BACKGROUND: Evidence suggests that cannabis may be a causal factor for development of schizophrenia. We aimed to investigate whether use of antipsychotic medication, benzodiazepines, and psychiatric service use differs among patients with schizophrenia depending on whether psychosis was precipitated by a diagnosis of cannabis use disorder (CUD). METHODS: We utilized the nationwide Danish registries to identify all individuals with an incident diagnosis of schizophrenia from 1995 to 2016. We also collected information on whether first CUD diagnosis preceded schizophrenia and thus defined a group of potentially cannabis-related schizophrenia. We compared the cannabis-related schizophrenia group both with all non-cannabis-related patients with schizophrenia and with non-cannabis-related patients with schizophrenia that were propensity-score matched to cases using a range of potentially confounding variables. RESULTS: We included 35 714 people with incident schizophrenia, including 4116 (11.5%) that were cannabis-related. In the unmatched-comparison analyses, there were no clear differences over time in use of antipsychotics and benzodiazepines related to whether the diagnosis of schizophrenia was cannabis-related. After propensity-score matching, use of antipsychotics and benzodiazepines was significantly lower among cannabis-related cases of schizophrenia. In the unmatched comparison, the cannabis-related group had significantly more days admitted than the non-cannabis-related group. This was markedly attenuated after propensity-score matching. CONCLUSIONS: Our findings indicate the importance of considering cannabis-related cases of schizophrenia as a potentially distinct disorder in terms of prognosis. It is unclear, however, if these differences are due to different biological types of schizophrenia being compared or if they rather indicate behavioral differences such as reduced adherence and treatment-seeking.
Good adherence to antipsychotic therapy helps prevent relapses in First Episode Psychosis (FEP). We used data from the FEP-CAUSAL Collaboration, an international consortium of observational cohorts to emulate a target trial comparing antipsychotics with treatment discontinuation as the primary outcome. Other outcomes included all-cause hospitalization. We benchmarked our results to estimates from EUFEST, a randomized trial conducted in the 2000s. We included 1097 patients with a psychotic disorder and less than 2 years since psychosis onset. Inverse probability weighting was used to control for confounding. The estimated 12-month risks of discontinuation for aripiprazole, first-generation agents, olanzapine, paliperidone, quetiapine, and risperidone (95% CI) were: 61.5% (52.5-70.6), 73.5% (60.5-84.9), 76.8% (67.2-85.3), 58.4% (40.4-77.4), 76.5% (62.1-88.5), and 74.4% (67.0-81.2) respectively. Compared with aripiprazole, the 12-month risk differences (95% CI) were -15.3% (-30.0, 0.0) for olanzapine, -12.8% (-25.7, -1.0) for risperidone, and 3.0% (-21.5, 30.8) for paliperidone. The 12-month risks of hospitalization were similar between agents. Our estimates support use of aripiprazole and paliperidone as first-line therapies for FEP. Benchmarking yielded similar results for discontinuation and absolute risks of hospitalization as in the original trial, suggesting that data from the FEP-CAUSAL Collaboration data sufficed to approximately remove confounding for these clinical questions.
Decades of psychosis research highlight the prevalence and the clinical significance of negative emotions, such as fear and anxiety. Translational evidence demonstrates the pivotal role of the amygdala in fear and anxiety. However, most of these approaches have used hypothesis-driven analyses with predefined regions of interest. A data-driven analysis may provide a complimentary, unbiased approach to identifying brain correlates of fear and anxiety. The aim of the current study was to identify the brain basis of fear and anxiety in early psychosis and controls using a data-driven approach. We analyzed data from the Human Connectome Project for Early Psychosis, a multi-site study of 125 people with psychosis and 58 controls with resting-state fMRI and clinical characterization. Multivariate pattern analysis of whole-connectome data was used to identify shared and psychosis-specific brain correlates of fear and anxiety using the NIH Toolbox Fear-Affect and Fear-Somatic Arousal scales. We then examined clinical correlations of Fear-Affect scores and connectivity patterns. Individuals with psychosis had higher levels of Fear-Affect scores than controls (p < 0.05). The data-driven analysis identified a cluster encompassing the amygdala and hippocampus where connectivity was correlated with Fear-Affect score (p < 0.005) in the entire sample. The strongest correlate of Fear-Affect was between this cluster and the anterior insula and stronger connectivity was associated with higher Fear-Affect scores (r = 0.31, p = 0.0003). The multivariate pattern analysis also identified a psychosis-specific correlate of Fear-Affect score between the amygdala/hippocampus cluster and a cluster in the ventromedial prefrontal cortex (VMPFC). Higher Fear-Affect scores were correlated with stronger amygdala/hippocampal-VMPFC connectivity in the early psychosis group (r = 0.33, p = 0.002), but not in controls (r = -0.15, p = 0.28). The current study provides evidence for the transdiagnostic role of the amygdala, hippocampus, and anterior insula in the neural basis of fear and anxiety and suggests a psychosis-specific relationship between fear and anxiety symptoms and amygdala/hippocampal-VMPFC connectivity. Our novel data-driven approach identifies novel, psychosis-specific treatment targets for fear and anxiety symptoms and provides complimentary evidence to decades of hypothesis-driven approaches examining the brain basis of threat processing.
BACKGROUND: Psychomotor disturbances are observed across psychiatric disorders and often manifest as psychomotor slowing, agitation, disorganized behavior, or catatonia. Psychomotor function includes both cognitive and motor components, but the neural circuits driving these subprocesses and how they relate to symptoms have remained elusive for centuries. METHODS: We analyzed data from the HCP-EP (Human Connectome Project for Early Psychosis), a multisite study of 125 participants with early psychosis and 58 healthy participants with resting-state functional magnetic resonance imaging and clinical characterization. Psychomotor function was assessed using the 9-hole pegboard task, a timed motor task that engages mechanical and psychomotor components of action, and tasks assessing processing speed and task switching. We used multivariate pattern analysis of whole-connectome data to identify brain correlates of psychomotor function. RESULTS: We identified discrete brain circuits driving the cognitive and motor components of psychomotor function. In our combined sample of participants with psychosis (n = 89) and healthy control participants (n = 52), the strongest correlates of psychomotor function (pegboard performance) (p < .005) were between a midline cerebellar region and left frontal region and presupplementary motor area. Psychomotor function was correlated with both cerebellar-frontal connectivity (r = 0.33) and cerebellar-presupplementary motor area connectivity (r = 0.27). However, the cognitive component of psychomotor performance (task switching) was correlated only with cerebellar-frontal connectivity (r = 0.19), whereas the motor component (processing speed) was correlated only with cerebellar-presupplementary motor area connectivity (r = 0.15), suggesting distinct circuits driving unique subprocesses of psychomotor function. CONCLUSIONS: We identified cerebellar-cortical circuits that drive distinct subprocesses of psychomotor function. Future studies should probe relationships between cerebellar connectivity and psychomotor performance using neuromodulation.
Community engagement is important for research, yet many researchers do not routinely seek feedback from people with lived experience. A key barrier to this engagement is that the resources required to create an advisory board may be unavailable to individual investigators, and creating an advisory board for a single study may often be impractical. In this column, the authors describe how to create a standing research advisory board that can serve as a shared resource for researchers and community members and provide a psychosis research advisory board example to aid discussion.
Advisory Committees , Psychotic Disorders , Humans , Research Personnel , Psychotic Disorders/therapy
Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.
BACKGROUND: Individuals with psychotic symptoms experience substantial morbidity and have shortened life expectancies; early treatment may mitigate the worst effects. Understanding care preceding a first psychotic disorder diagnosis is critical to inform early detection and intervention. STUDY DESIGN: In this observational cohort study using comprehensive information from the Massachusetts All-Payer Claims Database, we identified the first psychotic disorder diagnosis in 2016, excluding those with historical psychotic disorder diagnoses in the prior 48 months among those continuous enrollment data. We reviewed visits, medications, and hospitalizations 2012-2016. We used logistic regression to examine characteristics associated with pre-diagnosis antipsychotic use. STUDY RESULTS: There were 2505 individuals aged 15-35 years (146 per 100 000 similarly aged individuals in the database) with a new psychotic disorder diagnosis in 2016. Most (97%) had at least one outpatient visit in the preceding 48 months; 89% had a prior mental health diagnosis unrelated to psychosis (eg, anxiety [60%], depression [60%]). Many received psychotropic medications (77%), including antipsychotic medications (46%), and 68% had a visit for injury or trauma during the preceding 48 months. Characteristics associated with filling an antipsychotic medication before the psychotic disorder diagnosis included male sex and Medicaid insurance at psychosis diagnosis. CONCLUSIONS: In this insured population of Massachusetts residents with a new psychotic disorder diagnosis, nearly all had some healthcare utilization, visits for injury or trauma were common, and nearly half filled an antipsychotic medication in the preceding 48 months. These patterns of care could represent either pre-disease signals, delays, or both in receiving a formal diagnosis.
Antipsychotic Agents , Psychotic Disorders , United States , Humans , Male , Antipsychotic Agents/therapeutic use , Psychotic Disorders/diagnosis , Psychotic Disorders/epidemiology , Psychotic Disorders/therapy , Early Diagnosis , Logistic Models , Psychotherapy , Observational Studies as Topic
Background: The cerebellum contributes to the precise timing of non-motor and motor functions, and cerebellum abnormalities have been implicated in psychosis pathophysiology. In this study, we explored the effects of cerebellar theta burst stimulation (TBS), an efficient transcranial magnetic stimulation protocol, on temporal discrimination and self-reported mood and psychotic symptoms. Methods: We conducted a case-crossover study in which patients with psychosis (schizophrenias, schizoaffective disorders, or bipolar disorders with psychotic features) were assigned to three sessions of TBS to the cerebellar vermis: one session each of intermittent (iTBS), continuous (cTBS), and sham TBS. Of 28 enrolled patients, 26 underwent at least one TBS session, and 20 completed all three. Before and immediately following TBS, participants rated their mood and psychotic symptoms and performed a time interval discrimination task (IDT). We hypothesized that cerebellar iTBS and cTBS would modulate these measures in opposing directions, with iTBS being adaptive and cTBS maladaptive. Results: Reaction time (RT) in the IDT decreased significantly after iTBS vs. Sham (LS-mean difference = -73.3, p = 0.0001, Cohen's d = 1.62), after iTBS vs. cTBS (LS-mean difference = -137.6, p < 0.0001, d = 2.03), and after Sham vs. cTBS (LS-mean difference = -64.4, p < 0.0001, d = 1.33). We found no effect on IDT accuracy. We did not observe any effects on symptom severity after correcting for multiple comparisons. Conclusion: We observed a frequency-dependent dissociation between the effects of iTBS vs. cTBS to the cerebellar midline on the reaction time of interval discrimination in patients with psychosis. iTBS showed improved (adaptive) while cTBS led to worsening (maladaptive) speed of response. These results demonstrate behavioral target engagement in a cognitive dimension of relevance to patients with psychosis and generate testable hypotheses about the potential therapeutic role of cerebellar iTBS in this clinical population. Clinical Trial Registration: clinicaltrials.gov, identifier NCT02642029.
PURPOSE OF REVIEW: Schizophrenia (SZ) is a debilitating mental illness; existing treatments are partially effective and associated with significant side effect burden, largely due to our limited understanding of disease mechanisms and the trajectory of disease progression. Accumulating evidence suggests that metabolic changes associated with glucose metabolism, mitochondrial dysfunction, and redox imbalance play an important role in the pathophysiology of schizophrenia. However, the molecular mechanisms associated with these abnormalities in the brains of schizophrenia patients and the ways in which they change over time remain unclear. This paper aims to review the current literature on molecular mechanisms and in vivo magnetic resonance spectroscopy (MRS) studies of impaired energy metabolism in patients at clinical high risk for psychosis, with first-episode SZ, and with chronic SZ. Our review covers research related to high-energy phosphate metabolism, lactate, intracellular pH, redox ratio, and the antioxidant glutathione. RECENT FINDINGS: Both first-episode and chronic SZ patients display a significant reduction in creatine kinase reaction activity and redox (NAD + /NADH) ratio in the prefrontal cortex. Chronic, but not first-episode, SZ patients also show a trend toward increased lactate levels and decreased pH value. These findings suggest a progressive shift from oxidative phosphorylation to glycolysis for energy production over the course of SZ, which is associated with redox imbalance and mitochondrial dysfunction. Accumulating evidence indicates that aberrant brain energy metabolism associated with mitochondrial dysfunction and redox imbalance plays a critical role in SZ and will be a promising target for future treatments.
Psychotic Disorders , Schizophrenia , Humans , Schizophrenia/drug therapy , Psychotic Disorders/pathology , Magnetic Resonance Spectroscopy/methods , Brain/pathology , Energy Metabolism , Lactates/metabolism , Lactates/therapeutic use
OBJETIVE: Hospitalized COVID-19 patients with severe mental illness (SMI) have worse outcomes than counterparts without SMI. Barriers in access to acute care medical procedures among SMI patients may partially explain this phenomenon. Here, we examined differences in critical care admission and in-hospital mortality between hospitalized COVID-19 patients with and without SMI. METHODS: This population-based study used Spain's nationwide electronic health records. Based on International Classification Diseases, Tenth Revision, ICD-10-CM codes, we identified all patients aged ≥15 years hospitalized due to COVID-19 between July 1st-December 31st, 2020, and compared patients with and without SMI in terms of (i) critical care admission and (ii) in-hospital mortality - overall and stratified by age. We used logistic regression models including sex, age, and comorbidity burden as measured by Charlson Comorbidity Index Score as covariates. RESULTS: Of 118,691 hospital admissions due to COVID-19 of people aged ≥15 years, 1512 (1.3%) included a diagnosis of SMI. Compared to non-SMI patients, SMI patients had higher in-hospital mortality (OR,95%CI: 1.63,1.42-1.88) and were less frequently admitted to critical care (OR,95%CI: 0.70,0.58-0.85). Admission to critical care in SMI patients was lower than for non-SMI counterparts only among individuals aged ≥60 years. The magnitude of the difference in in-hospital mortality between SMI and non-SMI patients decreased as age increased. CONCLUSIONS: Individuals with SMI had reduced critical care admission and increased in-hospital mortality compared non-SMI counterparts, suggesting that differences in delivery of acute care medical procedures may partially explain higher risk of negative outcomes among COVID-19 patients with SMI.
COVID-19 , Mental Disorders , Humans , COVID-19/epidemiology , Mental Disorders/epidemiology , Mental Disorders/therapy , Mental Disorders/diagnosis , Comorbidity , Hospitalization
Most mental disorders have a typical onset between 12 and 25 years of age, highlighting the importance of this period for the pathogenesis, diagnosis, and treatment of mental ill-health. This perspective addresses interactions between risk and protective factors and brain development as key pillars accounting for the emergence of psychopathology in youth. Moreover, we propose that novel approaches towards early diagnosis and interventions are required that reflect the evolution of emerging psychopathology, the importance of novel service models, and knowledge exchange between science and practitioners. Taken together, we propose a transformative early intervention paradigm for research and clinical care that could significantly enhance mental health in young people and initiate a shift towards the prevention of severe mental disorders.
Mental Disorders , Mental Health , Humans , Adolescent , Mental Disorders/therapy , Mental Disorders/diagnosis , Psychopathology
Cortical thickness reductions differ between individuals with psychotic disorders and comparison subjects even in early stages of illness. Whether these reductions covary as expected by functional network membership or simply by spatial proximity has not been fully elucidated. Through orthonormal projective non-negative matrix factorization, cortical thickness measurements in functionally-annotated regions from MRI scans of early-stage psychosis and matched healthy controls were reduced in dimensionality into features capturing positive covariance. Rather than matching the functional networks, the covarying regions in each feature displayed a more localized spatial organization. With Bayesian belief networks, the covarying regions per feature were arranged into a network topology to visualize the dependency structure and identify key driving regions. The features demonstrated diagnosis-specific differences in cortical thickness distributions per feature, identifying reduction-vulnerable spatial regions. Differences in key cortical thickness features between psychosis and control groups were delineated, as well as those between affective and non-affective psychosis. Clustering of the participants, stratified by diagnosis and clinical variables, characterized the clinical traits that define the cortical thickness patterns. Longitudinal follow-up revealed that in select clusters with low baseline cortical thickness, clinical traits improved over time. Our study represents a novel effort to characterize brain structure in relation to functional networks in healthy and clinical populations and to map patterns of cortical thickness alterations among ESP patients onto clinical variables for a better understanding of brain pathophysiology.
Cerebral Cortex , Psychotic Disorders , Humans , Longitudinal Studies , Bayes Theorem , Cerebral Cortex/diagnostic imaging , Psychotic Disorders/diagnostic imaging , Magnetic Resonance Imaging
