TY - JOUR
T1 - Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression
AU - Yoshida, Kosuke
AU - Shimizu, Yu
AU - Yoshimoto, Junichiro
AU - Takamura, Masahiro
AU - Okada, Go
AU - Okamoto, Yasumasa
AU - Yamawaki, Shigeto
AU - Doya, Kenji
N1 - Publisher Copyright:
© 2017 Yoshida et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2017/7
Y1 - 2017/7
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pone.0179638
DO - 10.1371/journal.pone.0179638
M3 - Article
C2 - 28700672
AN - SCOPUS:85023177744
SN - 1932-6203
VL - 12
JO - PloS one
JF - PloS one
IS - 7
M1 - e0179638
ER -