Machine learning in resting-state fMRI analysis.

Publication Type	Review
Authors	Khosla M, Jamison K, Ngo G, Kuceyeski A, Sabuncu M
Journal	Magn Reson Imaging
Volume	64
Pagination	101-121
Date Published	06/05/2019
ISSN	1873-5894
Keywords	Brain Diseases, Image Interpretation, Computer-Assisted, Machine Learning, Magnetic Resonance Imaging
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.
DOI	10.1016/j.mri.2019.05.031
PubMed ID	31173849
PubMed Central ID	PMC6875692