Tracing Evolving Networks Using Tensor Factorizations vs. ICA-Based Approaches.

ABSTRACT: Analysis of time-evolving data is crucial to understand the functioning of dynamic systems such as the brain. For instance, analysis of functional magnetic resonance imaging (fMRI) data collected during a task may reveal spatial regions of interest, and how they evolve during the task. However, capturing underlying spatial patterns as well as their change in time is challenging. The traditional approach in fMRI data analysis is to assume that underlying spatial regions of interest are static. In this article, using fractional amplitude of low-frequency fluctuations (fALFF) as an effective way to summarize the variability in fMRI data collected during a task, we arrange time-evolving fMRI data as a subjects by voxels by time windows tensor, and analyze the tensor using

SUBMITTER: Acar E 

PROVIDER: S-EPMC9081795 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature