JP2008229238A - Brain activity analysis method and device - Google Patents
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Abstract
Description
本発明は、脳活動解析方法および装置に関する。 The present invention relates to a brain activity analysis method and apparatus.
脳活動の解析は、脳機能障害の原因部位の特定などに有効であり、従来から種々の方法が提案されている。例えば、特許文献１には、脳内の活性部位間を流れる情報の方向と流れ量を評価する生体情報解析装置が開示されている。この装置は、測定された頭皮電位分布の時系列データに基づいて、各時刻における複数の等価双極子を推定し、各等価双極子を時系列で一貫するように調整部により調整して、複数の等価双極子の位置及び相互間の情報の流れ量を表示するように構成されている。
上記従来の生体情報解析装置は、各時刻において種々の位置に種々の大きさの等価双極子が推定されるため、等価双極子の数を設定するのが困難であるだけでなく、同じカテゴリーに分類される双極子の分布が広範囲（例えば、２０ｍｍ四方以上）に拡がるおそれがあった。このため、複数の等価双極子の分布が空間的にオーバーラップするおそれがあり、脳活動部位を正確に特定することができないという問題があった。 In the above-described conventional biological information analyzing apparatus, since equivalent dipoles of various sizes are estimated at various positions at each time, it is difficult not only to set the number of equivalent dipoles but also to the same category. There is a possibility that the distribution of classified dipoles may spread over a wide range (for example, 20 mm square or more). For this reason, there is a possibility that the distribution of a plurality of equivalent dipoles may spatially overlap, and there is a problem that the brain activity site cannot be specified accurately.
このような問題は、様々な脳内部位が担う機能モデル（仮説）を検証する上で、特に顕著なものとなっていた。すなわち、脳内の神経活動の因果関係は、活動部位がピンポイントで特定されてはじめて生理学的に意味のある情報になる一方、複数の等価双極子が空間的にオーバーラップする状況では、提案されたモデルの整合性を評価することが極めて困難であった。 Such a problem has become particularly prominent when verifying a functional model (hypothesis) carried by various brain regions. In other words, the causal relationship of neural activity in the brain is proposed in situations where multiple equivalent dipoles spatially overlap while the active site is pinpointed and becomes physiologically meaningful information. It was extremely difficult to evaluate the consistency of the model.
そこで、本発明は、脳内部位の活動を正確に把握して、各部位間の活動の相関を表すモデルの検証を容易に精度良く行うことができる脳活動解析方法および装置を提供することを目的とする。 Therefore, the present invention provides a brain activity analysis method and apparatus capable of accurately grasping the activity of a part in the brain and verifying a model representing the correlation of the activity between the parts easily and accurately. Objective.
本発明の前記目的は、脳内に複数の電流要素を配置した脳神経モデルを設定するステップと、頭部周囲に生じる信号強度分布の時系列を計測するステップと、計測された時系列データに基づき、前記脳神経モデルにおける各電流要素の大きさの分布を再構成し、各電流要素の時系列データを取得するステップと、前記脳神経モデルから電流要素を抽出して脳活動モデルを設定し、前記電流要素の時系列データに基づき前記脳活動モデルの因果関係を推定することにより、前記脳活動モデルを検証するステップとを備える脳活動解析方法により達成される。 The object of the present invention is based on a step of setting a cranial nerve model in which a plurality of current elements are arranged in the brain, a step of measuring a time series of a signal intensity distribution generated around the head, and the measured time series data. Reconstructing the distribution of the size of each current element in the cranial nerve model, obtaining time series data of each current element, and extracting a current element from the cranial nerve model to set a brain activity model, And a step of verifying the brain activity model by estimating a causal relationship of the brain activity model based on time-series data of elements.
また、本発明の前記目的は、頭部周囲に生じる信号強度分布の時系列を計測する脳信号計測装置と、脳活動の解析を行う情報処理装置とを備え、前記情報処理装置は、前記脳信号計測装置により計測された時系列データに基づき、前記脳神経モデルにおける各電流要素の大きさの分布を再構成し、各電流要素の時系列データを取得する脳内活動再構成部と、前記脳神経モデルから電流要素を抽出して脳活動モデルを設定し、前記電流要素の時系列データに基づき前記脳活動モデルの因果関係を推定することにより、前記脳活動モデルを検証する脳活動モデル検証部とを備える脳活動解析装置により達成される。 The object of the present invention is also provided with a brain signal measuring device that measures a time series of a signal intensity distribution generated around the head and an information processing device that analyzes brain activity, and the information processing device includes the brain Based on the time series data measured by the signal measurement device, the brain activity reconstructing unit that reconstructs the distribution of the size of each current element in the cranial nerve model and obtains the time series data of each current element, and the cranial nerve A brain activity model verification unit that verifies the brain activity model by extracting a current element from the model, setting a brain activity model, and estimating a causal relationship of the brain activity model based on time-series data of the current element; This is achieved by a brain activity analysis apparatus comprising:
本発明によれば、脳内部位の活動を正確に把握して、各部位間の活動の相関を表すモデルの検証を容易に精度良く行うことができる脳活動解析方法および装置を提供することができる。 According to the present invention, it is possible to provide a brain activity analysis method and apparatus capable of accurately grasping the activity of a brain region and verifying a model representing the correlation of the activity between the regions easily and accurately. it can.
以下、本発明の実態形態について添付図面を参照して説明する。図１は、本発明の一実施形態に係る脳活動解析装置の概略構成を示すブロック図である。図１に示すように、脳活動解析装置１は、脳信号計測装置１０と、情報処理装置２０とを備えて構成されている。 Hereinafter, actual forms of the present invention will be described with reference to the accompanying drawings. FIG. 1 is a block diagram showing a schematic configuration of a brain activity analysis apparatus according to an embodiment of the present invention. As shown in FIG. 1, the brain activity analysis device 1 includes a brain signal measurement device 10 and an information processing device 20.
脳信号計測装置１０は、例えば、脳磁図（ＭＥＧ）や脳波（ＥＥＧ）などの脳活動に伴い発生する信号を計測する装置であり、図２に示すように、複数のセンサ１０ａを備えるヘルメット型のものを例示することができる。この脳信号計測装置１０は、頭部に装着することにより、頭部周囲に生じる磁界分布や電位分布などの信号強度分布の時系列を計測することができる。 The brain signal measuring device 10 is a device that measures a signal generated along with brain activity such as a magnetoencephalogram (MEG) and an electroencephalogram (EEG), for example, and as shown in FIG. 2, a helmet type equipped with a plurality of sensors 10a. Can be illustrated. The brain signal measuring apparatus 10 can measure a time series of signal intensity distributions such as a magnetic field distribution and a potential distribution generated around the head by wearing it on the head.
情報処理装置２０は、例えば、パーソナルコンピュータなどからなり、脳活動の解析を行う。情報処理装置２０は、複数の電流要素（電流双極子）が脳内に配置された脳神経モデルを格納する脳神経モデル記憶部２１と、脳信号計測装置１０の計測データに基づき脳神経モデルにおける各電流要素の大きさの分布を再構成する脳内活動再構成部２２と、再構成された各電流要素の時系列データを計測データと共に記憶する脳活動データ記憶部２３と、脳内部位間の相互作用を表す脳活動モデルを設定する脳活動モデル設定部２４と、各脳内部位に対応する電流要素の時系列データに基づき脳活動モデルを検証する脳活動モデル検証部２５と、検証結果を出力する検証結果出力部２６とを備えている。 The information processing apparatus 20 includes, for example, a personal computer and analyzes brain activity. The information processing apparatus 20 includes a cranial nerve model storage unit 21 that stores a cranial nerve model in which a plurality of current elements (current dipoles) are arranged in the brain, and each current element in the cranial nerve model based on the measurement data of the brain signal measurement apparatus 10. The brain activity reconstructing unit 22 for reconstructing the distribution of the size of the brain, the brain activity data storage unit 23 for storing the time series data of each reconstructed current element together with the measurement data, and the interaction between the regions in the brain A brain activity model setting unit 24 that sets a brain activity model that represents the brain activity model, a brain activity model verification unit 25 that verifies the brain activity model based on time-series data of current elements corresponding to each intracerebral region, and outputs a verification result And a verification result output unit 26.
このように構成された脳活動解析装置を用いて、脳活動の解析を行う方法を以下説明する。まず、脳神経モデル記憶部２１には、予め脳神経モデルを設定して格納しておく。 A method for analyzing brain activity using the thus configured brain activity analysis apparatus will be described below. First, the cranial nerve model storage unit 21 sets and stores a cranial nerve model in advance.
脳神経モデルは、脳内の各電流要素により頭部周囲にどのような信号強度分布（例えば、ＭＥＧの場合は磁界分布、ＥＥＧの場合は電位分布）が生じさせるかを算出するためのモデルである。各電流要素は、それぞれ脳内部位と一対一に対応付けることができ、脳の皮質領域にほぼ等間隔に多数（例えば、数千〜１万程度）配置することができる。 The cranial nerve model is a model for calculating what signal intensity distribution (for example, magnetic field distribution in the case of MEG, potential distribution in the case of EEG) is generated around the head by each current element in the brain. . Each current element can be associated with the brain region on a one-to-one basis, and a large number (for example, about several thousand to 10,000) can be arranged in the cortical region of the brain at almost equal intervals.
脳神経モデルにおける磁界分布あるいは電位分布は、一般的には、電流要素により生じる電流の分布範囲（頭蓋骨内部の空間の導電率分布）を設定し、マックスウェルの方程式を用いて求めることができる。 In general, the magnetic field distribution or potential distribution in the cranial nerve model can be obtained by setting the distribution range of the current generated by the current element (conductivity distribution in the space inside the skull) and using Maxwell's equations.
よばれるベクトル場でMEGセンサiおよびEEG電極iの脳領域内の位置rkにおける感度を表し
ている。つまり、MEGやEEGで計測される信号は、脳内の種々の位置にある各電極要素の大きさを、それぞれの位置に対するセンサ感度で重みづけ加算した結果である。こうして、脳神経モデルにおける各電流要素が所定の大きさの場合に、頭部周囲に生じる信号強度分布を求めることができる。
脳活動モデルを設定した後、脳信号計測装置１０を装着した被験者に刺激を与えるなどして解析対象となる状況や課題を生じさせ、脳活動に伴う信号を計測する。本実施形態においては、図３に示すように、画面中央に表示された注視点Ｆと共に、ターゲットＴ（赤色）または非ターゲットＮ（緑色）を時間的にランダムに表示し、頻度の少ないターゲットＴが表示された場合にのみ被験者がボタンを押す視覚ターゲット検出実験を行い、このときの磁界分布、電位分布などの信号強度分布の時系列を脳信号計測装置１０により計測する。この実験は、珍しい（新奇な）刺激に対する注意の振り向けや、視覚環境に内在するターゲットの検出に関わる脳活動を研究する際によく行われる。 After setting the brain activity model, the subject wearing the brain signal measuring apparatus 10 is stimulated to cause a situation or problem to be analyzed, and a signal associated with the brain activity is measured. In the present embodiment, as shown in FIG. 3, the target T (red) or non-target N (green) is randomly displayed temporally together with the gazing point F displayed at the center of the screen, and the target T with less frequency is displayed. The visual target detection experiment in which the subject presses the button is performed only when is displayed, and the time series of the signal intensity distribution such as the magnetic field distribution and the potential distribution at this time is measured by the brain signal measuring device 10. This experiment is often used to study brain activity related to the redirection of attention to unusual (novel) stimuli and the detection of targets inherent in the visual environment.
脳内活動再構成部２２は、脳信号計測装置１０からの入力に基づき、各時刻における脳神経モデルの各電流要素の大きさを算出する。まず、各時刻ｔにおけるＭＥＧまたはＥＥＧの計測結果(D(t))から、各電流要素の大きさの分布J(t)を再構成する線形演算子を求める。解かれるべき問題は、電流要素の大きさJ(t)を未知数とした以下の線型方程式である。 The intracerebral activity reconstructing unit 22 calculates the magnitude of each current element of the cranial nerve model at each time based on the input from the brain signal measuring apparatus 10. First, from the MEG or EEG measurement result (D (t)) at each time t, a linear operator for reconstructing the magnitude distribution J (t) of each current element is obtained. The problem to be solved is the following linear equation with the current element magnitude J (t) as an unknown.
逆行列L-を計算する手法を用いる。計算手法の詳細は、「離散インバース理論」(W.メン
ケ著，古今書院, 1997)が参考になる。時刻tにおける電流要素分布の推定値J＾(t)は、以下の式で計算される。
次に、脳活動モデル設定部２４は、脳活動モデルの設定を行うために、まず図４に示す脳内活動再構成画面をモニタに表示する。脳内活動再構成画面は、脳信号計測装置１０の各センサ１０ａによる計測信号の時系列データを、脳の周囲における各センサ１０ａの位置に対応させて表示する信号波形表示部３１と、脳及び各センサ１０ａの位置を表示する脳・センサ位置表示部３２と、任意の時刻における各電流要素の大きさを濃淡や色調の違い等で表示する脳活動マップ表示部３３とを備えている。脳活動マップ表示部３３は、脳の左側面及び右側面がそれぞれ表示されており、スライダーバー３４をマウス等で操作することにより、所望の時刻の電流要素分布が表示されるように変更可能である。操作者は、この画面上で、脳内部位の活動位置や活動の大きさが時間的にどのように変化するかを、視覚的に把握することができる。 Next, in order to set a brain activity model, the brain activity model setting unit 24 first displays the intracerebral activity reconstruction screen shown in FIG. 4 on the monitor. The brain activity reconstruction screen includes a signal waveform display unit 31 that displays time-series data of measurement signals from the sensors 10a of the brain signal measuring device 10 in correspondence with the positions of the sensors 10a around the brain, It includes a brain / sensor position display unit 32 that displays the position of each sensor 10a, and a brain activity map display unit 33 that displays the magnitude of each current element at an arbitrary time in terms of shades or color differences. The brain activity map display unit 33 displays the left side surface and the right side surface of the brain, and can be changed so that the current element distribution at a desired time is displayed by operating the slider bar 34 with a mouse or the like. is there. On this screen, the operator can visually grasp how the position of activity in the brain and the magnitude of the activity change with time.
脳活動モデル設定部２４は、操作者による上述した脳活動部位の確認後、画面切替操作
に基づき、図５に示すモデル設定支援画面を表示する。モデル設定支援画面は、図４の脳活動マップ上の関心領域となる脳内部位を画面操作により設定可能な関心領域設定部４１と、設定された関心領域における各電流要素の時系列データを表示する脳活動時系列表示部４２と、インターネットやＬＡＮ等を介した学術文献サーバへのアクセスにより複数の脳活動モデルの検索・表示が可能な文献表示部４３と、画面操作により脳活動モデルを設定可能なモデル設定編集部４４とを備えている。
The brain activity model setting unit 24 displays the model setting support screen shown in FIG. 5 based on the screen switching operation after the operator confirms the above-described brain activity site. The model setting support screen displays a region-of-brain setting unit 41 that can set a region in the brain as a region of interest on the brain activity map of FIG. 4 by screen operation and time-series data of each current element in the set region of interest. A brain activity time series display unit 42, a document display unit 43 capable of searching and displaying a plurality of brain activity models by accessing an academic literature server via the Internet, LAN, etc., and setting a brain activity model by screen operation A possible model setting editing unit 44.
操作者は、脳活動時系列表示部４２において、脳活動マップ上で大きな神経活動が観測された脳内部位を把握することができ、更に、文献表示部４３において学術論文等にアクセスすることにより、過去の神経科学研究等で、対象としている認知課題に関連する脳活動が報告されている部位を把握することができる。そして、これらの情報を参考に、関心領域設定部４１において、関心領域（例えば、（ａ）〜（ｄ））となる脳内部位を対話的に抽出し、設定することができる。 In the brain activity time-series display unit 42, the operator can grasp a part in the brain where a large neural activity is observed on the brain activity map, and further, by accessing an academic paper or the like in the document display unit 43. It is possible to grasp the part where the brain activity related to the cognitive task of interest is reported in the past neuroscience research. Then, with reference to these pieces of information, the region-of-interest setting unit 41 can interactively extract and set a region in the brain that becomes the region of interest (for example, (a) to (d)).
操作者は、関心領域の設定後、モデル設定編集部４４において脳活動モデルの構築を行う。モデル設定編集部４４は、ツールバー４４ａを備えており、脳内部位間の因果関係の設定や修正等を行うことができる。また、モデル設定編集部４４は、タブ４４ｂを備えており、複数の脳活動モデルを構築することができる。本実施形態においては、モデル設定編集部４４において操作者の操作により脳活動モデルの構築を行うようにしているが、文献表示部４３において抽出された学術文献などのデータからキーワード検索及び論理解析を行うことにより、関心領域の設定や脳活動モデルの構築を自動的に行うことも可能である。 After setting the region of interest, the operator constructs a brain activity model in the model setting editing unit 44. The model setting editing unit 44 includes a tool bar 44a, and can set and correct a causal relationship between brain regions. The model setting editing unit 44 includes a tab 44b and can construct a plurality of brain activity models. In this embodiment, the model setting editing unit 44 constructs a brain activity model by an operator's operation, but keyword search and logic analysis are performed from data such as academic literature extracted by the document display unit 43. By doing so, it is possible to automatically set a region of interest and build a brain activity model.
図６は、上述した視覚ターゲット検出実験（図３参照）において、課題の遂行に必要な脳部位間の相互作用（脳活動の因果関係）についての検証すべき脳活動モデルの２つの具体例を示す図である。これらのモデルは、各部位における活動の時系列を変数とした多重回帰モデルで、各脳部位における活動の因果関係が変数間の結合係数で表される。図６（ａ）及び（ｂ）に示す２つのモデルの主な違いは、神経情報処理の流れの中での頭頂後頭部（ＰＴ）の位置づけである。モデルの設定にあたっては、以下の文献を参考にした(T.W. Picton, J. Clin. Neurophysiol. 9: 456-479, 1992; G. McCarthy et al. J. Neurophysiol. 77: 1630-1634, 1997; E. Halgren et al. Electroenceph. Clin. Neurophysiol.
94: 191-220, 1995; R.T. Knight et al. Brain Res. 503: 109-116, 1989; M.M. Mesulam Ann. Neurol. 28: 597-613, 1990; J.J. Pardo et al. Nature 349: 61-64, 1991)。
FIG. 6 shows two specific examples of brain activity models to be verified for the interaction between brain parts (causal relationship of brain activity) necessary for performing the task in the visual target detection experiment described above (see FIG. 3). FIG. These models are multiple regression models in which the time series of activity in each part is a variable, and the causal relationship of the activity in each brain part is represented by a coupling coefficient between the variables. The main difference between the two models shown in FIGS. 6A and 6B is the position of the parietal head (PT) in the flow of neural information processing. In setting the model, the following references were used (TW Picton, J. Clin. Neurophysiol. 9: 456-479, 1992; G. McCarthy et al. J. Neurophysiol. 77: 1630-1634, 1997; E Halgren et al. Electroenceph. Clin. Neurophysiol.
94: 191-220, 1995; RT Knight et al. Brain Res. 503: 109-116, 1989; MM Mesulam Ann. Neurol. 28: 597-613, 1990; JJ Pardo et al. Nature 349: 61-64, 1991).
脳活動モデル検証部２５は、モデル設定編集部４４において設定された各脳活動モデルについて、因果モデルの推定に用いられる公知の分析方法により、係数を決定する。本実施形態においては、このような分析方法の一例として、共分散構造分析を行っている。すなわち、脳活動データ記憶部２３に格納された各電流要素の時系列を yとすると、共分散行列 Sは以下の式で表される。 The brain activity model verification unit 25 determines a coefficient for each brain activity model set by the model setting editing unit 44 by a known analysis method used for causal model estimation. In this embodiment, covariance structure analysis is performed as an example of such an analysis method. That is, if the time series of each current element stored in the brain activity data storage unit 23 is y, the covariance matrix S is expressed by the following equation.
位行列である。
すなわち、
That is,
したがって、因果モデルから推定される変数 xの共分散行列 Σは、以下の式で表され
る。
Therefore, the covariance matrix Σ of the variable x estimated from the causal model is expressed by the following equation.
共分散構造分析では，計測されたデータyの共分散行列 Sと，因果モデルから推定され
る変数 xの共分散行列 Σの間の差を最小化するようにパス係数 Bが決定される．例えば
，ML(最尤: maximum-likelihood)関数 FML（FML= log |Σ| + trace[ S・Σ-1] - log |S| - p）を最小化する因果モデルの係数が、例えばNewton-Raphson法やLevenberg-Marquardt法などの非線形最適化アルゴリズムを用いて決定される (pは因果モデルに含まれる脳
活動時系列の数)。
In the covariance structure analysis, the path coefficient B is determined so as to minimize the difference between the covariance matrix S of the measured data y and the covariance matrix Σ of the variable x estimated from the causal model. For example, the coefficient of the causal model that minimizes the ML (maximum-likelihood) function F ML (F ML = log | Σ | + trace [S · Σ-1]-log | S |-p) It is determined using a nonlinear optimization algorithm such as Newton-Raphson method or Levenberg-Marquardt method (p is the number of brain activity time series included in the causal model).
こうして各脳活動モデルの係数を決定した後、各モデルの良さを表すモデル推定評価指標を算出する。モデル推定評価指標としては、χ2検定，モデル適合度(GFI: Goodness-of-Fit Index)、AGFI、赤池情報量基準(AIC: Akaike Information Criteria)などを挙げる
ことができる。例えば、GFIおよびAICは、以下の式により算出することができる。
After determining the coefficient of each brain activity model in this way, a model estimation evaluation index representing the goodness of each model is calculated. Examples of the model estimation evaluation index include χ 2 test, goodness-of-fit index (GFI), AGFI, Akaike Information Criteria (AIC), and the like. For example, GFI and AIC can be calculated by the following equations.
ここで、
df = p ( p + 1)/2 - [因果モデルに含まれる自由パラメータの数]
χ2= ( n - 1 )・FML
図６に示す２つの脳活動モデルについて、係数及びモデル推定評価指標を算出した結果を、図７に示す。GFIは、モデルから予測される時系列と実際に計測された時系列との差
が小さいほど大きな値となり，AICでは、よりシンプルなモデルでより小さい誤差で観測
データを説明するほど小さい値となる。この結果からは、図６（ａ）に示すモデル１が、
より確からしい脳活動のモデルであることがわかる。
here,
df = p (p + 1) / 2-[number of free parameters in causal model]
χ 2 = (n-1) ・ F ML
FIG. 7 shows the results of calculating coefficients and model estimation evaluation indices for the two brain activity models shown in FIG. The GFI increases as the difference between the time series predicted from the model and the actual measured time series decreases, and the AIC decreases as the observation data is explained with a smaller error with a simpler model. . From this result, the model 1 shown in FIG.
It turns out that this is a more reliable model of brain activity.
検証結果出力部２６は、上述した脳活動モデル検証部２５による検証結果に基づき、図８に示すモデル評価結果表示画面を表示する。モデル評価結果表示画面は、設定された各脳活動モデルについて、脳活動モデル検証部２５により算出されたモデル推定評価指標５１ａを、脳活動モデルの構造と関連付けて表示するモデル推定結果表示部５１を備えており、各脳活動モデルの整合性は、モデル推定評価指標５１ａにより評価することができる。モデル評価指標を基準として、対象とするデータを最もよく説明する脳活動モデル（図８ではモデル２）は、ハイライト表示される。 The verification result output unit 26 displays the model evaluation result display screen shown in FIG. 8 based on the verification result by the brain activity model verification unit 25 described above. The model evaluation result display screen displays a model estimation result display unit 51 for displaying the model estimation evaluation index 51a calculated by the brain activity model verification unit 25 in association with the structure of the brain activity model for each set brain activity model. The consistency of each brain activity model can be evaluated by the model estimation evaluation index 51a. The brain activity model (model 2 in FIG. 8) that best explains the target data with the model evaluation index as a reference is highlighted.
以上のように、本実施形態に係る脳活動解析装置によれば、様々な脳内部位に対応するように複数の電流要素を予め設定することが可能であり、計測データに基づき電流要素の大きさ分布を再構成することにより、各電流要素の時系列データを取得するので、脳内部位の活動を正確に把握することができる。 As described above, according to the brain activity analysis apparatus according to the present embodiment, it is possible to preset a plurality of current elements so as to correspond to various parts of the brain, and the magnitude of the current element based on the measurement data. By reconstructing the depth distribution, time series data of each current element is acquired, so that the activity of the intracerebral region can be accurately grasped.
また、これによって、脳活動に関して既に得られている知見との整合性の検証が容易になり、各脳内部位間の活動の相関を表すモデルの検証を容易に精度良く行うことができる。特に、本実施形態においては、情報処理装置により複数の脳活動モデルを設定して、各脳活動モデルの評価指標を得ることができるので、最も適切なモデルを容易に選択することができる。 In addition, this makes it easy to verify the consistency with knowledge already obtained regarding brain activity, and it is possible to easily and accurately verify a model representing the correlation of activities between regions in each brain. In particular, in the present embodiment, since a plurality of brain activity models can be set by the information processing apparatus and the evaluation index of each brain activity model can be obtained, the most appropriate model can be easily selected.
１ 脳活動解析装置
１０ 脳信号計測装置
２０ 情報処理装置
２１ 脳神経モデル記憶部
２２ 脳内活動再構成部
２３ 脳活動データ記憶部
２４ 脳活動モデル設定部
２５ 脳活動モデル検証部
２６ 検証結果出力部
DESCRIPTION OF SYMBOLS 1 Brain activity analyzer 10 Brain signal measuring device 20 Information processing apparatus 21 Cranial nerve model memory | storage part 22 Brain activity reconstruction part 23 Brain activity data memory | storage part 24 Brain activity model setting part 25 Brain activity model verification part 26 Verification result output part
Claims (5)
頭部周囲に生じる信号強度分布の時系列を計測するステップと、
計測された時系列データに基づき、前記脳神経モデルにおける各電流要素の大きさの分布を再構成し、各電流要素の時系列データを取得するステップと、
前記脳神経モデルから電流要素を抽出して脳活動モデルを設定し、前記電流要素の時系列データに基づき前記脳活動モデルの因果関係を推定することにより、前記脳活動モデルを検証するステップとを備える脳活動解析方法。 Setting a cranial nerve model in which a plurality of current elements are arranged in the brain;
Measuring a time series of signal intensity distribution generated around the head; and
Reconstructing the distribution of the size of each current element in the cranial nerve model based on the measured time series data, obtaining the time series data of each current element;
Extracting a current element from the cranial nerve model to set a brain activity model, and verifying the brain activity model by estimating a causal relationship of the brain activity model based on time-series data of the current element. Brain activity analysis method.
前記情報処理装置は、
前記脳信号計測装置により計測された時系列データに基づき、前記脳神経モデルにおける各電流要素の大きさの分布を再構成し、各電流要素の時系列データを取得する脳内活動再構成部と、
前記脳神経モデルから電流要素を抽出して脳活動モデルを設定し、前記電流要素の時系列データに基づき前記脳活動モデルの因果関係を推定することにより、前記脳活動モデルを検証する脳活動モデル検証部とを備える脳活動解析装置。 A brain signal measuring device for measuring a time series of signal intensity distribution generated around the head, and an information processing device for analyzing brain activity;
The information processing apparatus includes:
Based on the time series data measured by the brain signal measuring device, reconstructing the distribution of the size of each current element in the cranial nerve model, and obtaining the time series data of each current element,
The brain activity model is verified by extracting a current element from the cranial nerve model, setting a brain activity model, and estimating the causal relationship of the brain activity model based on the time series data of the current element. And a brain activity analysis apparatus.
前記モデル推定評価指標を前記脳活動モデルの構造と関連付けて画面表示する検証結果出力部を更に備える請求項２に記載の脳活動解析装置。 The brain activity model verification unit determines a coefficient of the set brain activity model and calculates a model estimation evaluation index,
The brain activity analysis apparatus according to claim 2, further comprising a verification result output unit that displays the model estimation evaluation index on the screen in association with the structure of the brain activity model.
前記検証結果出力部は、前記モデル推定評価指標を、前記各脳活動モデル間で対比可能に表示する請求項３に記載の脳活動解析装置。 The brain activity model verification unit can set a plurality of the brain activity models,
The brain activity analysis apparatus according to claim 3, wherein the verification result output unit displays the model estimation evaluation index so as to be comparable between the brain activity models.
前記操作者は、前記脳活動時系列表示部及び前記文献表示部に表示された情報を見ながら、前記モデル設定編集部において前記脳活動モデルを設定可能に構成されている請求項２から４のいずれかに記載の脳活動解析装置。 The brain activity model verification unit includes a region-of-interest setting unit capable of setting a region in the brain to be a region of interest by a screen operation, and a brain activity time series displaying time-series data of each current element in the set region of interest A display unit, a document display unit capable of searching and displaying a plurality of brain activity models, and a model setting editing unit capable of setting a brain activity model by screen operation,
The said operator is comprised so that the said brain activity model can be set in the said model setting edit part, seeing the information displayed on the said brain activity time series display part and the said literature display part. The brain activity analysis apparatus according to any one of the above.
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