Untamed: Unconstrained Tensor Decomposition and Graph Node Embedding for Cortical Parcellation.

ABSTRACT: Cortical parcellation is fundamental to neuroscience, enabling the division of cerebral cortex into distinct, non-overlapping regions to support interpretation and comparison of complex neuroimaging data. Although extensive literature has investigated cortical parcellation and its connection to functional brain networks, the optimal spatial features for deriving parcellations from resting-state fMRI (rsfMRI) remain unclear. Traditional methods such as Independent Component Analysis (ICA) have been widely used to identify large-scale functional networks, while other approaches define disjoint cortical parcellations. However, bridging these perspectives through effective feature extraction remains an open challenge. To address this, we introduce Untamed, a novel framework that integra

SUBMITTER: Liu Y 

PROVIDER: S-EPMC10802375 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature