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Using Gradients of Connectivity to Predict Cognitive Impairment After Stroke

Project description

Predicting stroke’s impact on recovery

Stroke survivors often face long-term challenges with language, attention, cognitive control, and motor function. Predicting these effects has been difficult, as brain lesions alone cannot fully forecast the outcome. The ERC-funded STROKEGRAD project aims to change that by building on recent advances from another ERC project. By analysing brain connectivity patterns through resting-state fMRI, the team can predict the chronic consequences of stroke on cognitive abilities. Early results show promise, suggesting that these connectivity gradients could predict problems with language and cognitive control. The project will validate this approach across multiple domains, offering hope for more targeted rehabilitation and better support for stroke survivors in Europe.

Objective

Many stroke survivors have chronic problems with language, attention, cognitive control and motor function. There is an urgent need to predict these sequelae so that rehabilitation can be better focussed, but it has proved challenging to anticipate the effects of stroke from brain lesions alone. We will use recent advances from our ERC-funded FLEXSEM project to predict the long-term consequences of stroke. Our work decomposes whole-brain connectivity patterns derived from resting-state functional magnetic resonance imaging (fMRI) into components, known as ‘gradients’ that capture substantial variance in function, and links these gradients to individual differences in cognitive ability. Pilot work suggests that (i) connectivity gradients can predict the chronic effects of stroke on language comprehension and (ii) that changes to these gradients can be anticipated from the lesion alone. This suggests our approach has high clinical utility, since stroke survivors are typically assessed with structural but not functional MRI. This PoC project will validate our approach across a range of domains, including speech fluency, comprehension, phonology and cognitive control. We will use resting-state fMRI and structural scans along with high-quality neuropsychology. We will parcellate the fMRI data from patients, compute the connectivity between pairs of parcels, perform dimension reduction on the connectivity matrix to generate gradients, and identify associations with cognitive problems. We will then examine the extent to which post-stroke cognitive changes can be predicted from structural scans by estimating lesioned gradients using data from aged-matched controls. Focus groups with speech therapists and stroke survivors, co-production of the validation work and dissemination activities for clinicians will ensure the tools we develop are suited to European healthcare systems to identify targets for rehabilitation.

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Topic(s)

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HORIZON-ERC-POC - HORIZON ERC Proof of Concept Grants

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Call for proposal

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(opens in new window) ERC-2024-POC

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Host institution

UNIVERSITY OF YORK
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 150 000,00
Address
HESLINGTON
YO10 5DD YORK NORTH YORKSHIRE
United Kingdom

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Region
Yorkshire and the Humber North Yorkshire York
Activity type
Higher or Secondary Education Establishments
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Total cost

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Beneficiaries (1)

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