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 |