Image-Derived Phenotyping Informed by Independent Component Analysis-An Atlas-Based Approach

• Published in: Frontiers in neuroscience Vol. 14; p. 118
• Main Authors: Moradi, Mahdi, Ekhtiari, Hamed, Victor, Teresa A, Paulus, Martin, Kuplicki, Rayus
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 21-02-2020; Frontiers Media S.A
• Subjects: Automation; Biobanks; Datasets; Deep learning; group-level analysis; image-derived phenotype; independent component analysis; Labeling; Machine learning; Neuroscience; Noise; Phenotyping; Pipelines; rsfMRI; Studies; Subjectivity; United Kingdom > UK; image-derived phenotype; independent component analysis; group-level analysis; rsfMRI; machine learning
• ISSN: 1662-4548; 1662-453X; 1662-453X
• DOI: 10.3389/fnins.2020.00118

[...]the spatial components extracted through individual or group-level ICA are never identical between studies and are often labeled by visual inspection and expert opinion. The Critique on ICA Performing ICA-based rsfMRI studies involves: data preprocessing and clean-up (sometimes through subject-level ICA or SICA), group-level ICA (GICA) on the entire dataset (usually with temporal concatenation), separating signal from noise independent components (ICs), network labeling, and time-series and spatial map extraction based on selected ICs for all subjects (Nickerson et al., 2017). Because ICA is a time-consuming and computationally resource-demanding procedure, a significant reduction in runtime may be worthwhile, especially in large-scale studies. A Solution We propose to use the ICs resulting from prior studies, such as the UK Biobank and HCP, in an “atlas-like” manner. Because such ICs are already published (Miller et al., 2016), they could be well-studied and agreed upon by the experts across the field. Similar to other studies that use a common atlas, the subject population should be a reasonable representation of the subject group recruited in the reference studies. Since most large public datasets are from normal populations, one might question