Evidence map›Paper›PMID 40070441›Full record

ArticleBrain communications2025

Distinct spatiotemporal atrophy patterns in corticobasal syndrome are associated with different underlying pathologies.

William J Scotton, Cameron Shand, Emily G Todd, Martina Bocchetta, Christopher Kobylecki, David M Cash, Lawren VandeVrede, Hilary W Heuer, Annelies Quaegebeur, Alexandra L Young and 9 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Brain communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02966145 (The Four-Repeat Tauopathy Neuroimaging Initiative), which is not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT02966145 completednot on this map

The Four-Repeat Tauopathy Neuroimaging Initiative

TypeobservationalSponsorUniversity of California, San FranciscoRan2016 to 2024Enrolled293ConditionsCorticobasal Degeneration (CBD), Corticobasal Syndrome (CBS), Cortical-basal Ganglionic Degeneration (CBGD), Progressive Supranuclear Palsy (PSP)ArmsObservational Study
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

William J ScottonDementia Research Centre, Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK.ORCID https://orcid.org/0000-0003-0607-3190
Cameron ShandCentre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK.
Emily G ToddDementia Research Centre, Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK.ORCID https://orcid.org/0000-0003-1551-5691
Martina BocchettaDementia Research Centre, Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK.ORCID https://orcid.org/0000-0003-1814-5024
Christopher KobyleckiDepartment of Neurology, Manchester Centre for Clinical Neurosciences, Northern Care Alliance NHS Foundation Trust (Salford Royal Hospital), Salford M6 8HD, UK.
David M CashDementia Research Centre, Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK.ORCID https://orcid.org/0000-0001-7833-616X
Lawren VandeVredeDepartment of Neurology, Memory and Aging Center, University of California, San Francisco CA 94158, USA.
Hilary W HeuerDepartment of Neurology, Memory and Aging Center, University of California, San Francisco CA 94158, USA.
Annelies QuaegebeurCambridge University Department of Clinical Neurosciences, Cambridge University Hospitals NHS Trust, Cambridge CB2 0QQ, UK.
Alexandra L YoungCentre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK.ORCID https://orcid.org/0000-0002-7772-781X
Neil OxtobyCentre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK.ORCID https://orcid.org/0000-0003-0203-3909
Daniel AlexanderCentre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK.
James B RoweCambridge University Department of Clinical Neurosciences, Cambridge University Hospitals NHS Trust, Cambridge CB2 0QQ, UK.
Huw R MorrisDepartment of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London WC1N 3BG, UK.ORCID https://orcid.org/0000-0002-5473-3774
PROSPECT Consortium
Adam L BoxerDepartment of Neurology, Memory and Aging Center, University of California, San Francisco CA 94158, USA.
4RTNI Consortium
Jonathan D RohrerDementia Research Centre, Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK.
Peter A WijeratneCentre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK.ORCID https://orcid.org/0000-0002-4885-6241

Funding

The Four Repeat Tauopathy Neuroimaging InitiativeR01AG038791 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BOXER, ADAM L. · 2010 to 2020
$18.1M
Validation of Novel Plasma Biomarkers for Mixed Etiology DementiaK23AG073514 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI VANDEVREDE, LAWREN · 2021 to 2025
$1.0M
NIA NIH HHS K23 AG073514NIA NIH HHS R01 AG038791Wellcome Trust
6 · The paper itself

Abstract

Although the corticobasal syndrome was originally most closely linked with the pathology of corticobasal degeneration, the 2013 Armstrong clinical diagnostic criteria, without the addition of aetiology-specific biomarkers, have limited positive predictive value for identifying corticobasal degeneration pathology in life. Autopsy studies demonstrate considerable pathological heterogeneity in corticobasal syndrome, with corticobasal degeneration pathology accounting for only ∼50% of clinically diagnosed individuals. Individualized disease stage and progression modelling of brain changes in corticobasal syndrome may have utility in predicting this underlying pathological heterogeneity, and in turn improve the design of clinical trials for emerging disease-modifying therapies. The aim of this study was to jointly model the phenotypic and temporal heterogeneity of corticobasal syndrome, to identify unique imaging subtypes based solely on a data-driven assessment of MRI atrophy patterns and then investigate whether these subtypes provide information on the underlying pathology. We applied Subtype and Stage Inference, a machine learning algorithm that identifies groups of individuals with distinct biomarker progression patterns, to a large cohort of 135 individuals with corticobasal syndrome (52 had a pathological or biomarker defined diagnosis) and 252 controls. The model was fit using volumetric features extracted from baseline 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 the baseline subtype and stage assignments. We then investigated whether there were differences in associated pathology and clinical phenotype between the subtypes. Subtype and Stage Inference identified at least two distinct and longitudinally stable spatiotemporal subtypes of atrophy progression in corticobasal syndrome; four-repeat-tauopathy confirmed cases were most commonly assigned to the

Indexed as

biomarkerscorticobasal syndromedisease progressionmachine learningsubtype and stage inference

Identifiers

PMID40070441
PMCPMC11894806

What Socratic holds

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.