Large-scale classification of major depressive disorder via distributed Lasso

2017 ◽  
Author(s):  
Dajiang Zhu ◽  
Qingyang Li ◽  
Brandalyn C. Riedel ◽  
Neda Jahanshad ◽  
Derrek P. Hibar ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Large Scale ◽  
Major Depressive ◽  
Scale Classification

Twelve-month prevalence and disability of DSM-IV bipolar disorder in an Australian general population survey

2004 ◽  
Vol 34 (5) ◽  
pp. 777-785 ◽  
Author(s):  
P. B. MITCHELL ◽  
T. SLADE ◽  
G. ANDREWS
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
National Survey ◽  
Large Scale ◽  
Well Being ◽  
Major Depressive ◽  
Co Morbidity ◽  
Dsm Iv ◽  
Days Out Of Role

Background. There have been few large-scale epidemiological studies which have examined the prevalence of bipolar disorder. The authors report 12-month prevalence data for DSM-IV bipolar disorder from the Australian National Survey of Mental Health and Well-Being.Method. The broad methodology of the Australian National Survey has been described previously. Ten thousand, six hundred and forty-one people participated. The 12-month prevalence of euphoric bipolar disorder (I and II) – similar to the euphoric-grandiose syndrome of Kessler and co-workers – was determined. Those so identified were compared with subjects with major depressive disorder and the rest of the sample, on rates of co-morbidity with anxiety and substance use disorders as well as demographic features and measures of disability and service utilization. Polychotomous logistic regression was used to study the relationship between the three samples and these dependent variables.Results. There was a 12-month prevalence of 0·5% for bipolar disorder. Compared with subjects with major depressive disorder, those with bipolar disorder were distinguished by a more equal gender ratio; a greater likelihood of being widowed, separated or divorced; higher rates of drug abuse or dependence; greater disability as measured by days out of role; increased rates of treatment with medicines; and higher lifetime rates of suicide attempts.Conclusions. This large national survey highlights the marked functional impairment caused by bipolar disorder, even when compared with major depressive disorder.

scholarly journals Deep phenotyping of attention impairments and the ‘Inattention Biotype’ in Major Depressive Disorder

2019 ◽  
Vol 50 (13) ◽  
pp. 2203-2212 ◽  
Author(s):  
Arielle S. Keller ◽  
Tali M. Ball ◽  
Leanne M. Williams
Keyword(s):  
Major Depressive Disorder ◽  
Selective Attention ◽  
Depressive Disorder ◽  
Large Scale ◽  
Performance Measure ◽  
Behavioral Measure ◽  
Major Depressive ◽  
Attention Impairment ◽  
Attention Impairments

AbstractBackgroundAttention impairment is an under-investigated feature and diagnostic criterion of Major Depressive Disorder (MDD) that is associated with poorer outcomes. Despite increasing knowledge regarding mechanisms of attention in healthy adults, we lack a detailed characterization of attention impairments and their neural signatures in MDD.MethodsHere, we focus on selective attention and advance a deep multi-modal characterization of these impairments in MDD, using data acquired from n = 1008 patients and n = 336 age- and sex-matched healthy controls. Selective attention impairments were operationalized and anchored in a behavioral performance measure, assessed within a battery of cognitive tests. We sought to establish the accompanying neural signature using independent measures of functional magnetic resonance imaging (15% of the sample) and electroencephalographic recordings of oscillatory neural activity.ResultsGreater impairment on the behavioral measure of selective attention was associated with intrinsic hypo-connectivity of the fronto-parietal attention network. Not only was this relationship specific to the fronto-parietal network unlike other large-scale networks; this hypo-connectivity was also specific to selective attention performance unlike other measures of cognition. Selective attention impairment was also associated with lower posterior alpha (8–13 Hz) power at rest and was related to more severe negative bias (frequent misidentifications of neutral faces as sad and lingering attention on sad faces), relevant to clinical features of negative attributions and brooding. Selective attention impairments were independent of overall depression severity and of worrying or sleep problems.ConclusionsThese results provide a foundation for the clinical translational development of objective markers and targeted therapeutics for attention impairment in MDD.

scholarly journals Microstructural deficits in the thalamus of major depressive disorder

2021 ◽  
Author(s):  
Pengfei Xu ◽  
Gangqiang Hou ◽  
Yuxuan Zhang ◽  
Yingli Zhang ◽  
Hui Ai ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Large Scale ◽  
Gray Matter Volume ◽  
Structural Connectivity ◽  
Brain Network ◽  
Microstructural Properties ◽  
Major Depressive ◽  
T1 Relaxation ◽  
Structural Abnormalities

Macroscopic structural abnormalities in the thalamus and thalamic circuits have been shown to contribute to the neuropathology of major depressive disorder (MDD). However, cytoarchitectonic properties underlying these macroscopic abnormalities remain unknown. The purpose of this study was to identify systematic deficits of brain architecture in depression, from structural brain network organization to microstructural properties. A multi-modal neuroimaging approach including diffusion, anatomical and quantitative magnetic resonance imaging (MRI) was used to examine structural-related alternations in 56 MDD patients compared with 35 age- and sex-matched controls. Structural networks were constructed and analyzed using seed-based probabilistic tractography. Morphometric measurements, including cortical thickness and voxel-based morphometry (VBM), were evaluated across the whole brain. A conjunction analysis was then conducted to identify key regions showing common structural alternations across modalities. The microstructural properties, macromolecular tissue volume (MTV) and T1 relaxation times of identified key regions were then calculated. Results showed multiple alterations of structural connectivity within a set of subcortical areas and their connections to cortical regions in MDD patients. These subcortical regions included the putamen, thalamus and caudate, which are predominately involved in the limbic-cortical-striatal-pallidal-thalamic network (LCSPT). Structural connectivity was disrupted within and between large-scale networks, mainly including subcortical networks, default mode networks and salience/ventral attention networks. Consistently, these regions also exhibited widespread volume reductions in MDD patients, specifically the bilateral thalamus, left putamen and right caudate. Importantly, the microstructural properties, T1 relaxation time of left thalamus were increased and negatively correlated with its gray matter volume in MDD patients. The present work to date sheds light on the neuropathological disruptions of LCSPT circuit in MDD, providing the first multi-modal neuroimaging evidence for the macro-micro structural abnormalities of the thalamus in patients with MDD. These findings have implications in understanding the abnormal changes of brain structures across development of MDD.

scholarly journals CLASSIFICATION OF BIPOLAR DISORDER, MAJOR DEPRESSIVE DISORDER, AND HEALTHY STATE USING VOICE

2018 ◽  
Vol 11 (15) ◽  
pp. 89 ◽  
Author(s):  
Masakazu Higuchi ◽  
Shinichi Tokuno ◽  
Mitsuteru Nakamura ◽  
Shuji Shinohara ◽  
Shunji Mitsuyoshi ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Logistic Regression ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Mental Disease ◽  
Healthy Individuals ◽  
Major Depressive ◽  
Healthy State ◽  
Polytomous Logistic Regression

Objective: In this study, we propose a voice index to identify healthy individuals, patients with bipolar disorder, and patients with major depressive disorder using polytomous logistic regression analysis.Methods: Voice features were extracted from voices of healthy individuals and patients with mental disease. Polytomous logistic regression analysis was performed for some voice features.Results: With the prediction model obtained using the analysis, we identified subject groups and were able to classify subjects into three groups with 90.79% accuracy.Conclusion: These results show that the proposed index may be used as a new evaluation index to identify depression.

scholarly journals A Framework for Recruiting into a Remote Measurement Technologies (RMTs) study: Experiences from a major depressive disorder cohort

2021 ◽  
Author(s):  
Carolin Oetzmann ◽  
Katie White ◽  
Alina Ivan ◽  
Jessica Julie ◽  
Daniel Leightley ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Large Scale ◽  
Healthcare Research ◽  
Remote Measurement ◽  
Major Depressive ◽  
Successful Recruitment ◽  
Wide Range ◽  
The Uk ◽  
Common Barriers

The use of remote measurement technologies (RMTs) across mobile health (mHealth) studies is becoming increasingly popular, given their potential for high-frequency symptom monitoring outside of routine clinical appointments. However, many RMT studies fail to report on engagement and recruitment statistics, with the few who do citing a wide range of recruitment rates. There is a need for the standardisation of best practices for successful recruitment into RMT research, critical for both research validity and reproducibility. The current paper aims to create a framework for successful recruitment into RMT studies, reflecting on the experience of RADAR-MDD, a large-scale, multi-site prospective cohort study utilising RMT to explore the clinical course of people with major depressive disorder across the UK, Netherlands, and Spain. More specifically, the paper assesses four key strategies for successful recruitment, alongside a review of the common barriers to participation and how to avoid them. Finally, the strategies and barriers outlined are combined into a single model of recruitment, that can be used as a framework to inform future study design and evaluation. Such a model will be applicable to a variety of stakeholders using RMT in healthcare research and practice.

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