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

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.

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

International Psychogeriatrics ◽

10.1017/s1041610220003038 ◽

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.

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Brain Age in Early Stages of Bipolar Disorders or Schizophrenia

Schizophrenia Bulletin ◽

10.1093/schbul/sbx172 ◽

2017 ◽

Vol 45 (1) ◽

pp. 190-198 ◽

Cited By ~ 26

Author(s):

Tomas Hajek ◽

Katja Franke ◽

Marian Kolenic ◽

Jana Capkova ◽

Martin Matejka ◽

...

Keyword(s):

Bipolar Disorders ◽

Structural Mri ◽

Chronological Age ◽

First Episode ◽

Schizophrenia Spectrum Disorders ◽

Early Stages ◽

Age Gap ◽

Gap Estimate ◽

Brain Age ◽

The Brain

Abstract Background The greater presence of neurodevelopmental antecedants may differentiate schizophrenia from bipolar disorders (BD). Machine learning/pattern recognition allows us to estimate the biological age of the brain from structural magnetic resonance imaging scans (MRI). The discrepancy between brain and chronological age could contribute to early detection and differentiation of BD and schizophrenia. Methods We estimated brain age in 2 studies focusing on early stages of schizophrenia or BD. In the first study, we recruited 43 participants with first episode of schizophrenia-spectrum disorders (FES) and 43 controls. In the second study, we included 96 offspring of bipolar parents (48 unaffected, 48 affected) and 60 controls. We used relevance vector regression trained on an independent sample of 504 controls to estimate the brain age of study participants from structural MRI. We calculated the brain-age gap estimate (BrainAGE) score by subtracting the chronological age from the brain age. Results Participants with FES had higher BrainAGE scores than controls (F(1, 83) = 8.79, corrected P = .008, Cohen’s d = 0.64). Their brain age was on average 2.64 ± 4.15 years greater than their chronological age (matched t(42) = 4.36, P < .001). In contrast, participants at risk or in the early stages of BD showed comparable BrainAGE scores to controls (F(2,149) = 1.04, corrected P = .70, η2 = 0.01) and comparable brain and chronological age. Conclusions Early stages of schizophrenia, but not early stages of BD, were associated with advanced BrainAGE scores. Participants with FES showed neurostructural alterations, which made their brains appear 2.64 years older than their chronological age. BrainAGE scores could aid in early differential diagnosis between BD and schizophrenia.

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Brain age gap as a potential biomarker for schizophrenia: A multi-site structural MRI study

10.1109/embc46164.2021.9631085 ◽

2021 ◽

Author(s):

Weiqi Man ◽

Hao Ding ◽

Chao Chai ◽

Xingwei An ◽

Feng Liu ◽

...

Keyword(s):

Structural Mri ◽

Age Gap ◽

Potential Biomarker ◽

Mri Study ◽

Brain Age

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Plasma EFEMP1 Is Associated with Brain Aging and Dementia: The Framingham Heart Study

Journal of Alzheimer s Disease ◽

10.3233/jad-215053 ◽

2021 ◽

pp. 1-10

Author(s):

Emer R. McGrath ◽

Jayandra J. Himali ◽

Daniel Levy ◽

Qiong Yang ◽

Charles S. DeCarli ◽

...

Keyword(s):

Brain Injury ◽

Test Performance ◽

Matrix Protein ◽

Brain Aging ◽

Structural Mri ◽

Premature Aging ◽

Extracellular Matrix Protein ◽

Brain Volumes ◽

Total Brain ◽

Increased Risk

Background: Epidermal growth factor containing fibulin extracellular matrix protein-1 (EFEMP1) has been associated with increased white matter hyperintensities (WMH) burden and disorders of premature aging and may have a shared pathophysiological role in the development of WMH and dementia. Objective: To determine the association between plasma EFEMP1 levels and MRI markers of vascular brain injury and incident all-cause and Alzheimer’s disease (AD) dementia. Methods: We measured plasma EFEMP1 levels in 1597 [53% women, mean age 68.7 (SD 5.7) years] dementia-free Framingham Offspring cohort participants between 1998–2001 and subsequently followed them for incident dementia. Secondary outcomes included stroke, structural MRI brain measures and neurocognitive test performance. Results: During a median 11.8 [Q1, Q3 : 7.1, 13.3] year follow-up, 131 participants developed dementia. The highest quintile of plasma EFEMP1, compared to the bottom four quintiles, was associated with an increased risk of time to incident all-cause dementia (HR 1.77, 95% CI 1.18–2.64) and AD dementia (HR 1.76, 95% CI 1.11–2.81) but not with markers of vascular brain injury (WMH, covert brain infarcts or stroke). Higher circulating EFEMP1 concentrations were also cross-sectionally associated with lower total brain (β±SE, –0.28±0.11, p = 0.01) and hippocampal volumes (–0.006±0.003, p = 0.04) and impaired abstract reasoning (Similarities test, –0.18±0.08, p = 0.018 per standard deviation increment in EFEMP1). Conclusion: Elevated circulating EFEMP1 is associated with an increased risk of all-cause and AD dementia, smaller hippocampal and total brain volumes, and poorer cognitive performance. EFEMP1 may play an important biological role in the development of AD dementia. Further studies to validate these findings are warranted.

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

European Journal of Clinical Pharmacology ◽

10.1007/s00228-020-03068-8 ◽

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.

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Contributing Factors to Advanced Brain Aging in Depression and Anxiety Disorders

10.1101/2020.06.16.20132613 ◽

2020 ◽

Author(s):

Laura K.M. Han ◽

Hugo G. Schnack ◽

Rachel M. Brouwer ◽

Dick J. Veltman ◽

Nic J.A. van der Wee ◽

...

Keyword(s):

Brain Aging ◽

Antidepressant Medication ◽

Structural Mri ◽

Depression Symptoms ◽

Depression And Anxiety ◽

Contributing Factors ◽

Major Depressive ◽

Antidepressant Use ◽

Brain Age ◽

Age Prediction

ABSTRACTBrain aging has shown to be more advanced in patients with Major Depressive Disorder (MDD). This study examines which factors underlie this older brain age. Adults aged 18-57 years from the Netherlands Study of Depression and Anxiety underwent structural MRI. A pre-trained brain age prediction model based on >2,000 samples from the ENIGMA consortium was applied to predict age in 65 controls and 220 patients with current MDD and/or anxiety disorder. Brain-predicted age differences (brain-PAD) were calculated (predicted brain age minus chronological age) and associated with clinical, psychological, and biological factors. After correcting for antidepressant use, brain-PAD was significantly higher in MDD (+2.78 years) and anxiety patients (+2.91 years) compared to controls. Findings further indicate unique contributions of higher severity of somatic depression symptoms to advanced brain aging and a potential protective effect of antidepressant medication (-2.53 years).

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Deep learning-based brain age prediction in normal aging and dementia

10.21203/rs.3.rs-804454/v1 ◽

2021 ◽

Author(s):

Jeyeon Lee ◽

Brian Burkett ◽

Hoon-Ki Min ◽

Matthew Senjem ◽

Emily Lundt ◽

...

Keyword(s):

Deep Learning ◽

Cognitive Impairment ◽

Brain Aging ◽

Lewy Body Dementia ◽

Normal Aging ◽

Specific Pattern ◽

Normal Brain ◽

Age Gap ◽

Brain Age ◽

Age Prediction

Abstract Normal brain aging is accompanied by patterns of functional and structural change. Alzheimer's disease (AD), a representative neurodegenerative disease, has been linked to accelerated brain aging at respective age ranges. Here, we developed a deep learning-based brain age prediction model using fluorodeoxyglucose (FDG) PET and structural MRI and tested how the brain age gap relates to degenerative cognitive syndromes including mild cognitive impairment, AD, frontotemporal dementia, and Lewy body dementia. Occlusion analysis, performed to facilitate interpretation of the model, revealed that the model learns an age- and modality-specific pattern of brain aging. The elevated brain age gap in dementia cohorts was highly correlated with the cognitive impairment and AD biomarker. However, regions generating brain age gaps were different for each diagnosis group of which the AD continuum showed similar patterns to normal aging in the CU.

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Gray Matter Age Prediction as a Biomarker for Risk of Dementia

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1902376116 ◽

2019 ◽

Vol 116 (42) ◽

pp. 21213-21218 ◽

Cited By ~ 21

Author(s):

Johnny Wang ◽

Maria J. Knol ◽

Aleksei Tiulpin ◽

Florian Dubost ◽

Marleen de Bruijne ◽

...

Keyword(s):

Gray Matter ◽

Proportional Hazards ◽

Early Stage ◽

Hippocampal Volume ◽

Cox Proportional Hazards ◽

Intracranial Volume ◽

Age Gap ◽

Incident Dementia ◽

Brain Age ◽

Age Prediction

The gap between predicted brain age using magnetic resonance imaging (MRI) and chronological age may serve as a biomarker for early-stage neurodegeneration. However, owing to the lack of large longitudinal studies, it has been challenging to validate this link. We aimed to investigate the utility of such a gap as a risk biomarker for incident dementia using a deep learning approach for predicting brain age based on MRI-derived gray matter (GM). We built a convolutional neural network (CNN) model to predict brain age trained on 3,688 dementia-free participants of the Rotterdam Study (mean age 66 ± 11 y, 55% women). Logistic regressions and Cox proportional hazards were used to assess the association of the age gap with incident dementia, adjusted for age, sex, intracranial volume, GM volume, hippocampal volume, white matter hyperintensities, years of education, and APOE ε4 allele carriership. Additionally, we computed the attention maps, which shows which regions are important for age prediction. Logistic regression and Cox proportional hazard models showed that the age gap was significantly related to incident dementia (odds ratio [OR] = 1.11 and 95% confidence intervals [CI] = 1.05–1.16; hazard ratio [HR] = 1.11, and 95% CI = 1.06–1.15, respectively). Attention maps indicated that GM density around the amygdala and hippocampi primarily drove the age estimation. We showed that the gap between predicted and chronological brain age is a biomarker, complimentary to those that are known, associated with risk of dementia, and could possibly be used for early-stage dementia risk screening.

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

10.1101/859660 ◽

2019 ◽

Author(s):

Alba Xifra-Porxas ◽

Arna Ghosh ◽

Georgios D. Mitsis ◽

Marie-Hélène Boudrias

Keyword(s):

Dimensionality Reduction ◽

Brain Aging ◽

Principal Component ◽

Structural Mri ◽

Prediction Performance ◽

Subcortical Structures ◽

Mri Features ◽

Neuroimaging Data ◽

Brain Age ◽

Age Prediction

AbstractBrain age prediction studies aim at reliably estimating the difference between the chronological age of an individual and their predicted age based on neuroimaging data, which has been proposed as an informative measure of disease and cognitive decline. As most previous studies relied exclusively on magnetic resonance imaging (MRI) data, we hereby investigate whether combining structural MRI with functional magnetoencephalography (MEG) information improves age prediction using a large cohort of healthy subjects (N=613, age 18-88 yrs) from the Cam-CAN repository. To this end, we examined the performance of dimensionality reduction and multivariate associative techniques, namely Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA), to tackle the high dimensionality of neuroimaging data. Using MEG features (mean absolute error (MAE) of 9.60 yrs) yielded worse performance when compared to using MRI features (MAE of 5.33 yrs), but a stacking model combining both feature sets improved age prediction performance (MAE of 4.88 yrs). Furthermore, we found that PCA resulted in inferior performance, whereas CCA in conjunction with Gaussian process regression models yielded the best prediction performance. Notably, CCA allowed us to visualize the features that significantly contributed to brain age prediction. We found that MRI features from subcortical structures were more reliable age predictors than cortical features, and that spectral MEG measures were more reliable than connectivity metrics. Our results provide an insight into the underlying processes that are reflective of brain aging, yielding promise for the identification of reliable biomarkers of neurodegenerative diseases that emerge later during the lifespan.

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

10.31234/osf.io/7xcun ◽

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.

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