Identifying brain networks in synaptic density PET (¹¹C-UCB-J) with independent component analysis.

ABSTRACT:

Background

The human brain is inherently organized into distinct networks, as reported widely by resting-state functional magnetic resonance imaging (rs-fMRI), which are based on blood-oxygen-level-dependent (BOLD) signal fluctuations. ¹¹C-UCB-J PET maps synaptic density via synaptic vesicle protein 2A, which is a more direct structural measure underlying brain networks than BOLD rs-fMRI.

Methods

The aim of this study was to identify maximally independent brain source networks, i.e., "spatial patterns with common covariance across subjects", in ¹¹C-UCB-J data using independent component analysis (ICA), a data-driven analysis method. Using a population of 80 healthy controls, we applied ICA to two 40-sample subsets and compared source network replication

SUBMITTER: Fang XT 

PROVIDER: S-EPMC8452380 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature