The physical and social exposome affects human aging, and brain clocks may track its effects. However, most studies neglect multidomain exposures (physical, social and political) across diverse settings globally and their associations with brain aging. In this study, we characterized the associations between 73 country-level physical and social exposomal factors and multimodal brain age in 18,701 participants from 34 countries (healthy individuals and those with Alzheimer's disease, frontotemporal lobar degeneration or mild cognitive impairment). Exposome effects were assessed using generalized additive models and meta-analytic frameworks. Aggregated exposome models explained up to 15.5-fold more variance than individual exposures (delta Akaike information criterion (ΔAIC): 2,034-3,127). Physical exposome was primarily associated with accelerated structural brain aging (limbic, subcortical and cerebellar regions), whereas social exposome was more strongly associated with functional brain aging (frontotemporal and limbic networks). Exposome burden accounted for 3.3-9.1-fold higher risk of accelerated aging, exceeding effects of clinical diagnoses. Findings were out-of-sample validated in cross-sectional and longitudinal designs, remained consistent across clinical subgroups and persisted after adjustment for demographics, age correction bias, cognition, scanner type and data quality. The exposome accelerates brain aging in health and disease, underscoring the need to address physical, social and political inequities.

Hippocampal atrophy is a hallmark of Alzheimer’s disease and is linked to deficits in navigation. We investigated whether performance in a novel digital assessment, the Spatial Performance Assessment for Cognitive Evaluation (SPACE), is associated with hippocampal volume beyond traditional neuropsychological tests in older adults. Forty older adults (Mage = 67, SD = 6) underwent structural MRI and completed the spatial and navigation tasks in SPACE along with a battery of neuropsychological tests typically used to detect cognitive impairment. Regression analyses revealed that poorer performance in the path integration and mapping tasks was associated with smaller hippocampal volume after accounting for age, education, and neuropsychological test performance. Notably, individuals who accurately completed the path integration task and successfully learned the spatial configuration of landmarks required for subsequent reconstruction in the mapping task exhibited larger hippocampal volumes. Together, these findings suggest that SPACE may capture aspects of spatial cognition closely linked to hippocampal structural integrity and may complement existing cognitive assessments by providing increased sensitivity to hippocampal variation in non-clinical older adults.

BACKGROUND:Cerebral small vessel disease (SVD) is a major cause of ischemic stroke, intracerebral hemorrhage, and dementia. Despite its importance, there are few studies of its prevalence and how cerebral SVD varies across the world, different age ranges, sexes, and magnetic resonance imaging (MRI) parameters. SVD can be estimated using MRI neuroimaging markers, including white matter hyperintensities (WMHs), lacunes, cerebral microbleeds (CMBs), and perivascular spaces (PVS). AIMS:This study aimed to document the global prevalence of SVD based on population-based or large community-based MRI studies and to determine how SVD prevalence varies by region, mean age, and sex. With SVD neuroimaging markers being the standard to assess SVD prevalence, we aimed to investigate how different MRI acquisition parameters may influence its prevalence. SUMMARY OF REVIEW:In this systematic review and meta-analysis, articles were searched from the Ovid MEDLINE and EMBASE databases between 1 January 2000 and 31 March 2024, without language restrictions. Title and abstract screening, full-text review, and data extraction were performed by at least two independent reviewers. The prevalence of SVD, subject demographic information, and MRI acquisition parameters were extracted. The Risk of Bias for Non-randomized Studies tool was used. The protocol was registered on PROSPERO (CRD42022311133). Of 14,582 studies identified, 246 studies spanning 40 countries were included in the systematic review. In the meta-analysis, 85 studies (88 cohorts) from 17 regions (n = 1,562,765) were included. The quality of studies was high (mean score 7.67 out of 8, ranging between 5 and 8). The pooled prevalence of moderate-to-severe WMH was 18.9%, and the pooled mean of WMH volume was 4.4 mL. Pooled prevalences of lacunes, cerebral microbleeds (CMBs), and moderate-to-severe perivascular spaces (PVS) were 11.2%, 10.3%, and 22.6%, respectively. A lower lacune prevalence (7.3% vs 13.3%; adjusted OR (aOR) [95% confidence interval (CI)]: 0.45 [0.30-0.68]) but higher PVS prevalence (30.9% vs 19.6%; aOR [95% CI]: 12.15 [2.12-69.46]) was found in Europe compared with Asia. A higher mean age of the studies was associated with a higher prevalence of most SVD markers, except for PVS. There was an overall trend of more lacunes and CMBs in males. MRI field strength, sequence used, and slice thickness could potentially influence the reported SVD prevalence, especially for WMH volume and CMB count. There was high heterogeneity in the studies (>95%) that was not resolved by performing analyses stratified by Global Burden of Disease (GBD) regions, age groups, study design, or MRI parameters. CONCLUSION:This systematic review and meta-analysis based on large MRI studies demonstrated that SVD is a common health problem affecting about one-fifth of the adult population. SVD prevalence differs in regions separated by geographical regions. SVD prevalence is higher with increasing age. There is an overall trend of more lacunes and CMBs in males. WMH volume and CMB are SVD markers prone to the variability of MRI acquisition parameters, and a harmonised SVD scanning protocol should be used. More studies from middle- and low-income regions would benefit the estimation of a truly global prevalence of SVD.

Background Cognitive impairment and dementia are common in patients with heart failure, but the relationships between left ventricular (LV) function, cerebral small vessel disease (CSVD), and cognition remain poorly understood. This cross‐sectional study aims to investigate the associations between echocardiographic measures of LV function, magnetic resonance imaging markers of CSVD, and cognition. Methods LV systolic and diastolic function were assessed by echocardiography. CSVD markers were graded on 3 Tesla magnetic resonance imaging. Cognition was assessed using neuropsychological assessment and clinical diagnosis of cognitive impairment. Multivariable linear and logistic regression analyses were performed for continuous and binary outcomes, respectively, adjusting for demographics and cardiovascular risk factors. Mediation analysis was conducted to examine the mediating effect of CSVD markers on the association between LV function and cognition. Results A total of 261 patients (75.3±6.8 years) were recruited from a memory‐clinic cohort. LV function measures were associated with CSVD markers, with LV ejection fraction, average mitral inflow early‐diastolic velocity/annular early‐diastolic velocity, and tricuspid regurgitation velocity related to cortical cerebral microinfarcts, global longitudinal strain and mitral inflow early‐diastolic velocity/late‐diastolic velocity associated with lacunes, and impaired systolic function associated with white matter hyperintensities. Furthermore, LV systolic dysfunction was associated with lower global cognitive scores (β, −1.05 [95% CI, −1.76 to −0.34]) and increased odds of vascular cognitive impairment (OR, 7.91 [95% CI, 2.26–32.14]). CSVD markers mediated 22.5% of the association between systolic dysfunction and global cognition. Conclusions In a memory‐clinic population, significant associations were identified between LV function measures, CSVD markers, and cognition. The relationship between LV systolic function and global cognition was mediated by CSVD markers, suggesting the role of vascular pathologies in the heart–cognition connection.

Brain tissue segmentation is vital in Alzheimer's and dementia research for creating detailed neuroanatomical maps, diagnosing early-stage neurodegeneration, and guiding interventions. Although MRI remains the standard approach for its superior soft-tissue contrast, CT is a more accessible imaging modality in acute and resource-constrained settings. This study utilized paired CT-MRI datasets from the Gothenburg H70 Birth Cohort ( N  = 733) and the Memory Clinic Cohort of the National University Hospital, Singapore (NUS Dementia Cohort, N  = 210) to train and evaluate advanced segmentation models— nnUNet (2D & 3D models for 300-1000 epochs) and MedNeXt (3D- Small, Base, Medium and Large models for 3x3x3 & 5x5x5 kernels). MRI-derived labels were employed to guide CT segmentation, allowing accurate delineation of brain tissue segmentation (Gray Matter: GM, White Matter: WM and Cerebrospinal Fluid: CSF). Evaluation was conducted on all axial datasets for all variations of the models and for coronal & sagittal orientations the best performing models were utilized for inference. The 3D nnU-Net achieved average Dice Similarity Coefficients (DSCs) of 0.82, 0.72, and 0.76 for axial, coronal, and sagittal orientations, respectively, while MedNeXt demonstrated slightly superior performance with DSCs of 0.83, 0.73, and 0.78. MedNeXt also exhibited improved volumetric similarity in axial datasets, with scores ranging from 0.842 (CSF, sagittal) to 0.992 (WM, axial). When applied to dementia cohorts, MedNeXt achieved higher generalizability with an average DSC and volumetric similarity of 0.73 and 0.912, compared to 0.70 and 0.854 for nnU-Net. Extended training (1000 epochs) enhanced nnU-Net's performance, yet MedNeXt displayed superior scalability, handling larger kernel sizes and multi-modal imaging scenarios. However, significantly longer training times of up to 288 hours was required for the largest model. Automated CT brain segmentation guided by MRI-derived labels demonstrates clinically acceptable segmentation performance on untrained dementia cohort. nnU-Net is more resource-efficient and suitable for limited-resource settings, while MedNeXt has higher accuracy excelling in multi-orientation and multi-modal datasets. These findings validate the feasibility of using CT imaging with advanced segmentation frameworks to develop accessible neuroimaging tools for Alzheimer's and dementia research, addressing diagnostic challenges across diverse clinical contexts.

Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive accumulation of toxic amyloid species. The rising prevalence of AD in Asia has made it an increasing public health concern, placing a substantial burden on the economy and healthcare systems. Anti-amyloid therapies (AATs) have demonstrated statistically significant slowing of disease progression at the group level in pivotal trials in early symptomatic AD. We explore the clinical meaningfulness of AATs and considerations impacting on its meaning in East and Southeast Asia. We acknowledge that there is a lack of data on Asian populations, particularly from the perspectives of patients and caregivers, highlighting the need for such evidence to facilitate the successful adoption of AATs in the region. We also propose the conceptual Connect, Align, Reframe, Explain (CARE) communication framework and practical tools to support effective communication with patients and caregivers regarding the benefits of AATs. HIGHLIGHTS: Anti-amyloid therapies (AATs) have demonstrated statistically significant group-level effects in slowing of disease progression in early symptomatic Alzheimer's disease. Translating clinical trial outcomes into measures of benefit that are truly meaningful to patients and caregivers is critical for the adoption of AATs. Asia, with its rapidly aging societies, diverse cultural norms and heterogeneous healthcare and reimbursement systems, presents a unique perspective on the clinical meaningfulness of AATs. The proposed Connect, Align, Reframe, Explain (CARE) communication framework concept and practical support tools, such as goal-setting checklist, visual aids and motivational messages, can facilitate effective communication with patients and caregivers regarding the benefit of AATs. Optimizing the full potential of AATs in Asia requires focused efforts on understanding patients' and caregivers' perspectives on treatment benefits, building regional registries to collect real-world data, and aligning care frameworks.

OBJECTIVE:White matter hyperintensities (WMHs) moderate the association between diabetes and cognition, but the underlying mechanisms remain unknown. This study investigated the interaction effect between diabetes and WMHs on brain atrophy, the resulting atrophy patterns, and whether brain atrophy mediates the effect of diabetes on cognition. RESEARCH DESIGN AND METHODS:This study included individuals without dementia from two independent memory clinic-based cohorts: Harmonization (primary analysis, n = 112 case subjects with diabetes, n = 284 control subjects) and Alzheimer's Disease Neuroimaging Initiative (ADNI) (secondary analysis, n = 64 case subjects with diabetes, n = 600 control subjects). Participants underwent longitudinal brain MRI and cognitive assessments, along with plasma pTau181 measurement as a marker of Alzheimer disease (AD). WMHs and brain atrophy were quantified, with Schwarz signature, McEvoy signature, and hippocampal volume used as AD-specific atrophy measures. RESULTS:Diabetes was not associated with brain atrophy cross-sectionally or longitudinally. Instead, an interactive effect between diabetes and WMHs on brain atrophy was observed. In Harmonization, this interaction was significant in cross-sectional analyses, affecting cortical gray matter and the frontal lobe. No interactive effect was found for AD-specific atrophy, and the observed interactive effect remained significant after adjusting for plasma pTau181. Cortical gray matter mediated the effect of diabetes on cognition at higher WMHs burden. These results were replicated in ADNI, where diabetes and WMHs interacted to accelerate brain atrophy over time. CONCLUSIONS:Our study demonstrated diabetes and WMHs synergistically contribute to brain atrophy independent of AD, suggesting diabetes-associated cognitive impairment is primarily driven by cerebrovascular disease rather than Alzheimer pathology.

BACKGROUND:Cardiovascular-Kidney-Metabolic (CKM) syndrome assesses the interconnections among metabolic, kidney, and cardiovascular diseases, rendering significant prognostic value for age-related chronic diseases and mortality. We aimed to investigate the effects of CKM syndrome on transitions between healthy status, mental disorders, and dementia and evaluate the potential mediating role of a CKM-related metabolomic signature in these associations. METHODS:This prospective longitudinal study used UK Biobank data from 375,203 midlife and older adults at baseline and 188,018 with metabolomic information. CKM was staged from 0 to 4. Mental disorders and dementia were identified via ICD-10. Multi-state models analyzed the impact of CKM on transitions from healthy status to mental disorders and dementia. Competing risk (death) models assessed the associations of CKM with specific mental disorders and dementia. Mediation role of CKM-related metabolomic signature was evaluated. RESULTS:We show that per-stage CKM increase elevates hazards of transitioning from healthy to mental disorders (HR = 1.24[1.22-1.26]) and subsequently to dementia (HR = 1.38[1.21-1.58]), or directly to dementia (HR = 1.27[1.21-1.33]). Worsening CKM stages are associated with bipolar, depressive, and anxiety disorders; whilst only advanced stages (3/4) associated with all dementia types. The CKM metabolomic signature mediates 34.9% and 8.1% of associations of CKM with pre-dementia mental disorders and dementia, respectively. CONCLUSIONS:CKM syndrome is associated with pre-dementia mental disorders and dementia, emphasizing the need for regular monitoring and early intervention to manage CKM progression and reduce geriatric neuropsychiatric disturbances.

Supplementary Table 1. Statistical Assumption and Sample Size Estimates for MUCE Design

Cognitive flexibility supports efficient switching between mental sets and contributes to the preservation of general cognition in aging. It relies on the integration between brain functional dynamics and structural architecture. However, how this structure-function integration changes with age and contributes to cognitive flexibility decline in older adults remains unclear. In this study, we investigated longitudinal aging-related changes in multimodal structure-function integration, quantified as functional signal alignment (i.e., coupling) versus liberality (i.e., decoupling) relative to individual structural connectomes, which represent distinct spectral components, and tested their longitudinal associations with cognitive flexibility. Resting-state fMRI signals were decomposed based on diffusion MRI-derived structural networks using a graph signal processing framework. We focused on subnetworks within three core large-scale cognitive systems: the executive control network (ECN), default mode network (DMN), and salience network (SN). Across two independent datasets, the task-positive SN-A subnetwork, which includes core SN regions such as the anterior insula and dorsal anterior cingulate cortex, exhibited decreased coupling and increased decoupling with aging. Importantly, these changes were associated with a greater decline in cognitive flexibility (measured by the Trail Making Test and Color Trails Test) over time. In contrast, task-negative DMN-A (centered in the medial prefrontal and posterior cingulate cortex) showed aging-related changes in the opposite direction, with increased coupling and decreased decoupling over time. Together, these findings reveal network-specific trajectories of intrinsic structure-function integration in normal aging and indicate that preserved structure-function integration within the SN may be particularly important for maintaining cognitive flexibility in older adults.

INTRODUCTION:Cortical cerebral microinfarcts (CMIs) are associated with cognitive dysfunction and dementia, while their evolution on sequential magnetic resonance imaging (MRI) remains unclear. METHODS:The study enrolled 490 patients (72.5 ± 7.9 years) from a memory-clinic cohort, with 5-year follow-up. Cortical CMIs were graded at baseline and year 2 to identify incident lesions and other evolutionary patterns. Cognitive function was assessed annually. Clinical events, including dementia, stroke, and mortality, were recorded with time to event. RESULTS:Forty-one (8.4%) patients showed incident cortical CMIs at year 2. Additionally, 12 had CMIs becoming invisible, and six showed CMIs incorporated into new large infarctions. Baseline cortical CMIs and large cortical infarcts showed the strongest association with incident CMIs. Incident cortical CMIs were associated with cognitive decline, white matter hyperintensity progression, and incident dementia, independent of prevalent lesions. DISCUSSION:Cortical CMI evolution may reflect dynamic changes in brain vascular pathology and represent a potential target for interventions aimed at preserving cognitive function.

Obesity and lifelong body-shape fluctuation are associated with late-life structural brain damage, suggesting the involvement of metabolic pathways. The cerebral metabolic rate of oxygen (CMRO₂) reflects hemodynamic and oxidative stress and precedes structural atrophy, but its role in adiposity-related brain change remains unclear. We examined whether current and life-course adiposity relate to CMRO₂ and to structural change. A total of 303 community-dwelling adults aged 50 years and older were included. Body shape was assessed using Body Mass Index (BMI) and Body Roundness Index (BRI). Global CMRO₂ was derived from TRUST and phase-contrast MRI. T1-weighted MPRAGE provided volumetry, and medial temporal atrophy (MTA) grading. General linear models estimated associations of BMI and BRI with CMRO₂, including age interactions. Age-stratified mediation tested CMRO₂ as a mediator of adiposity to MTA associations. Body-shape trajectories at ages 25, 40, 60, and current age were modeled and related to CMRO₂ and metabolism-related regions. Adiposity was associated with lower CMRO₂: with overweight (β = -1.12 μmol/100 g/min, 95

The retina, as an extension of the central nervous system, shares a common embryological origin with the brain. In Alzheimer's disease (AD), studies of human tissue and animal models have revealed that hallmark AD pathologies, including amyloid-β (Aβ) deposits and pathological tau protein tangles, also appear in the retina. These findings, coupled with advances in high-resolution retinal imaging techniques, suggest the potential to detect and characterize AD-related molecular and structural changes in the retina. However, retinal findings across different AD mouse models have significant discrepancies and show limited concordance with human phenotypes, complicating the identification of AD-specific alterations and the selection of optimal models for translational research. Moreover, the temporal sequence and functional significance of retinal abnormalities across the AD continuum, from preclinical stages to mild cognitive impairment and overt dementia, remain poorly defined. Addressing these knowledge gaps is essential to establish the retina as a reliable, non-invasive screening and monitoring approach. This review synthesizes current evidence on the spectrum of retinal alterations in AD, including vascular dysfunction, neuroinflammation, impaired Aβ clearance, and neurodegeneration, as observed in diverse mouse models. We compare these manifestations across species and between different models, highlighting findings along the disease continuum to delineate convergent and divergent pathways. We further discuss how emerging technologies enable the identification of AD-specific retinal alterations, and advocate for a paradigm shift from non-specific morphological assessment (“seeing shapes”) toward molecular-level interrogation (“seeing components”). Interdisciplinary efforts and technological integration are crucial to establish retina as a dynamic mirror of pathology in AD.

Longitudinal dementia progression prediction is essential for clinical decision-making. However, models often degrade on external cohorts due to systemic missingness - where certain biomarkers available during training are completely absent at test time - compounded by distribution shifts and patient-specific variability. Here, we propose Progression-aware Feature Fusion with Test-Time Adaptation (ProFuse-TTA), a two-stage hierarchical Transformer for longitudinal dementia prediction. Stage 1 learns per-biomarker temporal representations from irregular observations without imputation. Stage 2 fuses them via cross-feature attention, with simulated modality dropout during training for robustness to systemic missingness. At inference, a lightweight test-time adaptation module performs per-individual calibration. We trained on ADNI and evaluated on three external cohorts comprising 2,316 participants and 13,205 timepoints, with controlled modality ablation experiments isolating the effect of systemic missingness. We compared against six baselines, four from a recent benchmark study and two new baselines including one built on a tabular foundation model. ProFuse-TTA achieved the best cross-dataset performance in 8 of 9 settings across clinical diagnosis, MMSE, and hippocampal volume prediction, and ranked first in 14 of 15 ablation scenarios. The model maintained superior performance across varying input lengths and prediction horizons up to 6 years. Pretrained ADNI models are available at XXX.

Background Neuroimaging-derived brain age is a promising biomarker of early neurodegeneration, but methodological variation in machine learning (ML) algorithms and input features as well as scarce evidence from various ethnic populations limit clinical translation. Objective To identify an accurate and interpretable machine learning-based brain age model for a multiethnic Asian population and examine its utility as a biomarker of early cognitive decline Methods Nine brain age prediction models were developed using 406 cognitively normal individuals (45-86 years) from two population-based studies using structural MRI features. Prediction performance was evaluated using mean absolute error (MAE) and Pearson's correlation coefficient (R2). Feature importance was assessed using the SHapley Additive exPlanations (SHAP) analysis based on best performing model. The model was applied to an independent cohort with no cognitive impairment (NCI), mild and moderate cognitive impairment no dementia (CIND), and dementia. Differences in BrainAGE across cognitive groups were examined using an ANOVA test. Results The chosen ensemble model, comprised of linear regression, lasso and SVR, was trained on 17 features (11 subcortical volumes and 6 lobe-level cortical thickness measures) and achieved an overall bias-corrected MAE and R2 of 4.04 years and 0.59 respectively. Feature importance analysis found thalamic, lateral ventricle, accumbens area and gray matter volume as important features for brain age prediction. Conclusions An interpretable ensemble ML model using structural MRI provides a robust BrainAGE biomarker capable of detecting early cognitive decline in multiethnic Asian populations.

Perivascular spaces (PVS), when abnormally enlarged and visible in magnetic resonance imaging (MRI) structural sequences, are important imaging markers of cerebral small vessel disease and potential indicators of neurodegenerative conditions. Despite their clinical significance, automatic enlarged PVS (EPVS) segmentation remains challenging due to their small size, variable morphology, similarity with other pathological features, and limited annotated datasets. This paper presents the EPVS Challenge organized at MICCAI 2024, which aims to advance the development of automated algorithms for EPVS segmentation across multi-site data. We provided a diverse dataset comprising 100 training, 50 validation, and 50 testing scans collected from multiple international sites (UK, Singapore, and China) with varying MRI protocols and demographics. All annotations followed the STRIVE protocol to ensure standardized ground truth and covered the full brain parenchyma. Seven teams completed the full challenge, implementing various deep learning approaches primarily based on U-Net architectures with innovations in multi-modal processing, ensemble strategies, and transformer-based components. Performance was evaluated using dice similarity coefficient, absolute volume difference, recall, and precision metrics. The winning method employed MedNeXt architecture with a dual 2D/3D strategy for handling varying slice thicknesses. The top solutions showed relatively good performance on test data from seen datasets, but significant degradation of performance was observed on the previously unseen Shanghai cohort, highlighting cross-site generalization challenges due to domain shift. This challenge establishes an important benchmark for EPVS segmentation methods and underscores the need for the continued development of robust algorithms that can generalize in diverse clinical settings.

Abstract Background and aims Recovery after a moderately severe ischaemic stroke remains limited. MLC601 (NeuroAiD) has been investigated as a neurorestorative therapy enhancing long-term functional recovery beyond spontaneous improvement. We will present meta-analytic findings on functional outcomes with MLC601 over 24 months. Methods A meta-analysis of published clinical studies of MLC601 in stroke was conducted, comparing MLC601 with placebo in patients with moderately severe ischaemic stroke, defined by a baseline NIH Stroke Scale (NIHSS) score of 8-14. Functional recovery was defined as a modified Rankin Scale (mRS) score of 0-1 at 3, 6, 12, 18, and 24 months. Adjusted odds ratios (ORs) with 95% confidence intervals were calculated, accounting for relevant covariates. Subgroup analyses focused on patients with significant baseline motor impairment, defined as a baseline NIHSS Limb Motor Score ≥3. Results In the overall population with moderately severe stroke, MLC601 was associated with higher odds of good functional recovery, with statistically significant benefits at 6 and 12 months and a peak clinical impact during this period. In patients with baseline motor impairment, the benefit was evident as early as Month 3 and remained statistically significant through Month 24, with consistently favourable ORs (>1). No attenuation or reversal of the treatment effect was observed over time. Conclusions This comprehensive meta-analysis indicates that MLC601 significantly increases the likelihood of achieving functional independence after a moderately severe ischaemic stroke, particularly in patients with early motor deficits. The early onset and long-term benefits support a neurorestorative mechanism of action addressing an important unmet need in stroke recovery. Conflict of interest Dr. N. Venketasubramanian: nothing to disclose. Pr. C. Chen: nothing to disclose

The convergence of neurological, psychiatric, neurodevelopmental, and public health approaches to brain health is reshaping global strategies for prevention, care, and policy. However, major gaps remain in the integration and implementation of brain and mental health frameworks across healthcare systems and regions.This paper describes the foundation and early development of the International Alliance on Brain Health (IABH), established in Switzerland in 2025 to promote interdisciplinary collaboration, reciprocal innovation, and implementation-oriented exchange between the global North and global South. Drawing on the Alliance's founding meeting in Bern, its contribution as a partner to the World Brain Health Forum organized by the Paris Brain Institute, and its subsequent meeting in Buenos Aires, the paper outlines the Alliance's global positioning and priorities for translating brain health frameworks into practice.Key themes included integrated neurological and mental health approaches, prevention across the life course, digital innovation, brain capital, workforce development, and stronger inclusion of global South perspectives in international policy dialogue. The Alliance also emphasized bidirectional learning and locally adaptable implementation strategies aligned with WHO brain health frameworks.Under the joint patronage of the World Federation of Neurology and the World Psychiatric Association, the IABH represents an emerging platform for cross-continental collaboration in brain health.

BACKGROUND:Blood-pressure reduction is the only proven treatment to prevent stroke. Whether a single pill that combines three antihypertensive drugs at low doses, in addition to standard antihypertensive treatment, can lower blood pressure more than standard care alone and reduce the risk of recurrent stroke after intracerebral hemorrhage is uncertain. METHODS:We conducted a multinational, double-blind, randomized, placebo-controlled trial involving patients with a history of intracerebral hemorrhage. Patients were eligible for the trial if they had a systolic blood pressure of 130 to 160 mm Hg at baseline and were in clinically stable condition. After a 2-week active run-in phase during which all the patients received a once-daily pill containing three antihypertensive agents at low doses (telmisartan at 20 mg, amlodipine at 2.5 mg, and indapamide at 1.25 mg; the triple pill), the patients were randomly assigned to continue receiving the triple pill or to receive matching placebo. The primary outcome was the first recurrent stroke. Secondary outcomes included blood-pressure control, major cardiovascular events, death from cardiovascular causes, and safety. RESULTS:Of 1670 patients who underwent randomization, 833 were assigned to receive the triple pill and 837 to receive placebo. The mean age of the patients was 58 years. At a median follow-up of 2.5 years, recurrent stroke had occurred in 38 patients (4.6%) in the triple-pill group and 62 (7.4%) in the placebo group (hazard ratio, 0.61; 95% confidence interval [CI], 0.41 to 0.92; P = 0.02). The mean systolic blood pressure during follow-up was 127 mm Hg and 138 mm Hg, respectively. The incidence of major cardiovascular events was lower with the triple pill than with placebo (6.6% vs. 9.8%; P = 0.04). Serious adverse events occurred in 23.2% of the patients in the triple-pill group and 26.0% of those in the placebo group. Early discontinuation of the trial regimen due to an adverse event occurred in 13.6% and 6.0%, respectively. The most common adverse event leading to discontinuation was an increase of 20% or more in the serum creatinine level. CONCLUSIONS:Among patients with intracerebral hemorrhage, treatment with a combination of three low-dose antihypertensive agents in a single pill, in addition to standard care, was associated with a lower incidence of recurrent stroke and major cardiovascular events than placebo. (Funded by the National Health and Medical Research Council of Australia and the Brazilian Ministry of Health; TRIDENT ClinicalTrials.gov number, NCT02699645; Australian New Zealand Clinical Trials Registry number, ACTRN12616000327482.).

Reduced left atrial (LA) strain is associated with cerebral small vessel disease (CSVD). In memory clinic patients without atrial fibrillation, LA reservoir strain and LA conduit strain were significantly associated with white matter hyperintensity volume and cerebral microinfarcts. From plasma proteomic profiling of 1,441 proteins, we identified 23 proteins significantly associated with LA reservoir strain, 425 with LA conduit strain, 199 with white matter hyperintensity volume, and 247 with cerebral microinfarcts. Among them, 13 proteins were commonly associated with both LA strain and CSVD, with 8 proteins showing evidence of significant mediation effects for the association between LA strain and CSVD. The mediators included tumor necrosis factor receptor superfamily member (TNFRSF)-11A, TNFRSF1B, TNFRSF10B, cystatin-C, nectin-4, insulin-like growth factor-binding protein, CD27, and brorin. Partial correlation network analysis also supported TNFRSF11A and insulin-like growth factor-binding protein as highly interconnected proteins in this study population. These plasma proteins may offer pathobiological clues linking LA dysfunction to CSVD.