KR102096357B1 - Device and method for estimating task-related spatial patterns using task-negative default-mode networks and recording medium for performing the method - Google Patents

Abstract

The present invention is a default-mode network implemented to estimate brain regions related to a task performed when an image is acquired without hypothesis or prior information using data obtained using magnetic resonance imaging (MRI) equipment. An apparatus and method for estimating a task-related area, and a recording medium for performing the method, performing first independent component analysis using fMRI data according to a subject's task as input data to perform first independent components of the entire brain Extracting; Selecting and integrating independent components related to DMN among the first independent components extracted for the entire brain; Extracting second independent components in the DMN by performing a second independent component analysis on the independent components related to the DMN; Estimating probability-based structural connectivity of the brain function-activated region using the subject's DTI data; And estimating a brain region related to the task paradigm based on the inverse correlation between the second independent components extracted from the DMN and the first independent components extracted from the entire brain and the probability-based structural connectivity. Includes.

Description

DEVICE AND METHOD FOR ESTIMATING TASK-RELATED SPATIAL PATTERNS USING TASK-NEGATIVE DEFAULT-MODE NETWORKS AND RECORDING MEDIUM FOR PERFORMING THE METHOD}

The present invention relates to an apparatus and method for estimating a task-related area using a default-mode network, and a recording medium for performing the method, and more specifically, data obtained using magnetic resonance imaging (MRI) equipment. The present invention relates to a task-related area estimation apparatus and method using a default-mode network implemented to estimate brain areas related to a task, and a recording medium for performing the method.

Neurologic diseases such as Mild Cognitive Impairment (MCI), Alzheimer's disease (AD) or Stroke, result in structural changes in the brain, and various methods for diagnosing these neurological diseases have been studied. Became.

In the conventional method of diagnosing Parkinson's disease by quantitative measurement of brain metabolites using magnetic resonance spectroscopy, the magnetic resonance spectroscopy of the brain tissue regions of the Parkinson's disease animal model and the normal animal model is obtained from the magnetic resonance imaging equipment to obtain the spectrum. There is a diagnosis of Parkinson's disease by comparing and comparing the spectrum of a normal animal model with a spectrum of brain metabolites glutamate complex / creatine.

However, a recent study reported that the dementia disease group such as mild cognitive impairment and Alzheimer's are related to abnormalities in the Default Mode Network (DMN). Here, the default mode network is a network that is activated when the brain is in a resting state.

Although studies on the default mode network have reported that the default mode network of patients with mild cognitive impairment and Alzheimer's disease is reduced in functional connectivity compared to normal people, some studies have shown that the functional connectivity of the default mode network in this group of dementia diseases The result was increased.

From these results, it can be said that both functional disability and compensation exist in the dementia disease group.

Accordingly, there is a need for a method capable of accurately defining an area in which a functional change of the default network mode appears in the process of dementia and distinguishing a change pattern of functional connectivity according to the progression of dementia.

However, the existing model-based analysis method, which is not suitable for processing brain signal data that changes in real time, was unable to estimate the brain region activated from the data itself.

In addition, in the case of the neuro / psychiatric disease patient group, the estimation of brain region activation according to the task paradigm may be limited, and there is a difficulty in estimation when the activation is insufficient in the damaged brain region.

Korean Patent Publication No. 10-2016-0044641 (2016.04.26.)

Quantitative and qualitative analysis of the default mode network for patients with mild cognitive impairment and Alzheimer's disease (Jae-Ho Cha, Graduate School of Hanyang University, published on 2011.02.)

In one aspect of the present invention, the present invention uses fMRI (functional MRI) and DTI (diffusion tensor imaging) data obtained using magnetic resonance imaging (MRI) equipment. Provided is an apparatus and method for estimating a task-related area using a default-mode network implemented to estimate brain areas related to a task performed when an image is acquired without hypothesis or prior information, and a recording medium for performing the method.

The technical problems of the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description.

The method for estimating a task-related area using a default-mode network according to an embodiment of the present invention includes a first independent component analysis, using fMRI (functional Magnetic Resonance Imaging) data according to a subject's task as input data. ICA) to extract the first independent components of the entire brain; Selecting and integrating independent components related to a default-mode network (DMN) among the first independent components extracted for the entire brain; Extracting second independent components in the DMN by performing a second independent component analysis on the independent components related to the DMN; Estimating probability-based structural connectivity (SC) of an area in which brain function is activated using DTI (Diffusion Tensor Imaging) data of a subject according to a subject's task performance; And estimating a brain region related to the task paradigm based on the inverse correlation between the second independent components extracted from the DMN and the first independent components extracted from the entire brain and the probability-based structural connectivity. Includes.

In one embodiment, in the step of extracting the first independent components, the input data of the first independent component analysis is mixed with a signal source of a blood-oxygen level dependency (BOLD) signal according to a subject's task performance. It may be a linear relationship of a mixing matrix.

In one embodiment, the step of extracting the second independent component comprises: selecting DMN and related components among the first independent components extracted in the step of extracting the first independent components; Generating a binary mask of voxels of a brain region corresponding to the selected DMN-related component; And extracting second independent components that are independent components of the DMN by performing a second independent component analysis in the generated binary mask.

In an embodiment, the step of extracting the first independent components may include subdividing the entire brain by performing a first independent component analysis using fMRI data as input data according to a subject's task performance; After the unmixing matrix is created to extract the first independent component for the entire segmented brain from the input data, the mutual information between the non-mixing matrix and the data outputs of the inner product of the input data (mutual- learning an unmixed matrix in which information) is minimized; And extracting the first independent components for the entire brain by dot product of the trained non-mixing matrix and input data.

In one embodiment, the non-mixing matrix learned in the step of learning the non-mixing matrix may be an inverse matrix and a mixing matrix in a linear relationship with input data of the first independent component analysis.

In one embodiment, estimating the structural connectivity may extract structural connectivity passing through regions estimated to be brain regions related to the task paradigm, and then grasp structural connectivity of brain regions related to the task paradigm.

In one embodiment, a method for estimating a task-related region using a default-mode network according to another embodiment of the present invention comprises: MRI before extracting the first independent components or estimating the probability-based structural connectivity. The method may further include pre-processing fMRI data or DTI data according to performance of a subject obtained by using the equipment.

In one embodiment, a method for estimating a task-related area using a default-mode network according to another embodiment of the present invention estimates a brain area related to a task paradigm in estimating the brain area using fMRI data It may further include the step of evaluating and verifying the domain.

In one embodiment, the step of evaluating and verifying comprises: subdividing a DMN that has an inverse correlation with a task performed by a subject; Estimating an area inversely correlated from the segmented DMN area as a brain area related to the task paradigm; And evaluating and verifying the area estimated as the brain area related to the task paradigm using real-time and non-real-time fMRI data.

A computer program for performing a task-related area estimation method using a default-mode network is recorded in a computer-readable recording medium according to another embodiment of the present invention.

An apparatus for estimating a task-related area using a default-mode network according to an embodiment of the present invention includes first independent component analysis (fMRI) as input data using functional magnetic resonance imaging (fMRI) data of a subject performing a task. ICA) to extract the first independent components of the entire brain to extract the first independent components; An independent component integration unit for selecting and integrating independent components related to a default-mode network (DMN) among the first independent components extracted for the entire brain by the first independent component extraction unit; A second independent component extraction unit that extracts second independent components in the DMN by performing a second independent component analysis on the independent components related to DMN integrated by the independent component integration unit; A structural connectivity estimator for estimating probability-based structural connectivity (SC) of an area in which brain function is activated by using subject's DTI (Diffusion Tensor Imaging) data according to a subject's task performance; And an inverse correlation between the second independent components extracted in the DMN by the second independent component extraction unit and the first independent components extracted from the entire brain by the first independent component extraction unit and the structural connectivity estimation unit. And a brain region estimator for estimating a brain region related to the task paradigm based on the estimated probability-based structural connectivity.

According to one aspect of the present invention described above, it is possible to estimate the brain region related to the task paradigm only with data obtained without any information, and it is possible to estimate the brain area related to the personalized task paradigm by reflecting the variability and real-time variability of each subject. It can help in solving potential problems when processing real-time brain function image data.

In addition, it can be applied to a patient group with brain damage and variability by region by allowing patient-specific analysis by applying it to a patient group of neuro / psychiatric disease.

1 is a control block diagram illustrating an apparatus for estimating a task-related area using a default-mode network according to an embodiment of the present invention.
2 is a control block diagram illustrating an apparatus for estimating a task-related area using a default-mode network according to another embodiment of the present invention.
3 is a flowchart illustrating a task-related area estimation method using a default-mode network according to an embodiment of the present invention.
4 is a flowchart illustrating the steps of extracting the first independent components of FIG. 3.
FIG. 5 is a flowchart illustrating the steps of extracting the second independent components of FIG. 3.
6 is a view for explaining cross-correlation analysis between independent component time-path data in the present invention.
7 is a diagram for explaining task-related independent networks showing connectivity with the DMN.
8 is a flowchart illustrating a task-related area estimation method using a default-mode network according to another embodiment of the present invention.
9 is a flowchart illustrating a task-related area estimation method using a default-mode network according to another embodiment of the present invention.
10 to 13 are views for explaining estimation results in each experimental example according to another embodiment of the present invention of FIG.

For a detailed description of the present invention, which will be described later, reference is made to the accompanying drawings that illustrate, by way of example, specific embodiments in which the present invention may be practiced. These examples are described in detail enough to enable those skilled in the art to practice the present invention. It should be understood that the various embodiments of the present invention are different, but need not be mutually exclusive. For example, certain shapes, structures, and properties described herein may be implemented in other embodiments without departing from the spirit and scope of the invention in connection with one embodiment. In addition, it should be understood that the location or placement of individual components within each disclosed embodiment can be changed without departing from the spirit and scope of the invention. Therefore, the following detailed description is not intended to be taken in a limiting sense, and the scope of the present invention, if appropriately described, is limited only by the appended claims, along with all ranges equivalent to those claimed. In the drawings, similar reference numerals refer to the same or similar functions across various aspects.

Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the drawings.

1 is a diagram illustrating a schematic configuration of a task-related area estimation apparatus using a default-mode network according to an embodiment of the present invention.

Referring to FIG. 1, the apparatus 10 for estimating a task-related area using a default-mode network according to an embodiment of the present invention includes a first independent component extraction unit 100, an independent component integration unit 200, and a second An independent component extraction unit 300 and a brain region estimation unit 500 are included.

The first independent component extracting unit 100 performs a first independent component analysis (ICA) using fMRI (functional Magnetic Resonance Imaging) data according to the subject's task performance as input data to remove the entire brain. 1 Extract the independent ingredients.

Here, the application of the independent component analysis to the entire brain by the first independent component extraction unit 100 is to divide functional independent components of the entire brain, where the number of independent components corresponds to the number of fMRI acquisition volumes.

In one embodiment, the first independent component extraction unit 100 performs a first independent component analysis (ICA) using fMRI (functional Magnetic Resonance Imaging) data according to a subject's task performance as input data. The whole brain can be subdivided.

Here, the input data (X) of the first independent component analysis includes a signal source (S) and a mixing matrix (A) of a blood-oxygen level dependency (BOLD) signal according to a subject's task performance. ) In a linear relationship.

That is, it is assumed that fMRI data is used as the input data (X) for independent component analysis, and the input data (X) for independent component analysis is a linear relationship between the signal source (S) and the mixing matrix (A) as shown in Equation 1 below. do.

Next, the first independent component extracting unit 100 creates an unmixing matrix to extract the first independent component for the entire segmented brain from the input data, and then internalizes the non-mixing matrix and the input data. (inner product) You will learn a non-mixing matrix that minimizes mutual-information between data outputs.

That is, to extract independent components from the input data (X), an unmixing matrix (W) is created, and a non-mixing matrix in which the amount of mutual information between data outputs (non-mixing matrix and dot product of input data) is minimized ( W).

Accordingly, the learned non-mixing matrix W has an inverse matrix relationship with the mixing matrix A as shown in Equation 2 to be described later, and through this, the learned non-mixing matrix W and the input data X are dot products. First independent components for the entire brain can be extracted.

Currently, fMRI is widely used as one of the representative methods of non-invasive measurement of the brain because the spatial resolution of data is relatively high.

Here, fMRI (functional MRI, functional magnetic resonance imaging) is a method of imaging various physical quantities that enable the identification of an active site of the brain as a measurement amount, and is one of effective methods for measurement of brain function.

fMRI reflects the density of protons, longitudinal relaxation time T1, and transverse relaxation time T2 of tissue in vivo, similar to the principle of anatomical MRI imaging the structure of the brain, but increases blood flow at the active site of the brain There is a characteristic in grasping the point.

In the active site of the brain, it is known that blood flow increases locally, and blood-oxygenation-level-dependent (BOLD) increases.

Therefore, using fMRI, it is possible to specify a brain function region related to the task based on a change in the BOLD signal when the subject is performing a task.

Here, Independent Component Analysis (ICA) for extracting independent components is an analysis method used in fMRI studies, and BOLD signals of voxels measured in a network that is a collection of brain regions in the entire brain. It is assumed that the amount of change in is linearly related.

In order to estimate the independent component corresponding to the common BOLD (Blood Oxygen Level Dependency) signal using independent component analysis, a non-mixing matrix (M) to minimize mutual-information between independent components ( By learning the unmixing matrix), independent components can be extracted by simultaneously considering time and domain information from fMRI data.

The independent component integration unit 200 selects and integrates independent components related to the default-mode network (DMN) among the first independent components extracted for the entire brain by the first independent component extraction unit 100.

The independent component integration unit 200 may select and integrate independent components belonging to a predefined DMN by selecting independent components related to the DMN, thereby generating a mask.

Here, the default-mode means a kind of basic setting in which activity decreases when the brain does something and then increases again during rest.

The default-mode network refers to an area of the brain activated in the resting state, and is inactivated when performing a task, and this aspect has been revealed in many studies.

Specifically, the brain regions associated with the default-mode network were found to be the posterior cingulate cortex, the center of the precuneus and the center of the medical prefrontal cortex (MPFC).

Thus, the default-mode network refers to a network that is known to be functionally tightly connected, although several areas are not contiguous when the brain is resting.

The second independent component extracting unit 300 extracts second independent components in the DMN by performing a second independent component analysis on the independent components related to the DMN integrated by the independent component integrating unit 200.

Here, the second independent component extracting unit 300 may extract reliable fine-grained independent components from the DMN through the ICASSO (total number of repetitions = 100) method by applying the independent component analysis in the DMN.

Here, the ICASSO method refers to a method capable of estimating reliable independent components by repeatedly learning such that learning results can be reported without bias by randomly setting an initial weight during independent component analysis.

In one embodiment, the second independent component extraction unit 300 selects DMN-related components from the first independent components extracted by the first independent component extraction unit 100, and binary methods of voxels corresponding to the DMN-related components A binary mask is generated and detailed second independent components of the DMN can be extracted by performing a second independent component analysis in the generated binary mask.

The brain region estimator 500 includes second independent components extracted from the DMN by the second independent component extracting unit 300 and first independent components extracted from the entire brain by the first independent component extracting unit 100. Through the correlation analysis between the brain regions, the brain regions showing inverse correlation with the second independent components, which are the detailed independent components of the DMN, are estimated as brain regions related to the task paradigm.

Here, the task paradigm (Task paradigm or Task) refers to a series of actions performed by a subject according to a given task in the process of acquiring a brain function image, and is generally a block-based task and an event (Event) -related) tasks.

The block-based task is a method of alternately performing a task execution cycle and a rest cycle according to a specific period, and an event-related task is a method in which a subject performs a series of actions on a task presented without a predetermined cycle.

In general, the task paradigm is used to analyze brain function activation patterns according to specific tasks by analyzing blood oxygen concentrations occurring in task execution cycles and rest cycles.

In performing the same task, interest in research and diagnosis applications on differences between patients with brain dysfunction and normal humans is increasing, and representatively, patients with mild cognitive impairment and dementia perform finger-finishing tasks while hippocampus brains There are prior arts for differences from normal people using features that weaken function.

In addition, there are prior studies on the difference in brain function activation patterns in exercise-related brain regions between stroke patients and normal persons in performing the tasks of hand gripping and unfolding of stroke patients.

By using the brain region associated with the task paradigm estimated in the brain region estimator 500, it can be used to specify the surgical scope by confirming the function of the brain region around the tumor before surgery of the brain tumor patient. , It can be used as a measure of the degree of recovery of patients by analyzing the size, strength, and connectivity of the brain function activation area according to the performance of stroke patients' tasks.

The structural connectivity estimator 400 estimates the probability-based structural connectivity (SC) of the brain function-activated region by using the subject's Diffusion Tensor Imaging (DTI) data according to the subject's task performance.

Accordingly, it is possible to extract the structural connections that pass through the defined areas, so that it is possible to understand what structural connections the brain areas related to the task paradigm have.

Here, functional connectivity (FC) is a method of analyzing the degree of similarity between voxels or regions in a network structure of the brain when analyzing fMRI data, rather than analyzing the degree of similarity between voxels or regions, and structural connectivity, SC) refers to a method of tracking a fiber pathway connecting brain regions or voxels during DTI data analysis.

The connectivity by the structural connectivity estimation unit 400 estimates structural connectivity (SC) using DTI data as described above.

Here, DTI (Diffusion Tensor Imaging) is an image obtained through MRI equipment like fMRI, and does not measure signals over time, unlike fMRI, but anatomical images that image the structure of the brain. As a way to obtain, it is possible to specify the structural connectivity of the subject's brain.

In one embodiment, the structural connectivity estimator 400 extracts structural connectivity passing through regions estimated as brain regions related to the task paradigm, and then identifies structural connectivity of brain regions related to the task paradigm. have.

The brain region estimator 500 is based on probability by the structural connectivity estimator 400, apart from the brain region estimation related to the task paradigm of the brain region that has an inverse correlation with the second independent components. It is assumed that the brain region in which the brain function having the estimated structural connectivity is activated is a brain region related to the task paradigm.

The default-mode network is a brain network that is known to have functional connectivity (FC) between the task and the minus, and is extracted using an independent component analysis (ICA) based on fMRI data and extracted from the network. Try to estimate brain regions that show negative functional connectivity.

On the other hand, the task-related area estimation apparatus 10 using the default-mode network according to another embodiment of the present invention having the above-described configuration, apart from the brain area estimation using the default-mode network, based on DTI data It is possible to additionally estimate brain regions that show structural connectivity (SC) through the estimated brain regions.

The area estimation method by each configuration of the task-related area estimation apparatus 10 using the default-mode network having the above-described configuration will be described later in the method description of FIG. 3 or below.

The task-related area estimation apparatus 10 using the default-mode network having the above-described configuration can execute or manufacture various software based on an operating system (OS), that is, a system. The operating system is a system program for enabling the software to use the hardware of the device, such as Android OS, iOS, Windows Mobile OS, Sea OS, Symbian OS, Blackberry OS mobile computer operating system and Windows, Linux, Unix, It can include any computer operating system such as MAC, AIX, HP-UX.

2 is a control block diagram illustrating an apparatus for estimating a task-related area using a default-mode network according to another embodiment of the present invention.

2, the task-related area estimation apparatus 20 using the default-mode network according to another embodiment of the present invention includes a first independent component extraction unit 100, an independent component integration unit 200, and a second It includes an independent component extraction unit 300, a brain region estimation unit 500, a structural connectivity estimation unit 400, and a data pre-processing unit 600. Here, the first independent component extraction unit 100, the independent component integration unit 200, the second independent component extraction unit 300, the brain region estimation unit 500 and the structural connectivity estimation unit 400, Figure 1 Since it is the same as the component, its description is omitted.

The data pre-processing unit 600 pre-processes the fMRI data or DTI data according to the performance of the subject obtained by using the MRI equipment, delivers the pre-processed fMRI data to the first independent component extraction unit 100, and pre-processes the The DTI data is transmitted to the structural connectivity estimation unit 400.

The area estimation method by each configuration of the task-related area estimation apparatus 20 using the default-mode network having the above-described configuration will be described later in the method description of FIG. 3 and below.

3 is a flowchart illustrating a task-related area estimation method using a default-mode network according to an embodiment of the present invention.

Referring to FIG. 3, in the method for estimating a task-related area using a default-mode network according to an embodiment of the present invention, first, a first step is using functional magnetic resonance imaging (fMRI) data according to a subject's task execution as input data. Independent Component Analysis (ICA) is performed to extract the first independent components of the entire brain (S110).

At this time, the input data of the first independent component analysis in the step of extracting the first independent components (S110), the signal source and the mixing matrix of the blood-oxygen level dependence (BOLD) signal according to the subject's task performance (mixing matrix) may be a linear relationship.

When the extraction of the first independent components is completed in the above-described step S110, the independent components related to the default-mode network (DMN) among the first independent components related to the entire brain extracted in the above-described step S110 are selected and integrated (S120). .

In the step of selecting and integrating the independent components (S120), a mask may be generated by selecting and integrating the independent components belonging to the predefined DMN by selecting the independent components related to the DMN.

When the selection and integration of the independent components related to DMN in step S120 is completed, the second independent component analysis is performed on the independent components related to DMN integrated in step S120 to extract the second independent components in DMN. (S130).

In the step of extracting the second independent components (S130), by applying the independent component analysis in the DMN, fine-grained independent components can be extracted from the DMN through the ICASSO (total number of repetitions = 100) method.

Apart from the above-described steps S110 to S130, probability-based structural connectivity (SC) of the brain function-activated region is estimated by using the subject's DTI (Diffusion Tensor Imaging) data according to the subject's task performance ( S140).

Here, in the step of estimating the structural connectivity (S140), after extracting the structural connectivity passing through the regions estimated as the brain region related to the task paradigm, it is possible to grasp the structural connectivity of brain regions related to the task paradigm.

The default-mode network used in the above-described steps S110 to S130 is a brain network known to have functional connectivity (FC) between the task and minus, and independent component analysis (ICA) based on fMRI data. The brain regions that are minus and functionally minus are extracted from the extracted network.

On the other hand, in the above-described step S140, apart from the above-described steps S110 to S130 for estimating the brain area using the default-mode network, structural connectivity (SC) through the estimated brain areas based on the DTI data Brain regions related to the task paradigm can be estimated by using brain regions showing.

When the extraction of the second independent components is completed in the above-described step S130 or the estimation of the structural connectivity is completed in the above-described step S140, the extraction of the second independent components in the DMN extracted in the above-described step S130 and the above-described step S110 Through the correlation analysis between the first independent components extracted from the entire brain, the region of the brain showing the inverse correlation with the second independent components is estimated as the brain region related to the task paradigm, and the The brain region in which probability-based structural connectivity is estimated in step S140 is also estimated as a brain region related to the task paradigm (S150).

The task-related area estimation method using the default-mode network according to an embodiment of the present invention having the steps as described above can estimate the brain area related to the task paradigm only with data acquired without any information, and it is variable for each subject. And by reflecting real-time variability, it is possible to estimate the brain area related to the personalized task paradigm, which can help in solving potential problems when processing real-time brain function image data.

In addition, it can be applied to a patient group with brain damage and variability by region by allowing patient-specific analysis by applying it to a patient group of neuro / psychiatric disease.

4 is a flowchart illustrating the steps of extracting the first independent components of FIG. 3.

Referring to FIG. 4, in the step of extracting the first independent components (S110), fMRI (functional Magnetic Resonance Imaging) data according to a subject's task performance is used as input data, and a first independent component analysis (ICA) is performed. Subdivide the entire brain by performing (S111).

Here, the input data (X) of the first independent component analysis includes a signal source (S) and a mixing matrix (A) of a blood-oxygen level dependency (BOLD) signal according to a subject's task performance. ).

That is, fMRI data is used as the input data (X) of the independent component analysis, the input data (X) of the independent component analysis as shown in Equation 1 described above in Figure 1 of the signal source (S) and the mixing matrix (A) It is assumed to be a linear relationship.

When the segmentation of the entire brain is completed in the above-described step S111, an unmixing matrix is created to extract the first independent component of the entire brain segmented from the input data from the input data, and then the non-mixing is performed. A non-mixing matrix that minimizes mutual-information between data outputs of a matrix and input data is learned (S112).

Then, in the step of learning the non-mixing matrix (S112), the trained non-mixing matrix and the input data may be internally extracted to extract the first independent components for the entire brain.

In one embodiment, the non-mixing matrix learned in step S112 of learning the non-mixing matrix may be a mixing matrix and an inverse matrix in a linear relationship with input data of the first independent component analysis.

When the learning of the non-mixing matrix is completed in the above-described step S112, the first non-mixing matrix and the input data learned in the above-described step S112 are dot product to extract the first independent components for the entire brain (S113).

FIG. 5 is a flowchart illustrating the steps of extracting the second independent components of FIG. 3.

Referring to FIG. 5, in the step of extracting the second independent components (S130), DMN and related components are selected from the first independent components extracted in the step of extracting the first independent components (S110) (S131).

If the DMN and related components are selected in the above-described step S131, a binary mask of voxels corresponding to the DMN-related component selected in the above-described step S131 is generated (S132).

When the binary mask of voxels is generated in the above-described step S132, the second independent component analysis is performed in the binary-mask generated in the above-described step S132 to extract the second independent components of the DMN (S133).

6 is a view for explaining cross-correlation analysis between independent component time-path data in the present invention.

Referring to FIG. 6, in order to verify a task-related area estimation method using a default-mode network according to an embodiment of the present invention, evaluation by applying a hypothesis-based method to select task-related independent components from the entire brain Can be confirmed.

To this end, ICA is applied to the entire brain by using the fMRI data in which the pre-processing is performed in the data pre-processing step S150 to be described later in FIG. 8 (that is, the functional independent components of the entire brain are divided, where the number of independent components is fMRI) After obtaining the number of acquired volumes), and selecting the DMN-related independent components (selecting the independent components belonging to the predefined DMN and integrating them to create a mask), and then applying the ICA in the DMN (by applying the ICASSO method to refine the DMN as independent components) The cross-correlation between independent component timepath data can be analyzed using Equation 3 below through a series of processes.

는 기능적 연결성,

는 전체 뇌로부터 추출된 제1 독립성분들 중 i번재 독립성분의 시간적 정보의 분산,

는 DMN으로부터 추출된 제2 독립성분들 중 j번재 독립성분의 시간적 정보의 분산이고,

는 제1 독립성분들 중 i번째 독립성분의 시간적 정보와 제2 독립성분들 중 j번째 독립성분 간의 공분산이다.here,

Functional connectivity,

Is the variance of temporal information of i- independent components among the first independent components extracted from the entire brain,

Is the variance of temporal information of the j independent component among the second independent components extracted from the DMN,

Is the covariance between the temporal information of the i- th independent component among the first independent components and the j- th independent component among the second independent components.

7 is an independent network related to the task showing connectivity with the DMN derived by the cross-correlation analysis of FIG. 6, using the cross-correlation derived in FIG. 6 to identify the independent components showing a significant inverse correlation with the DMN Make it possible.

8 is a flowchart illustrating a task-related area estimation method using a default-mode network according to another embodiment of the present invention.

Referring to FIG. 8, in a method for estimating a task-related area using a default-mode network according to another embodiment of the present invention, extracting the first independent components described in FIG. 3 (S110) or estimating probability-based structural connectivity Before the step (S140), further comprising the step of pre-processing the fMRI data or DTI data according to the performance of the subject's task obtained using the MRI equipment (S160).

The fMRI data pre-processed in step S160 described above is used to extract the first independent components in step S110 of extracting the first independent components, or the pre-processed DTI data is probability-based structural connectivity in step S160 of pre-processing DTI data. Can be used for estimation.

9 is a flowchart illustrating a task-related area estimation method using a default-mode network according to another embodiment of the present invention.

Referring to FIG. 9, a method for estimating a task-related area using a default-mode network according to another embodiment of the present invention relates to a task paradigm in a step of estimating a brain area using fMRI data after step S150 described above. Further comprising the step of evaluating and verifying the region estimated as the brain region (S170).

For the evaluation and verification of the brain region, the step of evaluating and verifying (S170), first, subdivides the DMN having an inverse correlation with the task performed by the subject.

Next, an area inversely correlated from the segmented DMN area is estimated as a brain area related to the task paradigm.

Finally, the area estimated as the brain area related to the task paradigm is evaluated and verified using real-time and non-real-time fMRI data.

Evaluation of the area estimated as the brain area related to the task paradigm according to each embodiment, using the task-related area estimation method using the default-mode network according to another embodiment of the present invention having the steps as described above And verification will be described with reference to FIGS. 10 to 13 below.

FIG. 10 is a diagram illustrating a result of functional connectivity estimation for a left hand movement task by a task-related area estimation method using a default-mode network according to another embodiment of the present invention of FIG. 9.

10 (A) are brain regions showing significant inverse correlation from anterior DMN (anterior DMN; aDMN), and FIG. 10 (B) are brain regions showing significant inverse correlation from posterior DMN (posterior DMN; pDMN) (C) is a circular chart showing a significant inverse correlation when the whole brain is subdivided.

Here, L is left, R is right, PoCG is postcentral gyrus (central regression), SMA is supplementary motor area (secondary exercise area), CB is cerebellum (cerebellum), and LG is lingual gyrus (speech).

Referring to FIG. 10, it can be seen that when the left hand movement is performed, it shows a major correlation that is a postcentral gyrus region, a right movement region, which is a region mainly activated from the DMN.

In addition, it can be seen that the supplementary motor area located in the center of the brain also has a significant inverse correlation with DMN. As shown in the pie chart, the right exercise area of the entire brain area has the most significant inverse with DMN. You can see that it shows a correlation.

FIG. 11 is a diagram illustrating a result of estimating functional connectivity to a right hand motion task by a task-related area estimation method using a default-mode network according to another embodiment of the present invention of FIG. 9.

11 (A) are brain regions showing significant inverse correlation from anterior DMN (anterior DMN; aDMN), and FIG. 11 (B) is brain regions showing significant inverse correlation from posterior DMN (posterior DMN; pDMN) (C) is a circular chart showing a significant inverse correlation when the whole brain is subdivided.

Here, L is left, R is right, PoCG is postcentral gyrus (central regression), SMA is supplementary motor area (supplementary motor area), SMG is supramarginal gyrus (corner gyrus), STG is superior temporal gyrus (upper temporal gyrus) , IOG is inferior occipital gyrus (lower occipital gyrus), MOG is middle occipital gyrus (metal occipital gyrus), FG is fusiform gyrus (excitation), CB is cerebellum (cerebellum), LG is lingual gyrus (serine), INS is insula (Sum cortex).

Referring to FIG. 11, it can be confirmed that the central regression region, which is a region mainly activated from the DMN when the right hand movement is performed, shows a major correlation.

In addition, it can be seen that the supplementary motor area located in the center of the brain also has a significant inverse correlation with DMN. Among the entire brain areas, as shown in the pie chart, the left motor area has the most significant inverse with DMN. It can be confirmed that there is a correlation, and the subjects are all right-handed, so it can be confirmed that the right hand movement shows a pattern in which the left and right brain regions are activated.

12 is a view for explaining a result of functional connectivity estimation for a visual stimulus gaze task by a task-related area estimation method using a default-mode network according to another embodiment of the present invention of FIG. 9.

12 (A) are brain regions showing significant inverse correlation from anterior DMN (anterior DMN; aDMN), and FIG. 12 (B) is brain regions showing significant inverse correlation from posterior DMN (posterior DMN; pDMN) (C) is a pie chart showing a significant inverse correlation when the whole brain is subdivided.

Here, L is left, R is right, CLC is calcarine (new claw furrow), CN is cuneus (snow lobe), MOG is middle occipital gyrus (medium laryngeal gyrus), FG is fusiform gyrus (spindle gyrus), LG is lingual gyrus (Speech).

Referring to FIG. 12, when the visual stimulation gaze task is performed, the visually active areas, such as the visual area, cuneus, the claws, the occipital gyrus, or the lingual gyrus, are from DMN. It can be seen that it shows a major correlation.

In addition, as shown in the circular diagram of FIG. 12, it can be confirmed that, among the entire brain regions, the visual regions show the most significant inverse correlation with DMN, and the rear DMN has a more significant inverse correlation than the front DMN. You can.

FIG. 13 is a view for explaining a result of functional connectivity estimation for data taken during real-time neurofeedback training for smokers by a task-related area estimation method using a default-mode network according to another embodiment of the present invention of FIG. 9. .

13 (A) are brain regions showing significant inverse correlation from anterior DMN (anterior DMN; aDMN), and FIG. 13 (B) is brain regions showing significant inverse correlation from posterior DMN (posterior DMN; pDMN) (C) of FIG. 13 is a circular chart showing a significant inverse correlation when the entire brain is subdivided.

Here, L is left, R is right, CLC is calcarine (claw furrow), INS is insula (islet cortex), SPL is superior parietal lobule (MT), middle temporal gyrus (MTG), IOG Inferior occipital gyrus (lower occipital gyrus), mSFG is medial superior frontal gyrus (middle and upper temporal gyrus), iOFC is inferior orbitofrontal cortex (lower and frontal cortex), CB is cerebellum (cerebellum), ACC is anterior cingulate cortex , And MCC is a middle cingulate cortex.

Referring to FIG. 13, as a result of verification on data obtained during training for self-regulation of smoking cessation based on real-time functional magnetic resonance imaging neurofeedback for smokers, brain areas related to smoking desire, such as insula and severe injuries It can be seen that the medial superior frontal gyrus, the inferior orbitofrontal cortex, and the anterior cingulate cortex show significant inverse correlation from DMN.

In addition, as shown in the circular diagram of FIG. 13, it can be seen that among the entire brain areas, smoking-related areas exhibit the most significant inverse correlation with DMN, and are seen in complex high-dimensional cognitive tasks as well as performing simple sensory tasks. It can be confirmed that the method according to the present invention can significantly estimate the brain region related to the task.

The task-related area estimation method using the default-mode network as described above may be implemented as an application or implemented in the form of program instructions that can be executed through various computer components to be recorded in a computer-readable recording medium. The computer-readable recording medium may include program instructions, data files, data structures, or the like alone or in combination.

The program instructions recorded on the computer-readable recording medium are specially designed and configured for the present invention, and may be known and usable by those skilled in the computer software field.

Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CDROMs, DVDs, and magneto-optical media such as floptical disks. And hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like.

Examples of program instructions include not only machine language codes produced by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules to perform processing according to the present invention, and vice versa.

Although described above with reference to embodiments, those skilled in the art can variously modify and change the present invention without departing from the spirit and scope of the present invention as set forth in the claims below. You will understand.

The present invention, as a data-based task-related brain area estimation method, enables a researcher, including a neuroimaging developer, to use a product that can be released by a MATLAB-based GUI environment support, and other products already implemented and released ( For example, SPM (Scanning 추가적인 Probe Microscope) can be provided as an additional toolbox to provide a more comprehensive service.

In addition, by conducting the analysis on a normal normal person, it is possible to estimate the brain activation pattern customized for individual subjects performing the task paradigm, so it is easy to apply to real-time fMRI neurofeedback technology and improves the performance. Can be estimated.

In addition, when the present invention is applied to the field of clinical application, it is possible to grasp the functional / structural connectivity related to a task in consideration of brain damage in a group of neuro / psychiatric patients.

10, 20: Task-related area estimation device using default-mode network
100: first independent component extraction unit
200: independent component integration
300: second independent component extraction unit
400; Structural connectivity estimation unit
500: brain region estimation unit
600: data preprocessing unit

Claims (11)

Extracting first independent components of the entire brain by performing a first independent component analysis (ICA) using fMRI (functional Magnetic Resonance Imaging) data according to a subject's task performance as input data;
Selecting and integrating independent components related to a default-mode network (DMN) among the first independent components extracted for the entire brain;
Extracting second independent components in the DMN by performing a second independent component analysis on the independent components related to the DMN;
Estimating probability-based structural connectivity (SC) of a brain function-activated region using DTI (Diffusion Tensor Imaging) data according to a subject's task performance; And
And estimating a brain region related to a task paradigm based on the inverse correlation between the second independent components extracted from the DMN and the first independent components extracted from the entire brain and the probability-based structural connectivity. The task-related area estimation method using a default-mode network.
According to claim 1,
In the step of extracting the first independent components, the input data of the first independent component analysis is a signal source and a mixing matrix of a blood-oxygen level dependent (BOLD) signal according to the subject's task performance. A task-related area estimation method using a default-mode network, which is a linear relationship of.
The method of claim 1, wherein the step of extracting the second independent components,
Selecting DMN and related components among the first independent components extracted in the step of extracting the first independent components;
Generating a binary mask of voxels of a brain region corresponding to the selected DMN-related component; And
And performing a second independent component analysis in the generated binary mask to extract second independent components, which are independent components of the DMN, a task-related area estimation method using a default-mode network.
The method of claim 1, wherein the step of extracting the first independent components,
Segmenting the entire brain by performing a first independent component analysis using fMRI data as input data according to a subject's task performance;
An unmixing matrix is created to extract the first independent component of the entire segmented brain from the input data, and then the mutual information between the non-mixing matrix and the data outputs of the inner product of the input data is mutual- learning an unmixed matrix in which information) is minimized; And
A method of estimating a task-related region using a default-mode network, comprising extracting first independent components for the entire brain by dot producting the trained non-mixing matrix and input data.
According to claim 4,
The non-mixing matrix learned in the step of learning the non-mixing matrix is an inverse matrix with a mixing matrix in a linear relationship with the input data of the first independent component analysis.
The method of claim 1, wherein estimating the structural connectivity comprises:
A method for estimating a task-related area using a default-mode network, after extracting structural connections passing through areas estimated as a brain area related to the task paradigm, and then grasping the structural connection of brain areas related to the task paradigm.
According to claim 1,
Before the step of extracting the first independent components or estimating the probability-based structural connectivity, the method further comprises preprocessing fMRI data or DTI data according to a subject's task performance obtained using MRI equipment. -Project-related area estimation method using mode network.
According to claim 1,
A method for estimating a task-related area using a default-mode network, further comprising evaluating and verifying an area estimated to be a brain area related to the task paradigm in the estimating the brain area using fMRI data.
The method of claim 8, wherein the step of evaluating and verifying,
Segmenting the DMN, which has an inverse correlation with the task performed by the subject;
Estimating an area inversely correlated from the segmented DMN area as a brain area related to the task paradigm; And
A method for estimating a task-related area using a default-mode network, comprising evaluating and verifying a region estimated as a brain region related to the task paradigm using real-time and non-real-time fMRI data.
A computer-readable recording medium in which a computer program is recorded, for performing the task-related area estimation method using the default-mode network according to any one of claims 1 to 9.
A first independent component extraction unit that extracts the first independent components of the entire brain by performing a first independent component analysis (ICA) using fMRI (functional Magnetic Resonance Imaging) data according to the subject's task performance as input data ;
An independent component integration unit for selecting and integrating independent components related to a default-mode network (DMN) among the first independent components extracted for the entire brain by the first independent component extraction unit;
A second independent component extraction unit that extracts second independent components in the DMN by performing a second independent component analysis on the independent components related to DMN integrated by the independent component integration unit;
A structural connectivity estimator for estimating probability-based structural connectivity (SC) of a brain function-activated region using DTI (Diffusion Tensor Imaging) data according to a subject's task performance; And
Inverse correlation between the second independent components extracted from the DMN by the second independent component extraction unit and the first independent components extracted from the entire brain by the first independent component extraction unit and estimated by the structural connectivity estimation unit An apparatus for estimating a task-related area using a default-mode network, including a brain area estimator for estimating a brain area related to a task paradigm based on the obtained probability-based structural connectivity.

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