Uncovering spatiotemporal patterns of atrophy in progressive supranuclear palsy using unsupervised machine learning
Author:
Scotton William J1ORCID, Shand Cameron2, Todd Emily1ORCID, Bocchetta Martina1ORCID, Cash David M1ORCID, VandeVrede Lawren3, Heuer Hilary3, Costantini Alyssa A, Houlden Henry, Kobylecki Christopher, Hu Michele T M, Leigh Nigel, Boeve Bradley F, Dickerson Brad C, Tartaglia Carmela M, Litvan Irene, Grossman Murray, Pantelyat Alex, Huey Edward D, Irwin David J, Fagan Anne, Baker Suzanne L, Toga Arthur W, Young Alexandra L4ORCID, Oxtoby Neil2ORCID, Alexander Daniel C2, Rowe James B5, Morris Huw R67ORCID, Boxer Adam L3, Rohrer Jonathan D1, Wijeratne Peter A2ORCID,
Affiliation:
1. Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London , London WC1N 3AR , UK 2. Centre for Medical Image Computing, Department of Computer Science, University College London , London WC1V 6LJ , UK 3. Department of Neurology, Memory and Aging Center, University of California , San Francisco, CA 94158 , USA 4. Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London , London SE5 8AF , UK 5. Cambridge University Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, Medical Research Council Cognition and Brain Sciences Unit , Cambridge CB2 0QQ , UK 6. Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology , London WC1N 3BG , UK 7. Movement Disorders Centre, University College London Queen Square Institute of Neurology , London WC1N 3BG , UK
Abstract
Abstract
To better understand the pathological and phenotypic heterogeneity of progressive supranuclear palsy and the links between the two, we applied a novel unsupervised machine learning algorithm (Subtype and Stage Inference) to the largest MRI data set to date of people with clinically diagnosed progressive supranuclear palsy (including progressive supranuclear palsy–Richardson and variant progressive supranuclear palsy syndromes).
Our cohort is comprised of 426 progressive supranuclear palsy cases, of which 367 had at least one follow-up scan, and 290 controls. Of the progressive supranuclear palsy cases, 357 were clinically diagnosed with progressive supranuclear palsy–Richardson, 52 with a progressive supranuclear palsy–cortical variant (progressive supranuclear palsy–frontal, progressive supranuclear palsy–speech/language, or progressive supranuclear palsy–corticobasal), and 17 with a progressive supranuclear palsy–subcortical variant (progressive supranuclear palsy–parkinsonism or progressive supranuclear palsy–progressive gait freezing). Subtype and Stage Inference was applied to volumetric MRI features extracted from baseline structural (T1-weighted) MRI scans and then used to subtype and stage follow-up scans. The subtypes and stages at follow-up were used to validate the longitudinal consistency of subtype and stage assignments. We further compared the clinical phenotypes of each subtype to gain insight into the relationship between progressive supranuclear palsy pathology, atrophy patterns, and clinical presentation.
The data supported two subtypes, each with a distinct progression of atrophy: a ‘subcortical’ subtype, in which early atrophy was most prominent in the brainstem, ventral diencephalon, superior cerebellar peduncles, and the dentate nucleus, and a ‘cortical’ subtype, in which there was early atrophy in the frontal lobes and the insula alongside brainstem atrophy. There was a strong association between clinical diagnosis and the Subtype and Stage Inference subtype with 82% of progressive supranuclear palsy–subcortical cases and 81% of progressive supranuclear palsy–Richardson cases assigned to the subcortical subtype and 82% of progressive supranuclear palsy–cortical cases assigned to the cortical subtype. The increasing stage was associated with worsening clinical scores, whilst the ‘subcortical’ subtype was associated with worse clinical severity scores compared to the ‘cortical subtype’ (progressive supranuclear palsy rating scale and Unified Parkinson’s Disease Rating Scale). Validation experiments showed that subtype assignment was longitudinally stable (95% of scans were assigned to the same subtype at follow-up) and individual staging was longitudinally consistent with 90% remaining at the same stage or progressing to a later stage at follow-up.
In summary, we applied Subtype and Stage Inference to structural MRI data and empirically identified two distinct subtypes of spatiotemporal atrophy in progressive supranuclear palsy. These image-based subtypes were differentially enriched for progressive supranuclear palsy clinical syndromes and showed different clinical characteristics. Being able to accurately subtype and stage progressive supranuclear palsy patients at baseline has important implications for screening patients on entry to clinical trials, as well as tracking disease progression.
Publisher
Oxford University Press (OUP)
Subject
Neurology,Cellular and Molecular Neuroscience,Biological Psychiatry,Psychiatry and Mental health
Cited by
1 articles.
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