A history of previous childbirths is linked to women's white matter brain age in midlife and older age

Parental status and markers of brain and cellular age: A 3D convolutional network and classification study

Recent research shows prominent effects of pregnancy and the parenthood transition on structural brain characteristics in humans. Here, we present a comprehensive study of how parental status and number of children born/fathered links to markers of brain and cellular ageing in 36,323 UK Biobank participants (age range 44.57-82.06 years; 52% female). To assess global effects of parenting on the brain, we trained a 3D convolutional neural network on T1-weighted magnetic resonance images, and estimated brain age in a held-out test set. To investigate regional specificity, we extracted cortical and subcortical volumes using FreeSurfer, and ran hierarchical clustering to group regional volumes based on covariance. Leukocyte telomere length (LTL) derived from DNA was used as a marker of cellular ageing. We employed linear regression models to assess relationships between number of children, brain age, regional brain volumes, and LTL, and included interaction terms to probe sex differences in associations. Lastly, we used the brain measures and LTL as features in binary classification models, to determine if markers of brain and cellular ageing could predict parental status. The results showed associations between a greater number of children born/fathered and younger brain age in both females and males, with stronger effects observed in females. Volume-based analyses showed maternal effects in striatal and limbic regions, which were not evident in fathers. We found no evidence for associations between number of children and LTL. Classification of parental status showed an Area under the ROC Curve (AUC) of 0.57 for the brain age model, while the models using regional brain volumes and LTL as predictors showed AUCs of 0.52. Our findings align with previous population-based studies of middle- and older-aged parents, revealing subtle but significant associations between parental experience and neuroimaging-based surrogate markers of brain health. The findings further corroborate results from longitudinal cohort studies following parents across pregnancy and postpartum, potentially indicating that the parenthood transition is associated with long-term influences on brain health.

Linking menopause-related factors, history of depression, APOE ε4, and proxies of biological aging in the UK biobank cohort

In a subset of females, postmenopausal status has been linked to accelerated aging and neurological decline. A complex interplay between reproductive-related factors, mental disorders, and genetics may influence brain function and accelerate the rate of aging in the postmenopausal phase. Using multiple regressions corrected for age, in this preregistered study we investigated the associations between menopause-related factors (i.e., menopausal status, menopause type, age at menopause, and reproductive span) and proxies of cellular aging (leukocyte telomere length, LTL) and brain aging (white and gray matter brain age gap, BAG) in 13,780 females from the UK Biobank (age range 39-82). We then determined how these proxies of aging were associated with each other, and evaluated the effects of menopause-related factors, history of depression (= lifetime broad depression), and APOE ε4 genotype on BAG and LTL, examining both additive and interactive relationships. We found that postmenopausal status and older age at natural menopause were linked to longer LTL and lower BAG. Surgical menopause and longer natural reproductive span were also associated with longer LTL. BAG and LTL were not significantly associated with each other. The greatest variance in each proxy of biological aging was most consistently explained by models with the addition of both lifetime broad depression and APOE ε4 genotype. Overall, this study demonstrates a complex interplay between menopause-related factors, lifetime broad depression, APOE ε4 genotype, and proxies of biological aging. However, results are potentially influenced by a disproportionate number of healthier participants among postmenopausal females. Future longitudinal studies incorporating heterogeneous samples are an essential step towards advancing female health.

Longitudinal associations of reproductive factors and exogeneous estrogens with neuroimaging biomarkers of Alzheimer's disease and cerebrovascular disease

INTRODUCTION

Female-specific reproductive factors and exogeneous estrogen use are associated with cognition in later life. However, the underlying mechanisms are not understood. The present study aimed to investigate the effect of reproductive factors on neuroimaging biomarkers of Alzheimer's disease (AD) and cerebrovascular pathologies.

METHODS

We evaluated 389 females (median age of 71.7 years) enrolled in the Mayo Clinic Study of Aging with reproductive history data and longitudinal magnetic resonance imaging (MRI) scans. We used linear mixed effect models to examine the associations between reproductive factors and changes in neuroimaging measures.

RESULTS

Ever hormonal contraception (HC) use was longitudinally associated with higher fractional anisotropy across the corpus callosum, lower white matter hyperintensity (WMH) volume, and greater cortical thickness in an AD meta-region of interest (ROI). The initiation of menopausal hormone therapy (MHT) > 5 years post menopause was associated with higher WMH volume.

DISCUSSION

HC use and initiation of MHT >5 years post menopause were generally associated with neuroimaging biomarkers of cerebrovascular pathologies.

HIGHLIGHTS

Hormonal contraception use was associated with better brain white matter (WM) integrity. Initiation of menopausal hormone therapy >5 years post menopause was associated with worsening brain WM integrity. Hormonal contraception use was associated with greater cortical thickness. Ages at menarche and menopause and number of pregnancies were not associated with imaging measures. There were few associations between reproductive factors or exogenous estrogens and amyloid or tau PET.

White matter microstructure organization across the transition to fatherhood

The transition to parenthood remains an understudied window of potential neuroplasticity in the adult brain. White matter microstructural (WMM) organization, which reflects structural connectivity in the brain, has shown plasticity across the lifespan. No studies have examined how WMM organization changes from the prenatal to postpartum period in men becoming fathers. This study investigates WMM organization in men transitioning to first-time fatherhood. We performed diffusion-weighted imaging to identify differences in WMM organization, as indexed by fractional anisotropy (FA). We also investigated whether FA changes were associated with fathers' postpartum mental health. Associations between mental health and WMM organization have not been rarely examined in parents, who may be vulnerable to mental health problems. Fathers exhibited reduced FA at the whole-brain level, especially in the cingulum, a tract associated with emotional regulation. Fathers also displayed reduced FA in the corpus callosum, especially in the forceps minor, which is implicated in cognitive functioning. Postpartum depressive symptoms were linked with increases and decreases in FA, but FA was not correlated with perceived or parenting stress. Findings provide novel insight into fathers' WMM organization during the transition to parenthood and suggest postpartum depression may be linked with fathers' neuroplasticity during the transition to parenthood.

Protective role of parenthood on age-related brain function in mid- to late-life

The experience of parenthood can profoundly alter one's body, mind, and environment, yet we know little about the long-term associations between parenthood and brain function and aging in adulthood. Here, we investigate the link between number of children parented (parity) and age on brain function in 19,964 females and 17,607 males from the UK Biobank. In both females and males, increased parity was positively associated with functional connectivity, particularly within the somato/motor network. Critically, the spatial topography of parity-linked effects was inversely correlated with the impact of age on functional connectivity across the brain for both females and males, suggesting that a higher number of children is associated with patterns of brain function in the opposite direction to age-related alterations. These results indicate that the changes accompanying parenthood may confer benefits to brain health across the lifespan, highlighting the importance of future work to understand the associated mechanisms.

Dissecting unique and common variance across body and brain health indicators using age prediction

Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.

Menopausal hormone therapy and the female brain: leveraging neuroimaging and prescription registry data from the UK Biobank cohort

Background and Objectives

Menopausal hormone therapy (MHT) is generally thought to be neuroprotective, yet results have been inconsistent. Here, we present a comprehensive study of MHT use and brain characteristics in middle- to older aged females from the UK Biobank, assessing detailed MHT data, APOE ε4 genotype, and tissue-specific gray (GM) and white matter (WM) brain age gap (BAG), as well as hippocampal and white matter hyperintensity (WMH) volumes.

Methods

A total of 19,846 females with magnetic resonance imaging data were included (current-users = 1,153, 60.1 ± 6.8 years; past-users = 6,681, 67.5 ± 6.2 years; never-users = 12,012, mean age 61.6 ± 7.1 years). For a sub-sample (n = 538), MHT prescription data was extracted from primary care records. Brain measures were derived from T1-, T2- and diffusion-weighted images. We fitted regression models to test for associations between the brain measures and MHT variables including user status, age at initiation, dosage and duration, formulation, route of administration, and type (i.e., bioidentical vs synthetic), as well as active ingredient (e.g., estradiol hemihydrate). We further tested for differences in brain measures among MHT users with and without a history of hysterectomy ± bilateral oophorectomy and examined associations by APOE ε4 status.

Results

We found significantly higher GM and WM BAG (i.e., older brain age relative to chronological age) as well as smaller left and right hippocampus volumes in current MHT users, not past users, compared to never-users. Effects were modest, with the largest effect size indicating a group difference of 0.77 years (~9 months) for GM BAG. Among MHT users, we found no significant associations between age at MHT initiation and brain measures. Longer duration of use and older age at last use post menopause was associated with higher GM and WM BAG, larger WMH volume, and smaller left and right hippocampal volumes. MHT users with a history of hysterectomy ± bilateral oophorectomy showed lower GM BAG relative to MHT users without such history. Although we found smaller hippocampus volumes in carriers of two APOE ε4 alleles compared to non-carriers, we found no interactions with MHT variables. In the sub-sample with prescription data, we found no significant associations between detailed MHT variables and brain measures after adjusting for multiple comparisons.

Discussion

Our results indicate that population-level associations between MHT use, and female brain health might vary depending on duration of use and past surgical history. Future research is crucial to establish causality, dissect interactions between menopause-related neurological changes and MHT use, and determine individual-level implications to advance precision medicine in female health care.

Prediction of brain age in individuals with and at risk for alcohol use disorder using brain morphological features

Brain age measures predicted from structural and functional brain features are increasingly being used to understand brain integrity, disorders, and health. While there is a vast literature showing aberrations in both structural and functional brain measures in individuals with and at risk for alcohol use disorder (AUD), few studies have investigated brain age in these groups. The current study examines brain age measures predicted using brain morphological features, such as cortical thickness and brain volume, in individuals with a lifetime diagnosis of AUD as well as in those at higher risk to develop AUD from families with multiple members affected with AUD (i.e., higher family history density (FHD) scores). The AUD dataset included a group of 30 adult males (mean age = 41.25 years) with a lifetime diagnosis of AUD and currently abstinent and a group of 30 male controls (mean age = 27.24 years) without any history of AUD. A second dataset of young adults who were categorized based on their FHD scores comprised a group of 40 individuals (20 males) with high FHD of AUD (mean age = 25.33 years) and a group of 31 individuals (18 males) with low FHD (mean age = 25.47 years). Brain age was predicted using 187 brain morphological features of cortical thickness and brain volume in an XGBoost regression model; a bias-correction procedure was applied to the predicted brain age. Results showed that both AUD and high FHD individuals showed an increase of 1.70 and 0.09 years (1.08 months), respectively, in their brain age relative to their chronological age, suggesting accelerated brain aging in AUD and risk for AUD. Increased brain age was associated with poor performance on neurocognitive tests of executive functioning in both AUD and high FHD individuals, indicating that brain age can also serve as a proxy for cognitive functioning and brain health. These findings on brain aging in these groups may have important implications for the prevention and treatment of AUD and ensuing cognitive decline.

Sex biology in amyotrophic lateral sclerosis

Although sex differences in amyotrophic lateral sclerosis (ALS) have not been studied systematically, numerous clinical and preclinical studies have shown sex to be influential in disease prognosis. Moreover, with the development of advanced imaging tools, the difference between male and female brain in structure and function and their response to neurodegeneration are more definitive. As discussed in this review, ALS patients exhibit a sex bias pertaining to the features of the disease, and their clinical, pathological, (and pathophysiological) phenotypes. Several epidemiological studies have indicated that this sex disparity stems from various aetiologies, including sex-specific brain structure and neural functioning, genetic predisposition, age, gonadal hormones, susceptibility to traumatic brain injury (TBI)/head trauma and lifestyle factors.

Brain asymmetries from mid- to late life 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.

Cardiometabolic health across menopausal years is linked to white matter hyperintensities up to a decade later

Introduction

The menopause transition is associated with several cardiometabolic risk factors. Poor cardiometabolic health is further linked to microvascular brain lesions, which can be detected as white matter hyperintensities (WMHs) using T2-FLAIR magnetic resonance imaging (MRI) scans. Females show higher risk for WMHs post-menopause, but it remains unclear whether changes in cardiometabolic risk factors underlie menopause-related increase in brain pathology.

Methods

In this study, we assessed whether cross-sectional measures of cardiometabolic health, including body mass index (BMI) and waist-to-hip ratio (WHR), blood lipids, blood pressure, and long-term blood glucose (HbA1c), as well as longitudinal changes in BMI and WHR, differed according to menopausal status at baseline in 9,882 UK Biobank females (age range 40-70 years, n premenopausal = 3,529, n postmenopausal = 6,353). Furthermore, we examined whether these cardiometabolic factors were associated with WMH outcomes at the follow-up assessment, on average 8.78 years after baseline.

Results

Postmenopausal females showed higher levels of baseline blood lipids (HDL β  = 0.14, p < 0.001, LDL β  = 0.20, p < 0.001, triglycerides β  = 0.12, p < 0.001) and HbA1c (β  = 0.24, p < 0.001) compared to premenopausal women, beyond the effects of age. Over time, BMI increased more in the premenopausal compared to the postmenopausal group (β  = -0.08, p < 0.001), while WHR increased to a similar extent in both groups (β  = -0.03, p = 0.102). The change in WHR was however driven by increased waist circumference only in the premenopausal group. While the group level changes in BMI and WHR were in general small, these findings point to distinct anthropometric changes in pre- and postmenopausal females over time. Higher baseline measures of BMI, WHR, triglycerides, blood pressure, and HbA1c, as well as longitudinal increases in BMI and WHR, were associated with larger WMH volumes (β range = 0.03-0.13, p ≤ 0.002). HDL showed a significant inverse relationship with WMH volume (β  = -0.27, p < 0.001).

Discussion

Our findings emphasise the importance of monitoring cardiometabolic risk factors in females from midlife through the menopause transition and into the postmenopausal phase, to ensure improved cerebrovascular outcomes in later years.

Sex steroids and the female brain across the lifespan: insights into risk of depression and Alzheimer's disease

Despite widespread sex differences in prevalence and presentation of numerous illnesses affecting the human brain, there has been little focus on the effect of endocrine ageing. Most preclinical studies have focused on males only, and clinical studies often analyse data by covarying for sex, ignoring relevant differences between the sexes. This sex- (and gender)-neutral approach is biased and contributes to the absence of targeted treatments and services for all sexes (and genders). Female health has been historically understudied, with grave consequences for their wellbeing and health equity. In this Review, we spotlight female brain health across the lifespan by informing on the role of sex steroids, particularly oestradiol, on the female brain and on risk for diseases more prevalent in females, such as depression and Alzheimer's disease.

Association of Cumulative Lifetime Exposure to Female Hormones With Cerebral Small Vessel Disease in Postmenopausal Women in the UK Biobank

BACKGROUND AND OBJECTIVES

Rates of cerebrovascular disease increase after menopause, which is often attributed to the absence of hormones. It remains unknown whether the cumulative exposure to hormones across a female person's premenopausal life extends the window of cerebrovascular protection to the postmenopausal period. To investigate this, we examined the relationship between lifetime hormone exposure (LHE) and cerebral small vessel disease in more than 9,000 postmenopausal women in the UK-Biobank.

METHODS

The cohort consisted of women (aged 40-69 years) who attended one of 22 research centers across the United Kingdom between 2006 and 2010. Women were excluded if they were premenopausal when scanned, had missing reproductive history data, self-reported neurologic disorders, brain cancer, cerebral vascular incidents, head or neurologic injury, and nervous system infection. Endogenous LHE (LHEEndo) was estimated by summing the number of years pregnant (LHEParity) with the duration of the reproductive period (LHECycle = age menopause - age menarche). Exogenous LHE (LHEExo) was estimated by summing the number of years on oral contraceptives and hormone replacement therapy. Cerebral small vessel disease was determined by estimating white matter hyperintensity volume (WMHV) from T2-fluid-attenuated inversion recovery brain MRI (acquired between 2014 and 2021), normalized to intracranial volume and log-transformed. Multiple linear regressions were used to assess the relationship between LHEEndo on WMHV adjusted for age, cardiovascular risk factors, sociodemographics, and LHEExo.

RESULTS

A total of 9,163 postmenopausal women (age 64.21 ± 6.81 years) were retained for analysis. Average LHEEndo was 39.77 ± 3.59 years. Women with higher LHEEndo showed smaller WMHV (adj-R 2 = 0.307, LHEEndo β = -0.007 [-0.012 to -0.002], p < 0.01). LHEParity and LHECycle were independent contributors to WMHV (adj-R 2 = 0.308, p << 0.001; LHEParity β = -0.022 [-0.042 to -0.002], p < 0.05; LHECycle β = -0.006 [-0.011 to -0.001], p < 0.05). LHEExo was not significantly related to WMHV (LHEExo β = 0.001 [-0.001 to 0.002], p > 0.05).

DISCUSSION

Women with more prolonged exposure to endogenous hormones show relatively smaller burden of cerebral small vessel disease independent of the history of oral contraceptive use or hormone replacement therapy. Our results highlight the critical role endogenous hormones play in female brain health and provide real-world evidence of the protective effects premenopausal endogenous hormone exposure plays on postmenopausal cerebrovascular health.

Interrelated effects of age and parenthood on whole-brain controllability: protective effects of parenthood in mothers

Background

Controllability is a measure of the brain's ability to orchestrate neural activity which can be quantified in terms of properties of the brain's network connectivity. Evidence from the literature suggests that aging can exert a general effect on whole-brain controllability. Mounting evidence, on the other hand, suggests that parenthood and motherhood in particular lead to long-lasting changes in brain architecture that effectively slow down brain aging. We hypothesize that parenthood might preserve brain controllability properties from aging.

Methods

In a sample of 814 healthy individuals (aged 33.9 ± 12.7 years, 522 females), we estimate whole-brain controllability and compare the aging effects in subjects with vs. those without children. We use diffusion tensor imaging (DTI) to estimate the brain structural connectome. The level of brain control is then calculated from the connectomic properties of the brain structure. Specifically, we measure the network control over many low-energy state transitions (average controllability) and the network control over difficult-to-reach states (modal controllability).

Results and conclusion

In nulliparous females, whole-brain average controllability increases, and modal controllability decreases with age, a trend that we do not observe in parous females. Statistical comparison of the controllability metrics shows that modal controllability is higher and average controllability is lower in parous females compared to nulliparous females. In men, we observed the same trend, but the difference between nulliparous and parous males do not reach statistical significance. Our results provide strong evidence that parenthood contradicts aging effects on brain controllability and the effect is stronger in mothers.

Conceiving complexity: Biological mechanisms underpinning the lasting effect of pregnancy on multiple sclerosis outcomes

Multiple sclerosis (MS) is an autoimmune, demyelinating disease with the highest incidence in women of childbearing age. The effect of pregnancy on disease activity and progression is a primary concern for women with MS and their clinical teams. It is well established that inflammatory disease activity is naturally suppressed during pregnancy, followed by an increase postpartum. However, the long-term effect of pregnancy on disease progression is less understood. Having had a pregnancy before MS onset has been associated with an older age at first demyelinating event, an average delay of 3.4 years. After MS onset, there is conflicting evidence about the impact of pregnancy on long-term outcomes. The study with the longest follow-up to date showed that pregnancy was associated with a 0.36-point lower disability score after 10-years of disease in 1830 women. Understanding the biological mechanism by which pregnancy induces long-term beneficial effects on MS outcomes could provide mechanistic insights into the elusive determinants of secondary progression. Here, we review potential biological processes underlying this effect, including evidence that acute sex hormone exposure induces lasting changes to neurobiological and DNA methylation patterns, and how sustained methylation changes in immune cells can alter immune composition and function long-term.

Evaluating test-retest reliability and sex-/age-related effects on temporal clustering coefficient of dynamic functional brain networks

The multilayer dynamic network model has been proposed as an effective method to understand the brain function. In particular, derived from the definition of clustering coefficient in static networks, the temporal clustering coefficient provides a direct measure of the topological stability of dynamic brain networks and shows potential in predicting altered brain functions. However, test-retest reliability and demographic-related effects on this measure remain to be evaluated. Using a data set from the Human Connectome Project (157 male and 180 female healthy adults; 22-37 years old), the present study investigated: (1) the test-retest reliability of temporal clustering coefficient across four repeated resting-state functional magnetic resonance imaging scans as measured by intraclass correlation coefficient (ICC); and (2) sex- and age-related effects on temporal clustering coefficient. The results showed that (1) the temporal clustering coefficient had overall moderate test-retest reliability (ICC > 0.40 over a wide range of densities) at both global and subnetwork levels, (2) female subjects showed significantly higher temporal clustering coefficient than males at both global and subnetwork levels, particularly within the default-mode and subcortical regions, and (3) temporal clustering coefficient of the subcortical subnetwork was positively correlated with age in young adults. The results of sex effects were robustly replicated in an independent REST-meta-MDD data set, while the results of age effects were not. Our findings suggest that the temporal clustering coefficient is a relatively reliable and reproducible approach for identifying individual differences in brain function, and provide evidence for demographically related effects on the human brain dynamic connectomes.

Matrescence: lifetime impact of motherhood on cognition and the brain

Profound environmental, hormonal, and neurobiological changes mark the transition to motherhood as a major biosocial life event. Despite the ubiquity of motherhood, the enduring impact of caregiving on cognition and the brain across the lifespan is not well characterized and represents a unique window of opportunity to investigate human neural and cognitive development. By integrating insights from the human and animal maternal brain literatures with theories of cognitive ageing, we outline a framework for understanding maternal neural and cognitive changes across the lifespan. We suggest that the increased cognitive load of motherhood provides an initial challenge during the peripartum period, requiring continuous adaptation; yet when these demands are sustained across the lifespan, they result in increased late-life cognitive reserve.

Mapping the effects of pregnancy on resting state brain activity, white matter microstructure, neural metabolite concentrations and grey matter architecture

While animal studies have demonstrated a unique reproduction-related neuroplasticity, little is known on the effects of pregnancy on the human brain. Here we investigated whether pregnancy is associated with changes to resting state brain activity, white matter microstructure, neural metabolite concentrations and grey matter architecture using a comprehensive pre-conception cohort study. We show that pregnancy leads to selective and robust changes in neural architecture and neural network organization, which are most pronounced in the Default Mode Network. These neural changes correlated with pregnancy hormones, primarily third-trimester estradiol, while no associations were found with other factors such as osmotic effects, stress and sleep. Furthermore, the changes related to measures of maternal-fetal bonding, nesting behavior and the physiological responsiveness to infant cues, and predicted measures of mother-infant bonding and bonding impairments. These findings suggest there are selective pregnancy-related modifications in brain structure and function that may facilitate peripartum maternal processes of key relevance to the mother-infant dyad.

Artificial neural networks for non-linear age correction of diffusion metrics in the brain

Human aging is characterized by progressive loss of physiological functions. To assess changes in the brain that occur with increasing age, the concept of brain aging has gained momentum in neuroimaging with recent advancements in statistical regression and machine learning (ML). A common technique to assess the brain age of a person is, first, fitting a regression model to neuroimaging data from a group of healthy subjects, and then, using the resulting model for age prediction. Although multiparametric MRI-based models generally perform best, models solely based on diffusion tensor imaging have achieved similar results, with the benefits of faster data acquisition and better replicability across scanners and field strengths. In the present study, we developed an artificial neural network (ANN) for brain age prediction based upon tract-based fractional anisotropy (FA). Consequently, we investigated if this age-prediction model could also be used for non-linear age correction of white matter diffusion metrics in healthy adults. The brain age prediction accuracy of the ANN (R 2 = 0.47) was similar to established multimodal models. The comparison of the ANN-based age-corrected FA with the tract-wise linear age-corrected FA resulted in an R 2 value of 0.90 [0.82; 0.93] and a mean difference of 0.00 [-0.04; 0.05] for all tract systems combined. In conclusion, this study demonstrated the applicability of complex ANN models to non-linear age correction of tract-based diffusion metrics as a proof of concept.

The Neuroanatomy of Pregnancy and Postpartum

Pregnancy and giving birth are exceptional states in a woman's life for many reasons. While the effects of pregnancy and childbirth on the female body are obvious, less is known about their impact on the female brain, especially in humans. The scientific literature is still sparse but we have identified 12 longitudinal neuroimaging studies conducted in women whose brains were scanned before pregnancy, during pregnancy, and/or after giving birth. This review summarizes and discusses the reported neuroanatomical changes during pregnancy, postpartum, and beyond. Some studies suggest that pregnancy is mainly associated with tissue decreases, and a few studies suggest that this tissue loss is mostly permanent. In contrast, the majority of studies seems to indicate that the postpartum period is accompanied by substantial tissue increases throughout the entire brain. Future research is clearly warranted to replicate and extend the current findings, while addressing various limitations and shortcomings of existing studies.

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

Cardiometabolic risk (CMR) factors 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 compared to females. Independent of APOE4 status, 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 (66-81 years), where greater BF% was linked to lower BAG. Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. In conclusion, the findings point to sex- and age-specific associations between CMRs and brain age. Incorporating sex as a factor of interest in studies addressing CMR may promote sex-specific precision medicine, consequently improving health care for both males and females.

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 in two population-based datasets, and assessed the effects of age range, sample size and age-bias correction on the model performance metrics Pearson's correlation coefficient (r), the coefficient of determination (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 generally indicate high accuracy, even for poorly performing models, inspection of uncorrected model results provides important information about underlying model attributes such as prediction variance.

Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women

The menopause transition involves changes in oestrogens and adipose tissue distribution, which may influence female brain health post-menopause. Although increased central fat accumulation is linked to risk of cardiometabolic diseases, adipose tissue also serves as the primary biosynthesis site of oestrogens post-menopause. It is unclear whether different types of adipose tissue play diverging roles in female brain health post-menopause, and whether this depends on lifetime oestrogen exposure, which can have lasting effects on the brain and body even after menopause. Using the UK Biobank sample, we investigated associations between brain characteristics and visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) in 10,251 post-menopausal females, and assessed whether the relationships varied depending on length of reproductive span (age at menarche to age at menopause). To parse the effects of common genetic variation, we computed polygenic scores for reproductive span. The results showed that higher VAT and ASAT were both associated with higher grey and white matter brain age, and greater white matter hyperintensity load. The associations varied positively with reproductive span, indicating more prominent associations between adipose tissue and brain measures in females with a longer reproductive span. The effects were in general small, but could not be fully explained by genetic variation or relevant confounders. Our findings indicate that associations between abdominal adipose tissue and brain health post-menopause may partly depend on individual differences in cumulative oestrogen exposure during reproductive years, emphasising the complexity of neural and endocrine ageing processes in females.

Recent Neuroscience Advances in Human Parenting

The transition to parenthood entails brain adaptations to the demands of caring for a newborn. This chapter reviews recent neuroscience findings on human parenting, focusing on neuroimaging studies. First, we describe the brain circuits underlying human maternal behavior, which comprise ancient subcortical circuits and more sophisticated cortical regions. Then, we present the short-term and long-term functional and structural brain adaptations that characterize the transition to motherhood, discuss the long-term effects of parenthood on the brain, and propose several underlying neural mechanisms. We also review neuroimaging findings in biological fathers and alloparents (such as other relatives or adoptive parents), who engage in parenting without directly experiencing pregnancy or childbirth. Finally, we describe perinatal mental illnesses and discuss the neural responses associated with such disorders. To date, studies indicate that parenthood is a period of enhanced brain plasticity within brain areas critical for cognitive and social processing and that both parenting experience and gestational-related factors can prime such plasticity.

Characterizing the Brain Structural Adaptations Across the Motherhood Transition

Women that become mothers face notable physiological adaptations during this life-period. Neuroimaging studies of the last decade have provided grounded evidence that women's brains structurally change across the transition into motherhood. The characterization of this brain remodeling is currently in its early years of research. The current article reviews this scientific field by focusing on our longitudinal (pre-to-post pregnancy) Magnetic Resonance Imaging (MRI) studies in first-time parents and other longitudinal and cross-sectional studies of parents. We present the questions that are currently being answered by the parental brain literature and point out those that have not yet been explored. We also highlight potential confounding variables that need to be considered when analyzing and interpreting brain changes observed during motherhood.

A history of previous childbirths is linked to women's white matter brain age in midlife and older age

Maternal brain adaptations occur in response to pregnancy, but little is known about how parity impacts white matter and white matter ageing trajectories later in life. Utilising global and regional brain age prediction based on multi-shell diffusion-weighted imaging data, we investigated the association between previous childbirths and white matter 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 white matter brain age, potentially indicating a protective effect of parity on white matter later in life. Both global white matter 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 white matter 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 white matter characteristics later in life, longitudinal studies are required to establish causality and determine how parity may influence women's white matter trajectories across the lifespan.