Projects per year
Background: Brain age is a biomarker that predicts chronological age using neuroimaging features. Deviations of this predicted age from chronological age is considered a sign of age-related brain changes, or commonly referred to as brain ageing. The aim of this systematic review is to identify and synthesize the evidence for an association between lifestyle, health factors and diseases in adult populations, with brain ageing. Methods: This systematic review was undertaken in accordance with the PRISMA guidelines. A systematic search of Embase and Medline was conducted to identify relevant articles using search terms relating to the prediction of age from neuroimaging data or brain ageing. The tables of two recent review papers on brain ageing were also examined to identify additional articles. Studies were limited to adult humans (aged 18 years and above), from clinical or general populations. Exposures and study design of all types were also considered eligible. Results: A systematic search identified 52 studies, which examined brain ageing in clinical and community dwelling adults (mean age between 21 to 78 years, ~ 37% were female). Most research came from studies of individuals diagnosed with schizophrenia or Alzheimer’s disease, or healthy populations that were assessed cognitively. From these studies, psychiatric and neurologic diseases were most commonly associated with accelerated brain ageing, though not all studies drew the same conclusions. Evidence for all other exposures is nascent, and relatively inconsistent. Heterogenous methodologies, or methods of outcome ascertainment, were partly accountable. Conclusion: This systematic review summarised the current evidence for an association between genetic, lifestyle, health, or diseases and brain ageing. Overall there is good evidence to suggest schizophrenia and Alzheimer’s disease are associated with accelerated brain ageing. Evidence for all other exposures was mixed or limited. This was mostly due to a lack of independent replication, and inconsistency across studies that were primarily cross sectional in nature. Future research efforts should focus on replicating current findings, using prospective datasets. Trial registration: A copy of the review protocol can be accessed through PROSPERO, registration number CRD42020142817.
|Number of pages||23|
|Publication status||Published - 12 Aug 2021|
- Age prediction
- Age-related brain changes
- Brain ageing
- Machine learning
- Predicted age difference
- 2 Finished
ARC Centre of Excellence for Integrative Brain Function
Egan, G., Rosa, M., Lowery, A., Stuart, G., Arabzadeh, E., Skafidas, E., Ibbotson, M., Petrou, S., Paxinos, G., Mattingley, J., Garrido, M., Sah, P., Robinson, P. A., Martin, P., Grunert, U., Tanaka, K., Mitra, P., Johnson, G., Diamond, M., Margrie, T., Leopold, D., Movshon, J., Markram, H., Victor, J., Hill, S. & Jirsa, V.
Australian National University (ANU), ETH Zurich, Australian Research Council (ARC), Karolinska Institute, QIMR Berghofer Medical Research Institute, Ecole Polytechnique Federale de Lausanne (EPFL) (Swiss Federal Institute of Technology in Lausanne) , Monash University, University of Melbourne, University of New South Wales (UNSW), University of Queensland , University of Sydney, Monash University – Internal University Contribution, National Institutes of Health (United States), Cornell University, New York University, MRC National Institute for Medical Research, Scuola Internazionale Superiore di Studi Avanzati (SISSA), Duke University, Cold Spring Harbor Laboratory, RIKEN
25/06/14 → 31/12/21
Biomedical Imaging (MBI)
Kylie Reid (Manager), Robert Brkljaca (Manager), Christoph Hagemeyer (Other) & David Wright (Other)Office of the Vice-Provost (Research and Research Infrastructure)