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Magn Reson Imaging. 2019 Jun 4. pii: S0730-725X(18)30685-4. doi: 10.1016/j.mri.2019.05.031. [Epub ahead of print]

Machine learning in resting-state fMRI analysis.

Author information

1
School of Electrical and Computer Engineering, Cornell University, United States of America.
2
Radiology, Weill Cornell Medical College, United States of America.
3
Radiology, Weill Cornell Medical College, United States of America; Brain and Mind Research Institute, Weill Cornell Medical College, United States of America.
4
School of Electrical and Computer Engineering, Cornell University, United States of America; Nancy E. & Peter C. Meinig School of Biomedical Engineering, Cornell University, United States of America. Electronic address: msabuncu@cornell.edu.

Abstract

Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We offer a methodical taxonomy of machine learning methods in resting-state fMRI. We identify three major divisions of unsupervised learning methods with regard to their applications to rs-fMRI, based on whether they discover principal modes of variation across space, time or population. Next, we survey the algorithms and rs-fMRI feature representations that have driven the success of supervised subject-level predictions. The goal is to provide a high-level overview of the burgeoning field of rs-fMRI from the perspective of machine learning applications.

KEYWORDS:

Brain connectivity; Functional MRI; Intrinsic networks; Machine learning; Resting-state

PMID:
31173849
DOI:
10.1016/j.mri.2019.05.031

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