Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

White matter brain age as a biomarker of cerebrovascular burden in the ageing brain.

As the brain ages, it almost invariably accumulates vascular pathology, which differentially affects the cerebral white matter. A rich body of research has investigated the link between vascular risk factors and the brain. One of the less studied questions is that among various modifiable vascular risk factors, which is the most debilitating one for white matter health? A white matter specific brain age was developed to evaluate the overall white matter health from diffusion weighted imaging, using a three-dimensional convolutional neural network deep learning model in both cross-sectional UK biobank participants (n = 37,327) and a longitudinal subset (n = 1409). White matter brain age gap (WMBAG) was the difference between the white matter age and the chronological age. Participants with one, two, and three or more vascular risk factors, compared to those without any, showed an elevated WMBAG of 0.54, 1.23, and 1.94 years, respectively. Diabetes was most strongly associated with an increased WMBAG (1.39 years, p < 0.001) among all risk factors followed by hypertension (0.87 years, p < 0.001) and smoking (0.69 years, p < 0.001). Baseline WMBAG was associated significantly with processing speed, executive and global cognition. Significant associations of diabetes and hypertension with poor processing speed and executive function were found to be mediated through the WMBAG. White matter specific brain age can be successfully targeted for the examination of the most relevant risk factors and cognition, and for tracking an individual's cerebrovascular ageing process. It also provides clinical basis for the better management of specific risk factors.

Brain structural covariances in the ageing brain in the UK Biobank.

The morphologic properties of brain regions co-vary or correlate with each other. Here we investigated the structural covariances of cortical thickness and subcortical volumes in the ageing brain, along with their associations with age and cognition, using cross-sectional data from the UK Biobank (N = 42,075, aged 45-83 years, 53% female). As the structural covariance should be estimated in a group of participants, all participants were divided into 84 non-overlapping, equal-sized age groups ranging from the youngest to the oldest. We examined 84 cortical thickness covariances and subcortical covariances. Our findings include: (1) there were significant differences in the variability of structural covariance in the ageing process, including an increased variance, and a decreased entropy. (2) significant enrichment in pairwise correlations between brain regions within the occipital lobe was observed in all age groups; (3) structural covariance in older age, especially after the age of around 64, was significantly different from that in the youngest group (median age 48 years); (4) sixty-two of the total 528 pairs of cortical thickness correlations and 10 of the total 21 pairs of subcortical volume correlations showed significant associations with age. These trends varied, with some correlations strengthening, some weakening, and some reversing in direction with advancing age. Additionally, as ageing was associated with cognitive decline, most of the correlations with cognition displayed an opposite trend compared to age associated patterns of correlations.

Association of Blood Pressure With Brain Ages: A Cohort Study of Gray and White Matter Aging Discrepancy in Mid-to-Older Adults From UK Biobank.

BACKGROUND: Gray matter (GM) and white matter (WM) impairments are both associated with raised blood pressure (BP), although whether elevated BP is differentially associated with the GM and WM aging process remains inadequately examined. METHODS: We included 37 327 participants with diffusion-weighted imaging (DWI) and 39 630 participants with T1-weighted scans from UK Biobank. BP was classified into 4 categories: normal BP, high-normal BP, grade 1, and grade 2 hypertension. Brain age gaps (BAGs) for GM (BAGGM) and WM (BAGWM) were derived from diffusion-weighted imaging and T1 scans separately using 3-dimensional-convolutional neural network deep learning techniques. RESULTS: There was an increase in both BAGGM and BAGWM with raised BP (P<0.05). BAGWM was significantly larger than BAGGM at high-normal BP (0.195 years older; P=0.006), grade 1 hypertension (0.174 years older; P=0.004), and grade 2 hypertension (0.510 years older; P<0.001), but not for normal BP. Mediation analysis revealed that the association between hypertension and cognitive decline was primarily mediated by WM impairment. Mendelian randomization analysis suggested a causal relationship between hypertension and WM aging acceleration (unstandardized B, 1.780; P=0.016) but not for GM (P>0.05). Sliding-window analysis indicated the association between hypertension and brain aging acceleration was moderated by chronological age, showing stronger correlations in midlife but weaker associations in the older age. CONCLUSIONS: Compared with GM, WM was more vulnerable to raised BP. Our study provided compelling evidence that concerted efforts should be directed towards WM damage in individuals with hypertension in clinical practice.

Brain-Derived Neurotrophic Factor Val66Met is Associated with Variation in Cortical Structure in Healthy Aging Subjects.

Aging is associated with progressive brain atrophy and declines in learning and memory, often attributed to hippocampal or cortical deterioration. The role of brain-derived neurotrophic factor (BDNF) in modulating the structural and functional changes in the brain and visual system, particularly in relation to BDNF Val66Met polymorphism, remains underexplored. In this present cross-sectional observational study, we aimed to assess the effects of BDNF polymorphism on brain structural integrity, cognitive function, and visual pathway alterations. A total of 108 older individuals with no evidence of dementia and a mean (SD) age of 67.3 (9.1) years were recruited from the Optic Nerve Decline and Cognitive Change (ONDCC) study cohort. The BDNF Met allele carriage had a significant association with lower entorhinal cortex volume (6.7% lower compared to the Val/Val genotype, P = 0.02) and posterior cingulate volume (3.2% lower than the Val/Val group, P = 0.03), after adjusting for confounding factors including age, sex and estimated total intracranial volumes (eTIV). No significant associations were identified between the BDNF Val66Met genotype and other brain volumetric or diffusion measures, cognitive performances, or vision parameters except for temporal retinal nerve fibre layer thickness. Small but significant correlations were found between visual structural and functional, cognitive, and brain morphological metrics. Our findings suggest that carriage of BDNF Val66Met polymorphism is associated with lower entorhinal cortex and posterior cingulate volumes and may be involved in modulating the cortical morphology along the aging process.

Exploring the link between brain topological resilience and cognitive performance in the context of aging and vascular risk factors: A cross-ethnicity population-based study.

Brain aging is typically associated with a significant decline in cognitive performance. Vascular risk factors (VRF) and subsequent atherosclerosis (AS) play a major role in this process. Brain resilience reflects the brain's ability to withstand external perturbations, but the relationship of brain resilience with cognition during the aging process remains unclear. Here, we investigated how brain topological resilience (BTR) is associated with cognitive performance in the face of aging and vascular risk factors. We used data from two cross-ethnicity community cohorts, PolyvasculaR Evaluation for Cognitive Impairment and Vascular Events (PRECISE, n = 2220) and Sydney Memory and Ageing Study (MAS, n = 246). We conducted an attack simulation on brain structural networks based on k-shell decomposition and node degree centrality. BTR was defined based on changes in the size of the largest subgroup of the network during the simulation process. Subsequently, we explored the negative correlations of BTR with age, VRF, and AS, and its positive correlation with cognitive performance. Furthermore, using structural equation modeling (SEM), we constructed path models to analyze the directional dependencies among these variables, demonstrating that aging, AS, and VRF affect cognition by disrupting BTR. Our results also indicated the specificity of this metric, independent of brain volume. Overall, these findings underscore the supportive role of BTR on cognition during aging and highlight its potential application as an imaging marker for objective assessment of brain cognitive performance.

Risk factors and cognitive correlates of white matter hyperintensities in ethnically diverse populations without dementia: The COSMIC consortium.

INTRODUCTION: White matter hyperintensities (WMHs) are an important imaging marker for cerebral small vessel diseases, but their risk factors and cognitive associations have not been well documented in populations of different ethnicities and/or from different geographical regions. METHODS: We investigated how WMHs were associated with vascular risk factors and cognition in both Whites and Asians, using data from five population-based cohorts of non-demented older individuals from Australia, Singapore, South Korea, and Sweden (N = 1946). WMH volumes (whole brain, periventricular, and deep) were quantified with UBO Detector and harmonized using the ComBat model. We also harmonized various vascular risk factors and scores for global cognition and individual cognitive domains. RESULTS: Factors associated with larger whole brain WMH volumes included diabetes, hypertension, stroke, current smoking, body mass index, higher alcohol intake, and insufficient physical activity. Hypertension and stroke had stronger associations with WMH volumes in Whites than in Asians. No associations between WMH volumes and cognitive performance were found after correction for multiple testing. CONCLUSION: The current study highlights ethnic differences in the contributions of vascular risk factors to WMHs.

The protocol for an observational Australian cohort study of CADASIL: The AusCADASIL study.

Introduction: Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL) is a rare genetic condition with a broad phenotypic presentation. This study aims to establish the first Australian cohort of individuals affected by CADASIL (AusCADASIL) and examine its clinical features and longitudinal course, and to investigate neuroimaging and blood biomarkers to assist in early diagnosis and identify disease progression. Methods: Participants will be recruited from six study centres across Australia for an observational study of CADASIL. We aim to recruit 150 participants with diagnosed CADASIL, family history of CADASIL or suspected CADASIL symptoms, and 150 cognitively normal NOTCH3 negative individuals as controls. Participants will complete: 1) online questionnaires on medical and family history, mental health, and wellbeing; 2) neuropsychological evaluation; 3) neurological examination and brain MRI; 4) ocular examination and 5) blood sample donation. Participants will have annual follow-up for 4 years to assess their progression and will be asked to invite a study partner to corroborate their self-reported cognitive and functional abilities.Primary outcomes include cognitive function and neuroimaging abnormalities. Secondary outcomes include investigation of genetics and blood and ocular biomarkers. Data from the cohort will contribute to an international consortium, and cohort participants will be invited to access future treatment/health intervention trials. Discussion: AusCADASIL will be the first study of an Australian cohort of individuals with CADASIL. The study will identify common pathogenic variants in this cohort, and characterise the pattern of clinical presentation and longitudinal progression, including imaging features, blood and ocular biomarkers and cognitive profile.

Cortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC.

Structural neuroimaging studies have identified a combination of shared and disorder-specific patterns of gray matter (GM) deficits across psychiatric disorders. Pooling large data allows for examination of a possible common neuroanatomical basis that may identify a certain vulnerability for mental illness. Large-scale collaborative research is already facilitated by data repositories, institutionally supported databases, and data archives. However, these data-sharing methodologies can suffer from significant barriers. Federated approaches augment these approaches by enabling access or more sophisticated, shareable and scaled-up analyses of large-scale data. We examined GM alterations using Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation, an open-source, decentralized analysis application. Through federated analysis of eight sites, we identified significant overlap in the GM patterns (n = 4,102) of individuals with schizophrenia, major depressive disorder, and autism spectrum disorder. These results show cortical and subcortical regions that may indicate a shared vulnerability to psychiatric disorders.