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 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 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 Lack of association between proton pump inhibitor use and brain aging: a cross-sectional study

2021 ◽  
Author(s):  
Nayeon Ahn ◽  
Stefan Frenzel ◽  
Katharina Wittfeld ◽  
Robin Bülow ◽  
Henry Völzke ◽  
...  
Keyword(s):  
Cognitive Function ◽  
Proton Pump ◽  
Brain Aging ◽  
Verbal Learning ◽  
Brain Magnetic Resonance Imaging ◽  
Primary Analysis ◽  
Cross Sectional ◽  
Brain Volumes ◽  
Increased Risk ◽  
Brain Age

Abstract Purpose Due to conflicting scientific evidence for an increased risk of dementia by intake of proton pump inhibitors (PPIs), this study investigates associations between PPI use and brain volumes, estimated brain age, and cognitive function in the general population. Methods Two surveys of the population-based Study of Health in Pomerania (SHIP) conducted in Northeast Germany were used. In total, 2653 participants underwent brain magnetic resonance imaging (MRI) and were included in the primary analysis. They were divided into two groups according to their PPI intake and compared with regard to their brain volumes (gray matter, white matter, total brain, and hippocampus) and estimated brain age. Multiple regression was used to adjust for confounding factors. Cognitive function was evaluated by the Verbal Learning and Memory Test (VLMT) and the Nuremberg Age Inventory (NAI) and put in relation to PPI use. Results No association was found between PPI use and brain volumes or the estimated brain age. The VLMT score was 1.11 lower (95% confidence interval: − 2.06 to − 0.16) in immediate recall, and 0.72 lower (95% CI: − 1.22 to − 0.22) in delayed recall in PPI users than in non-users. PPI use was unrelated to the NAI score. Conclusions The present study does not support a relationship between PPI use and brain aging.

scholarly journals 451 - Estimating “Brain Age Gaps” in patients with brain injury: Applying machine learning to advanced neuroimaging techniques

2020 ◽  
Vol 32 (S1) ◽  
pp. 171-171
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Brain Injury ◽  
Brain Aging ◽  
High Rate ◽  
Intracranial Volume ◽  
Increased Risk ◽  
Age Range ◽  
Brain Age ◽  
The Brain

Introduction:A single moderate or severe TBI is associated with accelerated brain aging and increased risk for dementia. Despite the high rate of falls that result in brain injury in older adults, numerous factors such as genetic predisposition to Alzheimer’s disease, sex, education, age are also known to affect multiple age-sensitive neuroimaging markers.METHODS:Here we use the “brain age” metric being tested by the global consortium, Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA), that employs machine learning to predict a person’s age from multiple age-sensitive imaging markers (e.g., hippocampal volume, regional cortical gray matter thickness, intracranial volume (ICV)), while also taking into account their sex and educational level. We will discuss results from brain injured patients ( n = 60; age range: 20-75 years) and healthy age-matched controls (n = 20 (20-75 years). We will compute the “brain age gap” – between a person’s actual chronological age and that predicted from their brain scan – and test relations between this measure and injury characteristics.RESULTS:In our pilot work, we predicted a person’s age from their MRI scan with a mean absolute error of about 5 years. ENIGMA’s current best model includes: (1) non-normalized brain volumetric measures as predictors including ICV, (2) separate models for males and females, (3) use of a large age range (12-80), and (4) Gaussian process regression (GPR).CONCLUSION:This “overall” marker of accelerated brain aging offers a metric that taps diverse sources of information, weighted by their relevance to brain aging, and is associated with decreased functionality in older adults.

scholarly journals Cross-Sectional and Longitudinal Brain Scans Reveal Accelerated Brain Aging in Multiple Sclerosis

2018 ◽  
Author(s):  
Einar A. Høgestøl ◽  
Tobias Kaufmann ◽  
Gro O. Nygaard ◽  
Mona K. Beyer ◽  
Piotr Sowa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Age Estimation ◽  
Brain Aging ◽  
Chronological Age ◽  
Aging Brain ◽  
Inflammatory Disorder ◽  
Cross Sectional ◽  
Chronological Aging ◽  
Brain Age

ABSTRACTMultiple sclerosis (MS) is an inflammatory disorder of the central nervous system. By combining longitudinal MRI-based brain morphometry and brain age estimation using machine learning, we tested the hypothesis that MS patients have higher brain age relative to chronological age than healthy controls (HC) and that longitudinal rate of brain aging in MS patients is associated with clinical course.Seventy-six MS patients, 71 % females and mean age 34.8 years (range 21-49) at inclusion, were examined with brain MRI at three time points with a mean total follow up period of 4.4 years. A machine learning model was applied on an independent training set of 3208 HC, estimating individual brain age and calculating the difference between estimated brain age and chronological age, termed brain age gap (BAG). We also assessed the longitudinal change rate in BAG in MS individuals. We used additional cross-sectional MRI data from 235 HC for case-control comparison.MS patients showed increased BAG (4.4 ±6.6 years) compared to HC (Cohen’s D = 0.69, p = 4.0 × 10−6). Longitudinal estimates of BAG in MS patients suggested an accelerated rate of brain aging corresponding to an annual increase of 0.41 (±1.23) years compared to chronological aging for the MS patients (p = 0.008).On average, patients with MS have significantly higher BAG compared to HC and accelerated rate of brain aging compared to chronological aging. Brain age estimation represents a promising method for evaluation of brain changes in MS, with potential for predicting future outcome and guide treatment.

scholarly journals Accelerated brain aging predicts impaired cognitive performance and greater disability in geriatric but not midlife adult depression

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Seth Christman ◽  
Camilo Bermudez ◽  
Lingyan Hao ◽  
Bennett A. Landman ◽  
Brian Boyd ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Performance ◽  
Brain Aging ◽  
Memory Performance ◽  
Accelerated Aging ◽  
Structural Mri ◽  
Chronological Age ◽  
Adult Lifespan ◽  
Depressed Group ◽  
Brain Age

Abstract Depression is associated with markers of accelerated aging, but it is unclear how this relationship changes across the lifespan. We examined whether a brain-based measure of accelerated aging differed between depressed and never-depressed subjects across the adult lifespan and whether it was related to cognitive performance and disability. We applied a machine-learning approach that estimated brain age from structural MRI data in two depressed cohorts, respectively 170 midlife adults and 154 older adults enrolled in studies with common entry criteria. Both cohorts completed broad cognitive batteries and the older subgroup completed a disability assessment. The machine-learning model estimated brain age from MRI data, which was compared to chronological age to determine the brain–age gap (BAG; estimated age-chronological age). BAG did not differ between midlife depressed and nondepressed adults. Older depressed adults exhibited significantly higher BAG than nondepressed elders (Wald χ2 = 8.84, p = 0.0029), indicating a higher estimated brain age than chronological age. BAG was not associated with midlife cognitive performance. In the older cohort, higher BAG was associated with poorer episodic memory performance (Wald χ2 = 4.10, p = 0.0430) and, in the older depressed group alone, slower processing speed (Wald χ2 = 4.43, p = 0.0354). We also observed a statistical interaction where greater depressive symptom severity in context of higher BAG was associated with poorer executive function (Wald χ2 = 5.89, p = 0.0152) and working memory performance (Wald χ2 = 4.47, p = 0.0346). Increased BAG was associated with greater disability (Wald χ2 = 6.00, p = 0.0143). Unlike midlife depression, geriatric depression exhibits accelerated brain aging, which in turn is associated with cognitive and functional deficits.

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