Model-based stationarity filtering of long-term memory data applied to resting-state blood-oxygen-level-dependent signal.

ABSTRACT: Resting-state blood-oxygen-level-dependent (BOLD) signal acquired through functional magnetic resonance imaging is a proxy of neural activity and a key mechanism for assessing neurological conditions. Therefore, practical tools to filter out artefacts that can compromise the assessment are required. On the one hand, a variety of tailored methods to preprocess the data to deal with identified sources of noise (e.g., head motion, heart beating, and breathing, just to mention a few) are in place. But, on the other hand, there might be unknown sources of unstructured noise present in the data. Therefore, to mitigate the effects of such unstructured noises, we propose a model-based filter that explores the statistical properties of the underlying signal (i.e., long-term memory). Specifically, w

SUBMITTER: Bansal IR 

PROVIDER: S-EPMC9328502 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature