Functional connectome organization predicts conversion to psychosis in clinical high-risk youth from the SHARP program

Advancements and Future Directions in Prevention Based on Evaluation for Individuals With Clinical High Risk of Psychosis: Insights From the SHARP Study

BACKGROUND AND HYPOTHESIS

This review examines the evolution and future prospects of prevention based on evaluation (PBE) for individuals at clinical high risk (CHR) of psychosis, drawing insights from the SHARP (Shanghai At Risk for Psychosis) study. It aims to assess the effectiveness of non-pharmacological interventions in preventing psychosis onset among CHR individuals.

STUDY DESIGN

The review provides an overview of the developmental history of the SHARP study and its contributions to understanding the needs of CHR individuals. It explores the limitations of traditional antipsychotic approaches and introduces PBE as a promising framework for intervention.

STUDY RESULTS

Three key interventions implemented by the SHARP team are discussed: nutritional supplementation based on niacin skin response blunting, precision transcranial magnetic stimulation targeting cognitive and brain functional abnormalities, and cognitive behavioral therapy for psychotic symptoms addressing symptomatology and impaired insight characteristics. Each intervention is evaluated within the context of PBE, emphasizing the potential for tailored approaches to CHR individuals.

CONCLUSIONS

The review highlights the strengths and clinical applications of the discussed interventions, underscoring their potential to revolutionize preventive care for CHR individuals. It also provides insights into future directions for PBE in CHR populations, including efforts to expand evaluation techniques and enhance precision in interventions.

Connectome dysfunction in patients at clinical high risk for psychosis and modulation by oxytocin

Abnormalities in functional brain networks (functional connectome) are increasingly implicated in people at Clinical High Risk for Psychosis (CHR-P). Intranasal oxytocin, a potential novel treatment for the CHR-P state, modulates network topology in healthy individuals. However, its connectomic effects in people at CHR-P remain unknown. Forty-seven men (30 CHR-P and 17 healthy controls) received acute challenges of both intranasal oxytocin 40 IU and placebo in two parallel randomised, double-blind, placebo-controlled cross-over studies which had similar but not identical designs. Multi-echo resting-state fMRI data was acquired at approximately 1 h post-dosing. Using a graph theoretical approach, the effects of group (CHR-P vs healthy control), treatment (oxytocin vs placebo) and respective interactions were tested on graph metrics describing the topology of the functional connectome. Group effects were observed in 12 regions (all pFDR < 0.05) most localised to the frontoparietal network. Treatment effects were found in 7 regions (all pFDR < 0.05) predominantly within the ventral attention network. Our major finding was that many effects of oxytocin on network topology differ across CHR-P and healthy individuals, with significant interaction effects observed in numerous subcortical regions strongly implicated in psychosis onset, such as the thalamus, pallidum and nucleus accumbens, and cortical regions which localised primarily to the default mode network (12 regions, all pFDR < 0.05). Collectively, our findings provide new insights on aberrant functional brain network organisation associated with psychosis risk and demonstrate, for the first time, that oxytocin modulates network topology in brain regions implicated in the pathophysiology of psychosis in a clinical status (CHR-P vs healthy control) specific manner.

Longitudinal trajectories of anterior cingulate glutamate and subclinical psychotic experiences in early adolescence: the impact of bullying victimization

Previous studies reported decreased glutamate levels in the anterior cingulate cortex (ACC) in non-treatment-resistant schizophrenia and first-episode psychosis. However, ACC glutamatergic changes in subjects at high-risk for psychosis, and the effects of commonly experienced environmental emotional/social stressors on glutamatergic function in adolescents remain unclear. In this study, adolescents recruited from the general population underwent proton magnetic resonance spectroscopy (MRS) of the pregenual ACC using a 3-Tesla scanner. We explored longitudinal data on the association of combined glutamate-glutamine (Glx) levels, measured by MRS, with subclinical psychotic experiences. Moreover, we investigated associations of bullying victimization, a risk factor for subclinical psychotic experiences, and help-seeking intentions, a coping strategy against stressors including bullying victimization, with Glx levels. Finally, path analyses were conducted to explore multivariate associations. For a contrast analysis, gamma-aminobutyric acid plus macromolecule (GABA+) levels were also analyzed. Negative associations were found between Glx levels and subclinical psychotic experiences at both Times 1 (n = 219, mean age 11.5 y) and 2 (n = 211, mean age 13.6 y), as well as for over-time changes (n = 157, mean interval 2.0 y). Moreover, effects of bullying victimization and bullying victimization × help-seeking intention interaction effects on Glx levels were found (n = 156). Specifically, bullying victimization decreased Glx levels, whereas help-seeking intention increased Glx levels only in bullied adolescents. Finally, associations among bullying victimization, help-seeking intention, Glx levels, and subclinical psychotic experiences were revealed. GABA+ analysis revealed no significant results. This is the first adolescent study to reveal longitudinal trajectories of the association between glutamatergic function and subclinical psychotic experiences and to elucidate the effect of commonly experienced environmental emotional/social stressors on glutamatergic function. Our findings may deepen the understanding of how environmental emotional/social stressors induce impaired glutamatergic neurotransmission that could be the underpinning of liability for psychotic experiences in early adolescence.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

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.

The schizophrenia syndrome, circa 2024: What we know and how that informs its nature

With new data about different aspects of schizophrenia being continually generated, it becomes necessary to periodically revisit exactly what we know. Along with a need to review what we currently know about schizophrenia, there is an equal imperative to evaluate the construct itself. With these objectives, we undertook an iterative, multi-phase process involving fifty international experts in the field, with each step building on learnings from the prior one. This review assembles currently established findings about schizophrenia (construct, etiology, pathophysiology, clinical expression, treatment) and posits what they reveal about its nature. Schizophrenia is a heritable, complex, multi-dimensional syndrome with varying degrees of psychotic, negative, cognitive, mood, and motor manifestations. The illness exhibits a remitting and relapsing course, with varying degrees of recovery among affected individuals with most experiencing significant social and functional impairment. Genetic risk factors likely include thousands of common genetic variants that each have a small impact on an individual's risk and a plethora of rare gene variants that have a larger individual impact on risk. Their biological effects are concentrated in the brain and many of the same variants also increase the risk of other psychiatric disorders such as bipolar disorder, autism, and other neurodevelopmental conditions. Environmental risk factors include but are not limited to urban residence in childhood, migration, older paternal age at birth, cannabis use, childhood trauma, antenatal maternal infection, and perinatal hypoxia. Structural, functional, and neurochemical brain alterations implicate multiple regions and functional circuits. Dopamine D-2 receptor antagonists and partial agonists improve psychotic symptoms and reduce risk of relapse. Certain psychological and psychosocial interventions are beneficial. Early intervention can reduce treatment delay and improve outcomes. Schizophrenia is increasingly considered to be a heterogeneous syndrome and not a singular disease entity. There is no necessary or sufficient etiology, pathology, set of clinical features, or treatment that fully circumscribes this syndrome. A single, common pathophysiological pathway appears unlikely. The boundaries of schizophrenia remain fuzzy, suggesting the absence of a categorical fit and need to reconceptualize it as a broader, multi-dimensional and/or spectrum construct.

Eye Movement Characteristics for Predicting a Transition to Psychosis: Longitudinal Changes and Implications

BACKGROUND AND HYPOTHESIS

Substantive inquiry into the predictive power of eye movement (EM) features for clinical high-risk (CHR) conversion and their longitudinal trajectories is currently sparse. This study aimed to investigate the efficiency of machine learning predictive models relying on EM indices and examine the longitudinal alterations of these indices across the temporal continuum.

STUDY DESIGN

EM assessments (fixation stability, free-viewing, and smooth pursuit tasks) were performed on 140 CHR and 98 healthy control participants at baseline, followed by a 1-year longitudinal observational study. We adopted Cox regression analysis and constructed random forest prediction models. We also employed linear mixed-effects models (LMMs) to analyze longitudinal changes of indices while stratifying by group and time.

STUDY RESULTS

Of the 123 CHR participants who underwent a 1-year clinical follow-up, 25 progressed to full-blown psychosis, while 98 remained non-converters. Compared with the non-converters, the converters exhibited prolonged fixation durations, decreased saccade amplitudes during the free-viewing task; larger saccades, and reduced velocity gain during the smooth pursuit task. Furthermore, based on 4 baseline EM measures, a random forest model classified converters and non-converters with an accuracy of 0.776 (95% CI: 0.633, 0.882). Finally, LMMs demonstrated no significant longitudinal alterations in the aforementioned indices among converters after 1 year.

CONCLUSIONS

Aberrant EMs may precede psychosis onset and remain stable after 1 year, and applying eye-tracking technology combined with a modeling approach could potentially aid in predicting CHRs evolution into overt psychosis.

Comprehensive review of multidimensional biomarkers in the ShangHai At Risk for Psychosis (SHARP) program for early psychosis identification

Psychosis is recognized as one of the largest contributors to nonfatal health loss, and early identification can largely improve routine clinical activity by predicting the psychotic course and guiding treatment. Clinicians have used the clinical high-risk for psychosis (CHR) paradigm to better understand the risk factors that contribute to the onset of psychotic disorders. Clinical factors have been widely applied to calculate the individualized risks for conversion to psychosis 1-2 years later. However, there is still a dearth of valid biomarkers to predict psychosis. Biomarkers, in the context of this paper, refer to measurable biological indicators that can provide valuable information about the early identification of individuals at risk for psychosis. The aim of this paper is to critically review studies assessing CHR and suggest possible biomarkers for application of prediction. We summarized the studies on biomarkers derived from the findings of the ShangHai at Risk for Psychosis (SHARP) program, including those that are considered to have the most potential. This comprehensive review was conducted based on expert opinions within the SHARP research team, and the selection of studies and results presented in this paper reflects the collective expertise of the team in the field of early psychosis identification. The three dimensions with potential candidates include neuroimaging dimension of brain structure and function, electrophysiological dimension of event-related potentials (ERPs), and immune dimension of inflammatory cytokines and complement proteins, which proved to be useful in supporting the prediction of psychosis from the CHR state. We suggest that these three dimensions could be useful as risk biomarkers for treatment optimization. In the future, when available for the integration of multiple dimensions, clinicians may be able to obtain a comprehensive report with detailed information of psychosis risk and specific indications about preferred prevention.

Recent Updates on Predicting Conversion in Youth at Clinical High Risk for Psychosis

PURPOSE OF REVIEW

This review highlights recent advances in the prediction and treatment of psychotic conversion. Over the past 25 years, research into the prodromal phase of psychotic illness has expanded with the promise of early identification of individuals at clinical high risk (CHR) for psychosis who are likely to convert to psychosis.

RECENT FINDINGS

Meta-analyses highlight conversion rates between 20 and 30% within 2-3 years using existing clinical criteria while research into more specific risk factors, biomarkers, and refinement of psychosis risk calculators has exploded, improving our ability to predict psychotic conversion with greater accuracy. Recent studies highlight risk factors and biomarkers likely to contribute to earlier identification and provide insight into neurodevelopmental abnormalities, CHR subtypes, and interventions that can target specific risk profiles linked to neural mechanisms. Ongoing initiatives that assess longer-term (> 5-10 years) outcome of CHR participants can provide valuable information about predictors of later conversion and diagnostic outcomes while large-scale international biomarker studies provide hope for precision intervention that will alter the course of early psychosis globally.

Quantifying brain connectivity signatures by means of polyconnectomic scoring

A broad range of neuropsychiatric disorders are associated with alterations in macroscale brain circuitry and connectivity. Identifying consistent brain patterns underlying these disorders by means of structural and functional MRI has proven challenging, partly due to the vast number of tests required to examine the entire brain, which can lead to an increase in missed findings. In this study, we propose polyconnectomic score (PCS) as a metric designed to quantify the presence of disease-related brain connectivity signatures in connectomes. PCS summarizes evidence of brain patterns related to a phenotype across the entire landscape of brain connectivity into a subject-level score. We evaluated PCS across four brain disorders (autism spectrum disorder, schizophrenia, attention deficit hyperactivity disorder, and Alzheimer's disease) and 14 studies encompassing ~35,000 individuals. Our findings consistently show that patients exhibit significantly higher PCS compared to controls, with effect sizes that go beyond other single MRI metrics ([min, max]: Cohen's d = [0.30, 0.87], AUC = [0.58, 0.73]). We further demonstrate that PCS serves as a valuable tool for stratifying individuals, for example within the psychosis continuum, distinguishing patients with schizophrenia from their first-degree relatives (d = 0.42, p = 4 × 10-3, FDR-corrected), and first-degree relatives from healthy controls (d = 0.34, p = 0.034, FDR-corrected). We also show that PCS is useful to uncover associations between brain connectivity patterns related to neuropsychiatric disorders and mental health, psychosocial factors, and body measurements.

Do antidepressants prevent transition to psychosis in individuals at clinical high-risk (CHR-P)? Systematic review and meta-analysis

BACKGROUND

Emerging meta-analytical evidence indicates that baseline exposure to antipsychotics in individuals at clinical high-risk for psychosis (CHR-P) is associated with a higher risk of an imminent transition to psychosis. Despite their tolerability profile and potential beneficial effects, baseline exposure to antidepressants (AD) in CHR-P has surprisingly received far less attention as a potential risk modulator for transition to psychosis. The current systematic review and meta-analysis were performed to fix such a knowledge gap.

METHODS

Systematic scrutiny of Medline and Cochrane library, performed up to 1 August 2021, searching for English-language studies on CHR-P reporting numeric data about the sample, the transition outcome at a predefined follow-up time and raw data on AD baseline exposure in relation to such outcome.

RESULTS

Of 1942 identified records, 16 studies were included in the systematic review and meta-analysis. 26% of the participants were already exposed to AD at baseline; at the end of the follow-up 13.5% (95% CI 10.2-17.1%) of them (n = 448) transitioned to psychosis against 21.0% (18.9 to 23.3%) of non-AD exposed CHR-P (n = 1371). CHR-P participants who were already under AD treatment at baseline had a lower risk of transition than non-AD exposed CHR-P. The RR was 0.71 (95% CI 0.56-0.90) in the fixed-effects model (z = -2.79; p = 0.005), and 0.78 (0.58-1.05) in the random-effects model (z = -1.77; p = 0.096; tau-squared = 0.059). There was no relevant heterogeneity (Cochran's Q = 18.45; df = 15; p = 0.239; I2 = 18.7%).

CONCLUSIONS

Ongoing AD exposure at inception in CHR-P is associated to a reduced risk of transition to psychosis at follow up.

A Review of Potential Neuroimaging Biomarkers of Schizophrenia-Risk

The risk for developing schizophrenia is increased among first-degree relatives of those with psychotic disorders, but the risk is even higher in those meeting established criteria for clinical high risk (CHR), a clinical construct most often comprising of attenuated psychotic experiences. Conversion to psychosis among CHR youth has been reported to be about 15-35% over three years. Accurately identifying individuals whose psychotic symptoms will worsen would facilitate earlier intervention, but this has been difficult to do using behavior measures alone. Brain-based risk markers have the potential to improve the accuracy of predicting outcomes in CHR youth. This narrative review provides an overview of neuroimaging studies used to investigate psychosis risk, including studies involving structural, functional, and diffusion imaging, functional connectivity, positron emission tomography, arterial spin labeling, magnetic resonance spectroscopy, and multi-modality approaches. We present findings separately in those observed in the CHR state and those associated with psychosis progression or resilience. Finally, we discuss future research directions that could improve clinical care for those at high risk for developing psychotic disorders.

The temporal dynamics of transition to psychosis in individuals at clinical high-risk (CHR-P) shows negative prognostic effects of baseline antipsychotic exposure: a meta-analysis

Meta-analytic evidence indicates that baseline exposure to antipsychotics (AP) in individuals at clinical high-risk for psychosis (CHR-P) is associated with an even higher risk of transition to psychosis. However, the temporal dynamics of such prognostic effect have not been clarified yet. This study was therefore designed to address this knowledge gap. We performed a systematic review and meta-analysis of all longitudinal studies published up to 31 December 2021 on CHR-P individuals identified according to a validated diagnostic procedure and reporting numeric data of transition to psychosis according to baseline antipsychotic exposure. 28 studies covering a total of 2405 CHR-P were included. 554 (23.0%) were exposed to AP at baseline, whereas 1851 (77.0%) were not. At follow-up (12 to 72 months), 182 individuals among AP-exposed (32.9%; 95% CI: 29.4% to 37.8%) and 382 among AP-naive CHR-P (20.6%; 18.8% to 22.8%) developed psychosis. Transition rates increased over time, with the best-fit for an ascending curve peaking at 24 months and reaching then a plateau, with a further increase at 48 months. Baseline AP-exposed CHR-P had higher transition risk at 12 months and then again at 36 and 48 months, with an overall higher risk of transition (fixed-effect model: risk ratio = 1.56 [95% CI: 1.32-1.85]; z = 5.32; p < 0.0001; Random-effect model: risk ratio = 1.56 [95% CI: 1.07-2.26]; z = 2.54; p = 0.0196). In conclusion, the temporal dynamics of transition to psychosis differ in AP-exposed vs. AP-naive CHR-P. Baseline AP exposure in CHR-P is associated with a persistently higher risk of transition at follow up, supporting the rationale for more stringent clinical monitoring in AP-exposed CHR-P. The insufficiency of more granular information in available primary literature (e.g., temporal and quantitative details of AP exposure as well as psychopathological dimensions in CHR-P) did not allow the testing of causal hypotheses on this negative prognostic association.

Functional connectivity signatures of NMDAR dysfunction in schizophrenia-integrating findings from imaging genetics and pharmaco-fMRI

Both, pharmacological and genome-wide association studies suggest N-methyl-D-aspartate receptor (NMDAR) dysfunction and excitatory/inhibitory (E/I)-imbalance as a major pathophysiological mechanism of schizophrenia. The identification of shared fMRI brain signatures of genetically and pharmacologically induced NMDAR dysfunction may help to define biomarkers for patient stratification. NMDAR-related genetic and pharmacological effects on functional connectivity were investigated by integrating three different datasets: (A) resting state fMRI data from 146 patients with schizophrenia genotyped for the disease-associated genetic variant rs7191183 of GRIN2A (encoding the NMDAR 2 A subunit) as well as 142 healthy controls. (B) Pharmacological effects of the NMDAR antagonist ketamine and the GABA-A receptor agonist midazolam were obtained from a double-blind, crossover pharmaco-fMRI study in 28 healthy participants. (C) Regional gene expression profiles were estimated using a postmortem whole-brain microarray dataset from six healthy donors. A strong resemblance was observed between the effect of the genetic variant in schizophrenia and the ketamine versus midazolam contrast of connectivity suggestive for an associated E/I-imbalance. This similarity became more pronounced for regions with high density of NMDARs, glutamatergic neurons, and parvalbumin-positive interneurons. From a functional perspective, increased connectivity emerged between striato-pallido-thalamic regions and cortical regions of the auditory-sensory-motor network, while decreased connectivity was observed between auditory (superior temporal gyrus) and visual processing regions (lateral occipital cortex, fusiform gyrus, cuneus). Importantly, these imaging phenotypes were associated with the genetic variant, the differential effect of ketamine versus midazolam and schizophrenia (as compared to healthy controls). Moreover, the genetic variant was associated with language-related negative symptomatology which correlated with disturbed connectivity between the left posterior superior temporal gyrus and the superior lateral occipital cortex. Shared genetic and pharmacological functional connectivity profiles were suggestive of E/I-imbalance and associated with schizophrenia. The identified brain signatures may help to stratify patients with a common molecular disease pathway providing a basis for personalized psychiatry.

Brain connectomes in youth at risk for serious mental illness: an exploratory analysis

BACKGROUND

Identifying early biomarkers of serious mental illness (SMI)-such as changes in brain structure and function-can aid in early diagnosis and treatment. Whole brain structural and functional connectomes were investigated in youth at risk for SMI.

METHODS

Participants were classified as healthy controls (HC; n = 33), familial risk for serious mental illness (stage 0; n = 31), mild symptoms (stage 1a; n = 37), attenuated syndromes (stage 1b; n = 61), or discrete disorder (transition; n = 9) based on clinical assessments. Imaging data was collected from two sites. Graph-theory based analysis was performed on the connectivity matrix constructed from whole-brain white matter fibers derived from constrained spherical deconvolution of the diffusion tensor imaging (DTI) scans, and from the correlations between brain regions measured with resting state functional magnetic resonance imaging (fMRI) data.

RESULTS

Linear mixed effects analysis and analysis of covariance revealed no significant differences between groups in global or nodal metrics after correction for multiple comparisons. A follow up machine learning analysis broadly supported the findings. Several non-overlapping frontal and temporal network differences were identified in the structural and functional connectomes before corrections.

CONCLUSIONS

Results suggest significant brain connectome changes in youth at transdiagnostic risk may not be evident before illness onset.

Schizophrenia: A Narrative Review of Etiopathogenetic, Diagnostic and Treatment Aspects

Although schizophrenia is currently conceptualized as being characterized as a syndrome that includes a collection of signs and symptoms, there is strong evidence of heterogeneous and complex underpinned etiological, etiopathogenetic, and psychopathological mechanisms, which are still under investigation. Therefore, the present viewpoint review is aimed at providing some insights into the recently investigated schizophrenia research fields in order to discuss the potential future research directions in schizophrenia research. The traditional schizophrenia construct and diagnosis were progressively revised and revisited, based on the recently emerging neurobiological, genetic, and epidemiological research. Moreover, innovative diagnostic and therapeutic approaches are pointed to build a new construct, allowing the development of better clinical and treatment outcomes and characterization for schizophrenic individuals, considering a more patient-centered, personalized, and tailored-based dimensional approach. Further translational studies are needed in order to integrate neurobiological, genetic, and environmental studies into clinical practice and to help clinicians and researchers to understand how to redesign a new schizophrenia construct.

Negative symptoms are associated with modularity and thalamic connectivity in schizophrenia

Negative symptoms, including avolition, anhedonia, asociality, blunted affect and alogia are associated with poor long-term outcome and functioning. However, treatment options for negative symptoms are limited and neurobiological mechanisms underlying negative symptoms in schizophrenia are still poorly understood. Diffusion-weighted magnetic resonance imaging scans were acquired from 64 patients diagnosed with schizophrenia and 35 controls. Global and regional network properties and rich club organization were investigated using graph analytical methods. We found that the schizophrenia group had higher modularity, clustering coefficient and characteristic path length, and lower rich connections compared to controls, suggesting highly connected nodes within modules but less integrated with nodes in other modules in schizophrenia. We also found a lower nodal degree in the left thalamus and left putamen in schizophrenia relative to the control group. Importantly, higher modularity was associated with greater negative symptoms but not with cognitive deficits in patients diagnosed with schizophrenia suggesting an alteration in modularity might be specific to overall negative symptoms. The nodal degree of the left thalamus was associated with both negative and cognitive symptoms. Our findings are important for improving our understanding of abnormal white-matter network topology underlying negative symptoms in schizophrenia.

Neurodegenerative model of schizophrenia: Growing evidence to support a revisit

Multidimensional progressive declines in the absence of standard biomarkers for neurodegeneration are observed commonly in the development of schizophrenia, and are accepted as consistent with neurodevelopmental etiological hypotheses to explain the origins of the disorder. Far less accepted is the possibility that neurodegenerative processes are involved as well, or even that key dimensions of function, such as cognition and aspects of biological integrity, such as white matter function, decline in chronic schizophrenia beyond levels associated with normal aging. We propose that recent research germane to these issues warrants a current look at the question of neurodegeneration. We propose the view that a neurodegenerative hypothesis provides a better explanation of some features of chronic schizophrenia, including accelerated aging, than is provided by neurodevelopmental hypotheses. Moreover, we suggest that neurodevelopmental influences in early life, including those that may extend to later life, do not preclude the development of neurodegenerative processes in later life, including some declines in cognitive and biological integrity. We evaluate these views by integrating recent findings in representative domains such as cognition and white and gray matter integrity with results from studies on accelerated aging, together with functional implications of neurodegeneration for our understanding of chronic schizophrenia.

Modular-Level Functional Connectome Alterations in Individuals With Hallucinations Across the Psychosis Continuum

Functional connectome alterations, including modular network organization, have been related to the experience of hallucinations. It remains to be determined whether individuals with hallucinations across the psychosis continuum exhibit similar alterations in modular brain network organization. This study assessed functional connectivity matrices of 465 individuals with and without hallucinations, including patients with schizophrenia and bipolar disorder, nonclinical individuals with hallucinations, and healthy controls. Modular brain network organization was examined at different scales of network resolution, including (1) global modularity measured as Qmax and Normalised Mutual Information (NMI) scores, and (2) within- and between-module connectivity. Global modular organization was not significantly altered across groups. However, alterations in within- and between-module connectivity were observed for higher-order cognitive (e.g., central-executive salience, memory, default mode), and sensory modules in patients with schizophrenia and nonclinical individuals with hallucinations relative to controls. Dissimilar patterns of altered within- and between-module connectivity were found bipolar disorder patients with hallucinations relative to controls, including the visual, default mode, and memory network, while connectivity patterns between visual, salience, and cognitive control modules were unaltered. Bipolar disorder patients without hallucinations did not show significant alterations relative to controls. This study provides evidence for alterations in the modular organization of the functional connectome in individuals prone to hallucinations, with schizophrenia patients and nonclinical individuals showing similar alterations in sensory and higher-order cognitive modules. Other higher-order cognitive modules were found to relate to hallucinations in bipolar disorder patients, suggesting differential neural mechanisms may underlie hallucinations across the psychosis continuum.

How Machine Learning is Powering Neuroimaging to Improve Brain Health

This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.

Big contributions of the little brain for precision psychiatry

Our previous work using 3T functional Magnetic Resonance Imaging (fMRI) parcellated the human dentate nuclei (DN), the primary output of the cerebellum, to three distinct functional zones each contributing uniquely to default-mode, salience-motor, and visual brain networks. In this perspective piece, we highlight the possibility to target specific functional territories within the cerebellum using non-invasive brain stimulation, potentially leading to the refinement of cerebellar-based therapeutics for precision psychiatry. Significant knowledge gap exists in our functional understanding of cerebellar systems. Intervening early, gauging severity of illness, developing intervention strategies and assessing treatment response, are all dependent on our understanding of the cerebello-cerebral networks underlying the pathology of psychotic disorders. A promising yet under-examined avenue for biomarker discovery is disruptions in cerebellar output circuitry. This is primarily because most 3T MRI studies in the past had to exclude cerebellum from the field of view due to limitations in spatiotemporal resolutions. Using recent technological advances in 7T MRI (e.g., parallel transmit head coils) to identify functional territories of the DN, with a focus on dentato-cerebello-thalamo-cortical (CTC) circuitry can lead to better characterization of brain-behavioral correlations and assessments of co-morbidities. Such an improved mechanistic understanding of psychiatric illnesses can reveal aspects of CTC circuitry that can aid in neuroprognosis, identification of subtypes, and generate testable hypothesis for future studies.

Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction

Schizophrenia (SCZ) is a debilitating neuropsychiatric disorder with high heritability and complex inheritance. In the past decade, successful identification of numerous susceptibility loci has provided useful insights into the molecular etiology of SCZ. However, applications of these findings to clinical classification and diagnosis, risk prediction, or intervention for SCZ have been limited, and elucidating the underlying genomic and molecular mechanisms of SCZ is still challenging. More recently, multiple Omics technologies - genomics, transcriptomics, epigenomics, proteomics, metabolomics, connectomics, and gut microbiomics - have all been applied to examine different aspects of SCZ pathogenesis. Integration of multi-Omics data has thus emerged as an approach to provide a more comprehensive view of biological complexity, which is vital to enable translation into assessments and interventions of clinical benefit to individuals with SCZ. In this review, we provide a broad survey of the single-omics studies of SCZ, summarize the advantages and challenges of different Omics technologies, and then focus on studies in which multiple omics data are integrated to unravel the complex pathophysiology of SCZ. We believe that integration of multi-Omics technologies would provide a roadmap to create a more comprehensive picture of interactions involved in the complex pathogenesis of SCZ, constitute a rich resource for elucidating the potential molecular mechanisms of the illness, and eventually improve clinical assessments and interventions of SCZ to address clinical translational questions from bench to bedside.

Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms

OBJECTIVE

Magnetic resonance imaging (MRI) has shown that estimated brain age is deviant from chronological age in various common brain disorders. Brain age estimation could be useful for investigating patterns of brain maturation and integrity, aiding to elucidate brain mechanisms underlying these heterogeneous conditions. Here, we examined functional brain age in two large samples of children and adolescents and its relation to mental health.

METHODS

We used resting-state fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC; n = 1126, age range 8-22 years) to estimate functional connectivity between brain networks, and utilized these as features for brain age prediction. We applied the prediction model to 1387 individuals (age range 8-22 years) in the Healthy Brain Network sample (HBN). In addition, we estimated brain age in PNC using a cross-validation framework. Next, we tested for associations between brain age gap and various aspects of psychopathology and cognitive performance.

RESULTS

Our model was able to predict age in the independent test samples, with a model performance of r = 0.54 for the HBN test set, supporting consistency in functional connectivity patterns between samples and scanners. Linear models revealed a significant association between brain age gap and psychopathology in PNC, where individuals with a lower estimated brain age, had a higher overall symptom burden. These associations were not replicated in HBN.

DISCUSSION

Our findings support the use of brain age prediction from fMRI-based connectivity. While requiring further extensions and validations, the approach may be instrumental for detecting brain phenotypes related to intrinsic connectivity and could assist in characterizing risk in non-typically developing populations.

Global fractional anisotropy predicts transition to psychosis after 12 months in individuals at ultra-high risk for psychosis

OBJECTIVE

Psychosis spectrum disorders are associated with cerebral changes, but the prognostic value and clinical utility of these findings are unclear. Here, we applied a multivariate statistical model to examine the predictive accuracy of global white matter fractional anisotropy (FA) for transition to psychosis in individuals at ultra-high risk for psychosis (UHR).

METHODS

110 UHR individuals underwent 3 Tesla diffusion-weighted imaging and clinical assessments at baseline, and after 6 and 12 months. Using logistic regression, we examined the reliability of global FA at baseline as a predictor for psychosis transition after 12 months. We tested the predictive accuracy, sensitivity and specificity of global FA in a multivariate prediction model accounting for potential confounders to FA (head motion in scanner, age, gender, antipsychotic medication, parental socioeconomic status and activity level). In secondary analyses, we tested FA as a predictor of clinical symptoms and functional level using multivariate linear regression.

RESULTS

Ten UHR individuals had transitioned to psychosis after 12 months (9%). The model reliably predicted transition at 12 months (χ²  = 17.595, p = 0.040), accounted for 15-33% of the variance in transition outcome with a sensitivity of 0.70, a specificity of 0.88 and AUC of 0.87. Global FA predicted level of UHR symptoms (R²  = 0.055, F = 6.084, p = 0.016) and functional level (R²  = 0.040, F = 4.57, p = 0.036) at 6 months, but not at 12 months.

CONCLUSION

Global FA provided prognostic information on clinical outcome and symptom course of UHR individuals. Our findings suggest that the application of prediction models including neuroimaging data can inform clinical management on risk for psychosis transition.

Subtyping Schizophrenia Patients Based on Patterns of Structural Brain Alterations

Schizophrenia is a complex and heterogeneous syndrome. Whether quantitative imaging biomarkers can identify discrete subgroups of patients as might be used to foster personalized medicine approaches for patient care remains unclear. Cross-sectional structural MR images of 163 never-treated first-episode schizophrenia patients (FES) and 133 chronically ill patients with midcourse schizophrenia from the Bipolar and Schizophrenia Network for Intermediate Phenotypes (B-SNIP) consortium and a total of 403 healthy controls were recruited. Morphometric measures (cortical thickness, surface area, and subcortical structures) were extracted for each subject and then the optimized subtyping results were obtained with nonsupervised cluster analysis. Three subgroups of patients defined by distinct patterns of regional cortical and subcortical morphometric features were identified in FES. A similar three subgroup pattern was identified in the independent dataset of patients from the multi-site B-SNIP consortium. Similarities of classification patterns across these two patient cohorts suggest that the 3-group typology is relatively stable over the course of illness. Cognitive functions were worse in subgroup 1 with midcourse schizophrenia than those in subgroup 3. These findings provide novel insight into distinct subgroups of patients with schizophrenia based on structural brain features. Findings of different cognitive functions among the subgroups support clinical differences in the MRI-defined illness subtypes. Regardless of clinical presentation and stage of illness, anatomic MR subgrouping biomarkers can separate neurobiologically distinct subgroups of schizophrenia patients, which represent an important and meaningful step forward in differentiating subtypes of patients for studies of illness neurobiology and potentially for clinical trials.

Emerging neurodevelopmental perspectives on mathematical learning

Strong foundational skills in mathematical problem solving, acquired in early childhood, are critical not only for success in the science, technology, engineering, and mathematical (STEM) fields but also for quantitative reasoning in everyday life. The acquisition of mathematical skills relies on protracted interactive specialization of functional brain networks across development. Using a systems neuroscience approach, this review synthesizes emerging perspectives on neurodevelopmental pathways of mathematical learning, highlighting the functional brain architecture that supports these processes and sources of heterogeneity in mathematical skill acquisition. We identify the core neural building blocks of numerical cognition, anchored in the posterior parietal and ventral temporal-occipital cortices, and describe how memory and cognitive control systems, anchored in the medial temporal lobe and prefrontal cortex, help scaffold mathematical skill development. We highlight how interactive specialization of functional circuits influences mathematical learning across different stages of development. Functional and structural brain integrity and plasticity associated with math learning can be examined using an individual differences approach to better understand sources of heterogeneity in learning, including cognitive, affective, motivational, and sociocultural factors. Our review emphasizes the dynamic role of neurodevelopmental processes in mathematical learning and cognitive development more generally.

Negative Prognostic Effect of Baseline Antipsychotic Exposure in Clinical High Risk for Psychosis (CHR-P): Is Pre-Test Risk Enrichment the Hidden Culprit?

INTRODUCTION

Sample enrichment is a key factor in contemporary early-detection strategies aimed at the identification of help-seekers at increased risk of imminent transition to psychosis. We undertook a meta-analytic investigation to ascertain the role of sample enrichment in the recently highlighted negative prognostic effect of baseline antipsychotic (AP) exposure in clinical high-risk (CHR-P) of psychosis individuals.

METHODS

Systematic review and meta-analysis of all published studies on CHR-P were identified according to a validated diagnostic procedure. The outcome was the proportion of transition to psychosis, which was calculated according to the Freeman-Tukey double arcsine transformation.

RESULTS

Thirty-three eligible studies were identified, including 16 samples with details on AP exposure at baseline and 17 samples with baseline AP exposure as exclusion criterion for enrollment. Those with baseline exposure to AP (n = 395) had higher transition rates (29.9%; 95% CI: 25.1%-34.8%) than those without baseline exposure to AP in the same study (n = 1289; 17.2%; 15.1%-19.4%) and those coming from samples that did not include people who were exposed to AP at baseline (n = 2073; 16.2%; 14.6%-17.8%; P < .05 in both the fixed-effects and the random-effects models). Heterogeneity within studies was substantial, with values above 75% in all comparisons.

CONCLUSIONS

Sample enrichment is not a plausible explanation for the higher risk of transition to psychosis of CHR-P individuals who were already exposed to AP at the enrollment in specialized early-detection programs. Baseline exposure to AP at CHR-P assessment is a major index of enhanced, imminent risk of psychosis.

Abnormal Function in Dentate Nuclei Precedes the Onset of Psychosis: A Resting-State fMRI Study in High-Risk Individuals

OBJECTIVE

The cerebellum serves a wide range of functions and is suggested to be composed of discrete regions dedicated to unique functions. We recently developed a new parcellation of the dentate nuclei (DN), the major output nuclei of the cerebellum, which optimally divides the structure into 3 functional territories that contribute uniquely to default-mode, motor-salience, and visual processing networks as indexed by resting-state functional connectivity (RsFc). Here we test for the first time whether RsFc differences in the DN, precede the onset of psychosis in individuals at risk of developing schizophrenia.

METHODS

We used the magnetic resonance imaging (MRI) dataset from the Shanghai At Risk for Psychosis study that included subjects at high risk to develop schizophrenia (N = 144), with longitudinal follow-up to determine which subjects developed a psychotic episode within 1 year of their functional magnetic resonance imaging (fMRI) scan (converters N = 23). Analysis used the 3 functional parcels (default-mode, salience-motor, and visual territory) from the DN as seed regions of interest for whole-brain RsFc analysis.

RESULTS

RsFc analysis revealed abnormalities at baseline in high-risk individuals who developed psychosis, compared to high-risk individuals who did not develop psychosis. The nature of the observed abnormalities was found to be anatomically specific such that abnormal RsFc was localized predominantly in cerebral cortical networks that matched the 3 functional territories of the DN that were evaluated.

CONCLUSIONS

We show for the first time that abnormal RsFc of the DN may precede the onset of psychosis. This new evidence highlights the role of the cerebellum as a potential target for psychosis prediction and prevention.

When to initiate antipsychotic treatment for psychotic symptoms: At the premorbid phase or first episode of psychosis?

OBJECTIVE

Antipsychotic drugs are widely used for treating patients with first episode of psychosis, targeting threshold psychotic symptoms. The clinical high risk of psychosis is characterized as subthreshold psychotic symptoms and it is unclear whether they can also benefit from antipsychotic drugs treatment. This study attempted to determine whether initiating antipsychotic drugs treatment in the clinical high risk of psychosis phase was superior to initiating antipsychotic drugs treatment in the first episode of psychosis phase, after the 2-year symptomatic and functional outcomes.

METHOD

Drawing on 517 individuals with clinical high risk of psychosis from the ShangHai At Risk for Psychosis program, we identified 105 patients who converted to first episode of psychosis within the following 2 years. Patients who initiated antipsychotic drugs while at clinical high risk of psychosis (CHR_AP; n = 70) were compared with those who initiated antipsychotic drugs during a first episode of psychosis (FEP_AP; n = 35). Summary scores on positive symptoms and the global function scores at baseline and at 2 months, 1 year and 2 years of follow-up were analyzed to evaluate outcomes.

RESULTS

The CHR_AP and FEP_AP groups were not different in the severity of positive symptoms and functioning at baseline. However, the CHR_AP group exhibited significantly more serious negative symptoms and total symptoms than the FEP_AP group. Both groups exhibited a significant reduction in positive symptoms and function (p < 0.001). Repeated-measures analysis of variance revealed group by time interaction for symptomatic (F = 3.196, df = 3, p = 0.024) and functional scores (F = 7.306, df = 3, p < 0.001). The FEP_AP group showed higher remission rates than the CHR_AP group (χ² = 22.270, p < 0.001). Compared to initiating antipsychotic drug treatments in the clinical high risk of psychosis state, initiating antipsychotic drugs treatments in the first episode of psychosis state predicted remission in a regression model for FEP_AP (odds ratio = 5.567, 95% confidence interval = [1.783, 17.383], p = 0.003).

CONCLUSION

For clinical high risk of psychosis, antipsychotic drugs might be not the first choice in terms of long-term remission, which is more reasonable to use at the first episode of psychosis phase.

Neuroimaging Markers of Resiliency in Youth at Clinical High Risk for Psychosis: A Qualitative Review

Psychotic disorders are highly debilitating and constitute a major public health burden. Identifying markers of psychosis risk and resilience is a necessary step toward understanding etiology and informing prevention and treatment efforts in individuals at clinical high risk (CHR) for psychosis. In this context, it is important to consider that neural risk markers have been particularly useful in identifying mechanistic determinants along with predicting clinical outcomes. Notably, despite a growing body of supportive literature and the promise of recent findings identifying potential neural markers, the current work on CHR resilience markers has received little attention. The present review provides a brief overview of brain-based risk markers with a focus on predicting symptom course. Next, the review turns to protective markers, examining research from nonpsychiatric and schizophrenia fields to build an understanding of framing, priorities, and potential, applying these ideas to contextualizing a small but informative body of resiliency-relevant CHR research. Four domains (neurocognition, emotion regulation, allostatic load, and sensory and sensorimotor function) were identified and are discussed in terms of behavioral and neural markers. Taken together, the literature suggests significant predictive value for brain-based markers for individuals at CHR for psychosis, and the limited but compelling resiliency work highlights the critical importance of expanding this promising area of inquiry.

Meta-analyzing the prevalence and prognostic effect of antipsychotic exposure in clinical high-risk (CHR): when things are not what they seem

BACKGROUND

The clinical high-risk (CHR) for psychosis paradigm is changing psychiatric practice. However, a widespread confounder, i.e. baseline exposure to antipsychotics (AP) in CHR samples, is systematically overlooked. Such exposure might mitigate the initial clinical presentation, increase the heterogeneity within CHR populations, and confound the evaluation of transition to psychosis at follow-up. This is the first meta-analysis examining the prevalence and the prognostic impact on transition to psychosis of ongoing AP treatment at baseline in CHR cohorts.

METHODS

Major databases were searched for articles published until 20 April 2020. The variance-stabilizing Freeman-Tukey double arcsine transformation was used to estimate prevalence. The binary outcome of transition to psychosis by group was estimated with risk ratio (RR) and the inverse variance method was used for pooling.

RESULTS

Fourteen studies were eligible for qualitative synthesis, including 1588 CHR individuals. Out of the pooled CHR sample, 370 individuals (i.e. 23.3%) were already exposed to AP at the time of CHR status ascription. Transition toward full-blown psychosis at follow-up intervened in 112 (29%; 95% CI 24-34%) of the AP-exposed CHR as compared to 235 (16%; 14-19%) of the AP-naïve CHR participants. AP-exposed CHR had higher RR of transition to psychosis (RR = 1.47; 95% CI 1.18-1.83; z = 3.48; p = 0.0005), without influence by age, gender ratio, overall sample size, duration of the follow-up, or quality of the studies.

CONCLUSIONS

Baseline AP exposure in CHR samples is substantial and is associated with a higher imminent risk of transition to psychosis. Therefore, such exposure should be regarded as a non-negligible red flag for clinical risk management.

Language as a biomarker for psychosis: A natural language processing approach

Human ratings of conceptual disorganization, poverty of content, referential cohesion and illogical thinking have been shown to predict psychosis onset in prospective clinical high risk (CHR) cohort studies. The potential value of linguistic biomarkers has been significantly magnified, however, by recent advances in natural language processing (NLP) and machine learning (ML). Such methodologies allow for the rapid and objective measurement of language features, many of which are not easily recognized by human raters. Here we review the key findings on language production disturbance in psychosis. We also describe recent advances in the computational methods used to analyze language data, including methods for the automatic measurement of discourse coherence, syntactic complexity, poverty of content, referential coherence, and metaphorical language. Linguistic biomarkers of psychosis risk are now undergoing cross-validation, with attention to harmonization of methods. Future directions in extended CHR networks include studies of sources of variance, and combination with other promising biomarkers of psychosis risk, such as cognitive and sensory processing impairments likely to be related to language. Implications for the broader study of social communication, including reciprocal prosody, face expression and gesture, are discussed.

Lowering costs for large-scale screening in psychosis: a systematic review and meta-analysis of performance and value of information for speech-based psychiatric evaluation

OBJECTIVE

Obstacles for computational tools in psychiatry include gathering robust evidence and keeping implementation costs reasonable. We report a systematic review of automated speech evaluation for the psychosis spectrum and analyze the value of information for a screening program in a healthcare system with a limited number of psychiatrists (Maputo, Mozambique).

METHODS

Original studies on speech analysis for forecasting of conversion in individuals at clinical high risk (CHR) for psychosis, diagnosis of manifested psychotic disorder, and first-episode psychosis (FEP) were included in this review. Studies addressing non-verbal components of speech (e.g., pitch, tone) were excluded.

RESULTS

Of 168 works identified, 28 original studies were included. Valuable speech features included direct measures (e.g., relative word counting) and mathematical embeddings (e.g.: word-to-vector, graphs). Accuracy estimates reported for schizophrenia diagnosis and CHR conversion ranged from 71 to 100% across studies. Studies used structured interviews, directed tasks, or prompted free speech. Directed-task protocols were faster while seemingly maintaining performance. The expected value of perfect information is USD 9.34 million. Imperfect tests would nevertheless yield high value.

CONCLUSION

Accuracy for screening and diagnosis was high. Larger studies are needed to enhance precision of classificatory estimates. Automated analysis presents itself as a feasible, low-cost method which should be especially useful for regions in which the physician pool is insufficient to meet demand.

Anterior Hippocampal-Cortical Functional Connectivity Distinguishes Antipsychotic Naïve First-Episode Psychosis Patients From Controls and May Predict Response to Second-Generation Antipsychotic Treatment

BACKGROUND

Converging evidence implicates the anterior hippocampus in the proximal pathophysiology of schizophrenia. Although resting state functional connectivity (FC) holds promise for characterizing anterior hippocampal circuit abnormalities and their relationship to treatment response, this technique has not yet been used in first-episode psychosis (FEP) patients in a manner that distinguishes the anterior from posterior hippocampus.

METHODS

We used masked-hippocampal-group-independent component analysis with dual regression to contrast subregional hippocampal-whole brain FC between healthy controls (HCs) and antipsychotic naïve FEP patients (N = 61, 36 female). In a subsample of FEP patients (N = 27, 15 female), we repeated this analysis following 8 weeks of second-generation antipsychotic treatment and explored whether baseline FC predicted treatment response using random forest.

RESULTS

Relative to HC, untreated FEP subjects displayed reproducibly lower FC between the left anteromedial hippocampus and cortical regions including the anterior cingulate and insular cortex (P < .05, corrected). Anteromedial hippocampal FC increased in FEP patients following treatment (P < .005), and no longer differed from HC. Random forest analysis showed baseline anteromedial hippocampal FC with four brain regions, namely the insular-opercular cortex, superior frontal gyrus, precentral gyrus, and postcentral gyrus predicted treatment response (area under the curve = 0.95).

CONCLUSIONS

Antipsychotic naïve FEP is associated with lower FC between the anterior hippocampus and cortical regions previously implicated in schizophrenia. Preliminary analysis suggests that random forest models based on hippocampal FC may predict treatment response in FEP patients, and hence could be a useful biomarker for treatment development.

Altered resting-state functional connectivity in young children at familial high risk for psychotic illness: A preliminary study

Multiple lines of evidence suggest that illness development in schizophrenia and other psychotic disorders predates the first psychotic episode by many years. In this study, we examined a sample of 15 pre-adolescent children, ages 7 through 12 years, who are at familial high-risk (FHR) because they have a parent or sibling with a history of schizophrenia or related psychotic disorder. Using multi-voxel pattern analysis (MVPA), a data-driven fMRI analysis, we assessed whole-brain differences in functional connectivity in the FHR sample as compared to an age- and sex-matched control (CON) group of 15 children without a family history of psychosis. MVPA analysis yielded a single cluster in right posterior superior temporal gyrus (pSTG/BA 22) showing significant group-differences in functional connectivity. Post-hoc characterization of this cluster through seed-to-voxel analysis revealed mostly reduced functional connectivity of the pSTG seed to a set of language and default mode network (DMN) associated brain regions including Heschl's gyrus, inferior temporal gyrus extending into fusiform gyrus, (para)hippocampus, thalamus, and a cerebellar cluster encompassing mainly Crus I/II. A height-threshold of whole-brain p < .001 (two-sided), and FDR-corrected cluster-threshold of p < .05 (non-parametric statistics) was used for post-hoc characterization. These findings suggest that abnormalities in functional communication in a network encompassing right STG and associated brain regions are present before adolescence in at-risk children and may be a risk marker for psychosis. Subsequent changes in this functional network across development may contribute to either disease manifestation or resilience in children with a familial vulnerability for psychosis.

Resting-State Functional Network Organization Is Stable Across Adolescent Development for Typical and Psychosis Spectrum Youth

BACKGROUND

Resting-state functional neuroimaging captures large-scale network organization; whether this organization is intact or disrupted during adolescent development across the psychosis spectrum is unresolved. We investigated the integrity of network organization in psychosis spectrum youth and those with first episode psychosis (FEP) from late childhood through adulthood.

METHODS

We analyzed data from Philadelphia Neurodevelopmental Cohort (PNC; typically developing = 450, psychosis spectrum = 273, 8-22 years), a longitudinal cohort of typically developing youth (LUNA; N = 208, 1-3 visits, 10-25 years), and a sample of FEP (N = 39) and matched controls (N = 34). We extracted individual time series and calculated correlations from brain regions and averaged them for 4 age groups: late childhood, early adolescence, late adolescence, adulthood. Using multiple analytic approaches, we assessed network stability across 4 age groups, compared stability between controls and psychosis spectrum youth, and compared group-level network organization of FEP to controls. We explored whether variability in cognition or clinical symptomatology was related to network organization.

RESULTS

Network organization was stable across the 4 age groups in the PNC and LUNA typically developing youth and PNC psychosis spectrum youth. Psychosis spectrum and typically developing youth had similar functional network organization during all age ranges. Network organization was intact in PNC youth who met full criteria for psychosis and in FEP. Variability in cognitive functioning or clinical symptomatology was not related to network organization in psychosis spectrum youth or FEP.

DISCUSSION

These findings provide rigorous evidence supporting intact functional network organization in psychosis risk and psychosis from late childhood through adulthood.

Brain functional connectivity data enhance prediction of clinical outcome in youth at risk for psychosis

The first episode of psychosis is typically preceded by a prodromal phase with subthreshold symptoms and functional decline. Improved outcome prediction in this stage is needed to allow targeted early intervention. This study assesses a combined clinical and resting-state fMRI prediction model in 137 adolescents and young adults at Clinical High Risk (CHR) for psychosis from the Shanghai At Risk for Psychosis (SHARP) program. Based on outcome at one-year follow-up, participants were separated into three outcome categories including good outcome (symptom remission, N = 71), intermediate outcome (ongoing CHR symptoms, N = 30), and poor outcome (conversion to psychosis or treatment-refractory, N = 36). Validated clinical predictors from the psychosis-risk calculator were combined with measures of resting-state functional connectivity. Using multinomial logistic regression analysis and leave-one-out cross-validation, a clinical-only prediction model did not achieve a significant level of outcome prediction (F₁ = 0.32, p = .154). An imaging-only model yielded a significant prediction model (F₁ = 0.41, p = .016), but a combined model including both clinical and connectivity measures showed the best performance (F₁ = 0.46, p < .001). Influential predictors in this model included functional decline, verbal learning performance, a family history of psychosis, default-mode and frontoparietal within-network connectivity, and between-network connectivity among language, salience, dorsal attention, sensorimotor, and cerebellar networks. These findings suggest that brain changes reflected by alterations in functional connectivity may be useful for outcome prediction in the prodromal stage.

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.

Connectome-Based Patterns of First-Episode Medication-Naïve Patients With Schizophrenia

Emerging evidence indicates that a disruption in brain network organization may play an important role in the pathophysiology of schizophrenia. The neuroimaging fingerprint reflecting the pathophysiology of first-episode schizophrenia remains to be identified. Here, we aimed at characterizing the connectome organization of first-episode medication-naïve patients with schizophrenia. A cross-sectional structural and functional neuroimaging study using two independent samples (principal dataset including 42 medication-naïve, previously untreated patients and 48 healthy controls; replication dataset including 39 first-episode patients [10 untreated patients] and 66 healthy controls) was performed. Brain network architecture was assessed by means of white matter fiber integrity measures derived from diffusion-weighted imaging (DWI) and by means of structural-functional (SC-FC) coupling measured by combining DWI and resting-state functional magnetic resonance imaging. Connectome rich club organization was found to be significantly disrupted in medication-naïve patients as compared with healthy controls (P = .012, uncorrected), with rich club connection strength (P = .032, uncorrected) and SC-FC coupling (P < .001, corrected for false discovery rate) decreased in patients. Similar results were found in the replication dataset. Our findings suggest that a disruption of rich club organization and functional dynamics may reflect an early feature of schizophrenia pathophysiology. These findings add to our understanding of the neuropathological mechanisms of schizophrenia and provide new insights into the early stages of the disorder.

Emerging Temporal Lobe Dysfunction in People at Clinical High Risk for Psychosis

Clinical high-risk (CHR) individuals have been increasingly utilized to investigate the prodromal phases of psychosis and progression to illness. Research has identified medial and lateral temporal lobe abnormalities in CHR individuals. Dysfunction in the medial temporal lobe, particularly the hippocampus, is linked to dysregulation of glutamate and dopamine via a hippocampal-striatal-midbrain network that may lead to aberrant signaling of salience underpinning the formation of delusions. Similarly, lateral temporal dysfunction may be linked to the disorganized speech and language impairments observed in the CHR stage. Here, we summarize the significance of these neurobiological findings in terms of emergent psychotic symptoms and conversion to psychosis in CHR populations. We propose key questions for future work with the aim to identify the neural mechanisms that underlie the development of psychosis.

Real-World Effectiveness and Safety of Antipsychotics in Individuals at Clinical High-Risk for Psychosis: Study Protocol for a Prospective Observational Study (ShangHai at Risk for Psychosis-Phase 2)

BACKGROUND

The clinical high-risk (CHR) state is identified as a critical period for early prevention and intervention during the development of psychosis and early treatment may reduce the risk of conversion to psychosis. However, it remains controversial whether antipsychotics are effective in CHR populations. Limited previous randomised controlled trials of antipsychotic treatment of CHR individuals indicated possible short-term efficacy on psychotic symptoms with unclear long-term effects. To answer this question, it is necessary to establish a high-quality real-world cohort study with large sample size to explore the effectiveness and safety of antipsychotics in CHR individuals.

METHODS

We plan to consecutively recruit 600 CHR individuals from Shanghai Mental Health Centre in the ongoing SHARP-2 (ShangHai At Risk for Psychosis-Phase 2) project between 2019 and 2022. At baseline, participants will be assessed by the Structured Interview for Prodromal Syndromes, the MATRICS Consensus Cognitive Battery, demographic information, and clinical medication history. They will be followed up in a naturalistic way in which the research team will not prescribe antipsychotics or provide pharmacological consultation. First, CHR participants and their families will be trained to record their medication daily and self-evaluate symptoms through smart-phone application-based assessment and report their information weekly. Second, telephone calls will be arranged monthly so that the researchers are informed about the participants' symptoms, medications and daily functions. Third, face-to-face interviews will be conducted annually for repeating assessment of baseline. The primary outcomes will include conversion to psychosis and functional outcome (scored with less than 60 in the Global Assessment of Function) at the end of the follow-up period.

CONCLUSION

The current study will improve our knowledge on the effectiveness and safety of the use of antipsychotics at the prodromal phase, and will eventually facilitate optimisation of individualised interventions for psychosis prevention and treatment.