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 |