Evidence map›Paper›PMID 31927499›Full record

Trial reportNeuroImage. Clinical2020

Brain age prediction in stroke patients: Highly reliable but limited sensitivity to cognitive performance and response to cognitive training.

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 and 2 more

Open access · goldAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in NeuroImage. Clinical, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 6 pooled it
7.1field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed, 6 syntheses or guidelines pooled it, 68 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. The effects of exercise intervention on cognition and motor function in stroke survivors: a systematic review and meta-analysis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2023
    Pooled it
  4. Occupational therapy for cognitive impairment in stroke patients.The Cochrane database of systematic reviews · 2022
    Pooled it
  5. Pooled it
  6. Pooled it
  7. Trial
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Acceleration of brain aging after small-volume infarcts.Frontiers in aging neuroscience · 2024
    Article
  15. Observational
  16. Review
  17. Total Sleep Deprivation Increases Brain Age Prediction Reversibly in Multisite Samples of Young Healthy Adults.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023
    Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 3 institutions in 3 countries.

Geneviève RichardNORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Norway. Electronic address: genevieve.richard@medisin.uio.no.
Knut KolskårNORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Norway.
Kristine M UlrichsenNORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Norway.
Tobias KaufmannNORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway.
Dag AlnæsNORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway.
Anne-Marthe SandersNORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Norway.
Erlend S DørumNORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Norway.
Jennifer Monereo SánchezFaculty of Health, Medicine and Life Sciences, Maastricht University, Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Netherlands.
Anders PetersenCenter for Visual Cognition, Department of Psychology, University of Copenhagen, Copenhagen, Denmark.
Hege Ihle-HansenDepartment of Geriatric Medicine, Oslo University Hospital, Oslo, Norway.
Jan Egil NordvikCatoSenteret Rehabilitation Center, Son, Norway.
Lars T WestlyeNORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway. Electronic address: l.t.westlye@psykologi.uio.no.
Oslo University Hospital · NOMaastricht University Medical Centre · NLUniversity of Copenhagen · DK

Funding

Biotechnology and Biological Sciences Research Council BB/H008217/1
6 · The paper itself

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.

Indexed as

AgingBrainCognitive DysfunctionCognitive RemediationStrokeTranscranial Direct Current StimulationAdolescentAdultAgedAged, 80 and overAge FactorsCombined Modality TherapyDouble-Blind MethodFemaleHumansLongitudinal StudiesBrain age predictionCerebral stroke, T1Computerized cognitive trainingMagnetic resonance imagingTranscranial direct current stimulation

Identifiers

PMID31927499
PMCPMC6953960
OpenAlexW2998099948

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.