The major anxiety disorders (ANX; including generalized anxiety disorder, panic disorder and phobias) are highly prevalent, often onset early and cause substantial global disability. Although distinct in their clinical presentations, they probably represent differential expressions of a dysregulated threat-response system. Here, we present a genome-wide association meta-analysis comprising 122,341 European ancestry ANX cases and 729,881 controls. We identified 58 independent genome-wide significant risk variants and 66 genes with robust biological support. In an independent sample of 1,175,012 self-report ANX cases and 1,956,379 controls, 51 out of the 58 associations replicated. As predicted by twin studies, we found substantial genetic correlation between ANX and depression, neuroticism and other internalizing phenotypes. Follow-up analyses demonstrated enrichment in all major brain regions and highlighted GABAergic signaling as one potential mechanism implicated in ANX genetic risk. These results advance our understanding of the genetic architecture of ANX and prioritize genes for functional follow-up studies.
INTRODUCTION:The sortilin-related receptor (SORL1) directs APP and Aβ trafficking within the retromer pathway. Cleavage at the cell surface releases soluble SORL1 (sSORL1) into cerebrospinal fluid (CSF). We examined whether CSF-sSORL1 can serve as an in vivo marker of genetically impaired SORL1. METHODS:CSF-sSORL1 was quantified by enzyme-linked immunosorbent assay (ELISA) in 218 participants: 90 carriers of SORL1 variants, 78 SORL1-wildtype (WT) AD patients, and 50 SORL1-WT controls. RESULTS:sSORL1 concentrations were significantly lower in carriers of protein-truncating and damaging missense variants. In SORL1-WT patients, CSF-sSORL1 correlated with pTau181 but not with Aβ42 among AD patients, and did not differ between patients and controls. DISCUSSION:These findings suggest that impaired SORL1 trafficking reduces receptor delivery to the cell surface and thereby decreases sSORL1 shedding, supporting its potential use as a pathway-specific biomarker. HIGHLIGHTS:Enzyme-linked immunosorbent assay (ELISA) enables quantitative measurement of soluble sortilin-related receptor (sSORL1) in cerebrospinal fluid (CSF). sSORL1 levels are reduced in CSF from carriers of a pathogenic SORL1 variant. CSF-sSORL1 levels correlate with tau pathology in Alzheimer's disease. sSORL1 levels represent an in vivo biomarker of SORL1 function.
Current psychiatric neuroimaging supports the view that major depressive disorder (MDD) is a dysconnection syndrome, characterized by structural brain dysconnectivity. Recent studies investigating this question, however, did not evaluate the involvement of comorbid disorders, of which anxiety disorders (ANX) are particularly prevalent. Here, we investigated the structural connectivity alterations observed in MDD with and without comorbid ANX. To this end, we reconstructed structural brain networks of n = 781 individuals with a diagnosis of MDD who had at least one diagnosis of an ANX (n = 249) and those without any diagnosis of ANX (n = 532), as well as n = 906 healthy controls (HC) from structural and diffusion-weighted MRI. The network-based statistic (NBS) toolbox was employed to evaluate network-level differences in structural connectivity among the three groups. Transdiagnostic analyses were conducted to explore the dimensional relationship between anxiety and structural connectivity. NBS revealed decreased structural connectivity in MDD patients without comorbid ANX and increased structural connectivity in MDD patients with comorbid ANX relative to HC, with both effects found in spatially overlapping white matter connections. Transdiagnostic analyses suggested that increases in anxiety were associated with increased structural connectivity across all groups. Our finding that hyperconnectivity rather than hypoconnectivity characterizes the structural connectome of MDD patients with comorbid ANX challenges the applicability of the dysconnection syndrome hypothesis to MDD with comorbid ANX, warranting symptom-based investigations of brain changes in mental disorders.
Abstract Brain disorders are increasingly understood as disorders of distributed brain circuits, yet functional connectivity (FC), the dominant framework for mapping them, treats the brain as a collection of pairwise relationships between regions and cannot represent pathology distributed across coordinated sets of connections. We introduce a Hodge-Laplacian topological framework that localizes higher-order “loop” (1-cycle) organization within functional connectome, maps each loop to specific edges and networks, and yields a subject-level measure of loop expression. Applied to resting-state fMRI from the ENIGMA-OCD consortium (1,024 patients and 1,028 controls across 28 sites), the framework identified 93 loop-level abnormalities in obsessive–compulsive disorder (OCD), concentrated in frontoparietal and somatomotor systems. The edges forming these loops largely showed no significant differences between groups, indicating that the abnormalities were invisible to conventional FC analysis. The frontoparietal and somatomotor loop clusters recurred across the clinical subgroups, suggesting convergence on a shared higher-order phenotype. Robustness analyses showed the loop signal reflected higher-order organization rather than an artifact of individual edges, the network backbone, or any single site. These results indicate that coordinated, multi-edge pathology exists and can be localized even when pairwise analyses fail to detect it, positioning higher-order topology as a generalizable axis for mapping circuit pathology across psychiatric and neurological disorders.
Traditionally, subgroups have been used to explore the effects of tau heterogeneity on cognition. However, categorization into rigid, exclusive subtypes, each with their own tau pattern, may overlook the fact that individual tau patterns are complex, and most individuals express features of multiple patterns. Mapping tau patterns in all their complexity has important clinical implications, as it may enable more accurate prognostication and support the development of personalized therapeutic strategies tailored to an individual’s unique tau profile. We applied a data-driven Bayesian model using Latent Dirichlet Allocation (LDA) to identify four covarying tau PET patterns in amyloid-positive individuals with symptomatic Alzheimer’s disease (AD) from the Amsterdam Dementia Cohort (ADC, N = 93, mean age = 65.3). The four latent tau spatial patterns identified via LDA were designated as follows: Limbic (Factor 1), characterized by predominant involvement of the limbic regions; Left TPC (Factor 2), centered on the left temporo-parietal cortex (TPC); Posterior (Factor 3), reflecting a posterior neocortical distribution; and MTL-sparing (Factor 4), showing relative sparing of limbic regions with diffuse neocortical tau deposition. Associations between individual loadings on each of these factors and cognitive domain scores were assessed using linear regression analyses. We then applied the ADC-derived model to an independent validation sample from the Alzheimer’s Disease Neuroimaging Initiative (ADNI, N = 162, mean age = 72.7) to extract individual factor loadings. Associations between factor loadings and contemporaneous cognitive performance were again tested using linear regression, and linear mixed-effects models were used to additionally explore associations with cognitive decline. All analyses were adjusted for age, sex, education, and clinical diagnosis. In both the discovery and validation cohort, we identified distinct associations between tau factor loadings and cognitive performance. Higher loading on Factor 1: limbic tau was generally associated with relatively better baseline cognition and slower cognitive decline. Factor 2: left TPC tau was linked to worse baseline scores and faster decline in memory, MMSE and language scores but only in the ADNI cohort. Factor 3: posterior tau was associated with worse baseline MMSE in the ADC cohort and to worse visuospatial performance both cross-sectionally and longitudinally in the ADNI cohort. Factor 4: MTL-sparing tau was related to better longitudinal memory in the ADNI cohort. This data-driven approach identified overlapping tau patterns that relate to distinct cognitive domains. The findings highlight the value of continuous factor modeling in understanding heterogeneity in Alzheimer’s disease. Such a refinement of quantifying tau heterogeneity may improve patient stratification, refine prognostic accuracy, and ultimately guide the development of individualized treatment strategies.
