Bipolar Disorder

A rapid LC-MS/MS method for simultaneous determination of quetiapine and duloxetine in rat plasma and its application to pharmacokinetic interaction study.

Combinations of new antidepressants like duloxetine and second-generation antipsychotics like quetiapine are used in clinical treatment of major depressive disorder, as well as in forensic toxicology scenarios. The drug-drug interaction (DDI) between quetiapine and duloxetine is worthy of attention to avoid unnecessary adverse effects. However, no pharmacokinetic DDI studies of quetiapine and dulo...

Self-Efficacy, Poststroke Depression, and Rehabilitation Outcomes: Is There a Correlation?

BACKGROUND: The sudden live changes of stroke survivors may lead to negative psychological and behavioral outcomes, including anxiety and depressive mood, which may compromise the rehabilitation process. Some personality features, such as self-efficacy, could play an important role in mediating the degree of post-stroke depression. Aim of this study is to investigate the possible correlation betwe...

Multiday EMG-Based Classification of Hand Motions with Deep Learning Techniques.

Pattern recognition of electromyography (EMG) signals can potentially improve the performance of myoelectric control for upper limb prostheses with re...

Is there a symptomatic distinction between the affective psychoses and schizophrenia? A machine learning approach.

Dubiety exists over whether clinical symptoms of schizophrenia can be distinguished from affective psychosis, the assumption being that absence of a "...

Ab Initio Calculations of the Redox Potentials of Additives for Lithium-Ion Batteries and Their Prediction through Machine Learning.

Ab initio molecular orbital calculations were carried out to examine the redox potentials of 149 electrolyte additives for lithium-ion batteries. Thes...

Disrupted functional connectivity within the default mode network and salience network in unmedicated bipolar II disorder.

BACKGROUND: Recent studies demonstrate that functional disruption in resting-state networks contributes to cognitive and affective symptoms of bipolar...

On the importance of hidden bias and hidden entropy in representational efficiency of the Gaussian-Bipolar Restricted Boltzmann Machines.

In this paper, we analyze the role of hidden bias in representational efficiency of the Gaussian-Bipolar Restricted Boltzmann Machines (GBPRBMs), whic...

Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study.

Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve majo...

Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens.

Dynamic treatment regimens (DTRs) are sequential treatment decisions tailored by patient's evolving features and intermediate outcomes at each treatme...

Machine learning improved classification of psychoses using clinical and biological stratification: Update from the bipolar-schizophrenia network for intermediate phenotypes (B-SNIP).

Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach.

Neuroimaging studies have been steadily explored in Bipolar Disorder (BD) in the last decades. Neuroanatomical changes tend to be more pronounced in p...

Emotional hyper-reactivity and cardiometabolic risk in remitted bipolar patients: a machine learning approach.

OBJECTIVE: Remitted bipolar disorder (BD) patients frequently present with chronic mood instability and emotional hyper-reactivity, associated with po...

A morphometric signature of depressive symptoms in unmedicated patients with mood disorders.

OBJECTIVE: A growing literature indicates that unipolar depression and bipolar depression are associated with alterations in grey matter volume. Howev...

Automated EEG-based screening of depression using deep convolutional neural network.

In recent years, advanced neurocomputing and machine learning techniques have been used for Electroencephalogram (EEG)-based diagnosis of various neur...

Predicting lithium treatment response in bipolar patients using gender-specific gene expression biomarkers and machine learning.

We sought to test the hypothesis that transcriptome-level gene signatures are differentially expressed between male and female bipolar patients, prio...

Predicting individual responses to the electroconvulsive therapy with hippocampal subfield volumes in major depression disorder.

Electroconvulsive therapy (ECT) is one of the most effective treatments for major depression disorder (MDD). ECT can induce neurogenesis and synaptoge...

A YinYang bipolar fuzzy cognitive TOPSIS method to bipolar disorder diagnosis.

BACKGROUND AND OBJECTIVE: Bipolar disorder is often mis-diagnosed as unipolar depression in the clinical diagnosis. The main reason is that, different...

GWAS-based machine learning approach to predict duloxetine response in major depressive disorder.

Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders and is commonly treated with antidepressant drugs. However, large v...

Music Intervention Leads to Increased Insular Connectivity and Improved Clinical Symptoms in Schizophrenia.

Schizophrenia is a syndrome that is typically accompanied by delusions and hallucinations that might be associated with insular pathology. Music inter...

The Early Psychosis Screener (EPS): Quantitative validation against the SIPS using machine learning.

Machine learning techniques were used to identify highly informative early psychosis self-report items and to validate an early psychosis screener (EP...