JPH07194566A - In-brain cortical activity tracing system and device therefor - Google Patents

Abstract

PURPOSE:To easily diagnose a human consciousness condition through an image screen by three-dimensionally forming an electric double layer in a brain cortex position of an in-brain model, finding and optimizing the electric potential distribution, and estimating an electric condition spatially and expansively distributing in a brain cortex. CONSTITUTION:A head part is formed as a multiple area head part model, and a brain cortex is set as an electric double layer in this model. In a processing part 23, the reference scalp electric potential distribution is found by this electric double layer, and operation is performed on a square error between this reference scalp electric potential distribution and the measured scalp electric potential distribution, and operation is performed on the electric potential distribution of the electric double layer to minimize this square error. This brain cortex electric potential distribution is estimated and traced. The electric potential distribution of this electric double layer is three-dimensionally synthesized with the multiple area head part model, and emphasis is also applied according to intensity of the electric potential distribution, and the brain cortex electric potential distribution is displayed on a display part 24.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention, in the field of ME (medical electronics: medical electronic equipment), measures the human electroencephalogram and tracks the electrical or magnetic activity of the brain cortex with time. Cortical activity tracking device (C
ortical Activity Tracing System. CAT-Sys
tem or CAT method. ), Especially thinking / recognition /
With memory recall / pleasantness / mental fatigue / tension, etc.
It seeks to find out to what extent the brain cortex spreads and becomes active over time.

In clinical terms, it is used for estimating the focal position during "epileptic" seizure and intracerebral condition, determining brain death, estimating intracerebral abnormality in psychosis, and monitoring the progress of senile dementia. Therefore, it is expected to establish a new diagnostic technique that has never been obtained by correlating these contents with phenomenological theory.

Further, in basic medicine and brain physiology, recognition,
It can be a powerful tool for elucidating the mechanisms of consciousness, emotions, and intellectual activity. Therefore, it is used not only for clinical application and basic medicine but also for objective and quantitative estimation of pleasantness / discomfort, mental fatigue, stress, etc. to prevent death from overwork and relieve stress. There are major industrial benefits such as the development of methods.

[0004]

2. Description of the Related Art For example, the electric potential appearing on the scalp reflects the electrical activity of a large number of neurons (nerve cells) in the brain, and causes thought processes, recognition, memory recall, and certain emotions. It has already been widely known by medical scientists that the association of these neuronal activities occurs with this.
That is, it was observed by researchers that the electric potential was locally strengthened according to the consciousness and the psychological state, and it was announced at academic conferences.

Based on the above facts, the inventor of the present invention uses a measuring device for measuring potential waveforms due to the activity of these neurons on the scalp, and a method for estimating the position / intensity of the active site in the brain by the dipole approximation method. Had already been developed.
The dipole approximation method is effective only when there are spatially limited active neurons. In addition, changes in the potential due to brain waves are spontaneous EEG and evoked potentials due to external factors, and these devices and methods of the present inventor are adapted to be applied to any of these.

[0006] These devices and methods have been applied for patents, and the details thereof are described in the following respective publications. (A) Measuring method: JP-A-2-31739 (b) Measuring device: JP-A-2-31736 (c) Measuring method: JP-A-3-99630

FIG. 7 shows an example of a conventional display method for displaying these measurement results. In FIG. 7, this method is, for example, a cross-section Z is scanned in the Z-axis direction by a shape measuring device such as a nuclear magnetic resonance imaging device and an X-ray device. Tomographic image (CT) 5
Is formed, and the current dipole 6 is marked and displayed on this image 5.

[0008]

However, these methods and apparatuses cannot be said to be able to make practical use of the valuable data obtained, and have the following problems. (1) Physiological knowledge about cortical structure is not taken into consideration, and everything is left to calculation, so the original goal of estimation is poor, and active neurons are limited as described above. It is effective only in a narrow region, and has no effect in estimating the spread of the electric double layer formed in the gray matter of the brain cortex. (2) Also, simply finding an equivalent current dipole, marking it, and displaying it does not reveal the above-mentioned planar spread of the electric double layer. Can not estimate the distribution of. (3) Furthermore, only by observing the local generation of current dipoles in the brain, there is no theoretical close contact with the cortex itself of the brain, which is the mother body of generation of these dipoles. Since the strata do not capture the spatial changes distributed throughout this cortex,
It is not possible to accurately diagnose the actual activity of neurons in the brain. (4) Since the estimated equivalent dipole is a vector-like weighted average in the activity of the neuron group, the neuron occupies a position quite different from the inside of the gray matter in which the neuron originally should exist, and the intensity in this activity is large. The position when the distribution changes changes unnaturally. (5) Even when the position of the active neuron is spatially local, if the brain cortex is bent with a small radius of curvature, the current dipole occupies a position different from the actual active neuron. (6) When estimating the position and strength of the equivalent dipole, the degree of approximation for numerically approximating the values actually measured and calculated is not quantitatively determined.

That is, the conventional technique for approximating the current dipole is to approximate the activity group of neurons in the brain by several current dipoles and estimate the position of the dipole. Is useful if it is locally unevenly distributed, but it is invalid if it is spatially spread. In addition, this position is imaginary, and it is inevitable to measure the potential waveform on the scalp with a limited number of electrodes to estimate active neurons for a large number of small regions, Equivalent dipoles had limited physiological, basic or clinical applications.

In the first place, this electrical activation state associated with a high degree of mental activity in the cerebrum most remarkably appears in the cerebral cortex, which is a surface layer, and this cerebral cortex has a complex surface structure having many valley-like folds. Therefore, it cannot be fully expressed by the conventional simple overlay technique.

Therefore, the present inventor has proposed the following assumption. (A) Many neurons (neurons) in the gray matter of the brain cortex form a column structure with each other, so that the active neurons have a planar spread and an electrical double layer is formed along the brain cortex. . (B) The distribution of the electrical activity changes momentarily while repeating dispersion and convergence, and the density of the dipole moment per unit area in this gray matter is nonuniform macroscopically.

(C) In other words, this electrical activity is no longer an isolated current dipole, but actually forms an electric double layer having a planar spread along the gray matter, and this gray matter is three-dimensional. Since the electric double layer has a complicated curved surface, the electric double layer also has a complicated shape. (D) Therefore, by dividing the gray matter into small regions having a uniform density of dipole moments and approximating the brain cortex as an aggregate of these small regions, this activity can be efficiently evaluated by a mathematically easy method. Can be estimated.

Therefore, the method of the present invention, which incorporates the cortical structure into the calculation as a known knowledge and finds out where it is active from the potential distribution on the scalp or the distribution of the brain magnetic field, is the conventional dipole estimation. The law is originally foreign. In view of the above problems, the present invention is to estimate the intensity distribution of active neurons having a spatial spread, and an object of the present invention is to realize a new method which is not available in the prior art.

[0014]

[Means for solving the problem]

(1) Cortex in the brain that estimates the cortical potential distribution, which is the distribution of potential in the cortex in the brain, from the distribution of scalp potential, which is the distribution of potential on the scalp, and tracks the spatial distribution of active neurons in the cortex An activity tracking method, comprising forming a multi-region head model simulating a multiple internal structure of a head, and setting an electric double layer having electric poles inside the multi-region head model. The electric 2
A setting step of electrically dividing the multi-layer into a plurality of small areas having unit strength, calculating the unit strength for each of the plurality of small areas, obtaining a reference scalp potential distribution by converting to the scalp potential distribution, By calculating a first squared error between the reference scalp potential distribution and the actually measured measured scalp potential distribution, a specific small area and its intensity for minimizing the first squared error are calculated. A method for tracking cortical activity in the brain, comprising: an estimating step of determining and tracking. (2) The estimating step includes a process of determining an intensity for minimizing a second squared error with respect to a peripheral area closest to the specific subarea, and the second squared value is included. It is characterized in that the estimation step is repeatedly performed while replacing the new plurality of small areas with the peripheral areas until the error reaches a predetermined limit value, and the brain cortical potential distribution is estimated. The method for tracking cortical activity in the brain according to the above item (1). (3) Subsequent to the estimation step, a composite image is formed that stereoscopically combines the respective sub-areas determined in the estimation step with the internal structure, and the respective sub-areas are combined in the composite image. It is configured by adding a drawing step of converting to an emphasized image to be emphasized based on the intensity and drawing the image, and the cerebral cortical potential distribution is stored and displayed as a distribution map that changes over time. The method for tracking cortical activity in the brain according to (1) or (2) above. (4) Cortex in the brain that estimates the cerebral cortical potential distribution, which is the distribution of potential in the cortex in the brain, from the scalp potential distribution, which is the distribution of potential on the scalp, and tracks the spatial distribution of active neurons in the cortex An activity tracking device, which forms a multi-region head model simulating a multiple internal structure of a head, and sets an electric double layer having electric poles inside the multi-region head model. The electric 2
A setting step of electrically dividing the multi-layer into a plurality of small areas having unit strength, calculating the unit strength for each of the plurality of small areas, obtaining a reference scalp potential distribution by converting to the scalp potential distribution, By calculating a first squared error between the reference scalp potential distribution and the actually measured measured scalp potential distribution, a specific small area and its intensity for minimizing the first squared error are calculated. An apparatus for tracking cortical activity in the brain, comprising: an estimation step of determining and tracking. (5) The estimating step includes a process of determining an intensity for minimizing a second squared error with respect to a peripheral area closest to the specific small area, and the second squared error is a predetermined value. The above-mentioned estimation step is repeatedly performed while replacing the plurality of new sub-areas with the surrounding area until the limit value is reached, and the brain cortical potential distribution is estimated. The cortical activity tracking device in the brain according to 4). (6) Subsequent to the estimation step, a composite image is formed that stereoscopically combines the respective small areas determined in the estimation step with the internal structure, and the respective small areas are described in the composite image. It is configured by adding a drawing step of converting to an emphasized image to be emphasized based on the intensity and drawing the image, and the cerebral cortical potential distribution is stored and displayed as a distribution map that changes with time. The cortical activity tracking device in the brain according to item (4) or (5) above.

[0015]

The head is formed in a multi-region head model, and the brain cortex is set as an electric double layer in this multi-region head model, and the reference scalp potential distribution is obtained by this electric double layer. The square error of the distribution and the measured scalp potential distribution is calculated, the potential distribution of the electric double layer for minimizing this square error is calculated, and the brain cortical potential distribution is estimated and tracked. Further, in a specific small area in the electric potential distribution of the electric double layer, the electric potential distribution in the peripheral area is repeatedly calculated, and the cerebral cortical electric potential distribution is traced more precisely. Furthermore, the electric potential distribution of these electric double layers is stereoscopically combined with the multi-region head model, and is emphasized based on the strength of this electric potential distribution, and the cerebral cortical potential distribution is displayed over time.

[0016]

EXAMPLE An example of the method of the present invention will be described below with reference to the electric potential distribution on the scalp with reference to the drawings. However, the distribution of the cerebral magnetic field can be carried out in substantially the same manner. Figure 1
It is a principle diagram which shows the Example by the method of this invention. In FIG. 1, the main part 6 of the system 5 in this embodiment is
The electric head model according to the present invention electrically 2
A first setting step 7 for setting a multistory layer, a first estimation step 8 for estimating a cerebral cortical potential distribution by this electric double layer and tracking it over time, and a 1st step for drawing this cerebral cortical potential distribution by image processing. It is configured by sequentially providing the drawing stage 9 of 1 and
The measured scalp potential distribution is introduced from the known measuring device DET, and the cerebral cortical potential distribution is sent to the organizing step 10 in which the operator observes and arranges.

The first setting step 7 forms a head model, which will be described later, on the basis of the outline image of the head and the tomographic image of the inside of the head model. A double layer is set up and the electric double layer is divided into a plurality of subsections. The first estimation stage 8 estimates the cortical potential distribution from the electric double layer in the plurality of sub-regions by using the head model and tracks the distribution over time. First
In the drawing step 9 of step 1, the brain cortical potential distribution is image-processed to form a composite image which is stereoscopically combined with the multi-region head model, and each small region in the composite image is emphasized based on its intensity. To display. In addition, 1 is a correction step for preprocessing and correcting the measured values, and 10 is a step for organizing the cerebral cortical potentials.

Therefore, in this embodiment, as shown in FIG. 1, the electric potential distribution on the scalp due to the electroencephalogram is detected by the measuring device DET and converted into an electric signal, and the error in the measurement of the obtained data is corrected. , And introduce the result into the system 5.
In this system 5, the brain cortical potential distribution is estimated based on the brain model assumed by the present inventor, and the brain cortical potential distribution and the tomographic image are combined and stereoscopically displayed. A doctor or the like can observe the screen to diagnose various symptoms, and can store these images and the screen as required.

In addition, the system 5 is preceded by a preliminary step (not shown) for performing a preliminary treatment in the main part 6, so that the components included in the waveform of the scalp potential distribution introduced are classified and analyzed. Has become. Here, this preliminary step is briefly described to help understanding in the following description. The waveform of the electric potential obtained by measuring the electroencephalogram has components of amplitude, phase, and frequency that are functions that change with time, and the activity of the neuron in the brain to be estimated is used as the source of information for each. It is designed to change these components in association with this activity while interrelated with the channels to be measured.

Such a waveform of the electric potential shows a calm control state in which there is no peculiar change due to the mental activity of the brain,
In the activated state where this peculiar change occurs, the values of these components largely fluctuate, and the changes that extremeize according to their respective peculiar distributions are temporally constructed, and the mental activity of the brain changes. As a result, with time, these components in the potential distribution on the scalp give rise to unique characteristics.

Therefore, in order to grasp this transition, in the above-mentioned correction step, for example, the waveform of the above-mentioned potential is subjected to a filtering operation in a predetermined period, a predetermined band including this filtered frequency is selected, and this band is selected. The electric potential distributions of the respective electric components of the signal are obtained in advance and then introduced into the system 5 in FIG.

Next, a brain model, which is an equivalent model for estimating the activity of neurons in the brain according to the present invention, will be schematically described. FIG. 2 is a diagram for explaining the above-mentioned in-brain model used in the present invention by the structure of the brain.
2A shows a vertical cross section of the head, and FIG.
The folds in (a) are partially enlarged.

In FIG. 2 (a), the head HD includes the outermost scalp (Scalp) SC and the inner skull (Skull).
It is an area where neurons in the brain are active, which has SK on the outside and through the interstitial gaps such as the eye, nostrils, and ear canals, and the intermediate layer GP formed by intermediate substances such as plasma and meninges. It has a brain-cortex BC inside these and is constructed. The surface of this brain cortex folds and many folds F are formed.
L is formed.

In FIG. 2 (b), in the brain cortex BC, the gray-white gray matter NC mainly composed of neurons (nerve cells) is arranged on the outer side, and the white white matter OC mainly composed of nerve fibers is arranged on the inner side. A large number of neurons formed almost parallel to each other along a column structure almost perpendicular to the surface,
This gray matter is contained in this gray matter, and the equivalent current dipole moment generated by the active neuron is generated almost parallel to this column group. Therefore, this gray matter is electrically charged along its center plane. It can be regarded as forming an electric double layer having both poles.

Therefore, in the brain model according to the method of the present invention, focusing on this gray matter NC, the activity of brain neurons in the brain cortex BC is represented by the electric double layer, and the scalp SC and the skull SK described above are represented. And the intermediate layer GP are regarded as a layered structure having a uniform conductivity in the thickness direction,
The brain cortical potential distribution in the stratum is estimated from the potential distribution of the scalp SC and is constructed.

Thus, the electrical state of the head HD is equivalently expressed, and the state of this electric double layer is estimated from the above potential distribution so that the electrical activity of neurons in the brain can be externally diagnosed. Has become. The brain model in the above description is the Scalp Skull Brain-cortex equivalent model (hereinafter, SSB model).

However, in this SSB model, the skull S
It does not consider the cerebrospinal fluid CS existing between K and the cerebrocortex BC, and ignoring the cerebrospinal fluid CS, which has a greater conductivity than the scalp, skull, and cortex, is the The estimation accuracy of the active neuron by the statistical equivalent model was significantly reduced. Therefore, the intracerebral model according to the method of the present invention considers cerebrospinal fluid CS, limits the above-mentioned electrical activity to the gray matter of the brain based on physiological knowledge, and determines the activity level in the gray matter itself. It is supposed to be calculated backwards, and if this is Scalp Skull Cerebrospinal Brain-cor
Multi-region head model (hereinafter MC
HM model).

Now, returning to the original story, an apparatus which is a system for carrying out the present invention will be described. FIG. 3 is a block diagram schematically showing the apparatus of the embodiment. In FIG. 3, the apparatus of this embodiment is provided with an interface IN for detecting the waveform of the electric potential by the measuring apparatus DET and introducing the data of the corrected electric potential distribution through the correction unit having the correction step described above. ing.

Furthermore, the data of the introduced potential distribution is
Processing unit 2 that processes according to the sequencer 27 that manages the procedure
3, a file memory FM for storing and storing numerical data and image data obtained by processing, and this memory FM
A display unit 24 for extracting and displaying data at any time from the
A printer 25 for printing these data and a keyboard 26 for instructing a processing procedure and setting parameters are provided and connected to each other via a bus.

The processing unit 23 includes means 11 for the first setting stage.
And similarly the means 12, 13, 14 of the first estimation stage,
Similarly, the synthesizing stage means 15, the analysis unit and the display unit 24
And a printer 25 and a keyboard 26, and means I / O for input / output processing are provided as a program module, and each command is processed according to a sequencer 27 that decodes commands and manages the entire system. It has become.

The file memory FM is provided with a file 28 of numerical data for classifying and storing numerical data in respective tables and a file 29 of image data for classifying and storing image data in respective screens. is doing.

The numerical value file contains a conversion table GT for arranging the data of the distribution obtained by analyzing each of the above-mentioned elements in a predetermined time domain and the coefficient to be converted according to the conversion function described later, and the parameters specified by the keyboard. It has a parameter table PT to be arranged and is configured to access each file by controlling the address by each program module in the processing unit.

The image file is obtained by conversion, and an estimated image developed by estimating and developing the brain cortical potential distribution in the MCHM model described above and a CT obtained by a shape measuring device such as a nuclear magnetic resonance imaging device and an X-ray device. Image and a combined image obtained by synthesizing these estimated and CT images with each other, and this CT image includes, in addition to the above-mentioned group of images in the head, the outline of the head. It is supposed to include images.

A method for obtaining an image of the outer shape of the head is known PO
ISOTRAK (registered trademark) manufactured by LHEMUS Co., Ltd. is used, and the method of obtaining an image of the outer shape of the measured object using this measuring device is described in the following patent application “3D by magnetic field” by the applicant of the present invention. Shape measurement and display device ". Patent application: Japanese Patent Application No. 5-28320. In addition, the shape measuring devices such as the nuclear magnetic resonance imaging device, the X-ray device, and the ISOTRAK (registered trademark) measuring device will be collectively referred to as M below.
Collectively referred to as RI.

Then, in the method of this embodiment, MC
A program for stereoscopically displaying the cortical potential screen estimated by the HM model will be described in detail with reference to FIGS. 1 to 4. FIG. 4 is a flow chart schematically showing this program, which is configured by subdividing the respective steps 7, 8 and 9 of the first setting, estimation and drawing in FIG. 1 into modules. Is designed to judge and execute the command specified by the analyst.

In FIG. 4, first, the first setting step 7
2 starts up the apparatus of FIG. 2 to bring up a predetermined menu screen to start up the system of the present invention, and loads the files of the above-mentioned numerical data of the potential distribution and the above-mentioned image data such as CT image on this system. FL means 71 to
From the above-mentioned image data file-loaded by the L means 71, the extracting means 72 for extracting the features such as the contour surface of the image data by a known technique, and these features extracted by the extracting means 72 are numerically analyzed and described above. The electric double layer in the cerebral cortex is set, and a standardizing means 73 for standardizing and expressing the head to be inspected in the MCHM model is sequentially provided.

Subsequently, in the first estimation step 8, a reference scalp potential distribution serving as a reference is obtained from the electric double layer potential distribution in the MCHM model expressed in the above-mentioned first setting step 7.
An approximation unit 81 that approximates the actually measured measurement scalp potential distribution and a conversion unit 82 that calculates the reference scalp potential distribution and converts the reference scalp potential distribution into the cerebral cortical potential distribution to be estimated are sequentially provided.

Finally, in the first drawing step 9, the cerebral cortical potential distribution calculated by the first estimating step 8 is developed into new image data based on the above-mentioned position, and this image is obtained. Emphasizing means 91 for emphasizing the color arrangement or density of the data by gradation based on the intensity of the cerebral cortical potential distribution at the above position to form an enhanced image of the cerebral cortical potential distribution.
And the above-mentioned emphasized image formed by this emphasizing means 91 is combined with the above-mentioned CT image to form a combined image, and this combined image is separated into another time region in the active period during which the neurons in the brain are active. In the activity period necessary for diagnosing the desired symptom, the synthesizing means 92 for further synthesizing with another synthetic image to form a new synthetic image, and each synthetic image formed by this synthesizing means 92 And a period determination means 93 for determining whether or not the measured values are covered are provided in order, and when the period determination means 93 is negative, the flow of the procedure to be executed is branched, F
The processing is returned to the L means 71.

Furthermore, when the period determination means 93 gives an affirmative result, the image data of these combined images obtained by the combining means 92 is displayed on the display device 24 while being stored in the image file IF. The display means 94 is provided at the end of the display. Also, after executing this display means 94,
The processing is shifted to the above-mentioned arrangement step 10.

Here, it is considered to be a real shape uniform head model in which the head is once separated from the MCHM model and the head is considered to be formed by an electrically uniform medium. Then, it is assumed that the brain cortex is an electric double layer having a unit dipole moment of 1 per unit area, this electric double layer is divided into J small sections, and the electric potential generated by the small section j at the position of the electrode n is Φn. , j. When this subsection j has a dipole moment Pj, the potential appearing on the electrode n is
Can be obtained by the conversion determinant of

[0041]

[U ⁿ ] = [Φ ^nj ] [P ^j ] where j = 1, 2, 3, ..., J; n = 1, 2,
3, ..., N.

Therefore, if there is an inverse matrix of this transformation matrix [Φ ^nj ], this is used as a transformation parameter group, and the measured potential group [U ⁿ ] on the scalp to the dipole moment group [P By solving the inverse problem for [ ^j ], the solution obtained shows the distribution of active neurons in the brain cortex. As a specific numerical calculation method, the finite element method, the boundary element method, or a mixture thereof may be used.

Now, the method of the present invention will be described in detail with reference to the drawings, using the above-mentioned numerical analysis. The MCHM model of the present invention is a multi-region head model that represents a multiple internal structure of the head, and by multiplying the real shape uniform head model described above, the estimation calculation from the electric double layer is performed. In this case, the reference scalp potential distribution serving as a reference can be obtained, but other operational equations may be used if the electric conductivity at each place in the multiple internal structure is appropriately initialized.

FIG. 5 (a) is a flow chart for explaining the standardization means in FIG. 1, and FIG. 5 (b) is a flow chart for explaining the estimation step. In FIG. 5A, the standardization means 73 is a region processing 7 for setting the model in a hypothetical region of the head where the operator expects that an electrical feature to be diagnosed exists.
31 and a deployment process 732 for deploying the above-mentioned electric double layer on the entire brain cortex set in this model, and a predetermined number range according to the scale of the system to which the electrical double layer on the entire brain cortex to be deployed is applied. A division process 733 for dividing by is sequentially provided.

Subsequently, with respect to these divided ranges, the above-mentioned numerical analysis is performed to obtain the electric potential at each measurement point from the electric double layer and analyze the electric potential distribution 734.
Further, regarding the analyzed potential distribution, an array processing 735 for arranging and numerically storing the numerical values of the respective components in the respective divided areas in a conversion table is provided in order. Note that the connector 730 initiates these processes,
The connectors 739 each indicate the end.

Therefore, this conversion table is determined by a general-purpose one determined by the standard physical structure of the head and an individual physical structure obtained by measuring the individual head each time. There is a dedicated one, and the former is selected for simple and real-time simple processing, and the latter is selected for high-accuracy diagnosis and research purposes.

Further, the conversion operation by the inverse matrix is
Considerable ambiguity is inevitable when estimating more than a large number of estimates from a small number of measurements due to the limited number of electrodes. Therefore, if the number J for dividing the electric double layer into the small areas in the above Equation 1 is reduced and the areas estimated by the conversion calculation are limited, the calculation accuracy is relatively improved as compared with the number of measurement electrodes. be able to. For example,
If this number J is the same as the number of measurement electrodes, the distribution of active neurons can be uniquely obtained.

FIG. 5 (b) shows a first example including a correction for this.
3 is a flowchart specifically showing the estimation stage of FIG. In FIG. 5 (b), first, each set intensity is calculated for each of the plurality of small areas divided in the first setting step 7, and the calculation result is converted into a scalp potential distribution by the MCHM model and used as a reference. Error processing 811 for calculating a squared error between the reference scalp potential distribution and the actually measured measured scalp potential distribution, and the value of this squared error is counted and minimized. Counting process 812 for determining the intensity of the electric double layer in each area for determining the sub-region to be estimated by weighting the obtained intensity in the distribution within a predetermined range,
A calculation process 821 for calculating the position in the small area to be estimated and the numerical value of its component, and a precision process 822 for verifying the accuracy at this time by collating the numerical value of the squared error with the target limit value. Are sequentially provided.

When the target limit value has not been reached in the precision processing 822, the peripheral area around the small area to be determined is set as a new range to be divided, and this range and its range are set. After designating the number of divisions in the range, the flow of execution is branched to the area processing 731 and the processing is returned. Note that the connector 80 indicates that the estimation step is started and the connector 89 ends it.

A standard cerebral cortical structure is constructed in advance in order to omit the process of taking a large number of nuclear magnetic resonance apparatuses or X-ray CT tomographic images for each subject, and this standard is determined from the subject's head shape. It can also be transformed into a model close to the brain structure of the person. Alternatively, it is possible to examine all brain structures for a typical head shape and select from them.

In the above, the method of approximating by minimizing the mean square error is used in the first estimation stage, but the method of back propagation in which the neural network is trained to approximate is also used. It can also be used. In this neural network, the scalp potential distribution at each measurement electrode is set at the input end, and the cerebral cortical potential distribution in each subregion of the brain cortex to be estimated is set at the output end to form a network. Each weighting coefficient and bias coefficient are set in each nonlinear circuit that is a component of the network. The second approximation means and the second conversion means in the second estimation stage using this neural network will be briefly described.

The second approximating means first selects a specific one from each of these sub-regions and gives an electric double layer having a unit intensity only to this one sub-region. The scalp potential distribution at this time is obtained by using the MCHM model of the present invention, similarly, each distribution is sequentially obtained for all the sub-regions, and these are filed in correspondence with each sub-region number.

Next, the network is set to a learning mode, a numerical group of a specific scalp potential distribution is read from a file and set at each input end, and a small area having an electric double layer of unit intensity at the corresponding output end. Adjust the value of each coefficient to indicate. Similarly, the value of each coefficient for all output terminals is sequentially obtained, and a neural network equivalent to the MCHM model of the present invention is obtained.
Forming a network.

Subsequently, the second conversion means first switches the network to the operation mode, measures the actual scalp potential distribution and supplies it to each input terminal, and activates this neural network to perform the operation processing. Then, the value of the actual cerebral cortical potential distribution based on this actual scalp potential distribution is formed at each output end.

That is, in the system for estimating and displaying the electrical activity of the neurons in the brain, the MCHM described above is used.
Using the model, the electrical state on the surface of the cerebral cortex is estimated from the electric potential distribution on the scalp, and the image obtained from this estimated result is synthesized into a tomographic image of the head, and the above-described on the surface of the cerebral cortex. Even individual folds can be displayed in close contact with each other accurately.

It should be noted that the method of the present invention is not limited to the above, and in each of the above-mentioned steps of extraction and analysis, the order of processing time and position may be executed first, Although the neural network preferably has two or more layers, it may be a single layer, and the constituent elements of the network may be only linear circuits, or may be those that combine linear and non-linear circuits, and various modifications are made without departing from the scope of the present invention. Needless to say, can be added.

[0057]

[Effects of the Invention] An equivalent model on a three-dimensional curved surface is estimated over time from the waveform of the potential measured on the scalp, and the active site is displayed on the actual brain structure. It became possible to perform multifaceted and reliable electrical activity of neurons in the cerebral cortex. Therefore, regarding the analysis of the electric potential on the scalp, the distribution is stereoscopically displayed by various signal processing, and the detailed state of information processing in the brain is identified by the position, vector, and time in accordance with the actual situation. Therefore, it becomes possible to make a realistic and reproducible diagnosis. Further, for a specific subject, this MCHM model is trained by a neural network, and the distribution patterns of the respective correlations that appear in the control state and the active state due to the mental activity are compared over time, and the differences are identified and quantified. It becomes possible to apply the expert system to unspecified subjects and point out the mental activity state.

[Brief description of drawings]

FIG. 1 is a diagram showing in principle an embodiment according to the method of the present invention.

FIG. 2 is an explanatory diagram for explaining the equivalent model used in FIG. 1 from the brain structure.

FIG. 3 is a block diagram schematically showing the apparatus used in FIG.

FIG. 4 is a flowchart schematically showing a program used in FIG.

FIG. 5 is a flowchart showing a part of FIG. 4 in detail.

FIG. 6 is a diagram illustrating a conventional display method.

[Explanation of symbols]

 DET measuring device MRI shape measuring device 1 correction part 5 system 6 main part 7 first or second setting step 8 first or second estimation step 9 first or second drawing step 10 rearrangement Stage

─────────────────────────────────────────────────── ─── Continuation of the front page (51) Int.Cl. ⁶ Identification code Internal reference number FI technical display location G06F 15/62 390 B

Claims (6)

[Claims] 1. A brain for estimating a cerebral cortical potential distribution, which is a potential distribution in a cortex in the brain, from a scalp potential distribution, which is a potential distribution on the scalp, and tracing a spatial distribution of active neurons in the cortex. In the method for tracking cortical activity in, a multi-region head model that simulates multiple internal structures of the head is formed, and an electric double layer having electrical both poles is formed inside the multi-region head model. And a setting step of electrically dividing the electric double layer into a plurality of small regions having unit intensity, calculating the unit intensity for each of the plurality of small regions, and converting the unit intensity into the scalp potential distribution. A reference scalp potential distribution is obtained, a first squared error between the reference scalp potential distribution and the actually measured measured scalp potential distribution is calculated, and a specific squared error for minimizing the first squared error is calculated. Subdivision and its strength Cortical activity tracking methods in the brain, characterized in that it is constructed by providing the estimation step of tracking to determine and. 2. The estimating step includes the step of determining an intensity for minimizing a second squared error with respect to a peripheral area closest to the specific sub-area, Until the squared error reaches a predetermined limit value, the estimation step is repeatedly performed while replacing the new plurality of small areas with the peripheral area, and the brain cortical potential distribution is estimated. The method for tracking cortical activity in the brain according to claim 1, wherein 3. Following the estimation step, a composite image is formed that stereoscopically combines the respective sub-areas determined in the estimation step with the internal structure, and the respective sub-areas are formed in the composite image. It is configured by adding a drawing step of converting to an emphasized image to be emphasized based on the intensity and drawing the image, and the cerebral cortical potential distribution is displayed while being stored as a distribution map that changes with time. The method for tracking cortical activity in the brain according to claim 1 or 2, wherein: 4. A brain for estimating a cerebral cortical potential distribution, which is a potential distribution in a cortex in the brain, from a scalp potential distribution, which is a potential distribution on the scalp, and tracing a spatial distribution of active neurons in the cortex. In the cortical activity tracking device according to claim 1, a multi-region head model that simulates multiple internal structures of the head is formed, and an electric double layer having electric bipolar is formed inside the multi-region head model. And a setting step of electrically dividing the electric double layer into a plurality of small regions having unit intensity, calculating the unit intensity for each of the plurality of small regions, and converting the unit intensity into the scalp potential distribution. A reference scalp potential distribution is obtained, a first squared error between the reference scalp potential distribution and the actually measured measured scalp potential distribution is calculated, and a specific squared error for minimizing the first squared error is calculated. Subdivision and its strength Cortical activity tracking device in the brain, characterized in that it is constructed by providing the estimation step of tracking to determine and. 5. The estimating step includes the step of determining an intensity for minimizing a second squared error for a peripheral area closest to the particular sub-area, wherein the second squared error is determined. Until a predetermined limit value is reached, the estimation step is repeatedly performed while replacing the new plurality of small areas with the peripheral area, and the brain cortical potential distribution is estimated. The cortical activity tracking device in the brain according to claim 4. 6. Following the estimation step, a composite image is formed that stereoscopically combines the respective sub-areas determined in the estimation step with the internal structure, and the respective sub-areas are formed in the composite image. It is configured by adding a drawing step of converting to an emphasized image to be emphasized based on the intensity and drawing the image, and the cerebral cortical potential distribution is displayed while being stored as a distribution map that changes with time. The cortical activity tracking device in the brain according to claim 4 or 5, wherein: