Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture.

ABSTRACT: Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and

SUBMITTER: Meszlenyi RJ 

PROVIDER: S-EPMC5651030 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature