Dementia is a growing global health burden,with mild cognitive impairment(MCI)serving as a critical transitional stage[1].Identifying the nutritional risk factors is crucial for prevention.While nutrition plays a key role in preventing age-related diseases and staple foods are a major dietary source,existing research on cognitive function has predominantly emphasized whole-grain consumption.There is limited evidence regarding the effects of specific staple food types on MCI.

Genes impacting the bioaccumulation of perfluoroalkyl and polyfluoroalkyl substances (PFASs)and their neurotoxic effects on the brain and behavior remain unclear. Here,we examined genome-wide associations with serum accumulation of 13 PFASs in 6,823 Chinese adults. We revealed that perfluoroheptanoic acid (PFHpA) accumulation was associated with genetic variants at two loci (3q29: P = 5.20 ×10-19; 6p22.2: P = 3.69 ×10-23), mapping to 56 genes.Blood expression of 27 of these genes was associated with PFHpA accumulation in 573 subsamples. Eight genes showed potential causal effects on PFHpA accumulation,functionally linked to innate immunity (TRIM38, ZDHHC19, MUC20)and organic solute transport (SLC51A and SLC17A3). We assessed the impact of PFASs on cortical thickness and surface area, white matter fractional anisotropy and mean diffusivity,along with 25 behavioral phenotypes. We identified that seven PFASs were correlated with reduced cortical morphology, primarily in the prefrontal cortex. We also found a statistical causal effect of PFHpA accumulation on the surface area in the right frontomarginal cortex, which mediated the effect of PFHpA on anxiety. These findings indicate that serum PFHpA accumulation may be regulated by genes related to innate immunity and solute transport, heightening anxiety by impairing the prefrontal cortex.

BACKGROUND:As an extension of diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI) quantifies non-Gaussian water diffusion and has been applied to explore brain disorders. However, the genetic architecture of brain DKI phenotypes remains unknown. METHODS:Here, we estimated heritability and conducted genome-wide association studies (GWASs) for 804 DKI phenotypes across 188 brain structures in 4183 participants. To determine whether DKI-GWASs provides genetic insights beyond DTI-GWASs, we compared results from 804 DKI-GWASs and 752 DTI-GWASs in the same cohort. To clarify the biological significance of DKI phenotypes, we examined associations between DKI phenotypes and brain health-related outcomes within the CHIMGEN, and explored associations between polygenic risk scores (PRSs) of DKI phenotypes and mental disorders in the UK Biobank. FINDINGS:Of 804 DKI phenotypes, 275 showed significant heritability (P < 0.05; h2 range: 0.143-0.602). We detected 280 significant associations (P < 5 × 10-8), with 38 surviving Bonferroni correction (P < 1.54 × 10-10). These associations were unevenly distributed across chromosomes, DKI phenotype subgroups, and brain structures. Among 229 independent variant-structure associations for DKI, 175 (76.4%) were DKI-specific. We observed 930 associations between DKI phenotypes and brain health-related outcomes (P < 0.05; ten Bonferroni-significant with P < 1.02 × 10-5), and 200 between PRSs and mental disorders (P < 0.05; one Bonferroni-significant with P < 9.61 × 10-5). INTERPRETATION:This study delineates the genetic architecture of brain DKI phenotypes, identifies complementary genetic insights into brain microstructure, and provides biologically relevant endophenotypes for investigating neural mechanisms underlying brain disorders. FUNDING:National Natural Science Foundation of China, National Key Research and Development Program of China, Tianjin Key Medical Discipline Construction Project, and Tianjin Natural Science Foundation.

Cerebral asymmetry is a core principle of human brain organization, showing dynamic changes across the lifespan and alterations in brain disorders. However, it remains unclear whether lifespan trajectories of asymmetry differ across populations. We compared lifespan structural asymmetry normative charts of 221 cerebral imaging phenotypes from 43,037 Chinese and 56,339 Western participants aged 0–100 years. The two populations showed distinct lifespan asymmetry patterns in 26.2% of the phenotypes. Chinese-minus-Western asymmetry difference curves displayed distinct patterns across brain phenotypes: rightward (45.7%), leftward (26.2%), rightward-to-leftward (11.8%), leftward-to-rightward (10.0%), and unclassified (6.3%). Population-matched normative models outperformed population-unmatched normative models in capturing normal asymmetry variability among healthy individuals and in detecting abnormal asymmetry deviations in patients with Alzheimer’s disease, mild cognitive impairment, schizophrenia, and major depressive disorder. These findings indicate that population mismatch can bias chart-based individual-level asymmetry assessment and underscore the need for population-representative brain asymmetry normative charts.

With the worldwide increase in only-child families, it is crucial to understand the effects of growing up without siblings (GWS) on the adult brain, behaviour and the underlying pathways. Using the CHIMGEN cohort, we investigated the associations of GWS with adult brain structure, function, connectivity, cognition, personality and mental health, as well as the pathway from GWS to GWS-related growth environments to brain and to behaviour development, in 2,397 pairs of individuals with and without siblings well matched in covariates. We found associations linking GWS to higher language fibre integrity, lower motor fibre integrity, larger cerebellar volume, smaller cerebral volume and lower frontotemporal spontaneous brain activity. Contrary to the stereotypical impression of associations between GWS and problem behaviours, we found positive correlations of GWS with neurocognition and mental health. Despite direct effects, GWS affects most brain and behavioural outcomes through modifiable environments, such as socioeconomic status, maternal care and family support, suggesting targets for interventions to enhance children's healthy growth.

The brainstem houses numerous nuclei and tracts that serve vital functions. Genome-wide associations with brainstem substructure volumes have been explored in European individuals, yet other ancestries remain under-represented. Here, we conduct cross-ancestry genome-wide association meta-analyses in 103,098 individuals for brainstem and 78,062 individuals for eight substructure volumes, including 7094 Chinese Han individuals. We identify 713 locus-trait associations with brainstem and substructure volumes at P < 5.56 ×10−9, comprising 569 new associations. Two associations show different effect sizes, while 496 associations have similar effect sizes between ancestries. We prioritize 186 genes associated with brainstem volumetric traits. We find both shared and distinct genetic loci, genes, and pathways for midbrain, pons, and medulla volumes, along with the shared genetic architectures related to disease phenotypes and physiological functions. The results provide new insights into the genetic architectures of brainstem and substructure volumes and their genetic associations with brainstem physiologies and pathologies. A cross-ancestry GWAS meta-analyses of brainstem structures identify 713 associations. It reveals shared/distinct genetic architectures across ancestries/substructures and overlaps with neuropsychiatric disorders and physiological functions.

The amygdala is a small but critical multi-nucleus structure for emotion, cognition and neuropsychiatric disorders. Although genetic associations with amygdala volumetric traits have been investigated in sex-combined European populations, cross-ancestry and sex-stratified analyses are lacking. Here we conducted cross-ancestry and sex-stratified genome-wide association analyses for 21 amygdala volumetric traits in 6,923 Chinese and 48,634 European individuals. We identified 191 variant-trait associations (P < 2.38 × 10-9), including 47 new associations (12 new loci) in sex-combined univariate analyses and seven additional new loci in sex-combined and sex-stratified multivariate analyses. We identified 12 ancestry-specific and two sex-specific associations. The identified genetic variants include 16 fine-mapped causal variants and regulate amygdala and fetal brain gene expression. The variants were enriched for brain development and colocalized with mood, cognition and neuropsychiatric disorders. These results indicate that cross-ancestry and sex-stratified genetic association analyses may provide insight into the genetic architectures of amygdala and subnucleus volumes.

National Medical Licensing Examination (NMLE) is the entrance exam for medical practice in China, and its general medical knowledge test (GMKT) evaluates abilities of medical students to comprehensively apply medical knowledge to clinical practice. This study aimed to identify nonacademic predictors of GMKT performance, which would benefit medical schools in designing appropriate strategies and techniques to facilitate the transition from medical students to qualified medical practitioners. In 1202 medical students, we conducted the deletion-substitution-addition (DSA) and structural equation model (SEM) analyses to identify nonacademic predictors of GMKT performance from 98 candidate variables including early life events, physical conditions, psychological and personality assessments, cognitive abilities, and socioeconomic conditions. The candidate variables were assessed using psychometrically or cognitively validated and accepted instruments. We identified seven nonacademic predictors for GMKT performance. Body mass index (BMI) and working memory reaction time showed direct negative effects on GMKT performance. Psychological and personality features (conscientiousness, state anxiety, and openness to experience) indirectly affected GMKT performance via BMI, while socioeconomic conditions (father’s education and mother’s occupation) indirectly affected GMKT performance by influencing psychological and personality features and further BMI. The identified nonacademic predictors for GMKT performance and their pathways may be useful for improving medical education by strengthening favorable and weakening, rectifying, or compensating unfavorable factors that are modifiable.

Brain structure and function show substantial individual differences, finely controlled by genes, environments, and their interactions. Despite the increasing knowledge about genetic and environmental main effects, gene-environment interaction effects on brain phenotypes remain elusive. This study investigates genome-wide by environment (41 exposures) interactions on 598 brain imaging phenotypes in 7084 healthy young adults. Both univariate and multivariate analyses identify 486 significant gene-environment interactions, scattered across the genome, exposome, and phenome. These interactions explain more variances of phenotypes than genetic and environmental main effects (100% of genetic and 96% of environmental main effects are non-significant). Variants with interactions are enriched in intronic and intergenic regions, comprising 79 regulatory variants and 145 associated with brain gene expression. Protein-protein interaction network analyses reveal distinct interaction networks for genes associated with air pollution (hubs: H4C6, SMARCA4, and RPS11) and urbanicity (hubs: CCND1, CALM3, and CDK2) exposures. Genes that interacted with air pollution exposures exhibit enrichment in pathways related to metal ion detoxification and homeostasis. For time-varying exposures, 144 interactions demonstrate sensitive periods, predominantly in childhood (ages 4-7) and adolescence (ages 12-15). These findings highlight the value of genome-wide by exposome-wide interaction studies, which may offer crucial information for optimizing brain health outcomes.

Patients with chronic subcortical stroke undergo regional and network morphometric reorganizations beyond the lesion site, but the interplay between network and regional reorganization remains poorly understood. We aimed to clarify the reorganization patterns of the individualized differential structural covariance networks (IDSCN) in chronic subcortical stroke and investigate their associations with regional gray matter volume (GMV) changes and functional recovery. Structural MRI from four datasets enrolled 112 patients with chronic subcortical stroke (81 male, age: 55.82 ± 7.79) and 122 matched healthy controls (HC) (74 male; age: 55.28 ± 7.54). Network-based statistics were employed to identify aberrant IDSCN, Spearman correlation was conducted to assess the association between IDSCN and regional GMV alterations, and partial correlation was utilized to investigate the association between abnormal IDSCN and functional recovery. We identified 133 connections with balanced increased and decreased IDSCN. Aberrant IDSCN involved more regions than local GMV alterations, local GMV alteration exhibited intricate correlations with IDSCN, which could explain partly IDSCN reorganization (p < 0.05, corrected). Finally, abnormal IDSCN showed a weak association with long-term clinical recovery (p < 0.01). These findings reinforce the theory of adaptive network reorganization post-stroke and suggest that IDSCN may provide further insights into cortical reorganization and functional rehabilitation beyond regional morphometric measures.

BackgroundThe thalamus, along with its component nuclei, possesses extensive connections with various brain regions and is engaged in diverse functions. However, it is unknown whether the gray matter volume (GMV) covariance networks of thalamic subfields are selectively affected in chronic capsular stroke.MethodsWe recruited 45 patients with chronic right capsular strokes (CS) and 93 normal controls (NC) from three centers. The thalamus was segmented into 25 subfields using FreeSurfer (v7.1.1). A general linear model was applied to investigate intergroup differences in the GMV covariance network of each thalamic subfield with each voxel of the entire brain between CS and NC, correcting for confounders such as age, gender, total intracranial volume (TIV), and scanners (voxel-wise p < 0.001, cluster-wise FWE corrected p < 0.05).ResultsOur findings revealed that all 25 ipsilesional thalamic subfields in CS were atrophied (p < 0.05, FDR correction). Among these, 16 ipsilesional thalamic subfields (including AV, LD, LP, VLa, VLp, VPL, VM, CeM, CL, MDm, LGN, PuM, PuI, CM, Pf, and Pt) exhibited significantly subfield-specific increased GMV covariance connectivity with the anterior orbital gyrus, superior occipital gyrus, calcarine, anterior cingulate cortex, precentral gyrus, and other regions. Additionally, although none of the contralesional thalamic subfields demonstrated regional GMV changes, 3/25 showed subfield-specific increased GMV covariance connectivity with the ipsilesional anterior orbital gyrus and subcortex.ConclusionThe GMV covariance networks of thalamic subfields are selectively involved in patients with chronic capsular stroke, which affect not only the ipsilesional thalamic subfields but also the contralesional ones.

Socioeconomic status (SES) is a time-varying multidimensional construct with ill-defined dimension-specific and age-specific effects on brain and behavior. We investigated these effects in 4,228 young adults. From 16 socioeconomic indicators, assessed for early (0-10 years) and late (>10 years) stages, we constructed family, provincial, family adverse and neighborhood adverse socioeconomic dimensions. Generally, family SES was associated with brain structure and connectivity along with cognitive function, whereas family adverse and neighborhood adverse SES were associated with personality and emotion. Most associations were observed for both early and late-stage SES; however, adjusting for the effect of early stage SES revealed late-stage-specific SES effects. Changes in SES were associated with personality and cognitive function. Cerebellar and medial frontal volumes and functional connectivity within the left frontoparietal network mediated the associations between family SES and memory and openness. These results inform both more precise interventions for reducing the consequences of adverse SES and experimental designs for excluding confounding socioeconomic effects on human health.

Human brain charts provide unprecedented opportunities for decoding neurodevelopmental milestones and establishing clinical benchmarks for precision brain medicine 1-7. However, current lifespan brain charts are primarily derived from European and North American cohorts, with Asian populations severely underrepresented. Here, we present the first population-specific brain charts for China, developed through the Chinese Lifespan Brain Mapping Consortium (Phase I) using neuroimaging data from 43,037 participants (aged 0-100 years) across 384 sites nationwide. We establish the lifespan normative trajectories for 296 structural brain phenotypes, encompassing global, subcortical, and cortical measures. Cross-population comparisons with Western brain charts (based on data from 56,339 participants aged 0-100 years) reveal distinct neurodevelopmental patterns in the Chinese population, including prolonged cortical and subcortical maturation, accelerated cerebellar growth, and earlier development of sensorimotor regions relative to paralimbic regions. Crucially, these Chinese-specific charts outperform Western-derived models in predicting healthy brain phenotypes and detecting pathological deviations in Chinese clinical cohorts. These findings highlight the urgent need for diverse, population-representative brain charts to advance equitable precision neuroscience and improve clinical validity across populations.

BACKGROUND:Cardio-cerebrovascular diseases are linked to neuroanatomical changes that may arise from genetic risk profiles, with structural brain alterations often preceding clinical onset. The pathways connecting genetic susceptibility to early neuroimaging phenotypes remain unclear. PURPOSE:To examine the effects of polygenic risk for cardio-cerebrovascular diseases (PRSCCVD) on brain structure in healthy middle-aged and older adults. STUDY TYPE:Observational cohort study. POPULATION:29,714 healthy White British individuals (mean age 54.86 ± 7.44 years; 13,691 male). FIELD STRENGTH/SEQUENCE:3T MRI; 3D-MPRAGE T1, DTI (monopolar Stejskal-Tanner). ASSESSMENT:Brain structural measurements were derived from 48 white matter tracts (Johns Hopkins University atlas) using eight diffusion MRI metrics for white matter microstructure, and from 62 cortical regions (Desikan-Killiany-Tourville atlas) for cortical thickness and surface area measurements. Confirmatory factor analysis was applied to calculate the polygenic risk score for PRSCCVD based on PRS scores for cardiovascular disease, coronary artery disease, and ischemic stroke. STATISTICAL TESTS:Associations between PRSCCVD and 508 brain structural MRI phenotypes were examined using multivariate linear regression with Bonferroni correction. Comparative analyses between gray and white matter phenotypes, as well as between older (≥ 60 years) and middle-aged (< 60 years) participants, were performed using Wilcoxon signed-rank tests (p < 0.05). Propensity score matching was applied to balance covariates in age-related effect analyses. RESULTS:PRSCCVD was significantly associated with 117 white matter (β range: -0.042 to 0.051, partial R 2 range: 0.29% to 0.05%) and two cortical phenotypes (bilateral insular: β = -0.023, partial R 2 range: 0.05%-0.06%) in healthy middle-aged and older adults. White matter microstructure was more vulnerable than cortical morphometry (W = 37,912). Older adults had more severe white matter microstructure damage than the middle-aged people (W = 49,046). DATA CONCLUSION:PRSCCVD preferentially affects white matter microstructure over cortical morphometry in asymptomatic individuals, with exacerbated effects in older adults. EVIDENCE LEVEL:3. TECHNICAL EFFICACY:2.

This study investigates abnormalities in cerebellar-cerebral static and dynamic functional connectivity among patients with acute pontine infarction, examining the relationship between these connectivity changes and behavioral dysfunction. Resting-state functional magnetic resonance imaging was utilized to collect data from 45 patients within seven days post-pontine infarction and 34 normal controls. Seed-based static and dynamic functional connectivity analyses identified divergences in cerebellar-cerebral connectivity features between pontine infarction patients and normal controls. Correlations between abnormal functional connectivity features and behavioral scores were explored. Compared to normal controls, left pontine infarction patients exhibited significantly increased static functional connectivity within the executive, affective-limbic, and motor networks. Conversely, right pontine infarction patients demonstrated decreased static functional connectivity in the executive, affective-limbic, and default mode networks, alongside an increase in the executive and motor networks. Decreased temporal variability of dynamic functional connectivity was observed in the executive and default mode networks among left pontine infarction patients. Furthermore, abnormalities in static and dynamic functional connectivity within the executive network correlated with motor and working memory performance in patients. These findings suggest that alterations in cerebellar-cerebral static and dynamic functional connectivity could underpin the behavioral dysfunctions observed in acute pontine infarction patients.

Depression, a widespread and highly heritable mental health condition, profoundly affects millions of individuals worldwide. Neuroimaging studies have consistently revealed volumetric abnormalities in subcortical structures associated with depression. However, the genetic underpinnings shared between depression and subcortical volumes remain inadequately understood. Here, we investigate the extent of polygenic overlap using the bivariate causal mixture model (MiXeR), leveraging summary statistics from the largest genome-wide association studies for depression (N = 674,452) and 14 subcortical volumetric phenotypes (N = 33,224). Additionally, we identify shared genomic loci through conditional/conjunctional FDR analyses. MiXeR shows that subcortical volumetric traits share a substantial proportion of genetic variants with depression, with 44 distinct shared loci identified by subsequent conjunctional FDR analysis. These shared loci are predominantly located in intronic regions (58.7%) and non-coding RNA intronic regions (25.4%). The 269 protein-coding genes mapped by these shared loci exhibit specific developmental trajectories, with the expression level of 55 genes linked to both depression and subcortical volumes, and 30 genes linked to cognitive abilities and behavioral symptoms. These findings highlight a shared genetic architecture between depression and subcortical volumetric phenotypes, enriching our understanding of the neurobiological underpinnings of depression. Depression affects millions of people worldwide. Here, the authors show a substantial polygenic overlap between depression and brain subcortical volumes, identifying 44 shared loci.

Potential changes in patterns of dynamic functional network connections at the cerebellar-cerebral level in pontine infarction (PI) patients remain unclear. The study aimed to investigate the abnormal patterns of dynamic functional connectivity (dFC) between the cerebellar subregions within networks and regions of the cerebral cortex in patients with PI. Forty-six chronic left pontine infarction (LPI), 32 chronic right pontine infarction (RPI), and 50 healthy controls (HCs) were recruited to undergo resting-state fMRI scans. Cerebellar-cerebral dFC was characterized using the sliding window method and seed-based connectivity analyses. Correlations between altered dFC values and clinical variables (The Rey Auditory Verbal Learning Test and Flanker task) in PI patients and healthy controls were investigated. Compared with HCs, the PI groups showed significantly aberrant cerebellar-cerebral dFC between cerebellar subregions within networks and supratentorial cerebral cortex, including executive, default-mode, and motor networks. Furthermore, Correlation analysis showed a decoupling between abnormal dFC and cognitive functions in PI patients. These findings indicate that PI patients are accompanied by damage to cerebellar subregions within networks and cerebellar-cerebral pathways, which may provide a potential target for treatment or an indication of therapeutic efficacy.

Genome-wide association studies of brain imaging phenotypes are mainly performed in European populations, but other populations are severely under-represented. Here, we conducted Chinese-alone and cross-ancestry genome-wide association studies of 3,414 brain imaging phenotypes in 7,058 Chinese Han and 33,224 white British participants. We identified 38 new associations in Chinese-alone analyses and 486 additional new associations in cross-ancestry meta-analyses at P < 1.46 × 10-11 for discovery and P < 0.05 for replication. We pooled significant autosomal associations identified by single- or cross-ancestry analyses into 6,443 independent associations, which showed uneven distribution in the genome and the phenotype subgroups. We further divided them into 44 associations with different effect sizes and 3,557 associations with similar effect sizes between ancestries. Loci of these associations were shared with 15 brain-related non-imaging traits including cognition and neuropsychiatric disorders. Our results provide a valuable catalog of genetic associations for brain imaging phenotypes in more diverse populations.

Background Internal capsule strokes often result in multidomain cognitive impairments across memory, attention, and executive function, typically due to disruptions in brain network connectivity. Our study examines these impairments by analyzing interactions within the triple-network model, focusing on both static and dynamic aspects. Methods We collected resting-state fMRI data from 62 left (CI_L) and 56 right (CI_R) internal capsule stroke patients, along with 57 healthy controls (HC). Using independent component analysis to extract the default mode (DMN), executive control (ECN), and salience networks (SAN), we conducted static and dynamic functional network connectivity analyses (DFNC) to identify differences between stroke patients and controls. For DFNC, we used k-means clustering to focus on temporal properties and multilayer network analysis to examine integration and modularity Q, where integration represents dynamic interactions between networks, and modularity Q measures how well the network is divided into distinct modules. We then calculated the correlations between SFNC/DFNC properties with significant inter-group differences and cognitive scales. Results Compared to HC, both CI_L and CI_R patients showed increased static FCs between SAN and DMN and decreased dynamic interactions between ECN and other networks. CI_R patients also had heightened static FCs between SAN and ECN and maintained a state with strongly positive FNCs across all networks in the triple-network model. Additionally, CI_R patients displayed decreased modularity Q. Conclusion These findings highlight that stroke can result in the disruption of static and dynamic interactions in the triple network model, aiding our understanding of the neuropathological basis for multidomain cognitive deficits after internal capsule stroke.

BackgroundThrough its extensive connection with the cortex, the thalamus constitutes the hub of cortico-subcortical circuits and participants in multi-dimensional functions. However, the differential involvements of thalamic functional connectivity in chronic capsular and pontine stroke are still unknown.MethodsThe research recruited 66 left-lesion chronic stroke patients, including 46 capsular strokes (CS) and 20 pontine stroke (PS) patients, and 67 normal controls (NC). The thalamic subfields functional connectivities were compared between groups using a two-way repeated analysis of variance (ANOVA), corrected for confounders including age, gender, education and scanners. Spearman partial correlation was used to explore the potential association between altered thalamic FC and clinical variables.ResultsThe ipsilesional thalamus of CS patients had abnormally decreased FC with widespread cognitive-related areas while increased FC with visual- and somatic-motor areas. In contrast, the ipsilesional thalamus of PS patients mainly demonstrated increased FC in these sensorimotor areas. Even in the contralesional thalamus, we observed similar (with the ipsilesional) but less extensive functional dysconnectivity patterns in both the CS and PS patients (P < 0.05, corrected using family-wise error [FWE] at the voxel level). Finally, we found significant group x subfields interactions on thalamic functional connectivity, where capsular vs. pontine stroke demonstrate varied functional dysconnectivity with specific thalamic subfields. Finally, a weak correlation was found between FC of both ipsilesional/contralesional thalamic subfields and motor, working and verbal memory.ConclusionsThe thalamic functional dysconnectivity after chronic stroke are lesion-location and subfields dependent. Moreover, functional dysconnectivity were shown in both the ipsilesional and contralesional thalamus with similar patterns.