Over 30 years of STEP: The Pittsburgh experience with first-episode psychosis.

AIMS: For over 30 years, combined research and treatment settings in the US have been critical to conceptualizing care for first-episode psychosis (FEP). Here we describe an early example of such a context, the Services for the Treatment of Early Psychosis (STEP) clinic, which is affiliated with the University of Pittsburgh. METHODS: We describe STEP's historical roots and establishment in the early 1990s; STEP's research and treatment contributions, alongside its growth and ongoing leadership. RESULTS: Research-based clinics, like STEP, preceded and helped pave the way for the Recovery After an Initial Schizophrenia Episode project in the US and the ensuing Coordinated Specialty Care (CSC) approach, now widely adopted in the US. Early clinic-based research at STEP helped establish protocols for psychopharmacology, the relevance of effective early treatment, including psychosocial approaches, and highlighted disparities in treatment outcomes across race/ethnicity. Multidisciplinary collaboration and dialogue with consumers contributed to early treatment, combining psychosocial and pharmacological approaches. STEP adopted CSC and is situated within a bi-state Learning Health System. STEP has retained a relatively unique 5-year treatment model and exists within continuum of care ideally suited to studying psychotic illness and treatment outcomes. CONCLUSIONS: STEP remains the largest academic FEP clinic in Pennsylvania. Academic FEP clinics like STEP will have a critical role within Learning Health Systems nationally to model participatory approaches, sustain early intervention treatment quality and ongoing treatment developments.

Diagnostically distinct resting state fMRI energy distributions: A subject-specific maximum entropy modeling study.

Objective: Existing neuroimaging studies of psychotic and mood disorders have reported brain activation differences (first-order properties) and altered pairwise correlation-based functional connectivity (second-order properties). However, both approaches have certain limitations that can be overcome by integrating them in a pairwise maximum entropy model (MEM) that better represents a comprehensive picture of fMRI signal patterns and provides a system-wide summary measure called energy. This study examines the applicability of individual-level MEM for psychiatry and identifies image-derived model coefficients related to model parameters. Method: MEMs are fit to resting state fMRI data from each individual with schizophrenia/schizoaffective disorder, bipolar disorder, and major depression (n=132) and demographically matched healthy controls (n=132) from the UK Biobank to different subsets of the default mode network (DMN) regions. Results: The model satisfactorily explained observed brain energy state occurrence probabilities across all participants, and model parameters were significantly correlated with image-derived coefficients for all groups. Within clinical groups, averaged energy level distributions were higher in schizophrenia/schizoaffective disorder but lower in bipolar disorder compared to controls for both bilateral and unilateral DMN. Major depression energy distributions were higher compared to controls only in the right hemisphere DMN. Conclusions: Diagnostically distinct energy states suggest that probability distributions of temporal changes in synchronously active nodes may underlie each diagnostic entity. Subject-specific MEMs allow for factoring in the individual variations compared to traditional group-level inferences, offering an improved measure of biologically meaningful correlates of brain activity that may have potential clinical utility.

Energy in functional brain states correlates with cognition in adolescent schizophrenia and healthy persons.

Adolescent-onset schizophrenia (AOS) is a relatively rare and under-studied form of schizophrenia with more severe cognitive impairments and poorer outcome compared to adult-onset schizophrenia. Several neuroimaging studies have reported alterations in regional activations that account for activity in individual regions (first-order model) and functional connectivity that reveals pairwise co-activations (second-order model) in AOS compared to controls. The pairwise maximum entropy model, also called the Ising model, can integrate both first-order and second-order terms to elucidate a comprehensive picture of neural dynamics and captures both individual and pairwise activity measures into a single quantity known as energy, which is inversely related to the probability of state occurrence. We applied the MEM framework to task functional MRI data collected on 23 AOS individuals in comparison with 53 healthy control subjects while performing the Penn Conditional Exclusion Test (PCET), which measures executive function that has been repeatedly shown to be more impaired in AOS compared to adult-onset schizophrenia. Accuracy of PCET performance was significantly reduced among AOS compared to controls as expected. Average cumulative energy achieved for a participant over the course of the fMRI negatively correlated with task performance, and the association was stronger than any first-order associations. The AOS subjects spent more time in higher energy states that represent lower probability of occurrence and were associated with impaired executive function suggesting that the neural dynamics may be less efficient compared to controls who spent more time in lower energy states occurring with higher probability and hence are more stable and efficient. The energy landscapes in both conditions featured attractors that corresponded to two distinct subnetworks, namely fronto-temporal and parieto-motor. Attractor basins were larger in the controls than in AOS; moreover, fronto-temporal basin size was significantly correlated with cognitive performance in controls but not among the AOS. The single trial trajectories for the AOS group also showed higher variability in concordance with shallow attractor basins among AOS. These findings suggest that the neural dynamics of AOS features more frequent occurrence of less probable states with narrower attractors, which lack the relation to executive function associated with attractors in control subjects suggesting a diminished capacity of AOS to generate task-effective brain states.

Gene Expressions Preferentially Influence Cortical Thickness of Human Connectome Project Atlas Parcellated Regions in First-Episode Antipsychotic-Naïve Psychoses.

Altered gene expressions may mechanistically link genetic factors with brain morphometric alterations. Existing gene expression studies have examined selected morphometric features using low-resolution atlases in medicated schizophrenia. We examined the relationship of gene expression with cortical thickness (CT), surface area (SA), and gray matter volume (GMV) of first-episode antipsychotic-naïve psychosis patients (FEAP = 85) and 81 controls, hypothesizing that gene expressions often associated with psychosis will differentially associate with different morphometric features. We explored such associations among schizophrenia and non-schizophrenia subgroups within FEAP group compared to controls. We mapped 360 Human Connectome Project atlas-based parcellations on brain MRI on to the publicly available brain gene expression data from the Allen Brain Institute collection. Significantly correlated genes were investigated using ingenuity pathway analysis to elucidate molecular pathways. CT but not SA or GMV correlated with expression of 1137 out of 15 633 genes examined controlling for age, sex, and average CT. Among these ≈19%, ≈39%, and 8% of genes were unique to FEAP, schizophrenia, and non-schizophrenia, respectively. Variants of 10 among these 1137 correlated genes previously showed genome-wide-association with schizophrenia. Molecular pathways associated with CT were axonal guidance and sphingosine pathways (common to FEAP and controls), selected inflammation pathways (unique to FEAP), synaptic modulation (unique to schizophrenia), and telomere extension (common to NSZ and healthy controls). We demonstrate that different sets of genes and molecular pathways may preferentially influence CT in different diagnostic groups. Genes with altered expressions correlating with CT and associated pathways may be targets for pathophysiological investigations and novel treatment designs.

Multipronged investigation of morphometry and connectivity of hippocampal network in relation to risk for psychosis using ultrahigh field MRI.

Hippocampal abnormalities are associated with psychosis-risk states. Given the complexity of hippocampal anatomy, we conducted a multipronged examination of morphometry of regions connected with hippocampus, and structural covariance network (SCN) and diffusion-weighted circuitry among 27 familial high-risk (FHR) individuals who were past the highest risk for conversion to psychoses and 41 healthy controls using ultrahigh-field high-resolution 7 Tesla (7T) structural and diffusion MRI data. We obtained fractional anisotropy and diffusion streams of white matter connections and examined correspondence of diffusion streams with SCN edges. Nearly 89 % of the FHR group had an axis-I disorder including 5 with schizophrenia. Therefore, we compared the entire FHR group regardless of the diagnosis (All_FHR = 27) and FHR-without-schizophrenia (n = 22) with 41 controls in this integrative multimodal analysis. We found striking volume loss in bilateral hippocampus, particularly the head, bilateral thalamus, caudate, and prefrontal regions. All_FHR and FHR-without-SZ SCNs showed significantly lower assortativity and transitivity but higher diameter compared to controls, but FHR-without-SZ SCN differed on every graph metric compared to All_FHR suggesting disarrayed network with no hippocampal hubs. Fractional anisotropy and diffusion streams were lower in FHR suggesting white matter network impairment. White matter edges showed significantly higher correspondence with SCN edges in FHR compared to controls. These differences correlated with psychopathology and cognitive measures. Our data suggest that hippocampus may be a "neural hub" contributing to psychosis risk. Higher correspondence of white matter tracts with SCN edges suggest that shared volume loss may be more coordinated among regions within the hippocampal white matter circuitry.

Threshold Selection for Brain Connectomes.

Introduction: Structural and functional brain connectomes represent macroscale data collected through techniques such as magnetic resonance imaging (MRI). Connectomes may contain noise that contributes to false-positive edges, thereby obscuring structure-function relationships and data interpretation. Thresholding procedures can be applied to reduce network density by removing low-signal edges, but there is limited consensus on appropriate selection of thresholds. This article compares existing thresholding methods and introduces a novel alternative "objective function" thresholding method. Methods: The performance of thresholding approaches, based on percolation and objective functions, is assessed by (1) computing the normalized mutual information (NMI) of community structure between a known network and a simulated, perturbed networks to which various forms of thresholding have been applied, and by (2) comparing the density and the clustering coefficient (CC) between the baseline and thresholded networks. An application to empirical data is provided. Results: Our proposed objective function-based threshold exhibits the best performance in terms of resulting in high similarity between the underlying networks and their perturbed, thresholded counterparts, as quantified by NMI and CC analysis on the simulated functional networks. Discussion: Existing network thresholding methods yield widely different results when graph metrics are subsequently computed. Thresholding based on the objective function maintains a set of edges such that the resulting network shares the community structure and clustering features present in the original network. This outcome provides a proof of principle that objective function thresholding could offer a useful approach to reducing the network density of functional connectivity data.

Modular architecture and resilience of structural covariance networks in first-episode antipsychotic-naive psychoses.

Structural covariance network (SCN) studies on first-episode antipsychotic-naïve psychosis (FEAP) have examined less granular parcellations on one morphometric feature reporting lower network resilience among other findings. We examined SCNs of volume, cortical thickness, and surface area using the Human Connectome Project atlas-based parcellation (n = 358 regions) from 79 FEAP and 68 controls to comprehensively characterize the networks using a descriptive and perturbational network neuroscience approach. Using graph theoretical methods, we examined network integration, segregation, centrality, community structure, and hub distribution across the small-worldness threshold range and correlated them with psychopathology severity. We used simulated nodal "attacks" (removal of nodes and all their edges) to investigate network resilience, calculated DeltaCon similarity scores, and contrasted the removed nodes to characterize the impact of simulated attacks. Compared to controls, FEAP SCN showed higher betweenness centrality (BC) and lower degree in all three morphometric features and disintegrated with fewer attacks with no change in global efficiency. SCNs showed higher similarity score at the first point of disintegration with ≈ 54% top-ranked BC nodes attacked. FEAP communities consisted of fewer prefrontal, auditory and visual regions. Lower BC, and higher clustering and degree, were associated with greater positive and negative symptom severity. Negative symptoms required twice the changes in these metrics. Globally sparse but locally dense network with more nodes of higher centrality in FEAP could result in higher communication cost compared to controls. FEAP network disintegration with fewer attacks suggests lower resilience without impacting efficiency. Greater network disarray underlying negative symptom severity possibly explains the therapeutic challenge.

Age-dependent effects of schizophrenia genetic risk on cortical thickness and cortical surface area: Evaluating evidence for neurodevelopmental and neurodegenerative models of schizophrenia.

Risk for schizophrenia peaks during early adulthood, a critical period for brain development. Although several influential theoretical models have been proposed for the developmental relationship between brain pathology and clinical onset, to our knowledge, no study has directly evaluated the predictions of these models for schizophrenia developmental genetic effects on brain structure. To address this question, we introduce a framework to estimate the effects of schizophrenia genetic variation on brain structure phenotypes across the life span. Five-hundred and six participants, including 30 schizophrenia probands, 200 of their relatives (aged 12-85 years) from 32 families with at least two first-degree schizophrenia relatives, and 276 unrelated controls, underwent MRI to assess regional cortical thickness (CT) and cortical surface area (CSA). Genetic variance decomposition analyses were conducted to distinguish among schizophrenia neurogenetic effects that are most salient before schizophrenia peak age-of-risk (i.e., early neurodevelopmental effects), after peak age-of-risk (late neurodevelopmental effects), and during the later plateau of age-of-risk (neurodegenerative effects). Genetic correlations between schizophrenia and cortical traits suggested early neurodevelopmental effects for frontal and insula CSA, late neurodevelopmental effects for overall CSA and frontal, parietal, and occipital CSA, and possible neurodegenerative effects for temporal CT and parietal CSA. Importantly, these developmental neurogenetic effects were specific to schizophrenia and not found with nonpsychotic depression. Our findings highlight the potentially dynamic nature of schizophrenia genetic effects across the lifespan and emphasize the utility of integrating neuroimaging methods with developmental behavior genetic approaches to elucidate the nature and timing of risk-conferring processes in psychopathology. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Age-dependent patterns of schizophrenia genetic risk affect cognition.

Cognition shares substantial genetic overlap with schizophrenia, yet it remains unclear whether such genetic effects become significant during developmental periods of elevated risk for schizophrenia, such as the peak age of onset. We introduce an investigative framework integrating epidemiological, developmental, and genetic approaches to determine whether genetic effects shared between schizophrenia and cognition are significant across periods of differing risk for schizophrenia onset, and whether these effects are shared with depression. 771 European-American participants, including 636 (ages 15-84 years) from families with at least two first-degree relatives with schizophrenia and 135 unrelated controls, were divided into three age-risk groups based on ages relative to epidemiological age of onset patterns for schizophrenia: Pre-Peak (before peak age-of-onset: 15 to 22 years), Post-Peak (after peak age-of-onset: 23-42 years), and Plateau (during plateau of age-of-onset: over 42 years). For general cognition and 11 specific cognitive traits, we estimated genetic correlations with schizophrenia and with depression within each age-risk group. Genetic effects shared between deficits in general cognition and schizophrenia were nonsignificant before peak age of onset, yet were high and significant after peak age of onset and during the plateau of onset. These age-dependent genetic effects were largely consistent across specific cognitive traits and not transdiagnostically shared with depression. Schizophrenia genetic effects appear to influence cognitive traits in an age-dependent manner, supporting late developmental and perhaps neurodegenerative models that hypothesize increased expression of schizophrenia risk genes during and after the peak age of risk. Our findings underscore the utility of cognitive traits for tracking schizophrenia genetic effects across the lifespan.

Structural covariance networks in schizophrenia: A systematic review Part I.

BACKGROUND: Schizophrenia is proposed as a disorder of dysconnectivity. However, examination of complexities of dysconnectivity has been challenging. Structural covariance networks (SCN) provide important insights into the nature of dysconnectivity. This systematic review examines the SCN studies that employed statistical approaches to elucidate covariation of regional morphometric variations. METHODS: A systematic search of literature was conducted for peer-reviewed publications using different keywords and keyword combinations for schizophrenia. Fifty-two studies met the criteria. RESULTS: Early SCN studies began using correlational structure of selected regions. Over the last 3 decades, methodological approaches have grown increasingly sophisticated from examining selected brain regions using correlation tests on small sample sizes to recent approaches that use advanced statistical methods to examine covariance structure of whole-brain parcellations on larger samples. Although the results are not fully consistent across all studies, a pattern of fronto-temporal, fronto-parietal and fronto-thalamic covariation is reported. Attempts to associate SCN alterations with functional connectivity, to differentiate between disease-related and neurodevelopment-related morphometric changes, and to develop "causality-based" models are being reported. Clinical correlation with outcome, psychotic symptoms, neurocognitive and social cognitive performance are also reported. CONCLUSIONS: Application of advanced statistical methods are beginning to provide insights into interesting patterns of regional covariance including correlations with clinical and cognitive data. Although these findings appear similar to morphometric studies, SCNs have the advantage of highlighting topology of these regions and their relationship to the disease and associated variables. Further studies are needed to investigate neurobiological underpinnings of shared covariance, and causal links to clinical domains.

Structural covariance networks in schizophrenia: A systematic review Part II.

BACKGROUND: Examination of structural covariance network (SCN) is gaining prominence among the strategies to delineate dysconnectivity that case-control morphometric comparisons cannot address. Part II of this review extends on the part I of the review that included SCN studies using statistical approaches by examining SCN studies applying graph theoretic approaches to elucidate network properties in schizophrenia. This review also includes SCN studies using graph theoretic or statistical approaches on persons at-risk for schizophrenia. METHODS: A systematic literature search was conducted for peer-reviewed publications using different keywords and keyword combinations for schizophrenia and risk for schizophrenia. Thirteen studies on schizophrenia and five on persons at risk for schizophrenia met the criteria. RESULTS: A variety of findings from over the last 1½ decades showing qualitative and quantitative differences in the global and local structural connectome in schizophrenia are described. These observations include altered hub patterns, disrupted network topology and hierarchical organization of the brain, and impaired connections that may be localized to default mode, executive control, and dorsal attention networks. Some of these connectomic alterations were observed in persons at-risk for schizophrenia before the onset of the illness. CONCLUSIONS: Observed disruptions may reduce network efficiency and capacity to integrate information. Further, global connectomic changes were not schizophrenia-specific but local network changes were. Existing studies have used different atlases for brain parcellation, examined different morphometric features, and patients at different stages of illness making it difficult to conduct meta-analysis. Future studies should harmonize such methodological differences to facilitate meta-analysis and also elucidate causal underpinnings of dysconnectivity.

Why does age of onset predict clinical severity in schizophrenia? A multiplex extended pedigree study.

Schizophrenia has substantial variation in symptom severity, course of illness, and overall functioning. Earlier age of onset (AOO) is consistently associated with negative outcomes and yet the causes of this association are still unknown. We used a multiplex, extended pedigree design (total N = 771; 636 relatives from 43 multigenerational families with at least 2 relatives diagnosed with schizophrenia and 135 matched controls) to examine among the schizophrenia relatives (N = 103) the relationship between AOO and negative and positive symptom severity, cognition, and community functioning. Most importantly, we assessed whether there are shared genetic effects between AOO and negative symptoms, positive symptoms, cognition, and community functioning. As expected, earlier AOO was significantly correlated with increased severity of negative and positive symptoms and poorer cognition and community functioning among schizophrenia patients. Notably, the genetic correlation between AOO of schizophrenia and negative symptoms was significant (Rg = -1.00, p = .007). Although the genetic correlations between AOO and positive symptoms, cognition, and community functioning were estimated at maximum and in the predicted direction, they were not statistically significant. AOO of schizophrenia itself was modestly heritable, although not significant and negative symptoms, positive symptoms, and cognition were all strongly and significantly heritable. In sum, we replicated prior findings indicating that earlier AOO is associated with increased symptom severity and extended the literature by detecting shared genetic effects between AOO and negative symptoms, suggestive of pleiotropy.

Managing Depression in Diabetes Mellitus: A Multicentric Randomized Controlled Trial Comparing Effectiveness of Fluoxetine and Mindfulness in Primary Care: Protocol for DIAbetes Mellitus ANd Depression (DIAMAND) Study.

BACKGROUND: Suboptimal management of depression in type 2 diabetes mellitus (T2DM) often translates into poor glycemic control, medical complications, and impaired quality of life. Feasibility and effectiveness of collaborative care models of depression in diabetes in low- and middle-income countries (LMICs) remain unexplored. DIAbetes Mellitus ANd Depression (DIAMAND) study, a multicentric single-blind randomized controlled trial (SBRCT) comparing effectiveness of fluoxetine and mindfulness in primary care settings, addresses this gap in scientific literature. METHODS: This trial conducted in diverse geographic settings of New Delhi, Bengaluru, and Bhubaneswar will comprise module-based training of primary care providers (PCPs) for screening, diagnosing, and managing depression in diabetes in phase I. Phase II will involve four-arm parallel group RCT on 350 participants with T2DM with comorbid depressive episode randomly allocated to receive fluoxetine, mindfulness therapy, fluoxetine plus mindfulness therapy, or treatment as usual at primary care settings. Interventions would include fluoxetine (up to 60 mg/day) and/or sessions of mindfulness for 16 weeks. Primary outcomes on standardized rating scales include depression scores (Hamilton Depression Rating Scale), treatment adherence (Adherence to Refill and Medication Scale), self-care (Diabetes Self-Management Questionnaire), diabetes-related distress (Diabetes Distress Scale), and glycemic control. Secondary outcomes include quality of life (World Health Organization Quality of Life Brief version [WHO-QOL BREF]) and mindfulness (Five Facets Mindfulness Questionnaire). DISCUSSION: This RCT will investigate the effectiveness of module-based training of PCPs and feasibility of collaborative care model for managing depression in T2DM in primary care settings in LMICs and effectiveness of fluoxetine and/or mindfulness in improving diverse outcomes of T2DM with major depression.

Neuroimaging in Schizophrenia.

Schizophrenia is a chronic psychotic disorder with a lifetime prevalence of about 1%. Onset is typically in adolescence or early adulthood; characteristic symptoms include positive symptoms, negative symptoms, and impairments in cognition. Neuroimaging studies have shown substantive evidence of brain structural, functional, and neurochemical alterations that are more pronounced in the association cortex and subcortical regions. These abnormalities are not sufficiently specific to be of diagnostic value, but there may be a role for imaging techniques to provide predictions of outcome. Incorporating multimodal imaging datasets using machine learning approaches may offer better diagnostic and predictive value in schizophrenia.

Regionally Distinct Alterations in Membrane Phospholipid Metabolism in Schizophrenia: A Meta-analysis of Phosphorus Magnetic Resonance Spectroscopy Studies.

BACKGROUND: Existing data on altered membrane phospholipid metabolism in schizophrenia are diverse. We conducted a meta-analysis of studies of phosphorus magnetic resonance spectroscopy, a noninvasive imaging approach that can assess molecular biochemistry of cortex by measuring phosphomonoester (PME) and phosphodiester (PDE) levels, which can provide evidence of altered biochemical processes involved in neuropil membrane expansion and contraction in schizophrenia. METHODS: We analyzed PME and PDE data in the frontal and temporal lobes in subjects with schizophrenia from 24 peer-reviewed publications using the MAVIS package in R by building random- and fixed-effects models. Heterogeneity of effect sizes, effects of publication bias, and file drawer analysis were also assessed. RESULTS: Subjects with schizophrenia showed lower PME levels in the frontal regions (p = .008) and elevated PDE levels in the temporal regions (p < .001) with significant heterogeneity. We noted significant publication bias and file drawer effect for frontal PME and PDE and temporal PDE levels, but not for temporal PME levels. Fail-safe analysis estimated that a high number of negative studies were required to provide nonsignificant results. CONCLUSIONS: Despite methodological differences, these phosphorus magnetic resonance spectroscopy studies demonstrate regionally specific imbalance in membrane phospholipid metabolism related to neuropil in subjects with schizophrenia compared with control subjects reflecting neuropil contraction. Specifically, decreased PME levels in the frontal regions and elevated PDE levels in the temporal regions provide evidence of decreased synthesis and increased degradation of neuropil membrane, respectively. Notwithstanding significant heterogeneity and publication bias, a large number of negative studies are required to render the results of this meta-analysis nonsignificant. These findings warrant further postmortem and animal studies.