Imaging and Genetic Biomarkers Predicting Transition to Psychosis

Sex- and Age-Specific Deviations in Cerebellar Structure and Their Link With Symptom Dimensions and Clinical Outcome in Individuals at Clinical High Risk for Psychosis

BACKGROUND

The clinical high-risk (CHR) period offers a temporal window into neurobiological deviations preceding psychosis onset, but little attention has been given to regions outside the cerebrum in large-scale studies of CHR. Recently, the North American Prodrome Longitudinal Study (NAPLS)-2 revealed altered functional connectivity of the cerebello-thalamo-cortical circuitry among individuals at CHR; however, cerebellar morphology remains underinvestigated in this at-risk population, despite growing evidence of its involvement in psychosis.

STUDY DESIGN

In this multisite study, we analyzed T1-weighted magnetic resonance imaging scans obtained from N = 469 CHR individuals (61% male, ages = 12-36 years) and N = 212 healthy controls (52% male, ages = 12-34 years) from NAPLS-2, with a focus on cerebellar cortex and white matter volumes separately. Symptoms were rated by the Structured Interview for Psychosis-Risk Syndromes (SIPS). The outcome by two-year follow-up was categorized as in-remission, symptomatic, prodromal-progression, or psychotic. General linear models were used for case-control comparisons and tests for volumetric associations with baseline SIPS ratings and clinical outcomes.

STUDY RESULTS

Cerebellar cortex and white matter volumes differed between the CHR and healthy control groups at baseline, with sex moderating the difference in cortical volumes, and both sex and age moderating the difference in white matter volumes. Baseline ratings for major psychosis-risk dimensions as well as a clinical outcome at follow-up had tissue-specific associations with cerebellar volumes.

CONCLUSIONS

These findings point to clinically relevant deviations in cerebellar cortex and white matter structures among CHR individuals and highlight the importance of considering the complex interplay between sex and age when studying the neuromaturational substrates of psychosis risk.

Psychotic disorders as a framework for precision psychiatry

People with psychotic disorders can show marked interindividual variations in the onset of illness, responses to treatment and relapse, but they receive broadly similar clinical care. Precision psychiatry is an approach that aims to stratify people with a given disorder according to different clinical outcomes and tailor treatment to their individual needs. At present, interindividual differences in outcomes of psychotic disorders are difficult to predict on the basis of clinical assessment alone. Therefore, current research in psychosis seeks to build models that predict outcomes by integrating clinical information with a range of biological measures. Here, we review recent progress in the application of precision psychiatry to psychotic disorders and consider the challenges associated with implementing this approach in clinical practice.

Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment

PURPOSE OF REVIEW

This review will cover the most relevant findings on the use of machine learning (ML) techniques in the field of non-affective psychosis, by summarizing the studies published in the last three years focusing on illness detection and treatment.

RECENT FINDINGS

Multiple ML tools that include mostly supervised approaches such as support vector machine, gradient boosting, and random forest showed promising results by applying these algorithms to various sources of data: socio-demographic information, EEG, language, digital content, blood biomarkers, neuroimaging, and electronic health records. However, the overall performance, in the binary classification case, varied from 0.49, which is to be considered very low (i.e., noise), to over 0.90. These results are fully justified by different factors, some of which may be attributable to the preprocessing of the data, the wide variety of the data, and the a-priori setting of hyperparameters. One of the main limitations of the field is the lack of stratification of results based on biological sex, given that psychosis presents differently in men and women; hence, the necessity to tailor identification tools and data analytic strategies. Timely identification and appropriate treatment are key factors in reducing the consequences of psychotic disorders. In recent years, the emergence of new analytical tools based on artificial intelligence such as supervised ML approaches showed promises as a potential breakthrough in this field. However, ML applications in everyday practice are still in its infancy.

Early Detection and Prevention of Schizophrenic Psychosis-A Review

Psychotic disorders often run a chronic course and are associated with a considerable emotional and social impact for patients and their relatives. Therefore, early recognition, combined with the possibility of preventive intervention, is urgently warranted since the duration of untreated psychosis (DUP) significantly determines the further course of the disease. In addition to established diagnostic tools, neurobiological factors in the development of schizophrenic psychoses are increasingly being investigated. It is shown that numerous molecular alterations already exist before the clinical onset of the disease. As schizophrenic psychoses are not elicited by a single mutation in the deoxyribonucleic acid (DNA) sequence, epigenetics likely constitute the missing link between environmental influences and disease development and could potentially serve as a biomarker. The results from transcriptomic and proteomic studies point to a dysregulated immune system, likely evoked by epigenetic alterations. Despite the increasing knowledge of the neurobiological mechanisms involved in the development of psychotic disorders, further research efforts with large population-based study designs are needed to identify suitable biomarkers. In conclusion, a combination of blood examinations, functional imaging techniques, electroencephalography (EEG) investigations and polygenic risk scores should be considered as the basis for predicting how subjects will transition into manifest psychosis.

Grey-matter abnormalities in clinical high-risk participants for psychosis

The current study examined the presence of abnormalities in cortical grey-matter (GM) in a sample of clinical high-risk (CHR) participants and examined relationships with psychosocial functioning and neurocognition. CHR-participants (n = 114), participants who did not fulfil CHR-criteria (CHR-negative) (n = 39) as well as a group of healthy controls (HC) (n = 49) were recruited. CHR-status was assessed using the Comprehensive Assessment of At-Risk Mental State (CAARMS) and the Schizophrenia Proneness Interview, Adult Version (SPI-A). The Brief Assessment of Cognition in Schizophrenia Battery (BACS) as well as tests for emotion recognition, working memory and attention were administered. In addition, role and social functioning as well as premorbid adjustment were assessed. No significant differences in GM-thickness and intensity were observed in CHR-participants compared to CHR-negative and HC. Circumscribed abnormalities in GM-intensity were found in the visual and frontal cortex of CHR-participants. Moreover, small-to-moderate correlations were observed between GM-intensity and neuropsychological deficits in the CHR-group. The current data suggest that CHR-participants may not show comprehensive abnormalities in GM. We discuss the implications of these findings for the pathophysiological theories of early stage-psychosis as well as methodological issues and the impact of different recruitment strategies.

Sleep spindle and slow wave abnormalities in schizophrenia and other psychotic disorders: Recent findings and future directions

Sleep spindles and slow waves are the two main oscillatory activities occurring during NREM sleep. Slow waves are ∼1 Hz, high amplitude, negative-positive deflections that are primarily generated and coordinated within the cortex, whereas sleep spindles are 12-16 Hz, waxing and waning oscillations that are initiated within the thalamus and regulated by thalamo-cortical circuits. In healthy subjects, these oscillations are thought to be responsible for the restorative aspects of sleep and have been increasingly shown to be involved in learning, memory and plasticity. Furthermore, deficits in sleep spindles and, to lesser extent, slow waves have been reported in both chronic schizophrenia (SCZ) and early course psychosis patients. In this article, we will first describe sleep spindle and slow wave characteristics, including their putative functional roles in the healthy brain. We will then review electrophysiological, genetic, and cognitive studies demonstrating spindle and slow wave impairments in SCZ and other psychotic disorders, with particularly emphasis on recent findings in early course patients. Finally, we will discuss how future work, including sleep studies in individuals at clinical high risk for psychosis, may help position spindles and slow waves as candidate biomarkers, as well as novel treatment targets, for SCZ and related psychotic disorders.

The benefit of foresight? An ethical evaluation of predictive testing for psychosis in clinical practice

Risk prediction for psychosis has advanced to the stage at which it could feasibly become a clinical reality. Neuroimaging biomarkers play a central role in many risk prediction models. Using such models to predict the likelihood of transition to psychosis in individuals known to be at high risk has the potential to meaningfully improve outcomes, principally through facilitating early intervention. However, this compelling benefit must be evaluated in light of the broader ethical ramifications of this prospective development in clinical practice. This paper advances ethical discussion in the field in two ways: firstly, through in-depth consideration of the distinctive implications of the clinical application of predictive tools; and, secondly, by evaluating the manner in which newer predictive models incorporating neuroimaging alter the ethical landscape. We outline the current state of the science of predictive testing for psychosis, with a particular focus on emerging neuroimaging biomarkers. We then proceed to ethical analysis employing the four principles of biomedical ethics as a conceptual framework. We conclude with a call for scientific advancement to proceed in tandem with ethical consideration, informed by empirical study of the views of high risk individuals and their families. This collaborative approach will help ensure that predictive testing progresses in an ethically acceptable manner that minimizes potential adverse effects and maximizes meaningful benefits for those at high risk of psychosis.

Structural and functional imaging markers for susceptibility to psychosis

The introduction of clinical criteria for the operationalization of psychosis high risk provided a basis for early detection and treatment of vulnerable individuals. However, about two-thirds of people meeting clinical high-risk (CHR) criteria will never develop a psychotic disorder. In the effort to increase prognostic precision, structural and functional neuroimaging have received growing attention as a potentially useful resource in the prediction of psychotic transition in CHR patients. The present review summarizes current research on neuroimaging biomarkers in the CHR state, with a particular focus on their prognostic utility and limitations. Large, multimodal/multicenter studies are warranted to address issues important for clinical applicability such as generalizability and replicability, standardization of clinical definitions and neuroimaging methods, and consideration of contextual factors (e.g., age, comorbidity).

Predicting major mental illness: ethical and practical considerations

SummaryAn increasing body of genetic and imaging research shows that it is becoming possible to forecast the onset of major psychiatric disorders such as depression and schizophrenia before people become ill with ever improving accuracy. Practical issues such as the optimal combination of clinical and biological variables are being addressed, but the application of predictive algorithms to individuals or in routine clinical settings have yet to be tested. The development of predictive methods in mental health comes with substantial ethical questions, including whether people wish to know their level of risk, as well as individual and societal attitudes to the potential adverse effects of data sharing, early diagnosis and treatment, which so far have been largely ignored. Preliminary data suggests that at least some people think predictive research is valuable and would take part in such studies, and some would welcome knowing the results. Future initiatives should systematically assess opinions and attitudes in conjunction with scientific and technical advances.Declaration of interestIn the past 3 years, S.M.L. has received personal fees from Otsuaka, Sunovion and Janssen, and research grant support from Janssen and Lundbeck. A.M.M. has received research support from the Sackler Trust, Eli Lilly and Janssen. S.M.L. is part of the PSYSCAN consortium.

Deconstructing Schizophrenia: Advances in Preclinical Models for Biomarker Identification

Schizophrenia is considered to develop as a consequence of genetic and environmental factors impacting on brain neural systems and circuits during vulnerable neurodevelopmental periods, thereby resulting in symptoms in early adulthood. Understanding of the impact of schizophrenia risk factors on brain biology and behaviour can help in identifying biologically relevant pathways that are attractive for informing clinical studies and biomarker development. In this chapter, we emphasize the importance of adopting a reciprocal forward and reverse translation approach that is iteratively updated when additional new information is gained, either preclinically or clinically, for offering the greatest opportunity for discovering panels of biomarkers for the diagnosis, prognosis and treatment of schizophrenia. Importantly, biomarkers for identifying those at risk may inform early intervention strategies prior to the development of schizophrenia.Given the emerging nature of this approach in the field, this review will highlight recent research of preclinical biomarkers in schizophrenia that show the most promise for informing clinical needs with an emphasis on relevant imaging, electrophysiological, cognitive behavioural and biochemical modalities. The implementation of this reciprocal translational approach is exemplified firstly by the production and characterization of preclinical models based on the glutamate hypofunction hypothesis, genetic and environmental risk factors for schizophrenia (reverse translation), and then the recent clinical recognition of the thalamic reticular thalamus (TRN) as an important locus of brain dysfunction in schizophrenia as informed by preclinical findings (forward translation).

Biomarkers in Neuropsychiatry: A Prospect for the Twenty-First Century?

The search for biomarkers to aid in the diagnosis and prognosis of psychiatric conditions and predict response to treatment is a focus of twenty-first century medicine. The current lack of biomarkers in routine use is attributable in part to the existing way mental health conditions are diagnosed, being based upon descriptions of symptoms rather than causal biological evidence. New ways of conceptualizing mental health disorders together with the enormous advances in genetic, epidemiological, and neuroscience research are informing the brain circuits and physiological mechanisms underpinning behavioural constructs that cut across current diagnostic DSM-5 categories. Combining these advances with 'Big Data', analytical approaches offer new opportunities for biomarker development. Here we provide an introductory perspective to this volume, highlighting methodological strategies for biomarker identification; ranging from stem cells, immune mechanisms, genomics, imaging, network science to cognition. Thereafter we emphasize key points made by contributors on affective disorders, psychosis, schizophrenia, and autism spectrum disorder. An underlying theme is how preclinical and clinical research are informing biomarker development and the importance of forward and reverse translation approaches. In considering the exploitation of biomarkers we note that there is a timely opportunity to improve clinical trial design informed by patient 'biological' and 'psychological' phenotype. This has the potential to reinvigorate drug development and clinical trials in psychiatry. In conclusion, we are poised to move from the descriptive and discovery phase to one where biomarker panels can be evaluated in real-life cohorts. This will necessitate resources for large-scale collaborative efforts worldwide. Ultimately this will lead to new interventions and personalized medicines and transform our ability to prevent illness onset and treat complex psychiatric disorders more effectively.