Tensorial independent component analysis reveals social and reward networks associated with major depressive disorder.

ABSTRACT: Major depressive disorder (MDD) has been associated with changes in functional brain connectivity. Yet, typical analyses of functional connectivity, such as spatial independent components analysis (ICA) for resting-state data, often ignore sources of between-subject variability, which may be crucial for identifying functional connectivity patterns associated with MDD. Typically, methods like spatial ICA will identify a single component to represent a network like the default mode network (DMN), even if groups within the data show differential DMN coactivation. To address this gap, this project applies a tensorial extension of ICA (tensorial ICA)-which explicitly incorporates between-subject variability-to identify functionally connected networks using functional MRI data from the Human Con

SUBMITTER: Dennison JB 

PROVIDER: S-EPMC10089091 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature