Brain‐wide associations between white matter and age highlight the role of fornix microstructure in brain ageing

Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age-associations of WM features from different diffusion approaches, we analyzed UK Biobank diffusion magnetic resonance imaging (dMRI) data across midlife and older age (N = 35,749, 44.6–82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM-age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities, and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.publishedVersio

Investigating the synergistic effects of hormone replacement therapy, apolipoprotein E and age on brain health in the UK Biobank

Global prevalence of Alzheimer's Disease has a strong sex bias, with women representing approximately two-thirds of the patients. Yet, the role of sex-specific risk factors during midlife, including hormone replacement therapy (HRT) and their interaction with other major risk factors for Alzheimer's Disease, such as apolipoprotein E (APOE)-e4 genotype and age, on brain health remains unclear. We investigated the relationship between HRT (i.e., use, age of initiation and duration of use) and brain health (i.e., cognition and regional brain volumes). We then consider the multiplicative effects of HRT and APOE status (i.e., e2/e2, e2/e3, e3/e3, e3/e4 and e4/e4) via a two-way interaction and subsequently age of participants via a three-way interaction. Women from the UK Biobank with no self-reported neurological conditions were included (N = 207,595 women, mean age = 56.25 years, standard deviation = 8.01 years). Generalised linear regression models were computed to quantify the cross-sectional association between HRT and brain health, while controlling for APOE status, age, time since attending centre for completing brain health measure, surgical menopause status, smoking history, body mass index, education, physical activity, alcohol use, ethnicity, socioeconomic status, vascular/heart problems and diabetes diagnosed by doctor. Analyses of structural brain regions further controlled for scanner site. All brain volumes were normalised for head size. Two-way interactions between HRT and APOE status were modelled, in addition to three-way interactions including age. Results showed that women with the e4/e4 genotype who have used HRT had 1.82% lower hippocampal, 2.4% lower parahippocampal and 1.24% lower thalamus volumes than those with the e3/e3 genotype who had never used HRT. However, this interaction was not detected for measures of cognition. No clinically meaningful three-way interaction between APOE, HRT and age was detected when interpreted relative to the scales of the cognitive measures used and normative models of ageing for brain volumes in this sample. Differences in hippocampal volume between women with the e4/e4 genotype who have used HRT and those with the e3/e3 genotype who had never used HRT are equivalent to approximately 1–2 years of hippocampal atrophy observed in typical health ageing trajectories in midlife (i.e., 0.98%–1.41% per year). Effect sizes were consistent within APOE e4/e4 group post hoc sensitivity analyses, suggesting observed effects were not solely driven by APOE status and may, in part, be attributed to HRT use. Although, the design of this study means we cannot exclude the possibility that women who have used HRT may have a predisposition for poorer brain health

Visual processing deficits in patients with schizophrenia spectrum and bipolar disorders and associations with psychotic symptoms, and intellectual abilities

Abstract Objective Low-level sensory disruption is hypothesized as a precursor to clinical and cognitive symptoms in severe mental disorders. We compared visual discrimination performance in patients with schizophrenia spectrum disorder or bipolar disorder with healthy controls, and investigated associations with clinical symptoms and IQ. Methods Patients with schizophrenia spectrum disorder (n = 32), bipolar disorder (n = 55) and healthy controls (n = 152) completed a computerized visual discrimination task. Participants responded whether the latter of two consecutive grids had higher or lower spatial frequency, and discrimination thresholds were estimated using an adaptive maximum likelihood procedure. Case-control differences in threshold were assessed using linear regression, F-test and post-hoc pair-wise comparisons. Linear models were used to test for associations between visual discrimination threshold and psychotic symptoms derived from the PANSS and IQ assessed using the Matrix Reasoning and Vocabulary subtests from the Wechsler Abbreviated Scale of Intelligence (WASI). Results Robust regression revealed a significant main effect of diagnosis on discrimination threshold (robust F = 6.76, p = .001). Post-hoc comparisons revealed that patients with a schizophrenia spectrum disorder (mean = 14%, SD = 0.08) had higher thresholds compared to healthy controls (mean = 10.8%, SD = 0.07, β = 0.35, t = 3.4, p = .002), as did patients with bipolar disorder (12.23%, SD = 0.07, β = 0.21, t = 2.42, p = .04). There was no significant difference between bipolar disorder and schizophrenia (β = −0.14, t = −1.2, p = .45). Linear models revealed negative associations between IQ and threshold across all participants when controlling for diagnostic group (β = −0.3, t = −3.43, p = .0007). This association was found within healthy controls (t = −3.72, p = .0003) and patients with bipolar disorder (t = −2.53, p = .015), and no significant group by IQ interaction on threshold (F = 0.044, p = .97). There were no significant associations between PANSS domain scores and discrimination threshold. Conclusion Patients with schizophrenia spectrum or bipolar disorders exhibited higher visual discrimination thresholds than healthy controls, supporting early visual deficits among patients with severe mental illness. Discrimination threshold was negatively associated with IQ among healthy controls and bipolar disorder patients. These findings elucidate perception-related disease mechanisms in severe mental illness, which warrants replication in independent samples.publishedVersio

Shared pattern of impaired social communication and cognitive ability in the youth brain across diagnostic boundaries

Background Abnormalities in brain structure are shared across diagnostic categories. Given the high rate of comorbidity, the interplay of relevant behavioural factors may also cross these classic boundaries. Methods We aimed to detect brain-based dimensions of behavioural factors using canonical correlation and independent component analysis in a clinical youth sample (n = 1732, 64 % male, age: 5–21 years). Results We identified two correlated patterns of brain structure and behavioural factors. The first mode reflected physical and cognitive maturation (r = 0.92, p = .005). The second mode reflected lower cognitive ability, poorer social skills, and psychological difficulties (r = 0.92, p = .006). Elevated scores on the second mode were a common feature across all diagnostic boundaries and linked to the number of comorbid diagnoses independently of age. Critically, this brain pattern predicted normative cognitive deviations in an independent population-based sample (n = 1253, 54 % female, age: 8–21 years), supporting the generalisability and external validity of the reported brain-behaviour relationships. Conclusions These results reveal dimensions of brain-behaviour associations across diagnostic boundaries, highlighting potent disorder-general patterns as the most prominent. In addition to providing biologically informed patterns of relevant behavioural factors for mental illness, this contributes to a growing body of evidence in favour of transdiagnostic approaches to prevention and intervention.publishedVersio

Development and validation of a dementia risk score in the UK Biobank and Whitehall II cohorts

BACKGROUND: Current dementia risk scores have had limited success in consistently identifying at-risk individuals across different ages and geographical locations. OBJECTIVE: We aimed to develop and validate a novel dementia risk score for a midlife UK population, using two cohorts: the UK Biobank, and UK Whitehall II study. METHODS: We divided the UK Biobank cohort into a training (n=176 611, 80%) and test sample (n=44 151, 20%) and used the Whitehall II cohort (n=2934) for external validation. We used the Cox LASSO regression to select the strongest predictors of incident dementia from 28 candidate predictors and then developed the risk score using competing risk regression. FINDINGS: Our risk score, termed the UK Biobank Dementia Risk Score (UKBDRS), consisted of age, education, parental history of dementia, material deprivation, a history of diabetes, stroke, depression, hypertension, high cholesterol, household occupancy, and sex. The score had a strong discrimination accuracy in the UK Biobank test sample (area under the curve (AUC) 0.8, 95% CI 0.78 to 0.82) and in the Whitehall cohort (AUC 0.77, 95% CI 0.72 to 0.81). The UKBDRS also significantly outperformed three other widely used dementia risk scores originally developed in cohorts in Australia (the Australian National University Alzheimer's Disease Risk Index), Finland (the Cardiovascular Risk Factors, Ageing, and Dementia score), and the UK (Dementia Risk Score). CLINICAL IMPLICATIONS: Our risk score represents an easy-to-use tool to identify individuals at risk for dementia in the UK. Further research is required to determine the validity of this score in other populations

Bio-psycho-social factors’ associations with brain age: a large-scale UK Biobank diffusion study of 35,749 participants

Brain age refers to age predicted by brain features. Brain age has previously been associated with various health and disease outcomes and suggested as a potential biomarker of general health. Few previous studies have systematically assessed brain age variability derived from single and multi-shell diffusion magnetic resonance imaging data. Here, we present multivariate models of brain age derived from various diffusion approaches and how they relate to bio-psycho-social variables within the domains of sociodemographic, cognitive, life-satisfaction, as well as health and lifestyle factors in midlife to old age (N = 35,749, 44.6–82.8 years of age). Bio-psycho-social factors could uniquely explain a small proportion of the brain age variance, in a similar pattern across diffusion approaches: cognitive scores, life satisfaction, health and lifestyle factors adding to the variance explained, but not socio-demographics. Consistent brain age associations across models were found for waist-to-hip ratio, diabetes, hypertension, smoking, matrix puzzles solving, and job and health satisfaction and perception. Furthermore, we found large variability in sex and ethnicity group differences in brain age. Our results show that brain age cannot be sufficiently explained by bio-psycho-social variables alone. However, the observed associations suggest to adjust for sex, ethnicity, cognitive factors, as well as health and lifestyle factors, and to observe bio-psycho-social factor interactions’ influence on brain age in future studies.publishedVersio

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  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

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 (Formula presented.) = 0.14, p &lt; 0.001, LDL (Formula presented.) = 0.20, p &lt; 0.001, triglycerides (Formula presented.) = 0.12, p &lt; 0.001) and HbA1c ((Formula presented.) = 0.24, p &lt; 0.001) compared to premenopausal women, beyond the effects of age. Over time, BMI increased more in the premenopausal compared to the postmenopausal group ((Formula presented.) = −0.08, p &lt; 0.001), while WHR increased to a similar extent in both groups ((Formula presented.) = −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 ((Formula presented.) range = 0.03–0.13, p ≤ 0.002). HDL showed a significant inverse relationship with WMH volume ((Formula presented.) = −0.27, p &lt; 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.</p

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

IntroductionThe 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.MethodsIn 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.ResultsPostmenopausal females showed higher levels of baseline blood lipids (HDL β = 0.14, p &lt; 0.001, LDL β = 0.20, p &lt; 0.001, triglycerides β = 0.12, p &lt; 0.001) and HbA1c (β = 0.24, p &lt; 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 &lt; 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 &lt; 0.001).DiscussionOur 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

Recommendations for a better understanding of sex and gender in neuroscience of mental health

There are prominent sex/gender differences in the prevalence, expression and lifespan course of mental health and neurodiverse conditions. Yet the underlying sex and gender related mechanisms and their interactions are still not fully understood. This lack of knowledge has harmful consequences for those suffering from mental health problems. Hence, we set up a co-creation session in a one week workshop with a multidisciplinary team of 25 researchers, clinicians and policy makers, to identify the main barriers in sex and gender research in neuroscience of mental health. Based on this work, we here provide recommendations for methodologies, translational research and stakeholder involvement. These include guidelines for recording, reporting, analysis beyond binary groups, and open science. Improved understanding of sex and gender related mechanisms in neuroscience may benefit public health as this is an important step towards precision medicine and may function as an archetype for studying diversity