Literature DB >> 36267328

Brain age predicts long-term recovery in post-stroke aphasia.

Sigfus Kristinsson1, Natalie Busby1, Christopher Rorden1,2, Roger Newman-Norlund1,2, Dirk B den Ouden1,3, Sigridur Magnusdottir4, Haukur Hjaltason4,5, Helga Thors4, Argye E Hillis1,6, Olafur Kjartansson5, Leonardo Bonilha1,7, Julius Fridriksson1,3.   

Abstract

The association between age and language recovery in stroke remains unclear. Here, we used neuroimaging data to estimate brain age, a measure of structural integrity, and examined the extent to which brain age at stroke onset is associated with (i) cross-sectional language performance, and (ii) longitudinal recovery of language function, beyond chronological age alone. A total of 49 participants (age: 65.2 ± 12.2 years, 25 female) underwent routine clinical neuroimaging (T1) and a bedside evaluation of language performance (Bedside Evaluation Screening Test-2) at onset of left hemisphere stroke. Brain age was estimated from enantiomorphically reconstructed brain scans using a machine learning algorithm trained on a large sample of healthy adults. A subsample of 30 participants returned for follow-up language assessments at least 2 years after stroke onset. To account for variability in age at stroke, we calculated proportional brain age difference, i.e. the proportional difference between brain age and chronological age. Multiple regression models were constructed to test the effects of proportional brain age difference on language outcomes. Lesion volume and chronological age were included as covariates in all models. Accelerated brain age compared with age was associated with worse overall aphasia severity (F(1, 48) = 5.65, P = 0.022), naming (F(1, 48) = 5.13, P = 0.028), and speech repetition (F(1, 48) = 8.49, P = 0.006) at stroke onset. Follow-up assessments were carried out ≥2 years after onset; decelerated brain age relative to age was significantly associated with reduced overall aphasia severity (F(1, 26) = 5.45, P = 0.028) and marginally failed to reach statistical significance for auditory comprehension (F(1, 26) = 2.87, P = 0.103). Proportional brain age difference was not found to be associated with changes in naming (F(1, 26) = 0.23, P = 0.880) and speech repetition (F(1, 26) = 0.00, P = 0.978). Chronological age was only associated with naming performance at stroke onset (F(1, 48) = 4.18, P = 0.047). These results indicate that brain age as estimated based on routine clinical brain scans may be a strong biomarker for language function and recovery after stroke.
© The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain.

Entities:  

Keywords:  age; ageing; aphasia; neuroimaging; stroke

Year:  2022        PMID: 36267328      PMCID: PMC9576153          DOI: 10.1093/braincomms/fcac252

Source DB:  PubMed          Journal:  Brain Commun        ISSN: 2632-1297


  85 in total

Review 1.  Neural mechanisms of ageing and cognitive decline.

Authors:  Nicholas A Bishop; Tao Lu; Bruce A Yankner
Journal:  Nature       Date:  2010-03-25       Impact factor: 49.962

2.  Structural brain changes in medically refractory focal epilepsy resemble premature brain aging.

Authors:  Heath R Pardoe; James H Cole; Karen Blackmon; Thomas Thesen; Ruben Kuzniecky
Journal:  Epilepsy Res       Date:  2017-04-03       Impact factor: 3.045

3.  Accelerating cortical thinning: unique to dementia or universal in aging?

Authors:  Anders M Fjell; Lars T Westlye; Håkon Grydeland; Inge Amlien; Thomas Espeseth; Ivar Reinvang; Naftali Raz; Anders M Dale; Kristine B Walhovd
Journal:  Cereb Cortex       Date:  2012-12-12       Impact factor: 5.357

4.  Epidemiology of aphasia attributable to first ischemic stroke: incidence, severity, fluency, etiology, and thrombolysis.

Authors:  Stefan T Engelter; Michal Gostynski; Susanna Papa; Maya Frei; Claudia Born; Vladeta Ajdacic-Gross; Felix Gutzwiller; Phillipe A Lyrer
Journal:  Stroke       Date:  2006-05-11       Impact factor: 7.914

5.  The impact of early-life intelligence quotient on post stroke cognitive impairment.

Authors:  Stephen Dj Makin; Fergus N Doubal; Kirsten Shuler; Francesca M Chappell; Julie Staals; Martin S Dennis; Joanna M Wardlaw
Journal:  Eur Stroke J       Date:  2018-01-08

6.  The Post Ischaemic Stroke Cardiovascular Exercise Study: Protocol for a randomised controlled trial of fitness training for brain health.

Authors:  Liam Johnson; Emilio Werden; Chris Shirbin; Laura Bird; Elizabeth Landau; Toby Cumming; Leonid Churilov; Julie A Bernhardt; Vincent Thijs; Amy Brodtmann
Journal:  Eur Stroke J       Date:  2018-07-10

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

Authors:  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
Journal:  Neuroimage Clin       Date:  2019-12-30       Impact factor: 4.881

8.  Predicted Brain Age After Stroke.

Authors:  Natalia Egorova; Franziskus Liem; Vladimir Hachinski; Amy Brodtmann
Journal:  Front Aging Neurosci       Date:  2019-12-10       Impact factor: 5.750

9.  Brain-age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth cohort.

Authors:  Maxwell L Elliott; Daniel W Belsky; Annchen R Knodt; David Ireland; Tracy R Melzer; Richie Poulton; Sandhya Ramrakha; Avshalom Caspi; Terrie E Moffitt; Ahmad R Hariri
Journal:  Mol Psychiatry       Date:  2019-12-10       Impact factor: 15.992

Review 10.  Brain age and other bodily 'ages': implications for neuropsychiatry.

Authors:  James H Cole; Riccardo E Marioni; Sarah E Harris; Ian J Deary
Journal:  Mol Psychiatry       Date:  2018-06-11       Impact factor: 15.992

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.