Multilayer brain network combined with deep convolutional neural network for detecting major depressive disorder

Acknowledgements This work was supported in part by the National Natural Science Foundation of China under Grants Nos. 61922062 and 61873181.Peer reviewedPostprin

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review

Mental disorders represent critical public health challenges as they are leading contributors to the global burden of disease and intensely influence social and financial welfare of individuals. The present comprehensive review concentrate on the two mental disorders: Major depressive Disorder (MDD) and Bipolar Disorder (BD) with noteworthy publications during the last ten years. There is a big need nowadays for phenotypic characterization of psychiatric disorders with biomarkers. Electroencephalography (EEG) signals could offer a rich signature for MDD and BD and then they could improve understanding of pathophysiological mechanisms underling these mental disorders. In this review, we focus on the literature works adopting neural networks fed by EEG signals. Among those studies using EEG and neural networks, we have discussed a variety of EEG based protocols, biomarkers and public datasets for depression and bipolar disorder detection. We conclude with a discussion and valuable recommendations that will help to improve the reliability of developed models and for more accurate and more deterministic computational intelligence based systems in psychiatry. This review will prove to be a structured and valuable initial point for the researchers working on depression and bipolar disorders recognition by using EEG signals.Comment: 29 pages,2 figures and 18 Table

Meaning in the noise: Neural signal variability in major depressive disorder

Clinical research has revealed aberrant activity and connectivity in default mode (DMN), frontoparietal (FPN), and salience (SN) network regions in major depressive disorder (MDD). Recent functional magnetic resonance imaging (fMRI) studies suggest that variability in brain activity, or blood oxygen level-dependent (BOLD) signal variability, may be an important novel predictor of psychopathology. However, to our knowledge, no studies have yet determined the relationship between resting-state BOLD signal variability and MDD nor applied BOLD signal variability features to the classification of MDD history using machine learning (ML). Thus, the current study had three aims: (i) to investigate the differences in the voxel-wise resting-state BOLD signal variability between varying depression histories; (ii) to examine the relationship between depressive symptom severity and resting-state BOLD signal variability; (iii) to explore the capability of resting-state BOLD signal variability to classify individuals by depression history. Using resting-state neuroimaging data for 79 women collected as a part of a larger NIH R01-funded study, we conducted (i) a one-way between-subjects ANCOVA, (ii) a multivariate multiple regression, and (iii) applied BOLD signal variability and average BOLD signal features to a supervised ML model. First, results indicated that individuals with any history of depression had significantly decreased BOLD signal variability in the left and right cerebellum and right parietal cortex in comparison to those with no depression history (pFWE \u3c .05). Second, and consistent with the results for depression history, depression severity was associated with reduced BOLD signal variability in the cerebellum. Lastly, a random forest model classified participant depression history with 76% accuracy, with BOLD signal variability features showing greater discriminative power than average BOLD signal features. These findings provide support for resting-state BOLD signal variability as a novel marker of neural dysfunction and implicate decreased neural signal variability as a neurobiological mechanism of depression

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

The motivational mechanisms driving the antidepressant effect of ketamine

Ketamine is a rapidly-acting antidepressant and has shown to be effective in depressed individuals who have previously failed to benefit from other available treatments. An important question is how ketamine works. Addressing this might help inform more targeted and efficient treatments in the future. The aim of this thesis was to examine the neural, cognitive, and computational mechanisms underpinning the antidepressant response to ketamine in treatment-resistant depression. The work has specifically focused on motivational processing, since ketamine is particularly effective in alleviating symptoms of anhedonia, which are thought to be related to impaired reward-related function. Following a general introduction (Chapter 1), the first experimental chapter (Chapter 2) focuses on identifying suitable reward and punishment tasks for repeated testing in a clinical trial. Test retest properties of various tasks are explored in healthy individuals, assessed by both traditional measures of task performance (e.g., accuracy) and computational parameters. Chapter 3 outlines a pilot simultaneous EEGfMRI study in healthy individuals probing the neural dynamics of the motivation to exert cognitive effort, an important but understudied process in depression. The third study (Chapter 4) uses resting-state fMRI to examine how ketamine modulates fronto-striatal circuitry, which is known to drive motivational behaviour, in depressed and healthy individuals. The final experimental chapter (Chapter 5) examines which cognitive and computational measures of motivational processing (using tasks identified in Chapter 2) change following a single dose of ketamine compared to placebo in depression, using a crossover design. Based on preliminary findings, it is tentatively proposed that ketamine might affect reward processing by enhancing fronto-striatal circuitry functional connectivity, as well as by increasing exploratory behaviours, and possibly punishment learning rates. The general discussion (Chapter 6) discusses these findings in relation to contemporary models of anhedonia and antidepressant action, considering both the limitations of the work presented and possible future directions