Clusters of anatomical disease-burden patterns in ALS: a data-driven approach confirms radiological subtypes

• Published in: Journal of neurology Vol. 269; no. 8; pp. 4404 - 4413
• Main Authors: Bede, Peter, Murad, Aizuri, Lope, Jasmin, Hardiman, Orla, Chang, Kai Ming
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2022; Springer Nature B.V
• Subjects: Amyotrophic lateral sclerosis; Brain stem; Cerebellum; Cluster analysis; Genotypes; Medicine; Medicine & Public Health; Motor neurone disease; Neuroimaging; Neurology; Neuroradiology; Neurosciences; Original Communication; Patients; Phenotypes; Sensory neurons; Substantia alba; Neuroimaging; Biomarkers; Diffusion imaging; Clinical trials; Amyotrophic lateral sclerosis; Motor neuron disease
• ISSN: 0340-5354; 1432-1459; 1432-1459
• DOI: 10.1007/s00415-022-11081-3

Amyotrophic lateral sclerosis (ALS) is associated with considerable clinical heterogeneity spanning from diverse disability profiles, differences in UMN/LMN involvement, divergent progression rates, to variability in frontotemporal dysfunction. A multitude of classification frameworks and staging systems have been proposed based on clinical and neuropsychological characteristics, but disease subtypes are seldom defined based on anatomical patterns of disease burden without a prior clinical stratification. A prospective research study was conducted with a uniform imaging protocol to ascertain disease subtypes based on preferential cerebral involvement. Fifteen brain regions were systematically evaluated in each participant based on a comprehensive panel of cortical, subcortical and white matter integrity metrics. Using min–max scaled composite regional integrity scores, a two-step cluster analysis was conducted. Two radiological clusters were identified; 35.5% of patients belonging to ‘Cluster 1’ and 64.5% of patients segregating to ‘Cluster 2’. Subjects in Cluster 1 exhibited marked frontotemporal change. Predictor ranking revealed the following hierarchy of anatomical regions in decreasing importance: superior lateral temporal, inferior frontal, superior frontal, parietal, limbic, mesial inferior temporal, peri-Sylvian, subcortical, long association fibres, commissural, occipital, ‘sensory’, ‘motor’, cerebellum, and brainstem. While the majority of imaging studies first stratify patients based on clinical criteria or genetic profiles to describe phenotype- and genotype-associated imaging signatures, a data-driven approach may identify distinct disease subtypes without a priori patient categorisation. Our study illustrates that large radiology datasets may be potentially utilised to uncover disease subtypes associated with unique genetic, clinical or prognostic profiles.