Evaluating the Exposome Score for Schizophrenia in a Transdiagnostic Psychosis Cohort: Associations With Psychosis Risk, Symptom Severity, and Personality Traits.

Background: Investigations of causal pathways for psychosis can be guided by the identification of environmental risk factors. A recently developed composite risk tool, the exposome score for schizophrenia (ES-SCZ), which controls for intercorrelations between risk factors, has shown fair to good performance. We tested the transdiagnostic psychosis classifier performance of the ES-SCZ with the Bipolar-Schizophrenia Network for Intermedial Phenotypes data and examined its relationship with clinical-level outcomes.

A spatially constrained independent component analysis jointly informed by structural and functional network connectivity.

There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. The brain connectivity of different modalities provides an insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multimodal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs).

Genetic analysis of psychosis Biotypes: shared Ancestry-adjusted polygenic risk and unique genomic associations.

The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) created psychosis Biotypes based on neurobiological measurements in a multi-ancestry sample. These Biotypes cut across DSM diagnoses of schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis. Two recently developed post hoc ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Ancestry-Adjusted PRSs (AAPRSs), which allow for PRS analysis of multi-ancestry samples.

Genetic Analysis of Psychosis Biotypes: Shared Ancestry-Adjusted Polygenic Risk and Unique Genomic Associations.

The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) created psychosis Biotypes based on neurobiological measurements in a multi-ancestry sample. These Biotypes cut across DSM diagnoses of schizophrenia, schizoaffective disorder and bipolar disorder with psychosis. Two recently developed ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Ancestry-Adjusted PRSs (AAPRSs), which allow for PRS analysis of multi-ancestry samples.

Categorical and Dimensional Approaches for Psychiatric Classification and Treatment Targeting: Considerations from Psychosis Biotypes.

Categorical diagnosis, a pillar of the medical model, has not worked well in psychiatry where most diagnoses are still exclusively symptom based. Uncertainty continues about whether categories or dimensions work better for the assessment and treatment of idiopathic psychoses. The Bipolar Schizophrenia Network for Intermediate Phenotypes (B-SNIP) examined multiple cognitive and electrophysiological biomarkers across a large transdiagnostic psychosis data set.

Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia.

Accurate diagnosis of mental disorders is expected to be achieved through the identification of reliable neuroimaging biomarkers with the help of cutting-edge feature selection techniques. However, existing feature selection methods often fall short in capturing the local structural characteristics among samples and effectively eliminating redundant features, resulting in inadequate performance in disorder prediction. To address this gap, we propose a novel supervised method named local-structure-preservation and redundancy-removal-based feature selection (LRFS), and then apply it to the identification of meaningful biomarkers for schizophrenia (SZ).

A randomized clinical trial to evaluate feasibility, tolerability, and preliminary target engagement for a novel executive working memory training in adolescents with ADHD.

Objective: Working memory training for Attention-Deficit/Hyperactivity Disorder (ADHD) has focused on increasing working memory capacity, with inconclusive evidence for its effectiveness. Alternative training targets are executive working memory (EWM) processes that promote flexibility or bolster stability of working memory contents to guide behavior via selective attention. This randomized, placebo-controlled study was designed to assess feasibility, tolerability, and behavioral target engagement of a novel EWM training for ADHD.

Unraveling the Neural Landscape of Mental Disorders using Double Functional Independent Primitives (dFIPs).

Mental illnesses extract a high personal and societal cost, and thus explorations of the links between mental illness and functional connectivity in the brain are critical. Investigating major mental illnesses, believed to arise from disruptions in sophisticated neural connections, allows us to comprehend how these neural network disruptions may be linked to altered cognition, emotional regulation, and social interactions. Although neuroimaging has opened new avenues to explore neural alterations linked to mental illnesses, the field still requires precise and sensitive methodologies to inspect these neural substrates of various psychological disorders.

4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia.

Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain networks in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.

Gyrification across psychotic disorders: A bipolar-schizophrenia network of intermediate phenotypes study.

Background: The profiles of cortical gyrification across schizophrenia, bipolar I disorder, and schizoaffective disorder have been studied to a limited extent, report discordant findings, and are rarely compared in the same study. Here we assess gyrification in a large dataset of psychotic disorder probands, categorized according to the DSM-IV. Furthermore, we explore gyrification changes with age across healthy controls and probands.

Disentangling negative and positive symptoms in schizophrenia and autism spectrum disorder.

Autism spectrum disorder (ASD) and schizophrenia (SZ) share traits, especially in social skills and negative symptoms, and to a lesser degree positive symptoms. Differential diagnosis can be challenging and discerning expressive and experiential negative symptoms may provide knowledge with potential diagnostic and functional relevance that can guide treatment. Two exploratory factor analyses (EFA) were conducted to reveal the underlying dimensions of negative and positive symptoms using the Positive and Negative Syndrome Scale (PANSS), the Scale for the Assessment of Positive Symptoms & Negative Symptoms (SAPS/SANS) and the Autism Diagnostic Observation Schedule-Generic (ADOS-G).

Association between the oral microbiome and brain resting state connectivity in schizophrenia.

Recent microbiome-brain axis findings have shown evidence of the modulation of microbiome community as an environmental mediator in brain function and psychiatric illness. This work is focused on the role of the microbiome in understanding a rarely investigated environmental involvement in schizophrenia (SZ), especially in relation to brain circuit dysfunction. We leveraged high throughput microbial 16s rRNA sequencing and functional neuroimaging techniques to enable the delineation of microbiome-brain network links in SZ.

Characterizing the Relationship between Personality Dimensions and Psychosis-Specific Clinical Characteristics.

Background: Past studies associating personality with psychosis have been limited by small nonclinical samples and a focus on general symptom burden. This study uses a large clinical sample to examine personality's relationship with psychosis-specific features and compare personality dimensions across clinically and neurobiologically defined categories of psychoses.

Methods: A total of 1352 participants with schizophrenia, schizoaffective disorder, and bipolar with psychosis, as well as 623 healthy controls (HC), drawn from the Bipolar-Schizophrenia Network for Intermediate Phenotypes (BSNIP-2) study, were included.

Evidence from comprehensive independent validation studies for smooth pursuit dysfunction as a sensorimotor biomarker for psychosis.

Smooth pursuit eye movements are considered a well-established and quantifiable biomarker of sensorimotor function in psychosis research. Identifying psychotic syndromes on an individual level based on neurobiological markers is limited by heterogeneity and requires comprehensive external validation to avoid overestimation of prediction models. Here, we studied quantifiable sensorimotor measures derived from smooth pursuit eye movements in a large sample of psychosis probands (N = 674) and healthy controls (N = 305) using multivariate pattern analysis.

A spatially constrained independent component analysis jointly informed by structural and functional network connectivity.

There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. Brain connectivity of different modalities provides insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multi-modal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs).

A confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer's disease using resting-state functional network connectivity.

Resting-state functional magnetic resonance imaging (rs-fMRI) has increasingly been used to study both Alzheimer's disease (AD) and schizophrenia (SZ). While most rs-fMRI studies being conducted in AD and SZ compare patients to healthy controls, it is also of interest to directly compare AD and SZ patients with each other to identify potential biomarkers shared between the disorders. However, comparing patient groups collected in different studies can be challenging due to potential confounds, such as differences in the patient's age, scan protocols, etc.

Functional and structural effects of repetitive transcranial magnetic stimulation (rTMS) for the treatment of auditory verbal hallucinations in schizophrenia: A systematic review.

Background: Auditory verbal hallucinations (AVH) are a disabling symptom for people with schizophrenia (SCZ), and do not always respond to antipsychotics. Repetitive transcranial magnetic stimulation (rTMS) has shown efficacy for medication-refractory AVH, though the underlying neural mechanisms by which rTMS produces these effects remain unclear. This systematic review evaluated the structural and functional impact of rTMS for AVH in SCZ, and its association with clinical outcomes.

More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method.

The integration of neuroimaging with artificial intelligence is crucial for advancing the diagnosis of mental disorders. However, challenges arise from incomplete matching between diagnostic labels and neuroimaging. Here, we propose a label-noise filtering-based dimensional prediction (LAMP) method to identify reliable biomarkers and achieve accurate prediction for mental disorders.

Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis.

This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community.