space to obtain the source estimation result of the standard space.

Step 2: mapping the source estimation result of the standard space into a brain map template of the standard space to obtain a brain map traceability reconstruction result of the brain map data to be processed.

Illustratively, the source estimation result of the source space is transformed into the standard space, resulting in the source estimation result of the standard space. The standard space is the space corresponding to the brain atlas, specifically, the source estimation result of the source space is mapped into the standard space through affine transformation and symmetrical differential registration deformation, so as to obtain the source estimation result of the standard space. Affine transformation is a linear transformation that can transform the source estimation result from the source space to the standard space, and symmetric differential registration transformation is a nonlinear transformation that optimizes the spatial alignment and morphological consistency of the source estimation result in the standard space by symmetric differential registration transformation.

And acquiring a brain spectrum template in the standard space, and mapping a source estimation result of the standard space into the brain spectrum template, so as to obtain a traceable reconstruction result of the brain spectrum level. Specifically, the standard space may include a plurality of brain map templates, for example, brain network group maps (Brainnetome Atlas, BNA) and human connection plan multi-modal segmentation maps (Human Connectome Project Multi-Modal Parcellation, HCP-MMP), and different brain map templates may be selected to perform brain map traceability reconstruction of the brain map data to be processed, so as to obtain brain map traceability reconstruction results of the brain map data to be processed. Further, analysis can be performed according to a brain map traceability reconstruction result of the brain map data to be processed, and universality of traceability reconstruction of the brain map data is improved.

In the implementation process, the source estimation result of the source space is mapped into the standard space, and the corresponding brain map traceability reconstruction result is generated according to the brain map template in the standard space, so that the reliability and the repeatability of the brain magnetic map data processing and analysis research are improved.

In this embodiment, another traceability reconstruction method of the magnetoencephalography data is also provided. Fig. 3 is a flowchart of an embodiment of a traceability reconstruction method of magnetoencephalography data according to an embodiment of the present application, as shown in fig. 3, where the flowchart includes the following steps:

step S301, performing specific processing on the acquired magnetoencephalography data.

Specifically, different magnetoencephalography data acquisition systems have specificity, and in order to ensure that the processing flow of magnetoencephalography data acquired by the different acquisition systems has uniformity, the acquired magnetoencephalography data is subjected to system-specific processing, so that the influence of the difference among the data acquired by the different acquisition systems on the subsequent standardized preprocessing flow is eliminated. The FIFF format data acquired by the Neuromag/Elekta/Megin system can be subjected to signal space separation processing, and the signal space separation processing is performed by relying on a specific calibration and crosstalk file of the system data, so that the signal source and the environmental noise are separated to reduce the interference of the environmental noise on the signal and improve the quality of the signal; gradient compensation processing is carried out on data acquired by the CTF system, so that the influence of magnetic field gradient non-uniformity on brain magnetic signals is eliminated, and the accuracy and the precision of the data are improved; and applying reference channel compensation channel weights to data acquired by the BTI/4D system and the KIT/Yokogawa system, and reducing noise and interference of other channels by utilizing the reference channel data weights acquired by the system, thereby improving the data quality. Because of the uniformity of the processing of the magnetoencephalography data collected by different systems in the subsequent processing flow, for convenience of description, the FIFF format MEG data collected by the Neuromag system is taken as an example in this embodiment for explanation.

Step S302, standardized preprocessing is carried out on the magnetoencephalography data after the specific processing, test data corresponding to the event are extracted, and event-related field analysis is carried out on the event data.

Specifically, standardized preprocessing is performed on the magnetoencephalography data after specific processing, and fig. 4 is a flowchart of standardized preprocessing of magnetoencephalography data provided by an embodiment of the present application, where the flowchart shown in fig. 4 includes:

step S1: eye movement or muscle artifact data in the magnetoencephalography data is identified.

Eye movement or muscle artifacts in the data are identified based on EOG channels of the electrooculogram, and muscle artifacts are identified based on magnetoencephalography magnetometers filtered in the range of 110-140 Hz.

Step S2: and removing bad track bad sections in the magnetoencephalography data.

The removal of bad track segments in the magnetoencephalography data refers to the removal of data segments or channels containing the components of the artifact or the components which are too flat.

Step S3: and reading and generating events from the stimulation channels of the data, and extracting test data related to the events.

The event is read from the stimulation channel of the data and generated, the event is stored in a specific data type, and then the epoch data under specific conditions are extracted according to the event, and in the embodiment, the event has left-looking and right-looking, so that the epoch data of left-looking or right-looking can be extracted for subsequent analysis.

Step S4: and carrying out event-related field analysis on the event-related test data.

Event-related field analysis is then performed, with event-related fields resulting from neuronal activity induced by a given event, which are commonly used in cognitive and clinical neuroscience to quantify brain activity associated with a given task, and in this embodiment, either "left-looking" epoch data or "right-looking" epoch data, respectively, may be averaged to obtain "left-looking" or "right-looking" event-related field results.

Step S303, constructing a head model according to the magnetic resonance structural image data of the target object.

In particular, a head model for source modeling is constructed from structural image data of magnetic resonance of a target object, which model encapsulates the geometry and conductivity of different tissue compartments. The first cortical reconstruction of the magnetic resonance structure image data of the target object can be realized generally by freeform software. FreeSterfer is a piece of open source software for magnetic resonance image processing and analysis, preprocessing of multi-modal data, cortical reconstruction and the like can be performed, and a make_scale_surfaces method of MNE toolkit can be used for creating a high-resolution head curved surface for subsequent coordinate alignment on the result of FreeSterfer cortical reconstruction. Then using the watershed algorithm to segment, the make_watershed_ bem method of the MNE kit can be used to obtain the curved surfaces of the inner skull, the outer skull and the scalp of the brain. Finally, based on these curved surfaces, the head part is divided into three layers of a head cortex, a skull layer and a brain tissue layer, and dielectric constant and magnetic permeability are defined in each layer, and a head model is constructed using a boundary element method. The construction of the head model does not depend on the magnetoencephalography data of the target object, and only needs the magnetic resonance structural image data of the target object.

Step S304, aligning the space coordinate system of the magnetoencephalography sensor, the space coordinate system of the head and the space coordinate system of the head model, and constructing a source space.

Specifically, the space coordinate system of the magnetoencephalography sensor, the space coordinate system of the head and the space coordinate system of the head model are aligned, namely a first coordinate transformation matrix between the space coordinate system of the magnetoencephalography sensor and the space coordinate system of the head needs to be determined, and a second coordinate transformation matrix between the space coordinate system of the head and the space coordinate system of the head model needs to be determined. The first coordinate transformation matrix can be calculated according to a position coordinate system of a magnetoencephalography sensor provided by magnetoencephalography equipment manufacturer and a digital sensor position and a scalp landmark coordinate system obtained when data are acquired, the second coordinate transformation matrix can be obtained according to tested magnetic resonance structure image data, an RAS coordinate system taking an anisotropic voxel center as an origin is obtained, and then a datum point of a head coordinate is fitted by using an iterative nearest point method, and the RAS coordinate system is calculated.

Further, according to a user instruction, constructing a cortex-based source space or a voxel-based source space, creating a proper extraction dipole grid on the white matter surface, setting a grid coverage range and a grid coverage interval, constructing the cortex-based source space by using a setup_source_space method of an MNE kit, and constructing the voxel-based source space by using the setup_volume_source_space method.

And step S305, calculating a forward solution based on the constructed source space, and calculating an inverse operator by using a beam former algorithm to obtain a tracing result of event related activation.

Specifically, the head model and the source space which are obtained in the above steps, and the alignment transformation matrix result among the three coordinate systems of the brain magnetic map sensor space coordinate, the head space coordinate and the head model coordinate, allocate a leading field to each source position related to the sensor head position to construct a forward solution, and can be realized by using a make_forward_solution method of an MNE kit. Next, a cross spectral density matrix of the epoch data is calculated, which is obtained by processing the frequency domain signal using a multi-window spectral estimation technique (multitaper spectral estimation) in this embodiment. Then, in this embodiment, a beam former algorithm is used to perform a traceable reconstruction, and the algorithm extracts brain activity source signals related to a given event by spatially filtering the cross spectral density matrix. Based on the resulting forward model and the cross spectral density matrix, a spatial filter used in the beamforming method is calculated. Finally, a filter is applied to the event related activation data resulting in a result of the source estimation, which is vertex based, the number of vertices depending on whether the source space used is cortical source space or voxel source space.

And step S306, mapping the result of the source estimation into a standard space, and obtaining a traceable reconstruction result of the brain spectrum level by using a brain spectrum template of the standard space.

The obtained source estimation results are all based on the cortical source space or voxel source space of the target object individual, the obtained results need to be deformed into the standard space, the source estimation results in the individual source space are mapped into the fserveverage standard source space, the source estimation results in the fseverage-ico-5 standard source space are mapped based on the results of the individual cortical source space, and the source estimation results in the fseverage-vol-5 standard source space are mapped based on the results of the individual voxel source space. The source estimated deformations mainly require affine transformations and symmetric differential registration deformations. Affine transformation is a linear transformation that can transform the source estimation result from the original reference space to the standard space. The symmetrical differential registration transformation is a nonlinear transformation, and can further optimize the spatial alignment and morphological consistency of the source estimation result. In this embodiment, the method of computer_source_morph of the mno toolkit is used to perform source space deformation, so as to obtain a source estimation result in the fserveverage standard space. Then, mapping the source estimation result in the fservverage standard space by using a brain map template in the standard space, and selecting different brain maps for mapping, for example, BNA (Brainnetome Atlas), HCP-MMP (Human Connectome Project Multi-Modal Parcellation) and the like, so as to finally obtain a traceable reconstruction result of the brain map level.

In the implementation process, the system specificity processing is carried out on the magnetoencephalography data, so that the influence of the difference between the acquired data of different systems on the subsequent standardized preprocessing flow is eliminated. The provided flow for standardized preprocessing of the magnetoencephalography data is beneficial to the reliability and the repeatability of magnetoencephalography data processing and analysis research, and can help common scientific researchers and medical professionals to rapidly start processing the magnetoencephalography data. Secondly, the invention provides a brain map level traceability reconstruction method for the brain map data, adds a certain commonality and comparability to the brain map data processing and analyzing flow by means of brain map tools commonly used in other nerve image fields, provides a new thought for brain map related research, and has important significance for brain function research of brain map angles.

Although the steps in the flowcharts according to the embodiments described above are shown in order as indicated by the arrows, these steps are not necessarily executed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.

In this embodiment, a traceability reconstruction device for magnetoencephalography data is further provided, and the traceability reconstruction device is used for implementing the foregoing embodiments and preferred embodiments, and is not described again. The terms "module," "unit," "sub-unit," and the like as used below may refer to a combination of software and/or hardware that performs a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementations in hardware, or a combination of software and hardware, are also possible and contemplated.

Fig. 5 is a structural block diagram of a traceability reconstruction device for magnetoencephalography data according to an embodiment of the present application, where, as shown in fig. 5, the device includes:

an obtaining module 501, configured to obtain magnetoencephalography data to be processed of a target object;

the standardized preprocessing module 502 is configured to perform standardized preprocessing on magnetoencephalography data to be processed, so as to obtain preprocessed magnetoencephalography data;

the relevant field analysis module 503 is configured to perform event relevant field analysis on the preprocessed magnetoencephalography data to obtain event relevant field results, where the event relevant field results are used to characterize an activity result of a neuron caused by a brain activity event of a target object;

A model building module 504 for building a head model of the target object based on magnetic resonance structural image data of the target object and building a source space based on user instructions, the source space comprising a cortical source space or a voxel source space;

a calculation module 505 for calculating a forward solution based on the source space and the head model;

the traceability reconstruction module 506 performs traceability reconstruction based on the event-related field result and forward solution to obtain a source estimation result of the magnetoencephalography data to be processed in the source space;

the brain map mapping module 507 is configured to generate a brain map traceability reconstruction result of the brain map data to be processed based on the source estimation result of the source space and the brain map template in the standard space.

In some of these embodiments, the acquisition module 501 is specifically configured to:

collecting a magnetoencephalography signal of a target object;

In some of these embodiments, the brain activity events of the target subject include a plurality of event types, and the standardized preprocessing module 502 is specifically configured to:

removing bad channels and data segments containing eye movement or muscle artifact components to obtain removed data;

and determining test data corresponding to each event type according to the stimulation channel of the removed data to obtain preprocessed magnetoencephalography data.

In some of these embodiments, the relevant field analysis module 503 is specifically configured to:

In some of these embodiments, the computing module 505 is specifically configured to:

determining a second transformation matrix between a head coordinate system of the target object and a magnetic resonance structural image coordinate system of the target object;

a forward solution is calculated by assigning a preamble field to each source location associated with the sensor head location based on the first transformation matrix, the second transformation matrix, the head model, and the source space.

In some of these embodiments, the traceability reconstruction module 506 is specifically configured to:

processing the frequency domain signals of the preprocessed magnetoencephalography data to obtain a cross spectral density matrix;

and carrying out spatial filtering on the event related field result based on a spatial filter to obtain a source estimation result of the magnetoencephalography data to be processed in a source space.

In some of these embodiments, the brain map mapping module 507 is specifically configured to:

performing linear transformation and nonlinear transformation on a source estimation result of a source space to obtain a source estimation result of a standard space;

mapping the source estimation result of the standard space into a brain map template of the standard space to obtain a brain map traceability reconstruction result of the brain map data to be processed.

The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.

In one embodiment, a computer device is provided, which may be a server, and fig. 6 is an internal structural diagram of the computer device provided in an embodiment of the present application, and as shown in fig. 6, the computer device includes a processor, a memory, and a network interface connected through a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing the magnetoencephalography data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, implements a traceable reconstruction method of magnetoencephalography data.

It will be appreciated by those skilled in the art that the structure shown in FIG. 6 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.

In one embodiment, there is also provided an electronic device including a memory and a processor, the memory storing a computer program, the processor implementing the steps of the method embodiments described above when executing the computer program.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.

The user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random AccessMemory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (StaticRandom Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the patent claims. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.

Claims

1. The traceability reconstruction method of the magnetoencephalography data is characterized by comprising the following steps of:

acquiring magnetoencephalography data to be processed of a target object;

calculating a forward solution based on the source space and the head model;

the computing a forward solution based on the source space and the head model, comprising:

assigning a lead field to each source location associated with a sensor head location based on the first transformation matrix, the second transformation matrix, the head model, and the source space to calculate a forward solution;

2. The traceable reconstruction method of magnetoencephalography data according to claim 1, wherein the acquiring magnetoencephalography data to be processed of the target object comprises:

collecting a magnetoencephalography signal of the target object;

and carrying out differential processing on the magnetoencephalography signals to obtain magnetoencephalography data to be processed of the target object.

3. The traceability reconstruction method of magnetoencephalography data according to claim 2, wherein the differential processing of the acquisition device comprises: at least one of signal spatial separation, gradient compensation, and channel compensation.

4. The method for traceable reconstruction of magnetoencephalography data according to claim 1, wherein brain activity events of the target object include a plurality of event types, the standardized preprocessing is performed on the magnetoencephalography data to be processed to obtain preprocessed magnetoencephalography data, and the method comprises:

5. The method for traceable reconstruction of magnetoencephalography data according to claim 4, wherein the performing event-related field analysis on the preprocessed magnetoencephalography data to obtain event-related field results comprises:

6. The method for traceable reconstruction of magnetoencephalography data according to claim 1, wherein the performing traceable reconstruction based on the event-related field result and the forward solution to obtain a source estimation result of the magnetoencephalography data to be processed in the source space comprises:

7. The method for traceable reconstruction of magnetoencephalography data according to claim 1, wherein the generating the traceable reconstruction of the brain atlas of the magnetoencephalography data to be processed based on the source estimation result of the source space and the brain atlas template in the standard space comprises:

8. The utility model provides a brain magnetic map data's rebuild device that traces to source which characterized in that includes:

the computing module is specifically configured to:

9. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the method of traceable reconstruction of magnetoencephalography data according to any of claims 1 to 7.

10. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the method for trace-source reconstruction of magnetoencephalography data according to any of claims 1 to 7.