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Resting connectivity predicts task activation in pre-surgical populations.
Parker Jones, O; Voets, N L; Adcock, J E; Stacey, R; Jbabdi, S.
Afiliación
  • Parker Jones O; FMRIB Centre, NDCN, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK.
  • Voets NL; FMRIB Centre, NDCN, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK; Oxford Epilepsy Research Group, NDCN, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.
  • Adcock JE; Oxford Epilepsy Research Group, NDCN, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK; Department of Neurology, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK.
  • Stacey R; Department of Neurosurgery, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK.
  • Jbabdi S; FMRIB Centre, NDCN, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK.
Neuroimage Clin ; 13: 378-385, 2017.
Article en En | MEDLINE | ID: mdl-28123949
Injury and disease affect neural processing and increase individual variations in patients when compared with healthy controls. Understanding this increased variability is critical for identifying the anatomical location of eloquent brain areas for pre-surgical planning. Here we show that precise and reliable language maps can be inferred in patient populations from resting scans of idle brain activity. We trained a predictive model on pairs of resting-state and task-evoked data and tested it to predict activation of unseen patients and healthy controls based on their resting-state data alone. A well-validated language task (category fluency) was used in acquiring the task-evoked fMRI data. Although patients showed greater variation in their actual language maps, our models successfully learned variations in both patient and control responses from the individual resting-connectivity features. Importantly, we further demonstrate that a model trained exclusively on the more-homogenous control group can be used to predict task activations in patients. These results are the first to show that resting connectivity robustly predicts individual differences in neural response in cases of pathological variability.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Encefalopatías / Conectoma / Lenguaje Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Neuroimage Clin Año: 2017 Tipo del documento: Article Pais de publicación: Países Bajos
Texto completo
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XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Encefalopatías / Conectoma / Lenguaje Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Neuroimage Clin Año: 2017 Tipo del documento: Article Pais de publicación: Países Bajos