Ensembling complex network 'perspectives' for mild cognitive impairment detection with artificial neural networks

TL;DR

This paper introduces a novel approach combining complex network measures and neural networks to improve early detection of mild cognitive impairment using brain imaging data.

Contribution

It proposes an ensembling method that integrates complex network analysis with neural networks for better MCI detection, addressing data balancing issues.

Findings

Effective in early MCI detection on benchmark data

Shows robustness against data balancing issues

Enhances interpretability of brain connectivity features

Abstract

In this paper, we propose a novel method for mild cognitive impairment detection based on jointly exploiting the complex network and the neural network paradigm. In particular, the method is based on ensembling different brain structural "perspectives" with artificial neural networks. On one hand, these perspectives are obtained with complex network measures tailored to describe the altered brain connectivity. In turn, the brain reconstruction is obtained by combining diffusion-weighted imaging (DWI) data to tractography algorithms. On the other hand, artificial neural networks provide a means to learn a mapping from topological properties of the brain to the presence or absence of cognitive decline. The effectiveness of the method is studied on a well-known benchmark data set in order to evaluate if it can provide an automatic tool to support the early disease diagnosis. Also, the effects of balancing issues are investigated to further assess the reliability of the complex network approach to DWI data.