What Questions Arise when Studying Cultural Universals in Depression? Lessons from Abnormal Psychology Textbooks

We examined 70 abnormal psychology textbooks published from 1920s to the present to identify consistent cross-cultural themes with regard to human depressive experiences over time and across regions of the world. The cultural and cross-cultural literature on abnormality and depression, in particular, has contributed to widening the scope of abnormal psychology textbooks over time. However, the texts are almost entirely dependent on Western diagnostic categories, particularly with regard to definitions of depression. Within the Western classification framework, authors of abnormal psychology textbooks have increasingly recognized the role of culture in depressive experiences and their communication. On the basis of our content analysis of the textbooks, we propose 10 culturally informed questions to study the relationship between culture and depression

An empirical comparison of classification algorithms for diagnosis of depression from brain sMRI scans

To be diagnostically effective, structural magnetic resonance imaging (sMRI) must reliably distinguish a depressed individual from a healthy individual at individual scans level. One of the tasks in the automated diagnosis of depression from brain sMRI is the classification. It determines the class to which a sample belongs (i.e., depressed/not depressed, remitted/not-remitted depression) based on the values of its features. Thus far, very limited works have been reported for identification of a suitable classification algorithm for depression detection. In this paper, different types of classification algorithms are compared for effective diagnosis of depression. Ten independent classification schemas are applied and a comparative study is carried out. The algorithms are: Naïve Bayes, Support Vector Machines (SVM) with Radial Basis Function (RBF), SVM Sigmoid, J48, Random Forest, Random Tree, Voting Feature Intervals (VFI), LogitBoost, Simple KMeans Classification Via Clustering (KMeans) and Classification Via Clustering Expectation Minimization (EM) respectively. The performances of the algorithms are determined through a set of experiments on sMRI brain scans. An experimental procedure is developed to measure the performance of the tested algorithms. A classification accuracy evaluation method was employed for evaluation and comparison of the performance of the examined classifiers

Plasma protein biomarkers for depression and schizophrenia by multi analyte profiling of case-control collections.

Despite significant research efforts aimed at understanding the neurobiological underpinnings of psychiatric disorders, the diagnosis and the evaluation of treatment of these disorders are still based solely on relatively subjective assessment of symptoms. Therefore, biological markers which could improve the current classification of psychiatry disorders, and in perspective stratify patients on a biological basis into more homogeneous clinically distinct subgroups, are highly needed. In order to identify novel candidate biological markers for major depression and schizophrenia, we have applied a focused proteomic approach using plasma samples from a large case-control collection. Patients were diagnosed according to DSM criteria using structured interviews and a number of additional clinical variables and demographic information were assessed. Plasma samples from 245 depressed patients, 229 schizophrenic patients and 254 controls were submitted to multi analyte profiling allowing the evaluation of up to 79 proteins, including a series of cytokines, chemokines and neurotrophins previously suggested to be involved in the pathophysiology of depression and schizophrenia. Univariate data analysis showed more significant p-values than would be expected by chance and highlighted several proteins belonging to pathways or mechanisms previously suspected to be involved in the pathophysiology of major depression or schizophrenia, such as insulin and MMP-9 for depression, and BDNF, EGF and a number of chemokines for schizophrenia. Multivariate analysis was carried out to improve the differentiation of cases from controls and identify the most informative panel of markers. The results illustrate the potential of plasma biomarker profiling for psychiatric disorders, when conducted in large collections. The study highlighted a set of analytes as candidate biomarker signatures for depression and schizophrenia, warranting further investigation in independent collections

Fitting Prediction Rule Ensembles with R Package pre

Prediction rule ensembles (PREs) are sparse collections of rules, offering highly interpretable regression and classification models. This paper presents the R package pre, which derives PREs through the methodology of Friedman and Popescu (2008). The implementation and functionality of package pre is described and illustrated through application on a dataset on the prediction of depression. Furthermore, accuracy and sparsity of PREs is compared with that of single trees, random forest and lasso regression in four benchmark datasets. Results indicate that pre derives ensembles with predictive accuracy comparable to that of random forests, while using a smaller number of variables for prediction

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

Mixed Anxiety Depression Should Not Be Included in DSM-5

Contains fulltext : 102705.pdf (publisher's version ) (Closed access)Subthreshold anxiety and subthreshold depressive symptoms often co-occur in the general population and in primary care. Based on their associated significant distress and impairment, a psychiatric classification seems justified. To enable classification, mixed anxiety depression (MAD) has been proposed as a new diagnostic category in DSM-5. In this report, we discuss arguments against the classification of MAD. More research is needed before reifying a new category we know so little about. Moreover, we argue that in patients with MAD symptoms and a history of an anxiety or depressive disorder, symptoms should be labeled as part of the course trajectories of these disorders, rather than calling it a different diagnostic entity. In patients with incident co-occurring subthreshold anxiety and subthreshold depression, subthreshold categories of both anxiety and depression could be classified to maintain a consistent classification system at both threshold and subthreshold levels.5 p

Developing a health state classification system from NEWQOL for epilepsy using classical psychometric techniques and Rasch analysis: a technical report

Aims: Resource allocation amongst competing health care interventions is informed by evidence of both clinical- and cost-effectiveness. Cost-utility analysis is increasingly used to assess cost effectiveness through the use of Quality Adjusted Life Years (QALYs). This requires health state values. Generic measures of health related quality of life (HRQL) are usually used to produce these values, but there are concerns about their relevance and sensitivity in epilepsy. This study develops a health state classification system for epilepsy from the NEWQOL battery, a validated questionnaire measuring QoL in epilepsy. The classification system will be amenable to valuation for calculating QALYs. Methods: Factor and other psychometric analyses were undertaken to investigate the factor structure of the battery, and assess the validity and responsiveness of the items. These analyses were used alongside Rasch analysis to select the dimensions included in the classification system, and the items used to represent each domain. Analysis was carried out on a trial dataset of patients with epilepsy (n=1611). Rasch and factor analysis were performed on one half of the sample and validated on the remaining half. Dimensions and items were selected that performed well across all analyses. Results: The battery was found to demonstrate reliability and validity but responsiveness across time periods for many of the items was low. A six dimension classification system was developed: worry about seizures, depression, memory, cognition, stigmatism and control, each with four response levels. Conclusions: It is feasible to develop a health state classification system from a battery of instruments using a combination of classical psychometric, factor and Rasch analysis. This is the first condition-specific health state classification developed for epilepsy and the next stage will produce preference weights to enable the measure to be used in cost-utility analysis.quality adjusted life years; health related quality of life; Rasch analysis; preference-based measures of health; health states; epilepsy