Journal article
Latent disconnectome prediction of long-term cognitive-behavioural symptoms in stroke
Brain (London, England : 1878), Vol.146(5), pp.1963-1978
05/2023
DOI: 10.1093/brain/awad013
PMCID: PMC10151183
PMID: 36928757
Abstract
Stroke significantly impacts the quality of life. However, the long-term cognitive evolution in stroke is poorly predictable at the individual level. There is an urgent need to better predict long-term symptoms based on acute clinical neuroimaging data. Previous works have demonstrated a strong relationship between the location of white matter disconnections and clinical symptoms. However, rendering the entire space of possible disconnection-deficit associations optimally surveyable will allow for a systematic association between brain disconnections and cognitive-behavioural measures at the individual level. Here we present the most comprehensive framework, a composite morphospace of white matter disconnections (disconnectome) to predict neuropsychological scores 1 year after stroke. Linking the latent disconnectome morphospace to neuropsychological outcomes yields biological insights that are available as the first comprehensive atlas of disconnectome-deficit relations across 86 scores-a Neuropsychological White Matter Atlas. Our novel predictive framework, the Disconnectome Symptoms Discoverer, achieved better predictivity performances than six other models, including functional disconnection, lesion topology and volume modelling. Out-of-sample prediction derived from this atlas presented a mean absolute error below 20% and allowed personalize neuropsychological predictions. Prediction on an external cohort achieved an R2 = 0.201 for semantic fluency. In addition, training and testing were replicated on two external cohorts achieving an R2 = 0.18 for visuospatial performance. This framework is available as an interactive web application (http://disconnectomestudio.bcblab.com) to provide the foundations for a new and practical approach to modelling cognition in stroke. We hope our atlas and web application will help to reduce the burden of cognitive deficits on patients, their families and wider society while also helping to tailor future personalized treatment programmes and discover new targets for treatments. We expect our framework's range of assessments and predictive power to increase even further through future crowdsourcing.
Details
- Title: Subtitle
- Latent disconnectome prediction of long-term cognitive-behavioural symptoms in stroke
- Creators
- Lia Talozzi - Stanford University School of MedicineStephanie J Forkel - Technical University of MunichValentina Pacella - Scuola Universitaria Superiore IUSS, Pavia, 27100, ItalyVictor Nozais - Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, 75006, FranceEtienne Allart - InsermCéline Piscicelli - Service de Rééducation Neurologique, Institut de Rééducation, Hôpital Sud, CHU de Grenoble-Alpes, Échirolles, 38834, FranceDominic Pérennou - Laboratoire Psychology and Neurocognition, University Grenoble-Alpes, Service de Rééducation Neurologique, Institut de Rééducation, Hôpital sud-CHU Grenoble-Alpes, 38043 Grenoble, FranceDaniel Tranel - Department of Neurology, Carver College of Medicine, Iowa City, IA 52242, USAAaron Boes - Departments of Neurology, Psychiatry, and Pediatrics, Carver College of Medicine, Iowa City, IA 52242, USAMaurizio Corbetta - Venetian Institute of Molecular Medicine, VIMM, Padova, 32122, ItalyParashkev Nachev - Department of Brain Repair and Rehabilitation, Institute of Neurology, UCL, London, WC1N 3AZ, UKMichel Thiebaut de Schotten - Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, 75006, France
- Resource Type
- Journal article
- Publication Details
- Brain (London, England : 1878), Vol.146(5), pp.1963-1978
- DOI
- 10.1093/brain/awad013
- PMID
- 36928757
- PMCID
- PMC10151183
- NLM abbreviation
- Brain
- ISSN
- 0006-8950
- eISSN
- 1460-2156
- Grant note
- 818521 / European Research Council SOE_0000130 / NextGenerationEU PNRR 101028551 / Marie Skłodowska-Curie 2401515 / Donders Mohrmann
- Language
- English
- Electronic publication date
- 03/16/2023
- Date published
- 05/2023
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Neurology; Psychiatry; Stead Family Department of Pediatrics; Psychological and Brain Sciences; Iowa Neuroscience Institute; Neurology (Pediatrics)
- Record Identifier
- 9984378333102771
Metrics
14 Record Views