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Dynamical brain state transitions are critical for flexible working memory but the network mechanisms are incompletely understood. Here, we show that working memory performance entails brain-wide switching between activity states using a combination of functional magnetic resonance imaging in healthy controls and individuals with schizophrenia, pharmacological fMRI, genetic analyses and network control theory. The stability of states relates to dopamine D1 receptor gene expression while state transitions are influenced by D2 receptor expression and pharmacological modulation. Individuals with schizophrenia show altered network control properties, including a more diverse energy landscape and decreased stability of working memory representations. Our results demonstrate the relevance of dopamine signaling for the steering of whole-brain network dynamics during working memory and link these processes to schizophrenia pathophysiology. Working memory requires the brain to switch between cognitive states and activity patterns. Here, the authors show that the steering of these neural network dynamics is influenced by dopamine D1- and D2-receptor function and altered in schizophrenia.

Schizophrenia is increasingly recognized as a disorder of distributed neural dynamics, but the molecular and genetic contributions are poorly understood. Recent work highlights a role for altered N-methyl-D-aspartate (NMDA) receptor signaling and related impairments in the excitation–inhibitory balance and synchrony of large-scale neural networks. Here, we combined a pharmacological intervention with novel techniques from dynamic network neuroscience applied to functional magnetic resonance imaging (fMRI) to identify alterations in the dynamic reconfiguration of brain networks related to schizophrenia genetic risk and NMDA receptor hypofunction. We quantified “network flexibility,” a measure of the dynamic reconfiguration of the community structure of time-variant brain networks during working memory performance. Comparing 28 patients with schizophrenia, 37 unaffected first-degree relatives, and 139 healthy controls, we detected significant differences in network flexibility [F(2,196) = 6.541, P = 0.002] in a pattern consistent with the assumed genetic risk load of the groups (highest for patients, intermediate for relatives, and lowest for controls). In an observer-blinded, placebo-controlled, randomized, cross-over pharmacological challenge study in 37 healthy controls, we further detected a significant increase in network flexibility as a result of NMDA receptor antagonism with 120 mg dextromethorphan [F(1,34) = 5.291, P = 0.028]. Our results identify a potential dynamic network intermediate phenotype related to the genetic liability for schizophrenia that manifests as altered reconfiguration of brain networks during working memory. The phenotype appears to be influenced by NMDA receptor antagonism, consistent with a critical role for glutamate in the temporal coordination of neural networks and the pathophysiology of schizophrenia.

Oxytocin plays a vital role in social behavior and homeostatic processes, with animal models indicating that oxytocin receptor (OXTR) expression patterns in the brain influence behavior and physiology. However, the developmental trajectory of OXTR gene expression is unclear. By analyzing gene expression data in human post-mortem brain samples, from the prenatal period to late adulthood, we demonstrate distinct patterns of OXTR gene expression in the developing brain, with increasing OXTR expression along the course of the prenatal period culminating in a peak during early childhood. This early life OXTR expression peak pattern appears slightly earlier in a comparative macaque sample, which is consistent with the relative immaturity of the human brain during early life compared to macaques. We also show that a network of genes with strong spatiotemporal couplings with OXTR is enriched in several psychiatric illness and body composition phenotypes. Taken together, these results demonstrate that oxytocin signaling plays an important role in a diverse set of psychological and somatic processes across the lifespan.

•Structural brain networks can be modeled as a trade-off between wiring cost and topological features.•Schizophrenia patients show lower spatial constraints and topological facilitation.•Individual model parameters are associated with genetic risk for schizophrenia and cognition. Alterations in the structural connectome of schizophrenia patients have been widely characterized, but the mechanisms remain largely unknown. Generative network models have recently been introduced as a tool to test the biological underpinnings of altered brain network formation. We evaluated different generative network models in healthy controls (n=152), schizophrenia patients (n=66), and their unaffected first-degree relatives (n=32), and we identified spatial and topological factors contributing to network formation. We further investigated how these factors relate to cognition and to polygenic risk for schizophrenia. Our data show that among the four tested classes of generative network models, structural brain networks were optimally accounted for by a two-factor model combining spatial constraints and topological neighborhood structure. The same wiring model explained brain network formation across study groups. However, relatives and schizophrenia patients exhibited significantly lower spatial constraints and lower topological facilitation compared to healthy controls. Further exploratory analyses point to potential associations of the model parameter reflecting spatial constraints with the polygenic risk for schizophrenia and cognitive performance. Our results identify spatial constraints and local topological structure as two interrelated mechanisms contributing to regular brain network formation as well as altered connectomes in schizophrenia and healthy individuals at familial risk for schizophrenia. On an exploratory level, our data further point to the potential relevance of spatial constraints for the genetic risk for schizophrenia and general cognitive functioning, thereby encouraging future studies in following up on these observations to gain further insights into the biological basis and behavioral relevance of model parameters.

Multi-task learning (MTL) is a learning paradigm that enables the simultaneous training of multiple communicating algorithms, and has been widely applied in the biomedical analysis for shared biomarker identification. Although MTL has successfully supported either regression or classification tasks, incorporating mixed types of tasks into a unified MTL framework remains challenging, especially in biomedicine, where it can lead to biased biomarker identification. To address this issue, we propose an improved method of multi-task learning, MTLComb, which balances the weights of regression and classification tasks to promote unbiased biomarker identification. We demonstrate the algorithmic efficiency and clinical utility of MTLComb through analyses on both simulated data and actual biomedical studies pertaining to sepsis and schizophrenia. The code is available at https://github.com/transbioZI/MTLComb .

Background Understanding the relationship between psychopathology and major domains of human neurobehavioral functioning may identify new transdiagnostic treatment targets. However, studies examining the interrelationship between psychopathological symptoms, sensorimotor, cognitive, and global functioning in a transdiagnostic sample are lacking. We hypothesized a close relationship between sensorimotor and cognitive functioning in a transdiagnostic patient sample. Methods We applied network analysis and community detection methods to examine the interplay and centrality [expected influence (EI) and strength] between psychopathological symptoms, sensorimotor, cognitive, and global functioning in a transdiagnostic sample consisting of 174 schizophrenia spectrum (SSD) and 38 mood disorder (MOD) patients. All patients ( n  = 212) were examined with the Positive and Negative Syndrome Scale (PANSS), the Heidelberg Neurological Soft Signs Scale (NSS), the Global Assessment of Functioning (GAF), and the Brief Cognitive Assessment Tool for Schizophrenia consisted of trail making test B (TMT-B), category fluency (CF) and digit symbol substitution test (DSST). Results NSS showed closer connections with TMT-B, CF, and DSST than with GAF and PANSS. DSST, PANSS general, and NSS motor coordination scores showed the highest EI. Sensory integration, DSST, and CF showed the highest strength. Conclusions The close connection between sensorimotor and cognitive impairment as well as the high centrality of sensorimotor symptoms suggests that both domains share aspects of SSD and MOD pathophysiology. But, because the majority of the study population was diagnosed with SSD, the question as to whether sensorimotor symptoms are really a transdiagnostic therapeutic target needs to be examined in future studies including more balanced diagnostic groups.

Copy number variations (CNV) involving multiple genes are ideal models to study polygenic neuropsychiatric disorders. Since 22q11.2 deletion is regarded as the most important single genetic risk factor for developing schizophrenia, characterizing the effects of this CNV on neural networks offers a unique avenue towards delineating polygenic interactions conferring risk for the disorder. We used a Df(h22q11)/+ mouse model of human 22q11.2 deletion to dissect gene expression patterns that would spatially overlap with differential resting-state functional connectivity (FC) patterns in this model (N = 12 Df(h22q11)/+ mice, N = 10 littermate controls). To confirm the translational relevance of our findings, we analyzed tissue samples from schizophrenia patients and healthy controls using machine learning to explore whether identified genes were co-expressed in humans. Additionally, we employed the STRING protein-protein interaction database to identify potential interactions between genes spatially associated with hypo- or hyper-FC. We found significant associations between differential resting-state connectivity and spatial gene expression patterns for both hypo- and hyper-FC. Two genes, Comt and Trmt2a, were consistently over-expressed across all networks. An analysis of human datasets pointed to a disrupted co-expression of these two genes in the brain in schizophrenia patients, but not in healthy controls. Our findings suggest that COMT and TRMT2A form a core genetic component implicated in differential resting-state connectivity patterns in the 22q11.2 deletion. A disruption of their co-expression in schizophrenia patients points out a prospective cause for the aberrance of brain networks communication in 22q11.2 deletion syndrome on a molecular level.

PurposePerigenual anterior cingulate cortex (pACC) is a neural convergence site for social stress-related risk factors for mental health, including ethnic minority status. Current social status, a strong predictor of mental and somatic health, has been related to gray matter volume in this region, but the effects of social mobility over the lifespan are unknown and may differ in minorities. Recent studies suggest a diminished health return of upward social mobility for ethnic minority individuals, potentially due to sustained stress-associated experiences and subsequent activation of the neural stress response system.MethodsTo address this issue, we studied an ethnic minority sample with strong upward social mobility. In a cross-sectional design, we examined 64 young adult native German and 76 ethnic minority individuals with comparable sociodemographic attributes using whole-brain structural magnetic resonance imaging.ResultsResults showed a significant group-dependent interaction between perceived upward social mobility and pACC gray matter volume, with a significant negative association in the ethnic minority individuals. Post-hoc analysis showed a significant mediation of the relationship between perceived upward social mobility and pACC volume by perceived chronic stress, a variable that was significantly correlated with perceived discrimination in our ethnic minority group.ConclusionOur findings extend prior work by pointing to a biological signature of the “allostatic costs” of socioeconomic attainment in socially disadvantaged upwardly mobile individuals in a key neural node implicated in the regulation of stress and negative affect.

Background Although the association between smoking and depression is well-established, the underlying mechanisms and contextual factors remain insufficiently understood. We examined the association between smoking and depression, including detailed dose-response and timing-related relationships, using baseline data from a large population-based cohort, the German National Cohort (NAKO). Methods The analysis comprised 173,890 participants (19-72 years, 50.21% female). Lifetime and current depression were assessed via self-reported physician’s diagnosis, the Major Depressive Disorder module of the MINI International Neuropsychiatric Interview (MINI), and the depression scale of the Patient Health Questionnaire (PHQ-9). Smoking behavior was assessed using self-reported smoking status, age at initiation, cigarettes per day, and time since smoking cessation. Associations between smoking and depression measures were analyzed using regression models adjusted for sex, age, age², education, Body Mass Index, and alcohol consumption. Results Lifetime depression was more prevalent among individuals who currently or formerly smoked compared to those who never smoked. Currently smoking individuals also reported most current depressive symptoms, followed by formerly smoking individuals and those who never smoked. A dose-response relationship was observed, with more cigarettes per day being associated with more current depressive symptoms. Later age at smoking initiation was associated with later depression onset. Time since smoking cessation was positively associated with time since last depressive episode and negatively with current depressive symptoms. Conclusions Our findings support an association between smoking and depression. Robust dose-response relationships were found, with higher cigarette consumption associated with more severe depressive symptoms, and longer time since cessation linked to lower depression levels. These results highlight smoking as a meaningful and modifiable contributor to current and lifetime depression, suggesting that quitting smoking or reducing cigarette consumption may benefit mental health. Early prevention of smoking initiation, along with integrated approaches that combine smoking cessation support with mental health care, may help reduce both smoking rates and depression burden.

In this proof of concept study, we demonstrated how Large Language Models (LLMs) can automate the conversion of unstructured case reports into clinical ratings. By leveraging instructions from a standardized clinical rating scale and evaluating the LLM’s confidence in its outputs, we aimed to refine prompting strategies and enhance reproducibility. Using this strategy and case reports of drug-induced Parkinsonism, we showed that LLM-extracted data closely align with clinical rater manual extraction, achieving an accuracy of 90%.