Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age.

ABSTRACT: Recent works have extensively investigated the possibility to predict brain aging from T1-weighted MRI brain scans. The main purposes of these studies are the investigation of subject-specific aging mechanisms and the development of accurate models for age prediction. Deviations between predicted and chronological age are known to occur in several neurodegenerative diseases; as a consequence, reaching higher levels of age prediction accuracy is of paramount importance to develop diagnostic tools. In this work, we propose a novel complex network model for brain based on segmenting T1-weighted MRI scans in rectangular boxes, called patches, and measuring pairwise similarities using Pearson's correlation to define a subject-specific network. We fed a deep neural network with nodal metrics, ev

SUBMITTER: Amoroso N 

PROVIDER: S-EPMC6538815 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature