Rxivist logo

Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 70,077 bioRxiv papers from 306,093 authors.

Reliable longitudinal brain age prediction in stroke patients: Associations with cognitive function and response to cognitive training

By Geneviève Richard, Knut Kolskår, Kristine M. Ulrichsen, Tobias Kaufmann, Dag Alnæs, Anne-Marthe Sanders, Erlend S. Dørum, Jennifer Monereo Sánchez, Anders Petersen, Hege Ihle-Hansen, Jan Egil Nordvik, Lars T. Westlye

Posted 29 Jun 2019
bioRxiv DOI: 10.1101/687079

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.

Download data

  • Downloaded 170 times
  • Download rankings, all-time:
    • Site-wide: 55,481 out of 70,077
    • In neuroscience: 9,867 out of 12,597
  • Year to date:
    • Site-wide: 50,048 out of 70,077
  • Since beginning of last month:
    • Site-wide: 24,736 out of 70,077

Altmetric data

Downloads over time

Distribution of downloads per paper, site-wide


Sign up for the Rxivist weekly newsletter! (Click here for more details.)