Investigating machine learning techniques for detection of depression using structural MRI volumetric features

Structural MRI offers anatomical details and high sensitivity to pathological changes. It can demonstrate certain patterns of brain changes present at a structural level. Research to date has shown that volumetric analysis of brain regions has importance in depression detection. However, such analysis has had very minimal use in depression detection studies at individual level. Optimally combining various brain volumetric features/attributes, and summarizing the data into a distinctive set of variables remain difficult. This study investigates machine learning algorithms that automatically identify relevant data attributes for depression detection. Different machine learning techniques are studied for depression classification based on attributes extracted from structural MRI (sMRI) data. The attributes include volume calculated from whole brain, white matter, grey matter and hippocampus. Attributes subset selection is performed aiming to remove redundant attributes using three filtering methods and one hybrid method, in combination with ranker search algorithms. The highest average classification accuracy, obtained by using a combination of both SVM-EM and IG-Random Tree algorithms, is 85.23%. The classification approach implemented in this study can achieve higher accuracy than most reported studies using sMRI data, specifically for detection of depression.

Depression, anxiety and metabolic syndrome in farm men and women

This research found that Depression was associated with the development of metabolic syndrome, whilst both Depression and Anxiety are associated with the maintenance of metabolic syndrome in Farm men and women. Future interventions in metabolic syndrome should consider screening for and treating these psychological factors to improve health outcomes.

Role of immune-inflammatory and oxidative and nitrosative stress pathways in the etiology of depression: therapeutic implications

Accumulating data have led to a re-conceptualization of depression that emphasizes the role of immuneinflammatory processes, coupled to oxidative and nitrosative stress (O&NS). These in turn drive the production of neuroregulatory tryptophan catabolites (TRYCATs), driving tryptophan away from serotonin, melatonin, and Nacetylserotonin production, and contributing to central dysregulation. This revised perspective better encompasses the diverse range of biological changes occurring in depression and in doing so provides novel and readily attainable treatment targets, as well as potential screening investigations prior to treatment initiation. We briefly review the role that immune-inflammatory, O&NS, and TRYCAT pathways play in the etiology, course, and treatment of depression. We then discuss the pharmacological treatment implications arising from this, including the potentiation of currently available antidepressants by the adjunctive use of immune- and O&NS- targeted therapies. The use of such a frame of reference and the treatment benefits attained are likely to have wider implications and utility for depression-associated conditions, including the neuroinflammatory and (neuro)degenerative disorders.

Protocol for a collaborative meta-analysis of 5-HTTLPR, stress, and depression

Background: Debate is ongoing about what role, if any, variation in the serotonin transporter linked polymorphic region (5-HTTLPR) plays in depression. Some studies report an interaction between 5-HTTLPR variation and stressful life events affecting the risk for depression, others report a main effect of 5-HTTLPR variation on depression, while others find no evidence for either a main or interaction effect. Meta-analyses of multiple studies have also reached differing conclusions.

Methods/Design: To improve understanding of the combined roles of 5-HTTLPR variation and stress in the development of depression, we are conducting a meta-analysis of multiple independent datasets. This coordinated approach utilizes new analyses performed with centrally-developed, standardized scripts. This publication documents the protocol for this collaborative, consortium-based meta-analysis of 5-HTTLPR variation, stress, and depression.

Study eligibility criteria: Our goal is to invite all datasets, published or unpublished, with 5-HTTLPR genotype and assessments of stress and depression for at least 300 subjects. This inclusive approach is to minimize potential impact from publication bias.

Data sources: This project currently includes investigators from 35 independent groups, providing data on at least N = 33,761 participants.  The analytic plan was determined prior to starting data analysis. Analyses of individual study datasets will be performed by the investigators who collected the data using centrally-developed standardized analysis scripts to ensure a consistent analytical approach across sites. The consortium as a group will review and interpret the meta-analysis results.

Discussion: Variation in 5-HTTLPR is hypothesized to moderate the response to stress on depression. To test specific hypotheses about the role of 5-HTTLPR variation on depression, we will perform coordinated meta-analyses of de novo results obtained from all available data, using variables and analyses determined a priori. Primary analyses, based on the original 2003 report by Caspi and colleagues of a GxE interaction will be supplemented by secondary analyses to help interpret and clarify issues ranging from the mechanism of effect to heterogeneity among the contributing studies. Publication of this protocol serves to protect this project from biased reporting and to improve the ability of readers to interpret the results of this specific meta-analysis upon its completion.