Dynamic brain states and molecular signatures in primary angle-closure glaucoma.

Altered homotopic functional connectivity in primary angle-closure glaucoma (PACG) and their underlying molecular mechanisms remain poorly understood. In our study, we investigated voxel-mirrored homotopic connectivity (VMHC) alterations in patients with PACG and the molecular mechanisms of VMHC. In this study, we investigated alterations in VMHC among 47 patients with PACG and 45 matched healthy controls. We then integrated these spatial patterns with cortical transcriptomic data from the Allen Human Brain Atlas using partial least squares (PLS) regression to identify gene expression profiles associated with VMHC alterations. In this study, we identified widespread reductions in interhemispheric functional connectivity in patients with PACG using VMHC analysis. Multivariate spatial correlation with gene expression data revealed that VMHC alterations were significantly associated with a distinct transcriptional signature captured by the first PLS component. Functional enrichment of these genes indicated downregulation of pathways related to synaptic and metabolic maintenance, and upregulation of immune, stress, and chromatin regulatory processes. Cell-type analysis showed that astrocytes and endothelial cells were selectively enriched with VMHC-related genes, reflecting glial and vascular involvement. Moreover, spatial alignment with neurotransmitter receptor maps highlighted significant associations with serotonergic, dopaminergic, GABAergic, cholinergic, and opioidergic pathways, suggesting a neuromodulatory basis for VMHC disruption. Together, these findings suggest that interhemispheric dysconnectivity in PACG is not only a reflection of functional brain changes but is also grounded in molecular and cellular mechanisms. This integrative approach advances our understanding of PACG as a brain-wide neurodegenerative condition and offers new perspectives for targeting glial, vascular, and neuromodulatory pathways in future therapeutic interventions.

Altered dynamic large-scale brain networks and combined machine learning in primary angle-closure glaucoma

Unprecedented 4D spatiotemporal infant regional cerebral blood flow framework and region-specific physiology–function coupling across infancy were elucidated, highlighting strong physiology–function coupling specifically at the default-mode network to meet extraneuronal metabolic demand for network emergence.

A growing body of evidence suggests the presence of functional and structural abnormalities in patients with primary angle-closure glaucoma (PACG). The aim of this study was to examine the topological characteristics of the large-scale functional connectivity (FC) network, structural connectivity (SC) network, and strength of FC-SC coupling in patients with PACG. This study included 47 patients with PACG and 48 healthy controls (HCs) matched for age, sex, and education. All participants underwent a detailed ophthalmological examination and cognitive assessment via the Montreal Cognitive Assessment scale. Resting-state functional magnetic resonance imaging and diffusion tensor imaging data of all participants were acquired separately. Large-scale FC and SC networks were constructed based on the Automated Anatomical Labeling 90 (AAL90) region atlas. Graph theoretic analysis was used for the computation of global attribute and node attribute indices. Subsequently, whole-brain FC-SC coupling was evaluated by analyzing the correspondence between FC and SC matrices to determine how brain anatomy constrains functional dynamics. Finally, the relationships between topological properties and coupling strengths with ophthalmic parameters and cognitive scales were assessed. Bonferroni correction was applied to all multiple comparisons. Compared with the HC group, the PACG group had a lower normalized clustering coefficient (t=-2.339; P=0.024) and small-worldness (t=-2.017; P=0.047) for the FC network and lower normalized characteristic path length (t=-2.054; P=0.043) for the SC network. In terms of node attributes, the PACG group showed abnormal node degree, node betweenness, and node efficiency for FC and SC, mainly in the frontal, temporal, and occipital lobes. In addition, the strength of FC-SC coupling in the whole brain of patients with PACG was reduced (t=-2.622; P=0.01). The topological characteristics of these functional and structural abnormalities were correlated with visual acuity, disease duration, and Montreal Cognitive Assessment score (P<0.05). We observed significantly weaker FC-SC coupling in patients with PACG compared to HCs, indicating impaired integration of brain structure and function. This decoupling suggests widespread network-level disruption, and this finding advances our understanding of optic nerve damage and cognitive deficits in PACG.

ObjectivePrimary angle-closure glaucoma (PACG), an incurable ophthalmic disease, is a serious risk to human visual health. Previous studies have demonstrated a strong link between PACG and neuroimaging changes in the brain. This study utilizes dynamic low-frequency fluctuation amplitude (dALFF) with the aim of resolving the potential dynamic alterations in neurological function in PACG and integrating transcriptomics profiles with spatial distribution characteristics of neuromodulatory receptors/transporters to systematically elucidate the underlying neurophysiopathological mechanisms.MethodsWe used sliding time windows of 30TR, 50TR and 80TR to calculate dALFF values and performed partial least squares regression (PLS) analysis of t-values after two-sample test of dALFF values under the sliding window of 50 TR against the Allen Human Brain Atlas (AHBA) to screen genes. Enrichment analysis, tissue-specific expression analysis and protein–protein interactions (PPI) network construction were implemented. The t-values were also analyzed for spatial correlation with neurotransmitter receptor/transporter density profiles distributed throughout the brain.ResultsThe two-sample tests under three sliding windows revealed extensive brain alterations in PACG and each abnormal brain region showed elevation (the Gaussian Random Field method, with significance at the voxel level set at p < 0.005 (two-tailed) and at the cluster level at p < 0.01), which was mainly in the occipital lobe and angular gyrus. Enrichment analysis were mainly “regulation of neuron projection development” and “membrane organization” pathways (p < 0.05, no corrected). Specific expression analysis revealed that the relevant genes were involved in all stages of thalamic development. PPI analysis demonstrated the role of PACG-associated genes in the formation of functional network. Neurotransmitter receptor/transporter correlation analysis revealed significant associations with 5-HT4R and mGlu5R (p < 0.05, FDR corrected).ConclusionThe present study reveals that a wide range of brain regions in PACG patients show significant functional remodeling, elucidating the molecular regulatory network behind this type of pathological alteration.

Differences in cerebral spontaneous neural activity correlate with gene-specific transcriptional signatures in primary angle-closure glaucoma.

Combining 7 Tesla fMRI and MEG data collected during a challenging visual recognition task revealed distinct neural representational formats in ventral visual and frontoparietal regions, and the emergence of recognition-related signals prior to category-related information.

Oral frailty, an age-related decline in oral function and health linked to adverse geriatric outcomes, involves multiple phenotypes. Chronic periodontitis, a key inflammatory driver of tooth loss and systemic disease, is a cornerstone of this syndrome, yet the shared genetic architecture connecting it to other oral conditions remains uncharacterised. We employed genomic structural equation modelling (genomic SEM) to integrate genome-wide association studies (GWAS) summary statistics from five oral frailty-related phenotypes, defining a common latent factor reflecting their shared genetics. We further integrated several post-GWAS analytical methods, including locus and gene discovery (MAGMA, TWAS/FOCUS), fine-mapping (SuSiE, FINEMAP), pathway and cell-type enrichment (S-LDSC, CELLECT), spatial mapping (gsMap), and Polygenic Risk Score analyses. The genomic SEM model demonstrated a good fit and revealed a common genetic factor underlying oral frailty. We identified four genome-wide significant loci, three of which are novel for oral frailty. Fine-mapping prioritised rs150699482 (KIAA0247), rs78975199 (SPG11), and rs2705755 (SNORA77) as likely causal variants. MAGMA highlighted 13 candidate susceptibility genes, with SPG11 and CCDC91 among the top candidates. TWAS and FOCUS analyses robustly implicated RP11-967K21.1 as a putative causal gene. Cell-type enrichment analysis indicated significant involvement of brain endothelial cells, immune cells, and mammary gland stromal cells. Heritability was enriched in evolutionarily conserved regions and active regulatory elements. Notably, gsMap analysis showed that genetic risk for oral frailty is enriched not only in jaw and tooth tissues but also across multiple embryonic tissues, including adipose tissue, dorsal root ganglion, mucosal epithelium, and connective tissue. This study provides the first comprehensive genomic SEM-based characterisation of the shared architecture underlying oral frailty-related traits. By demonstrating a common genetic basis linking periodontitis with other oral dysfunctions, it provides novel insights into shared aetiological pathways and reinforces the concept of oral frailty as a systemic issue.

Large-scale brain dynamics recently started to be modeled numerically based on both heterogeneous large-scale networks build from diffusion MRI, that is, a connectome, and local homogeneous connectivity kernel representing intracortical synaptic connections. However, topological properties of a connectome can significantly change with resolution and parcellation [1]. Furthermore, the sampling of the cerebral surfaces, resulting in a geometric model, and, in this way, the local connectivity kernel play crucial roles in the formation of spatial patterns on the cerebral surfaces as well as on the sensor level (e.g., EEG electrodes) by a forward calculation [2]. However, the effect of sampling and parcellation on modeling brain dynamics has not been studied so far. Here, we investigate qualitatively and quantitatively: (i) how different parcellation resolutions affect the dynamics of the network; and (ii) how the local connectivity affects the network dynamics. To do so, we used the neuroinformatics platform for large-scale brain simulations, called The Virtual Brain (TVB) [3] and developed a preprocessing pipeline to incorporate experimental data (e.g., structural MRI, diffusion-weighted MRI) in TVB [4]. We prepared ten individual models based on ten randomly selected subjects from the Human Connectome Project dataset [5]. For each individual model we performed simulations under two conditions during rest: (i) noise driven, using a bistable neural mass model, and (ii) after stimulation, using an excitable neural mass model. We investigated the effect of heterogeneous and homogeneous connectivity on large-scale brain dynamics by different numbers of regions in the parcellation (70 to 2240) and by varying the local connectivity coupling strength. To introduce experimental data (i.e., structural and diffusion MRI) into TVB we tackled issues such as surface downsampling (for achieving moderate simulation times) and mapping between surface and parcellation (to consistently use heterogeneous and homogeneous connectivity) by developing the Surface and Connectivity Reconstruction with an Imaging Pipeline for TVB Simulations, short SCRIPTS [4]. When considering slow dynamics, the major fiber bundles best reflected in the coarsest parcellation appeared to be mainly responsible for the emergence of the network attractors with limited changes over different parcellations and different local coupling strengths. For fast dynamics, new qualitative solutions appeared, but only in the presence of delays. Figure 1 A. Spatial attractors. For each value of the global (G) or local (G_local) coupling parameter, correlation with in-strength (blue points) and with s-core (red points) for ten different initial conditions. The blue square indicates the critical interval. ...

Potential associations between immune signaling genes, deactivated microglia, and oligodendrocytes and cortical gray matter loss in patients with long-term remitted Cushing’s disease

Neurochemical and molecular characteristics of altered brain functional activity in the anti-NMDAR encephalitis.

To explore altered patterns of static and dynamic functional brain network connectivity (sFNC and dFNC) in Primary angle-closure glaucoma (PACG) patients. Clinically confirmed 34 PACG patients and 33 age- and gender-matched healthy controls (HCs) underwent evaluation using T1 anatomical and functional MRI on a 3 T scanner. Independent component analysis, sliding window, and the K-means clustering method were employed to investigate the functional network connectivity (FNC) and temporal metrics based on eight resting-state networks. Differences in FNC and temporal metrics were identified and subsequently correlated with clinical variables. For sFNC, compared with HCs, PACG patients showed three decreased interactions, including SMN-AN, SMN-VN and VN-AN pairs. For dFNC, we derived four highly structured states of FC that occurred repeatedly between individual scans and subjects, and the results are highly congruent with sFNC. In addition, PACG patients had a decreased fraction of time in state 3 and negatively correlated with IOP (p < 0.05). PACG patients exhibit abnormalities in both sFNC and dFNC. The high degree of overlap between static and dynamic results suggests the stability of functional connectivity networks in PACG patients, which provide a new perspective to understand the neuropathological mechanisms of optic nerve damage in PACG patients.

The causes of distinct patterns of reduced cortical thickness in the common human epilepsies, detectable on neuroimaging and with important clinical consequences, are unknown. We investigated the underlying mechanisms of cortical thinning using a systems-level analysis. Imaging-based cortical structural maps from a large-scale epilepsy neuroimaging study were overlaid with highly spatially resolved human brain gene expression data from the Allen Human Brain Atlas. Cell-type deconvolution, differential expression analysis and cell-type enrichment analyses were used to identify differences in cell-type distribution. These differences were followed up in post-mortem brain tissue from humans with epilepsy using Iba1 immunolabelling. Furthermore, to investigate a causal effect in cortical thinning, cell-type-specific depletion was used in a murine model of acquired epilepsy. We identified elevated fractions of microglia and endothelial cells in regions of reduced cortical thickness. Differentially expressed genes showed enrichment for microglial markers and, in particular, activated microglial states. Analysis of post-mortem brain tissue from humans with epilepsy confirmed excess activated microglia. In the murine model, transient depletion of activated microglia during the early phase of the disease development prevented cortical thinning and neuronal cell loss in the temporal cortex. Although the development of chronic seizures was unaffected, the epileptic mice with early depletion of activated microglia did not develop deficits in a non-spatial memory test seen in epileptic mice not depleted of microglia. These convergent data strongly implicate activated microglia in cortical thinning, representing a new dimension for concern and disease modification in the epilepsies, potentially distinct from seizure control.

ObjectiveAttention-deficit/hyperactivity disorder (ADHD) is characterized by atypical brain development, yet the molecular and cellular mechanisms underlying its characteristic gray matter volume (GMV) alterations remain poorly understood. This study aimed to integrate neuroimaging and transcriptomic data to identify cell-type-specific transcriptional signatures associated with GMV changes in adolescents with ADHD.MethodsVoxel-based morphometry was performed on structural MRI data from 27 adolescents with ADHD and 34 typically developing (TD) controls to map regional GMV differences. The spatial pattern of these alterations was then correlated with whole-brain gene expression profiles from the Allen Human Brain Atlas using partial least squares (PLS) regression. Functional and cell-type enrichment analyses were conducted on significant gene sets. Finally, three machine-learning models (support vector machine, random forest, and decision tree) were developed to evaluate the diagnostic utility of GMV changes.ResultsIncreased GMV was observed in the bilateral precuneus, and decreased GMV was found in the left middle occipital gyrus and orbital part of the right inferior frontal gyrus in ADHD compared to TD. The spatial distribution of these GMV changes was significantly correlated with a specific gene expression pattern. Functional enrichment analysis revealed that positively correlated genes were involved in fundamental cellular processes, while negatively correlated genes were associated with synaptic organization and brain development. Cell-type analysis demonstrated significant enrichment of positively correlated genes in microglia, and negatively correlated genes in excitatory and inhibitory neurons. Random Forest achieved the highest accuracy in distinguishing between ADHD and TD (AUC = 0.871 ± 0.029).ConclusionIn summary, this study provides unique insights into the brain structural development of attention-deficit/hyperactivity disorder (ADHD) and offers new perspectives for the future diagnosis and treatment of ADHD.

In multiple sclerosis (MS), functional network abnormalities arise as structural damage accumulates. However their biological basis and spatial distribution remain unclear. This study investigated the associations between MS-related functional network abnormalities and physiological gene expression using the Allen Human Brain Atlas (AHBA). Five-hundred fifty-eight MS patients and 214 healthy controls (HC) underwent neurological assessment and 3 T MRI; 491 patients also completed a neuropsychological evaluation. Resting-state functional MRI was used to generate degree centrality maps to identify network topography alterations. Spatial correlations between centrality abnormalities (p < 0.01 uncorrected) and the expression of 3634 MS-related genes was evaluated using AHBA and the Multimodal Environment for Neuroimaging and Genomic Analysis. Genes showing significant associations (p < 0.001, R2 ≥ 0.15) underwent pathway enrichment analysis (p < 0.05, Bonferroni-corrected). Compared to HC, MS patients showed higher centrality mainly in the default-mode network (DMN), linked to genes regulating inflammation resolution and immune functions, and lower centrality in regions mostly located in the salience network and cerebellum, associated with genes implicated in cytokine response. Compared to HC and relapsing-remitting MS, progressive MS patients showed higher centrality in DMN and cerebellar regions, correlating with genes related to epigenetic and mitochondrial functions. Of the MS cohort, 144 (29.3%) patients were cognitively impaired. Compared to cognitively preserved MS and HC, they showed higher centrality in DMN and mesial temporal lobe regions, negatively correlated with expression of DNASE1, regulating DNA degradation, and CP, encoding ceruloplasmin, involved in iron homeostasis and potentially iron-driven neurodegeneration. Physiological regional gene expression spatially correlates with MS-related functional network alterations. Biological factors may shape regional vulnerability or resilience to MS pathology, influencing functional reorganization.