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Background: Biological brain age estimates from structural MRI data and their difference from chronological age — the brain age gap (BAG) — have been successfully applied in a range of diseases, but remain limited to capturing structural characteristics only. To incorporate physiological properties, we operationalized ‘Cerebrovascular brain age’ using a combination of structural, and arterial spin labelling (ASL) image data, investigate their optimal feature and algorithm combinations, and evaluate its repeatability. Methods: Healthy participants (n = 341, 62 % female, age 59.7 ± 14.8 years, range: 21 - 95 years) were scanned at baseline and after 1.7 ± 0.5 years (n = 248, 62.9 % female, mean age 62.4 ± 13.3 years, range: 27 - 86). At 3 T MRI, 3D structural T1-weighted (T1w) and Fluid Attenuated Inversion Recovery (FLAIR), and 3D ASL image data were acquired to extract within grey matter (GM) and deep white matter (WM) ROIs: volumetrics, WM hyperintensity volume and count; and cerebral blood flow (CBF) and spatial coefficient of variation (CoV). Multiple combinations of features and machine learning algorithms were evaluated to train brain age algorithms on 70 % of the subjects and evaluated on the remainder, for 300 Monte-Carlo cross-validations, using the Mean Absolute Error (MAE). Feature importance of the best performing model was assessed by determining the feature weights. Model repeatability of the best model was assessed by comparing the BAGs between baseline and follow-up, also using T1w + FLAIR or ASL-only features. Results: The lowest MAE was observed for the ElasticNetCV algorithm using T1w + FLAIR + ASL (MAE = 5.03 ± 0.34 years) and significantly better compared to using T1w + FLAIR (MAE = 6.01 ± 0.39, p < 0.01) and ASL-only features (MAE = 6.04 ± 0.39, R2 = 0.70 ± 0.04, p < 0.01). The three most important features were GM CBF (6.2 ± 1.18), GM/ICV (5.34 ± 0.6), and WM CBF (4.16 ± 0.36). Average baseline and follow-up BAGs were not different (-1.51 ± 6.29 and -1.14 ± 6.40 years respetively, ICC = 0.85, 95% CI: 0.79 - 0.90, p = 0.16). The ElasticNetCV model with T1w+FLAIR+ASL performed similar to the same model with the T1w + FLAIR feature set (0.37 ± 3.48 years and 0.01 ± 2.95 years respectively, p = 0.14), and the ASL-only feature set (0.29 ± 4.03, p = 0.39). Conclusion: The addition of ASL features to structural brain age improved brain age prediction, with the ElasticNetCV algorithm and a combination of all tested features (T1w+FLAIR+ASL) performing the best in a cross-sectional and repeatability comparison. These findings encourage future studies to explore the value of ASL in brain age in various pathologies.

Intracranial volume (ICV) is frequently used in volumetric brain magnetic resonance imaging (MRI) studies, both as an adjustment factor for head size and as a variable of interest. Associations with age have been reported in both longitudinal and cross-sectional studies, but results have varied, potentially due to differences in ICV estimation methods. Here, we compared five commonly used ICV estimation methods and their cross-sectional and longitudinal associations with age. T1-weighted cross-sectional MRI data was included for 651 healthy individuals recruited through the NORMENT Centre (mean age = 46.1 years, range = 12.0-85.8 years) and 2,410 healthy individuals recruited through the UK Biobank study (UKB, mean age = 63.2 years, range = 47.0-80.3 years), where follow-up data was also available with a mean follow-up interval of 2.3 years. ICV was estimated with FreeSurfer (eTIV and sbTIV), SPM12, CAT12, and FSL. We assessed Pearson correlations, performed Bland-Altman analysis, and tested the explained variance of sex, height, body weight, and age on pairwise differences between ICV estimation methods. We fitted regression models to test linear and non-linear cross-sectional associations between age and ICV. For the UKB dataset, we further assessed longitudinal ICV change using linear mixed-effects (LME) models. We found overall high correlations across ICV estimation method, with the lowest correlations between FSL and eTIV (r=0.87) and between FSL and CAT12 (r=0.89). Widespread proportional bias was found in the Bland-Altman analyses, i.e., agreement between methods varying as a function of head size. Body weight, age, and sex explained the most variance in the differences between ICV estimation methods, indicating possible confounding by these variables for some estimation methods. In the NORMENT dataset, cross-sectional associations with age were found only for FSL and SPM12, indicating a positive association. For the UKB dataset, we observed negative cross-sectional associations with age for all ICV estimation methods. Longitudinal associations with age were found for all ICV estimation methods, with estimated annual percentage change ranging from −0.291 % to −0.416 % across the sampled age range. This convergence of longitudinal results across ICV estimation methods, in the largest dataset to date, offers strong evidence for age-related ICV reductions in mid- to late adulthood.HighlightsCorrelations between the five assessed estimation methods were very high (r>0.90) with the exception of FSL and eTIV (r=0.87), and FSL and CAT12 (r=0.89).Explained variance of estimated ICV differences by body weight, age, and sex indicate possible confounding for some ICV estimation methods.Positive cross-sectional associations with age, from adolescence to old age, were observed for the SPM12 and FSL estimation methods in one dataset.In the other dataset, negative cross-sectional associations with age, from mid- to late adulthood, were found for all estimation methods.Longitudinal ICV changes were observed for all estimation methods, indicating an annual percentage ICV reduction of −0.29 % to −0.42 % in mid- to late adulthood.

There are prominent sex differences in the prevalence of numerous mental health and neurodiverse conditions. Both sex and gender related factors affect the expression and diagnosis of mental health conditions. Even though, sex and gender research has been integrated in research policies, the underlying mechanisms and interactions between them are still not fully understood. This lack of knowledge has harmful consequences for diagnosis and treatment of mental health conditions. Hence, we have set up a co-creation session in a one week workshop, including a multidisciplinary team of 25 scientists, clinicians and policy makers, to identify the main barriers in sex and gender research in neuroscience of mental health. We provide recommendations and solutions for three focus areas including neuroscientific methods, bridging the gap between research and the clinic, and stakeholder collaborations. These recommendations include guidelines for documenting sex and gender methods and results, analysis beyond the binary and open science, which will drive our understanding of sex and gender related mechanisms in neuroscience. This will ultimately benefit public health as this is an important step towards precision medicine and may function as an archetype for studying diversity.

BackgroundStudies have linked auditory hallucinations (AH) in schizophrenia-spectrum disorders (SCZ) to altered cerebral white matter microstructure within the language and auditory processing circuitry (LAPC). However, the neuroanatomical distribution and specificity to the LAPC remains unclear. Here, we investigated the relationship between AH and DTI among patients with SCZ using diffusion tensor imaging (DTI).MethodsWe included patients with SCZ with (AH+; n=59) and without (AH-; n=81) current AH, and 140 age-and-sex-matched controls. Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) were extracted from 39 fibre tracts. We used principal component analysis (PCA) to identify general factors of variation across fibre tracts and DTI metrics. Regression models adjusted for sex, age, and age2were used to compare tract-wise DTI metrics and PCA factors between AH+, AH-, and healthy controls and to assess associations with clinical characteristics.ResultsWidespread differences relative to controls were observed for MD and RD in patients without current AH. Only limited differences in two fibre tracts were observed between AH+ and controls. Unimodal PCA factors based on MD, RD, and AD, as well as multimodal PCA factors, differed significantly relative to controls for AH-, but not AH+. We did not find any significant associations between PCA factors and clinical characteristics.ConclusionsContrary to previous studies, DTI metrics differed mainly in patientswithoutcurrent AH compared to controls, indicating a widespread neuroanatomical distribution. Our results challenge the notion that altered DTI metrics in the LAPC is a specific feature underlying AH.

SummaryA child’s socioeconomic environment can shape central aspects of their life, including vulnerability to mental disorders. Negative environmental influences in youth may interfere with the extensive and dynamic brain development occurring at this time. Indeed, there are numerous yet diverging reports of associations between parental socioeconomic status (SES) and child cortical brain morphometry. Most of these studies have used single metric-or unimodal analyses that downplay the probable scenario that separate sources of variability, from numerous independent biological pathways, are the basis for SES-related cortical differences in youth. To comprehensively capture such variability, using data from 9758 children aged 8.9-11.1 years from the ABCD Study®, we employed linked independent component analysis (LICA) and fused vertex-wise cortical thickness, surface area, curvature and grey-/white-matter contrast (GWC). LICA revealed 70 uni- and multimodal components. We then assessed the linear relationships between parental education, parental income and each of the cortical components, controlling for age, sex, genetic ancestry, and family relatedness. We also assessed whether cortical structure moderated the negative relationships between parental SES and child general psychopathology. Parental education and income were both associated with larger surface area and higher GWC globally, in addition to local increases in surface area and to a lesser extent bidirectional GWC and cortical thickness patterns. The negative relation between parental income and child psychopathology were attenuated in children with a multimodal pattern of larger frontal- and smaller occipital surface area, and lower medial occipital thickness and GWC. In conclusion, structural brain MRI is sensitive to SES-related variability in childhood, and in low-income families, having a more developed cortex across MRI metrics, is beneficial for mental health.

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 analysed UK Biobank diffusion Magnetic Resonance Imaging (dMRI) data across midlife and older age (N= 35,749, 44.6 to 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.

BackgroundResearch has demonstrated associations between pubertal development and brain maturation. However, existing studies have been limited by small samples, cross-sectional designs, and inconclusive findings regarding coupling directionality and sex differences.MethodsWe examined the longitudinal temporal coupling of puberty status assessed using the Pubertal Development Scale (PDS) and magnetic resonance imaging (MRI)-based brain grey and white matter structure. Our sample consisted of 8,896 children and adolescents at baseline (mean age = 9.9) and 6,099 at follow-up (mean age = 11.9) from the Adolescent Brain and Cognitive Development (ABCD) Study.ResultsApplying multigroup Bivariate Latent Change Score (BLCS) models, we found that baseline pubertal status predicted the rate of change in cortical thickness among females only, while it predicted rate of change in cortical surface area for both males and females, with a significantly stronger association for females. For cortical surface area, we also found a correlation between baseline pubertal status and area and co-occurring changes over time, with both associations present in males only. Diffusion tensor imaging (DTI) analysis revealed correlated change between pubertal status and fractional anisotropy (FA) for both males and females, but no significant associations for mean diffusivity (MD).ConclusionsOur results suggest that pubertal status in early adolescence predicts cortical maturation, and that the strength of the associations differ between sex. Further research is needed to understand how the associations between puberty and brain maturation are related to environmental and lifestyle factors, and the impact on mental health.

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 to 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: cognitve 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.

Background: Increased intraindividual variability (IIV) in reaction times (RT) has been suggested as a key cognitive and behavioral marker of attention problems, but findings for other dimensions of psychopathology are less consistent. Moreover, while studies have linked IIV to brain white matter microstructure, large studies testing the robustness of these associations are needed. Methods: We utilized data from the Adolescent Brain Cognitive Development (ABCD) Study baseline assessment to test the associations between IIV and psychopathology (n=8622, age=8.9-11.1), and between IIV and white matter microstructure (n=7958, age=8.9-11.1). IIV was investigated using an ex-Gaussian distribution analysis of RTs in correct response go trials in the stop signal task. Psychopathology was measured by the Child Behavior Checklist and a bifactor structural equation model was performed to extract a general p-factor and specific factors reflecting internalizing, externalizing, and attention problems. To investigate white matter microstructure, fractional anisotropy (FA) and mean (MD), axial (AD), and radial diffusivity (RD) were examined in 23 atlas-based tracts. Results: Increased IIV in both short and long reaction times was positively associated with the specific attention problems factor (Cohen’s d=.13 and d=.15). Increased IIV in long reaction times was also positively associated with RD in the left and right corticospinal tract (d=.12 (both tracts)). Conclusions: Using a large sample and a data-driven dimensional approach to psychopathology, the results provide novel evidence for a small but specific association between IIV and attention problems in children and supports previous findings on the relevance of white matter microstructure for IIV.

The term free-water volume fraction (FWVF) refers to the cerebrospinal and interstitial fluids in the extracellular space of the white matter (WM) of the brain, which has been demonstrated as a sensitive biomarker that correlates with the cognitive performance and the neuropathological processes modifying the interstitial extracellular spaces. It can be quantified by properly fitting the isotropic compartment of the magnetic resonance (MR) signal in diffusion-sensitized sequences. Using N = 287 healthy subjects aged 25-94, this study examines in detail the evolution of the FWVF in the human brain WM across the adult lifespan, which has been previously reported to exhibit a positive trend. We found evidence of a noticeably non-linear gain after the sixth decade of life, with a region-specific variate and varying change rate of the FWVF parameter with age, at the same time a heteroskedastic pattern across the adult lifespan is suggested. On the other hand, the FW-compensated MR signal leads to a region-dependent flattened age-related evolution of the mean diffusivity (MD) and fractional anisotropy (FA), along with a considerable reduction in their variability, as compared to standard studies conducted over the raw MR signal. This way, our study provides a new perspective on the trajectory-based assessment of the brain and explains the source of the variations observed in FA and MD parameters across the lifespan with previous studies with the standard diffusion tensor imaging.

BackgroundCerebral blood flow (CBF) is critical for brain metabolism and overall function. Age-related changes in CBF have been associated with cognitive deficits and increased risk of neurocognitive disorders and vascular events such as stroke. Exercise is considered among the protective factors for age-related brain and cognitive impairment, but how different lifestyle characteristics, such as low or moderate-to-vigorous physical activity or frequency of everyday activities, relate to cross-sectional and longitudinal measures of CBF has not been established.ObjectiveWith the main aim of identifying potential targets for interventions, our objective was to examine associations between cortical and subcortical CBF and frequency of diverse everyday activities in healthy community-dwelling adults aged 65-89 years, and assess to which degree activity level at baseline is associated with longitudinal changes in CBF across a one-to-two years interval.MethodOne hundred nineteen (N = 119) adults underwent brain magnetic resonance imaging (MRI), neurocognitive, physical, and activity assessment at baseline. CBF was obtained using pseudo-continuous arterial spin labelling (ASL) MRI. Frequency of everyday activities were measured using Frenchay Activities index, while minutes of low and moderate-to-vigorous intensity physical activity were measured using a StepWatch Activity Monitor. Eighty-six participants completed a follow-up ASL MRI, on average 506 (SD = 113) days after the baseline scan.ResultsBayesian multilevel modelling revealed positive associations between baseline cortical and subcortical CBF and various everyday activities. Higher baseline accumbens, putamen and pallidum CBF was associated with more time spent on low intensity (> 0 steps/minute to < 100 steps/minute) physical activity, higher accumbens and caudate CBF with more moderate to vigorous intensity (≥ 100 steps/minute) physical activity, higher cerebral cortical CBF with more participation in social activity, higher cortical and thalamic CBF with more reading, and higher baseline pallidum CBF with more actively pursuing hobbies. We did not find evidence for an association between baseline activity level and longitudinal changes in CBF.ConclusionThe identified associations between everyday activity measures and CBF provide new knowledge on malleable lifestyle factors that may indicate or contribute to healthy brain aging. In addition to the relevance for prioritizing targets for public health guidelines, our findings contribute to disclose parts of the intricate connection between brain metabolism and everyday activities in aging.

Cardiometabolic risk factors (CMRs) are associated with accelerated brain aging and increased risk for sex-dimorphic illnesses such as Alzheimer’s Disease (AD). Yet, it is unknown how CMRs interact with sex and apolipoprotein E-ε4 (APOE4), a known genetic risk factor for AD, to influence brain age across different life stages. Using age prediction based on multi-shell diffusion-weighted imaging data in 21,308 UK Biobank participants, we investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BF%), and APOE4 status varied i) between males and females, ii) according to age at menopause in females, and iii) across different age groups in males and females. We report sex differences in associations between BAG and all three CMRs, with stronger positive associations among males com- pared to females. Higher BAG (older brain age relative to chronological age) was associated with greater BMI, WHR, and BF% in males, whereas in females, higher BAG was associated with greater WHR, but not BMI and BF%. These divergent associations were most prominent within the oldest group of females (66-81yrs), where higher BF% was linked to lower BAG (younger brain age relative to chronological age). Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. APOE4 status was not significantly associated with BAG, and no significant interactions with CMRs were found. In conclusion, the findings point to sex- and age-specific associations between body fat composition and brain age. Incorporating sex as a factor of interest in studies addressing cardiometabolic risk may promote sex-specific precision medicine, consequently improving health care for both males and females.

Schizophrenia (SCZ) spectrum and bipolar disorder (BD) are severe mental disorders with unknown pathophysiology. Altered visual evoked potential (VEP), an electroencephalogram signal reflecting function in the primary visual cortex (V1), abnormal visual processing and visual hallucinations reported in these patients, all point towards V1 dysfunction. While the mechanisms contributing to V1 dysfunction remain unknown, structural alterations are possible candidates. Lack of insight into neural substrates of structure and functional in V1 has limited our ability to determine implications of altered V1 function. While combining VEP and magnetic resonance imaging has increased our understanding of the structure-function relationship in V1 in healthy individuals, no previous study has examined the same structure-function relationship in patients with SCZ spectrum and BD. Here, we aimed to confirm previous findings of a selective positive correlation between the amplitude of the P100 component of the VEP and V1 surface area (SA) in 307 healthy individuals and to examine whether this relationship was altered in patients with SCZ spectrum (n=30) and BD (n=45). The correlation between the P100 amplitude and the total, (r=0.16, p=0.006), right (r=0.14, p=0.013) and left V1 surface area (r=0.13, p=0.02) was significant in healthy individuals, but not in patients. The current results support previous findings of a selective relationship between P100 amplitude and V1 surface area in healthy individuals and suggests that other factors than V1 surface area or thickness explain V1 dysfunction reported in these patients.

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., 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. Corpus callosum contributed uniquely to this association, and showed a stronger relationship relative to several other tracts.

There is an intimate body-brain connection in ageing, and obesity is a key risk factor for poor cardiometabolic health and neurodegenerative conditions. Although research has demonstrated deleterious effects of obesity on brain structure and function, the majority of studies have used conventional measures such as waist-to-hip ratio, waist circumference, and body mass index. While sensitive to gross features of body composition, such global anthropomorphic features fail to describe regional differences in body fat distribution and composition, and to determine visceral adiposity, which is related to a range of metabolic conditions. In this mixed cross-sectional and longitudinal design (interval mean and standard deviation = 19.7 ± 0.5 months), including 790 healthy individuals (mean (range) age = 46.7 (18-94) years, 53% women), we investigated cross-sectional body magnetic resonance imaging (MRI, n = 286) measures of adipose tissue distribution in relation to longitudinal brain structure using MRI-based morphometry and diffusion tensor imaging (DTI). We estimated tissue-specific brain age at two time points and performed Bayesian multilevel modelling to investigate the associations between adipose measures at follow-up and brain age gap (BAG) at baseline and follow-up. We also tested for interactions between BAG and both time and age on each adipose measure. The results showed credible associations between T1-based BAG and liver fat, muscle fat infiltration (MFI), and weight-to-muscle ratio (WMR), indicating older-appearing brains in people with higher measures of adipose tissue. Longitudinal evidence supported interaction effects between time and MFI and WMR on T1-based BAG, indicating accelerated ageing over the course of the study period in people with higher measures of adipose tissue. The results show that specific measures of fat distribution are associated with brain ageing and that different compartments of adipose tissue may be differentially linked with increased brain ageing, with potential to identify key processes involved in age-related transdiagnostic disease processes.

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 women’s WM trajectories across the lifespan.

The macro- and microstructural architecture of human brain white matter undergoes substantial alterations throughout development and ageing. Most of our understanding of the spatial and temporal characteristics of these lifespan adaptations come from magnetic resonance imaging (MRI), including diffusion MRI (dMRI), which enables visualisation and quantification of brain white matter with unprecedented sensitivity and detail. However, with some notable exceptions, previous studies have relied on cross-sectional designs, limited age ranges, and diffusion tensor imaging (DTI) based on conventional single-shell dMRI. In this mixed cross-sectional and longitudinal study (mean interval: 15.2 months) including 702 multi-shell dMRI datasets, we combined complementary dMRI models to investigate age trajectories in healthy individuals aged 18 to 94 years (57.12% women). Using linear mixed effect models and machine learning based brain age prediction, we assessed the age-dependence of diffusion metrics, and compared the age prediction accuracy of six different diffusion models, including diffusion tensor (DTI) and kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), restriction spectrum imaging (RSI), spherical mean technique multi-compartment (SMT-mc), and white matter tract integrity (WMTI). The results showed that the age slopes for conventional DTI metrics (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], radial diffusivity [RD]) were largely consistent with previous research, and that the highest performing advanced dMRI models showed comparable age prediction accuracy to conventional DTI. Linear mixed effects models and Wilks theorem analysis showed that the FA fine metric of the RSI model and orientation dispersion (OD) metric of the NODDI model showed the highest sensitivity to age. The results indicate that advanced diffusion models (DKI, NODDI, RSI, SMT mc, WMTI) provide sensitive measures of age-related microstructural changes of white matter in the brain that complement and extend the contribution of conventional DTI. Key words: ageing, white matter, multi-shell, longitudinal, diffusion, brain age

BackgroundPrevious structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression.MethodsWe used linked independent component analysis to fuse cortical macrostructure (thickness, area, gray matter density), white matter diffusion properties and resting-state fMRI default mode network amplitude in patients with a history of depression (n = 170) and controls (n = 71). We used univariate and machine learning approaches to assess the relationship between age, sex, case-control status, and symptom loads for depression and anxiety with the resulting brain components.ResultsUnivariate analyses revealed strong associations between age and sex with mainly global but also regional specific brain components, with varying degrees of multimodal involvement. In contrast, there were no significant associations with case-control status, nor symptom loads for depression and anxiety with the brain components, nor any interaction effects with age and sex. Machine learning revealed low model performance for classifying patients from controls and predicting symptom loads for depression and anxiety, but high age prediction accuracy.ConclusionMultimodal fusion of brain imaging data alone may not be sufficient for dissecting the clinical and neurobiological heterogeneity of depression. Precise clinical stratification and methods for brain phenotyping at the individual level based on large training samples may be needed to parse the neuroanatomy of depression.

Experience-dependent modulation of the visual evoked potential (VEP) is a promising proxy measure of synaptic plasticity in the cerebral cortex. However, existing studies are limited by small to moderate sample sizes as well as by considerable variability in how VEP modulation is quantified. In the present study, we used a large sample (n = 415) of healthy volunteers to compare different quantifications of VEP modulation with regards to effect sizes and retention of the modulation effect over time. We observed significant modulation for VEP components C1 (Cohen’s d = 0.53), P1 (d = 0.66), N1 (d = −0.27), N1b (d = −0.66), but not P2 (p = 0.1), and in one time-frequency cluster (~30 Hz and ~70 ms post-stimulus; d = −0.48), 2-4 minutes after 2 Hz prolonged visual stimulation. For components N1 (d = −0.21) and N1b (d = −0.38), as well for the time-frequency cluster (d = −0.33), this effect was retained after 54-56 minutes. Moderate to high correlations (ρ = [0.39, 0.69]) between modulation at different postintervention blocks revealed a relatively high temporal stability in the modulation effect for each VEP component. However, different VEP components also showed markedly different temporal retention patterns. Finally, P1 modulation correlated positively with age (t = 5.26), and was larger for female participants (t = 3.91), with no effects of either age or sex on N1 and N1b potentiation. These results provide strong support for VEP modulation, and especially N1b modulation, as a robust measure of synaptic plasticity, but underscore the need to differentiate between components, and to control for demographic confounders.

BACKGROUND: Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts.METHODS: We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18-94 years of age) and applied the models to the test sets including 648 patients with SZ (18-66 years of age), 185 patients with BD (18-64 years of age), and 990 HC subjects (17-68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results.RESULTS: Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen's d = -20.29) and patients with BD (Cohen's d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy-based models showed larger group differences than the models based on other DTI-derived metrics.CONCLUSIONS: Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.

BackgroundSchizophrenia (SZ) and bipolar disorders (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, BD, and healthy controls across 10 cohorts.MethodsWe trained six cross-validated models using different combinations of DTI data from 927 healthy controls (HC, 18-94 years), and applied the models to the test sets including 648 SZ (18-66 years) patients, 185 BD patients (18-64 years), and 990 HC (17-68 years), estimating brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results.Results10-fold cross-validation revealed high accuracy for all models. Compared to controls, the model including all feature sets significantly over-estimated the age of patients with SZ (d=-.29) and BD (d=.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy (FA) based models showed larger group differences than the models based on other DTI-derived metrics.ConclusionsBrain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.

Highlights • Examining associations between physical activity, physical capability and brain health. • Higher global FA were observed in older adults with higher walking speed. • Lower subcortical brain age gap (BAG) were observed in more physically active women., Maintaining high levels of daily activity and physical capability have been proposed as important constituents to promote healthy brain and cognitive aging. Studies investigating the associations between brain health and physical activity in late life have, however, mainly been based on self-reported data or measures designed for clinical populations. In the current study, we examined cross-sectional associations between physical activity, recorded by an ankle-positioned accelerometer for seven days, physical capability (grip strength, postural control, and walking speed), and neuroimaging based surrogate markers of brain health in 122 healthy older adults aged 65–88 years. We used a multimodal brain imaging approach offering complementary structural MRI based indicators of brain health: global white matter fractional anisotropy (FA) and mean diffusivity (MD) based on diffusion tensor imaging, and subcortical and global brain age based on brain morphology inferred from T1-weighted MRI data. In addition, based on the results from the main analysis, follow-up regression analysis was performed to test for association between the volume of key subcortical regions of interest (hippocampus, caudate, thalamus and cerebellum) and daily steps, and a follow-up voxelwise analysis to test for associations between walking speed and FA across the white matter Tract-Based Spatial Statistics (TBSS) skeleton. The analyses revealed a significant association between global FA and walking speed, indicating higher white matter integrity in people with higher pace. Voxelwise analysis supported widespread significant associations. We also found a significant interaction between sex and subcortical brain age on number of daily steps, indicating younger-appearing brains in more physically active women, with no significant associations among men. These results provide insight into the intricate associations between different measures of brain and physical health in old age, and corroborate established public health advice promoting physical activity.

The human cerebral cortex is highly regionalized. We aimed to test whether principles of regionalization could be traced from embryonic development throughout the human lifespan. A data-driven fuzzy-clustering approach was used to identify regions of coordinated longitudinal development of cortical surface area (SA) and thickness (CT) over 1.5 years (n = 301, 4-12 years). First, the SA clusters were compared to patterns from embryonic cortical development. The earliest sign of cortical regionalization is the emergence of morphometric gradients in the cerebral vesicles, with a major gradient running along the anterior-posterior (AP) axis. We found that the principal divide for the developmental SA clusters extended from the inferior-posterior to the superior-anterior cortex, corresponding to the embryonic morphometric AP gradient. Second, embryonic factors showing a clear AP gradient were identified, and tests revealed significant differences in gene expression of these factors between the anterior and posterior clusters. Further, each identified developmental SA and CT cluster showed distinguishable lifespan trajectories in a larger longitudinal dataset (4-88 years, 1633 observations). This means that regions that developed together also changed together throughout life, demonstrating continuity in regionalization of cortical changes. The AP divide in SA development also characterized genetic patterning obtained in an adult twin sample, but otherwise regionalized CT development adhered more to the genetic boundaries. Finally, SA and CT clusters showed differential relationships to cognitive functions. In sum, the results suggest that development of cortical regionalization is a continuous process from the embryonic stage throughout human life.Significance statement (120 words)The protomap and the radial unit theories of brain development have shown that graded expression patterns of several factors are responsible for shaping the ultimately highly partitioned and specialized neocortical landscape. The present study shows that the major anterior-posterior gradient of embryonic development can be detected in the regional cortical expansion profiles of children. The study further demonstrates that brain regions that develop together during childhood also tend to change together throughout the lifespan. This suggests that regional cortical development is a continuous process through the entire life, and that early-life factors have life-long impacts on this process.

Objective This study was undertaken to characterize primary peritoneal carcinoma (PPC) compared with ovarian carcinoma (OvC). Study design Within the framework of a nationwide epidemiologic Israeli study, 95 PPC patients were identified and compared with 117 FIGO stage III-IV epithelial OvC patients matched by age and continent of birth. Data were abstracted from medical records and personal interviews. Results Our data confirm the similarities between PPC and OvC. A higher rate of abdominal distention, volume of ascites, and malignant cells in ascitic fluid and lower rate of pelvic palpable mass and personal breast cancer history were found in the PPC compared with the OvC group. The overall survival was similar in both groups (30-33 months). In optimally cytoreduced patients, survival was better in the OvC group. Diameter of residual disease was associated with better survival only in the OvC group. Conclusion The clinical differences do not enable a preoperative distinction between the neoplasms.