Prediction of brain age and cognitive age: quantifying brain and cognitive maintenance in aging

Anaturk, M; Kaufmann, Tobias; Cole, James H.; Suri, Sana; Griffanti, Ludovica; Zsoldos, Enikő; Filippini, Nicola; Singh‐Manoux, Archana; Kivimäki, Mika; Westlye, Lars T.; Ebmeier, Klaus P.; A-M., de Lange

doi:10.31234/osf.io/gwqnt

Cited by 12 publications

(17 citation statements)

References 48 publications

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“…However, the exact neurobiological underpinnings of diffusion metrics cannot be directly inferred, and although we utilised advanced diffusion modelling which is sensitive to biophysical tissue properties [38], the biological substrates underlying these metrics remain to be elucidated by future studies. In addition, controlling for the effect of extracellular water or indices of hydration [122] as well as including measures of WM hyperintensities [123, 124] could potentially provide more accurate models of WM ageing.…”

Section: Discussionmentioning

confidence: 99%

A history of previous childbirths is linked to women’s white matter brain age in midlife and older age

Voldsbekk

Barth

Maximov

et al. 2020

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Maternal brain adaptations occur in response to pregnancy, but little is known about how parity impacts white matter (WM) microstructure and WM ageing trajectories later in life. Utilising global and regional brain-age prediction based on multi-shell diffusion MRI data, we investigated the association between previous childbirths and WM brain age in 8,895 women in the UK Biobank cohort (age range = 54 - 81 years). The results showed that a higher number of previous childbirths was associated with lower WM brain age, in line with previous studies showing less evident grey matter (GM) brain ageing in parous relative to nulliparous women. Both global WM and GM brain age estimates showed unique contributions to the association with previous childbirths, suggesting partly independent processes. Corpus callosum contributed uniquely to the global WM association with previous childbirths, and showed a stronger relationship relative to several other tracts. While our findings demonstrate a link between reproductive history and brain WM characteristics later in life, longitudinal studies are required to understand how parity influences women’s WM trajectories across the lifespan.

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Section: Discussionmentioning

confidence: 99%

A history of previous childbirths is linked to women’s white matter brain age in midlife and older age

Voldsbekk

Barth

Maximov

et al. 2020

Preprint

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“…[42,40,38]). In other words, it provides the same results as regressing out chronological age from brain age delta and using the residuals [41,6,9,25,12], or including chronological age as a covariate in regressions/correlations between brain age delta and other variables of interest [41,42,49,31]. The orange line shows the linear fit applied to model the age bias.…”

Section: Age-bias Correctionmentioning

confidence: 99%

“…The difference between an individual's brain-predicted and chronological age (brain age delta) provides a proxy for deviations from expected age trajectories, and has been associated with clinical risk factors [10,11,23] as well as neurological and neuropsychiatric conditions [2,9,13,20,24,25,26,27,28,29]. Brain age delta estimates have also been linked to biomedical variables and lifestyle factors in healthy population cohorts [3,22,11,12,30,31,32], and the overall evidence supports the use of brain-predicted age as a surrogate marker for brain integrity and health [21].…”

Section: Introductionmentioning

confidence: 99%

Mind the gap: performance metric evaluation in brain-age prediction

Lange

Anatürk

Rokicki

et al. 2021

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Estimating age based on neuroimaging-derived data has become a popular approach to developing markers for brain integrity and health. While a variety of machine-learning algorithms can provide accurate predictions of age based on brain characteristics, there is significant variation in model accuracy reported across studies. We predicted age based on neuroimaging data in two population-based datasets, and assessed the effects of age range, sample size, and age-bias correction on the model performance metrics r, R2, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The results showed that these metrics vary considerably depending on cohort age range; r and R2 values are lower when measured in samples with a narrower age range. RMSE and MAE are also lower in samples with a narrower age range due to smaller errors/brain age delta values when predictions are closer to the mean age of the group. Across subsets with different age ranges, performance metrics improve with increasing sample size. Performance metrics further vary depending on prediction variance as well as mean age difference between training and test sets, and age-bias corrected metrics indicate high accuracy - also for models showing poor initial performance. In conclusion, performance metrics used for evaluating age prediction models depend on cohort and study-specific data characteristics, and cannot be directly compared across different studies. Since age-bias corrected metrics in general indicate high accuracy, even for poorly performing models, inspection of uncorrected model results provides important information about underlying model attributes such as prediction variance.

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“…BMI, smoking; [47]) and rates of disease in the UKB are lower than in the general population [48]. Our analytical samples were also significantly younger, had a lower proportion of females and were more educated than those who were excluded (SI Table 1).…”

Section: Strengths and Limitations……………………………………………………………………………………mentioning

confidence: 77%

Development and Validation of a Novel Dementia Risk Score in the UK Biobank Cohort

Anatürk¹,

Patel²,

Georgiopoulos³

et al. 2021

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INTRODUCTION: Current prognostic models of dementia have had limited success in consistently identifying at-risk individuals. We aimed to develop and validate a novel dementia risk score (DRS) using the UK Biobank cohort.METHODS: After randomly dividing the sample into a training (n=166,487, 80%) and test set (n=41,621, 20%), logistic LASSO regression and standard logistic regression were used to develop the UKB-DRS.RESULTS: The score consisted of age, sex, education, apolipoprotein E4 genotype, a history of diabetes, stroke, and depression, and a family history of dementia. The UKB-DRS had good-to-strong discrimination accuracy in the UKB hold-out sample (AUC [95%CI]=0.79 [0.77, 0.82]) and in an external dataset (Whitehall II cohort, AUC [95%CI]=0.83 [0.79,0.87]). The UKB-DRS also significantly outperformed four published risk scores (i.e., Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI), Cardiovascular Risk Factors, Aging, and Dementia score (CAIDE), Dementia Risk Score (DRS), and the Framingham Cardiovascular Risk Score (FRS) across both test sets.CONCLUSION: The UKB-DRS represents a novel easy-to-use tool that could be used for routine care or targeted selection of at-risk individuals into clinical trials.

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Prediction of brain age and cognitive age: quantifying brain and cognitive maintenance in aging

Cited by 12 publications

References 48 publications

A history of previous childbirths is linked to women’s white matter brain age in midlife and older age

A history of previous childbirths is linked to women’s white matter brain age in midlife and older age

Mind the gap: performance metric evaluation in brain-age prediction

Development and Validation of a Novel Dementia Risk Score in the UK Biobank Cohort

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