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 - Article
C2 - 31927499
VL - 25
JO - NeuroImage: Clinical
JF - NeuroImage: Clinical
SN - 2213-1582
M1 - 102159
ER -