History of childbirths relates to region-specific brain aging patterns in middle and older-aged women

AbstractPregnancy 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 was 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.

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Women with a history of previous childbirths show less evident white matter brain ageing

10.1101/2020.11.20.391698 ◽

2020 ◽

Author(s):

Irene Voldsbekk ◽

Claudia Barth ◽

Ivan I. Maximov ◽

Tobias Kaufmann ◽

Dani Beck ◽

...

Keyword(s):

White Matter ◽

Nulliparous Women ◽

Brain Ageing ◽

Maternal Brain ◽

History Of ◽

The Uk ◽

Age Range ◽

Brain Age ◽

Age Estimates ◽

Age Prediction

AbstractMaternal 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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Population-based neuroimaging reveals traces of childbirth in the maternal brain

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1910666116 ◽

2019 ◽

Vol 116 (44) ◽

pp. 22341-22346 ◽

Cited By ~ 14

Author(s):

Ann-Marie G. de Lange ◽

Tobias Kaufmann ◽

Dennis van der Meer ◽

Luigi A. Maglanoc ◽

Dag Alnæs ◽

...

Keyword(s):

Brain Aging ◽

Population Based ◽

Uk Biobank ◽

Long Term Effects ◽

Pregnancy And Postpartum ◽

Maternal Brain ◽

The Uk ◽

Prospective Longitudinal ◽

The Relationship

Maternal brain adaptations have been found across pregnancy and postpartum, but little is known about the long-term effects of parity on the maternal brain. Using neuroimaging and machine learning, we investigated structural brain characteristics in 12,021 middle-aged women from the UK Biobank, demonstrating that parous women showed less evidence of brain aging compared to their nulliparous peers. The relationship between childbirths and a “younger-looking” brain could not be explained by common genetic variation or relevant confounders. Although prospective longitudinal studies are needed, the results suggest that parity may involve neural changes that could influence women’s brain aging later in life.

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Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders

10.1101/2020.06.29.20142810 ◽

2020 ◽

Author(s):

Jaroslav Rokicki ◽

Thomas Wolfers ◽

Wibeke Nordhøy ◽

Natalia Tesli ◽

Daniel S. Quintana ◽

...

Keyword(s):

Cognitive Impairment ◽

Neurological Disorders ◽

Prediction Accuracy ◽

Brain Aging ◽

Autism Spectrum ◽

Clinical Group ◽

Operating Characteristics ◽

Prediction Approach ◽

Brain Age ◽

Age Prediction

BackgroundThe deviation between chronological age and age predicted using brain MRI is a putative marker of brain health and disease-related deterioration. Age prediction based on structural MRI data shows high accuracy and sensitivity to common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the biological processes involved. Here, we implemented a multimodal age prediction approach and tested the predictive value across patients with a range of disorders with distinct etiologies and clinical features.MethodsWe 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) calculated from functional arterial spin labeling (ASL) data. For each of the 11 models we assessed the age prediction accuracy in HC n=761 and compared the resulting brain age gaps (BAGs) between each clinical group and age-matched subsets of HC in patients with Alzheimer’s disease (AD, n=54), mild cognitive impairment (MCI, n=88), subjective cognitive impairment (SCI, n=55), schizophrenia (SZ, n=156), bipolar disorder (BD, n=136), autism spectrum disorder (ASD, n=28).ResultsAmong the 11 models, we found highest age prediction accuracy in HC when integrating all modalities (mean absolute error=6.5 years). Beyond this global BAG, the area under the curve for the receiver-operating characteristics based on two-group case-control classifications showed strongest effects for AD and ASD in global T1-weighted BAG (T1w-BAG), while MCI, SCI, BD and SZ showed strongest effects in CBF-based BAGs.ConclusionsCombining 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 controls were often larger for BAGs based on single modalities. These findings demonstrate that multidimensional phenotyping provides a mapping of overlapping and distinct pathophysiology in common disorders of the brain, and specifically suggest metabolic and neurovascular aberrations in SZ and at-risk and early stage dementia.

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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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Longitudinal trajectories of brain age in young individuals at familial risk of mood disorder from the Scottish Bipolar Family Study

Wellcome Open Research ◽

10.12688/wellcomeopenres.15617.3 ◽

2020 ◽

Vol 4 ◽

pp. 206

Author(s):

Laura de Nooij ◽

Mathew A. Harris ◽

Emma L. Hawkins ◽

Toni-Kim Clarke ◽

Xueyi Shen ◽

...

Keyword(s):

Mood Disorder ◽

Brain Structure ◽

Family Study ◽

Brain Aging ◽

Familial Risk ◽

Age Difference ◽

Age Related ◽

Prospective Longitudinal ◽

Brain Age

Background: Within young individuals, mood disorder onset may be related to changes in trajectory of brain structure development. To date, however, longitudinal prospective studies remain scarce and show partly contradictory findings, with a lack of emphasis on changes at the level of global brain patterns. Cross-sectional adult studies have applied such methods and show that mood disorders are associated with accelerated brain aging. Currently, it remains unclear whether young individuals show differential brain structure aging trajectories associated with onset of mood disorder and/or presence of familial risk. Methods: Participants included young individuals (15-30 years, 53%F) from the prospective longitudinal Scottish Bipolar Family Study with and without close family history of mood disorder. All were well at time of recruitment. Implementing a structural MRI-based brain age prediction model, we globally assessed individual trajectories of age-related structural change using the difference between predicted brain age and chronological age (brain-predicted age difference (brain-PAD)) at baseline and at 2-year follow-up. Based on follow-up clinical assessment, individuals were categorised into three groups: (i) controls who remained well (C-well, n = 93), (ii) high familial risk who remained well (HR-well, n = 74) and (iii) high familial risk who developed a mood disorder (HR-MD, n = 35). Results: At baseline, brain-PAD was comparable between groups. Results showed statistically significant negative trajectories of brain-PAD between baseline and follow-up for HR-MD versus C-well (β = -0.60, pcorrected < 0.001) and HR-well (β = -0.36, pcorrected = 0.02), with a potential intermediate trajectory for HR-well (β = -0.24 years, pcorrected = 0.06). Conclusions: These preliminary findings suggest that within young individuals, onset of mood disorder and familial risk may be associated with a deceleration in brain structure aging trajectories. Extended longitudinal research will need to corroborate findings of emerging maturational lags in relation to mood disorder risk and onset.

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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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Accurate brain age prediction using recurrent slice-based networks

10.1101/2020.08.04.235069 ◽

2020 ◽

Author(s):

Pradeep K. Lam ◽

Vigneshwaran Santhalingam ◽

Parth Suresh ◽

Rahul Baboota ◽

Alyssa H. Zhu ◽

...

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Brain Aging ◽

Convergence Time ◽

Information Theoretic ◽

Current State ◽

Proposed Model ◽

Neuroimaging Data ◽

Brain Age ◽

Age Prediction

ABSTRACTBrainAge (a subject’s apparent age predicted from neuroimaging data) is an important biomarker of brain aging. The deviation of BrainAge from true age has been associated with psychiatric and neurological disease, and has proven effective in predicting conversion from mild cognitive impairment (MCI) to dementia. Conventionally, 3D convolutional neural networks and their variants are used for brain age prediction. However, these networks have a larger number of parameters and take longer to train than their 2D counterparts. Here we propose a 2D slice-based recurrent neural network model, which takes in an ordered sequence of sagittal slices as input to predict the brain age. The model consists of two components: a 2D convolutional neural network (CNN), which encodes the relevant features from the slices, and a recurrent neural network (RNN) that learns the relationship between slices. We compare our method to other recently proposed methods, including 3D deep convolutional regression networks, information theoretic models, and bag-of-features (BoF) models (such as BagNet) - where the classification is based on the occurrences of local features, without taking into consideration their global spatial ordering. In our experiments, our proposed model performs comparably to, or better than, the current state of the art models, with nearly half the number of parameters and a lower convergence time.

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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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1008 Brain Age Based on Sleep Encephalography is Elevated in HIV+ Adults on ART

SLEEP ◽

10.1093/sleep/zsaa056.1004 ◽

2020 ◽

Vol 43 (Supplement_1) ◽

pp. A383-A383

Author(s):

M J Leone ◽

H Sun ◽

C Boutros ◽

L Sullivan ◽

R J Thomas ◽

...

Keyword(s):

Brain Aging ◽

Sleep Eeg ◽

Delta Power ◽

Promising Tool ◽

Stage 2 Sleep ◽

History Of ◽

The Difference ◽

Inference Methods ◽

Brain Age ◽

Eeg Features

Abstract Introduction Sleep EEG is a promising tool to measure brain aging in vulnerable populations such as people with HIV, who are high risk of brain aging due to co-morbidities, increased inflammation, and antiretroviral neurotoxicity. Our lab previously developed a machine learning model that estimates age from sleep EEG (brain age, BA), which reliably predicts chronological age (CA) in healthy adults. The difference between BA and CA, the brain age index (BAI), independently predicts mortality, and is increased by cardiovascular co-morbidities. Here, we assessed BAI in HIV+ compared to matched HIV- adults. Methods Sleep EEGs from 43 treated HIV+ adults were gathered and matched to controls (HIV-, n=284) by age, gender, race, alcoholism, smoking and substance use history. We compared BAI between groups and used additional causal interference methods to ensure robustness. Individual EEG features that underlie BA prediction were also compared. We performed a sub-analysis of BAI between HIV+ with or without a history of AIDS. Results After matching, mean CA of HIV+ vs HIV- adults were 49 and 48 years, respectively (n.s.). The mean HIV+ BAI was 3.04 years higher than HIV- (4.4 vs 1.4 yr; p=0.048). We found consistent and significant results with alternative causal inference methods. Several EEG features predictive of BA were different in the HIV+ and HIV- cohorts. Most notably, non-REM stage 2 sleep (N2) delta power (1-4Hz) was decreased in HIV+ vs. HIV- adults, while theta (4-8Hz) and alpha (8-12Hz) power were increased. Those with AIDS (n=19, BAI=4.40) did not have significantly different BAI than HIV+ without AIDS (n=23, BAI=5.22). HIV+ subjects had higher rates of insomnia (56% vs 29%, p<0.001), obstructive apnea (47% vs 30%, p=0.03), depression (49% vs 23%, p<0.001), and bipolar disorder (19% vs 4%, p<0.001). Conclusion HIV+ individuals on ART have excess sleep-EEG based brain age compared to matched controls. This excess brain age is partially due to reduction in delta power during N2, suggesting decreased sleep depth. These results suggest sleep EEG could be a valuable brain aging biomarker for the HIV population. Support This research is supported by the Harvard Center for AIDS Research HU CFAR NIH/NIAID 5P30AI060354-16.

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Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study

10.1101/2020.01.28.923094 ◽

2020 ◽

Cited By ~ 5

Author(s):

Ann-Marie G. de Lange ◽

Melis Anatürk ◽

Tobias Kaufmann ◽

James H. Cole ◽

Ludovica Griffanti ◽

...

Keyword(s):

Cardiovascular Risk ◽

White Matter ◽

Functional Connectivity ◽

Prediction Accuracy ◽

Brain Aging ◽

Training Sample ◽

Resting State Functional Connectivity ◽

White Matter Microstructure ◽

Brain Age ◽

Age Prediction

AbstractBrain age is becoming a widely applied imaging-based biomarker of neural aging and potential proxy for brain integrity and health. We estimated multimodal and modality-specific brain age in the Whitehall II MRI cohort using machine learning and imaging-derived measures of gray matter morphology, diffusion-based white matter microstructure, and resting state functional connectivity. Ten-fold cross validation yielded multimodal and modality-specific brain age estimates for each participant, and additional predictions based on a separate training sample was included for comparison. The results showed equivalent age prediction accuracy between the multimodal model and the gray and white matter models (R2 of 0.34, 0.31, and 0.31, respectively), while the functional connectivity model showed a lower prediction accuracy (R2 of 0.01). Cardiovascular risk factors, including high blood pressure, alcohol intake, and stroke risk score, were each associated with more apparent brain aging, with consistent associations across modalities.

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