scholarly journals Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort

2021 ◽  
Author(s):  
Sivaniya Subramaniapillai ◽  
Sana Suri ◽  
Claudia Barth ◽  
Ivan Maximov ◽  
Irene Voldsbekk ◽  
...  
Keyword(s):  
White Matter ◽  
Body Fat ◽  
Cardiometabolic Risk ◽  
Brain Aging ◽  
Chronological Age ◽  
Uk Biobank ◽  
Imaging Data ◽  
Increased Risk ◽  
Males And Females ◽  
Brain Age

Cardiometabolic risk factors (CMRs) 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-ε4 (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 com- pared to females. 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 (66-81yrs), where higher BF% was linked to lower BAG (younger brain age relative to chronological age). Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. APOE4 status was not significantly associated with BAG, and no significant interactions with CMRs were found. In conclusion, the findings point to sex- and age-specific associations between body fat composition and brain age. Incorporating sex as a factor of interest in studies addressing cardiometabolic risk may promote sex-specific precision medicine, consequently improving health care for both males and females.

scholarly journals Predicting Brain Age at Slice Level: Convolutional Neural Networks and Consequences for Interpretability

2021 ◽  
Vol 12 ◽  
Author(s):  
Pedro L. Ballester ◽  
Laura Tomaz da Silva ◽  
Matheus Marcon ◽  
Nathalia Bianchini Esper ◽  
Benicio N. Frey ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Brain Aging ◽  
Brain Slices ◽  
Psychiatric Research ◽  
Chronological Age ◽  
Whole Brain ◽  
Increased Risk ◽  
Model Predictions ◽  
Brain Age

Problem: Chronological aging in later life is associated with brain degeneration processes and increased risk for disease such as stroke and dementia. With a worldwide tendency of aging populations and increased longevity, mental health, and psychiatric research have paid increasing attention to understanding brain-related changes of aging. Recent findings suggest there is a brain age gap (a difference between chronological age and brain age predicted by brain imaging indices); the magnitude of the gap may indicate early onset of brain aging processes and disease. Artificial intelligence has allowed for a narrowing of the gap in chronological and predicted brain age. However, the factors that drive model predictions of brain age are still unknown, and there is not much about these factors that can be gleaned from the black-box nature of machine learning models. The goal of the present study was to test a brain age regression approach that is more amenable to interpretation by researchers and clinicians.Methods: Using convolutional neural networks we trained multiple regressor models to predict brain age based on single slices of magnetic resonance imaging, which included gray matter- or white matter-segmented inputs. We evaluated the trained models in all brain image slices to generate a final prediction of brain age. Unlike whole-brain approaches to classification, the slice-level predictions allows for the identification of which brain slices and associated regions have the largest difference between chronological and neuroimaging-derived brain age. We also evaluated how model predictions were influenced by slice index and plane, participant age and sex, and MRI data collection site.Results: The results show, first, that the specific slice used for prediction affects prediction error (i.e., difference between chronological age and neuroimaging-derived brain age); second, the MRI site-stratified separation of training and test sets removed site effects and also minimized sex effects; third, the choice of MRI slice plane influences the overall error of the model.Conclusion: Compared to whole brain-based predictive models of neuroimaging-derived brain age, slice-based approach improves the interpretability and therefore the reliability of the prediction of brain age using MRI data.

scholarly journals No evidence for accelerated brain aging in patients with chronic non-cancer pain

2019 ◽  
Author(s):  
Peter Sörös ◽  
Carsten Bantel
Keyword(s):  
Chronic Pain ◽  
White Matter ◽  
Cancer Pain ◽  
Brain Structure ◽  
Healthy Aging ◽  
Brain Aging ◽  
T Test ◽  
Chronological Age ◽  
The Individual ◽  
Brain Age

AbstractChronic pain is often associated with changes in brain structure and function, and also cognitive deficits. It has been noted that these chronic pain-related alterations may resemble changes found in healthy aging, and thus may represent accelerated or pre-mature aging of the brain. Here we test the hypothesis that patients with chronic non-cancer pain demonstrate accelerated brain aging compared to healthy control subjects. The predicted brain age of 59 patients with chronic pain (mean chronological age ± standard deviation: 53.0 ± 9.0 years; 43 women) and 60 pain-free healthy controls (52.6 ± 9.0 years; 44 women) was determined using the softwarebrainageR. This software segments the individual T1-weighted structural MR images into gray and white matter and compares gray and white matter images to a large (n = 2001) training set of structural images, using machine learning. Finally, brain age delta, which is the predicted brain age minus chronological age, was calculated and compared across groups. This study provided no evidence for the hypothesis that chronic pain is associated with accelerated brain aging (Welch’s t-test, p = 0.74, Cohen’s d = 0.061). A Bayesian independent samples t-test indicated moderate evidence in favor of the null hypothesis (BF01 = 4.875, i.e. group means were equal). Our results provide indirect support for recent models of pain related-changes of brain structure, brain function, and cognitive functions. These models postulate network-specific maladaptive plasticity, rather than wide-spread or global neural degeneration, leading to synaptic, dendritic, and neuronal remodeling.

scholarly journals Brain-age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth-cohort

2019 ◽  
Author(s):  
Maxwell L. Elliott ◽  
Daniel W. Belsky ◽  
Annchen R. Knodt ◽  
David Ireland ◽  
Tracy R. Melzer ◽  
...  
Keyword(s):  
Cognitive Decline ◽  
Birth Cohort ◽  
Brain Aging ◽  
Chronological Age ◽  
Biological Aging ◽  
Imaging Data ◽  
Cohort Differences ◽  
Age Related ◽  
Brain Age ◽  
The Brain

AbstractAn individual’s brain-age is the difference between chronological age and age predicted from machine-learning models of brain-imaging data. Brain-age has been proposed as a biomarker of age-related deterioration of the brain. Having an older brain-age has been linked to Alzheimer’s, dementia and mortality. However, these findings are largely based on cross-sectional associations which can confuse age differences with cohort differences. To illuminate the validity of brain-age a biomarker of accelerated brain aging, a study is needed of a large cohort all born the same year who nevertheless vary on brain-age. In a population-representative 1972-73 birth cohort we measured brain-age at age 45, as well as the pace of biological aging and cognitive decline in longitudinal data from childhood to midlife (N=869). In this cohort, all chronological age 45 years, brain-age was measured reliably (ICC=.81) and ranged from 24 to 72 years. Those with older midlife brain-ages tended to have poorer cognitive function in both adulthood and childhood, as well as impaired brain health at age 3. Furthermore, those with older brain-ages had an accelerated pace of biological aging, older facial appearance and early signs of cognitive decline from childhood to midlife. These findings help to validate brain-age as a potential surrogate biomarker for midlife intervention studies that seek to measure treatment response to dementia-prevention efforts in midlife. However, the findings also caution against the assumption that brain-age scores represent only age-related deterioration of the brain as they may also index central nervous system variation present since childhood.

scholarly journals Associations of cigarette smoking with gray and white matter in the UK Biobank

2019 ◽  
Author(s):  
Joshua Gray ◽  
Matthew Thompson ◽  
Chelsie Benca-Bachman ◽  
Max Michael Owens ◽  
Mikela Murphy ◽  
...  
Keyword(s):  
White Matter ◽  
Cigarette Smoking ◽  
Gray Matter ◽  
Brain Structure ◽  
Mean Diffusivity ◽  
Medial Lemniscus ◽  
Uk Biobank ◽  
Matter Volume ◽  
Increased Risk ◽  
The Uk

Chronic cigarette smoking is associated with increased risk for myriad health consequences including cognitive decline and dementia, but research on the link between smoking and brain structure is nascent. We assessed the relationship of cigarette smoking (ever smoked, cigarettes per day, and duration) with gray and white matter using the UK Biobank cohort (gray matter N = 19,615; white matter N = 17,760), adjusting for numerous demographic and health confounders. Ever smoked and duration were associated with smaller total gray matter volume. Ever smoked was associated with reduced volume of the right VIIIa cerebellum, as well as elevated white matter hyperintensity volumes. Smoking duration was associated with reduced total white matter volume. With regard to specific tracts, ever smoked was associated with reduced fractional anisotropy in the left cingulate gyrus part of the cingulum, left posterior thalamic radiation, and bilateral superior thalamic radiation and increased mean diffusivity in the middle cerebellar peduncle, right medial lemniscus, bilateral posterior thalamic radiation, and bilateral superior thalamic radiation. Overall, we found significant associations of cigarette exposure with global measures of gray and white matter. Furthermore, we found select associations of ever smoked, but not cigarettes per day or duration, with specific gray and white matter regions. These findings inform our understanding of the connections between smoking and variation in brain structure and clarify potential mechanisms of risk for common neurological sequelae.

scholarly journals Increased Brain Age Gap Estimate (BrainAGE) in Young Adults After Premature Birth

2021 ◽  
Vol 13 ◽  
Author(s):  
Dennis M. Hedderich ◽  
Aurore Menegaux ◽  
Benita Schmitz-Koep ◽  
Rachel Nuttall ◽  
Juliana Zimmermann ◽  
...  
Keyword(s):  
Premature Birth ◽  
Brain Aging ◽  
Structural Mri ◽  
Full Term ◽  
Intracranial Volume ◽  
Age Gap ◽  
Increased Risk ◽  
Aging Rates ◽  
Brain Age ◽  
Premature Born

Recent evidence suggests increased metabolic and physiologic aging rates in premature-born adults. While the lasting consequences of premature birth on human brain development are known, its impact on brain aging remains unclear. We addressed the question of whether premature birth impacts brain age gap estimates (BrainAGE) using an accurate and robust machine-learning framework based on structural MRI in a large cohort of young premature-born adults (n = 101) and full-term (FT) controls (n = 111). Study participants are part of a geographically defined population study of premature-born individuals, which have been followed longitudinally from birth until young adulthood. We investigated the association between BrainAGE scores and perinatal variables as well as with outcomes of physical (total intracranial volume, TIV) and cognitive development (full-scale IQ, FS-IQ). We found increased BrainAGE in premature-born adults [median (interquartile range) = 1.4 (−1.3–4.7 years)] compared to full-term controls (p = 0.002, Cohen’s d = 0.443), which was associated with low Gestational age (GA), low birth weight (BW), and increased neonatal treatment intensity but not with TIV or FS-IQ. In conclusion, results demonstrate elevated BrainAGE in premature-born adults, suggesting an increased risk for accelerated brain aging in human prematurity.

scholarly journals Predicting brain-age from multimodal imaging data captures cognitive impairment

2016 ◽  
Author(s):  
Franziskus Liem ◽  
Gaël Varoquaux ◽  
Jana Kynast ◽  
Frauke Beyer ◽  
Shahrzad Kharabian Masouleh ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Biological Information ◽  
Future Application ◽  
Chronological Age ◽  
Imaging Data ◽  
Adult Lifespan ◽  
Training Models ◽  
Brain Data ◽  
Brain Age ◽  
Age Prediction

The disparity between the chronological age of an individual and their brain-age measured based on biological information has the potential to offer clinically-relevant biomarkers of neurological syndromes that emerge late in the lifespan. While prior brain-age prediction studies have relied exclusively on either structural or functional brain data, here we investigate how multimodal brain-imaging data improves age prediction. Using cortical anatomy and whole-brain functional connectivity on a large adult lifespan sample (N = 2354, age 19-82), we found that multimodal data improves brain-based age prediction, resulting in a mean absolute prediction error of 4.29 years. Furthermore, we found that the discrepancy between predicted age and chronological age captures cognitive impairment. Importantly, the brain-age measure was robust to confounding effects: head motion did not drive brain-based age prediction and our models generalized reasonably to an independent dataset acquired at a different site (N = 475). Generalization performance was increased by training models on a larger and more heterogeneous dataset. The robustness of multimodal brain-age prediction to confounds, generalizability across sites, and sensitivity to clinically-relevant impairments, suggests promising future application to the early prediction of neurocognitive disorders.

scholarly journals Individual variations in ‘brain age’ relate to early-life factors more than to longitudinal brain change

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Didac Vidal-Pineiro ◽  
Yunpeng Wang ◽  
Stine K Krogsrud ◽  
Inge K Amlien ◽  
William FC Baaré ◽  
...  
Keyword(s):  
Early Life ◽  
Brain Aging ◽  
Average Rate ◽  
Aging Brain ◽  
Imaging Data ◽  
Cross Sectional ◽  
Polygenic Scores ◽  
Longitudinal Imaging ◽  
Brain Age ◽  
Brain Change

Brain age is a widely used index for quantifying individuals’ brain health as deviation from a normative brain aging trajectory. Higher-than-expected brain age is thought partially to reflect above-average rate of brain aging. Here, we explicitly tested this assumption in two independent large test datasets (UK Biobank [main] and Lifebrain [replication]; longitudinal observations ≈ 2750 and 4200) by assessing the relationship between cross-sectional and longitudinal estimates of brain age. Brain age models were estimated in two different training datasets (n ≈ 38,000 [main] and 1800 individuals [replication]) based on brain structural features. The results showed no association between cross-sectional brain age and the rate of brain change measured longitudinally. Rather, brain age in adulthood was associated with the congenital factors of birth weight and polygenic scores of brain age, assumed to reflect a constant, lifelong influence on brain structure from early life. The results call for nuanced interpretations of cross-sectional indices of the aging brain and question their validity as markers of ongoing within-person changes of the aging brain. Longitudinal imaging data should be preferred whenever the goal is to understand individual change trajectories of brain and cognition in aging.

scholarly journals Individual variations in “Brain age” relate to early life factors more than to longitudinal brain change

2021 ◽  
Author(s):  
D. Vidal-Piñeiro ◽  
Y. Wang ◽  
SK. Krogsrud ◽  
IK. Amlien ◽  
WFC. Baaré ◽  
...  
Keyword(s):  
Early Life ◽  
Brain Aging ◽  
Average Rate ◽  
Aging Brain ◽  
Individual Change ◽  
Imaging Data ◽  
Cross Sectional ◽  
Polygenic Scores ◽  
Longitudinal Imaging ◽  
Brain Age

AbstractBrain age is a widely used index for quantifying individuals’ brain health as deviation from a normative brain aging trajectory. Higher than expected brain age is thought partially to reflect above-average rate of brain aging. We explicitly tested this assumption in two large datasets and found no association between cross-sectional brain age and steeper brain decline measured longitudinally. Rather, brain age in adulthood was associated with early-life influences indexed by birth weight and polygenic scores. The results call for nuanced interpretations of cross-sectional indices of the aging brain and question their validity as markers of ongoing within-person changes of the aging brain. Longitudinal imaging data should be preferred whenever the goal is to understand individual change trajectories of brain and cognition in aging.

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