Classifying individuals at high-risk for psychosis based on functional brain activity during working memory processing

Unveiling the potential of machine learning in schizophrenia diagnosis: A meta-analytic study of task-based neuroimaging data

The emergence of machine learning (ML) techniques has opened up new avenues for identifying biomarkers associated with schizophrenia (SCZ) using task-related fMRI (t-fMRI) designs. To evaluate the effectiveness of this approach, we conducted a comprehensive meta-analysis of 31 t-fMRI studies using a bivariate model. Our findings revealed a high overall sensitivity of 0.83 and specificity of 0.82 for t-fMRI studies. Notably, neuropsychological domains modulated the classification performance, with selective attention demonstrating a significantly higher specificity than working memory (β = 0.98, z = 2.11, P = 0.04). Studies involving older, chronic patients with SCZ reported higher sensitivity (P <0.015) and specificity (P <0.001) than those involving younger, first-episode patients or high-risk individuals for psychosis. Additionally, we found that the severity of negative symptoms was positively associated with the specificity of the classification model (β = 7.19, z = 2.20, P = 0.03). Taken together, these results support the potential of using task-based fMRI data in combination with machine learning techniques to identify biomarkers related to symptom outcomes in SCZ, providing a promising avenue for improving diagnostic accuracy and treatment efficacy. Future attempts to deploy ML classification should consider the factors of algorithm choice, data quality and quantity, as well as issues related to generalization.

A Review of Potential Neuroimaging Biomarkers of Schizophrenia-Risk

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

Precuneus and insular hypoactivation during cognitive processing in first-episode psychosis: Systematic review and meta-analysis of fMRI studies

INTRODUCTION

The neural correlates of the cognitive dysfunction in first-episode psychosis (FEP) are still unclear. The present review and meta-analysis provide an update of the location of the abnormalities in the fMRI-measured brain response to cognitive processes in individuals with FEP.

METHODS

Systematic review and voxel-based meta-analysis of cross-sectional fMRI studies comparing neural responses to cognitive tasks between individuals with FEP and healthy controls (HC) according to PRISMA guidelines.

RESULTS

Twenty-six studies were included, comprising 598 individuals with FEP and 567 HC. Individual studies reported statistically significant hypoactivation in the dorsolateral prefrontal cortex (6 studies), frontal lobe (8 studies), cingulate (6 studies) and insula (5 studies). The meta-analysis showed statistically significant hypoactivation in the left anterior insula, precuneus and bilateral striatum.

CONCLUSIONS

While the studies tend to highlight frontal hypoactivation during cognitive tasks in FEP, our meta-analytic results show that the left precuneus and insula primarily display aberrant activation in FEP that may be associated with salience attribution to external stimuli and related to deficits in perception and regulation.

Functional imaging in youth at risk for transdiagnostic serious mental illness: Initial results from the PROCAN study

BACKGROUND

In their early stages, serious mental illnesses (SMIs) are often indistinguishable from one another, suggesting that studying alterations in brain activity in a transdiagnostic fashion could help to understand the neurophysiological origins of different SMI. The purpose of this study was to examine brain activity in youth at varying stages of risk for SMI using functional magnetic resonance imaging tasks (fMRI) that engage brain systems believed to be affected.

METHODS

Two hundred and forty three participants at different stages of risk for SMI were recruited to the Canadian Psychiatric Risk and Outcome (PROCAN) study, however only 179 were scanned. Stages included asymptomatic participants at no elevated risk, asymptomatic participants at elevated risk due to family history, participants with undifferentiated general symptoms of mental illness, and those experiencing attenuated versions of diagnosable psychiatric illnesses. The fMRI tasks included: (1) a monetary incentive delay task; (2) an emotional Go-NoGo and (3) an n-back working memory task.

RESULTS

Strong main effects with each of the tasks were found in brain regions previously described in the literature. However, there were no significant differences in brain activity between any of the stages of risk for SMI for any of the task contrasts, after accounting for site, sex and age. Furthermore, results indicated no significant differences even when participants were dichotomized as asymptomatic or symptomatic.

CONCLUSIONS

These results suggest that univariate BOLD responses during typical fMRI tasks are not sensitive markers of SMI risk and that further study, particularly longitudinal designs, will be necessary to understand brain changes underlying the early stages of SMI.

Cost-effectiveness of neuroimaging technologies in management of psychiatric and insomnia disorders: A meta-analysis and prospective cost analysis

BACKGROUND

The optimal diagnostic strategy for patients with psychiatric and insomnia disorders has not been established yet.

PURPOSE

The purpose of this study was to perform cost-effectiveness analysis of six neuroimaging technologies in diagnosis of patients with psychiatric and insomnia disorders.

METHODS

An economic evaluation study was conducted in three parts, including a systematic review for determining diagnostic accuracy, a descriptive cross-sectional study with Activity-Based Costing (ABC) technique for tracing resource consumption, and a cost-effectiveness analysis using a short-term decision-analytic model.

RESULTS

In the first phase, 93 diagnostic accuracy studies were included in the systematic review. The accumulated results (meta-analysis) showed that the highest diagnostic accuracy for psychiatric and insomnia disorders was attributed to PET (sensitivity of 90% and specificity of 80%) and MRI (sensitivity of 76% and specificity of 78%) respectively. In the second phase of the study, we calculated the cost of each technology. The results showed that MRI has the lowest cost. Based on the results in the model of cost-effectiveness sMRI ($ 50.08 per accurate diagnosis) and MRI ($ 58.54 per accurate diagnosis) were more cost-effective neuroimaging technologies.

CONCLUSION

In psychiatric disorders, no single strategy was characterized by both low cost and high accuracy. However, MRI and PET scan had lower cost and higher accuracy for psychiatric disorders, respectively. MRI was the least costly with the highest diagnostic accuracy in insomnia disorders. Based on our model, sMRI in psychiatric disorders and MRI in insomnia disorders were the most cost-effective technologies.

Precuneus and insular hypoactivation during cognitive processing in first-episode psychosis: Systematic review and meta-analysis of fMRI studies

INTRODUCTION

The neural correlates of the cognitive dysfunction in first-episode psychosis (FEP) are still unclear. The present review and meta-analysis provide an update of the location of the abnormalities in the fMRI-measured brain response to cognitive processes in individuals with FEP.

METHODS

Systematic review and voxel-based meta-analysis of cross-sectional fMRI studies comparing neural responses to cognitive tasks between individuals with FEP and healthy controls (HC) according to PRISMA guidelines.

RESULTS

Twenty-six studies were included, comprising 598 individuals with FEP and 567 HC. Individual studies reported statistically significant hypoactivation in the dorsolateral prefrontal cortex (6 studies), frontal lobe (8 studies), cingulate (6 studies) and insula (5 studies). The meta-analysis showed statistically significant hypoactivation in the left anterior insula, precuneus and bilateral striatum.

CONCLUSIONS

While the studies tend to highlight frontal hypoactivation during cognitive tasks in FEP, our meta-analytic results show that the left precuneus and insula primarily display aberrant activation in FEP that may be associated with salience attribution to external stimuli and related to deficits in perception and regulation.

Individualized Diagnostic and Prognostic Models for Patients With Psychosis Risk Syndromes: A Meta-analytic View on the State of the Art

BACKGROUND

The clinical high risk (CHR) paradigm has facilitated research into the underpinnings of help-seeking individuals at risk for developing psychosis, aiming at predicting and possibly preventing transition to the overt disorder. Statistical methods such as machine learning and Cox regression have provided the methodological basis for this research by enabling the construction of diagnostic models (i.e., distinguishing CHR individuals from healthy individuals) and prognostic models (i.e., predicting a future outcome) based on different data modalities, including clinical, neurocognitive, and neurobiological data. However, their translation to clinical practice is still hindered by the high heterogeneity of both CHR populations and methodologies applied.

METHODS

We systematically reviewed the literature on diagnostic and prognostic models built on Cox regression and machine learning. Furthermore, we conducted a meta-analysis on prediction performances investigating heterogeneity of methodological approaches and data modality.

RESULTS

A total of 44 articles were included, covering 3707 individuals for prognostic studies and 1052 individuals for diagnostic studies (572 CHR patients and 480 healthy control subjects). CHR patients could be classified against healthy control subjects with 78% sensitivity and 77% specificity. Across prognostic models, sensitivity reached 67% and specificity reached 78%. Machine learning models outperformed those applying Cox regression by 10% sensitivity. There was a publication bias for prognostic studies yet no other moderator effects.

CONCLUSIONS

Our results may be driven by substantial clinical and methodological heterogeneity currently affecting several aspects of the CHR field and limiting the clinical implementability of the proposed models. We discuss conceptual and methodological harmonization strategies to facilitate more reliable and generalizable models for future clinical practice.

Multivariate classification of schizophrenia and its familial risk based on load-dependent attentional control brain functional connectivity

Patients with schizophrenia (SCZ), as well as their unaffected siblings (SIB), show functional connectivity (FC) alterations during performance of tasks involving attention. As compared with SCZ, these alterations are present in SIB to a lesser extent and are more pronounced during high cognitive demand, thus possibly representing one of the pathways in which familial risk is translated into the SCZ phenotype. Our aim is to measure the separability of SCZ and SIB from healthy controls (HC) using attentional control-dependent FC patterns, and to test to which extent these patterns span a continuum of neurofunctional alterations between HC and SCZ. 65 SCZ with 65 age and gender-matched HC and 39 SIB with 39 matched HC underwent the Variable Attentional Control (VAC) task. Load-dependent connectivity matrices were generated according to correct responses in each VAC load. Classification performances of high, intermediate and low VAC load FC on HC-SCZ and HC-SIB cohorts were tested through machine learning techniques within a repeated nested cross-validation framework. HC-SCZ classification models were applied to the HC-SIB cohort, and vice-versa. A high load-related decreased FC pattern discriminated between HC and SCZ with 66.9% accuracy and with 57.7% accuracy between HC and SIB. A high load-related increased FC network separated SIB from HC (69.6% accuracy), but not SCZ from HC (48.5% accuracy). Our findings revealed signatures of attentional FC abnormalities shared by SCZ and SIB individuals. We also found evidence for potential, SIB-specific FC signature, which may point to compensatory neurofunctional mechanisms in persons at familial risk for schizophrenia.

Social cognitive endophenotypes in schizophrenia: A study comparing first episode schizophrenia patients and, individuals at clinical- and familial- 'at-risk' for psychosis

Impairments in specific domains of social cognition have been suggested as possible endophenotypes for schizophrenia and clinical markers for accurate identification of 'at-risk' (AR) states. Aim of the present study was to find out whether performance on social cognition tasks will distinguish 'clinical at-risk (CAR)' and 'familial at-risk (FAR)' individuals from remitted first episode schizophrenia (FES) patients and healthy controls. Fifty in each of these four groups were included for analysis. Schizophrenia psychopathology in FES group was assessed using the Positive and Negative Syndrome Scale (PANSS). Theory of mind (ToM; first and second order (SOT and FOT), and faux pas composite (FPC)), attributional bias (AB) and social perception (SP) were assessed using the Social Cognition Rating Tool in Indian Setting (SOCRATIS). Facial emotion recognition task was used to assess emotional-expression recognition (ER). Significant differences in ToM, SP and ER between the four groups were found, even after controlling for performance on various neurocognitive tasks. ToM and SP were identified to follow an endophenotype pattern. While, both ToM and SP classified FES from healthy with large accuracy rates, SP, specifically, distinguished at-risk from disease groups. None of the social cognitive domains accurately classified familial at-risk from clinical at-risk groups. We conclude that social cognitive measures may be used as reliable endophenotype markers for schizophrenia and its sub-domains may be used for valid identification of AR individuals.

Selective Review of Neuroimaging Findings in Youth at Clinical High Risk for Psychosis: On the Path to Biomarkers for Conversion

First episode psychosis (FEP), and subsequent diagnosis of schizophrenia or schizoaffective disorder, predominantly occurs during late adolescence, is accompanied by a significant decline in function and represents a traumatic experience for patients and families alike. Prior to first episode psychosis, most patients experience a prodromal period of 1-2 years, during which symptoms first appear and then progress. During that time period, subjects are referred to as being at Clinical High Risk (CHR), as a prodromal period can only be designated in hindsight in those who convert. The clinical high-risk period represents a critical window during which interventions may be targeted to slow or prevent conversion to psychosis. However, only one third of subjects at clinical high risk will convert to psychosis and receive a formal diagnosis of a primary psychotic disorder. Therefore, in order for targeted interventions to be developed and applied, predicting who among this population will convert is of critical importance. To date, a variety of neuroimaging modalities have identified numerous differences between CHR subjects and healthy controls. However, complicating attempts at predicting conversion are increasingly recognized co-morbidities, such as major depressive disorder, in a significant number of CHR subjects. The result of this is that phenotypes discovered between CHR subjects and healthy controls are likely non-specific to psychosis and generalized for major mental illness. In this paper, we selectively review evidence for neuroimaging phenotypes in CHR subjects who later converted to psychosis. We then evaluate the recent landscape of machine learning as it relates to neuroimaging phenotypes in predicting conversion to psychosis.

Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual

Schizophrenia is a severe psychiatric disorder associated with both structural and functional brain abnormalities. In the past few years, there has been growing interest in the application of machine learning techniques to neuroimaging data for the diagnostic and prognostic assessment of this disorder. However, the vast majority of studies published so far have used either structural or functional neuroimaging data, without accounting for the multimodal nature of the disorder. Structural MRI and resting‐state functional MRI data were acquired from a total of 295 patients with schizophrenia and 452 healthy controls at five research centers. We extracted features from the data including gray matter volume, white matter volume, amplitude of low‐frequency fluctuation, regional homogeneity and two connectome‐wide based metrics: structural covariance matrices and functional connectivity matrices. A support vector machine classifier was trained on each dataset separately to distinguish the subjects at individual level using each of the single feature as well as their combination, and 10‐fold cross‐validation was used to assess the performance of the model. Functional data allow higher accuracy of classification than structural data (mean 82.75% vs. 75.84%). Within each modality, the combination of images and matrices improves performance, resulting in mean accuracies of 81.63% for structural data and 87.59% for functional data. The use of all combined structural and functional measures allows the highest accuracy of classification (90.83%). We conclude that combining multimodal measures within a single model is a promising direction for developing biologically informed diagnostic tools in schizophrenia.

Machine learning in mental health: a scoping review of methods and applications

BACKGROUND

This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.

METHODS

We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.

RESULTS

Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.

CONCLUSIONS

Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

Models of persecutory delusions: a mechanistic insight into the early stages of psychosis

Identifying robust markers for predicting the onset of psychosis has been a key challenge for early detection research. Persecutory delusions are core symptoms of psychosis, and social cognition is particularly impaired in first-episode psychosis patients and individuals at risk for developing psychosis. Here, we propose new avenues for translation provided by hierarchical Bayesian models of behaviour and neuroimaging data applied in the context of social learning to target persecutory delusions. As it comprises a mechanistic model embedded in neurophysiology, the findings of this approach may shed light onto inference and neurobiological causes of transition to psychosis.

Basic Self-Disturbances Related to Reduced Anterior Cingulate Volume in Subjects at Ultra-High Risk for Psychosis

Introduction: Alterations of the "pre-reflective" sense of first-person perspective (e.g., of the "basic self") are characteristic features of schizophrenic spectrum disorders and are significantly present in the prodromal phase of psychosis and in subjects at ultra-high risk for psychosis (UHR). Studies in healthy controls suggest that neurobiological substrate of the basic self involves cortical midline structures, such as the anterior and posterior cingulate cortices. Neuroimaging studies have identified neuroanatomical cortical midline structure abnormalities in schizophrenic spectrum disorders. Objectives: i) To compare basic self-disturbances levels in UHR subjects and controls and ii) to assess the relationship between basic self-disturbances and alterations in cortical midline structures volume in UHR subjects. Methods: Thirty-one UHR subjects (27 antipsychotic-naïve) and 16 healthy controls were assessed using the 57-item semistructured Examination of Anomalous Self-Experiences (EASE) interview. All subjects were scanned using magnetic resonance imaging (MRI) at 3 T, and gray matter volume was measured in a priori defined regions of interest (ROIs) in the cortical midline structures. Results: EASE scores were much higher in UHR subjects than controls (p < 0.001). The UHR group had smaller anterior cingulate volume than controls (p = 0.037). There were no structural brain imaging alterations between UHR individuals with or without self-disturbances. Within the UHR sample, the subgroup with higher EASE scores had smaller anterior cingulate volumes than UHR subjects with lower EASE scores and controls (p = 0.018). In the total sample, anterior cingulate volume was inversely correlated with the EASE score (R = 0.52, p < 0.016). Conclusions: Basic self-disturbances in UHR subjects appear to be related to reductions in anterior cingulate volume.

Is It Possible to Predict the Future in First-Episode Psychosis?

The outcome of first-episode psychosis (FEP) is highly variable, ranging from early sustained recovery to antipsychotic treatment resistance from the onset of illness. For clinicians, a possibility to predict patient outcomes would be highly valuable for the selection of antipsychotic treatment and in tailoring psychosocial treatments and psychoeducation. This selective review summarizes current knowledge of prognostic markers in FEP. We sought potential outcome predictors from clinical and sociodemographic factors, cognition, brain imaging, genetics, and blood-based biomarkers, and we considered different outcomes, like remission, recovery, physical comorbidities, and suicide risk. Based on the review, it is currently possible to predict the future for FEP patients to some extent. Some clinical features-like the longer duration of untreated psychosis (DUP), poor premorbid adjustment, the insidious mode of onset, the greater severity of negative symptoms, comorbid substance use disorders (SUDs), a history of suicide attempts and suicidal ideation and having non-affective psychosis-are associated with a worse outcome. Of the social and demographic factors, male gender, social disadvantage, neighborhood deprivation, dysfunctional family environment, and ethnicity may be relevant. Treatment non-adherence is a substantial risk factor for relapse, but a small minority of patients with acute onset of FEP and early remission may benefit from antipsychotic discontinuation. Cognitive functioning is associated with functional outcomes. Brain imaging currently has limited utility as an outcome predictor, but this may change with methodological advancements. Polygenic risk scores (PRSs) might be useful as one component of a predictive tool, and pharmacogenetic testing is already available and valuable for patients who have problems in treatment response or with side effects. Most blood-based biomarkers need further validation. None of the currently available predictive markers has adequate sensitivity or specificity used alone. However, personalized treatment of FEP will need predictive tools. We discuss some methodologies, such as machine learning (ML), and tools that could lead to the improved prediction and clinical utility of different prognostic markers in FEP. Combination of different markers in ML models with a user friendly interface, or novel findings from e.g., molecular genetics or neuroimaging, may result in computer-assisted clinical applications in the near future.

Abnormal neural hierarchy in processing of verbal information in patients with schizophrenia

Previous research indicates abnormal comprehension of verbal information in patients with schizophrenia. Yet the neural mechanism underlying the breakdown of verbal information processing in schizophrenia is poorly understood. Imaging studies in healthy populations have shown a network of brain areas involved in hierarchical processing of verbal information over time. Here, we identified critical aspects of this hierarchy, examining patients with schizophrenia. Using functional magnetic resonance imaging, we examined various levels of information comprehension elicited by naturally presented verbal stimuli; from a set of randomly shuffled words to an intact story. Specifically, patients with first episode schizophrenia (N = 15), their non-manifesting siblings (N = 14) and healthy controls (N = 15) listened to a narrated story and randomly scrambled versions of it. To quantify the degree of dissimilarity between the groups, we adopted an inter-subject correlation (inter-SC) approach, which estimates differences in synchronization of neural responses within and between groups. The temporal topography found in healthy and siblings groups were consistent with our previous findings - high synchronization in responses from early sensory toward high order perceptual and cognitive areas. In patients with schizophrenia, stimuli with short and intermediate temporal scales evoked a typical pattern of reliable responses, whereas story condition (long temporal scale) revealed robust and widespread disruption of the inter-SCs. In addition, the more similar the neural activity of patients with schizophrenia was to the average response in the healthy group, the less severe the positive symptoms of the patients. Our findings suggest that system-level neural indication of abnormal verbal information processing in schizophrenia reflects disease manifestations.

Identifying Individuals at High Risk of Psychosis: Predictive Utility of Support Vector Machine using Structural and Functional MRI Data

The identification of individuals at high risk of developing psychosis is entirely based on clinical assessment, associated with limited predictive potential. There is, therefore, increasing interest in the development of biological markers that could be used in clinical practice for this purpose. We studied 25 individuals with an at-risk mental state for psychosis and 25 healthy controls using structural MRI, and functional MRI in conjunction with a verbal memory task. Data were analyzed using a standard univariate analysis, and with support vector machine (SVM), a multivariate pattern recognition technique that enables statistical inferences to be made at the level of the individual, yielding results with high translational potential. The application of SVM to structural MRI data permitted the identification of individuals at high risk of psychosis with a sensitivity of 68% and a specificity of 76%, resulting in an accuracy of 72% (p < 0.001). Univariate volumetric between-group differences did not reach statistical significance. By contrast, the univariate fMRI analysis identified between-group differences (p < 0.05 corrected), while the application of SVM to the same data did not. Since SVM is well suited at identifying the pattern of abnormality that distinguishes two groups, whereas univariate methods are more likely to identify regions that individually are most different between two groups, our results suggest the presence of focal functional abnormalities in the context of a diffuse pattern of structural abnormalities in individuals at high clinical risk of psychosis.