WEKO3
アイテム
{"_buckets": {"deposit": "889e475d50d74135b47bde0302f06b78"}, "_deposit": {"created_by": 1, "id": "766", "owners": [1], "pid": {"revision_id": 0, "type": "depid", "value": "766"}, "status": "published"}, "_oai": {"id": "oai:ir.soken.ac.jp:00000766", "sets": ["17"]}, "author_link": ["9112", "9110", "9111"], "item_1_biblio_info_21": {"attribute_name": "書誌情報（ソート用）", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "20040324", "bibliographicIssueDateType": "Issued"}, "bibliographic_titles": [{}]}]}, "item_1_creator_2": {"attribute_name": "著者名", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "山下, 宙人"}], "nameIdentifiers": [{"nameIdentifier": "9110", "nameIdentifierScheme": "WEKO"}]}]}, "item_1_creator_3": {"attribute_name": "フリガナ", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "ヤマシタ, オキト"}], "nameIdentifiers": [{"nameIdentifier": "9111", "nameIdentifierScheme": "WEKO"}]}]}, "item_1_date_granted_11": {"attribute_name": "学位授与年月日", "attribute_value_mlt": [{"subitem_dategranted": "20040324"}]}, "item_1_degree_grantor_5": {"attribute_name": "学位授与機関", "attribute_value_mlt": [{"subitem_degreegrantor": [{"subitem_degreegrantor_name": "総合研究大学院大学"}]}]}, "item_1_degree_name_6": {"attribute_name": "学位名", "attribute_value_mlt": [{"subitem_degreename": "博士（学術）"}]}, "item_1_description_1": {"attribute_name": "ID", "attribute_value_mlt": [{"subitem_description": "2004010", "subitem_description_type": "Other"}]}, "item_1_description_12": {"attribute_name": "要旨", "attribute_value_mlt": [{"subitem_description": " Human being has long been challenging to understand functions and organizations of the brain. With striking developments of various measurement apparatus and methodology after twentieth century, we have accumulated not only the knowledge about the mechanism of our brain but also measurements of brain activities from various aspects. In order to make the best use of these data combined with a priori knowledge, the development of statistical methods is indispensable. \u003cbr /\u003e Nowadays the functional Magnetic Resonance Imaging (fMRI)technique and the electroencephalography (EEG) are two common tools for the understanding of human cognition as well as for the clinical diagnosis. By the fMRI technique, the change of regional cerebral blood flow, which is supposed to result from electrical neuronal activities on the corresponding local region, is measured as temporally successive images covering the whole brain volume with high spatial resolution but low temporal resolution. By the EEG, evoke potentials can be measured in several tens positions on the scalp surface with high temporal resolution as a consequence of the transmission of electric currents (a collection of electrical neuronal activities) inside the brain.\u003cbr /\u003e In this thesis, for the purpose of analyzing these two kind of the data sets, the methodology in the field of time series analysis will be applied and developed. Since these two data sets have distinct properties, the purpose and the tool for analysis are also distinct. Therefore this thesis consists of two parts, the inverse problem of the EEG and the causal analysis for the fMRI data.\u003cbr /\u003e In the first part of this thesis, the dynamical inverse problem of the EEG generation will be discussed. Since the EEG recording is an indirect observation of electrical sources inside the brain, the inference to localize the sources, called the \u0027inverse problem\u0027 are necessary. In general, in order to solve the inverse problem we have to combine additional information to the observation because it is impossible to uniquely determine the solution from the observation itself. In this thesis, we will consider the dynamical inverse problem so that general spatiotemporal constraints can be incorporated. This aspect has been neglected in many previous studies of the inverse problem of the EEG generation in spite of its importance. \u003cbr /\u003e Mathematically the dynamical inverse problem will be formulated as the state estimation problem. The system equation in the state space representation describes general spatiotemporal constraints. By assuming a parametric model for the dynamics, we can choose in a sense the \u0027best fitting\u0027 constraints onto the solution. In principle both the parameter estimation and the state estimation (the solution) can be done by means of the celebrated Kalman filtering algorithm. \u003cbr /\u003e However due to high dimensionality of the state in the EEG application, the difficulty occurs in the computational aspect. As alternatives of ordinary Kalman filtering, the author will propose three approximate filtering algorithms; the recursive penalized least squares (RPLS) method, observable projection Kalman filtering and partitioned (spatiotemporal) Kalman filtering. The different ways of approximation of covariance matrices of the filtered and predicion states are employed in these algorithms. The simulation study will demonstrate similarity of the solutions via three methods in the case of simple dynamics. However the difference of three solutions could become larger when the dynamics becomes complex. It would be necessary to examine the situation of problems and validity of the assumption. \u003cbr /\u003e The data analysis of real α wave will show two sources located in the occipital region of both the left and right hemisphere, which has been reported in the previous studies. In addition, the estimated dynamics inside and outside the occipital region is observed to differ in periodicity using a regional AR model as the dynamics. \u003cbr /\u003e In the latter part of this thesis, the methodology to evaluate the effective connectivity of the fMRI data will be investigated. In the fMRl studies, recently, more attention has been paid to the analysis of the effective connectivity defined as \"the influence that one neural system exerts over another\" (Friston 1995). In order to accomplish this purpose, the method developed in the multivariate time series analysis will be applied. It is a crucial advantage of this approach that no assumption about the direction of connectivity is required, whereas the structural equation model, the most common approach to evaluate the effective connectivity so far, requires to determine and to restrict the direction of connectivity apriori. \u003cbr /\u003e For this purpose, the author proposes to apply the Akaike\u0027s noise contribution ratio (ANCR), which quantifies the influence on one time series from another time series. Using the data from the random dot experiment, the change of the connectivity between two conditions will be evaluated by the ANCR as a measure. As a result, the increase of the connectivity on the task condition is observed compared with the connectivity on the control condition.", "subitem_description_type": "Other"}]}, "item_1_description_7": {"attribute_name": "学位記番号", "attribute_value_mlt": [{"subitem_description": "総研大甲第742号", "subitem_description_type": "Other"}]}, "item_1_select_14": {"attribute_name": "所蔵", "attribute_value_mlt": [{"subitem_select_item": "有"}]}, "item_1_select_8": {"attribute_name": "研究科", "attribute_value_mlt": [{"subitem_select_item": "数物科学研究科"}]}, "item_1_select_9": {"attribute_name": "専攻", "attribute_value_mlt": [{"subitem_select_item": "15 統計科学専攻"}]}, "item_1_text_10": {"attribute_name": "学位授与年度", "attribute_value_mlt": [{"subitem_text_value": "2003"}]}, "item_1_text_20": {"attribute_name": "業務メモ", "attribute_value_mlt": [{"subitem_text_value": "（2018年2月14日）本籍など個人情報の記載がある旧要旨・審査要旨を個人情報のない新しいものに差し替えた。承諾書等未確認。要確認該当項目修正のこと。"}]}, "item_creator": {"attribute_name": "著者", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "YAMASHITA, Okito", "creatorNameLang": "en"}], "nameIdentifiers": [{"nameIdentifier": "9112", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "ファイル情報", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "20160217"}], "displaytype": "simple", "download_preview_message": "", "file_order": 0, "filename": "甲742_要旨.pdf", "filesize": [{"value": "320.7 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_11", "mimetype": "application/pdf", "size": 320700.0, "url": {"label": "要旨・審査要旨 / Abstract, Screening Result", "url": "https://ir.soken.ac.jp/record/766/files/甲742_要旨.pdf"}, "version_id": "ec2904b014104436917c20da505a056b"}]}, "item_language": {"attribute_name": "言語", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "資源タイプ", "attribute_value_mlt": [{"resourcetype": "thesis", "resourceuri": "http://purl.org/coar/resource_type/c_46ec"}]}, "item_title": "Dynamical EEG Inverse Problem and Causality Analysis of fMRI Data", "item_titles": {"attribute_name": "タイトル", "attribute_value_mlt": [{"subitem_title": "Dynamical EEG Inverse Problem and Causality Analysis of fMRI Data"}, {"subitem_title": "Dynamical EEG Inverse Problem and Causality Analysis of fMRI Data", "subitem_title_language": "en"}]}, "item_type_id": "1", "owner": "1", "path": ["17"], "permalink_uri": "https://ir.soken.ac.jp/records/766", "pubdate": {"attribute_name": "公開日", "attribute_value": "20100222"}, "publish_date": "20100222", "publish_status": "0", "recid": "766", "relation": {}, "relation_version_is_last": true, "title": ["Dynamical EEG Inverse Problem and Causality Analysis of fMRI Data"], "weko_shared_id": 1}
Dynamical EEG Inverse Problem and Causality Analysis of fMRI Data
https://ir.soken.ac.jp/records/766
https://ir.soken.ac.jp/records/766b5c4c79110b743f996157add1f011254
名前 / ファイル  ライセンス  アクション 

要旨・審査要旨 / Abstract, Screening Result (320.7 kB)

Item type  学位論文 / Thesis or Dissertation(1)  

公開日  20100222  
タイトル  
タイトル  Dynamical EEG Inverse Problem and Causality Analysis of fMRI Data  
タイトル  
言語  en  
タイトル  Dynamical EEG Inverse Problem and Causality Analysis of fMRI Data  
言語  
言語  eng  
資源タイプ  
資源タイプ識別子  http://purl.org/coar/resource_type/c_46ec  
資源タイプ  thesis  
著者名 
山下, 宙人
× 山下, 宙人 

フリガナ 
ヤマシタ, オキト
× ヤマシタ, オキト 

著者 
YAMASHITA, Okito
× YAMASHITA, Okito 

学位授与機関  
学位授与機関名  総合研究大学院大学  
学位名  
学位名  博士（学術）  
学位記番号  
内容記述タイプ  Other  
内容記述  総研大甲第742号  
研究科  
値  数物科学研究科  
専攻  
値  15 統計科学専攻  
学位授与年月日  
学位授与年月日  20040324  
学位授与年度  
2003  
要旨  
内容記述タイプ  Other  
内容記述  Human being has long been challenging to understand functions and organizations of the brain. With striking developments of various measurement apparatus and methodology after twentieth century, we have accumulated not only the knowledge about the mechanism of our brain but also measurements of brain activities from various aspects. In order to make the best use of these data combined with a priori knowledge, the development of statistical methods is indispensable. <br /> Nowadays the functional Magnetic Resonance Imaging (fMRI)technique and the electroencephalography (EEG) are two common tools for the understanding of human cognition as well as for the clinical diagnosis. By the fMRI technique, the change of regional cerebral blood flow, which is supposed to result from electrical neuronal activities on the corresponding local region, is measured as temporally successive images covering the whole brain volume with high spatial resolution but low temporal resolution. By the EEG, evoke potentials can be measured in several tens positions on the scalp surface with high temporal resolution as a consequence of the transmission of electric currents (a collection of electrical neuronal activities) inside the brain.<br /> In this thesis, for the purpose of analyzing these two kind of the data sets, the methodology in the field of time series analysis will be applied and developed. Since these two data sets have distinct properties, the purpose and the tool for analysis are also distinct. Therefore this thesis consists of two parts, the inverse problem of the EEG and the causal analysis for the fMRI data.<br /> In the first part of this thesis, the dynamical inverse problem of the EEG generation will be discussed. Since the EEG recording is an indirect observation of electrical sources inside the brain, the inference to localize the sources, called the 'inverse problem' are necessary. In general, in order to solve the inverse problem we have to combine additional information to the observation because it is impossible to uniquely determine the solution from the observation itself. In this thesis, we will consider the dynamical inverse problem so that general spatiotemporal constraints can be incorporated. This aspect has been neglected in many previous studies of the inverse problem of the EEG generation in spite of its importance. <br /> Mathematically the dynamical inverse problem will be formulated as the state estimation problem. The system equation in the state space representation describes general spatiotemporal constraints. By assuming a parametric model for the dynamics, we can choose in a sense the 'best fitting' constraints onto the solution. In principle both the parameter estimation and the state estimation (the solution) can be done by means of the celebrated Kalman filtering algorithm. <br /> However due to high dimensionality of the state in the EEG application, the difficulty occurs in the computational aspect. As alternatives of ordinary Kalman filtering, the author will propose three approximate filtering algorithms; the recursive penalized least squares (RPLS) method, observable projection Kalman filtering and partitioned (spatiotemporal) Kalman filtering. The different ways of approximation of covariance matrices of the filtered and predicion states are employed in these algorithms. The simulation study will demonstrate similarity of the solutions via three methods in the case of simple dynamics. However the difference of three solutions could become larger when the dynamics becomes complex. It would be necessary to examine the situation of problems and validity of the assumption. <br /> The data analysis of real α wave will show two sources located in the occipital region of both the left and right hemisphere, which has been reported in the previous studies. In addition, the estimated dynamics inside and outside the occipital region is observed to differ in periodicity using a regional AR model as the dynamics. <br /> In the latter part of this thesis, the methodology to evaluate the effective connectivity of the fMRI data will be investigated. In the fMRl studies, recently, more attention has been paid to the analysis of the effective connectivity defined as "the influence that one neural system exerts over another" (Friston 1995). In order to accomplish this purpose, the method developed in the multivariate time series analysis will be applied. It is a crucial advantage of this approach that no assumption about the direction of connectivity is required, whereas the structural equation model, the most common approach to evaluate the effective connectivity so far, requires to determine and to restrict the direction of connectivity apriori. <br /> For this purpose, the author proposes to apply the Akaike's noise contribution ratio (ANCR), which quantifies the influence on one time series from another time series. Using the data from the random dot experiment, the change of the connectivity between two conditions will be evaluated by the ANCR as a measure. As a result, the increase of the connectivity on the task condition is observed compared with the connectivity on the control condition.  
所蔵  
値  有 