Modeling Brain Networks with Artificial Neural Networks

TL;DR

This paper introduces neural network-based methods to model functional brain connectivities from fMRI data, improving the accuracy of cognitive process classification and outperforming existing models.

Contribution

It presents novel neural network architectures for estimating directed and undirected brain networks, enhancing the discrimination of cognitive states from fMRI data.

Findings

Both undirected and directed brain networks outperform existing models.

Directed networks provide more discriminative features than undirected networks.

The proposed models achieve higher classification accuracy on HCP and CPS datasets.

Abstract

In this study, we propose a neural network approach to capture the functional connectivities among anatomic brain regions. The suggested approach estimates a set of brain networks, each of which represents the connectivity patterns of a cognitive process. We employ two different architectures of neural networks to extract directed and undirected brain networks from functional Magnetic Resonance Imaging (fMRI) data. Then, we use the edge weights of the estimated brain networks to train a classifier, namely, Support Vector Machines(SVM) to label the underlying cognitive process. We compare our brain network models with popular models, which generate similar functional brain networks. We observe that both undirected and directed brain networks surpass the performances of the network models used in the fMRI literature. We also observe that directed brain networks offer more discriminative features compared to the undirected ones for recognizing the cognitive processes. The representation power of the suggested brain networks are tested in a task-fMRI dataset of Human Connectome Project and a Complex Problem Solving dataset.