Machine learning in resting-state fMRI analysis

• Published in: Magnetic resonance imaging Vol. 64; pp. 101 - 121
• Main Authors: Khosla, Meenakshi, Jamison, Keith, Ngo, Gia H., Kuceyeski, Amy, Sabuncu, Mert R.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.12.2019
• Subjects: Algorithms; Brain - diagnostic imaging; Brain connectivity; Brain Diseases - diagnostic imaging; Brain Mapping - methods; Female; Functional MRI; Humans; Image Interpretation, Computer-Assisted - methods; Intrinsic networks; Machine Learning; Magnetic Resonance Imaging - methods; Male; Rest; Resting-state; Resting-state; Intrinsic networks; Functional MRI; Brain connectivity; Machine learning
• ISSN: 0730-725X; 1873-5894; 1873-5894
• DOI: 10.1016/j.mri.2019.05.031

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.