Parsing the heterogeneity of Major Depression:Biological subtyping and other statistical approaches to unravel the causes of Major Depression

Major Depression (MD) is the largest contributor to the global burden of disease. Unfortunately, standard pharmacological treatments are not always effective. Combined with the heterogeneity of the patient population, this indicates that there is likely no biological disturbance (e.g., impaired serotonin) underlying depression in all MD patients. Indeed, many potential risk factors for MD have been identified, ranging from genetic and environmental variables to different types of biological disturbances. The first part of this thesis provided more insight into the etiology of MD by using rich datasets and novel methodology to identify the most important predictors of MD. Family history of depression and anxiety was one of the most important predictors of both onset and recurrence of MD. This thesis also showed that the gender gap in MD prevalence arises early in life and remains stable over the lifetime. The second part of this thesis addressed the heterogeneity of the MD population by investigating if and how well studies based on biological data might enable the discovery of more homogeneous subtypes of MD. The results indicated that although subtyping based on etiology and pathophysiology is a promising research avenue, no definitive conclusions can be drawn as of yet. Importantly, the results showed that this kind of subtyping research is a very complex endeavor that requires elaborate and costly data collection as well as intricate research designs that enable the evaluation of the robustness of the model results

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

A deep phenotyping approach to understand major depressive disorder and responses to antidepressant pharmacotherapy

Major depressive disorder (MDD) is a debilitating psychiatric disorder characterised by a complex underlying biology and poor response to pharmacological antidepressant strategies. Given the heterogeneity of MDD and the diverse range of available treatment options, there is an increasing desire to develop and implement precision medicine approaches to tailor existing treatment strategies to the biological system of the individual. In this thesis, high-resolution omics data (connectomics [fMRI], metabolomics [1H NMR] and immunomics [inflammatory cytokines]) collected from the Canadian Biomarker Integration Network in Depression (CAN-BIND) study has been integrated to facilitate the deep phenotyping of MDD. In addition, this approach has been used to predict the treatment response to two common antidepressant drugs, monotherapy with the selective serotonin reuptake inhibitor (SSRI) escitalopram (10-20 mg) or combination therapy with escitalopram and the dopaminergic antipsychotic aripiprazole (2-10 mg). This approach identified a multi-modal panel of sex-specific biomarkers of MDD and treatment response, highlighting a strong immunometabolic component in depressed males, but not females. Unsupervised clustering methods indicated the superiority of biological (neuroimaging) over symptom-based (clinical questionnaires) data for the stratification of patients into MDD subtypes with differential response to treatment. More importantly, a set of multi-modal, sex-specific biomarkers were identified that predicted treatment response with escitalopram monotherapy (84.7% accuracy) or aripiprazole augmentation (88.5% accuracy). In addition to highlighting potential new aspects of the biology of MDD (e.g. relevance of lipoprotein size and density for their relation to depression), this work is one of the first attempts to apply systems biology approaches to high-resolution biological data from a large clinical trial to predict later treatment outcome. With the validation of the findings presented in this thesis in independent cohorts, and with further development of omics technologies, leading to cheaper and high-throughput screening of the patient population, pre-dose biomarkers have the potential to achieve personalised treatment. Each year, escitalopram and aripiprazole are prescribed to an estimated 26 million and 7 million individuals respectively, and over one third of them do not respond. Thus, being able to predict response to antidepressant medication from baseline biomarkers has enormous clinical and socioeconomic benefits.Open Acces

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research

By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks have advanced to a powerful tool in medical imaging. Here, we first give an introduction into methodological key concepts and resulting methodological promises including representation and transfer learning, as well as modelling domain-specific priors. After reviewing recent applications within neuroimaging-based psychiatric research, such as the diagnosis of psychiatric diseases, delineation of disease subtypes, normative modeling, and the development of neuroimaging biomarkers, we discuss current challenges. This includes for example the difficulty of training models on small, heterogeneous and biased data sets, the lack of validity of clinical labels, algorithmic bias, and the influence of confounding variables

Dimensionality reduction and unsupervised learning techniques applied to clinical psychiatric and neuroimaging phenotypes

Unsupervised learning and other multivariate analysis techniques are increasingly recognized in neuropsychiatric research. Here, finite mixture models and random forests were applied to clinical observations of patients with major depression to detect and validate treatment response subgroups. Further, independent component analysis and agglomerative hierarchical clustering were combined to build a brain parcellation solely on structural covariance information of magnetic resonance brain images. Übersetzte Kurzfassung: Unüberwachtes Lernen und andere multivariate Analyseverfahren werden zunehmend auf neuropsychiatrische Fragestellungen angewendet. Finite mixture Modelle wurden auf klinische Skalen von Patienten mit schwerer Depression appliziert, um Therapieantwortklassen zu bilden und mit Random Forests zu validieren. Unabhängigkeitsanalysen und agglomeratives hierarchisches Clustering wurden kombiniert, um die strukturelle Kovarianz von Magnetresonanz­tomographie-Bildern für eine Hirnparzellierung zu nutzen

Neuropsychiatric and cognitive symptoms in Parkinson’s disease: the contribution to subtype classification, to differential diagnosis, their clinical and instrumental correlations

Il piano di ricerca è volto ad approfondire il contributo dei sintomi neuropsichiatrici e cognitivi nelle diverse fasi della Malattia di Parkinson (MP). In particolare, l’argomento di studio è focalizzato sull’analisi dei sintomi cognitivi e neuropsichiatrici nella MP, affrontando queste tematiche anche mediante l’utilizzo di tecniche di neuroimaging, in pazienti drug-naïve, in fase precoce di malattia ed in fase avanzata. Nei pazienti drug-naïve, la ricerca è stata finalizzata alla caratterizzazione dei sintomi neuropsichiatrici e cognitivi nei sottotipi motori (i.e., tremorigeni vs acinetico-rigidi) e rispetto alla lateralità di esordio degli stessi (i.e., lateralità destra vs lateralità sinistra). Nei pazienti in fase precoce di malattia, è stato indagato il contributo dei sintomi neuropsichiatrici e cognitivi nella diagnosi differenziale tra MP e Paralisi Sopranucleare Progressiva (PSP) in pazienti valutati entro i 24 mesi dall’esordio motorio, finestra temporale in cui spesso si assiste ad un overlapping dei sintomi motori. Nei pazienti in fase avanzata di malattia, la ricerca è stata finalizzata alla caratterizzazione, mediante i sintomi neuropsichiatrici e cognitivi, del Gioco D’Azzardo Patologico (gambling) rispetto agli altri tipi di Disturbi del controllo degli Impulsi (ICDs). Ancora nell’ambito dell’ICDs, è stato sviluppato uno studio di neuroimaging, volto ad identificare i correlati morfostrutturali (spessori corticali e volumi dei nuclei sottocorticali) di tali disturbi. Infine, si sono identificati i sintomi neuropsichiatrici e cognitivi che possono impedire l’esecuzione di un esame di Risonanza Magnetica (RM), al fine, in ambito clinico, di preparare adeguatamente all’esame i pazienti più a rischio di mancato svolgimento e con l’intento di indagare, in ambito di ricerca, la reale rappresentatività campionaria dei pazienti inseriti in studi di RM

Examining the Viability of Computational Psychiatry: Approaches into the Future

As modern medicine becomes increasingly personalized, psychiatry lags behind, using poorly-understood drugs and therapies to treat mental disorders. With the advent of methods that capture large quantities of data, such as genome-wide analyses or fMRI, machine learning (ML) approaches have become prominent in neuroscience. This is promising for studying the brain’s function, but perhaps more importantly, these techniques can potentially predict the onset of disorder and treatment response. Experimental approaches that use naive machine learning algorithms have dominated research in computational psychiatry over the past decade. In a critical review and analysis, I argue that biologically realistic approaches will be more effective in clinical practice, and research trends should reflect this. Hybrid models are considered, and a brief case study on major depressive disorder is presented. Finally, I propose a novel four-step approach for the future implementation of computational methods in psychiatric clinics