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2022 Fiscal Year Final Research Report

Specificity and reliability of neuroiamging analysis of rewarding motivational contexts using a data integration framework

Research Project

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Project/Area Number 20K07727
Research Category

Grant-in-Aid for Scientific Research (C)

Allocation TypeMulti-year Fund
Section一般
Review Section Basic Section 51020:Cognitive and brain science-related
Research InstitutionGunma University (2022)
Keio University (2020-2021)

Principal Investigator

Jimura Koji  群馬大学, 情報学部, 教授 (80431766)

Project Period (FY) 2020-04-01 – 2023-03-31
Keywords機能的MRI / 認知制御
Outline of Final Research Achievements

We developped an integrated analysis framework for small sample data collected in an original experiment using functional MRI to measure brain activity related to behavioral tasks and big data distributed by the Human Connectome Project (HCP). By using the big data to train a classifier that classifies behavioral situations based on brain activity and then classifying the original data, we ensured generalization of the classifier and independence of the data. We also obtained behavioral task and index data of the same design as the HCP in conjunction with the data collection of the original behavioral tasks, enabling an integrated analysis using standard functional MRI analysis and machine learning.

Free Research Field

認知神経科学,神経情報学

Academic Significance and Societal Importance of the Research Achievements

機能的MRIを用いたヒト認知神経科学では,結果の再現性と信頼性が問題となっている.本研究では,公開されているビッグデータと,特異的な仮説を検証するための小サンプルサイズデータを統合的に解析することにより,再現性と信頼性をあげることに寄与した.とりわけ,独立のサイズが大きいサンプルを用いて,機械学習の分類器を訓練し,オリジナルのデータをテストする手法は,信頼性を確保する一つの標準的手法になると考えられる.また,オリジナル実験のデータを収集する際に,ビッグデータと相同のデータを収集することの有効性を示し,信頼性の高さを示した.

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Published: 2024-01-30  

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