Understanding a dynamical mechanism of alcoholism onset using intensive longitudinal behavioral data and the development of its early detection method

2020 Fiscal Year Final Research Report

• Project/Area Number: 18KT0069
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 特設分野
• Research Field: Complex Systems Disease Theory
• Research Institution: Osaka University
• Principal Investigator: Nakamura Toru 大阪大学, 基礎工学研究科, 特任教授(常勤) (80419473)
• Project Period (FY): 2018-07-18 – 2021-03-31
• Keywords: アルコール依存症 / 行動解析 / 早期検知 / 強縦断データ

Outline of Final Research Achievements

This study aimed: 1) to understand the mathematical mechanisms behind disease onset or pathological transitions in terms of changes in behavioral dynamics, and then 2) to develop an early detection method for transition phenomena based on revealed mechanisms. We developed a data-driven method to infer a dynamical system from obtained time series, and further applied it to intensive longitudinal behavioral data obtained from patients with psychiatric disorders and alcoholic model rats. In patients, we found that parameter values of estimated systems ware significantly different among diseases or pathological states. Furthermore, the decrease of stability of the estimated system was confirmed around pathological transitions. On the other hand, as an alternative approach to reveal dynamical mechanism of disease onset or pathological transitions, we newly developed a novel method for estimating the bifurcation structure only from time series using deep neural networks.

Free Research Field

健康情報工学

Academic Significance and Societal Importance of the Research Achievements

強縦断データに基づき疾患発症・病態遷移等の疾患動態の動力学構造を数理学的に理解し、その早期検知に活かすことは、臨床医学分野における動力学理論の新たな活用方法を開拓するものである。