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Machine learning in resting-state fMRI analysis
| Content Provider | Scilit |
|---|---|
| Author | Khosla, Meenakshi Jamison, Keith Ngo, Gia H. Kuceyeski, Amy Sabuncu, Mert R. |
| Copyright Year | 2019 |
| Description | Journal: Magnetic Resonance Imaging 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. |
| Related Links | http://arxiv.org/pdf/1812.11477 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6875692/pdf |
| Ending Page | 121 |
| Page Count | 21 |
| Starting Page | 101 |
| ISSN | 0730725X |
| e-ISSN | 18735894 |
| DOI | 10.1016/j.mri.2019.05.031 |
| Journal | Magnetic Resonance Imaging |
| Volume Number | 64 |
| Language | English |
| Publisher | Elsevier BV |
| Publisher Date | 2019-06-05 |
| Access Restriction | Open |
| Subject Keyword | Journal: Magnetic Resonance Imaging Intrinsic Networks Brain Connectivity |
| Content Type | Text |
| Resource Type | Article |
| Subject | Radiology, Nuclear Medicine and Imaging Biophysics Biomedical Engineering |
