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 1,359 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 brainpredicted age differences and reduced cognitive function in some domains that are implicated in cognitive ageing.

Bias-adjustment in neuroimaging-based brain age frameworks: A robust scheme

“…As can be seen, there is a significant dependence of the brain age delta on chronological age ( r = −0.88, p < 0.001; equation line: y = −0.7 x +50). This dependence seems independent of the method being used, as it has been reported by multiple authors (Cole et al, 2019; Boyle et al. 2019) and is likely a result of regression dilution bias (Le et al., 2018).…”

“…scanner properties, imaging modality, pre-processing, training size and population characteristics), the models might also suffer from an age-dependency issue due to regression dilution bias (Le et al., 2018). This bias has been reported in a series of brain age estimation studies (Cole et al., 2019; Boyle et al. 2019; Cole et al., 2018).…”

“…2019; Cole et al., 2018). For instance, Boyle and colleagues (Boyle et al. 2019) reported a significant age-dependency between delta age and chronological age among their training set on the basis of voxel-based features ( r = −0.44, p < 0.001).…”

“…2019) reported a significant age-dependency between delta age and chronological age among their training set on the basis of voxel-based features ( r = −0.44, p < 0.001). Using Cole's method, they removed this age-dependency from the prediction results followed by a MAE of 7.28 years on training set (Boyle et al. 2019).…”

Estimating brain age from structural MRI and MEG data: Insights from dimensionality reduction techniques

“…Additionally, brain age prediction has also been extended beyond understanding neurological disorders such as in the context of testing the positive influence of meditation (Luders et al, 2016), as well as education and physical exercise (Steffener et al, 2016b) on brain age. Recent work has also shown a relationship between the brain age delta and specific cognitive functions, namely visual attention, cognitive flexibility, and semantic verbal fluency (Boyle et al, 2020).…”