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  • 1
    In: Alzheimer's & Dementia, Wiley, Vol. 17, No. S10 ( 2021-12)
    Abstract: Globally, up to 40% of dementia cases may be prevented if several key risk factors, such as low education and obesity, are targeted. This has motivated interest into the development of risk scores that aim to quantify an individual’s risk of developing dementia within a given time frame. However, translation to a clinical setting has been hampered by either a lack of external validation, poor out‐of‐sample performance, or the integration of measures (e.g., MRI, cognitive testing) that are costly or time intensive to administer. This study aimed to develop a novel dementia risk score suitable for a primary care setting and compare its discrimination accuracy with other risk scores. Method 208,108 UK Biobank (UKB) participants were examined, with cases of incident dementia (mean time to diagnosis ± SD = 5.91 years ± 2.02) identified through self‐report, hospital inpatient records and death certificates (n = 1,270, 0.6%). The dataset was randomly split into a training (80%) and test set (20%). Potential predictors (n = 30) included Apolipoprotein E4 (APOE4) status and cardiovascular, medical and lifestyle factors collected at baseline. Logistic LASSO regression was employed for variable selection and logistic regression was then used to derive beta coefficients for each predictor included in the UKB dementia risk score (UKB‐DRS). The area under the curve (i.e., AUC) was used to compare the discriminative ability of the UKB‐DRS to the CAIDE, ANU‐ADRI, Dementia Risk Score (i.e., DRS) and Framingham risk score, in both the test set and the Whitehall II study (WHIII, n = 2,974), which served as an external dataset. Result The UKB‐DRS consisted of age, sex, education, APOE4 status, a medical history of diabetes, stroke and depression and a family history of dementia. The UKB‐DRS demonstrated good discrimination accuracy in both the test set (AUC [95% ] = 0.79 [0.77, 0.82] ) and external dataset (AUC [95% CI] = 0.83 [0.79,0.87] ). Additionally, the UKB‐DRS outperformed all other risk scores (the DRS had the next best AUC [95% CI]: UKB = 0.77 [0.74, 0.8] ; WHII = 0.77 [0.73, 0.81]). Conclusion This study contributes a novel dementia risk score that may be used to guide primary prevention strategies.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 2201940-6
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  • 2
    In: Human Brain Mapping, Wiley, Vol. 41, No. 18 ( 2020-12-15), p. 5141-5150
    Abstract: Sex hormones such as estrogen fluctuate across the female lifespan, with high levels during reproductive years and natural decline during the transition to menopause. Women's exposure to estrogen may influence their heightened risk of Alzheimer's disease (AD) relative to men, but little is known about how it affects normal brain aging. Recent findings from the UK Biobank demonstrate less apparent brain aging in women with a history of multiple childbirths, highlighting a potential link between sex‐hormone exposure and brain aging. We investigated endogenous and exogenous sex‐hormone exposure, genetic risk for AD, and neuroimaging‐derived biomarkers for brain aging in 16,854 middle to older‐aged women. The results showed that as opposed to parity, higher cumulative sex‐hormone exposure was associated with more evident brain aging, indicating that i) high levels of cumulative exposure to sex‐hormones may have adverse effects on the brain, and ii) beneficial effects of pregnancies on the female brain are not solely attributable to modulations in sex‐hormone exposure. In addition, for women using hormonal replacement therapy (HRT), starting treatment earlier was associated with less evident brain aging, but only in women with a genetic risk for AD. Genetic factors may thus contribute to how timing of HRT initiation influences women's brain aging trajectories.
    Type of Medium: Online Resource
    ISSN: 1065-9471 , 1097-0193
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 1492703-2
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  • 3
    In: Human Brain Mapping, Wiley, Vol. 41, No. 16 ( 2020-11), p. 4718-4729
    Abstract: Pregnancy involves maternal brain adaptations, but little is known about how parity influences women's brain aging trajectories later in life. In this study, we replicated previous findings showing less apparent brain aging in women with a history of childbirths, and identified regional brain aging patterns linked to parity in 19,787 middle‐ and older‐aged women. Using novel applications of brain‐age prediction methods, we found that a higher number of previous childbirths were linked to less apparent brain aging in striatal and limbic regions. The strongest effect was found in the accumbens—a key region in the mesolimbic reward system, which plays an important role in maternal behavior. While only prospective longitudinal studies would be conclusive, our findings indicate that subcortical brain modulations during pregnancy and postpartum may be traceable decades after childbirth.
    Type of Medium: Online Resource
    ISSN: 1065-9471 , 1097-0193
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2020
    detail.hit.zdb_id: 1492703-2
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  • 4
    In: Human Brain Mapping, Wiley, Vol. 43, No. 12 ( 2022-08-15), p. 3759-3774
    Abstract: Cardiometabolic risk (CMR) factors are associated with accelerated brain aging and increased risk for sex‐dimorphic illnesses such as Alzheimer's disease (AD). Yet, it is unknown how CMRs interact with sex and apolipoprotein E‐ ϵ 4 ( APOE4 ), a known genetic risk factor for AD, to influence brain age across different life stages. Using age prediction based on multi‐shell diffusion‐weighted imaging data in 21,308 UK Biobank participants, we investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist‐to‐hip ratio (WHR), body fat percentage (BF%), and APOE4 status varied (i) between males and females, (ii) according to age at menopause in females, and (iii) across different age groups in males and females. We report sex differences in associations between BAG and all three CMRs, with stronger positive associations among males compared to females. Independent of APOE4 status, higher BAG (older brain age relative to chronological age) was associated with greater BMI, WHR, and BF% in males, whereas in females, higher BAG was associated with greater WHR, but not BMI and BF%. These divergent associations were most prominent within the oldest group of females (66–81 years), where greater BF% was linked to lower BAG. Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. In conclusion, the findings point to sex‐ and age‐specific associations between CMRs and brain age. Incorporating sex as a factor of interest in studies addressing CMR may promote sex‐specific precision medicine, consequently improving health care for both males and females.
    Type of Medium: Online Resource
    ISSN: 1065-9471 , 1097-0193
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 1492703-2
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  • 5
    In: Human Brain Mapping, Wiley, Vol. 42, No. 10 ( 2021-07), p. 3141-3155
    Abstract: Deriving reliable information about the structural and functional architecture of the brain in vivo is critical for the clinical and basic neurosciences. In the new era of large population‐based datasets, when multiple brain imaging modalities and contrasts are combined in order to reveal latent brain structural patterns and associations with genetic, demographic and clinical information, automated and stringent quality control (QC) procedures are important. Diffusion magnetic resonance imaging (dMRI) is a fertile imaging technique for probing and visualising brain tissue microstructure in vivo, and has been included in most standard imaging protocols in large‐scale studies. Due to its sensitivity to subject motion and technical artefacts, automated QC procedures prior to scalar diffusion metrics estimation are required in order to minimise the influence of noise and artefacts. However, the QC procedures performed on raw diffusion data cannot guarantee an absence of distorted maps among the derived diffusion metrics. Thus, robust and efficient QC methods for diffusion scalar metrics are needed. Here, we introduce Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM), a computationally efficient QC method utilising structural similarity to evaluate diffusion map quality and mean diffusion metrics. As an example, we applied YTTRIUM in the context of tract‐based spatial statistics to assess associations between age and kurtosis imaging and white matter tract integrity maps in U.K. Biobank data ( n  = 18,608). To assess the influence of outliers on results obtained using machine learning (ML) approaches, we tested the effects of applying YTTRIUM on brain age prediction. We demonstrated that the proposed QC pipeline represents an efficient approach for identifying poor quality datasets and artefacts and increases the accuracy of ML based brain age prediction.
    Type of Medium: Online Resource
    ISSN: 1065-9471 , 1097-0193
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 1492703-2
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  • 6
    In: Human Brain Mapping, Wiley, Vol. 42, No. 6 ( 2021-04-15), p. 1626-1640
    Abstract: The concept of brain maintenance refers to the preservation of brain integrity in older age, while cognitive reserve refers to the capacity to maintain cognition in the presence of neurodegeneration or aging‐related brain changes. While both mechanisms are thought to contribute to individual differences in cognitive function among older adults, there is currently no “gold standard” for measuring these constructs. Using machine‐learning methods, we estimated brain and cognitive age based on deviations from normative aging patterns in the Whitehall II MRI substudy cohort ( N = 537, age range = 60.34–82.76), and tested the degree of correspondence between these constructs, as well as their associations with premorbid IQ, education, and lifestyle trajectories. In line with established literature highlighting IQ as a proxy for cognitive reserve, higher premorbid IQ was linked to lower cognitive age independent of brain age. No strong evidence was found for associations between brain or cognitive age and lifestyle trajectories from midlife to late life based on latent class growth analyses. However, post hoc analyses revealed a relationship between cumulative lifestyle measures and brain age independent of cognitive age. In conclusion, we present a novel approach to characterizing brain and cognitive maintenance in aging, which may be useful for future studies seeking to identify factors that contribute to brain preservation and cognitive reserve mechanisms in older age.
    Type of Medium: Online Resource
    ISSN: 1065-9471 , 1097-0193
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 1492703-2
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  • 7
    In: Human Brain Mapping, Wiley, Vol. 43, No. 10 ( 2022-07), p. 3113-3129
    Abstract: 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 in two population‐based datasets, and assessed the effects of age range, sample size and age‐bias correction on the model performance metrics Pearson's correlation coefficient ( r ), the coefficient of determination ( R 2 ), 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 R 2 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 generally indicate high accuracy, even for poorly performing models, inspection of uncorrected model results provides important information about underlying model attributes such as prediction variance.
    Type of Medium: Online Resource
    ISSN: 1065-9471 , 1097-0193
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 1492703-2
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  • 8
    In: Human Brain Mapping, Wiley, Vol. 42, No. 13 ( 2021-09), p. 4372-4386
    Abstract: Maternal brain adaptations occur in response to pregnancy, but little is known about how parity impacts white matter and white matter ageing trajectories later in life. Utilising global and regional brain age prediction based on multi‐shell diffusion‐weighted imaging data, we investigated the association between previous childbirths and white matter brain age in 8,895 women in the UK Biobank cohort (age range = 54–81 years). The results showed that number of previous childbirths was negatively associated with white matter brain age, potentially indicating a protective effect of parity on white matter later in life. Both global white matter and grey matter brain age estimates showed unique contributions to the association with previous childbirths, suggesting partly independent processes. Corpus callosum contributed uniquely to the global white matter 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 white matter characteristics later in life, longitudinal studies are required to establish causality and determine how parity may influence women's white matter trajectories across the lifespan.
    Type of Medium: Online Resource
    ISSN: 1065-9471 , 1097-0193
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 1492703-2
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  • 9
    In: Human Brain Mapping, Wiley, Vol. 42, No. 6 ( 2021-04-15), p. 1714-1726
    Abstract: The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the underlying biological processes. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub‐cortical volumes, cortical and subcortical T1/T2‐weighted ratios, and cerebral blood flow (CBF) based on arterial spin labeling. For each of the 11 models we assessed the age prediction accuracy in healthy controls (HC, n = 750) and compared the obtained brain age gaps (BAGs) between age‐matched subsets of HC and patients with Alzheimer's disease (AD, n = 54), mild (MCI, n = 90) and subjective (SCI, n = 56) cognitive impairment, schizophrenia spectrum (SZ, n = 159) and bipolar disorder (BD, n = 135). We found highest age prediction accuracy in HC when integrating all modalities. Furthermore, two‐group case–control classifications revealed highest accuracy for AD using global T1‐weighted BAG, while MCI, SCI, BD and SZ showed strongest effects in CBF‐based BAGs. Combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and HC were often larger for BAGs based on single modalities. These findings indicate that multidimensional neuroimaging of patients may provide a brain‐based mapping of overlapping and distinct pathophysiology in common disorders.
    Type of Medium: Online Resource
    ISSN: 1065-9471 , 1097-0193
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2021
    detail.hit.zdb_id: 1492703-2
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  • 10
    In: Human Brain Mapping, Wiley, Vol. 43, No. 2 ( 2022-02), p. 700-720
    Abstract: The structure and integrity of the ageing brain is interchangeably linked to physical health, and cardiometabolic risk factors (CMRs) are associated with dementia and other brain disorders. In this mixed cross‐sectional and longitudinal study (interval mean = 19.7 months), including 790 healthy individuals (mean age = 46.7 years, 53% women), we investigated CMRs and health indicators including anthropometric measures, lifestyle factors, and blood biomarkers in relation to brain structure using MRI‐based morphometry and diffusion tensor imaging (DTI). We performed tissue specific brain age prediction using machine learning and performed Bayesian multilevel modeling to assess changes in each CMR over time, their respective association with brain age gap (BAG), and their interaction effects with time and age on the tissue‐specific BAGs. The results showed credible associations between DTI‐based BAG and blood levels of phosphate and mean cell volume (MCV), and between T1‐based BAG and systolic blood pressure, smoking, pulse, and C‐reactive protein (CRP), indicating older‐appearing brains in people with higher cardiometabolic risk (smoking, higher blood pressure and pulse, low‐grade inflammation). Longitudinal evidence supported interactions between both BAGs and waist‐to‐hip ratio (WHR), and between DTI‐based BAG and systolic blood pressure and smoking, indicating accelerated ageing in people with higher cardiometabolic risk (smoking, higher blood pressure, and WHR). The results demonstrate that cardiometabolic risk factors are associated with brain ageing. While randomized controlled trials are needed to establish causality, our results indicate that public health initiatives and treatment strategies targeting modifiable cardiometabolic risk factors may also improve risk trajectories and delay brain ageing.
    Type of Medium: Online Resource
    ISSN: 1065-9471 , 1097-0193
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 1492703-2
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