Age Related Functional Connectivity Signature Extraction Using Energy-Based Machine Learning Techniques.

ABSTRACT: The study of brain connectivity plays an important role in understanding the functional organizations of the brain. It also helps to identify connectivity signatures that can be used for evaluating neural disorders and monitoring treatment efficacy. In this work, age-related changes in brain connectivity are studied to obtain aging signatures based on various modeling techniques. These include an energy-based machine learning technique to identify brain network interaction differences between two age groups with a large (30 years) age gap between them. Disconnectivity graphs and activation maps of the seven prominent resting-state networks (RSN) were obtained from functional MRI data of old and young adult subjects. Two-sample t-tests were performed on the local minimums with Bonfer

SUBMITTER: Varanasi S 

PROVIDER: S-EPMC9920122 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature