Analysis of brain word networks in schizophrenia using graph theory and natural language processing techniques

2022 Fiscal Year Final Research Report

• Project/Area Number: 20K21567
• Research Category: Grant-in-Aid for Challenging Research (Exploratory)
• Allocation Type: Multi-year Fund
• Review Section: Medium-sized Section 52:General internal medicine and related fields
• Research Institution: Tokyo Medical and Dental University
• Principal Investigator: Takahashi Hidehiko 東京医科歯科大学, 大学院医歯学総合研究科, 教授 (60415429)
• Project Period (FY): 2020-07-30 – 2023-03-31
• Keywords: 統合失調症 / 自然言語処理技術 / グラフ理論

Outline of Final Research Achievements

We applied the natural language processing algorithm Word2vec and encoding modeling to the video information and brain activity data, and quantified various semantic representations in the brain as brain activity patterns. We evaluated the structural characteristics of the semantic network in the brain, which was constructed based on the similarity between semantic representations. The semantic networks in the brains of healthy subjects showed high small-worldness as in natural language, suggesting that small-worldness is a universal property of networks related to semantic knowledge. On the other hand, small-worldness was decreased in the brain semantic network of schizophrenia patients and was negatively correlated with the severity of delusions.

Free Research Field

精神医学

Academic Significance and Societal Importance of the Research Achievements

研究により統合失調症の連合弛緩は、脳内において意味ネットワークのランダム化として表れていることが明らかになった。また、本研究の手法は精神疾患患者の主観的体験を患者の発話に依ることなく脳活動から直接評価できる点で、診断、治療の新たな可能性をひらくものと期待される。