Longitudinal brain age prediction and cognitive function after stroke

Eva B Aamodt; Dag Alnæs; Ann-Marie G de Lange; Stina Aam; Till Schellhorn; Ingvild Saltvedt; Mona K Beyer; Lars T Westlye

doi:10.1016/j.neurobiolaging.2022.10.007

Longitudinal brain age prediction and cognitive function after stroke

Neurobiol Aging. 2023 Feb:122:55-64. doi: 10.1016/j.neurobiolaging.2022.10.007. Epub 2022 Oct 29.

Authors

Eva B Aamodt¹, Dag Alnæs², Ann-Marie G de Lange³, Stina Aam⁴, Till Schellhorn⁵, Ingvild Saltvedt⁴, Mona K Beyer⁵, Lars T Westlye⁶

Affiliations

¹ Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway. Electronic address: [email protected].
² Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
³ Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychiatry, University of Oxford, Oxford, UK.
⁴ Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway; Department of Geriatrics, Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Norway.
⁵ Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
⁶ Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Norway.

PMID: 36502572
DOI: 10.1016/j.neurobiolaging.2022.10.007

Abstract

Advanced age is associated with post-stroke cognitive decline. Machine learning based on brain scans can be used to estimate brain age of patients, and the corresponding difference from chronological age, the brain age gap (BAG), has been investigated in a range of clinical conditions, yet not thoroughly in post-stroke neurocognitive disorder (NCD). We aimed to investigate the association between BAG and post-stroke NCD over time. Lower BAG (younger appearing brain compared to chronological age) was found associated with lower risk of post-stroke NCD up to 36 months after stroke, even among those showing no evidence of impairments 3 months after hospital admission. For patients with no NCD at baseline, survival analysis suggested that higher baseline BAG was associated with higher risk of post-stroke NCD at 18 and 36 months. In conclusion, a younger appearing brain is associated with a lower risk of post-stroke NCD.

Keywords: Aging; Biomarker; Cognitive decline; Imaging; Longitudinal; Post-stroke dementia.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Brain / diagnostic imaging
Cognition
Cognitive Dysfunction* / psychology
Humans
Neurocognitive Disorders
Stroke* / complications