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Machine learning in resting-state fMRI analysis
یادگیری ماشین در تجزیه و تحلیل fMRI وضعیت استراحت-2019 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. Keywords: Machine learning | Resting-state | Functional MRI | Intrinsic networks | Brain connectivity |
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