Identifying neural signatures mediating behavioral symptoms and psychosis onset: High-dimensional whole brain functional mediation analysis

Unique Functional Neuroimaging Signatures of Genetic Versus Clinical High Risk for Psychosis

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Shape Mediation Analysis in Alzheimer's Disease Studies

As a crucial tool in neuroscience, mediation analysis has been developed and widely adopted to elucidate the role of intermediary variables derived from neuroimaging data. Typically, structural equation models (SEMs) are employed to investigate the influences of exposures on outcomes, with model coefficients being interpreted as causal effects. While existing SEMs have proven to be effective tools for mediation analysis involving various neuroimaging-related mediators, limited research has explored scenarios where these mediators are derived from the shape space. In addition, the linear relationship assumption adopted in existing SEMs may lead to substantial efficiency losses and decreased predictive accuracy in real-world applications. To address these challenges, we introduce a novel framework for shape mediation analysis, designed to explore the causal relationships between genetic exposures and clinical outcomes, whether mediated or unmediated by shape-related factors while accounting for potential confounding variables. Within our framework, we apply the square-root velocity function to extract elastic shape representations, which reside within the linear Hilbert space of square-integrable functions. Subsequently, we introduce a two-layer shape regression model to characterize the relationships among neurocognitive outcomes, elastic shape mediators, genetic exposures, and clinical confounders. Both estimation and inference procedures are established for unknown parameters along with the corresponding causal estimands. The asymptotic properties of estimated quantities are investigated as well. Both simulated studies and real-data analyses demonstrate the superior performance of our proposed method in terms of estimation accuracy and robustness when compared to existing approaches for estimating causal estimands.

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.

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.

The role of left insula mediating impaired error processing in response inhibition in adult heavy drinkers

Identification of neurobiological mechanisms underlying development of alcohol use disorder is critical to ensuring the appropriate early-phase treatment and prevention of the disorder. To this aim, we tried to elucidate the disturbance of neural functions in heavy drinking, which can lead to alcohol use disorder. Because response inhibition is affected by alcohol use disorder, we examined neural activation and task performance for response inhibition using the Go/No-Go task in an fMRI paradigm in adult non-dependent heavy and light drinkers. We examined the neural activation for error processing and inhibitory control, components of response inhibition. We then investigated the mediating effect of the relevant neural substrate on the relationship between the level of alcohol drinking and task performance using mediation analysis. We found that heavy drinking significantly decreased activation in the left insula during error processing and increased the mean commission error rate for No-Go trials compared with light drinking. Mediation analysis demonstrated full mediation of the left insula activation during error processing for the relationship between drinking level and commission error rate. Our results suggested that left insula activation may be a neural marker pivotal for potential conversion to alcohol use disorder in individuals with high clinical risk such as heavy drinking.

Bayesian network mediation analysis with application to the brain functional connectome

The brain functional connectome, the collection of interconnected neural circuits along functional networks, facilitates a cutting-edge understanding of brain functioning, and has a potential to play a mediating role within the effect pathway between an exposure and an outcome. While existing mediation analytic approaches are capable of providing insight into complex processes, they mainly focus on a univariate mediator or mediator vector, without considering network-variate mediators. To fill the methodological gap and accomplish this exciting and urgent application, in the article, we propose an integrative mediation analysis under a Bayesian paradigm with networks entailing the mediation effect. To parameterize the network measurements, we introduce individually specified stochastic block models with unknown block allocation, and naturally bridge effect elements through the latent network mediators induced by the connectivity weights across network modules. To enable the identification of truly active mediating components, we simultaneously impose a feature selection across network mediators. We show the superiority of our model in estimating different effect components and selecting active mediating network structures. As a practical illustration of this approach's application to network neuroscience, we characterize the relationship between a therapeutic intervention and opioid abstinence as mediated by brain functional sub-networks.