Deep learning reveals the common spectrum underlying multiple brain disorders in youth and elders from brain functional networks

TL;DR

This study uses deep learning on multi-site fMRI data to identify common functional brain network alterations across multiple disorders in youth and elders, revealing a spectrum of disorders that enhances understanding of comorbidities.

Contribution

The paper introduces a deep learning model that uncovers shared neuroimaging features across diverse brain disorders, supporting the spectrum hypothesis in lifespan brain pathology.

Findings

Achieved 62.6% classification accuracy across disorders.

Identified common affected networks including default mode and limbic systems.

Discovered a continuous distribution of disorder features across age groups.

Abstract

Brain disorders in the early and late life of humans potentially share pathological alterations in brain functions. However, the key evidence from neuroimaging data for pathological commonness remains unrevealed. To explore this hypothesis, we build a deep learning model, using multi-site functional magnetic resonance imaging data (N=4,410, 6 sites), for classifying 5 different brain disorders from healthy controls, with a set of common features. Our model achieves 62.6(1.9)% overall classification accuracy on data from the 6 investigated sites and detects a set of commonly affected functional subnetworks at different spatial scales, including default mode, executive control, visual, and limbic networks. In the deep-layer feature representation for individual data, we observe young and aging patients with disorders are continuously distributed, which is in line with the clinical concept of the "spectrum of disorders". The revealed spectrum underlying early- and late-life brain disorders promotes the understanding of disorder comorbidities in the lifespan.