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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\u2013max scaled composite regional integrity scores, a two-step cluster analysis was conducted. Two radiological clusters were identified; 35.5% of patients belonging to \u2018Cluster 1\u2019 and 64.5% of patients segregating to \u2018Cluster 2\u2019. 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, \u2018sensory\u2019, \u2018motor\u2019, 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.<\/jats:p>","DOI":"10.1007\/s00415-022-11081-3","type":"journal-article","created":{"date-parts":[[2022,3,25]],"date-time":"2022-03-25T06:03:00Z","timestamp":1648188180000},"page":"4404-4413","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Clusters of anatomical disease-burden patterns in ALS: a data-driven approach confirms radiological subtypes"],"prefix":"10.1007","volume":"269","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0492-4772","authenticated-orcid":false,"given":"Peter","family":"Bede","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aizuri","family":"Murad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jasmin","family":"Lope","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Orla","family":"Hardiman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai Ming","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,25]]},"reference":[{"issue":"10","key":"11081_CR1","doi-asserted-by":"publisher","first-page":"1866","DOI":"10.3174\/ajnr.A2658","volume":"32","author":"L Sarro","year":"2011","unstructured":"Sarro L, Agosta F, Canu E, Riva N, Prelle A, Copetti M, Riccitelli G, Comi G, Filippi M (2011) Cognitive functions and white matter tract damage in amyotrophic lateral sclerosis: a diffusion tensor tractography study. 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