A Tensor-Based Framework for rs-fMRI Classification and Functional Connectivity Construction.

ABSTRACT: Recently, machine learning methods have gained lots of attention from researchers seeking to analyze brain images such as Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) to obtain a deeper understanding of the brain and such related diseases, for example, Alzheimer's disease. Finding the common patterns caused by a brain disorder through analysis of the functional connectivity (FC) network along with discriminating brain diseases from normal controls have long been the two principal goals in studying rs-fMRI data. The majority of FC extraction methods calculate the FC matrix for each subject and then use simple techniques to combine them and obtain a general FC matrix. In addition, the state-of-the-art classification techniques for finding subjects with brain disorders also r

SUBMITTER: Noroozi A 

PROVIDER: S-EPMC7734298 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature