Modelling of Predicting Functional and Structural Change in the Brain of Language Learners--Based on fMRI, Machine Learning and Morphometry

2016 Fiscal Year Final Research Report

• Project/Area Number: 26330246
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Intelligent informatics
• Research Institution: Tokyo Institute of Technology
• Principal Investigator: AKAMA HIROYUKI 東京工業大学, リベラルアーツ研究教育院, 准教授 (60242301)
• Project Period (FY): 2014-04-01 – 2017-03-31
• Keywords: 脳 / 言語 / fMRI / 機械学習 / 複雑ネットワーク

Outline of Final Research Achievements

Multi-Voxel Pattern Analysis (MVPA) in functional magnetic resonance imaging (fMRI) studies is considered effective for studying how the human brain represents the meanings of words. We published a review paper in Behaviormetrika to emphasize the importance of holistic approaches in embodiment semantics using naturalistic and ecologically valid tasks of language comprehension and production, or elucidating semantic spaces of individual participants. In our PLoS ONE paper, we developed an original distance definition for graphs, called the Markov-inverse-F measure (MiF), and measured its effectiveness for predicting a neural activity recorded during conceptual processing in the human brain. Now we are submitting a paper to show that functional connectivity of the brain can be modulated by interventional stimuli and serve as an indicator of brain plasticity focusing the executive control of an oral motor function.

Free Research Field

神経言語科学