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.
N1 - Funding Information:
The healthy controls used as training set for the age prediction model were obtained from the Cambridge centre for Ageing and Neuroscience (Cam-CAN) sample (http://www.mrc-cbu.cam.ac.uk/datasets/camcan/; (Shafto et al., 2014; Taylor et al., 2017)). Briefly, volunteers were recruited to Cam-CAN through a large-scale collaborative research project funded by the Biotechnology and Biological Sciences Research Council (BBSRC, grant number BB/H008217/1), the UK Medical Research Council and University of Cambridge. For more information, see http://www.cam-can.org. Data from 628 individuals (age = 18–87, mean = 54.2, SD = 18.3, 324 females) were included in the training set (Richard et al., 2018).This study was supported by the Norwegian Extra Foundation for Health and Rehabilitation (2015/FO5146), the Research Council of Norway (249795, 248238), the South-Eastern Norway Regional Health Authority (2014097, 2015044, 2015073, 2018076), Sunnaas Rehabilitation Hospital, the Department of Psychology, University of Oslo, and the European Research Council under the European Union's Horizon 2020 research and Innovation program (ERC StG, Grant 802998).
Funding Information:
This study was supported by the Norwegian Extra Foundation for Health and Rehabilitation ( 2015/FO5146 ), the Research Council of Norway ( 249795 , 248238 ), the South-Eastern Norway Regional Health Authority ( 2014097 , 2015044 , 2015073 , 2018076 ), Sunnaas Rehabilitation Hospital, the Department of Psychology, University of Oslo , and the European Research Council under the European Union's Horizon 2020 research and Innovation program ( ERC StG, Grant 802998 ).
Publisher Copyright:
© 2019 The Author(s)
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
SN - 2213-1582
VL - 25
JO - NeuroImage: Clinical
JF - NeuroImage: Clinical
M1 - 102159
ER -