Machine Learning Methods for Diagnosis, Prognosis and Prediction of Long-term Treatment Outcome of Major Depression

abstract: Major Depression, clinically called Major Depressive Disorder, is a mood disorder that affects about one eighth of population in US and is projected to be the second leading cause of disability in the world by the year 2020. Recent advances in biotechnology have enabled us to collect a great variety of data which could potentially offer us a deeper understanding of the disorder as well as advancing personalized medicine. This dissertation focuses on developing methods for three different aspects of predictive analytics related to the disorder: automatic diagnosis, prognosis, and prediction of long-term treatment outcome. The data used for each task have their specific characteristics and demonstrate unique problems. Automatic diagnosis of melancholic depression is made on the basis of metabolic profiles and micro-array gene expression profiles where the presence of missing values and strong empirical correlation between the variables is not unusual. To deal with these problems, a method of generating a representative set of features is proposed. Prognosis is made on data collected from rating scales and questionnaires which consist mainly of categorical and ordinal variables and thus favor decision tree based predictive models. Decision tree models are known for the notorious problem of overfitting. A decision tree pruning method that overcomes the shortcomings of a greedy nature and reliance on heuristics inherent in traditional decision tree pruning approaches is proposed. The method is further extended to prune Gradient Boosting Decision Tree and tested on the task of prognosis of treatment outcome. Follow-up studies evaluating the long-term effect of the treatments on patients usually measure patients' depressive symptom severity monthly, resulting in the actual time of relapse upper bounded by the observed time of relapse. To resolve such uncertainty in response, a general loss function where the hypothesis could take different forms is proposed to predict the risk of relapse in situations where only an interval for time of relapse can be derived from the observed data.Dissertation/ThesisDoctoral Dissertation Computer Science 201

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder:COORDINATE-MDD consortium design and rationale

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project

Case complexity as a guide for psychological treatment selection

Objective: Some cases are thought to be more complex and difficult to treat, although there is little consensus on how to define complexity in psychological care. This study proposes an actuarial, data-driven method of identifying complex cases based on their individual characteristics. Method: Clinical records for 1512 patients accessing low and high intensity psychological treatments were partitioned in 2 random subsamples. Prognostic indices (PI) predicting post-treatment reliable and clinically significant improvement (RCSI) in depression (PHQ-9) and anxiety (GAD-7) symptoms were estimated in one subsample using penalized (Lasso) regressions with optimal scaling. A PI-based algorithm was used to classify patients as standard (St) or complex (Cx) cases in the second (cross-validation) subsample. RCSI rates were compared between Cx cases that accessed treatments of different intensities using logistic regression. Results: St cases had significantly higher RCSI rates compared to Cx cases (OR = 1.81 to 2.81). Cx cases tended to attain better depression outcomes if they were initially assigned to high intensity (vs. low intensity) interventions (OR = 2.23); a similar pattern was observed for anxiety but the odds ratio (1.74) was not statistically significant. Conclusions: Complex cases could be detected early and matched to high intensity interventions to improve outcomes

Modern Views of Machine Learning for Precision Psychiatry

In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research

Moderators of treatment effect of Prompt Mental Health Care compared to treatment as usual: Results from a randomized controlled trial

Background In this exploratory study, we investigated a comprehensive set of potential moderators of response to the primary care service Prompt Mental Health Care (PMHC). Methods Data from an RCT of PMHC (n = 463) versus treatment as usual (TAU, n = 215) were used. At baseline mean age was 34.8, 66.7% were women, and 91% scored above caseness for depression (PHQ-9) and 87% for anxiety (GAD-7). Outcomes: change in symptoms of depression and anxiety and change in remission status from baseline to six- and 12- months follow-up. Potential moderators: sociodemographic, lifestyle, social, and cognitive variables, variables related to (mental) health problem and care. Each moderator was examined in generalized linear mixed models with robust maximum likelihood estimation. Results Effect modification was only identified for anxiolytic medication for change in symptoms of depression and anxiety; clients using anxiolytic medication showed less effect of PMHC relative to TAU (all p < 0.001), although this result should be interpreted with caution due to the low number of anxiolytic users in the sample. For remission status, none of the included variables moderated the effect of treatment. Conclusion As a treatment for depression and/or anxiety, PMHC mostly seems to work equally well as compared to TAU across a comprehensive set of potential moderators.publishedVersio

Examining the Relationship between Intrinsic Drivers of Motivation and Functional Outcomes in a Cross-Section of Individuals with Psychotic Disorders

Impaired functioning is recognized as a major barrier to recovery among individuals with psychotic disorders. Research on the role of negative symptomatology on functioning has identified avolition (i.e. lack of motivation) as being highly correlated with functional outcomes. However, current measures of avolition fail to consider more intrinsic factors that influence motivation. There is a need for more nuanced research on the drivers of motivation and their relationship with functioning to inform the observed relationship between avolition and impaired functioning. This cross-sectional study uses data obtained from the Prevention and Early Intervention Program for Psychoses, in London, Ontario. 105 clients of PEPP were assessed using validated measures of motivational drivers. Multivariate analyses did not show a statistically significant relationship between the intrinsic drivers of motivation and functional outcomes. Findings demonstrate the need for updated measures of negative symptoms as well as the need for further research on motivation and functional outcomes