Ruben C. Gur

Genetic studies of serious mental illness (SMI, including schizophrenia and bipolar disorder) have implicated two classes of risk variant: common variants ^1–3 , each of small effect but collectively accounting for most population-level risk, and ultra-rare coding variants ^4,5 which confer large risk in a small number of individuals. Most such studies cannot directly assess the impact of specific variants on SMI, transdiagnostically, as the phenotypic information that they collect is focused on a single diagnosis. Here we report results of association analysis of predicted deleterious variants from exome sequencing of 21,958 cases (SMI and severe major depressive disorder) and 19,826 controls from the Mision Origen biobank in the Paisa genetic isolate of Colombia. We identified transdiagnostic associations to seven genes, three at an exome-wide and four at a false discovery rate significance threshold. Five of these genes have not previously been implicated in SMI ( ADAP1 , WIPI1 , NFE2L1 , FBXO10 , and HLA-DPB1 ); for the first four of these the association is driven by variants enriched in the Paisa due to a founder effect, filling the gap between ultra-rare and common variants. The strongest association that we observe is to a frameshift variant (7:904169:T:TC) in ADAP1 that is genome-wide significant for SMI, schizophrenia, and bipolar disorder and increases risk for both diagnoses by more than threefold; it truncates ADAP1, a brain-enriched Arf6 GTPase-activating/scaffolding protein that has been mechanistically linked to post-mitotic neuronal morphogenesis and regulation of dendritic differentiation ⁶ . Variant carriers differ significantly from other SMI cases in Mision Origen for psychotic, manic, and cognitive item-level phenotypes assessed through analyses of electronic health records ^7–9 and displayed impaired executive function on neurocognitive testing. Variant carriers differ significantly from other SMI cases in Mision Origen for multiple item-level phenotypes (psychotic, manic, and cognitive) assigned through natural language processing of longitudinal electronic health records. Although the 7:904169:T:TC variant is not significantly associated with major depressive disorder, carriers compared to others with this diagnosis display a significant increase in symptoms typical of bipolar disorder. The variant’s high allele frequency in Mision Origen (∼0.4%) will enable population-level studies to stratify SMI based on causation and identify additional factors influencing risk and resilience.

Symptom severity in schizophrenia has been repeatedly associated with thinner cortical gray matter. While global and regional white matter microstructure alterations in schizophrenia are well-documented, their association with clinical symptom severity remains unclear. As this is likely due to methodological heterogeneity across studies, we tested whether symptom severity in schizophrenia was associated with regional and global white matter alterations using standardized methods. We hypothesized that positive symptom severity would be associated with temporal white matter changes and that negative symptom severity would be associated with alterations in frontal white matter. Using a standardized fractional anisotropy (FA) analysis pipeline developed by the ENIGMA consortium, we conducted a meta-analysis of the association between white matter microstructure and symptom severity in n = 1025 (ages 16–68 years; 369 women/656 men) across 19 ENIGMA sites. Where significant heterogeneity was detected across sites, we examined whether variation in association strength between white matter microstructure and symptom severity was explained by duration of illness and/or current antipsychotic use. Positive symptom severity was significantly associated with white matter microstructure as measured using temporal lobe FA and global FA. Negative symptom severity showed no significant association with white matter microstructure as measured using frontal lobe FA or global FA. Significant heterogeneity across sites was observed for the negative symptom analysis, explained partly by duration of illness. Post-hoc exploratory analyses identified one site as disproportionately contributing to this heterogeneity, and when removed, negative symptom severity was significantly associated with both global and frontal FA. These findings support the view of schizophrenia as a disorder of brain connectivity, in a manner relevant to understanding variation in clinical symptom severity.

Neuroanatomical findings on panic disorder (PD) are typically difficult to replicate, with inconsistent effects. These concerns prompted a paradigm shift towards large-scale collaborations, focused on harmonized data extraction and processing for robust examination of PD brain correlates. Hence, leveraging the largest-ever multi-site neuroimaging database on PD (Age: 10–66 years; global sites: 28), compiled by the ENIGMA-Anxiety Working Group, we report on cortical and subcortical differences in individuals with PD (N = 1146) versus healthy controls (HC: N = 3778). The analyses revealed lower thickness and smaller cortical surface area within fronto-temporo-parietal regions in PD (Cohen’s ds: −0.08–0.13), along with lower thalamic and caudate volumes (Cohen’s ds: −0.07–0.12). Diagnosis-by-age² interactions (Cohen’s ds: 0.07–0.12) revealed lower thickness in individuals with PD compared to HC in certain regions during adulthood (25–55 years), with relative absence of such differences during youth (55 years). Finally, patient subgroup analyses showed that early disease onset (≤21 years) in PD was associated with larger lateral ventricles (Cohen’s ds: 0.31–0.38), whilst no medication, comorbidity, or severity effects were found. These findings lend support to neurocircuitry models of PD, which postulate differences within fronto-striato-limbic circuits and temporo-parietal regions. Moreover, findings highlight the potential importance of abnormal development and aging in neuroanatomical differences related to PD. Given its unprecedented scale, the current study is an important milestone towards identifying the structural brain correlates of PD.

Children’s cognitive abilities vary across short and long timescales, from circadian fluctuations to year-by-year developmental changes. “Summer slide” refers to seasonal variation in academic performance, characterized by decreased performance following an extended school vacation. However, it remains unclear whether this effect generalizes across varied assessments and the extent of this effect across sociodemographic groups has been debated. Though seasonal variation in cognitive performance is often attributed to “forgetting” of learned material, no large-scale studies have systematically investigated seasonal variation in general cognitive skills (e.g., executive functioning) on standardized laboratory-based assessments in school-age youth compared to adults. Across four datasets with geographic, demographic, mental health, and measurement variation (total n = 23,251), we quantify cyclical, seasonal variation in children’s cognitive performance using generalized additive models with cyclic cubic splines. In school-age children but not young adults, we found consistent cognitive performance minima following school vacation (July–September in the United States; November–January in Singapore) across nearly all cognitive domains investigated. These results demonstrate a generalizable small-magnitude effect of lower cognitive performance aligning with school vacation even after adjusting for socioeconomic status or ADHD diagnosis. We contextualize summer cognitive minima relative to environmental and developmental effects, noting that the largest effect size is still seven times smaller than effects of the socioeconomic environment on cognition, and provide recommendations for future data collection and analysis of behavior across the calendar year.

Deriving white matter (WM) bundles in vivo has thus far mainly been applied in research settings, leveraging high angular resolution, multi‐shell diffusion MRI (dMRI) acquisitions that enable modern reconstruction methods. However, these advanced acquisitions are both time‐consuming and costly to acquire. The ability to reconstruct WM bundles in the massive amounts of existing single‐shelled, lower angular resolution data from legacy research studies and healthcare systems would offer much broader clinical applications and population‐level generalizability. While legacy scans may offer a valuable, large‐scale complement to contemporary research datasets, the reliability of white matter bundles derived from these scans remains unclear. Here, we leverage a large research dataset where each 64‐direction dMRI scan was acquired as two independent 32‐direction runs per subject. To investigate how a state‐of‐the‐art bundle‐specific reconstruction method generalizes to this data, we evaluated the test–retest reliability of WM bundles reconstructed from the two 32‐direction scans across three orientation distribution function (ODF) reconstruction methods: generalized q‐sampling imaging (GQI), constrained spherical deconvolution (CSD), and single‐shell three‐tissue CSD (SS3T). We found that the majority of WM bundles could be reliably extracted from dMRI scans that were acquired using the 32‐direction, single‐shell acquisition scheme. The mean Dice coefficient of reconstructed WM bundles was consistently higher within subject than between subject for all WM bundles and ODF reconstruction methods, illustrating preservation of person‐specific anatomy. Further, when using features of the bundles to predict complex reasoning assessed using a computerized cognitive battery, we observed stable prediction accuracies ( r : 0.15–0.36) across the test–retest data. Among the three ODF reconstruction methods, SS3T had a good balance between sensitivity and specificity when comparing the reconstructed bundles to atlas bundles, a high intra‐class correlation of extracted features, more plausible bundles, and strong predictive performance. More broadly, these results demonstrate that bundle‐specific reconstruction can achieve robust performance even on lower angular resolution, single‐shell dMRI, with particular advantages for ODF methods optimized for single‐shell data. This highlights the considerable potential for dMRI collected in healthcare settings and legacy research datasets to accelerate and expand the scope of WM research.

Executive function (EF) develops rapidly during adolescence. However, deficits in EF also emerge in adolescence, representing a transdiagnostic symptom associated with many forms of psychopathology. To promote transdiagnostic research on EF during development, we introduce a new data resource – the Penn Longitudinal Executive functioning in Adolescent Development study (Penn LEAD) – that combines longitudinal multimodal imaging data with rich clinical and cognitive phenotyping. These data include 225 imaging sessions from 132 individuals (8-16 years old at the time of enrollment) who are typically developing (27.3%), or meet criteria for attention-deficit hyperactivity disorder (20.5%) or the psychosis-spectrum (52.3%). In addition to phenotypic data from multiple cognitive tasks focused on EF, the study includes data from structural MRI, diffusion MRI, n -back task fMRI, resting-state fMRI, and arterial spin-labeled MRI. Notably, all raw data, fully-processed derived data, and detailed quality control recommendations are publicly shared on OpenNeuro. We anticipate that such analysis-ready data will accelerate research on EF development in psychiatry.

Psychiatric conditions share common genes, but mechanisms that differentiate diagnoses remain unclear. We present a multidimensional framework for functional analysis of rare copy number variants (CNVs) across 6 diagnostic categories, including schizophrenia (SCZ), autism (ASD), bipolar disorder (BD), depression (MDD), PTSD, and ADHD (N = 574,965). Using gene-set burden analysis (GSBA), we tested duplication (DUP) and deletion (DEL) burden across 2,645 functional gene sets defined by the intersections of pathways, cell types, and cortical regions. While diagnoses converge on shared pathways, mixed-effects modeling revealed divergence of pathway effects by cell type, brain region, and gene dosage. Factor analysis identified latent dimensions aligned with clinical axes. A primary factor (F1) captured reciprocal dose-dependent effects of DUP and DEL in SCZ reflecting positive and negative effects in excitatory versus inhibitory neurons and association versus sensory cortex. SCZ and ASD were both strongly aligned with F1 but with opposing directionalities. Orthogonal factors highlighted neuronal versus non-neuronal effects in mood disorders (F2) and differential spatial distributions of DEL effects in ADHD and MDD (F3). High-impact CNVs at 16p11.2 and 22q11.2 were enriched for combinations of cell-type-specific genes involved in pathways consistent with our broader findings. These results reveal molecular and cellular mechanisms that are broadly shared across psychiatric traits but differ between diagnostic categories in context and directionality.

Background and Hypothesis: Neurodevelopmental risk-factor models of psychosis highlight the importance of early developmental deviations in the emergence of psychosis. However, few longitudinal studies map neurodevelopment and neurocognitive trajectories across age in preclinical psychosis. We investigated longitudinal trajectories in neurocognition and brain volume in a community cohort of adolescents with recurrent psychosis spectrum (PS) symptoms, tracking their development into young adulthood compared to their typically developing (TD) peers. Study Design: Utilizing the Philadelphia Neurodevelopmental Cohort, we analyzed data of 231 youths aged 8-30 with at least one follow-up assessment, including 88 with PS. Study Results: Individuals with PS showed similar developmental trajectories but demonstrated significant impairments in executive functioning (t = −2.81, q = 0.010), memory (t = −2.34, q = 0.019), complex cognition (t = −3.72, q = 0.001), social cognition (t = −2.73, q = 0.010), motor (t = −2.50, q = 0.015), and general cognition (t = −3.20, q = 0.004). Lower cortical (t = −2.46, P = .014) and subcortical (t = −2.41, P = .016) gray matter volume in the recurrent PS group compared to the TD group were documented with age-related group differences becoming less pronounced by young adulthood. Further analyses revealed age-by-group interactions (qs < 0.05) observed in a few temporal and frontal regions, with differences between groups at earlier ages. Conclusions: These findings suggest that recurrent PS symptoms are linked to early neurocognitive and brain structure deficits, highlighting the need for interventions to reduce psychosis risk and support healthy neurodevelopment.