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

Frontiers in Neuroinformatics
Regina J MeszlényiZoltán Vidnyánszky

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 that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.

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Citations

Oct 29, 2018·Journal of Medical Systems·Brankica BratićZoran Bosnić
Aug 23, 2019·Neurology and Therapy·Aly Al-Amyn VallianiEric Karl Oermann
Dec 10, 2019·Human Brain Mapping·Xiaoxiao WangBensheng Qiu
Jan 11, 2020·Frontiers in Neuroscience·Alex Novaes SantanaPedro Montoya
Apr 9, 2020·Frontiers in Neuroinformatics·Pál VakliZoltán Vidnyánszky
Jan 31, 2020·NeuroImage. Clinical·Sukrit GuptaUNKNOWN Alzheimer’s Disease Neuroimaging Initiative

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Software Mentioned

SVM
MATLAB
LASSO
TensorFlow
BrinNetCNN
Python
SPM12

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