Relationships between motor and cognitive functions and subsequent post-stroke mood disorders revealed by machine learning analysis

Seiji Hama, Kazumasa Yoshimura, Akiko Yanagawa, Koji Shimonaga, Akira Furui, Zu Soh, Shinya Nishino, Harutoyo Hirano, Shigeto Yamawaki, Toshio Tsuji

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Mood disorders (e.g. depression, apathy, and anxiety) are often observed in stroke patients, exhibiting a negative impact on functional recovery associated with various physical disorders and cognitive dysfunction. Consequently, post-stroke symptoms are complex and difficult to understand. In this study, we aimed to clarify the cross-sectional relationship between mood disorders and motor/cognitive functions in stroke patients. An artificial neural network architecture was devised to predict three types of mood disorders from 36 evaluation indices obtained from functional, physical, and cognitive tests on 274 patients. The relationship between mood disorders and motor/cognitive functions were comprehensively analysed by performing input dimensionality reduction for the neural network. The receiver operating characteristic curve from the prediction exhibited a moderate to high area under the curve above 0.85. Moreover, the input dimensionality reduction retrieved the evaluation indices that are more strongly related to mood disorders. The analysis results suggest a stress threshold hypothesis, in which stroke-induced lesions promote stress vulnerability and may trigger mood disorders.

Original languageEnglish
Article number19571
JournalScientific reports
Volume10
Issue number1
DOIs
Publication statusPublished - 12-2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General

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