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Machine learning in resting-state fMRI analysis

Author:
Khosla, Meenakshi   Jamison, Keith   Ngo, Gia H.   Kuceyeski, Amy   Sabuncu, Mert R.   


Journal:
Magnetic Resonance Imaging


Issue Date:
2019


Abstract(summary):

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. Copyright =C2=A9 2019 Elsevier Inc. All rights reserved.


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