Network Analysis of multidimensional biosignals focused on non-linear correlation
| Project/Area Number |
18K04184
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 21030:Measurement engineering-related
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| Research Institution | Kindai University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
小濱 剛 近畿大学, 生物理工学部, 准教授 (90295577)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2019: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2018: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
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| Keywords | ネットワーク解析 / コネクティビティ強度 / 相互相関関数 / 偏相関関数 / 平均コネクティビティ強度追跡法 / 皮質脳波解析 / てんかん / Granger因果性 / コネクティビティ強 / F検定 / 非線形相関 / 有向最小木 / 脳波 / fNIRS / 相互情報量 |
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| Outline of Final Research Achievements |
Network analysis of multidimensional biological signals is a very useful tool to understand the mechanism of signal generation. Although network analysis has been widely used in brain function analysis, most of them are based on linear correlation analysis using the Pearson product-moment correlation coefficients. In this study, we conducted a research on multidimensional network analysis methods using network analysis of intractable epilepsy as a touchstone. We introduced a partial correlation function to accurately capture the network structure in multivariate connectivity analysis, introduced a directional graph that takes into account time delays. We have also developed a macroscopic network analysis method called "average connectivity strength tracking.
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| Academic Significance and Societal Importance of the Research Achievements |
てんかんは脳内で発生する異常興奮脳波の伝播によって意識障害や痙攣などを引き起こす慢性的な脳神経疾患疾患である。有病率は約1％に上り、最も多い慢性疾患の一つである。一般にてんかんの治療では抗てんかん薬を用いた投薬治療が行われるが、投薬によって発作を抑制できない場合、外科治療が考慮される。外科治療を行う際にはてんかん焦点や異常興奮脳波伝播経路を正確に同定することが重要である。本研究ではこのてんかん患者の皮質脳波を用いて、多次元生体信号のネットワーク解析法の開発を行ったものである。てんかんの病態解明（異常興奮脳波の伝播経路や焦点の特定など）につながる成果は臨床上も極めて重要である。
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Report
(4 results)
Research Products
(23 results)
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[Presentation] How to select training data to segment mammary gland region using a deep-learning approach for reliable individualized screening mammography2021
Author(s)
Mika Yamamuro, Yoshiyuki Asai, Naomi Hashimoto, Nao Yasuda, Takahiro Yamada, Mitsutaka Nemoto, Yuichi Kimura, Hisashi Handa, Hisashi Yoshida, Koji Abe, Masahiro Tada, Hitoshi Habe, Takashi Nagaoka, Yoshiaki Ozaki, Seiun Nin, Kazunari Ishii, Yongbum Lee
Organizer
Proceedings Volume 11597, Medical Imaging 2021: Computer-Aided Diagnosis; 115972V (2021)
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