An efficient functional magnetic resonance imaging data reduction strategy using neighborhood preserving embedding algorithm.

ABSTRACT: High dimensionality data have become common in neuroimaging fields, especially group-level functional magnetic resonance imaging (fMRI) datasets. fMRI connectivity analysis is a widely used, powerful technique for studying functional brain networks to probe underlying mechanisms of brain function and neuropsychological disorders. However, data-driven technique like independent components analysis (ICA), can yield unstable and inconsistent results, confounding the true effects of interest and hindering the understanding of brain functionality and connectivity. A key contributing factor to this instability is the information loss that occurs during fMRI data reduction. Data reduction of high dimensionality fMRI data in the temporal domain to identify the important information within group da

SUBMITTER: Zhao W 

PROVIDER: S-EPMC8886658 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature