Blurred magnitude homology of functional connectome for ASD diagnosis

TL;DR

This paper introduces a new method using blurred magnitude homology to analyze brain networks for early detection of autism spectrum disorder.

Contribution

The novelty lies in applying blurred magnitude homology to directed functional connectomes for ASD diagnosis.

Findings

Blurred magnitude homology effectively distinguishes directed brain networks of individuals with ASD.

The proposed method shows promise for early prediction of ASD using fMRI data.

Directed functional connectivity analysis outperforms undirected approaches in this context.

Abstract

Autism spectrum disorder (ASD) is one of the most common neurodevelopmental disorders. Existing studies show that adults with ASD may experience accelerated or altered neurocognitive aging. Consequently, cognitive decline in people with ASD can be delayed if timely measures are taken to treat this disorder. This study focuses on the development of a new algorithm for the early prediction of ASD from fMRI images. Autism spectrum disorder alters functional connectivity between brain regions. Therefore, it is important to develop methods for diagnosing this condition based on the analysis of a brain network. Functional brain networks are usually studied using undirected correlations, while functional connections in the brain are inherently directed. Blurred magnitude homology is an algebro-topological tool that enables the analysis of directed graphs, including directed functional connectomes. The method proposed in this work is based on applying a fully connected neural network to blurred magnitude homology-based features of a directed functional connectivity network. Experiments on empirically derived connectomes from fMRI images show that blurred magnitude homology is a useful invariant for distinguishing directed brain networks of individuals with ASD and typically developing individuals.

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