Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression

Kosuke Yoshida, Yu Shimizu, Junichiro Yoshimoto, Masahiro Takamura, Go Okada, Yasumasa Okamoto, Shigeto Yamawaki, Kenji Doya

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)

Abstract

In diagnostic applications of statistical machine learning methods to brain imaging data, common problems include data high-dimensionality and co-linearity, which often cause over-fitting and instability. To overcome these problems, we applied partial least squares (PLS) regression to resting-state functional magnetic resonance imaging (rs-fMRI) data, creating a low-dimensional representation that relates symptoms to brain activity and that predicts clinical measures. Our experimental results, based upon data from clinically depressed patients and healthy controls, demonstrated that PLS and its kernel variants provided significantly better prediction of clinical measures than ordinary linear regression. Subsequent classification using predicted clinical scores distinguished depressed patients from healthy controls with 80% accuracy. Moreover, loading vectors for latent variables enabled us to identify brain regions relevant to depression, including the default mode network, the right superior frontal gyrus, and the superior motor area.

Original languageEnglish
Article numbere0179638
JournalPloS one
Volume12
Issue number7
DOIs
Publication statusPublished - 07-2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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