Journal
MAGNETIC RESONANCE IMAGING
Volume 64, Issue -, Pages 101-121Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.mri.2019.05.031
Keywords
Machine learning; Resting-state; Functional MRI; Intrinsic networks; Brain connectivity
Funding
- NIH R01 grants [R01LM012719, R01AG053949]
- NSF NeuroNex grant [1707312]
- NSF CAREER grant [1748377]
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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 restingstate 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 subjectlevel predictions. The goal is to provide a high-level overview of the burgeoning field of rs-fMRI from the perspective of machine learning applications.
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