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Reliable longitudinal brain age prediction in stroke patients: Associations with cognitive function and response to cognitive training
Open accessPreprint — published as 10.1016/j.nicl.2019.102159
Abstract
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<7 at hospital discharge) underwent 3-weeks CCT and MRI before and after the intervention. In addition, patients were randomized to one of two groups receiving either active or sham transcranial direct current stimulation (tDCS). We tested for main effects of brain age gap (estimated age – chronological age) on cognitive performance, and associations between brain age gap and task improvement. Finally, we tested if longitudinal changes in brain age gap during the intervention were sensitive to treatment response. Briefly, our results suggest that longitudinal brain age prediction based on automated brain morphometry is feasible and reliable in stroke patients. However, no significant association between brain age and both performance and response to cognitive training were found.
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Feigin, V. L., Forouzanfar, M. H., Krishnamurthi, R., Mensah, G. A., Connor, M., Bennett, D. A., Moran, A. E., Sacco, R. L., Anderson, L., Truelsen, T., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(13)61953-4 · Cited by 3,899 · cited 1×
“Stroke is among the most common causes of acquired cognitive disabilities during adulthood, with a projected increase in prevalence over the next decades due to the aging population (Feigin et al. 2014; Feigin et al. 2017).”
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Metadata
- Authors
- Richard, G., Kolskår, K., Ulrichsen, K. M., Kaufmann, T., Alnæs, D., Sanders, A.-M., Dørum, E. S., Sánchez, J. M., Petersen, A., Ihle-Hansen, H., Nordvik, J. E., & Westlye, L. T.
- Affiliations
- Oslo University Hospital (NO)
- Maastricht University Medical Centre (NL)
- University of Copenhagen (DK)
- CatoSenteret (NO)
- Corresponding author
- Richard, Geneviève
- Westlye, Lars T.
- ORCID
- Venue
- NeuroImage Clinical
- Publisher
- openRxiv
- Published
- 2019-06-29
- Volume
- 25
- Pages
- 102159-102159
- ISSN
- 2213-1582 (ISSN-L)
- PMC
- PMC6953960
- OpenAlex
- W2998099948
- Cited by
- 65
- References
- 1
- Citations by year
- 2026: 6 · 2025: 7 · 2024: 8 · 2023: 8 · 2022: 18 · 2021: 7 · 2020: 9 · 2019: 2
- FWCI
- 7.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- NeuroImage Clinical (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, cc-by-nc-nd)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by-nc-nd)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Funders
- EkstraStiftelsen Helse og Rehabilitering (Stiftelsen Dam) (2015/FO5146)
- Sunnaas sykehus HF
- University of Cambridge
- European Commission (HORIZON2020, 802998)
- Universitetet i Oslo
- Norges Forskningsråd (2015044, 2018076, 2015/FO5146, #248238, 802998, #249795)
- Medical Research Council
- Biotechnology and Biological Sciences Research Council (BB/H008217/1)
- European Research Council (#802998)
- Helse Sør-Øst RHF (802998, 2018076, #2014-097, 248238, 2015-073, 2015044, 2015/FO5146)
- Horizon 2020
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Neurology › Transcranial Magnetic Stimulation Studies
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Keywords
- brain age prediction, stroke, computerized cognitive training, brain age gap, brain morphometry, transcranial direct current stimulation, cognitive performance, treatment response prediction, chronic stroke, cognitive impairment
- MeSH terms
- Cognitive Remediation, Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Aging, Brain, Combined Modality Therapy, Double-Blind Method, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Sensitivity and Specificity, Reproducibility of Results, Outcome Assessment, Health Care, Stroke, Young Adult, Cognitive Dysfunction, Transcranial Direct Current Stimulation
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