Matthias Kirschner

Background: Although most individuals with schizophrenia do not show aggressive behavior, rates are higher than in the general population. Identifying the neurobiological correlates of aggression may inform prevention. Methods: We included 2260 individuals with schizophrenia spectrum disorders and 3058 healthy controls (HC) from 26 sites in the ENIGMA-Schizophrenia Working Group. The former were classified as showing at least moderate aggression (SZ+A; N = 231) or not (SZ−A; N = 2029) based on symptom rating scales. Structural covariance networks were constructed from cortical and subcortical volumes. We examined global and nodal network topology, as well as density within six large-scale brain networks and between each network and the rest of the brain. Metrics were summarized as the area under the curve (AUC) across a prespecified range of densities ( K = 0.10–0.25, Δ K = 0.01). Group differences were assessed using pairwise permutation tests with Benjamini–Hochberg correction for multiple comparisons. Results: Global and nodal network topology did not differ significantly between SZ+A and HC. Network–rest density of the salience network (SN) was higher in SZ+A than in HC, although this difference did not survive multiple-comparison correction (ΔAUC = 0.018, p _BH = .083). In exploratory subgroup analyses, higher SN network–rest density was observed only in SZ+A with high delusion severity (ΔAUC = 0.021, p = .007). By contrast, SZ−A demonstrated extensive alterations in global network topology compared with SZ+A and HC, alongside selective alterations in nodal topology and large-scale network density compared with HC. Results were broadly consistent across sensitivity analyses using alternative definitions of aggression and adjusting for current antipsychotic dose. Conclusions: SZ+A was characterized by fewer—not more—abnormalities in structural covariance network organization than SZ−A, which may reflect etiological heterogeneity. In SZ+A with high delusion severity, higher network–rest density of the SN may point to aberrant salience processing in aggressive behavior.

Altered white matter microstructure, commonly indexed by fractional anisotropy (FA), has been reported in individuals at clinical high-risk of psychosis (CHR-P) across multiple white matter tracts. However, the relationship between these alterations and later transition to psychosis remains unclear. To address this gap, we investigated whether baseline and longitudinally updated FA measurements were associated with risk of transition to psychosis in a large cohort of individuals at CHR-P. We analysed diffusion tensor imaging data from N = 205 participants at CHR-P, 23 of whom developed psychosis within 3 years of follow-up. Data were collected at baseline, 6 months, and 12 months. Associations between global and tract-specific FA and transition risk were examined using Cox proportional hazards models of baseline data and longitudinally updated data. Point estimates were directionally consistent, with hazard ratios .05). The directionally consistent estimates are compatible with small, distributed effects across multiple white matter pathways being associated with transition to psychosis, although this requires replication. Longitudinally updated FA measurements did not yield stronger associations than were available from baseline imaging data alone. Studies at an even larger scale are needed to establish whether these associations are robust and clinically meaningful.

The human hippocampus is a unique cortical structure central to brain function, plasticity, and disease. Unravelling its complex organization requires the integration of multiscale data, linking molecular features to mesoscale anatomy and macroscale functional patterns. Gene expression is a fundamental microscale phenotype, and its profiling can provide a reference description of how molecular features are distributed across the brain. Capitalizing on recent imaging-transcriptomic analyses, we introduce HippoGenes, a repository of fine-grained gene expression patterns across human hippocampal subregions. We leveraged spatial statistical models and hippocampal surface mapping to reconstruct dense transcriptomic maps from sparse post-mortem tissue samples of the Allen Human Brain Atlas, generating continuous expression estimates for thousands of genes aligned to a common surface-based coordinate system. We illustrate the utility of HippoGenes to (i) map medial-lateral and anterior-posterior transcriptomic gradients that align with subfield and tripartite subdivisions of the hippocampal formation, (ii) examine associations between gene expression and canonical microstructural and functional features of the hippocampus, and (iii) perform a molecular decoding of subregional alterations in neurological patients with hippocampal pathology. HippoGenes provides a framework for exploring the molecular organization of the hippocampus, opening avenues for multiscale integration in health and disease, and is openly available on https://hippogenes.readthedocs.io/.

Psychosis has been linked to changes in diffusion-derived fractional anisotropy (FA) across multiple white matter tracts, yet most evidence is cross-sectional. Longitudinal findings are inconsistent, and the clinical relevance of white matter changes over time remains unclear. To address this gap, we investigated longitudinal white matter changes and their clinical correlates in early psychosis in a large, multi-centre diffusion-tensor imaging study. Across 18 sites, 407 participants (healthy controls: n = 97, 59.8% men, mean age 23.9±4.6 years; clinical high risk of psychosis: n = 158, 53.5% men, mean age 23.0±4.9 years; first-episode psychosis: n = 152, 71.1% men, mean age 25.1±5.4 years) were scanned at up to three time points over 12 months. FA was assessed in the cingulum bundle, the superior longitudinal fasciculus, the inferior fronto-occipital fasciculus, and at the whole brain level. FA trajectories were analysed using linear mixed-effects models to test effects of group, social and occupational functioning, and (attenuated) psychotic symptoms, while controlling for demographic and socioeconomic covariates. No significant group differences in FA were observed, either globally or within tracts ( p > .05). Longitudinal FA trajectories were not associated with changes in (attenuated) psychotic symptoms or functioning ( p > .05). In contrast, higher baseline antipsychotic medication dose was significantly associated with lower FA ( p _corr < .05). Overall, these findings indicate that white matter microstructure is relatively stable during the clinical-high-risk and first-episode phases of psychosis, and that it is not clearly associated with variation in symptom severity or functional outcomes over time.

Background and Hypothesis: Schizophrenia (SZ) and bipolar disorder (BD) share overlapping yet distinct clinical profiles and system-wide brain alterations. Macroscale functional connectivity gradients capture principal axes of cortical organization, including the separation of unimodal and transmodal systems, offering a low-dimensional lens on individual differences in brain architecture. Whether these axes reflect shared or diagnosis-specific variation across the SZ-BD spectrum is unknown. Study Design: Using resting-state fMRI from 187 adults (110 HC, 37 SZ, 40 BD) from the UCLA Consortium for Neuropsychiatric Phenomics, we derived individual low-dimensional gradients and applied three analyses: case-control comparisons at both the cortical network and subcortical region-of-interest level, Partial Least Squares (PLS) regression linking gradients to clinical phenotypes, and individual-level similarity indices (SI-PLS) positioning participants within a gradient-behaviour space. Study Results: While the gradient structure (G1: visual-somatomotor and G2: unimodal-transmodal) was preserved across groups, patient groups showed greater deviations along both axes. Network analyses revealed transdiagnostic frontoparietal compression in G2, alongside disorder-specific effects: visual pole contraction and subcortical amygdala displacement in SZ, and somatomotor displacement in BD. PLS identified a BD-associated profile of preserved gradient architecture and lower symptom burden, contrasting with an SZ-associated profile of greater cognitive impairment and symptom severity. SI-PLS scores placed SZ and BD in distinct regions of a shared two-dimensional neural space, with HC between them. Conclusions: Differences across the SZ-BD spectrum organize along two principal axes, revealing transdiagnostic alterations in higher-order association systems alongside disorder-specific sensory signatures. These findings support a multi-axis dimensional framework for understanding clinical heterogeneity in psychosis.

Schizophrenia is often conceptualized as a brain network disorder, yet the organizational principles and heterogeneity underlying widespread cortical abnormalities remain poorly understood. Leveraging multisite MRI data from 3,958 individuals diagnosed with schizophrenia and 5,489 neurotypical individuals, we studied the cortical organization and its subtyping by analyzing individualized cortical network similarity. We used eigenvector decompositions to study spatial patterning of the gradients and graph theory to study small–world topology. Individuals with schizophrenia showed widespread alterations of gradient loadings, which followed inferior–superior and frontal–temporal axes. Alterations in small–world topology were localized in key network hubs, including the insula and anterior cingulate cortex. Brain–symptom association analyses identified a latent dimension linking disorganization symptoms to topological alterations. Finally, clustering cortical alterations identified two robust subtypes, characterized by divergent anterior cingulate (S1) versus temporoparietal (S2) thickness differences aligned with the intrinsic gradient-topology patterns. Both subtypes were present early in the illness and stable across disease stages and age groups. These findings reveal systematic disruptions of cortical organization in schizophrenia, providing a network-level framework for macroscale brain organization and inter-individual heterogeneity.

Background: Chronic cocaine use is associated with physical, neuropsychiatric, and social consequences. While cocaine‑induced neuroplasticity is well-studied in animal models, less is known about recovery of the human brain after prolonged exposure. This longitudinal functional MRI (fMRI) study therefore examined whether (1) whole‑brain and fronto‑striatal resting‑state functional connectivity (rsFC) are modulated by reduced cocaine use and (2) baseline fronto‑striatal rsFC predicts future cocaine intake in individuals with chronic cocaine use (ICCU). Methods: We analysed rsFC data from 32 ICCU (15 sustained users [SU], 17 decreased users [DU]) and 38 matched cocaine‑naive controls (CNC) collected at baseline and long-term follow-up (median interval: 10.3 months). Whole‑brain rsFC was compared between ICCU and CNC and fronto‑striatal rsFC was examined within ICCU subgroups. Additionally, we tested whether baseline rsFC predicted future cocaine use and intensity or craving. Results: At TP1, no striatal‑prefrontal rsFC differences emerged between SU and DU. At TP2, SU exhibited widespread whole‑brain hyperconnectivity versus CNC and stronger striatal rsFC (pallidum/nucleus accumbens to superior frontal gyrus; caudate anti‑correlation to inferior frontal gyrus) versus DU (rm‑ANCOVA, p<0.05, cluster‑corrected). Baseline TP1 striatal‑prefrontal rsFC significantly predicted TP2 cocaine use (R²=0.892; caudate fronto‑striatal connectivity surviving FDR correction), whereas the craving model showed high in‑sample fit (R²=0.975) but no single seed survived FDR correction. Conclusion: Our findings indicate that altered rsFC in ICCU reflects widespread network disruption, while rsFC changes partially normalize toward control‑like patterns with reduced cocaine use. Finally, fronto‑striatal rsFC might be useful to predict cocaine use trajectories for individualized treatment of cocaine use disorder.

Positive and negative schizotypy reflect distinct patterns of subclinical traits in the general population associated with neurodevelopmental and schizophrenia-spectrum pathologies. Yet, a comprehensive characterization of the unique and shared neuroanatomical signatures of these schizotypy dimensions is lacking. Leveraging 3D brain MRI data from 2730 unmedicated healthy individuals, we identified neuroanatomical profiles of positive and negative schizotypy and systematically compared them with disorder-specific, microarchitectural, neurotransmitter-level, and connectome measures. Positive and negative schizotypy were associated with distinct cortical signatures, of predominantly thinner frontal and thicker paralimbic cortical areas, respectively. These cortical signatures of positive and negative schizotypy were differentially linked to brain-wide cortical patterns of schizophrenia-spectrum (clinical high-risk for psychosis, schizophrenia) and neurodevelopmental conditions (ADHD, autism spectrum disorder and 22q11.2 deletion syndrome). Additionally, the positive and negative schizotypy-related cortical profiles mapped onto different local attributes of gene expression, cortical myelination, D1, and histamine receptor distributions. Network models further showed that positive and negative schizotypy cortical signatures were spatially associated with cortical hubs, suggesting that highly interconnected regions are more vulnerable to the morphological differences associated with both schizotypy dimensions. Finally, predominantly sensorimotor-to-association and paralimbic areas emerged as epicenters with connectivity profiles significantly linked to the schizotypy-related cortical patterns. Collectively, this study identified cortical signatures of positive and negative schizotypy traits that are embedded along multiple scales of cortical organization and neuropsychiatric pathologies. Our work yields novel insights into how neurobiology and brain architecture may guide neuroanatomical vulnerability and resilience to psychopathology in the general population.

Treatment-resistant schizophrenia (TRS) poses a considerable challenge in psychiatric care, with approximately 10-60% of patients exhibiting non-responsiveness to antipsychotic medication. While early treatment could improve clinical outcomes, the absence of objective biomarkers hinders timely intervention. General structural changes have been suggestively associated with TRS. However, there is a lack of clear and robust conclusions due to challenges in obtaining data from TRS cases with large sample sizes. To address this knowledge gap, we collaborated with Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium to investigate brain structural changes associated with TRS. Leveraging data from multiple institutions spanning various continents and ethnic groups, our meta- and mega-analysis revealed a significant reduction in the volume of bilateral putamen and bilateral hippocampus, as well as the left superior frontal gyrus (SFG) surface area in TRS patients compared with non-TRS patients with sufficient statistical power. Further analysis, incorporating data from healthy controls (HCs), unveiled a decreasing trend in bilateral hippocampal volumes and left SFG surface area from HCs to non-TRS patients and further to TRS patients. There were no significant effects of either clozapine dose or cumulative antipsychotic exposure on bilateral hippocampal volumes or left SFG surface area. In contrast, the putamen volume exhibited an increase in non-TRS patients compared with HCs, with no significant difference between TRS patients and HCs. Antipsychotic exposure was significantly correlated with the putamen volume with small effect size. In conclusion, this study underscores the volume of the hippocampus and SFG as potential biomarkers for TRS.

The human brain requires a continuous supply of energy to function effectively. Here, we investigated how the low-dimensional organization of intrinsic functional connectivity patterns based on resting-state functional magnetic resonance imaging relates to brain energy expenditure measured by fluorodeoxyglucose positron emission tomography. By incrementally adding more dimensions of brain organization (via functional gradients), we show that increasing amounts of variance in the map of brain energy expenditure are accounted for. Dimensions of brain organization that explained much of the variance in intrinsic brain function also accounted for a substantial share of regional variance in energy expenditure maps. This relationship was especially pronounced for maps based on the strongest connections, suggesting that weaker connections may contribute less to explaining regional energy variance. Notably, our topological model was more effective than random brain organization configurations, suggesting that brain organization may be specifically associated with energy optimization. Our results demonstrate how the spatial organization of functional connections is systematically linked to optimized energy expenditure in the human brain, providing new insights into the metabolic basis of brain function.