KR101323318B1 - Data processing method of functional magnetic resonance imaging for brain functional training - Google Patents

Abstract

PURPOSE: A functional magnetic resonance imaging data processing method for training brain function measures the neuroactivity rate in two or more interested areas, updating neuro feedback data in real time. CONSTITUTION: Two or more interested areas are set up (S100). A first measurement data is generated in the interested area (S200). Neuro feedback data is generated based on the first measurement data (S300). The neuro feedback data is provided to an experimentee (S400). The neuro feedback data is updated in real time based on a second measurement data (S500). [Reference numerals] (S100) Two or more interested areas are set up among multiple brain areas; (S200) First measurement data is created; (S300) Neurofeedback data is created; (S400) Neurofeedback data is provided; (S500) Second measurement data is created and the neurofeedback data is updated in real time based on the second measurement data; (S600) Evaluation data is created

Description

DATA PROCESSING METHOD OF FUNCTIONAL MAGNETIC RESONANCE IMAGING FOR BRAIN FUNCTIONAL TRAINING}

The present invention relates to a method of processing functional magnetic resonance image data by a device for helping a brain function self-training of a subject using a functional magnetic resonance imaging (fMRI).

The study of human brain function has been mainly conducted through lesion studies of patients with brain lesions. In addition, intraoperative cortical stimulation and subdural electrode insertion methods ( subdural electrode insertion, brain induced potential tests, and brain magnetoencephalography (magnetoencephalography) may be used. Such conventional brain function research has an invasive method, and there are limitations in that it cannot exclude the effects of impairment of other brain functions, and it is impossible to grasp the brain function of an active person.

On the other hand, representative examples of non-invasive methods of measuring brain function include Functional Magnetic Resonance Imaging (fMRI), Positron Emission Tomography (PET), and Electroencephalograph (EEG), and Brain Map (magnetoencephalography: MEG).

Among them, functional brain magnetic resonance imaging (fMRI) has no advantage of irradiation and has a relatively high spatial and temporal resolution, which is the most widely used.

Functional brain magnetic resonance imaging (fMRI) is a method of measuring the activity of the brain nerves by the change in the magnetic resonance signal, and visualizes the measurement results. That is, according to the activity of the brain nerve, at least one of the local brain blood volume (CBV), cerebral blood flow (CBF), and oxygen intake changes the magnetic resonance signal. By detecting as, the area of the neuron activated in each brain area is measured.

In particular, S. Ogawa, R.S., 1993, published in the journal Biophysical. Menon, D.W. Tank, S.G. Kim, H. Merkle, J.M. The article "Functional brain mapping by blood oxygenation level-dependent contrast magnetic resonance imaging.A comparison of signal characteristics with a biophysical model" by Ellermann JM and K. Ugurgill, BOLD (Blood) using functional brain magnetic resonance imaging (fMRI) By measuring the Oxygen Level Dependent signal, we proposed a method of detecting changes in CBF using changes in deoxyhemoglobin (deoxyHb) levels in peripheral blood vessels and venous blood without injecting a control material.

Specifically, when neurons are activated in some brain regions by a predetermined paradigm, hemoglobin is reduced and converted to deoxyhemoglobin (deoxyHb) while activated neurons deodorize oxygen from hemoglobin in the blood. At this time, since deoxyhemoglobin (deoxyHb) is an intrinsic paramagnetic agent that causes magnetic field inhomogeneity, when the concentration of deoxyhemoglobin (deoxyHb) increases, the T2 weighted image The difference is detected by the magnetic resonance signal. In this way, the brain region in which the nerve cells are activated can be estimated.

Here, the T2-weighted image is an image that emphasizes the difference in signal strength between tissues. In addition, T2 is a transverse relaxation time, and refers to a time taken for the hydrogen atoms to collide with each other after applying high frequency to return in a direction different from the original axis.

Using the BOLD-based functional brain magnetic resonance imaging (fMRI) as described above, the magnetic resonance signal corresponding to each brain region in each subject performing period for activating a predetermined brain function and rest period for resting the brain function Can be estimated and based on the measured change in the magnetic resonance signal, the brain region related to the task can be estimated.

In general, BOLD-based functional brain magnetic resonance imaging (fMRI) data is processed into visualized information through statistical analysis and anatomical labeling and is provided to the user.

At this time, the visualized information can be interpreted only by a user possessing professional knowledge. In other words, it is virtually impossible for a subject to determine his or her brain function state using only the visualized information without an expert explanation.

In addition, since it takes a relatively long time to process the functional magnetic resonance image data into visualized information, real-time processing is virtually impossible.

Due to these shortcomings, functional magnetic resonance imaging (fMRI) could not be used for subject's self brain function training, despite the advantage of having relatively good spatial and temporal resolution.

An embodiment of the present disclosure provides a method for a functional magnetic resonance imaging device (fMRI) that can be used in real-time processing the functional magnetic resonance imaging data (fMRI data) device for helping the brain function self-training of the subject.

In order to achieve the above object, an embodiment of the present invention is a method of processing a functional magnetic resonance image data in a device for helping the brain function self-training of the subject using a functional magnetic resonance imaging device, (a) a predetermined brain For a subject undergoing a paradigm related to function, the functional magnetic resonance imaging device includes two or more regions of interest in which the neural activity of the paradigm is greater than or equal to a predetermined threshold among a plurality of brain regions spatially divided by the subject's brain. Generating first measurement data corresponding to neural activity in at least some; (b) generating neurofeedback data based on the first measurement data, the neurofeedback data being data on a neuronal activity state of the subject brain corresponding to the paradigm, and an improvement target of the brain function; (c) providing the neurofeedback data to the subject; And (d) generating, by the functional magnetic resonance imaging device, second measurement data corresponding to neural activity in the two or more regions of interest and subjecting the subject undergoing the paradigm to the generated second measurement data. On the basis of this, there is provided a functional magnetic resonance image data processing method comprising the step of updating the neurofeedback data in real time.

Functional magnetic resonance image data (fMRI data) processing method according to an embodiment of the present application after deriving two or more regions of interest that responds more than a threshold to a paradigm related to a predetermined brain function, and then for training the brain function of the subject With neurofeedback data provided, neuronal activity in two or more regions of interest is measured to update the neurofeedback data being provided to the subject in real time.

As such, during the self-training of the brain function using the neurofeedback data, two or more of the brain regions of the subject are not generated by magnetic resonance imaging data corresponding to neural activity in the entire brain region of the subject. Since neurofeedback data is updated by measuring neural activity only for the region of interest, the update and provision of neurofeedback data may be performed in real time.

The neurofeedback data may include state data including Blood Oxygen Level Dependent (BOLD) signals over time in each of the two or more regions of interest, functional correlation data between a pair of regions of interest in the two or more regions of interest, and And at least one of causal association data between the pair of regions of interest, and further comprising target data indicating a brain function training goal of the subject corresponding to the paradigm. In this case, the functional degree of association indicates the degree to which the pair of regions of interest are activated together for a predetermined time frame, and the causality of degree indicates the degree of influence of the pair of regions of interest. And the target data indicates a target for raising or lowering at least one of blood oxygen concentration signal, functional correlation, and causal association.

By providing such neurofeedback data to the subject, effective self-training can be performed for each brain function associated with multiple brain regions rather than individual brain regions.

Neurofeedback data is also provided through the subject's hearing or vision. In this case, when the neurofeedback data is provided through vision, the subject is displayed as at least one of a bar graph, a box plot, a line graph, and a superimposed image, instead of being provided as a magnetic resonance image or an irregular signal waveform that the subject cannot understand. Makes it easier for them to recognize their brain function status and goals. Therefore, the brain function self-training of the subject can be carried out more effectively.

1 is a flowchart illustrating a method of processing functional magnetic resonance image data according to an embodiment of the present application.
FIG. 2A is a timing diagram illustrating a block based paradigm, and FIG. 2B is a timing diagram illustrating an event based paradigm.
3 is a flowchart illustrating a step of deriving two or more regions of interest of FIG. 1.
4 is a flowchart illustrating a step of generating neurofeedback data of FIG. 1.
5A through 5D are examples of providing neurofeedback data in providing the neurofeedback data of FIG. 1.
6 is a flowchart illustrating a process of updating the neurofeedback data of FIG. 1 in real time.
FIG. 7 is a flowchart illustrating a step of deriving at least one of the state data, the functional relevance data, and the causal relevance data of FIG. 6.
FIG. 8A is a flowchart illustrating a step of deriving functional correlation data of FIG. 7.
FIG. 8B is a flowchart illustrating steps of deriving causality relevance data of FIG. 7.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, which will be readily apparent to those skilled in the art. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In order to clearly illustrate the present invention, parts not related to the description are omitted, and similar parts are denoted by like reference characters throughout the specification.

Throughout the specification, when a part is referred to as being "connected" to another part, it includes not only "directly connected" but also "electrically connected" with another part in between . Also, when an element is referred to as "comprising ", it means that it can include other elements as well, without departing from the other elements unless specifically stated otherwise.

Hereinafter, with reference to the accompanying drawings, a method of performing self-training brain function of the subject by the brain function training apparatus including a functional magnetic resonance imaging apparatus according to an embodiment of the present application will be described.

1 is a flowchart illustrating a method of processing functional magnetic resonance image data according to an embodiment of the present application. 2A is a timing diagram illustrating a block based paradigm, and FIG. 2B is a timing diagram illustrating an event based paradigm.

FIG. 3 is a flowchart illustrating steps of deriving two or more regions of interest of FIG. 1, FIG. 4 is a flowchart illustrating steps of generating neurofeedback data of FIG. 1, and FIGS. 5A to 5D illustrate the neurofeedback data of FIG. 1. In the providing step, examples of providing neurofeedback data are provided.

6 is a flowchart illustrating updating the neurofeedback data of FIG. 1 in real time, and FIG. 7 illustrates deriving at least one of the state data, the functional correlation data, and the causal association data of FIG. 6. Flowchart. FIG. 8A is a flowchart illustrating the derivation of the functional relevance data of FIG. 7, and FIG. 8B is a flowchart illustrating the derivation of the causal association data of FIG. 7.

As shown in FIG. 1, the functional magnetic resonance image data processing method according to an exemplary embodiment of the present application has a predetermined threshold of nerve activity due to a paradigm related to a predetermined brain function among a plurality of brain regions spatially divided by a subject brain. In step S100 of setting the two or more regions of interest, the subject corresponding to neural activity in at least a portion including two or more regions of interest of the plurality of brain regions is performed using a functional magnetic resonance imaging device. 1 generating measurement data (S200), generating neurofeedback data which is data on a neuronal activity state of a subject's brain corresponding to a paradigm based on the first measurement data, and an improvement target of brain function (S300); Providing the generated neurofeedback data to the subject (S400), for the subject undergoing a paradigm, functional magnetic resonance Generating second measurement data corresponding to neural activity in two or more regions of interest using an imager, and updating neurofeedback data in real time based on the generated second measurement data (S500), and step S500 After repeating at least once, generating the evaluation data indicating whether the brain function is improved (S600).

The paradigms in steps S100, S200, S500, and S600 include repeating at least one task performing period for activating a predetermined brain function and a rest period for resting a predetermined brain function.

For example, in order to examine the state of brain function related to the motor function of the right hand, a task of moving the right hand may be presented in the task execution period, and a task of maintaining a stationary posture in the resting period or moving a muscle other than the right hand may be presented. Can be.

In this case, the paradigm may be block-related or event-related, depending on the length and the length of the task execution period. That is, in the block-based paradigm, the task execution period is longer than the rest period, whereas the event based paradigm may be the task execution period is shorter than the rest period.

For example, as illustrated in FIG. 2A, in the case of a block-based paradigm, the length TT of the task execution period Task is generally 10 seconds or more. The length RT of the resting period Rest is set to a time at which neural activity due to the task is sufficiently reduced. Generally, the length RT of the rest period is at least 10 seconds.

On the other hand, as shown in Figure 2b, in the event-based paradigm, the length (TT ') of the task execution period (Task) is generally 10 seconds or less. In addition, the length RT 'of the rest period Rest is set to a time at which neural activity due to the task is sufficiently reduced. Generally, the length of the rest period RT 'is at least 10 seconds.

As shown in FIG. 3, in the step S100 of setting two or more regions of interest, the test measurement data corresponding to the nerve activity in the brain of the subject is measured using a functional magnetic resonance imaging device for a subject undergoing a paradigm. Generating (S110), preprocessing the generated inspection measurement data (S120), generating statistical data for the preprocessed inspection measurement data (S130), registration based on the preprocessed inspection measurement data ( generating anatomical labeling through registration (S140), and deriving two or more regions of interest based on the generated anatomical labeling (S150).

In step S110 of generating measurement data for examination, while the subject located inside the functional MRI performs the paradigm provided thereto, the concentration of blood oxygen in each brain region of the subject is measured using the functional MRI. By measuring the corresponding signals over time, test measurement data is generated. In this case, the measurement data for inspection may be generated at least once in each of the task execution period and the rest period.

In the step S120 of preliminary processing of the measurement data for examination, the measurement data for examination transmitted from the functional magnetic resonance imager is preprocessed to correct an error due to a subject's movement or averaging.

Accordingly, step S120 may be performed using at least one of slice timing correction, head motion correction, spatial normalization to template, and spatial smoothing. And preprocessing the measurement data for inspection.

In step S130 of generating statistical data, statistical analysis is performed on the measured measurement data for preprocessing using a general linear model (GLM), and statistical data is generated based on the statistical analysis result.

Specifically, generating the statistical data (S130) is a step of estimating the individual neuronal activation pattern based on the pre-processed measurement data using the general linear model (GLM), and based on the individual neuronal activation pattern, statistics Generating data. In this case, the method may include generating statistical data based on an individual neural activation pattern using a t-test. In addition, the generating of the statistical data may further include estimating the neural activation pattern of the group level based on the individual neural activation pattern using a fixed-effect model (FFX). In this case, statistical data may be generated based further on population level neural activation patterns.

Generating anatomical labeling (S140) is to generate brain images by mapping test measurement data corresponding to neuronal activity of the subject's brain by a paradigm to a standardized brain image. In this case, anatomical labeling may be generated through registration using at least one of automated anatomical labeling (AAL) and Broadmann's area (BA).

Meanwhile, steps S120, S130, and S140 may be collectively performed using a general algorithm (eg, SPM2, etc.) for deriving a brain image from signals generated by the magnetic resonance imager.

Subsequently, based on anatomical labeling, two or more regions of interest are derived whose neuronal activity is above a predetermined threshold for a paradigm of the plurality of brain regions of the subject. For example, two or more regions of interest may be derived to represent signals of patterns different from the other brain regions with respect to a paradigm among brain regions of a subject. Or, it may be derived to represent a signal of higher intensity than the rest of the brain region.

Unlike the illustration of FIG. 3, two or more regions of interest may be preset through anatomical examination using a plurality of experimental groups. (S100)

Subsequently, in the step S200 of generating the first measurement data of FIG. 1, while the subject located inside the functional MRI performs a paradigm provided thereto, a plurality of brain regions of the subject using the functional MRI The first measurement data is generated by measuring signals corresponding to blood oxygen levels in at least a portion including two or more regions of interest over time. In this case, the first measurement data may be generated at least once in each of the task execution period and the rest period.

As shown in FIG. 4, the generating of the neurofeedback data based on the first measurement data (S300) may include preprocessing the first measurement data (S310) and based on the preprocessed first measurement data, based on the state data. The neurofeedback data is generated based on the data generated by the step S320, the generation of the target data S330, and the steps S320 and S330. It generates a step (S340).

Pre-processing the first measurement data (S310) may include performing head motion correction on the first measurement data transmitted from the functional magnetic resonance imager. In this case, the preprocessing of the first measurement data may be performed as quickly as possible through an essential correction process.

Based on the preprocessed first measurement data, at least one of state data, functional relevance data, and causal relevance data is generated. (S320)

In this case, the state data includes Blood Oxygen Level Dependent (BOLD) signals in each of the two or more regions of interest. That is, state data is generated for each ROI. And, Functional Connectivity (FC) data indicates the extent to which a pair of regions of interest of two or more regions of interest are activated together for a predetermined time frame, and Effectivie Connectivity (EC) data indicates The degree of interest of the pair indicates the degree of mutual influence.

The process of generating such functional and causality data will be described in more detail below.

In addition, it generates target data. (S330). In this case, the target data represents a training target for improving brain function of the subject corresponding to the paradigm. That is, the target data is for guiding the subject in a direction to self-train the brain function of the subject corresponding to the paradigm, and up or down at least one provided to the subject among state data, functional relevance data, and causal relevance data. Indicates a command. Such target data may be set based on at least one of state data, functional relevance, and causal relevance, and the intention of the experimenter.

Next, neurofeedback data including at least one of the state data, the functional correlation data, and the causal association data generated by the step S320 and the target data generated by the step S330 is generated. (S340)

The neurofeedback data generated as described above is provided to the subject through a device that stimulates the subject's vision or hearing. In this case, the subject may recognize his or her brain function state and training direction through neurofeedback data provided as visual or auditory data.

In exemplary embodiments, the neurofeedback data may be displayed by at least one of a bar graph, a boxplot, a line graph, and an overlapped image, and may be provided through the subject's vision. .

That is, as shown in FIG. 5A, when the neurofeedback data is displayed as a bar graph, at least one of the state data, the functional correlation data, and the causal association data of the neurofeedback data based on the first measurement data is a bar size. The target data may be indicated by an arrow.

Alternatively, as shown in FIG. 5B, when the neurofeedback data is displayed as a line graph, at least one of state data, functional relevance data, and causal relevance data of the neurofeedback data based on the first and second measurement data. May be displayed as a line graph indicating a change in signal size with time. In this case, although not shown in detail in FIG. 5B, the target data may be indicated by an arrow that raises or lowers the line, or may be provided as a separate auditory material.

Alternatively, the neurofeedback data may be displayed as an overlapping image in which at least one of the state data, the functional relevance data, and the causal relevance data and the target data are different depending on the degree of similarity.

For example, as illustrated in FIG. 5C, when a subject self-trains a brain function related to smoking cessation, neurofeedback data may be provided as a smoking image and a non-smoking image overlapping each other.

When the subject effectively trains his / her brain function, that is, at least one of the state data, the functional correlation data, and the causal correlation data is similar to the target data, the non-smoking image of the superimposed image is higher than the smoking image. Clearly displayed.

On the other hand, as shown in FIG. 5D, when a subject fails to effectively train his or her brain function, that is, at least one of state data, functional relevance data, and causal relevance data differs from the target data, Smoking images of the overlapping images are displayed more clearly than non-smoking images.

On the other hand, it is not equipped with an imaging device for providing neurofeedback data through the subject's vision, or because the imaging device may cause a malfunction in the functional magnetic resonance imager or the error rate of the measurement data may be large, to prevent this The neurofeedback data may be provided through the subject's hearing as a voice or sound by a speaker. That is, the neurofeedback data may be converted into auditory data and provided to the subject in sound. In addition, neurofeedback data may be provided both through the subject's visual and auditory hearing.

As such, when the neurofeedback data is provided through the subject's vision or hearing, the subject located inside the functional magnetic resonance imager activates his brain function according to the neurofeedback data.

At the same time, the second measurement data is generated by measuring signals corresponding to blood oxygen levels in each of the two or more regions of interest using the functional magnetic resonance imaging device over time, based on the generated second measurement data. Updates the neurofeedback data provided to the user in real time. (S500)

As illustrated in FIG. 6, the updating of the neurofeedback data (S500) may include generating second measurement data corresponding to neural activity in two or more regions of interest using a functional magnetic resonance imager (S510). Preprocessing the 2 measurement data (S520); deriving at least one of state data, functional correlation data, and causal association data based on the preprocessed second measurement data (S530); based on the derived data Thus, updating the neurofeedback data in real time (S540), and providing the updated neurofeedback data to the subject (S550).

Preprocessing the second measurement data (S520) includes performing head motion correction on the second measurement data. In this case, since the update of the neurofeedback data is to be performed in real time, the second measurement data should be performed as a correction process that can be simply processed for a short time such as head movement correction.

Next, at least one of state data, functional relevance data, and causal relevance data is derived based on the preprocessed second measurement data. (S530)

In detail, as illustrated in FIG. 7, the deriving of at least one of the state data, the functional relevance data, and the causal relevance data (S530) may be performed to derive the state data based on the preprocessed second measurement data. Step S531 is included. The method further includes deriving functional relevance data based on the state data (S532), or deriving causal relevance data based on the state data (S533).

At this time, the state data includes a blood oxygen concentration (BOLD) signal in each of the two or more regions of interest, as mentioned above.

As shown in FIG. 8A, the step of deriving functional correlation data (S532) may include: dimining the signal intensity of the blood oxygen concentration signal corresponding to a predetermined time frame in each of the pair of ROIs; performing demeaning (S5321), performing BPF (bandpass filtering) to filter the demineralized blood coral concentration signal into an effective frequency band (S5322), and converting the signal passing through the BPF into a frequency domain. And generating a valid signal by performing Fast Fourier Transform, and generating functional correlation data by estimating a functional correlation between a pair of ROIs according to the valid signal (S5324).

In addition, as shown in FIG. 8B, deriving causality data (S533) may include: dimining to reduce the signal intensity of the blood oxygen concentration signal corresponding to a predetermined time frame in each of the pair of ROIs; (demeaning) (S5331), performing a BPF (bandpass filtering) for filtering the demineralized blood coral concentration signal into an effective frequency band (S5332), and the signal passing through the BPF vector self-regression model ( Vector autoregression (VAR) to estimate causal associations between the pair of regions of interest, thereby generating causal association data (S5333).

At this time, the equation for estimating the causality by applying the signal passing through the BPF to the vector autoregression (VAR) can be expressed as Equation 1 below.

In Equation 1, X _k (t) is the signal intensity corresponding to t time in the k region, and R _{kl , j} is the value of X _l (t) before j time is the value of X _k (t) Is a regression coefficient representing the contribution to, and ε _k (t) is the residual error measured at time t.

The neurofeedback data is updated in real time based on at least one of the state data, the functional association data, and the causal association data generated by the step S530 (S540), and the updated neurofeedback data is updated at this time. Provide to the subject in real time. (S550)

As described above, when the neurofeedback data is updated in real time (S500) at least once, the subject recognizes his / her brain function status and actively activates his / her brain function according to the training target based on the target data. By changing, the brain function self training of the subject can be carried out.

Thereafter, the evaluation data for evaluating the brain function self-training of the subject carried out in step S500 is generated. (S600)

Generating evaluation data (S600) may be performed based on the measurement data for the evaluation, which corresponds to the neuronal activity in the brain of the subject using a functional magnetic resonance imaging device, for the subject undergoing a paradigm, and the measurement data for evaluation. And generating evaluation data indicating whether the brain function is improved in response to the paradigm.

On the other hand, the step of setting two or more regions of interest (S100) and the generation of evaluation data (S600) is processed in non-real-time (non-real-time). However, the step (S500) of updating the neurofeedback data, which is a step in which the subject's brain function self-training is actually executed, is processed in real-time.

As described above, the functional magnetic resonance image data processing method according to an embodiment of the present application is based on the result of selectively measuring neural activity in only two or more regions of interest associated with the brain function as a training subject using a functional magnetic resonance imager. Neurofeedback data is provided to the subject. Accordingly, while using a functional magnetic resonance image having an advantage of having a relatively excellent spatial and temporal resolution, the brain can be provided to the subject in real time, and the brain function self-training of the subject can be performed.

The foregoing description of the present invention is intended for illustration, and it will be understood by those skilled in the art that the present invention may be easily modified in other specific forms without changing the technical spirit or essential features of the present invention. will be. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive. For example, each component described as a single entity may be distributed and implemented, and components described as being distributed may also be implemented in a combined form.

The scope of the present invention is shown by the following claims rather than the above description, and all changes or modifications derived from the meaning and scope of the claims and their equivalents should be construed as being included in the scope of the present invention. do.

Claims (11)

In the method for the functional magnetic resonance imaging data processing apparatus using the functional magnetic resonance imaging device to assist the self-training of the subject,
(a) For a subject who is carrying out a paradigm related to a predetermined brain function, the neural activity of the paradigm among a plurality of brain regions in which the brain of the subject is spatially separated using the functional magnetic resonance imaging device is greater than or equal to a predetermined threshold. Generating first measurement data corresponding to neural activity in at least some including at least two regions of interest;
(b) generating neurofeedback data based on the first measurement data, the neurofeedback data being data on a neuronal activity state of the subject brain corresponding to the paradigm, and an improvement target of the brain function;
(c) providing the neurofeedback data to the subject; And
(d) generating, based on the generated second measurement data, second measurement data corresponding to neural activity in the two or more regions of interest, using the functional magnetic resonance imaging device, for a subject in the paradigm; And updating the neurofeedback data in real time. The method of claim 1,
The neurofeedback data is
State data comprising a Blood Oxygen Level Dependent (BOLD) signal in each of the two or more regions of interest;
Functional connectivity (FC) data indicative of a degree to which a pair of regions of interest of the two or more regions of interest are activated together during a predetermined time frame; And
And at least one of causality (Effective Connectivity) data indicating an extent to which the pair of ROIs influence each other. 3. The method of claim 2,
The neurofeedback data is
Further comprising target data indicating a training target for improving brain function of the subject corresponding to the paradigm,
And the target data indicates a target of raising or lowering at least one of the state data, the functional relevance data, and the causal relevance data. The method of claim 3, wherein
In the step (c)
The neurofeedback data is provided through hearing or vision of the subject,
The neurofeedback data is represented by at least one of a bar graph, a boxplot, a line graph, and an overlapped image, and provides functional magnetic resonance image data provided through the subject's vision. Treatment method. 3. The method of claim 2,
The step (d)
Preprocessing the second measurement data;
Generating at least one of the state data, the functional relevance data and the causal relevance data based on the preprocessed second measurement data;
Updating the neurofeedback data based on the generated at least one data; And
Providing the subject with updated neurofeedback data;
And preprocessing the at least two second measurement data comprises performing head motion correction on the second measurement data. The method of claim 5, wherein
Generating at least one of the state data, the functional relevance data and the causal relevance data,
And generating the state data based on the preprocessed second measurement data. The method according to claim 6,
Generating at least one of the state data, the functional relevance data and the causal relevance data,
Demeaning the signal strength of the blood oxygen concentration signal corresponding to the time frame in each of the pair of regions of interest, filtering to an effective frequency band, and converting to a frequency domain to generate an effective signal, and generating And generating the functional association data by estimating the functional association between the pair of ROIs according to the valid signal. The method according to claim 6,
Generating at least one of the state data, the functional relevance data and the causal relevance data,
The pair of ROIs are applied to a vector autoregression (VAR) by demeaning the signal strength of the blood oxygen concentration signal in each of the pairs of ROIs, filtering them into an effective frequency band. Estimating a causal association between the liver and generating the causal association data based on the result of the estimation. The method of claim 1,
After repeating step (d) at least once, further comprising generating evaluation data indicating whether the brain function is improved.
Generating the evaluation data
Generating measurement data for evaluation corresponding to neural activity in the brain of the subject using the functional magnetic resonance imaging device for the subject in the paradigm; And
And generating evaluation data indicating whether the brain function corresponding to the paradigm is improved based on the measurement data for evaluation. The method of claim 1,
Before the step (a), further comprising the step of setting the two or more regions of interest of the plurality of brain regions,
Setting the two or more regions of interest
Generating test measurement data corresponding to nerve activity in the brain of the subject using the functional magnetic resonance imaging device for the subject in the paradigm; And
And based on the measurement data for examination, setting two or more regions of interest in which nerve activity responds to the paradigm among the plurality of brain regions by a predetermined threshold or more. The method of claim 1,
The paradigm may include repeating the task performing period for activating the predetermined brain function and at least one rest period for resting the predetermined brain function,
The method according to claim 1, wherein the task execution period is block-related longer than the idle period, or the task execution period is event-related shorter than the idle period.

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