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Brain-predicted age difference score is related to specific cognitive functions: a multi-site replication analysis

Abstract

Brain-predicted age difference scores are calculated by subtracting chronological age from ‘brain’ age, which is estimated using neuroimaging data. Positive scores reflect accelerated ageing and are associated with increased mortality risk and poorer physical function. To date, however, the relationship between brain-predicted age difference scores and specific cognitive functions has not been systematically examined using appropriate statistical methods. First, applying machine learning to 1359 T1-weighted MRI scans, we predicted the relationship between chronological age and voxel-wise grey matter data. This model was then applied to MRI data from three independent datasets, significantly predicting chronological age in each dataset: Dokuz Eylül University (n = 175), the Cognitive Reserve/Reference Ability Neural Network study (n = 380), and The Irish Longitudinal Study on Ageing (n = 487). Each independent dataset had rich neuropsychological data. Brain-predicted age difference scores were significantly negatively correlated with performance on measures of general cognitive status (two datasets); processing speed, visual attention, and cognitive flexibility (three datasets); visual attention and cognitive flexibility (two datasets); and semantic verbal fluency (two datasets). As such, there is firm evidence of correlations between increased brain-predicted age differences and reduced cognitive function in some domains that are implicated in cognitive ageing.

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Acknowledgements

The authors would like to thank all participants who participated in the various studies which are used here.

Funding

RB is supported by the Irish Research Council under grant number EPSPG/2017/277. LMRD and RW are supported by the Science Foundation Ireland under grant number 16/ERCD/3797. RR is supported by a PhD scholarship funded by the Region Calabria. Data collection in Dokuz Eylul University, managed and supervised by GGY and DDS, was partially supported by the Turkish National Science and Research Council (TUBITAK, Grant number: 112S459) and the Dokuz Eylul University Scientific Research Projects (Grant number: 2018.KB.SAG.084). The Irish Longitudinal Study on Ageing is funded by core grants from the Health Research Board, Atlantic Philanthropies and Irish Life. MRI data collection in TILDA was supported by the Centre for Advanced Medical Imaging (CAMI) at St. James’ Hospital, Dublin. IHR thanks The Atlantic Philanthropies for their grant to the Global Brain Health Institute. YS is supported by NIA RF1 AG038465 and R01 AG026158. The funding agencies had no involvement in the conduct of the research or preparation of the article.

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Contributions

Author contributions included conception and study design (RW and RB), data collection or acquisition (GGY, DDES, YS, JPM, SPK, DC, and RAK), preprocessing and quality control of MRI data (RR and RB), statistical analysis (LMRD, LJ, RW, and RB), interpretation of results (RW and RB), drafting the manuscript work (RW and RB), revising the manuscript critically for important intellectual content (DC, DDES, IHR, LMRD, LJ, YS, RAK, RW and RB) and approval of final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work (All authors).

Corresponding author

Correspondence to Robert Whelan.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.

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The authors report no conflict of interest.

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Boyle, R., Jollans, L., Rueda-Delgado, L.M. et al. Brain-predicted age difference score is related to specific cognitive functions: a multi-site replication analysis. Brain Imaging and Behavior 15, 327–345 (2021). https://doi.org/10.1007/s11682-020-00260-3

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  • DOI: https://doi.org/10.1007/s11682-020-00260-3

Keywords

  • MRI
  • Brain ageing
  • Cognitive ageing
  • Cognitive function
  • Machine learning
  • Biomarkers