Classification of amyotrophic lateral sclerosis by brain volume, connectivity, and network dynamics. Issue 2 (16th October 2021)

• Record Type: Journal Article
• Authors: Thome, Janine; Steinbach, Robert; Grosskreutz, Julian; Durstewitz, Daniel; Koppe, Georgia
• Is Part Of: Human brain mapping. Volume 43:Issue 2(2022)
• Page Start: 681
• Page End: 699
• Publication Date: 2021-10-16
• Subjects: ALS -- amyotrophic lateral sclerosis -- brain volume -- classification -- deep learning -- dynamical systems -- functional connectivity -- network dynamics -- neurodegeneration -- neuroimaging -- recurrent neural networks -- resting state fMRI; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.25679
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: Emerging studies corroborate the importance of neuroimaging biomarkers and machine learning to improve diagnostic classification of amyotrophic lateral sclerosis (ALS). While most studies focus on structural data, recent studies assessing functional connectivity between brain regions by linear methods highlight the role of brain function. These studies have yet to be combined with brain structure and nonlinear functional features. We investigate the role of linear and nonlinear functional brain features, and the benefit of combining brain structure and function for ALS classification. ALS patients ( N = 97) and healthy controls ( N = 59) underwent structural and functional resting state magnetic resonance imaging. Based on key hubs of resting state networks, we defined three feature sets comprising brain volume, resting state functional connectivity (rsFC), as well as (nonlinear) resting state dynamics assessed via recurrent neural networks. Unimodal and multimodal random forest classifiers were built to classify ALS. Out‐of‐sample prediction errors were assessed via five‐fold cross‐validation. Unimodal classifiers achieved a classification accuracy of 56.35–61.66%. Multimodal classifiers outperformed unimodal classifiers achieving accuracies of 62.85–66.82%. Evaluating the ranking of individual features' importance scores across all classifiers revealed that rsFC features were most dominant in classification. While univariate analyses revealed reduced rsFC in ALS patients, functional features more generally indicated deficits in information integration across resting state brain networks in ALS. The present work undermines that combining brain structure and function provides an additional benefit to diagnostic classification, as indicated by multimodal classifiers, while emphasizing the importance of capturing both linear and nonlinear functional brain properties to identify discriminative biomarkers of ALS. Abstract : The current study aims at identifying neuroimaging biomarkers for diagnostic classification of amyotrophic lateral sclerosis (ALS). We investigate the potential of combining brain structure and function for the classification of ALS and examine a novel feature set capturing nonlinear functional features from network dynamics based on recurrent neural networks. We demonstrate that combining different modalities improves classification, and that both linear and nonlinear functional brain features indeed deliver discriminative biomarkers of the disease.