TY - JOUR

T1 - Brain age prediction in stroke patients

T2 - Highly reliable but limited sensitivity to cognitive performance and response to cognitive training

AU - Richard, Genevieve

AU - Kolskar, Knut

AU - Ulrichsen, Kristine M.

AU - Kaufmann, Tobias

AU - Alnaes, Dag

AU - Sanders, Anne-Marthe

AU - Dorum, Erlend S.

AU - Sanchez, Jennifer Monereo

AU - Petersen, Anders

AU - Ihle-Hansen, Hege

AU - Nordvik, Jan Egil

AU - Westlye, Lars T.

PY - 2020

Y1 - 2020

N2 - Cognitive deficits are important predictors for outcome, independence and quality of life after stroke, but often remain unnoticed and unattended because other impairments are more evident. Computerized cognitive training (CCT) is among the candidate interventions that may alleviate cognitive difficulties, but the evidence supporting its feasibility and effectiveness is scarce, partly due to the lack of tools for outcome prediction and monitoring. Magnetic resonance imaging (MRI) provides candidate markers for disease monitoring and outcome prediction. By integrating information not only about lesion extent and localization, but also regarding the integrity of the unaffected parts of the brain, advanced MRI provides relevant information for developing better prediction models in order to tailor cognitive intervention for patients, especially in a chronic phase.Using brain age prediction based on MRI based brain morphometry and machine learning, we tested the hypotheses that stroke patients with a younger-appearing brain relative to their chronological age perform better on cognitive tests and benefit more from cognitive training compared to patients with an older-appearing brain. In this randomized double-blind study, 54 patients who suffered mild stroke ( > 6 months since hospital admission, NIHSS

AB - Cognitive deficits are important predictors for outcome, independence and quality of life after stroke, but often remain unnoticed and unattended because other impairments are more evident. Computerized cognitive training (CCT) is among the candidate interventions that may alleviate cognitive difficulties, but the evidence supporting its feasibility and effectiveness is scarce, partly due to the lack of tools for outcome prediction and monitoring. Magnetic resonance imaging (MRI) provides candidate markers for disease monitoring and outcome prediction. By integrating information not only about lesion extent and localization, but also regarding the integrity of the unaffected parts of the brain, advanced MRI provides relevant information for developing better prediction models in order to tailor cognitive intervention for patients, especially in a chronic phase.Using brain age prediction based on MRI based brain morphometry and machine learning, we tested the hypotheses that stroke patients with a younger-appearing brain relative to their chronological age perform better on cognitive tests and benefit more from cognitive training compared to patients with an older-appearing brain. In this randomized double-blind study, 54 patients who suffered mild stroke ( > 6 months since hospital admission, NIHSS

KW - Computerized cognitive training

KW - Transcranial direct current stimulation

KW - Magnetic resonance imaging

KW - Brain age prediction

KW - Cerebral stroke, T1

KW - QUALITY-OF-LIFE

KW - GLOBAL BURDEN

KW - IMPAIRMENT

U2 - 10.1016/j.nicl.2019.102159

DO - 10.1016/j.nicl.2019.102159

M3 - Journal article

C2 - 31927499

VL - 25

JO - NeuroImage: Clinical

JF - NeuroImage: Clinical

SN - 2213-1582

M1 - 102159

ER -