Abstract
Image-Derived Phenotyping Informed by Independent Component Analysis-An Atlas-Based Approach.
Highlights
Independent Component Analysis (ICA) is a widely used, unsupervised, exploratory machine learning method (Comon, 1994) and is often applied to resting-state fMRI data (Nickerson et al, 2017)
The utility of ICA to extract meaningful functional connectivity patterns without the need for prior knowledge has been established by its application to large-scale studies like Human Connectome Project (HCP) and United Kingdom (UK) BioBank cohort (Miller et al, 2016; Smitha et al, 2017)
ICA has been successfully applied to a wide range of applications in rsfMRI, there have long been some concerns about reproducibility and the subjectivity of ICA results (Friston, 1998)
Summary
Independent Component Analysis (ICA) is a widely used, unsupervised, exploratory machine learning method (Comon, 1994) and is often applied to resting-state fMRI (rsfMRI) data (Nickerson et al, 2017). The goal of this study was to establish a spatial component approach based on well-documented atlases derived from large-scale investigations.
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