JP2005143722A - Biomagnetism measuring apparatus, program for biomagnetism measuring apparatus, recording medium for biomagnetism measuring apparatus and biomagnetism measuring method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To analyze what course an information processing of a region of interest is performed along by specifying a signal transmitting course and an interaction (a network to identify the information processing course at the region of interest of the living body) which are the cause of activity at the region of interest when the biomagnetism which is generated following a bioactivity current in the region of interest of an examinee is measured and a condition of the bioactivity current in the region of interest of the examinee is grasped on the basis of the measured biomagnetism data. <P>SOLUTION: The activity change of the bioactivity current is calculated (refer S7) applying a Spatial Filter method to the measured (refer S1-S6) biomagnetism data and a multivariable autoregressive model is applied (refer S7) to the calculated activity change. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to a biomagnetic measurement apparatus that grasps the state of a bioactive current in a region of interest of a subject, a program used for the apparatus, a recording medium that records the program, and a biomagnetic measurement method.

  A minute biomagnetism is generated from a living body in accordance with a bioactive current generated in the living body. For example, the biomagnetism that occurs in response to the bioactive current in the brain is called the magnetoencephalogram, and it naturally occurs from the brain, such as the induced magnetoencephalogram that occurs when a stimulus is applied to the living body, or the spike wave of an alpha wave or epilepsy. There are spontaneous magnetoencephalograms that occur.

  In recent years, a multi-channel SQUID sensor using a SQUID (Superconducting Quantum Interference Device) has been developed as a magnetic sensor for measuring minute biomagnetism in a living body (see, for example, Patent Document 1). . This multi-channel SQUID sensor stores a large number of magnetic sensors immersed in a refrigerant such as liquid nitrogen in a container called a dewar. This magnetic sensor includes a coil unit that measures a magnetic field and a SQUID sensor unit that converts a detected magnetic field into a voltage.

  This multi-channel SQUID sensor is placed outside the head, which is a region of interest of the subject, for example, and minute biomagnetism generated from bioactive current generated in the brain is non-invasively detected by each magnetic sensor in the multi-channel SQUID sensor. Can be measured. From this measured biomagnetic information, it is possible to grasp the state such as the position and direction where the bioactive current of the region of interest of the subject is generated.

  Conventionally, in order to grasp the state of the bioactive current generated in the region of interest of the subject such as a living body, the following is performed. First, the positional relationship between the multi-channel SQUID sensor and the region of interest of the subject is captured. As means for capturing this positional relationship, there are various methods such as JP-A-5-237065 and JP-A-6-788925.

  Next, a weighting coefficient is calculated so that biomagnetic information based on a micromagnetic field generated from a bioactive current flowing at a specific position in the region of interest of the subject is emphasized. This weighting factor is calculated for each magnetic sensor housed in the multi-channel SQUID sensor. As a method for calculating the weighting coefficient, there is a method based on S.E. Robinson, D.F. Rose, “Current Source Image Estimation by Spatially Filtered MGE” Biomaguetisum: Clinical aspects (1992) (hereinafter referred to as “SF method”).

  A convolution operation is performed between the measured biomagnetic information obtained by measuring the biomagnetism from the region of interest of the subject and the weighting coefficient that emphasizes only the biomagnetism from the specific position. As a result, only the biomagnetism generated from the bioactive current at a specific position in the region of interest is emphasized, so that the state of the bioactive current at an arbitrary position in the region of interest of the subject can be grasped.

If the state of the bioactive current is captured at a number of positions in the region of interest of the subject, the state of the bioactive current generated in the entire region of interest of the subject can be grasped. By displaying this bioactive current on the tomographic image of the region of interest of the subject obtained with an X-ray CT (Computerized Tomography) device or MRI (Magnetic Resonance Imaging) device, It is possible to grasp the state of the biological activity current.
JP-A-11-221198 (page 4-6, second)

  However, in the past, since the (original) biological data signal measured by the SF method is averaged and only the activity intensity change of the brain power source is calculated from the added biological signal, no analysis is performed. It was difficult to identify brain signaling pathways and interactions (intra-brain networks) that cause activity.

  The present invention has been made in view of the above-described circumstances, and biomagnetism generated by a plurality of magnetic sensors arranged in proximity to a region of interest of a subject along with a biological activity current flowing in the region of interest of the subject. , And based on the measured biomagnetic data obtained by this measurement, when grasping the state of the bioactive current flowing in the region of interest of the subject, the signal transmission path and the interaction causing the activity in the region of interest An object is to analyze what route the information processing of the region of interest is performed by specifying (network indicating the information processing route in the region of interest of the living body).

  In order to achieve the above-mentioned object, the invention according to claim 1 measures biomagnetism generated in accordance with a biological activity current flowing in the region of interest by a plurality of magnetic sensors arranged close to the region of interest of the subject. The biomagnetic measuring device grasps the state of the bioactive current flowing in the region of interest based on the measured biomagnetic data obtained by this measurement, and the bioactive current is calculated based on the measured biomagnetic data. A calculation means for calculating an activity change; and an identification means for identifying a signal transmission path and an interaction in the region of interest that causes the activity based on the activity change calculated by the calculation means. It is the biomagnetic measuring device characterized.

  Here, as the “calculation means”, for example, a spatial filter method such as Spatial Filter method (minimum norm type) or wiener type is applied to the live magnetoencephalogram. As the “identification means”, for example, identification (estimation) is performed by applying a multivariate autoregressive model.

  In particular, the identification unit applies a multivariate autoregressive model to the activity change calculated by the calculation unit according to claim 2, whereby a signal transmission path and a mutual path in the region of interest causing the activity are determined. It is characterized by identifying an action.

  The invention according to claim 3 is obtained by measuring biomagnetism generated along with the bioactive current flowing in the region of interest by a plurality of magnetic sensors arranged close to the region of interest of the subject. A program used in a computer-controlled biomagnetic measuring device for grasping a state of a bioactive current flowing in the region of interest based on measured biomagnetic data, the bioactive current based on the measured biomagnetic data Calculation means for calculating the activity change of the computer, and identification means for identifying the signal transmission path and interaction in the region of interest that causes the activity based on the activity change calculated by the calculation means. It is the program for biomagnetism measuring devices for realizing.

  Here, the “program” in the present invention means an ordered sequence of instructions suitable for processing by a computer, such as those installed in a computer's HD (Hard Disk), CD-RW, etc. , Recorded on various recording media such as CD-ROM, DVD, FD, semiconductor memory, and computer HDD, and distributed via an external network such as the Internet.

  In particular, the identification unit applies a multivariate autoregressive model to the activity change calculated by the calculation unit according to claim 4, thereby causing a signal transmission path and a mutual path in the region of interest causing the activity. It is characterized by identifying an action.

  According to a fifth aspect of the present invention, there is provided the computer-readable recording medium on which the biomagnetism measuring device program according to the third or fourth aspect is recorded.

  Here, the “recording medium” in the present invention is only required to be used for reading a program for causing the computer to function the above means, and how information is recorded using physical characteristics of the medium. It does not depend on the physical recording method such as For example, FD (Flexible Disk), CD-ROM (R, RW) (Compact Disc Read Only Memory (CD Recordable, CD Rewritable)), DVD-ROM (RAM, R, RW) (Digital Versatile Disk Read Only Memory (DVD Random) Acess Memory, DVD Recordable, DVD Rewritable)), semiconductor memory, MO (Magneto Optical Disk), MD (Mini Disk), magnetic tape, and the like.

  The invention according to claim 6 is a measurement obtained by measuring biomagnetism generated in accordance with a biological activity current flowing in the region of interest by a plurality of magnetic sensors arranged in proximity to the region of interest. A biomagnetic measurement method for grasping a state of a bioactive current flowing in the region of interest based on biomagnetic data, and a calculating step for calculating an activity change of the bioactive current based on the measured biomagnetic data ( And an identification step (see S7) for identifying a signal transmission path and an interaction in the region of interest that causes the activity based on the activity change calculated in the calculation step (see S7). This is a biomagnetic measurement method characterized.

  The method according to claim 6 can be executed by using the biomagnetism measuring apparatus according to claim 1, but is not limited to using this apparatus.

  In particular, in the fixing step according to claim 7, by applying a multivariate autoregressive model to the activity change calculated in the calculation step, a signal transmission path and a mutual path in the region of interest causing the activity are determined. It is characterized by identifying an action.

  The method according to claim 7 can also be executed by using the biomagnetism measuring apparatus according to claim 2 above, but is not limited to using this apparatus.

  As described above, according to the present invention, the activity change of the life activity current is calculated based on the measured biomagnetic data, and the signal transmission path in the region of interest causing the activity is calculated based on the calculated activity change. Identifying the interaction and identifying the signal transmission path and interaction that cause the activity in the region of interest, and analyzing the route of information processing of the region of interest in the living body There is an effect that can be.

  Hereinafter, an embodiment of the present invention will be described.

  An average of individual biological signals is added, and a spatial filter for identifying (estimating) the brain power source is obtained from the average added biological signal, and this spatial filter is further applied to the raw (non-added) biological signal, thereby obtaining an unadded brain. Seeking power activity. Applying a multivariate autoregressive model makes sense because of fluctuations in brain power activity.

  A multivariate autoregressive model is identified, a response function specifying transfer characteristics is calculated from the parameters of the model, and a signal transmission path and interaction between each brain region are analyzed.

  The main point of this embodiment is that a multivariate autoregressive model is applied to data obtained from a magnetoencephalograph (multi-channel SQUID sensor) in order to analyze signal transmission paths and interactions in the brain region. It is in. This is a new analysis method at the world level.

  In general, only a brain power source is identified from a signal using a magnetoencephalograph. It is difficult to grasp the fluctuation state of brain activity only by identifying the brain power source. There is no point in applying the multivariate autoregressive model to data obtained directly from the magnetoencephalograph. The present applicant applied the spatial filter method to the raw magnetoencephalogram obtained from the magnetoencephalograph to obtain the raw brain power supply activity. By obtaining the data, it became possible to apply the multivariate autoregressive model to the fluctuation of those data, and as a result, the signal transduction pathway and the interaction could be identified.

  Conventionally, as means for identifying a signal-signal interaction, most of the methods are based on cross-correlation and coherency. They are methods that can be applied on the assumption that the input and noise are independent in the feedforward system. It is natural that fluctuations in brain activity are assumed to be a closed feedback system, and the correlation analysis for such a system is a rather rough application, and it is difficult to grasp the detailed correlation. The advantage of applying a multivariate autoregressive model is that it can calculate the open-loop response between individual subsystems for phenomena occurring in a closed system, and the interaction based on the transfer characteristics from one signal to another It is to be able to confirm.

  Hereinafter, examples specifically showing the embodiment of the invention will be described with reference to the drawings.

  FIG. 1 is a block diagram showing a schematic configuration of an embodiment of a biomagnetic measuring apparatus according to the present invention. In the figure, reference numeral 2 denotes a magnetic shield room, a bed 3 on which the subject M is supine in the magnetic shield room 2, and a biologically active current source generated in the brain in the proximity of the subject M, for example, the brain. And a multi-channel SQUID sensor 1 for non-invasively measuring a minute magnetic field due to. As described above, the multi-channel SQUID sensor 1 has a large number of magnetic sensors immersed in a refrigerant and housed in a dewar. In this embodiment, each magnetic sensor is composed of a pair of coils that detect a magnetic field component in the radial direction when the brain of the subject M is assumed to be a sphere.

  The magnetic field data detected by the multichannel SQUID sensor 1 is given to the data conversion unit 4 and converted into digital data, and then collected in the data collection unit 5. The stimulation device 6 is for applying electrical stimulation (or sound, light stimulation, etc.) to the subject M. The positioning unit 7 is a device for grasping the positional relationship of the subject M with respect to the three-dimensional coordinate system with the multi-channel SQUID sensor 1 as a reference. For example, small coils are attached to a plurality of locations of the subject M, and power is supplied from the positioning unit 7 to these small coils. Then, the magnetic field generated from each coil is detected by the multichannel SQUID sensor 1, thereby grasping the positional relationship of the subject M with respect to the multichannel SQUID sensor 1. In addition to this, a method for grasping the positional relationship of the subject M with respect to the SQUID sensor 1 is to attach a projector to the Dewar and irradiate the subject M with a light beam to grasp the positional relationship between the two, Alternatively, various methods disclosed in JP-A-5-237065 and JP-A-6-788925 are used.

  The data analysis unit 8 is a device that performs processing such as identification and analysis of a current source in the diagnosis target region of the subject M based on the magnetic field data collected by the data collection unit 5. The data analysis unit 8 is constituted by a computer or the like, and a biomagnetism measuring apparatus program (p) for causing the computer to execute processing such as identification and analysis is recorded. The recording and installation work of the program (p) can be read by the biomagnetic measuring device (data analysis unit 8) and uses a recording medium such as a CD-ROM in which the program (p) is recorded. It is also possible to do this.

  A magneto-optical disk 9 provided in association with the data analysis unit 8 stores a tomographic image obtained by, for example, an X-ray CT apparatus or an MRI apparatus, and the current source identified by the data analysis unit 8 is stored. Is superimposed on these tomographic images and displayed on the color monitor 10 or printed out on the color printer 11. The tomographic image obtained by the X-ray CT apparatus or the MRI apparatus may be directly transmitted to the data analysis unit 8 via the communication line 12 such as a LAN (Local Area Network) shown in FIG. Good.

  As described above, the positional relationship of the subject M with respect to the three-dimensional coordinate system based on the multichannel SQUID sensor 1 is measured and stored, and the diagnosis target region of the subject M is detected by the multichannel SQUID sensor 1, for example, the brain After measuring a minute magnetic field from the internal biological activity current source and collecting the magnetic field data in the data collection unit 5, the data analysis unit 8 executes the current source identification process in the following process.

  Hereinafter, the process of each part performed by the biomagnetic measurement apparatus will be described with reference to the flowchart of FIG.

Step S1 (detects the position of the region of interest)
First, a current is supplied from the positioning unit 3 to a plurality of small coils (not shown) mounted on the head. The magnetic field generated from each small coil is detected by the magnetic sensors S1 to Sm. The data conversion unit 4 digitally converts the detected magnetic field and sends it to the data collection unit 5 as coil position information.

Step S2 (derived transfer function)
The data analysis unit 8 obtains the positional relationship between the subject M and the magnetic sensors S1 to Sm based on the coil position information sent to the data collection unit 5. The method for obtaining this positional relationship can be calculated using the least square method, as in the prior art. When the positional relationship between the magnetic sensors S1 to Sm and the subject M is obtained, the data analysis unit 8 is, for example, three-dimensional with an arbitrary position in the region of interest of the subject M as the origin O as shown in FIG. Set the coordinate system. In this coordinate system, a transfer function indicating the degree to which biomagnetism generated from, for example, the specific position Px in the region of interest of the subject M is transmitted to the magnetic sensors S1 to Sm is derived.

As shown in FIG. 3, the data analysis unit 8 determines the degree of biomagnetism transmission from the specific position Px to the magnetic sensor Si when the bioactive current exists at the specific position Px at the distance r from the origin O. The transfer function Gi (r) indicating is derived as follows. For example, when a bioactive current J (r) (J (r) indicates current density) is present at a specific position Px, the biomagnetism detected by the magnetic sensor Si is expressed by the following equation (1) from Bio-Savart's law: ).

In equation (1), μ ₀ is the permeability in vacuum, ri is the distance from the origin O to the magnetic sensor Si, and ei is a unit normal vector passing through the coil surface of the magnetic sensor Si. Therefore, Bi represents the magnetic flux density detected by the i-th magnetic sensor. The above formula (1) is modified to derive the following formulas (2) and (3).

  As shown in Expression (2), the magnetic flux density Bi can be expressed by an expression obtained by integrating the inner product of the life activity currents J (r) and Gi (r). Gi (r) shown in the above equation (3) is a transfer function from the specific position Px to the magnetic sensor Si. It should be noted that specific numerical values of the distance r from the origin O of the specific position Px within the region of interest of the subject M and the distances from the magnetic sensors S1 to Sm are substituted into the transfer coefficient Gi (r) shown in the equation (3). The value calculated in this way is the transmission coefficient from the specific position in the region of interest to each magnetic sensor in this embodiment. Hereinafter, the specific position Px within the region of interest of the subject M will be described using the transfer function Gi (r).

Step S3 (derived weighting factor calculation formula)
If only the biomagnetism generated from the specific position Px can be emphasized from the measured biomagnetism information obtained by measuring the biomagnetism from the entire region of interest of the subject M, the bioactive current existing at every position within the region of interest Can be accurately grasped. That is, if the transfer function Gi (r) transmitted from the specific position Px to the magnetic sensors S1 to Sm is weighted, the biomagnetism transmitted based on the transfer function Gi (r) is emphasized. . Therefore, the data analysis unit 8 virtually sets the current δ (r) represented by the δ function (unit magnitude of current) at the specific position Px. Furthermore, the following equation (4) including a weight coefficient Wi that makes this current δ (r) equal to the sum of transfer functions Gl (r) to Gm (r) from the specific position Px to the magnetic sensors S1 to Sm: ) Is set. In addition, by setting this current δ (r) in an arbitrary direction in the three-axis directions, it is possible to emphasize an arbitrary directional component of the biological activity current generated in the region of interest of the subject M.

  Next, in order to calculate the weighting coefficient Wi that minimizes the evaluation function f, the linear equation shown in the following equation (5) is derived by differentially transforming the equation (4).

W = Γ-1B (5)
In equation (5), W is a matrix having weighting factor Wi as an element, Γ-1 is a matrix having Γij = ∫Gi (r) Gj (r) dr3, and B is B = ∫Gi (r) δ ( r) Indicates a matrix whose elements are dr3.

  The data analysis unit 8 adds a value corresponding to noise superimposed when measuring biomagnetism, for example, λI, previously input by an operator (not shown) by the operator, for example, to Γ in the above equation (4). λ indicates the magnitude of the input noise, and I indicates a unit matrix. The linear equation after adding λI is shown in the following equation (6) as a weighting factor calculation equation W.

W = (Γ + λI) −1B (6)
By adding the value λI to Γ, the solution of the matrix (Γ + λI) becomes a value farther from zero even when the solution of the matrix Γ is very close to zero. That is, the regularized matrix (Γ + λI) is in a state in which the so-called matrix property is stable. The data analysis unit 8 stores the weight coefficient calculation formula W in a memory (m) (not shown) in the data analysis unit 8. Step S3 corresponds to the function of the predetermined value adding means in the present invention.

Step S4 (Measure biomagnetism)
When the derivation of the weight coefficient calculation formula W is completed, the data analysis unit 8 instructs the stimulation apparatus 6 to start stimulation. The stimulating device 6 stimulates the subject M to generate a bioactive current in the region of interest. A minute magnetic field is generated from the region of interest of the subject M along with the biological activity current. The data conversion unit 4 digitally converts the biomagnetism detected by the magnetic sensors S1 to Sm and sends the biomagnetism to the data collection unit 5 as measured biomagnetic information M1 to Mm.

Step S5 (calculates a weighting factor)
The data analysis unit 8 substitutes the positions on the three-dimensional coordinates of the designated positions P1 to Pn of the subject M designated in advance by the operator into the weighting coefficient calculation formula W, and biomagnetism from the designated positions P1 to Pn. A weighting coefficient for emphasizing is calculated. For example, for the designated position P1, weighting factors P1W1 to P1Wm for weighting the measured biomagnetic information of the magnetic sensors S1 to Sm are obtained. Similarly, the weighting factors P2W1 to P2Wm,..., PnW1 to PnWm are obtained for the designated positions P2 to Pn. This weight coefficient is stored in the memory (m).

Step S6 (superimpose weighting factor)
The data analysis unit 8 calls the weighting factors PkW1 to PkWm at the designated position Pk (k = 1 to n) from the memory (m), and the weighting factors PkW1 to PkWm are collected in the data collection unit 5 for measurement biomagnetism. The information is superimposed on the information M1 to Mm. Thereby, the enhanced biomagnetic information DPk1 to DPkm in which only the biomagnetism from the designated position Pk is emphasized is obtained. Furthermore, by calculating the sum of the enhanced biomagnetic information DPk1 to DPkm, the enhanced life activity current Dk at the designated position Pk can be calculated. The calculation performed in step S6 is expressed by the following equation (7).

Dk = Σ (PkWm · Mm) (7)
The bioactive currents D1 to Dn at the designated positions P1 to Pn are obtained by performing the calculation of the enhanced biomagnetic information by the expression (7) for the designated positions P1 to Pn.

Step S7 (multivariate autoregressive model analysis)
The data analysis unit 8 calculates a brain activity change from the average added magnetoencephalogram data obtained in steps S1 to S6. In other words, a spatial filter method such as Spatial Filter method (minimum norm type) is applied to the magnetoencephalogram to determine the unpowered brain power supply activity. Next, a multivariate autoregressive model (VAR) analysis described later is applied to this live brain power supply activity to identify (estimate) the signal transmission pathway and interaction at the site of interest that causes the activity. .

  In steps S1 to S6, SF (minimum norm type) is used as an example of the spatial filter method. However, the present invention is not limited to this, and a multivariate autoregressive model is used even after using a spatial filter method such as Wiener type. Analysis can be performed.

Step S8 (result display / print)
The analysis result obtained in step S7 is displayed on the color monitor 10 or printed by the color printer 11.

  Subsequently, the experimental method is shown in FIGS. 4 and 5, and the experimental result is shown in FIG.

  When a visual stimulus (SM) in which two random dot regions that touch the boundary move in opposite directions is presented, a phenomenon that a contour that does not actually exist on the boundary is recognized (contour recognition by movement) occurs. . This was called the subjective contour, and the brain activity to the stimulus was measured by deriving the subjective contour, and analyzed using a multivariate autoregressive model. As a result of the analysis, the signal transmission from V5 (5th visual cortex) to V2 / 3 (2nd and 3rd visual cortex) by SM stimulation is the strongest compared to other interactions. This indicates that the contour recognition by movement is performed by the interaction between V5 and V2 / 3. When an adaptation stimulus in which the dots always move in a certain direction was given, the signal transmission from V5 to V2 / 3 was remarkably reduced, confirming the validity of the multivariate autoregressive model analysis.

Description of multivariate autoregressive model analysis Multivariate autoregressive model analysis is a technique that applies multivariate autoregressive models to output correlations from subsystems that have feedback relationships. . This technique is described in the following Reference 1 and the like, but the outline will be described here.

  In order to investigate the interrelationship between multivariate, the main method is to identify system characteristics from autocorrelation and cross-correlation in an open loop system. However, when there is a feedback relationship in an actual system, it is difficult to calculate an accurate response characteristic, coherency, etc. with a conventional analysis method that assumes that the input and noise are independent. The technique that should solve the problem is the analysis by the multivariate autoregressive model applied in this embodiment. The analysis theory is briefly explained below.

FIG. 7 shows a simple feedback system. When the outputs χ ₁ and χ ₂ from each subsystem are represented by their respective inputs,

It becomes. Ψ ₁ and Ψ ₂ are impulse response functions, and u ₁ and u ₂ are noise sources. Since it is a feedback system, the noise source that drives each subsystem is not independent of the output from the subsystem. In this case, the response characteristic cannot be obtained as a correct least square identification value. Therefore, the noise source needs to be uncorrelated, that is, whitened, and is expressed by the following equation (12).

ε ₁ and ε ₂ are white noises.

When these are used to organize each output, the following multivariate autoregressive model equation (13) is obtained.

Using these autoregressive coefficients, the impulse response function expressed by equation (11) is expressed as follows:

This corresponds to an open loop impulse response function. This response function directly indicates the interrelationship between subsystems in the feedback system. The multivariate autoregressive coefficient was calculated using the Yule-Walker method (see Reference 1). A rough program for feedback analysis based on these theories is published in the Fortran language according to Reference Document 2 below.
1. To integrate a series of data analysis flows into a configuration of "data reading, identification of multivariate autoregressive model based on information criterion, calculation of open / closed loop impulse response from model parameters", and to function as a biological signal analyzer Written in the Matlab language (a programming language for scientific and technical calculations developed by Math Work).

2. Since there are a lot of data on the sphere of the head that you want to focus on on the head sphere, the average value of the data was calculated for each field, and the model was applied using that value as the representative point data for each field. Then, the respective representative points were calculated for V1, V2 / 3, and V5, and the correlation between the three variables was examined).

Reference 1
Ozaki Osamu, Kitagawa Genshiro (1998). "Method of time series analysis" Asakura Shoten Reference 2
M. Ishiguro, H. kato, H. Akaike (1999), ARDOCK, An Auto-regressive Model Analyzer Cumputer Science Monographas No. 30, The Institute of statistical Mathematics
As described above, conventionally, by adding and averaging the original data measured by the SQUID sensor 1 and reducing random noise, a periodic data signal is found as a result, and then the SF method is applied to the outside of the brain. The brain activity intensity (final result) was derived by obtaining the measured magnetic field. In contrast, in this embodiment, the SF method is applied without averaging the original data measured by the SQUID sensor 1, and then the multivariate autoregressive model is applied, so that the periodic noise is buried in the random noise. By actively extracting data signals (having a certain periodic tendency) (final result), it is possible to identify the signal transmission path that is the brain network and the interaction.

  In addition, conventionally, the reason for adding and averaging the measured original data is that the random noise due to the spatial environment is much larger than the magnetic field coming out of the brain, so by performing the averaging to reduce the random noise, This is to find a periodic data signal buried in random noise.

  On the other hand, when applying the multivariate autoregressive model, the reason for not averaging the measured original data is to include important information related to transfer characteristics in random noise by averaging. This is because information may be lost. To do so, we apply a multivariate autoregressive model to raw data that is not processed. In addition, the data signal analyzed many times for each stimulus given as a task remains buried in random noise, but the data signal of the analysis result has a certain period (a somewhat similar brain activity tendency). Therefore, only this data signal can be positively extracted from random noise.

  As described above, according to this embodiment, the SF method is applied to the measured biomagnetic data to calculate the activity change of the life activity current, and the multivariate autoregressive model is applied to the calculated activity change. By identifying the signal transmission path and interaction in the region of interest that causes the activity, it is possible to analyze what path in the brain the information processing of the brain is processed through Play.

  In the above embodiment, the network analysis in the human brain is performed. However, the present invention is not limited to this, and the network analysis is performed in a region of interest other than the brain or a region of interest other than a human body (animal body, etc.). May be.

1 is a block diagram showing a schematic configuration of a biomagnetic measurement device according to an embodiment of the present invention. The flowchart which shows the process sequence performed in an Example. The figure which shows the relationship between each magnetic sensor and a specific position. The figure which shows the experimental method regarding an Example. The figure which shows the experimental method regarding an Example. The figure which shows the analysis result regarding an Example. The figure which shows the feedback system used for description of multivariate autoregressive model analysis.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Multichannel SQUID sensor 2 Magnetic shield room 3 Bed 4 Data conversion unit 5 Data collection unit 6 Stimulator 7 Positioning unit 8 Data analysis unit 10 Color monitor 11 Color monitor 12 Communication line M Subject
(m) Memory S1 to Sm magnetic sensor (in data analysis unit 4)

Claims (7)

A plurality of magnetic sensors arranged in proximity to the region of interest of the subject measures the biomagnetism generated along with the bioactive current flowing in the region of interest, and based on the measured biomagnetic data obtained by this measurement, A biomagnetism measuring device for grasping the state of the bioactive current flowing in the region of interest,
Calculating means for calculating an activity change of the bioactive current based on the measured biomagnetic data;
Identification means for identifying a signal transmission path and an interaction in the region of interest that causes the activity based on the activity change calculated by the calculation means;
A biomagnetism measuring device comprising:   The identification means identifies a signal transmission path and interaction in the region of interest that causes the activity by applying a multivariate autoregressive model to the activity change calculated by the calculation means. The biomagnetic measurement apparatus according to claim 1. A plurality of magnetic sensors arranged in proximity to the region of interest of the subject measures the biomagnetism generated along with the bioactive current flowing in the region of interest, and based on the measured biomagnetic data obtained by this measurement, A program used in a computer-controlled biomagnetic measurement device for grasping the state of a bioactive current flowing in the region of interest,
Calculating means for calculating an activity change of the bioactive current based on the measured biomagnetic data;
Identification means for identifying a signal transmission path and an interaction in the region of interest that causes the activity based on the activity change calculated by the calculation means;
A program for a biomagnetism measuring apparatus for causing the computer to realize the above.   The identification means identifies a signal transmission path and interaction in the region of interest that causes the activity by applying a multivariate autoregressive model to the activity change calculated by the calculation means. The biomagnetism measuring apparatus program according to claim 3.   5. The computer-readable recording medium on which the biomagnetism measuring apparatus program according to claim 3 is recorded. A plurality of magnetic sensors arranged in proximity to the region of interest of the subject measures the biomagnetism generated along with the bioactive current flowing in the region of interest, and based on the measured biomagnetic data obtained by this measurement, A biomagnetic measurement method for grasping a state of a bioactive current flowing in the region of interest,
A calculation step of calculating an activity change of the bioactive current based on the measured biomagnetic data;
An identification step for identifying a signal transmission path and interaction in the region of interest based on the activity change calculated by the calculation step;
The biomagnetism measuring method characterized by performing. The identifying step identifies a signal transmission path and interaction in the region of interest causing the activity by applying a multivariate autoregressive model to the activity change calculated in the calculating step. The biomagnetic measurement method according to claim 6.

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