BRAIN FUNCTION DETERMINATION APPARATUS, BRAIN FUNCTION DETERMINATION METHOD, AND COMPUTER-READABLE MEDIUM

An aspect of the present invention, a brain function determination apparatus includes a first acquisition unit, a first conversion unit, and an identification unit. The first acquisition unit is configured to acquire brain function data including a temporal change, indicating a brain function state measured by a measurement apparatus. The first conversion unit is configured to convert the brain function data acquired by the first acquisition unit, to first converted data including information on at least a time and a space as dimensions. The identification unit is configured to perform an identification process of determining a brain disease and identifying a brain disease region, using the first converted data as an input of a deep learning model constructed by predetermined deep learning.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority under 35 U.S.C. § 119 to Japanese Patent Application No. 2022-044158, filed on Mar. 18, 2022. The contents of which are incorporated herein by reference in their entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a brain function determination apparatus, a brain function determination method, and a computer-readable medium.

2. Description of the Related Art

Due to the influence of declining birthrate and aging population, improvement in life expectancy, and the like, in recent years, the elderly aged 65 and over accounts for about 30% of the total population in Japan. In the accelerated super-aging society, there is an urgent need to increase healthy life expectancy of the people, and dementia is one of issues for which countermeasures need to be taken. As for dementia, it is possible to improve symptoms and slow down the disease to some extent by rehabilitation or medication treatment. However, if once symptoms progress, it is difficult to recover an original state; therefore, as for various kinds of brain diseases including dementia, it is important to detect a disease at an early stage at which no subjective symptom is observed, read a sign at a very early stage, and take preventive measures against the disease.

As a technique for detecting the brain disease at an early stage as described above, there is a known technique for detecting a brain disease of a subject at an early stage by extracting, from brain waves, a feature that is originated from the brain disease, obtaining data by adding, as a label, disease information indicating content of a disease to the extracted feature, and classifying the data by machine learning. In this manner, electro-encephalography data and magneto-encephalography data are information that are close to brain neural activity as compared to brain metabolic rate data or the like, and are widely used in a technique for detecting a brain disease at an early stage.

As a brain disease diagnosis support system capable of determining a brain disease as described above, a certain system is disclosed that obtains electro-encephalography feature data by extracting, from a brain wave, a feature amount of the brain wave, acquires a plurality of pieces of learning data in each of which disease information indicating a brain disease corresponding to the electroencephalography feature data is added to the electroencephalography feature data, classifies the pieces of acquired learning data into a plurality of clusters, generates a classifier that classifies the learning data for each piece of the disease information based on the disease information added to the learning data in each of the classified clusters, acquires electro-encephalography feature data of a subject, identifies a cluster into which the electroencephalography feature data of the subject is classified, and determines a brain disease corresponding to the electro-encephalography feature data of the subject from among a plurality of brain diseases by the generated classifier (for example, Japanese Unexamined Patent Application Publication No. 2016-106940).

However, the conventional technique for detecting a brain disease at an early stage is based on the assumption that a feature is extracted, and it is difficult to perform derivation through an analysis based on multidimensional data, and therefore, it is difficult to accurately determine a brain disease and identify a brain disease region from data including a temporal change, which is a problem.

SUMMARY OF THE INVENTION

According to an aspect of the present invention, a brain function determination apparatus includes a first acquisition unit, a first conversion unit, and an identification unit. The first acquisition unit is configured to acquire brain function data including a temporal change, indicating a brain function state measured by a measurement apparatus. The first conversion unit is configured to convert the brain function data acquired by the first acquisition unit, to first converted data including information on at least a time and a space as dimensions. The identification unit is configured to perform an identification process of determining a brain disease and identifying a brain disease region, using the first converted data as an input of a deep learning model constructed by predetermined deep learning.

DESCRIPTION OF THE EMBODIMENTS

Embodiments of a brain function determination apparatus, a brain function determination method and a computer-readable medium according to the present invention will be described in detail below with reference to the drawings. The present invention is not limited by the embodiments below, and components in the embodiments below include one that can easily be thought of by a person skilled in the art, one that is practically identical, one that is what is called an equivalent, and the like. Furthermore, various omission, replacement, modifications, and combinations of the components may be made without departing from the gist of the embodiments described below.

An embodiment has an object to provide a brain function determination apparatus, a brain function determination method, and a computer-readable medium capable of accurately determining a brain disease and identifying a brain disease region from data including a temporal change.

Overview of Brain Function Determination System

FIG.1is a schematic configuration diagram of a brain function determination system according to one embodiment. An overview of a brain function determination system1according to the present embodiment will be described below with reference toFIG.1.

The brain function determination system1is a system that measure and acquires brain function imaging data (one example of brain function data) that is a plurality of kinds of biological signals (for example, magneto-encephalography (MEG) data, electro-encephalography (EEG) data, and the like) of a subject, determines a brain disease, identifies a brain disease region, and visualizes a portion corresponding to the brain disease on data or a brain image. The brain function imaging data is data that includes a temporal change and that is obtained by measuring physiologically active (function) state of each of portions in the brain by various kinds of methods. Meanwhile, the biological signal as the brain function imaging data that is a measurement target is not limited to data that includes the magneto-encephalography data and the electro-encephalography data.

As illustrated inFIG.1, the brain function determination system1includes a measurement apparatus3that measures one or more kinds of brain function imaging data of a subject, a server40that accumulates the one or more kinds of brain function imaging data measured by the measurement apparatus3, and an information processing apparatus50(brain function determination apparatus) that analyzes the one or more kinds of brain function imaging data recorded in the server40. Meanwhile, inFIG.1, the server40and the information processing apparatus50are illustrated as separate apparatuses; however, for example, at least a part of functions of the server40may be incorporated in the information processing apparatus50. Furthermore, inFIG.1, the information processing apparatus50is illustrated as a single information processing apparatus, but embodiments are not limited to this example, and an information processing system (one example of the brain function determination system) that includes a plurality of information processing apparatuses may be applicable.

In the example illustrated inFIG.1, a subject (to-be-measured person) lies down on a measurement table4with face up while electrodes (or sensors) for electroencephalography are mounted on his/her head, and a head portion is inserted in a hollow32of a dewar31of the measurement apparatus3. The dewar31is a holding container in an extremely low temperature environment using liquid helium, and a large number of magnetic sensors for magnetoencephalography are arranged inside the hollow32of the dewar31. The measurement apparatus3collects electro-encephalography data from the electrodes and magneto-encephalography data from the magnetic sensors, and outputs brain function imaging data that includes the collected electro-encephalography data and the collected magneto-encephalography data to the server40. The brain function imaging data that is output to the server40is read, displayed, and analyzed by the information processing apparatus50. In general, the dewar31in which the magnetic sensors are incorporated and the measurement table4are arranged in a magnetic shielding room, but illustration of the magnetic shielding room is omitted inFIG.1for the sake of convenience.

The information processing apparatus50is an apparatus that analyzes the magneto-encephalography data obtained from the plurality of magnetic sensors and the electro-encephalography data obtained from the plurality of electrodes. The electro-encephalography data is a signal that represents electrical activity of a nerve cell (ion charge flow that occurs in dendrites of a neuron at the time of synaptic transmission) as a voltage value between the electrodes. The magneto-encephalography data is a signal that represents minute magnetic field variation that occurs due to electrical activity of a brain. The brain's magnetic field is detected by a high-sensitive superconducting quantum interference device (SQUID) sensor. The electro-encephalography data and the magneto-encephalography data are one example of a “biological signal” and “brain function imaging data”.

Hardware Configuration of Information Processing Apparatus

FIG.2is a diagram illustrating an example of a hardware configuration of the information processing apparatus according to the embodiment. The hardware configuration of the information processing apparatus50according to the present embodiment will be described below with reference toFIG.2.

As illustrated inFIG.2, the information processing apparatus50includes a central processing unit (CPU)101, a random access memory (RAM)102, a read only memory (ROM)103, an auxiliary storage device104, a network interface (I/F)105, an input device106, and a display device107, all of which are connected to one another via a bus108.

The CPU101is an arithmetic device that controls entire operation of the information processing apparatus50and performs various kinds of information processing. The CPU101executes a program that is stored in the ROM103or the auxiliary storage device104and controls a learning process and an identification process using deep learning (to be described later) and display operation, such as visualization of an identification result.

The RAM102is a volatile storage device that is used as a work area of the CPU101and that stores therein main control parameters and information. The ROM103is a non-volatile storage device that stores therein a basic input-output program or the like. For example, it may be possible to store the program as described above in the ROM103.

The auxiliary storage device104is a non-volatile storage device, such as a hard disk drive (HDD) or a solid state drive (SSD). The auxiliary storage device104stores therein, for example, a program for controlling the operation of the information processing apparatus50, various kinds of data and files that are needed for the operation of the information processing apparatus50, and the like.

The network I/F105is a communication interface for performing communication with an apparatus, such as the server40, on a network. The network I/F105is implemented by, for example, a network interface card (NIC) or the like that is compliant with transmission control protocol/Internet protocol (TCP/IP).

The input device106is an input function of a touch panel, a user interface, such as a keyboard, a mouse, or an operation button, or the like. The display device107is a display device that displays various kinds of information. The display device107is implemented by, for example, a display function of a touch panel, a liquid crystal display (LCD), an organic electro-luminescence (EL), or the like.

Meanwhile, the hardware configuration of the information processing apparatus50illustrated inFIG.2is one example, and a different device may be added. Further, the information processing apparatus50illustrated inFIG.2has the hardware configuration based on the assumption that the information processing apparatus50is a personal computer (PC) for example, but embodiments are not limited to this example, and a mobile terminal, such as a tablet, may be adopted. In this case, it is sufficient that the network I/F105is a communication interface with a wireless communication function.

Functional Block Configuration and Operation of Information Processing Apparatus

FIG.3is a diagram illustrating an example of a functional block configuration of the information processing apparatus according to the embodiment.FIGS.4A and4Bare diagrams for explaining an overview of entire operation of the brain function determination system according to the embodiment. The functional block configuration and the operation of the information processing apparatus50according to the present embodiment will be described below with reference toFIG.3andFIGS.4A and4B.

As illustrated inFIG.3, the information processing apparatus50includes a communication unit201, a second acquisition unit202, a second dividing unit203, a second conversion unit204, a pre-processing unit205(standardization unit), a learning unit206, a first acquisition unit207, a first dividing unit208, a first conversion unit209, an identification unit210, a display control unit211, a storage unit212, and an input unit213.

The communication unit201is a functional unit that performs data communication with the measurement apparatus3, the server40, or the like. For example, the communication unit201receives the brain function imaging data from the server40and stores the brain function imaging data in the storage unit212. Meanwhile, the communication unit201may directly receive the brain function imaging data from the measurement apparatus3. The communication unit201is implemented by the network I/F105illustrated inFIG.2.

The second acquisition unit202is a functional unit that acquires the brain function imaging data that is received by the communication unit201. In this case, the brain function imaging data that is acquired by the second acquisition unit202has a disease label added, the disease label indicating content of a brain disease or a healthy state, and is used as learning data (hereinafter, may be referred to as training data) that is used for a learning process of deep learning by the learning unit206. Meanwhile, the second acquisition unit202need not always acquire the brain function imaging data from the communication unit201, but may acquire the brain function imaging data that is stored in the storage unit212.

The second dividing unit203is a functional unit that performs an epoching process of dividing the brain function imaging data that is acquired by the second acquisition unit202by an arbitrary time interval (time window).

The second conversion unit204is a functional unit that converts the brain function imaging data that has been divided by the second dividing unit203into data (hereinafter, may be referred to as converted data) (second converted data) that includes information on at least a time and a space as dimensions. For example, the second conversion unit204is able to obtain converted data that includes information on signal intensity (power) based on an amplitude, a frequency, a space, and a time as dimensions by performing frequency conversion based on the Fourier transform or the like for each channel and each division for which the brain function imaging data is measured. By performing the conversion by the second conversion unit204as described above, it is possible to obtain the converted data without losing a feature of the brain function imaging data. Meanwhile, it may be possible to use the brain function imaging data as it is in a learning process performed by the learning unit206on the subsequent stage, and, in this case, the second conversion unit204performs identity transform as the conversion. Furthermore, examples of the conversion process performed by the second conversion unit204include extraction and enhancement of a sensor, down-sampling, application of a frequency filter, elimination of artifacts, a defective channel process, extraction of a time window, and standardization of magnetic field data.

The pre-processing unit205is a functional unit that performs a predetermined standardization process on the converted data that is obtained by the second conversion unit204because the brain function imaging data is multidimensional and a data scale varies. Examples of the standardization process include a process of aligning ranges of the converted data for which the ranges are different, and, with this process, it becomes possible to stabilize the learning process performed by the learning unit206on the subsequent stage.

Meanwhile, the learning data that is divided by the second dividing unit203, the converted data that is converted by the second conversion unit204, and the converted data that is subjected to the standardization process by the pre-processing unit205may also be referred to as the training data, in addition to the brain function imaging data that is acquired by the second acquisition unit202, because these pieces of the data are used for the learning process performed by the learning unit206.

The learning unit206is a functional unit that performs the learning process, using the converted data, which is subjected to the standardization process by the pre-processing unit205and to which the disease label is added, as an input through deep learning with a time series analysis function. For example, the learning unit206performs a learning process by internally constructing a neural network based on an algorithm, such as a convolutional neural network (CNN), to extract a feature on spatial information, and constructing a neural network based on an algorithm, such as a recurrent neural network (RNN) or an attention, to extract a feature on temporal information. With this configuration, it is possible to extract a feature that is peculiar to a brain disease while emphasizing a brain region and a time, so that it is possible to construct a neural network capable of accurately determining the brain disease. Meanwhile, in the feature extraction as described above, it is not needed for a human being to define a type of feature data to be extracted from the learning data in advance as in the machine learning, but, in the deep learning, a type of the feature data to be extracted from the learning data is automatically determined during the learning process. Furthermore, construction of the neural network indicates, in particular, a process of adjusting and determining a weight or the like that is strength of synaptic connections in the neural network. The neural network (hereinafter, may be referred to as a deep learning model) that is constructed through the learning process performed by the learning unit206is stored in the storage unit212. Specifically, data of the determined weight or the like for the neural network is stored in the storage unit212. In this manner, with use of the deep learning model that is obtained through the learning process performed by the learning unit206, it becomes possible to determine presence or absence of a brain disease, such as dementia, developmental disorders, or psychosis, determine a brain disease, determine a disease type, and identify a brain disease region.

The first acquisition unit207is a functional unit that acquires the brain function imaging data that is received by the communication unit201. In this case, the brain function imaging data that is acquired by the first acquisition unit207is data for which a type of a brain disease is to be identified, to which the disease label is not added, and which is used as data (hereinafter, may be referred to as visualized data) for performing an identification process using the deep learning model and visualizing a result of the identification. Meanwhile, the first acquisition unit207need not always acquire the brain function imaging data from the communication unit201, but may acquire the brain function imaging data that is stored in the storage unit212.

The first dividing unit208is a functional unit that performs an epoching process of dividing the brain function imaging data that is acquired by the first acquisition unit207by an arbitrary time interval (time window).

The first conversion unit209is a functional unit that converts the brain function imaging data that is divided by the first dividing unit208into data (hereinafter, may be referred to as converted data) (first converted data) that includes information on at least a time and a space as dimensions. For example, the first conversion unit209is able to obtain converted data that includes information on signal intensity (power) based on an amplitude, a frequency, a space, and a time as dimensions by performing frequency conversion based on the Fourier transform or the like for each channel and each division for which the brain function imaging data is measured. By performing the first conversion unit209as described above, it is possible to obtain the converted data without losing a feature of the brain function imaging data. Meanwhile, it may be possible to use the brain function imaging data as it is in a learning process performed by the identification unit210on the subsequent stage, and, in this case, the first conversion unit209performs identity transform as the conversion. Furthermore, examples of the conversion process performed by the first conversion unit209include extraction and enhancement of a sensor, down-sampling, application of a frequency filter, elimination of artifacts, a defective channel process, extraction of a time window, and standardization of magnetic field data.

The converted data obtained by the first conversion unit209is, as illustrated inFIGS.4A and4B, data that is to be input to the deep learning model that is constructed through the learning process performed by the learning unit206. In the example illustrated inFIGS.4A and4B, signal intensity is illustrated by a heat map in a three-dimensional region in which a horizontal axis represents a time, a vertical axis represents a frequency, and a depth represents a space (region). Here, the space as the depth indicates a brain region, such as a frontal lobe, a temporal lobe, or an occipital lobe of the brain, which is determined in advance, and each brain region is associated with the depth axis for the sake of convenience.

Meanwhile, the data that is divided by the first dividing unit208, the converted data that is converted by the first conversion unit209, and the converted data that is subjected to the standardization process by the pre-processing unit205may be referred to as, in addition to the brain function imaging data that is acquired by the first acquisition unit207, visualized data because these pieces of data are data that are used for the identification process using the deep learning model by the identification unit210and that are to be visualized as the identification result.

The identification unit210is a functional unit that reads the deep learning model that is constructed through the learning process performed by the learning unit206from the storage unit212, and performs the identification process, using the converted data obtained by the first conversion unit209as an input for the deep learning model. Here, in particular, the identification process indicates a process of determining presence or absence of a brain disease, determining a brain disease, determining a disease type, and identifying a brain disease region, using the deep learning model. Meanwhile, the identification unit210may input, as a result of the identification process, the converted data to the deep learning model, and obtain probabilities of each disease type of each brain disease, such as dementia, and a healthy state, or may be able to calculate and obtain the probabilities based on an output of the deep learning model. Furthermore, the converted data obtained by the first conversion unit209may be subjected to the standardization process in the same manner as performed by the pre-processing unit205, and may thereafter be input to the deep learning model.

The display control unit211is a functional unit that causes the display device107to display, as the identification result obtained by the identification unit210, a result of presence or absence of a brain disease, a determination result of the brain disease, a determination result of a disease type, an identified brain disease region, and the like. For example, as illustrated inFIGS.4A and4B, the display control unit211may visualize a data portion that is a basis for the determination on the brain disease and the disease type by enclosing the data portion by a rectangle or the like on the heat map that is arranged in the three-dimensional region in which the horizontal axis represents a time, the vertical axis represents a frequency, and the depth represents a space (region). In the example illustrated inFIG.4A, if the probability of the healthy state is calculated as 60% as the identification result, the display control unit211visualizes a data portion indicating a feature portion as the healthy state by enclosing the data portion by a rectangle or the like on the heat map of the signal intensity of a specific brain region (depth). Furthermore, in the example illustrated inFIG.4B, if the probability of a disease type A, which indicates dementia, is calculated as 30% as the identification result, the display control unit211visualizes a data portion indicating a feature portion as the disease type A by enclosing the data portion by a rectangle or the like on the heat map of the signal intensity of a specific brain region (depth). In other words, the display control unit211is able to visualize the identification result obtained by the deep learning model for each brain disease (or healthy state). Moreover, as illustrated inFIG.4AandFIG.4B, the display control unit211may display, as the identification results, the probabilities of the healthy state and each disease type of each brain disease. By visualization of the identification result by the display control unit211as described above, it is possible to indicate, on the visualized data, a data portion of a time, a frequency, a brain region, and signal intensity that are identified as a basis for the determination on the brain disease or the healthy state. In other words, it is possible to visualize the identification result with respect to the same-dimensional data as the visualized data (converted data) that is input to the deep learning model, so that it is possible to identify a brain disease region that is less likely to be affected by a temporal change.

The storage unit212is a functional unit that stores therein the brain function imaging data that is received by the communication unit201, the deep learning model that is constructed through the learning process performed by the learning unit206, and the like. The storage unit212is implemented by the RAM102or the auxiliary storage device104illustrated inFIG.2.

The second acquisition unit202, the second dividing unit203, the second conversion unit204, the pre-processing unit205, the learning unit206, the first acquisition unit207, the first dividing unit208, the first conversion unit209, the identification unit210, and the display control unit211as described above are implemented by causing the CPU101to load a program that is stored in the ROM103or the like onto the RAM102and execute the loaded program. Meanwhile, a part or all of the second acquisition unit202, the second dividing unit203, the second conversion unit204, the pre-processing unit205, the learning unit206, the first acquisition unit207, the first dividing unit208, the first conversion unit209, the identification unit210, and the display control unit211may be implemented by a hardware circuit, such as an application specific integrated circuit (ASIC) or a field-programmable gate array (FPGA), instead of a program that is software.

Meanwhile, each of the functional units illustrated inFIG.3is a functionally conceptual, and need not always be configured in the same manner. For example, a plurality of functional units that are illustrated as independent functional units inFIG.3may be configured as a signal functional unit. In contrast, a function included in a single functional unit inFIG.3may be divided into a plurality of functions, and may be configured as a plurality of functional units.

Furthermore, in the information processing apparatus50illustrated inFIG.3, it is assumed that the learning process through the deep learning using the brain function imaging data and the identification process using the deep learning model are performed in the same apparatus, but embodiments are not limited to this example. For example, the learning process through the deep learning may be performed by an external apparatus (one example of a second apparatus) that is different from the information processing apparatus50(one example of a first apparatus). In this case, it is sufficient that the external apparatus includes at least the same functional units as the second acquisition unit202, the second dividing unit203, the second conversion unit204, the pre-processing unit205, and the learning unit206.

Entire Operation of Brain Function Determination System

FIG.5is a flowchart illustrating an example of the flow of the entire operation of the brain function determination system according to the embodiment.FIG.6is a diagram illustrating an example of a screen in which a brain disease region that is identified through the identification process performed by the information processing apparatus according to the embodiment is visualized. The flow of the entire operation of the brain function determination system1according to the present embodiment will be described below with reference toFIG.5andFIG.6.

The information processing apparatus50receives (acquires) the brain function imaging data from the communication unit201. Meanwhile, the information processing apparatus50may read the stored brain function imaging data that is the brain function imaging data received in advance. Then, the process goes to Step S12.

If the brain function imaging data that is received (acquired) by the information processing apparatus50is the training data to which the disease label is added (Step S12: training data), the second acquisition unit202acquires the brain function imaging data, and the process goes to Step S13. In contrast, if the brain function imaging data that is received (acquired) by the information processing apparatus50is the visualized data to which the disease label is not added (Step S12: the visualized data), the first acquisition unit207acquires the brain function imaging data, and the process goes to Step S18.

The second dividing unit203of the information processing apparatus50performs an epoching process of dividing the brain function imaging data that is acquired by the second acquisition unit202by an arbitrary time interval (time window). Then, the process goes to Step S14.

The second conversion unit204of the information processing apparatus50converts the brain function imaging data that is divided by the second dividing unit203into data (converted data) that includes information on at least a time and a space as dimensions. Then, the process goes to Step S15.

The pre-processing unit205of the information processing apparatus50performs a predetermined standardization process on the converted data that is obtained by the second conversion unit204because the brain function imaging data is multidimensional and a data scale varies. Then, the process goes to Step S16.

The learning unit206of the information processing apparatus50performs a learning process, using the converted data, which is subjected to the standardization process by the pre-processing unit205and to which the disease label is added, as an input through deep learning with a time series analysis function. For example, the learning unit206performs a learning process by internally constructing a neural network based on an algorithm, such as a CNN, to extract a feature on spatial information, and constructing a neural network based on an algorithm, such as an RNN or an attention, to extract a feature on temporal information. Then, the process goes to Step S17.

The deep learning model that is constructed through the learning process performed by the learning unit206is stored in the storage unit212. Specifically, data of the determined weight or the like for the neural network is stored in the storage unit212. Through the flow as described above, the learning process in the operation of the brain function determination system1is terminated.

The first dividing unit208of the information processing apparatus50performs an epoching process of dividing the brain function imaging data that is acquired by the first acquisition unit207by an arbitrary time interval (time window). Then, the process goes to Step S19.

The first conversion unit209of the information processing apparatus50converts the brain function imaging data that is divided by the first dividing unit208into data (converted data) that includes information on at least a time and a space as dimensions. Then, the process goes to Step S20.

The identification unit210of the information processing apparatus50reads the deep learning model that is constructed through the learning process performed by the learning unit206from the storage unit212, and performs the identification process, using the converted data obtained by the first conversion unit209as an input for the deep learning model. Then, the process goes to Step S21.

The display control unit211of the information processing apparatus50causes the display device107to display, as the identification result obtained by the identification unit210, a result of presence or absence of a brain disease, a determination result of the brain disease, a determination result of a disease type, an identified brain disease region, and the like. For example, the display control unit211may visualize a data portion that is a basis for the determination on the brain disease and the disease type by enclosing the data portion by a rectangle or the like on a heat map that is arranged in the three-dimensional region in which the horizontal axis represents a time, the vertical axis represents a frequency, and the depth represents a space (region). Further, the display control unit211may display, as the identification result, the probability of the healthy state or each disease type of each brain disease. Furthermore, as illustrated inFIG.6, the display control unit211may display, as the identification result, a heat map that represents a time and signal intensity of a frequency selected by a user based on the converted data that is the basis for the brain disease determined by the identification unit210, to be superimposed on a corresponding brain disease region on a brain image. Moreover, it may be possible to allow the user to select a brain disease (or a healthy state) to be visualized. With this configuration, it is possible to display distributions of a time, a frequency, a brain region, and signal intensity that are identified as the basis for the determination of the brain disease on the brain image.

As described above, in the brain function determination system1according to the present embodiment, the first acquisition unit207acquires the brain function imaging data that is measured by the measurement apparatus3, the first conversion unit209converts the brain function imaging data that is acquired by the first acquisition unit207to converted data that includes information on at least a time and a space as dimensions, the identification unit210performs the identification process of determining a brain disease and identifying a brain disease region, using the converted data as an input of a deep learning model that is constructed by predetermined deep learning. With this configuration, it is possible to accurately determine a brain disease and identify a brain disease region from data including a temporal change.

Furthermore, in the brain function determination system1according to the present embodiment, the display control unit211causes the display device107to display an identification result of the identification process performed by the identification unit210. With this configuration, it is possible to recognize the determination result of the brain disease and the identified brain disease region.

Moreover, in the brain function determination system1according to the present embodiment, the display control unit211displays, as the identification result obtained by the identification unit210, a data portion that is a basis for determination on the brain disease or the healthy state such that the data portion on the converted data is identifiable. By the visualization of the identification result by the display control unit211, it is possible to indicate, on the visualized data, a data portion of a time, a frequency, a brain region, and signal intensity that are identified as the basis for the determination on the brain disease or the healthy state.

Furthermore, in the brain function determination system1according to the present embodiment, the display control unit211displays the identification result with respect to the same-dimensional data as the converted data that is input to the deep learning model. With this configuration, it is possible to visualize the identification result with respect to the same-dimensional data as the visualized data (converted data) that is input to the deep learning model.

Moreover, in the brain function determination system1according to the present embodiment, the display control unit211displays, as the identification result, a heat map that represents a specific time and signal intensity of a frequency, to be superimposed on a corresponding brain disease region of a brain image, for each of specific brain diseases. With this configuration, it is possible to display distributions of a time, a frequency, a brain region, and signal intensity that are identified as the basis for the determination of the brain disease on the brain image.

Furthermore, in the brain function determination system1according to the present embodiment, the identification unit210calculates, as the identification result, probabilities of each disease type of each brain disease and the healthy state based on an output of the deep learning model, and the display control unit211displays the probabilities. With this configuration, it is possible to recognize the probabilities of each disease type of each brain disease and the healthy state.

Moreover, in the brain function determination system1according to the present embodiment, the second acquisition unit202acquires the brain function imaging data that is measured by the measurement apparatus3and that has the disease label added the disease label indicating content of a brain disease or a healthy state, the second conversion unit204converts the brain function imaging data that is acquired by the second acquisition unit202to converted data that includes information on at least a time and a space as dimensions, and the learning unit206constructs a deep learning model through a learning process based on the deep learning, using the converted data to which the disease label is added as an input. With this configuration, it is possible to construct a deep learning model that is able to accurately determine a brain disease and identify a brain disease region from data including a temporal change.

Furthermore, in the brain function determination system1according to the present embodiment, the pre-processing unit205performs a predetermined standardization process on the converted data, and the learning unit206constructs the deep learning model, using the converted data that is subjected to the standardization process as an input. With this configuration, it is possible to stabilize learning using deep learning.

Moreover, in the brain function determination system1according to the present embodiment, the deep learning model is constructed by deep learning with a time series analysis function. With this configuration, it is possible to process data including various kinds of temporal changes with high accuracy.

Meanwhile, in the embodiment as described above, if at least one of the functional units of the brain function determination system1(the information processing apparatus50) is implemented by execution of a program, the program is provided by being incorporated in a ROM or the like in advance. Further, the program that is executed by the brain function determination system1(the information processing apparatus50) according to the embodiment as described above may be provided by being recorded in a computer readable recording medium, such as a compact disc (CD)-ROM, a flexible disk (FD), a compact disk recordable (CD-R), or a digital versatile disk, in a computer-installable or computer-executable file format. Furthermore, the program that is executed by the brain function determination system1(the information processing apparatus50) of the embodiment as described above may be provided by being stored in a computer that is connected to a network, such as the Internet, and by being downloaded via the network. Moreover, the program that is executed by the brain function determination system1(the information processing apparatus50) of the embodiment as described above may be provided or distributed via a network, such as the Internet. Furthermore, the program that is executed by the brain function determination system1(the information processing apparatus50) of the embodiment as described above has a module structure that includes at least any of the functional units as described above, and as an actual hardware, each of the functional units as described above is loaded and generated on a main storage device by causing the CPU to read the program from the ROM or the like.

According to an embodiment, it is possible to accurately determine a brain disease and identify a brain disease region from data including a temporal change.