Machine learning and brain imaging in psychosis

Over the past years early detection and intervention in schizophrenia have become a major objective in psychiatry. Early intervention strategies are intended to identify and treat psychosis prior to fulfilling diagnostic criteria for the disorder. To this aim, reliable early diagnostic biomarkers are needed in order to identify a high-risk state for psychosis and also predict transition to frank psychosis in those high-risk individuals destined to develop the disorder. Recently, machine learning methods have been successfully applied in the diagnostic classification of schizophrenia and in predicting transition to psychosis at an individual level based on magnetic resonance imaging (MRI) data and also neurocognitive variables. This work investigates the application of machine learning methods for the early identification of schizophrenia in subjects at high risk for developing the disorder. The dataset used in this work involves data from the Edinburgh High Risk Study (EHRS), which examined individuals at a heightened risk for developing schizophrenia for familial reasons, and the FePsy (Fruherkennung von Psychosen) study that was conducted in Basel and involves subjects at a clinical high-risk state for psychosis. The overriding aim of this thesis was to use machine learning, and specifically Support Vector Machine (SVM), in order to identify predictors of transition to psychosis in high-risk individuals, using baseline structural MRI data. There are three aims pertaining to this main one. (i) Firstly, our aim was to examine the feasibility of distinguishing at baseline those individuals who later developed schizophrenia from those who did not, yet had psychotic symptoms using SVM and baseline data from the EHRS study. (ii) Secondly, we intended to examine if our classification approach could generalize to clinical high-risk cohorts, using neuroanatomical data from the FePsy study. (iii) In a more exploratory context, we have also examined the diagnostic performance of our classifier by pooling the two datasets together. With regards to the first aim, our findings suggest that the early prediction of schizophrenia is feasible using a MRI-based linear SVM classifier operating at the single-subject level. Additionally, we have shown that the combination of baseline neuroanatomical data with measures of neurocognitive functioning and schizotypal cognition can improve predictive performance. The application of our pattern classification approach to baseline structural MRI data from the FePsy study highly replicated our previous findings. Our classification method identified spatially distributed networks that discriminate at baseline between subjects that later developed schizophrenia and other related psychoses and those that did not. Finally, a preliminary classification analysis using pooled datasets from the EHRS and the FePsy study supports the existence of a neuroanatomical pattern that differentiates between groups of high-risk subjects that develop psychosis against those who do not across research sites and despite any between-sites differences. Taken together, our findings suggest that machine learning is capable of distinguishing between cohorts of high risk subjects that later convert to psychosis and those that do not based on patterns of structural abnormalities that are present before disease onset. Our findings have some clinical implications in that machine learning-based approaches could advise or complement clinical decision-making in early intervention strategies in schizophrenia and related psychoses. Future work will be, however, required to tackle issues of reproducibility of early diagnostic biomarkers across research sites, where different assessment criteria and imaging equipment and protocols are used. In addition, future projects may also examine the diagnostic and prognostic value of multimodal neuroimaging data, possibly combined with other clinical, neurocognitive, genetic information

Implementing Precision Psychiatry: a Systematic Review of Individualized Prediction Models for Clinical Practice

BACKGROUND: The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders. METHODS: PRISMA/RIGHT/CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models. FINDINGS: Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (beta = .29, P = .03) and diagnostic compared to prognostic (beta = .84, p < .0001) and predictive (beta = .87, P = .002) models were associated with increased accuracy. INTERPRETATION: To date, several validated prediction models are available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gapThis study was supported by the King’s College London Confidence in Concept award from the Medical Research Council (MC_PC_16048) to Dr Fusar-Poli. Dr Salazar de Pablo and Dr Vaquerizo-Serrano are supported by the Alicia Koplowitz Foundation. Dr Danese was funded by the Medical Research Council (grant no. P005918) and the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, and King’s College Londo

Schizotypy: The Way Ahead

Background: Empirical evidence suggests that schizotypy is a useful construct for analyzing and understanding psychotic disorders. However, several issues remain to be resolved. Method: This selective, critical review, addresses some questions and limitations, and discusses future directions of work. Results: First, we present a conceptual outline and discuss the evidence from translational and interdisciplinary studies on schizotypy. Next, we examine and discuss newer analytical and methodological approaches, including network and machine learning approaches. We also discuss newer psychometric identification approaches, such as those using biobehavioral and ambulatory assessment. Next, we review recent cross-cultural studies in schizotypy research. Finally, we identify new challenges and directions and draw conclusions. Conclusions: This selective, critical review suggests that new methods can contribute to the construction of a solid scientific model of schizotypy as a risk construct. // Esquizotipia: el Camino a Seguir. Antecedentes: la evidencia empírica ha demostrado que la esquizotipia es un constructo útil para analizar y comprender los trastornos psicóticos. Sin embargo, todavía quedan por resolver varias cuestiones. Método: en esta revisión selectiva y crítica se abordan algunas limitaciones, se discuten interrogantes y se comentan direcciones futuras de trabajo. Resultados: en primer lugar, se presenta una delimitación conceptual y se comenta la evidencia acumulada en diferentes estudios y niveles de análisis en el campo de la esquizotipia. A continuación, se examinan nuevos modelos psicopatológicos, como el modelo de red, y se presentan las diferentes herramientas desarrolladas y validadas para su evaluación. Seguidamente, se abordan algunas inquietudes metodológicas de fondo y se presentan nuevas técnicas y procedimientos psicométricos, como la evaluación ambulatoria y bioconductual. También se analizan algunos de los problemas inherentes en la investigación entre países y culturas. Finalmente, se establecen las conclusiones y se abordan nuevos desafíos y direcciones futuras de investigación. Conclusiones: esta revisión selectiva y crítica plantea que es necesario continuar trabajando en la construcción de un modelo científico sólido y refutable e incorporar nuevas pruebas científicas en el campo de la esquizotipia

Toward precision psychiatry in bipolar disorder : staging 2.0

Personalized treatment is defined as choosing the “right treatment for the right person at the right time.” Although psychiatry has not yet reached this level of precision, we are on the way thanks to recent technological developments that may aid to detect plausible molecular and genetic markers. At the moment there are some models that are contributing to precision psychiatry through the concept of staging. While staging was initially presented as a way to categorize patients according to clinical presentation, course, and illness severity, current stagingmodels integratemultiple levels of information that can help to define each patient’s characteristics, severity, and prognosis in a more precise and individualized way. Moreover, staging might serve as the foundation to create a clinical decision-making algorithm on the basis of the patient’s stage. In this review we will summarize the evolution of the bipolar disorder staging model in relation to the new discoveries on the neurobiology of bipolar disorder. Furthermore, we will discuss how the latest and future progress in psychiatry might transform current staging models into precision staging models

Peripheral biomarkers for first-episode psychosis-opportunities from the neuroinflammatory hypothesis of schizophrenia

Objective Schizophrenia is a disabling disorder of unknown aetiology, lacking definite diagnostic method and cure. A reliable biological marker of schizophrenia is highly demanded, for which traceable immune mediators in blood could be promising candidates. We aimed to gather the best findings of neuroinflammatory markers for first-episode psychosis (FEP). Methods We performed an extensive narrative review of online literature on inflammation-related markers found in human FEP patients only. Results Changes to cytokine levels have been increasingly reported in schizophrenia. The peripheral levels of IL-1 (or its receptor antagonist), soluble IL-2 receptor, IL-4, IL-6, IL-8, and TNF-a have been frequently reported as increased in FEP, in a suggestive continuum from high-risk stages for psychosis. Microglia and astrocytes establish the link between this immune signalling and the synthesis of noxious tryptophan catabolism products, that cause structural damage and directly hamper normal neurotransmission. Amongst these, only 3-hydroxykynurenine has been consistently described in the blood of FEP patients. Conclusion Peripheral molecules stemming from brain inflammation might provide insightful biomarkers of schizophrenia, as early as FEP or even prodromal phases, although more timeand clinically-adjusted studies are essential for their validation.This work has been conducted with the support of the Portuguese Foundation for Science and Technology

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group.

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data