Bipolar and schizophrenia network for intermediate phenotypes: outcomes across the psychosis continuum

The Common Structure of the Major Psychoses: More Similarities Than Differences in the Network Structures of Schizophrenia, Schizoaffective Disorder, and Psychotic Bipolar Disorder

BACKGROUND AND HYPOTHESIS

There has been a century-long debate about whether the major psychoses (eg, bipolar disorder, schizophrenia, and schizoaffective disorder) are one disorder with various manifestations or different disease entities. Traditional approaches using dimensional models have not provided decisive findings. Here, we address this question by examining the network constellation of affective and psychotic syndromes.

DESIGN

Comparable symptom data of 1882 patients with psychotic bipolar disorder, schizoaffective disorders, and schizophrenia were extracted from three datasets: B-SNIP 1, B-SNIP2, and PARDIP. Twenty-six items from the Positive and Negative Syndrome Scale, YMRS, and the Montgomery-Asberg Depression Rating Scale were selected for the analysis using a principled approach to eliminate overlapping/redundant items. Gaussian graphical models were estimated and assessed for stability, and their communities were identified using bootstrapped exploratory graph analysis. The structures and global densities of the networks were compared with network comparison tests.

RESULTS

The network structures were highly similar (r >. 80) across diagnostic groups. For all diagnoses, manic symptoms were more connected with positive symptoms while depressive symptoms were more linked with negative symptoms. The depressive and negative symptoms were the strongest indicators of depressive and psychotic communities. Theoretically interesting variability in network edge weights between symptoms was found relating to thought disorder and pessimistic thinking.

CONCLUSIONS

The same broad structure of psychopathology underlies the symptom expressions of bipolar disorder, schizoaffective disorder, and schizophrenia. Future studies should build on the present finding by comparing specific inter-relations between symptoms in the different diagnostic groups using methods capable of detecting causality.

Cognitive profiles across the psychosis continuum

Cognitive impairments are core features in individuals across the psychosis continuum and predict functional outcomes. Nevertheless, substantial variability in cognitive functioning within diagnostic groups, along with considerable overlap with healthy controls, hampers the translation of research findings into personalized treatment planning. Aligned with precision medicine, we employed a data driven machine learning method, self-organizing maps, to conduct transdiagnostic clustering based on cognitive functions in a sample comprising 228 healthy controls, 200 individuals at ultra-high risk for psychosis, and 98 antipsychotic-naïve patients with first-episode psychosis. The self-organizing maps revealed six clinically distinct cognitive profiles that significantly predicted baseline functional level and changes in functional level after one year. Cognitive flexibility in particular, as well as specific executive functions emerged as cardinal in differentiating the profiles. The application of self-organizing maps appears to be a promising approach to inform clinical decision-making based on individualized cognitive profiles, including patient allocation to different interventions. Moreover, this method has the potential to enable cross-diagnostic stratification in research trials, utilizing data-driven subgrouping informed by categories from underlying dimensions of cognition rather than from clinical diagnoses. Finally, the method enables cross-diagnostic profiling across other data modalities, such as brain networks or metabolic subtypes.

Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech

Background

Psychosis poses substantial social and healthcare burdens. The analysis of speech is a promising approach for the diagnosis and monitoring of psychosis, capturing symptoms like thought disorder and flattened affect. Recent advancements in Natural Language Processing (NLP) methodologies enable the automated extraction of informative speech features, which has been leveraged for early psychosis detection and assessment of symptomology. However, critical gaps persist, including the absence of standardized sample collection protocols, small sample sizes, and a lack of multi-illness classification, limiting clinical applicability. Our study aimed to (1) identify an optimal assessment approach for the online and remote collection of speech, in the context of assessing the psychosis spectrum and evaluate whether a fully automated, speech-based machine learning (ML) pipeline can discriminate among different conditions on the schizophrenia-bipolar spectrum (SSD-BD-SPE), help-seeking comparison subjects (MDD), and healthy controls (HC) at varying layers of analysis and diagnostic complexity.

Methods

We adopted online data collection methods to collect 20 minutes of speech and demographic information from individuals. Participants were categorized as "healthy" help-seekers (HC), having a schizophrenia-spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), or being on the psychosis spectrum with sub-clinical psychotic experiences (SPE). SPE status was determined based on self-reported clinical diagnosis and responses to the PHQ-8 and PQ-16 screening questionnaires, while other diagnoses were determined based on self-report from participants. Linguistic and paralinguistic features were extracted and ensemble learning algorithms (e.g., XGBoost) were used to train models. A 70%-30% train-test split and 30-fold cross-validation was used to validate the model performance.

Results

The final analysis sample included 1140 individuals and 22,650 minutes of speech. Using 5-minutes of speech, our model could discriminate between HC and those with a serious mental illness (SSD or BD) with 86% accuracy (AUC = 0.91, Recall = 0.7, Precision = 0.98). Furthermore, our model could discern among HC, SPE, BD and SSD groups with 86% accuracy (F1 macro = 0.855, Recall Macro = 0.86, Precision Macro = 0.86). Finally, in a 5-class discrimination task including individuals with MDD, our model had 76% accuracy (F1 macro = 0.757, Recall Macro = 0.758, Precision Macro = 0.766).

Conclusion

Our ML pipeline demonstrated disorder-specific learning, achieving excellent or good accuracy across several classification tasks. We demonstrated that the screening of mental disorders is possible via a fully automated, remote speech assessment pipeline. We tested our model on relatively high number conditions (5 classes) in the literature and in a stratified sample of psychosis spectrum, including HC, SPE, SSD and BD (4 classes). We tested our model on a large sample (N = 1150) and demonstrated best-in-class accuracy with remotely collected speech data in the psychosis spectrum, however, further clinical validation is needed to test the reliability of model performance.

Abnormal resting-state EEG phase dynamics distinguishes major depressive disorder and bipolar disorder

Changes in EEG have been reported in both major depressive disorder (MDD) and bipolar disorder (BD). Specifically, power changes in EEG alpha and theta frequency bands during rest and task are known in both disorders. This leaves open whether there are changes in yet another component of the electrophysiological EEG signal, namely phase-related processes that may allow for distinguishing MDD and BD. For that purpose, we investigate EEG-based spontaneous phase in the resting state of MDD, BD and healthy controls. Our main findings show: (i) decreased spontaneous phase variability in frontal theta of both MDD and BD compared to HC; (ii) decreased spontaneous phase variability in central-parietal alpha in MDD compared to both BD and HC; (iii) increased delays or lags of alpha phase cycles in MDD (but not in BD), which (iv) correlate with the decreased phase variability in MDD. Together, we show similar (decreased frontal theta variability) and distinct (decreased central-parietal alpha variability with increased lags or delays) findings in the spontaneous phase dynamics of MDD and BD. This suggests potential relevance of theta and alpha phase dynamics in distinguishing MDD and BD in clinical differential-diagnosis.

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. In this study, we employ a hierarchical methodology to derive double functionally independent primitives (dFIPs) from resting state functional magnetic resonance neuroimaging data (rs-fMRI). These dFIPs encapsulate canonical overlapping patterns of functional network connectivity (FNC) within the brain. Our investigation focuses on the examination of how combinations of these dFIPs relate to different mental disorder diagnoses. The central aim is to unravel the complex patterns of FNC that correspond to the diverse manifestations of mental illnesses. To achieve this objective, we used a large brain imaging dataset from multiple sites, comprising 5805 total individuals diagnosed with schizophrenia (SCZ), autism spectrum disorder (ASD), bipolar disorder (BPD), major depressive disorder (MDD), and controls. The key revelations of our study unveil distinct patterns associated with each mental disorder through the combination of dFIPs. Notably, certain individual dFIPs exhibit disorder-specific characteristics, while others demonstrate commonalities across disorders. This approach offers a novel, data-driven synthesis of intricate neuroimaging data, thereby illuminating the functional changes intertwined with various mental illnesses. Our results show distinct signatures associated with psychiatric disorders, revealing unique connectivity patterns such as heightened cerebellar connectivity in SCZ and sensory domain hyperconnectivity in ASD, both contrasted with reduced cerebellar-subcortical connectivity. Utilizing the dFIP concept, we pinpoint specific functional connections that differentiate healthy controls from individuals with mental illness, underscoring its utility in identifying neurobiological markers. In summary, our findings delineate how dFIPs serve as unique fingerprints for different mental disorders.

Exploring the potential of representation and transfer learning for anatomical neuroimaging: Application to psychiatry

The perspective of personalized medicine for brain disorders requires efficient learning models for anatomical neuroimaging-based prediction of clinical conditions. There is now a consensus on the benefit of deep learning (DL) in addressing many medical imaging tasks, such as image segmentation. However, for single-subject prediction problems, recent studies yielded contradictory results when comparing DL with Standard Machine Learning (SML) on top of classical feature extraction. Most existing comparative studies were limited in predicting phenotypes of little clinical interest, such as sex and age, and using a single dataset. Moreover, they conducted a limited analysis of the employed image pre-processing and feature selection strategies. This paper extensively compares DL and SML prediction capacity on five multi-site problems, including three increasingly complex clinical applications in psychiatry namely schizophrenia, bipolar disorder, and Autism Spectrum Disorder (ASD) diagnosis. To compensate for the relative scarcity of neuroimaging data on these clinical datasets, we also evaluate three pre-training strategies for transfer learning from brain imaging of the general healthy population: self-supervised learning, generative modeling and supervised learning with age. Overall, we find similar performance between randomly initialized DL and SML for the three clinical tasks and a similar scaling trend for sex prediction. This was replicated on an external dataset. We also show highly correlated discriminative brain regions between DL and linear ML models in all problems. Nonetheless, we demonstrate that self-supervised pre-training on large-scale healthy population imaging datasets (N≈10k), along with Deep Ensemble, allows DL to learn robust and transferable representations to smaller-scale clinical datasets (N≤1k). It largely outperforms SML on 2 out of 3 clinical tasks both in internal and external test sets. These findings suggest that the improvement of DL over SML in anatomical neuroimaging mainly comes from its capacity to learn meaningful and useful abstract representations of the brain anatomy, and it sheds light on the potential of transfer learning for personalized medicine in psychiatry.

Multimodal Neuroimaging Summary Scores as Neurobiological Markers of Psychosis

BACKGROUND AND HYPOTHESIS

Structural brain alterations are well-established features of schizophrenia but they do not effectively predict disease/disease risk. Similar to polygenic risk scores in genetics, we integrated multifactorial aspects of brain structure into a summary "Neuroscore" and examined its potential as a marker of disease.

STUDY DESIGN

We extracted measures from T1-weighted scans and diffusion tensor imaging (DTI) models from three studies with schizophrenia and healthy individuals. We calculated individual-level summary scores (Neuroscores) for T1-weighted and DTI measures and a combined score (Multimodal Neuroscore-MM). We assessed each score's ability to differentiate schizophrenia cases from controls and its relationship to clinical symptomatology, intelligence quotient (IQ), and medication dosage. We assessed Neuroscore specificity by performing all analyses in a more inclusive psychosis sample and by using scores generated from MDD effect sizes.

STUDY RESULTS

All Neuroscores significantly differentiated schizophrenia cases from controls (T1 d = 0.56, DTI d = 0.29, MM d = 0.64) to a greater degree than individual brain regions. Higher Neuroscores (ie, increased liability) were associated with lower IQ (T1 β = -0.26, DTI β = -0.15, MM β = -0.30). Higher T1-weighted Neuroscores were associated with higher positive and negative symptom severity (Positive β = 0.21, Negative β = 0.16); Higher Multimodal Neuroscores were associated with higher positive symptom severity (β = 0.30). SZ Neuroscores outperformed MDD Neuroscores in predicting IQ (T1: z = 3.5, q = 0.0007; MM: z = 1.8, q = 0.05).

CONCLUSIONS

Neuroscores are a step toward leveraging widespread structural brain alterations in psychosis to identify robust neurobiological markers of disease. Future studies will assess ways to improve neuroscore calculation, including developing the optimal methods to calculate neuroscores and considering disorder overlap.

Assessing regional intracortical myelination in schizophrenia spectrum and bipolar disorders using the optimized T1w/T2w-ratio

BACKGROUND

Dysmyelination could be part of the pathophysiology of schizophrenia spectrum (SCZ) and bipolar disorders (BPD), yet few studies have examined myelination of the cerebral cortex. The ratio of T1- and T2-weighted magnetic resonance images (MRI) correlates with intracortical myelin. We investigated the T1w/T2w-ratio and its age trajectories in patients and healthy controls (CTR) and explored associations with antipsychotic medication use and psychotic symptoms.

METHODS

Patients with SCZ (n = 64; mean age = 30.4 years, s.d. = 9.8), BPD (n = 91; mean age 31.0 years, s.d. = 10.2), and CTR (n = 155; mean age = 31.9 years, s.d. = 9.1) who participated in the TOP study (NORMENT, University of Oslo, Norway) were clinically assessed and scanned using a General Electric 3 T MRI system. T1w/T2w-ratio images were computed using an optimized pipeline with intensity normalization and field inhomogeneity correction. Vertex-wise regression models were used to compare groups and examine group × age interactions. In regions showing significant differences, we explored associations with antipsychotic medication use and psychotic symptoms.

RESULTS

No main effect of diagnosis was found. However, age slopes of the T1w/T2w-ratio differed significantly between SCZ and CTR, predominantly in frontal and temporal lobe regions: Lower T1w/T2w-ratio values with higher age were found in CTR, but not in SCZ. Follow-up analyses revealed a more positive age slope in patients who were using antipsychotics and patients using higher chlorpromazine-equivalent doses.

CONCLUSIONS

While we found no evidence of reduced intracortical myelin in SCZ or BPD relative to CTR, different regional age trajectories in SCZ may suggest a promyelinating effect of antipsychotic medication.

Pons-to-Cerebellum Hypoconnectivity Along the Psychosis Spectrum and Associations With Sensory Prediction and Hallucinations in Schizophrenia

BACKGROUND

Sensory prediction allows the brain to anticipate and parse incoming self-generated sensory information from externally generated signals. Sensory prediction breakdowns may contribute to perceptual and agency abnormalities in psychosis (hallucinations, delusions). The pons, a central node in a cortico-ponto-cerebellar-thalamo-cortical circuit, is thought to support sensory prediction. Examination of pons connectivity in schizophrenia and its role in sensory prediction abnormalities is lacking.

METHODS

We examined these relationships using resting-state functional magnetic resonance imaging and the electroencephalography-based auditory N1 event-related potential in 143 participants with psychotic spectrum disorders (PSPs) (with schizophrenia, schizoaffective disorder, or bipolar disorder); 63 first-degree relatives of individuals with psychosis; 45 people at clinical high risk for psychosis; and 124 unaffected comparison participants. This unique sample allowed examination across the psychosis spectrum and illness trajectory. Seeding from the pons, we extracted average connectivity values from thalamic and cerebellar clusters showing differences between PSPs and unaffected comparison participants. We predicted N1 amplitude attenuation during a vocalization task from pons connectivity and group membership. We correlated participant-level connectivity in PSPs and people at clinical high risk for psychosis with hallucination and delusion severity.

RESULTS

Compared to unaffected comparison participants, PSPs showed pons hypoconnectivity to 2 cerebellar clusters, and first-degree relatives of individuals with psychosis showed hypoconnectivity to 1 of these clusters. Pons-to-cerebellum connectivity was positively correlated with N1 attenuation; only PSPs with heightened pons-to-postcentral gyrus connectivity showed this pattern, suggesting a possible compensatory mechanism. Pons-to-cerebellum hypoconnectivity was correlated with greater hallucination severity specifically among PSPs with schizophrenia.

CONCLUSIONS

Deficient pons-to-cerebellum connectivity linked sensory prediction network breakdowns with perceptual abnormalities in schizophrenia. Findings highlight shared features and clinical heterogeneity across the psychosis spectrum.

The overlap across psychotic disorders: A functional network connectivity analysis

Functional network connectivity (FNC) has previously been shown to distinguish patient groups from healthy controls (HC). However, the overlap across psychiatric disorders such as schizophrenia (SZ), bipolar (BP), and schizoaffective disorder (SAD) is not evident yet. This study focuses on studying the overlap across these three psychotic disorders in both dynamic and static FNC (dFNC/sFNC). We used resting-state fMRI, demographics, and clinical information from the Bipolar-Schizophrenia Network on Intermediate Phenotypes cohort (BSNIP). The data includes three groups of patients with schizophrenia (SZ, N = 181), bipolar (BP, N = 163), and schizoaffective (SAD, N = 130) and HC (N = 238) groups. After estimating each individual's dFNC, we group them into three distinct states. We evaluated two dFNC features, including occupancy rate (OCR) and distance travelled over time. Finally, the extracted features, including both sFNC and dFNC, are tested statistically across patients and HC groups. In addition, we explored the link between the clinical scores and the extracted features. We evaluated the connectivity patterns and their overlap among SZ, BP, and SAD disorders (false discovery rate or FDR corrected p < 0.05). Results showed dFNC captured unique information about overlap across disorders where all disorder groups showed similar pattern of activity in state 2. Moreover, the results showed similar patterns between SZ and SAD in state 1 which was different than BP. Finally, the distance travelled feature of SZ (average R = 0.245, p < 0.01) and combined distance travelled from all disorders was predictive of the PANSS symptoms scores (average R = 0.147, p < 0.01).

Multi-modal deep learning from imaging genomic data for schizophrenia classification

Background

Schizophrenia (SZ) is a psychiatric condition that adversely affects an individual's cognitive, emotional, and behavioral aspects. The etiology of SZ, although extensively studied, remains unclear, as multiple factors come together to contribute toward its development. There is a consistent body of evidence documenting the presence of structural and functional deviations in the brains of individuals with SZ. Moreover, the hereditary aspect of SZ is supported by the significant involvement of genomics markers. Therefore, the need to investigate SZ from a multi-modal perspective and develop approaches for improved detection arises.

Methods

Our proposed method employed a deep learning framework combining features from structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), and genetic markers such as single nucleotide polymorphism (SNP). For sMRI, we used a pre-trained DenseNet to extract the morphological features. To identify the most relevant functional connections in fMRI and SNPs linked to SZ, we applied a 1-dimensional convolutional neural network (CNN) followed by layerwise relevance propagation (LRP). Finally, we concatenated these obtained features across modalities and fed them to the extreme gradient boosting (XGBoost) tree-based classifier to classify SZ from healthy control (HC).

Results

Experimental evaluation on clinical dataset demonstrated that, compared to the outcomes obtained from each modality individually, our proposed multi-modal approach performed classification of SZ individuals from HC with an improved accuracy of 79.01%.

Conclusion

We proposed a deep learning based framework that selects multi-modal (sMRI, fMRI and genetic) features efficiently and fuse them to obtain improved classification scores. Additionally, by using Explainable AI (XAI), we were able to pinpoint and validate significant functional network connections and SNPs that contributed the most toward SZ classification, providing necessary interpretation behind our findings.

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. Three biomarker-derived biotypes were used to separately categorize the probands. Mean personality factors (openness, conscientiousness, extraversion, agreeableness, and neuroticism) were compared between HC and proband subgroups using independent sample t-tests. A robust linear regression was utilized to determine personality differences across biotypes and diagnostic subgroups. Associations between personality factors and cognition were determined through Pearson's correlation. A canonical correlation was run between the personality factors and general functioning, positive symptoms, and negative symptoms to delineate the relationship between personality and clinical outcomes of psychosis.

Results

There were significant personality differences between the proband and HC groups across all five personality factors. Overall, the probands had higher neuroticism and lower extraversion, agreeableness, conscientiousness, and openness. Openness showed the greatest difference across the diagnostic subgroups and biotypes, and greatest correlation with cognition. Openness, agreeableness, and extraversion had the strongest associations with symptom severity.

Conclusions

Individuals with psychosis have different personality profiles compared to HC. In particular, openness may be relevant in distinguishing psychosis-specific phenotypes and experiences, and associated with biological underpinnings of psychosis, including cognition. Further studies should identify potential causal factors and mediators of this relationship.

Enlarged pituitary gland volume: a possible state rather than trait marker of psychotic disorders

BACKGROUND

Enlarged pituitary gland volume could be a marker of psychotic disorders. However, previous studies report conflicting results. To better understand the role of the pituitary gland in psychosis, we examined a large transdiagnostic sample of individuals with psychotic disorders.

METHODS

The study included 751 participants (174 with schizophrenia, 114 with schizoaffective disorder, 167 with psychotic bipolar disorder, and 296 healthy controls) across six sites in the Bipolar-Schizophrenia Network on Intermediate Phenotypes consortium. Structural magnetic resonance images were obtained, and pituitary gland volumes were measured using the MAGeT brain algorithm. Linear mixed models examined between-group differences with controls and among patient subgroups based on diagnosis, as well as how pituitary volumes were associated with symptom severity, cognitive function, antipsychotic dose, and illness duration.

RESULTS

Mean pituitary gland volume did not significantly differ between patients and controls. No significant effect of diagnosis was observed. Larger pituitary gland volume was associated with greater symptom severity (F = 13.61, p = 0.0002), lower cognitive function (F = 4.76, p = 0.03), and higher antipsychotic dose (F = 5.20, p = 0.02). Illness duration was not significantly associated with pituitary gland volume. When all variables were considered, only symptom severity significantly predicted pituitary gland volume (F = 7.54, p = 0.006).

CONCLUSIONS

Although pituitary volumes were not increased in psychotic disorders, larger size may be a marker associated with more severe symptoms in the progression of psychosis. This finding helps clarify previous inconsistent reports and highlights the need for further research into pituitary gland-related factors in individuals with psychosis.

Resting-state fMRI is associated with trauma experiences, mood and psychosis in Afro-descendants with bipolar disorder and schizophrenia

BACKGROUND

Bipolar disorder (BD) and schizophrenia (SCZ) may exhibit functional abnormalities in several brain areas, including the medial temporal and prefrontal cortex and hippocampus; however, a less explored topic is how brain connectivity is linked to premorbid trauma experiences and clinical features in non-Caucasian samples of SCZ and BD.

METHODS

Sixty-two individuals with SCZ (n = 20), BD (n = 21), and healthy controls (HC, n = 21) from indigenous and African ethnicity were submitted to clinical screening (Di-PAD), traumata experiences (ETISR-SF), cognitive and functional MRI assessment. The item psychosis/hallucinations in SCZ patients showed a negative correlation with the global efficiency (GE) in the right dorsal attention network. The items mania, irritable mood, and racing thoughts in the Di-PAD scale had a significant negative correlation with the GE in the parietal right default mode network.

CONCLUSIONS

Differences in the activation of specific networks were associated with earlier disease onset, history of physical abuse, and more severe psychotic and mood symptoms in SCZ and BD subjects of indigenous and black ethnicity. Findings provide further evidence on SZ and BD's brain connectivity disturbances, and their clinical significance, in non-Caucasian samples.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds

Objective

fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging.

Methods

We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevel-opmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP).

Results

We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity.

Conclusion

Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics.

Significance

Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

A Demographic-Conditioned Variational Autoencoder for fMRI Distribution Sampling and Removal of Confounds

Objective

fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging.

Methods

We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP).

Results

We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity.

Conclusion

Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics.

Significance

Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.

Bipolar disorders and schizophrenia: discrete disorders?

Background

With similarities in heritability, neurobiology and symptomatology, the question has been raised whether schizophrenia and bipolar disorder are truly distinctive disorders or belong to a continuum. This narrative review summarizes common and distinctive findings from genetics, neuroimaging, cognition and clinical course that may help to solve this ethiopathogenetic puzzle.

Methods

The authors conducted a literature search for papers listed in PubMed and Google Scholar, using the search terms "schizophrenia" and "bipolar disorder" combined with different terms such as "genes", "neuroimaging studies", "phenomenology differences", "cognition", "epidemiology". Articles were considered for inclusion if they were written in English or Spanish, published as full articles, if they compared subjects with schizophrenia and bipolar disorder, or subjects with either disorder with healthy controls, addressing differences between groups.

Results

Several findings support the hypothesis that schizophrenia and bipolar disorder are discrete disorders, yet some overlapping of findings exists. The evidence for heritability of both SZ and BD is obvious, as well as the environmental impact on individual manifestations of both disorders. Neuroimaging studies support subtle differences between disorders, it appears to be rather a pattern of irregularities than an unequivocally unique finding distinguishing schizophrenia from bipolar disorder. The cognitive profile displays differences between disorders in certain domains, such as premorbid intellectual functioning and executive functions. Finally, the timing and trajectory of cognitive impairment in both disorders also differs.

Conclusion

The question whether SZ and BD belong to a continuum or are separate disorders remains a challenge for further research. Currently, our research tools may be not precise enough to carve out distinctive, unique and undisputable differences between SZ and BD, but current evidence favors separate disorders. Given that differences are subtle, a way to overcome diagnostic uncertainties in the future could be the application of artificial intelligence based on BigData.

Limitations

Despite the detailed search, this article is not a full and complete review of all available studies on the topic. The search and selection of papers was also limited to articles in English and Spanish. Selection of papers and conclusions may be biased by the personal view and clinical experience of the authors.

A Brainwide Risk Score for Psychiatric Disorder Evaluated in a Large Adolescent Population Reveals Increased Divergence Among Higher-Risk Groups Relative to Control Participants

BACKGROUND

Accurate psychiatric risk assessment requires biomarkers that are both stable and adaptable to development. Functional network connectivity (FNC), which steadily reconfigures over time, potentially contains abundant information to assess psychiatric risks. However, the absence of suitable analytical methodologies has constrained this area of investigation.

METHODS

We investigated the brainwide risk score (BRS), a novel FNC-based metric that contrasts the relative distances of an individual's FNC to that of psychiatric disorders versus healthy control references. To generate group-level disorder and healthy control references, we utilized a large brain imaging dataset containing 5231 total individuals diagnosed with schizophrenia, autism spectrum disorder, major depressive disorder, and bipolar disorder and their corresponding healthy control individuals. The BRS metric was employed to assess the psychiatric risk in 2 new datasets: Adolescent Brain Cognitive Development (ABCD) Study (n = 8191) and Human Connectome Project Early Psychosis (n = 170).

RESULTS

The BRS revealed a clear, reproducible gradient of FNC patterns from low to high risk for each psychiatric disorder in unaffected adolescents. We found that low-risk ABCD Study adolescent FNC patterns for each disorder were strongly present in over 25% of the ABCD Study participants and homogeneous, whereas high-risk patterns of each psychiatric disorder were strongly present in about 1% of ABCD Study participants and heterogeneous. The BRS also showed its effectiveness in predicting psychosis scores and distinguishing individuals with early psychosis from healthy control individuals.

CONCLUSIONS

The BRS could be a new image-based tool for assessing psychiatric vulnerability over time and in unaffected individuals, and it could also serve as a potential biomarker, facilitating early screening and monitoring interventions.

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. Our method proposes to utilize a label-noise filtering model to automatically filter out unclear cases from a neuroimaging perspective, and then the typical subjects whose diagnostic labels align with neuroimaging measures are used to construct a dimensional prediction model to score independent subjects. Using fMRI data of schizophrenia patients and healthy controls (n = 1,245), our method yields consistent scores to independent subjects, leading to more distinguishable relabeled groups with an enhanced classification accuracy of 31.89%. Additionally, it enables the exploration of stable abnormalities in schizophrenia. In summary, our LAMP method facilitates the identification of reliable biomarkers and accurate diagnosis of mental disorders using neuroimages.

Identifying psychosis subtypes use individualized covariance structural differential networks and multi-site clustering

BACKGROUND

Similarities among schizophrenia (SZ), schizoaffective disorder (SAD) and bipolar disorder (BP) including clinical phenotypes, brain alterations and risk genes, make it challenging to perform reliable separation among them. However, previous subtype identification that transcend traditional diagnostic boundaries were based on group-level neuroimaging features, ignoring individual-level inferences.

METHODS

455 psychoses (178 SZs, 134 SADs and 143 BPs), their first-degree relatives (N = 453) and healthy controls (HCs, N = 220) were collected from Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP I) consortium. Individualized covariance structural differential networks (ICSDNs) were constructed for each patient and multi-site clustering was used to identify psychosis subtypes. Group differences between subtypes in clinical phenotypes and voxel-wise fractional amplitude of low frequency fluctuation (fALFF) were calculated, as well as between the corresponding relatives.

RESULTS

Two psychosis subtypes were identified with increased whole brain structural covariance, with decreased connectivity between amygdala-hippocampus and temporal-occipital cortex in subtype I (S-I) compared to subtype II (S-II), which was replicated under different clustering methods, number of edges and across datasets (B-SNIP II) and different brain atlases. S-I had higher emotional-related symptoms than S-II and showed significant fALFF decrease in temporal and occipital cortex, while S-II was more similar to HC. This pattern was consistently validated on relatives of S-I and S-II in both fALFF and clinical symptoms.

CONCLUSIONS

These findings reconcile categorical and dimensional perspectives of psychosis neurobiological heterogeneity, indicating that relatives of S-I might have greater predisposition in developing psychosis, while relatives of S-II are more likely to be healthy. This study contributes to the development of neuroimaging informed diagnostic classifications within psychosis spectrum.

Differentiating Biomarker Features and Familial Characteristics of B-SNIP Psychosis Biotypes

Idiopathic psychosis shows considerable biological heterogeneity across cases. B-SNIP used psychosis-relevant biomarkers to identity psychosis Biotypes, which will aid etiological and targeted treatment investigations. Psychosis probands from the B-SNIP consortium (n = 1907), their first-degree biological relatives (n = 705), and healthy participants (n = 895) completed a biomarker battery composed of cognition, saccades, and auditory EEG measurements. ERP quantifications were substantially modified from previous iterations of this approach. Multivariate integration reduced multiple biomarker outcomes to 11 "bio-factors". Twenty-four different approaches indicated bio-factor data among probands were best distributed as three subgroups. Numerical taxonomy with k-means constructed psychosis Biotypes, and rand indices evaluated consistency of Biotype assignments. Psychosis subgroups, their non-psychotic first-degree relatives, and healthy individuals were compared across bio-factors. The three psychosis Biotypes differed significantly on all 11 bio-factors, especially prominent for general cognition, antisaccades, ERP magnitude, and intrinsic neural activity. Rand indices showed excellent consistency of clustering membership when samples included at least 1100 subjects. Canonical discriminant analysis described composite bio-factors that simplified group comparisons and captured neural dysregulation, neural vigor, and stimulus salience variates. Neural dysregulation captured Biotype-2, low neural vigor captured Biotype-1, and deviations of stimulus salience captured Biotype-3. First-degree relatives showed similar patterns as their Biotyped proband relatives on general cognition, antisaccades, ERP magnitudes, and intrinsic brain activity. Results extend previous efforts by the B-SNIP consortium to characterize biologically distinct psychosis Biotypes. They also show that at least 1100 observations are necessary to achieve consistent outcomes. First-degree relative data implicate specific bio-factor deviations to the subtype of their proband and may inform studies of genetic risk.

Different levels of prepulse inhibition among patients with first-episode schizophrenia, bipolar disorder and major depressive disorder

BACKGROUND

Deficits in prepulse inhibition may be a common feature in first-episode schizophrenia, bipolar disorder (BD) and major depressive disorder (MDD). We sought to explore the levels and viability of prepulse inhibition to differentiate first-episode schizophrenia, BD and MDD in patient populations.

METHODS

We tested patients with first-episode schizophrenia, BD or MDD and healthy controls using prepulse inhibition paradigms, namely perceived spatial co-location (PSC-PPI) and perceived spatial separation (PSS-PPI).

RESULTS

We included 53 patients with first-episode schizophrenia, 30 with BD and 25 with MDD, as well as 82 healthy controls. The PSS-PPI indicated that the levels of prepulse inhibition were smallest to largest, respectively, in the first-episode schizophrenia, BD, MDD and control groups. Relative to the healthy controls, the prepulse inhibition deficits in the first-episode schizophrenia group were significant (p < 0.001), but the prepulse inhibitions were similar between patients with BD and healthy controls, and between patients with MDD and healthy controls. The receiver operating characteristic curve analysis showed that PSS-PPI (area under the curve [AUC] 0.73, p < 0.001) and latency (AUC 0.72, p < 0.001) were significant for differentiating patients with first-episode schizophrenia or BD from healthy controls.

LIMITATIONS

The demographics of the 4 groups were not ideally matched. We did not perform cognitive assessments. The possible confounding effect of medications on prepulse inhibition could not be eliminated.

CONCLUSION

The level of prepulse inhibition among patients with first-episode schizophrenia was the lowest, with levels among patients with BD, patients with MDD and healthy controls increasingly higher. The PSS-PPI paradigm was more effective than PSC-PPI to recognize deficits in prepulse inhibition. These results provide a basis for further research on biological indicators that can assist differential diagnoses in psychosis.

Emotional interference and attentional control in schizophrenia-spectrum disorders: The special case of neutral faces

BACKGROUND AND OBJECTIVES

Schizophrenia-spectrum disorders (SSD) are characterized by impaired emotion processing and attention. SSD patients are more sensitive to the presence of emotional distractors. But despite growing interest on the emotion-attention interplay, emotional interference in SSD is far from fully understood. Moreover, research to date has not established the link between emotional interference and attentional control in SSD. This study thus aimed to investigate the effects of facial expression and attentional control in SSD, by manipulating perceptual load.

METHODS

Twenty-two SSD patients and 22 healthy controls performed a target-letter discrimination task with task-irrelevant angry, happy, and neutral faces. Target-letter was presented among homogenous (low load) or heterogenous (high load) distractor-letters. Accuracy and RT were analysed using (generalized) linear mixed-effect models.

RESULTS

Accuracy was significantly lower in SSD patients than controls, regardless of perceptual load and facial expression. Concerning RT, SSD patients were significantly slower than controls in the presence of neutral faces, but only at high load. No group differences were observed for angry and happy faces.

LIMITATIONS

Heterogeneity of SSD, small sample size, lack of clinical control group, medication.

CONCLUSIONS

One possible explanation is that neutral faces captured exogenous attention to a greater extent in SSD, thus challenging attentional control in perceptually demanding conditions. This may reflect abnormal processing of neutral faces in SSD. If replicated, these findings will help to understand the interplay between exogenous attention, attentional control, and emotion processing in SSD, which may unravel the mechanism underlying socioemotional dysfunction in SSD.

ImageNomer: Description of a functional connectivity and omics analysis tool and case study identifying a race confound

Most packages for the analysis of fMRI-based functional connectivity (FC) and genomic data are used with a programming language interface, lacking an easy-to-navigate GUI frontend. This exacerbates two problems found in these types of data: demographic confounds and quality control in the face of high dimensionality of features. The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset. FC in particular usually contains tens of thousands of features per subject, and can only be summarized and efficiently explored using visualizations. To remedy this situation, we have developed ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. The software is Python-based, runs in a self-contained Docker image, and contains a browser-based GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores in the Philadelphia Neurodevelopmental Cohort (PNC) dataset, which contains multitask fMRI and single nucleotide polymorphism (SNP) data of healthy adolescents. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer to visualize trends in achievement scores between races, we find a clear potential for confounding effects if race can be predicted using FC. Using correlation analysis in the ImageNomer software, we show that FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more highly correlated with race. Investigating further, we find that whereas both FC and SNP (genomic) features can account for 10-15% of WRAT score variation, this predictive ability disappears when controlling for race. We also use ImageNomer to investigate race-FC correlation in the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP) dataset. In this work, we demonstrate the advantage of our ImageNomer GUI tool in data exploration and confound detection. Additionally, this work identifies race as a strong confound in FC data and casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents.

Microstructural and Microvascular Alterations in Psychotic Spectrum Disorders: A Three-Compartment Intravoxel Incoherent Imaging and Free Water Model

BACKGROUND AND HYPOTHESIS

Microvascular and inflammatory mechanisms have been hypothesized to be involved in the pathophysiology of psychotic spectrum disorders (PSDs). However, data evaluating these hypotheses remain limited.

STUDY DESIGN

We applied a three-compartment intravoxel incoherent motion free water imaging (IVIM-FWI) technique that estimates the perfusion fraction (PF), free water fraction (FW), and anisotropic diffusion of tissue (FAt) to examine microvascular and microstructural changes in gray and white matter in 55 young adults with a PSD compared to 37 healthy controls (HCs).

STUDY RESULTS

We found significantly increased PF, FW, and FAt in gray matter regions, and significantly increased PF, FW, and decreased FAt in white matter regions in the PSD group versus HC. Furthermore, in patients, but not in the HC group, increased PF, FW, and FAt in gray matter and increased PF in white matter were significantly associated with poor performance on several cognitive tests assessing memory and processing speed. We additionally report significant associations between IVIM-FWI metrics and myo-inositol, choline, and N-acetylaspartic acid magnetic resonance spectroscopy imaging metabolites in the posterior cingulate cortex, which further supports the validity of PF, FW, and FAt as microvascular and microstructural biomarkers of PSD. Finally, we found significant relationships between IVIM-FWI metrics and the duration of psychosis in gray and white matter regions.

CONCLUSIONS

The three-compartment IVIM-FWI model provides metrics that are associated with cognitive deficits and may reflect disease progression.

Endophenotype trait domains for advancing gene discovery in autism spectrum disorder

Autism spectrum disorder (ASD) is associated with a diverse range of etiological processes, including both genetic and non-genetic causes. For a plurality of individuals with ASD, it is likely that the primary causes involve multiple common inherited variants that individually account for only small levels of variation in phenotypic outcomes. This genetic landscape creates a major challenge for detecting small but important pathogenic effects associated with ASD. To address similar challenges, separate fields of medicine have identified endophenotypes, or discrete, quantitative traits that reflect genetic likelihood for a particular clinical condition and leveraged the study of these traits to map polygenic mechanisms and advance more personalized therapeutic strategies for complex diseases. Endophenotypes represent a distinct class of biomarkers useful for understanding genetic contributions to psychiatric and developmental disorders because they are embedded within the causal chain between genotype and clinical phenotype, and they are more proximal to the action of the gene(s) than behavioral traits. Despite their demonstrated power for guiding new understanding of complex genetic structures of clinical conditions, few endophenotypes associated with ASD have been identified and integrated into family genetic studies. In this review, we argue that advancing knowledge of the complex pathogenic processes that contribute to ASD can be accelerated by refocusing attention toward identifying endophenotypic traits reflective of inherited mechanisms. This pivot requires renewed emphasis on study designs with measurement of familial co-variation including infant sibling studies, family trio and quad designs, and analysis of monozygotic and dizygotic twin concordance for select trait dimensions. We also emphasize that clarification of endophenotypic traits necessarily will involve integration of transdiagnostic approaches as candidate traits likely reflect liability for multiple clinical conditions and often are agnostic to diagnostic boundaries. Multiple candidate endophenotypes associated with ASD likelihood are described, and we propose a new focus on the analysis of "endophenotype trait domains" (ETDs), or traits measured across multiple levels (e.g., molecular, cellular, neural system, neuropsychological) along the causal pathway from genes to behavior. To inform our central argument for research efforts toward ETD discovery, we first provide a brief review of the concept of endophenotypes and their application to psychiatry. Next, we highlight key criteria for determining the value of candidate endophenotypes, including unique considerations for the study of ASD. Descriptions of different study designs for assessing endophenotypes in ASD research then are offered, including analysis of how select patterns of results may help prioritize candidate traits in future research. We also present multiple candidate ETDs that collectively cover a breadth of clinical phenomena associated with ASD, including social, language/communication, cognitive control, and sensorimotor processes. These ETDs are described because they represent promising targets for gene discovery related to clinical autistic traits, and they serve as models for analysis of separate candidate domains that may inform understanding of inherited etiological processes associated with ASD as well as overlapping neurodevelopmental disorders.

COMPLEXITY MEASURES OF PSYCHOTIC BRAIN ACTIVITY IN THE FMRI SIGNAL

When viewing the brain as a sophisticated, nonlinear dynamic system, employing complexity measures offers a valuable way to measure the intricate and dynamic aspects of spontaneous psychotic brain activity. These measures can help us identify irregularities and patterns in complex systems. In our study, we utilized fuzzy recurrence plots and sample entropy to evaluate the dynamic characteristics of psychiatric disorders. This assessment focused on understanding the temporal and spatial neural activity patterns, and more specifically, we applied complexity measures to investigate the functional connectivity within the psychotic brain. This involves understanding how different brain regions synchronize their activity, and complexity measures can reveal the patterns of these connections. It provides a means to understand how different brain regions interact and communicate under resting-state abnormal conditions. This study offers evidence demonstrating that fuzzy recurrence plots can serve as descriptors for functional connectivity and discusses their relevance to sample entropy in the context of the psychotic brain. In summary, complexity measures offer valuable insights that enrich our comprehension of atypical brain activity and the complexities present in the psychotic brain.

Double dissociation between P300 components and task switch error type in healthy but not psychosis participants

Event-related potentials (ERPs) during oddball tasks and the behavioral performance on the Penn Conditional Exclusion Task (PCET) measure context-appropriate responding: P300 ERPs to oddball targets reflect detection of input changes and context updating in working memory, and PCET performance indexes detection, adherence, and maintenance of mental set changes. More specifically, PCET variables quantify cognitive functions including inductive reasoning (set 1 completion), mental flexibility (perseverative errors), and working memory maintenance (regressive errors). Past research showed that both P300 ERPs and PCET performance are disrupted in psychosis. This study probed the possible neural correlates of 3 PCET abnormalities that occur in participants with psychosis via the overlapping cognitive demands of the two study paradigms. In a two-tiered analysis, psychosis (n = 492) and healthy participants (n = 244) were first divided based on completion of set 1 - which measures subjects' ability to use inductive reasoning to arrive at the correct set. Results showed that participants who failed set 1 produced lower parietal P300, independent of clinical status. In the second tier of analysis, a double dissociation was found among healthy set 1 completers: frontal P300 amplitudes were negatively associated with perseverative errors, and parietal P300 was negatively associated with regressive errors. In contrast, psychosis participants showed global P300 reductions regardless of PCET performance. From this we conclude that in psychosis, overall activations evoked by the oddball task are reduced while the cognitive functions required by PCET are still somewhat supported, showing some level of independence or compensatory physiology in psychosis between neural activities underlying the two tasks.

Brain Growth Charts for Quantitative Analysis of Pediatric Clinical Brain MRI Scans with Limited Imaging Pathology

Background Clinically acquired brain MRI scans represent a valuable but underused resource for investigating neurodevelopment due to their technical heterogeneity and lack of appropriate controls. These barriers have curtailed retrospective studies of clinical brain MRI scans compared with more costly prospectively acquired research-quality brain MRI scans. Purpose To provide a benchmark for neuroanatomic variability in clinically acquired brain MRI scans with limited imaging pathology (SLIPs) and to evaluate if growth charts from curated clinical MRI scans differed from research-quality MRI scans or were influenced by clinical indication for the scan. Materials and Methods In this secondary analysis of preexisting data, clinical brain MRI SLIPs from an urban pediatric health care system (individuals aged ≤22 years) were scanned across nine 3.0-T MRI scanners. The curation process included manual review of signed radiology reports and automated and manual quality review of images without gross pathology. Global and regional volumetric imaging phenotypes were measured using two image segmentation pipelines, and clinical brain growth charts were quantitatively compared with charts derived from a large set of research controls in the same age range by means of Pearson correlation and age at peak volume. Results The curated clinical data set included 532 patients (277 male; median age, 10 years [IQR, 5-14 years]; age range, 28 days after birth to 22 years) scanned between 2005 and 2020. Clinical brain growth charts were highly correlated with growth charts derived from research data sets (22 studies, 8346 individuals [4947 male]; age range, 152 days after birth to 22 years) in terms of normative developmental trajectories predicted by the models (median r = 0.979). Conclusion The clinical indication of the scans did not significantly bias the output of clinical brain charts. Brain growth charts derived from clinical controls with limited imaging pathology were highly correlated with brain charts from research controls, suggesting the potential of curated clinical MRI scans to supplement research data sets. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Ertl-Wagner and Pai in this issue.

Multimodal subspace independent vector analysis captures latent subspace structures in large multimodal neuroimaging studies

We present Multimodal Subspace Independent Vector Analysis (MSIVA), a methodology to capture both joint and unique vector sources across multiple data modalities by defining linked and modality-specific subspaces. In particular, MSIVA enables the estimation of independent subspaces of various sizes within modalities and their one-to-one linkage to corresponding subspaces across modalities. We compare MSIVA to a fully unimodal initialization baseline and a fully multimodal initialization baseline, and evaluate all three approaches with five distinct subspace structures on synthetic and neuroimaging datasets. We first demonstrate that MSIVA and the unimodal baseline can identify the correct ground-truth subspace structures from the incorrect ones in multiple synthetic datasets, while the multimodal baseline fails at detecting high-dimensional subspace structures. We then show that MSIVA can better capture the latent subspace structure with the minimum loss value in two large multimodal neuroimaging datasets compared to the unimodal baseline. Our results from subsequent per-subspace canonical correlation analysis (CCA) and brain-phenotype modeling demonstrate that the sources from the optimal subspace structure are strongly associated with phenotype measures, including age, sex and schizophrenia-related effects. Our proposed methodology MSIVA can be applied to capture linked and unique biomarkers from multimodal neuroimaging data.

Identifiability in Functional Connectivity May Unintentionally Inflate Prediction Results

Functional magnetic resonance (fMRI) is an invaluable tool in studying cognitive processes in vivo. Many recent studies use functional connectivity (FC), partial correlation connectivity (PC), or fMRI-derived brain networks to predict phenotypes with results that sometimes cannot be replicated. At the same time, FC can be used to identify the same subject from different scans with great accuracy. In this paper, we show a method by which one can unknowingly inflate classification results from 61% accuracy to 86% accuracy by treating longitudinal or contemporaneous scans of the same subject as independent data points. Using the UK Biobank dataset, we find one can achieve the same level of variance explained with 50 training subjects by exploiting identifiability as with 10,000 training subjects without double-dipping. We replicate this effect in four different datasets: the UK Biobank (UKB), the Philadelphia Neurodevelopmental Cohort (PNC), the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP), and an OpenNeuro Fibromyalgia dataset (Fibro). The unintentional improvement ranges between 7% and 25% in the four datasets. Additionally, we find that by using dynamic functional connectivity (dFC), one can apply this method even when one is limited to a single scan per subject. One major problem is that features such as ROIs or connectivities that are reported alongside inflated results may confuse future work. This article hopes to shed light on how even minor pipeline anomalies may lead to unexpectedly superb results.

Confidence, accuracy judgments and feedback in schizophrenia and bipolar disorder: a time series network analysis

BACKGROUND

Inaccurate self-assessment of performance is common among people with serious mental illness, and it is associated with poor functional outcomes independent from ability. However, the temporal interdependencies between judgments of performance, confidence in accuracy, and feedback about performance are not well understood.

METHODS

We evaluated two tasks: the Wisconsin Card Sorting Test (WCST) and the Penn Emotion recognition task (ER40). These tasks were modified to include item-by-item confidence and accuracy judgments, along with feedback on accuracy. We evaluated these tasks as time series and applied network modeling to understand the temporal relationships between momentary confidence, accuracy judgments, and feedback. The sample constituted participants with schizophrenia (SZ; N = 144), bipolar disorder (BD; N = 140), and healthy controls (HC; N = 39).

RESULTS

Network models for both WCST and ER40 revealed denser and lagged connections between confidence and accuracy judgments in SZ and, to a lesser extent in BD, that were not evidenced in HC. However, associations between feedback regarding accuracy with subsequent accuracy judgments and confidence were weaker in SZ and BD. In each of these comparisons, the BD group was intermediate between HC and SZ. In analyses of the WCST, wherein incorporating feedback is crucial for success, higher confidence predicted worse subsequent performance in SZ but not in HC or BD.

CONCLUSIONS

While network models are exploratory, the results suggest some potential mechanisms by which challenges in self-assessment may impede performance, perhaps through hyperfocus on self-generated judgments at the expense of incorporation of feedback.

Subfield-specific longitudinal changes of hippocampal volumes in patients with early-stage bipolar disorder

BACKGROUND

The hippocampus is a heterogeneous structure composed of biologically and functionally distinct subfields. Hippocampal aberrations are proposed to play a fundamental role in the etiology of psychotic symptoms. Bipolar disorder (BPD) has substantial overlap in symptomatology and genetic liability with schizophrenia (SZ), and reduced hippocampal volumes, particularly at the chronic illness stages, are documented in both disorders. Studies of hippocampal subfields in the early stage of BPD are limited and cross-sectional findings to date report no reduction in hippocampal volumes. To our knowledge, there have been no longitudinal studies of BPD evaluating hippocampal volumes in the early phase of illness. We investigated the longitudinal changes in hippocampal regions and subfields in BPD mainly and in early stage of psychosis (ESP) patients more broadly and compared them to those in controls (HC).

METHODS

Baseline clinical and structural MRI data were acquired from 88 BPD, from a total of 143 ESP patients, and 74 HCs. Of those, 66 participants (23 HC, 43 patients) completed a 12-month follow-up visit. The hippocampus regions and subfields were segmented using Freesurfer automated pipeline.

RESULTS

We found general baseline deficits in hippocampal volumes among BPD and ESP cohorts. Both cohorts displayed significant increases in the anterior hippocampal region and dentate gyrus compared with controls. Additionally, antipsychotic medications were positively correlated with the posterior region at baseline.

CONCLUSION

These findings highlight brain plasticity in BPD and in ESP patients providing evidence that deviations in hippocampal volumes are adaptive responses to atypical signaling rather than progressive degeneration.

Resting-state fMRI functional connectivity and clinical correlates in Afro-descendants with schizophrenia and bipolar disorder

Schizophrenia (SCZ) and bipolar disorder (BD) exhibited altered activation in several brain areas, including the prefrontal and temporal cortex; however, a less explored topic is how brain connectivity and functional disturbances occur in non-Caucasian samples of SCZ and BD. Individuals with SCZ (n=20), BD (n=21), and healthy controls (HC, n=21) from indigenous and African ethnicity were submitted to clinical screening and functional assessments. Mood, compulsive and psychotic symptoms were also correlated to network dysfunction in each group. Two distinct networks' subcomponents demonstrated significant lower global efficiency (GE) in SCZ versus HC, corresponding to left posterior dorsal attention and medial left ventral attention (VA) networks. Lower GE was found in BD versus controls in four subcomponents, including the left medial and right VA. Higher compulsion scores correlated in BD with lower GE in the left VA, whereas increased report of alcohol abuse was associated with higher GE in left default mode network. Although preliminary, differences in the activation of specific networks, notably the left hemisphere, in SCZ versus controls, and lower activation in VA areas, in BD versus controls. Results highlight default mode and salient network as relevant for the emotional processing of SCZ and BD of indigenous and black ethnicity. Abstract: schizophrenia, bipolar disorder, functional neuroimaging, ethnicity, default network.

Temporal imprecision and phase instability in schizophrenia resting state EEG

Schizophrenia is characterized by temporal imprecision and irregularities on neuronal, psychological cognitive, and behavioral levels which are usually tested during task-related activity. This leaves open whether analogous temporal imprecision and irregularities can already be observed in the brain's spontaneous activity as measured during the resting state; this is the goal of our study. Building on recent task-related data, we, using EEG, aimed to investigate the temporal precision and regularity of phase coherence over time in healthy, schizophrenia, and bipolar disorder participants. To this end, we developed a novel methodology, nominal frequency phase stability (NFPS), that allows to measure stability over phase angles in selected frequencies. By applying sample entropy quantification to the time-series of the nominal frequency phase angle time series, we found increased irregularities in theta activity over a frontocentral electrode in schizophrenia but not in bipolar disorder. We therefore assume that temporal imprecision and irregularity already occur in the brain's spontaneous activity in schizophrenia.

Identification of Homogeneous Subgroups from Resting-State fMRI Data

The identification of homogeneous subgroups of patients with psychiatric disorders can play an important role in achieving personalized medicine and is essential to provide insights for understanding neuropsychological mechanisms of various mental disorders. The functional connectivity profiles obtained from functional magnetic resonance imaging (fMRI) data have been shown to be unique to each individual, similar to fingerprints; however, their use in characterizing psychiatric disorders in a clinically useful way is still being studied. In this work, we propose a framework that makes use of functional activity maps for subgroup identification using the Gershgorin disc theorem. The proposed pipeline is designed to analyze a large-scale multi-subject fMRI dataset with a fully data-driven method, a new constrained independent component analysis algorithm based on entropy bound minimization (c-EBM), followed by an eigenspectrum analysis approach. A set of resting-state network (RSN) templates is generated from an independent dataset and used as constraints for c-EBM. The constraints present a foundation for subgroup identification by establishing a connection across the subjects and aligning subject-wise separate ICA analyses. The proposed pipeline was applied to a dataset comprising 464 psychiatric patients and discovered meaningful subgroups. Subjects within the identified subgroups share similar activation patterns in certain brain areas. The identified subgroups show significant group differences in multiple meaningful brain areas including dorsolateral prefrontal cortex and anterior cingulate cortex. Three sets of cognitive test scores were used to verify the identified subgroups, and most of them showed significant differences across subgroups, which provides further confirmation of the identified subgroups. In summary, this work represents an important step forward in using neuroimaging data to characterize mental disorders.

Psychotic Symptom, Mood, and Cognition-associated Multimodal MRI Reveal Shared Links to the Salience Network Within the Psychosis Spectrum Disorders

Schizophrenia (SZ), schizoaffective disorder (SAD), and psychotic bipolar disorder share substantial overlap in clinical phenotypes, associated brain abnormalities and risk genes, making reliable diagnosis among the three illness challenging, especially in the absence of distinguishing biomarkers. This investigation aims to identify multimodal brain networks related to psychotic symptom, mood, and cognition through reference-guided fusion to discriminate among SZ, SAD, and BP. Psychotic symptom, mood, and cognition were used as references to supervise functional and structural magnetic resonance imaging (MRI) fusion to identify multimodal brain networks for SZ, SAD, and BP individually. These features were then used to assess the ability in discriminating among SZ, SAD, and BP. We observed shared links to functional and structural covariation in prefrontal, medial temporal, anterior cingulate, and insular cortices among SZ, SAD, and BP, although they were linked with different clinical domains. The salience (SAN), default mode (DMN), and fronto-limbic (FLN) networks were the three identified multimodal MRI features within the psychosis spectrum disorders from psychotic symptom, mood, and cognition associations. In addition, using these networks, we can classify patients and controls and distinguish among SZ, SAD, and BP, including their first-degree relatives. The identified multimodal SAN may be informative regarding neural mechanisms of comorbidity for psychosis spectrum disorders, along with DMN and FLN may serve as potential biomarkers in discriminating among SZ, SAD, and BP, which may help investigators better understand the underlying mechanisms of psychotic comorbidity from three different disorders via a multimodal neuroimaging perspective.

Exploring the Hypothesis of a Schizophrenia and Bipolar Disorder Continuum: Biological, Genetic and Pharmacologic Data

Present time nosology has its roots in Kraepelin's demarcation of schizophrenia and bipolar disorder. However, accumulating evidence has shed light on several commonalities between the two disorders, and some authors have advocated for the consideration of a disease continuum. Here, we review previous genetic, biological and pharmacological findings that provide the basis for this conceptualization. There is a cross-disease heritability, and they share single-nucleotide polymorphisms in some common genes. EEG and imaging patterns have a number of similarities, namely reduced white matter integrity and abnormal connectivity. Dopamine, serotonin, GABA and glutamate systems have dysfunctional features, some of which are identical among the disorders. Finally, cellular calcium regulation and mitochondrial function are, also, impaired in the two.

Examination of the Relationship Between the Theory of Mind, Neurocognitive Functions and Thought-Language Features in the Schizophrenia and Bipolar Affective Disorder I Groups

OBJECTIVE

In this study, it was aimed to examine the relationship of ToM with face-emotion recognition, executive functions and thought disorders in the schizophrenia (SC) and bipolar affective disorder I (BAD I) groups.

METHOD

40 SC, 40 BAD I and 40 healthy control groups were included, matched for age, gender, and educational status. Dokuz Eylül Theory of Mind Scale (DEToMS)) and Reading Mind in Eyes (RMET) test, Benton face recognition test (BFRT), Ekman emotion recognition battery, Wisconsin Card Sorting Test (WCST), Stroop test, Thought and Language Index (TLI) were used. Positive and Negative Symptoms Rating Scale (PANNS) in the SZ group, Young Mania Rating Scale (YMRS) in the BAD I group were used.

RESULTS

The number of perseverative responses in WSCT was higher in the SC group than the BAD I group. Recognition of the fearful expression scores, DEToMS total and subscale scores except irony were higher and scores of TLI were lower in healthy controls more than patients group. Recognition of the fearful expression scores, DEToMS total and subscale scores except irony were higher and scores of TLI were lower in BAD I group more than SC group. There was no difference between SC and BAD I groups between BFRT, emotion recognition except fearful expression and RMET scores. The best predictors of DEToMS were executive functions and TLI total score in the SC group and was emotion recognition in the BAD I group. The best predictors of the RMET score were executive functions and emotion recognition for both groups.

CONCLUSION

Our findings suggest that social cognition remains a biomarker in patients with SZ and BAD I.

Towards mapping neuro-behavioral heterogeneity of psychedelic neurobiology in humans

Precision psychiatry aims to identify markers of inter-individual variability that allow predicting the right treatment for each patient. However, bridging the gap between molecular-level manipulations and neural systems-level functional alterations remains an unsolved problem in psychiatry. After decades of low success rates in pharmaceutical R&D for psychiatric drugs, multiple studies now point to the potential of psychedelics as a promising fast-acting and long-lasting treatment for some psychiatric symptoms. Yet, given the highly psychoactive nature of these substances, a precision medicine approach is essential to map the neural signals related to clinical efficacy in order to identify patients who can maximally benefit from this treatment. Recent studies have shown that bridging the gap between pharmacology, systems-level neural response in humans and individual experience is possible for psychedelic substances, therefore paving the way for a precision neuropsychiatric therapeutic development. Specifically, it has been shown that the integration of brain-wide PET or transcriptomic data, i.e. receptor distribution for the serotonin 2A receptor, with computational neuroimaging methods can simulate the effect of psychedelics on the human brain. These novel 'computational psychiatry' approaches allow for modeling inter-individual differences in neural as well as subjective effects of psychedelic substances. Collectively, this review provides a deep dive into psychedelic pharmaco-neuroimaging studies with a core focus on how recent computational psychiatry advances in biophysically based circuit modeling can be leveraged to predict individual responses. Finally, we emphasize the importance of human pharmacological neuroimaging for the continued precision therapeutic development of psychedelics.

Decreased basal ganglia and thalamic iron in early psychotic spectrum disorders are associated with increased psychotic and schizotypal symptoms

Iron deficits have been reported as a risk factor for psychotic spectrum disorders (PSD). However, examinations of brain iron in PSD remain limited. The current study employed quantitative MRI to examine iron content in several iron-rich subcortical structures in 49 young adult individuals with PSD (15 schizophrenia, 17 schizoaffective disorder, and 17 bipolar disorder with psychotic features) compared with 35 age-matched healthy controls (HC). A parametric approach based on a two-pool magnetization transfer model was applied to estimate longitudinal relaxation rate (R₁), which reflects both iron and myelin, and macromolecular proton fraction (MPF), which is specific to myelin. To describe iron content, a synthetic effective transverse relaxation rate (R₂*) was modeled using a linear fitting of R₁ and MPF. PSD patients compared to HC showed significantly reduced R₁ and synthetic R₂* across examined regions including the pallidum, ventral diencephalon, thalamus, and putamen areas. This finding was primarily driven by decreases in the subgroup with schizophrenia, followed by schizoaffective disorder. No significant group differences were noted for MPF between PSD and HC while for regional volume, significant reductions in patients were only observed in bilateral caudate, suggesting that R₁ and synthetic R₂* reductions in schizophrenia and schizoaffective patients likely reflect iron deficits that either occur independently or precede structural and myelin changes. Subcortical R₁ and synthetic R₂* were also found to be inversely related to positive symptoms within the PSD group and to schizotypal traits across the whole sample. These findings that decreased iron in subcortical regions are associated with PSD risk and symptomatology suggest that brain iron deficiencies may play a role in PSD pathology and warrant further study.

A novel neighborhood rough set-based feature selection method and its application to biomarker identification of schizophrenia

Feature selection can disclose biomarkers of mental disorders that have unclear biological mechanisms. Although neighborhood rough set (NRS) has been applied to discover important sparse features, it has hardly ever been utilized in neuroimaging-based biomarker identification, probably due to the inadequate feature evaluation metric and incomplete information provided under a single-granularity. Here, we propose a new NRS-based feature selection method and successfully identify brain functional connectivity biomarkers of schizophrenia (SZ) using functional magnetic resonance imaging (fMRI) data. Specifically, we develop a new weighted metric based on NRS combined with information entropy to evaluate the capacity of features in distinguishing different groups. Inspired by multi-granularity information maximization theory, we further take advantage of the complementary information from different neighborhood sizes via a multi-granularity fusion to obtain the most discriminative and stable features. For validation, we compare our method with six popular feature selection methods using three public omics datasets as well as resting-state fMRI data of 393 SZ patients and 429 healthy controls. Results show that our method obtained higher classification accuracies on both omics data (100.0%, 88.6%, and 72.2% for three omics datasets, respectively) and fMRI data (93.9% for main dataset, and 76.3% and 83.8% for two independent datasets, respectively). Moreover, our findings reveal biologically meaningful substrates of SZ, notably involving the connectivity between the thalamus and superior temporal gyrus as well as between the postcentral gyrus and calcarine gyrus. Taken together, we propose a new NRS-based feature selection method that shows the potential of exploring effective and sparse neuroimaging-based biomarkers of mental disorders.

Identifying transdiagnostic biological subtypes across schizophrenia, bipolar disorder, and major depressive disorder based on lipidomics profiles

Emerging evidence has demonstrated overlapping biological abnormalities underlying schizophrenia (SCZ), bipolar disorder (BP), and major depressive disorder (MDD); these overlapping abnormalities help explain the high heterogeneity and the similarity of patients within and among diagnostic categories. This study aimed to identify transdiagnostic subtypes of these psychiatric disorders based on lipidomics abnormalities. We performed discriminant analysis to identify lipids that classified patients (N = 349, 112 with SCZ, 132 with BP, and 105 with MDD) and healthy controls (N = 198). Ten lipids that mainly regulate energy metabolism, inflammation, oxidative stress, and fatty acylation of proteins were identified. We found two subtypes (named Cluster 1 and Cluster 2 subtypes) across patients with SCZ, BP, and MDD by consensus clustering analysis based on the above 10 lipids. The distribution of clinical diagnosis, functional impairment measured by Global Assessment of Functioning (GAF) scales, and brain white matter abnormalities measured by fractional anisotropy (FA) and radial diffusivity (RD) differed in the two subtypes. Patients within the Cluster 2 subtype were mainly SCZ and BP patients and featured significantly elevated RD along the genu of corpus callosum (GCC) region and lower GAF scores than patients within the Cluster 1 subtype. The SCZ and BP patients within the Cluster 2 subtype shared similar biological patterns; that is, these patients had comparable brain white matter abnormalities and functional impairment, which is consistent with previous studies. Our findings indicate that peripheral lipid abnormalities might help identify homogeneous transdiagnostic subtypes across psychiatric disorders.

Sex-Specific Changes in Choroid Vasculature Among Patients with Schizophrenia and Bipolar Disorder

Purpose

While structural changes within the retina of psychosis patients have been established, no detailed studies of choroidal microvasculature in these patients have been performed. Given evidence of microvascular disruption in psychosis patients, this study sought to determine whether there exists evidence of microvascular disruption in the choroids in these patients.

Methods

Fifty-six subjects (20 controls and 36 psychosis patients) were recruited from April 2018 to February 2020. Five were excluded due to imaging artifact or missing demographic information. Swept-source optical coherence tomography angiography (SS-OCTA) images were obtained. Choroid vascular enface images (12 mm × 9mm) were exported every 2.6 μm from Bruch’s membrane to the choroid–scleral interface from Topcon to ImageJ. The images were binarized using Otsu’s method, signal from the optic disk and retinal vasculature was removed, and average choroid vascular density (CVD) was calculated as the average of percent area occupied by choroidal vasculature across images in the stack. Choroid vascular volume (CVV) was calculated as the CVD multiplied by maximum CT and image area. During image analysis, study staff were blinded to the phenotype of the study subjects.

Results

Compared with same-sex controls, male psychiatric patients had significantly lower CVD. Compared with same-sex controls, female psychiatric patients had significantly lower maximum CT with correspondingly decreased CVV, after adjusting for age. When all psychiatric patients were compared with all healthy controls, no significant differences in CT, CVD, or CVV were noted.

Conclusion

These results suggest that the pathogenesis of psychotic illness affects choroidal microvasculature in a sex-specific manner.

Default mode network dynamic functional network connectivity predicts psychotic symptom severity

Neuropsychiatric disorders affect millions of people worldwide every year. Recent studies showed that the symptomatic overlaps across neuropsychiatric disorders mislead schizophrenia and bipolar disorder diagnosis. Additionally, recent studies claimed that schizoaffective disorder as a condition overlapped with both schizophrenia and bipolar disorder. Since symptomatic overlap among these disorders causes misdiagnosis, a need for neuroimaging biomarkers differentiating these disorders for a more accurate diagnosis is crucial. This study investigates dynamics functional network connectivity (dFNC) in the default mode network (DMN) of schizophrenia, bipolar, and schizoaffective disorder patients and compares them with their relative and healthy control. Additionally, it explored whether DMN dFNC features can predict the symptom severity of these neuropsychiatric disorders. Here, we found that dFNC features can differentiate schizophrenia from bipolar disorder. At the same time, we did not see a significant difference between schizoaffective with other conditions. Additionally, we found dFNC features can predict symptom severity of these three conditions.

Mapping relationships among schizophrenia, bipolar and schizoaffective disorders: A deep classification and clustering framework using fMRI time series

BACKGROUND

Psychiatric disorders are categorized using self-report and observational information rather than biological data. There is also considerable symptomatic overlap between different types of psychiatric disorders, which makes diagnostic categorization and multi-class classification challenging.

METHODS

In this work, we propose a unified framework for supervised classification and unsupervised clustering of psychotic disorders using brain imaging data. A new multi-scale recurrent neural network (MsRNN) model was developed and applied to fMRI time courses (TCs) for multi-class classification. The high-level representations of the original TCs were then submitted to a tSNE clustering model for visualizing the group differences between disorders. A leave-one-feature-out approach was used for disorder-related biomarker identification.

RESULTS

When studying fMRI from schizophrenia, psychotic bipolar disorder, schizoaffective disorder, and healthy individuals, the accuracy of a 4-class classification reached 46%, significantly above chance. The hippocampus, supplementary motor area and paracentral lobule were discovered as the most contributing regional TCs in the multi-class classification. Beyond this, visualization of the tSNE clustering suggested that the disease severity can be captured and schizoaffective disorder (SAD) may be separated into two subtypes. SAD cluster1 has significantly higher Positive And Negative Syndrome Scale (PANSS) scores than SAD cluster2 in PANSS negative2 (emotional withdrawal), general2 (anxiety), general3 (guilt feelings), general4 (tension).

CONCLUSIONS

The proposed deep classification and clustering framework is not only able to identify psychiatric disorders with high accuracy, but also interpret the correlation between brain networks and specific psychiatric disorders, and reveal the relationship between them. This work provides a promising way to investigate a spectrum of similar disorders using neuroimaging-based measures.

Searching for Imaging Biomarkers of Psychotic Dysconnectivity

BACKGROUND

Progress in precision psychiatry is predicated on identifying reliable individual-level diagnostic biomarkers. For psychosis, measures of structural and functional connectivity could be promising biomarkers given consistent reports of dysconnectivity across psychotic disorders using magnetic resonance imaging.

METHODS

We leveraged data from four independent cohorts of patients with psychosis and control subjects with observations from approximately 800 individuals. We used group-level analyses and two supervised machine learning algorithms (support vector machines and ridge regression) to test within-, between-, and across-sample classification performance of white matter and resting-state connectivity metrics.

RESULTS

Although we replicated group-level differences in brain connectivity, individual-level classification was suboptimal. Classification performance within samples was variable across folds (highest area under the curve [AUC] range = 0.30) and across datasets (average support vector machine AUC range = 0.50; average ridge regression AUC range = 0.18). Classification performance between samples was similarly variable or resulted in AUC values of approximately 0.65, indicating a lack of model generalizability. Furthermore, collapsing across samples (resting-state functional magnetic resonance imaging, N = 888; diffusion tensor imaging, N = 860) did not improve model performance (maximal AUC = 0.67). Ridge regression models generally outperformed support vector machine models, although classification performance was still suboptimal in terms of clinical relevance. Adjusting for demographic covariates did not greatly affect results.

CONCLUSIONS

Connectivity measures were not suitable as diagnostic biomarkers for psychosis as assessed in this study. Our results do not negate that other approaches may be more successful, although it is clear that a systematic approach to individual-level classification with large independent validation samples is necessary to properly vet neuroimaging features as diagnostic biomarkers.

Sensory gating is related to positive and disorganised schizotypy in contrast to smooth pursuit eye movements and latent inhibition

Since the characteristics and symptoms of both schizophrenia and schizotypy are manifested heterogeneously, it is possible that different endophenotypes and neurophysiological measures (sensory gating and smooth pursuit eye movement errors) represent different clusters of symptoms. Participants (N = 205) underwent a standard conditioned-pairing paradigm to establish their sensory gating ratio, a smooth-pursuit eye-movement task, a latent inhibition task, and completed the Schizotypal Personality Questionnaire. A Multidimensional Scaling analysis revealed that sensory gating was related to positive and disorganised dimensions of schizotypy. Latent inhibition and prepulse inhibition were not related to any dimension of schizotypy. Smooth pursuit eye movement error was unrelated to sensory gating and latent inhibition, but was related to negative dimensions of schizotypy. Our findings suggest that the symptom clusters associated with two main endophenotypes are largely independent. To fully understand symptomology and outcomes of schizotypal traits, the different subtypes of schizotypy (and potentially, schizophrenia) ought to be considered separately rather than together.