Biological Psychiatry最新文献

Background: Neurocognitive impairment is a well-known phenomenon in schizophrenia that begins prior to psychosis onset. Connectome-wide association studies have inconsistently linked cognitive performance to resting-state functional magnetic resonance imaging. We hypothesized that a carefully selected cognitive instrument and refined population would allow identification of reliable brain-behavior associations with connectome-wide association studies. To test this hypothesis, we first identified brain-cognition correlations via a connectome-wide association study in early psychosis. We then asked, in an independent dataset, if these brain-cognition relationships would generalize to individuals who develop psychosis in the future.

Methods: The Seidman Auditory Continuous Performance Task (ACPT) effectively differentiates healthy participants from those with psychosis. Our connectome-wide association study used the HCP-EP (Human Connectome Project for Early Psychosis) (n = 183) to identify links between connectivity and ACPT performance. We then analyzed data from the NAPLS2 (North American Prodrome Longitudinal Study 2) (n = 345), a multisite prospective study of individuals at risk for psychosis. We tested the connectome-wide association study-identified cognition-connectivity relationship in both individuals at risk for psychosis and control participants.

Results: Our connectome-wide association study in early-course psychosis identified robust associations between better ACPT performance and higher prefrontal-somatomotor connectivity (p < .005). Prefrontal-somatomotor connectivity was also related to ACPT performance in at-risk individuals who would develop psychosis (n = 17). This finding was not observed in nonconverters (n = 196) or control participants (n = 132).

Conclusions: This connectome-wide association study identified reproducible links between connectivity and cognition in separate samples of individuals with psychosis and at-risk individuals who would later develop psychosis. A carefully selected task and population improves the ability of connectome-wide association studies to identify reliable brain-phenotype relationships.

Background: Numerous studies have established the presence of gray matter atrophy and brain activation abnormalities during neurocognitive and social cognitive tasks in schizophrenia. Despite a growing consensus that diseases localize better to distributed brain networks than individual anatomical regions, relatively few studies have examined brain network localization of gray matter atrophy and neurocognitive and social cognitive dysfunction in schizophrenia.

Methods: To address this gap, we initially identified brain locations of structural and functional abnormalities in schizophrenia from 301 published neuroimaging studies with 8712 individuals with schizophrenia and 9275 healthy control participants. By applying novel functional connectivity network mapping to large-scale resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to 3 brain abnormality networks of schizophrenia.

Results: The gray matter atrophy network of schizophrenia comprised a broadly distributed set of brain areas predominantly implicating the ventral attention, somatomotor, and default networks. The neurocognitive dysfunction network was also composed of widespread brain areas primarily involving the frontoparietal and default networks. By contrast, the social cognitive dysfunction network consisted of circumscribed brain regions mainly implicating the default, subcortical, and visual networks.

Conclusions: Our findings suggest shared and unique brain network substrates of gray matter atrophy and neurocognitive and social cognitive dysfunction in schizophrenia, which may not only refine the understanding of disease neuropathology from a network perspective but may also contribute to more targeted and effective treatments for impairments in different cognitive domains in schizophrenia.

Background: Patients with early psychosis (EP) (within 3 years after psychosis onset) show significant variability, which makes predicting outcomes challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, which limits the development of early interventions.

Methods: A data-driven approach, partial least squares correlation, was used across 2 independent datasets to examine multivariate relationships between white matter properties and symptomatology and to identify stable and generalizable signatures in EP. The primary cohort included patients with EP from the Human Connectome Project for Early Psychosis (n = 124). The replication cohort included patients with EP from the Feinstein Institute for Medical Research (n = 78) as part of the MEND (Multimodal Evaluation of Neural Disorders) Project. Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders.

Results: In both cohorts, a significant latent component corresponded to a symptom profile that combined negative symptoms, primarily diminished expression, with specific somatic symptoms. Both latent components captured comprehensive features of white matter disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the partial least squares model trained on the primary cohort accurately predicted microstructural features and symptoms in the replication cohort. Findings were not driven by diagnosis, medication, or substance use.

Conclusions: This data-driven transdiagnostic approach revealed a stable and replicable neurobiological signature of microstructural white matter alterations in EP across diagnoses and datasets, showing strong covariance of these alterations with a unique profile of negative and somatic symptoms. These findings suggest the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings.

Background: 22q11.2 deletion syndrome (22qDel) is a copy number variant that is associated with psychosis and other neurodevelopmental disorders. Adolescents who are at clinical high risk for psychosis (CHR) are identified based on the presence of subthreshold psychosis symptoms. Whether common neural substrates underlie these distinct high-risk populations is unknown. We compared functional brain measures in 22qDel and CHR cohorts and mapped the results to biological pathways.

Methods: We analyzed 2 large multisite cohorts with resting-state functional magnetic resonance imaging data: 1) a 22qDel cohort (n = 164, 47% female) and typically developing (TD) control participants (n = 134, 56% female); and 2) a cohort of CHR individuals (n = 240, 41% female) and TD control participants (n = 149, 46% female) from the NAPLS-2 (North American Prodrome Longitudinal Study-2). We computed global brain connectivity (GBC), local connectivity (LC), and brain signal variability (BSV) across cortical regions and tested case-control differences for 22qDel and CHR separately. Group difference maps were related to published brain maps using autocorrelation-preserving permutation.

Results: BSV, LC, and GBC were significantly disrupted in individuals with 22qDel compared with TD control participants (false discovery rate-corrected q < .05). Spatial maps of BSV and LC differences were highly correlated with each other, unlike GBC. In the CHR group, only LC was significantly altered versus the control group, with a different spatial pattern than the 22qDel group. Group differences mapped onto biological gradients, with 22qDel effects being strongest in regions with high predicted blood flow and metabolism.

Conclusions: 22qDel carriers and CHR individuals exhibited different effects on functional magnetic resonance imaging temporal variability and multiscale functional connectivity. In 22qDel carriers, strong and convergent disruptions in BSV and LC that were not seen in CHR individuals suggest distinct functional brain alterations.

Background: Mosaic chromosomal alterations are implicated in neuropsychiatric disorders, but the contribution to schizophrenia (SCZ) risk for somatic copy number variations (sCNVs) emerging in early developmental stages has not been fully established.

Methods: We analyzed blood-derived genotype arrays from 9715 patients with SCZ and 28,822 control participants of Chinese descent using a computational tool (MoChA) based on long-range chromosomal information to detect mosaic chromosomal alterations. We focused on probable early developmental sCNVs through stringent filtering. We assessed the burden of sCNVs across varying cell fraction cutoffs, as well as the frequency with which genes were involved in sCNVs. We integrated this data with the PGC (Psychiatric Genomics Consortium) dataset, which comprises 12,834 SCZ cases and 11,648 controls of European descent, and complemented it with genotyping data from postmortem brain tissue of 936 participants (449 cases and 487 controls).

Results: Patients with SCZ had a significantly higher somatic losses detection rate than control participants (1.00% vs. 0.52%; odds ratio = 1.91; 95% CI, 1.47-2.49; two-sided Fisher's exact test, p = 1.49 × 10^-6). Further analysis indicated that the odds ratios escalated proportionately (from 1.91 to 2.78) with the increment in cell fraction cutoffs. Recurrent sCNVs associated with SCZ (odds ratio > 8; Fisher's exact test, p < .05) were identified, including notable regions at 10q21.1 (ZWINT), 3q26.1 (SLITRK3), 1q31.1 (BRINP3) and 12q21.31-21.32 (MGAT4C and NTS) in the Chinese cohort, and some regions were validated with PGC data. Cross-tissue validation pinpointed somatic losses at loci like 1p35.3-35.2 and 19p13.3-13.2.

Conclusions: The study highlights the significant impact of mosaic chromosomal alterations on SCZ, suggesting their pivotal role in the disorder's genetic etiology.

The mechanisms of psychotic symptoms such as hallucinations and delusions are often investigated in fully formed illness, well after symptoms emerge. These investigations have yielded key insights but are not well positioned to reveal the dynamic forces underlying symptom formation itself. Understanding symptom development over time would allow us to identify steps in the pathophysiological process leading to psychosis, shifting the focus of psychiatric intervention from symptom alleviation to prevention. We propose a model for understanding the emergence of psychotic symptoms within the context of an adaptive, developing neural system. We make the case for a pathophysiological process that begins with cortical hyperexcitability and bottom-up noise transmission, which engenders inappropriate belief formation via aberrant prediction error signaling. We argue that this bottom-up noise drives learning about the (im)precision of new incoming sensory information because of diminished signal-to-noise ratio, causing a compensatory relative overreliance on prior beliefs. This overreliance on priors predisposes to hallucinations and covaries with hallucination severity. An overreliance on priors may also lead to increased conviction in the beliefs generated by bottom-up noise and drive movement toward conversion to psychosis. We identify predictions of our model at each stage, examine evidence to support or refute those predictions, and propose experiments that could falsify or help select between alternative elements of the overall model. Nesting computational abnormalities within longitudinal development allows us to account for hidden dynamics among the mechanisms driving symptom formation and to view established symptoms as a point of equilibrium among competing biological forces.

Background: Disruptions of axonal connectivity are thought to be a core pathophysiological feature of psychotic illness, but whether they are present early in the illness, prior to antipsychotic exposure, and whether they can predict clinical outcome remain unknown.

Methods: We acquired diffusion-weighted magnetic resonance images to map structural connectivity between each pair of 319 parcellated brain regions in 61 antipsychotic-naïve individuals with first-episode psychosis (15-25 years, 46% female) and a demographically matched sample of 27 control participants. Clinical follow-up data were also acquired in patients 3 and 12 months after the scan. We used connectome-wide analyses to map disruptions of inter-regional pairwise connectivity and connectome-based predictive modeling to predict longitudinal change in symptoms and functioning.

Results: Individuals with first-episode psychosis showed disrupted connectivity in a brainwide network linking all brain regions compared with controls (familywise error-corrected p = .03). Baseline structural connectivity significantly predicted change in functioning over 12 months (r = 0.44, familywise error-corrected p = .041), such that lower connectivity within fronto-striato-thalamic systems predicted worse functional outcomes.

Conclusions: Brainwide reductions of structural connectivity exist during the early stages of psychotic illness and cannot be attributed to antipsychotic medication. Moreover, baseline measures of structural connectivity can predict change in patient functional outcomes up to 1 year after engagement with treatment services.

Background: Psychotic experiences are thought to emerge from various interrelated patterns of disrupted belief updating, such as overestimating the reliability of sensory information and misjudging task volatility, yet these substrates have never been jointly addressed under one computational framework, and it is not clear to what degree they reflect trait-like computational patterns.

Methods: We introduce a novel hierarchical Bayesian model that describes how individuals simultaneously update their beliefs about the task volatility and noise in observation. We applied this model to data from a modified predictive inference task in a test-retest study with healthy volunteers (N = 45, 4 sessions) and examined the relationship between model parameters and schizotypal traits in a larger online sample (N = 437) and in a cohort of patients with schizophrenia (N = 100).

Results: The interclass correlations were moderate to high for model parameters and excellent for averaged belief trajectories and precision-weighted learning rates estimated through hierarchical Bayesian inference. We found that uncertainty about the task volatility was related to schizotypal traits and to positive symptoms in patients, when learning to gain rewards. In contrast, negative symptoms in patients were associated with more rigid beliefs about observational noise, when learning to avoid losses.

Conclusions: These findings suggest that individuals with schizotypal traits across the psychosis continuum are less likely to learn or use higher-order statistical regularities of the environment and showcase the potential of clinically relevant computational phenotypes for differentiating symptom groups in a transdiagnostic manner.