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- Meenakshi Khosla, Keith Jamison, Gia H Ngo, Amy Kuceyeski, and Mert R Sabuncu.
- School of Electrical and Computer Engineering, Cornell University, United States of America.
- Magn Reson Imaging. 2019 Dec 1; 64: 101-121.
AbstractMachine 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 © 2019 Elsevier Inc. All rights reserved.
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