Biology subjects

de Lange, A.-M. G.

Publications and source records attributed to de Lange, A.-M. G..

9 recordsLinked to original sources

bioRxiv2024

Dimensions of early life adversity are differentially associated with patterns of delayed and accelerated brain maturation

BackgroundDifferent types of early-life adversity have been associated with childrens brain structure and function. However, understanding the disparate influence of distinct adversity exposures on the developing brain remains a major challenge. MethodsThis study investigates the neural correlates of 10 robust dimensions of early-life adversity identified through exploratory factor analysis in a large community sample of youth from the Adolescent Brain Cognitive Development (ABCD) Study. Brain age models were trained, validated, and tested separately on T1-weighted (T1; N = 9524), diffusion tensor (DTI; N = 8834), and resting-state functional (rs-fMRI; N = 8233) magnetic resonance imaging (MRI) data from two time points (mean age = 10.7 years, SD = 1.2, range = 8.9-13.8 years). ResultsBayesian multilevel modelling supported distinct associations between different types of early-life adversity exposures and younger- and older-looking brains. Dimensions generally related to emotional neglect, such as lack of primary and secondary caregiver support, and lack of caregiver supervision, were associated with lower brain age gaps (BAGs), i.e., younger-looking brains. In contrast, dimensions generally related to caregiver psychopathology, trauma exposure, family aggression, substance use and separation from biological parent, and socio-economic disadvantage and neighbourhood safety were associated with higher BAGs, i.e., older-looking brains. ConclusionsThe findings suggest that dimensions of early-life adversity are differentially associated with distinct neurodevelopmental patterns, indicative of dimension-specific delayed and accelerated brain maturation.

neuroscience↗

bioRxiv2023

Brain asymmetries from midlife to old adulthood and hemispheric brain age

The human brain demonstrates structural and functional asymmetries which have implications for ageing and mental and neurological disease development. We used a set of magnetic resonance imaging (MRI) metrics derived from structural and diffusion MRI data in N =48,040 UK Biobank participants to evaluate age-related differences in brain asymmetry. Most regional grey and white matter metrics presented asymmetry, which were higher later in life. Informed by these results, we conducted hemispheric brain age (HBA) predictions from left/right multimodal MRI metrics. HBA was concordant to conventional brain age predictions, using metrics from both hemispheres, but offers a supplemental general marker of brain asymmetry when setting left/right HBA into relationship with each other. In contrast to WM brain asymmetries, left/right discrepancies in HBA are lower at higher ages. Our findings outline various sex-specific differences, particularly important for brain age estimates, and the value of further investigating the role of brain asymmetries in brain ageing and disease development.

neuroscience↗

bioRxiv2021

Mind the gap: performance metric evaluation in brain-age prediction

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 based on neuroimaging data in two population-based datasets, and assessed the effects of age range, sample size, and age-bias correction on the model performance metrics r, R2, 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 R2 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 in general indicate high accuracy, even for poorly performing models, inspection of uncorrected model results provides important information about underlying model attributes such as prediction variance.

neuroscience↗

bioRxiv2020

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

Maternal brain adaptations occur in response to pregnancy, but little is known about how parity impacts white matter (WM) 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 number of previous childbirths was negatively associated with WM brain age, potentially indicating a protective effect of parity on brain WM later in life. Both global WM 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 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 establish causality and determine how parity may influence womens WM trajectories across the lifespan.

neuroscience↗

bioRxiv2020

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

Pregnancy involves maternal brain adaptations, but little is known about how parity influences womens 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.

neuroscience↗

bioRxiv2020

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

Brain 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.

neuroscience↗

bioRxiv2019

Cumulative estrogen exposure, APOE genotype, and women’s brain aging - a population-based neuroimaging study

Sex hormones such as estrogen fluctuate across the female lifespan, with high levels during reproductive years and natural decline during the transition to menopause. Womens exposure to estrogen may influence their heightened risk of Alzheimers 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 womens brain aging trajectories.

neuroscience↗

bioRxiv2019

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

Pregnancy and childbirth involve maternal brain adaptations that promote attachment to and protection of the newborn. Using brain imaging and machine learning, we provide evidence for a positive relationship between number of childbirths and a younger-looking brain in 12,021 women, which could not be explained by common genetic variation. The findings demonstrate that parity can be linked to brain health later in life.

neuroscience↗

bioRxiv2018

Longitudinal increase in sleep problems is related to amyloid deposition in cortical regions with high HOMER1 gene expression

Older adults who report more sleep problems tend to have elevated levels of the Alzheimers disease (AD) biomarker {beta}-amyloid (A{beta}), but the mechanisms responsible for this relationship are largely unknown. Molecular markers of sleep problems are now emerging from rodent research, yielding opportunities to generate hypotheses about the causes of the sleep-A{beta} relationship. A major molecular marker of sleep deprivation is Homer1a, a neural protein coded by the HOMER1 gene, involved in control of sleep homeostasis and also implied in A{beta} accumulation. Here, in a sample of 109 cognitively healthy middle-aged and older adults, we tested whether the relationship between cortical A{beta} accumulation and self-reported sleep quality, as well as changes in sleep quality over three years, was stronger in cortical regions with high HOMER1 mRNA expression levels. A{beta} correlated with poorer sleep quality cross-sectionally and longitudinally. This relationship was stronger in the younger (50-67 years) than the older (68-81 years) participants. Effects were mainly found in regions with high expression of HOMER1, suggesting a possible molecular pathway between sleep problems and A{beta} accumulation. The anatomical distribution of the sleep-A{beta} relationships followed closely the A{beta} accumulation pattern in 69 patients with mild cognitive impairment (MCI) or AD. Thus, the results indicate that the relationship between sleep problems and A{beta}-accumulation may involve Homer1 activity in the cortical regions that harbor A{beta} in AD. Analysis of cortical gene expression patterns represent a promising avenue to unveil molecular mechanisms behind the relationship between sleep problems and AD risk.

neuroscience↗