Bipolar Disorder

Disrupted rich-club network organization and individualized identification of patients with major depressive disorder.

BACKGROUND: Altered structural and functional brain networks have been extensively studied in major depressive disorder (MDD) patients. However, whether the differential connectivity patterns in the rich-club organization, assessed from structural brain network analyses, and the associated connections of these regions are particularly susceptible to depression remain unclear.

Construction of gene-classifier and co-expression network analysis of genes in association with major depressive disorder.

Because the pathogenesis of major depressive disorder (MDD) is still unclear and the accurate diagnosis remains unavailable, we aimed to analyze its molecular mechanisms and develop a gene classifier to improve diagnostic accuracy. We extracted differentially expressed genes from two datasets, GSE45642 (from brain tissue samples) and GSE98793 (from blood samples), and found three key modules to ha...

Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood.

BACKGROUND: Visual and auditory signs of patient functioning have long been used for clinical diagnosis, treatment selection, and prognosis. Direct me...

Robot-assisted Nerve Plane-sparing Eradication of Deep Endometriosis with Double-bipolar Method.

OBJECTIVE: To demonstrate anatomic and technical highlights of a robot-assisted nerve plane-sparing eradication of deep endometriosis (DE).

Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach.

Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologist...

A deep learning model for detecting mental illness from user content on social media.

Users of social media often share their feelings or emotional states through their posts. In this study, we developed a deep learning model to identif...

Automated design and optimization of multitarget schizophrenia drug candidates by deep learning.

Complex neuropsychiatric diseases such as schizophrenia require drugs that can target multiple G protein-coupled receptors (GPCRs) to modulate complex...

Predicting the chemical reactivity of organic materials using a machine-learning approach.

Stability and compatibility between chemical components are essential parameters that need to be considered in the selection of functional materials i...

Machine learning for genetic prediction of psychiatric disorders: a systematic review.

Machine learning methods have been employed to make predictions in psychiatry from genotypes, with the potential to bring improved prediction of outco...

Speech Quality Feature Analysis for Classification of Depression and Dementia Patients.

Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases. However, major depressive disorder may...

Translating big data to better treatment in bipolar disorder - a manifesto for coordinated action.

Bipolar disorder (BD) is a major healthcare and socio-economic challenge. Despite its substantial burden on society, the research activity in BD is mu...

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach.

The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental...

Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial.

IMPORTANCE: Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant m...

Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection.

To solve major limitations in algorithms for the metabolite-based prediction of psychiatric phenotypes, a novel prediction model for depressive sympto...

Towards a brain-based predictome of mental illness.

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognit...

Machine-learning models for depression and anxiety in individuals with immune-mediated inflammatory disease.

OBJECTIVE: Individuals with immune-mediated inflammatory disease (IMID) have a higher prevalence of psychiatric disorders than the general population....

Noise can speed backpropagation learning and deep bidirectional pretraining.

We show that the backpropagation algorithm is a special case of the generalized Expectation-Maximization (EM) algorithm for iterative maximum likeliho...

Smartphone as a monitoring tool for bipolar disorder: a systematic review including data analysis, machine learning algorithms and predictive modelling.

BACKGROUND: Bipolar disorder (BD) is a chronic illness with a high recurrence rate. Smartphones can be a useful tool for detecting prodromal symptoms ...

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions.

OBJECTIVES: The existence of anatomofunctional brain abnormalities in bipolar disorder (BD) is now well established by magnetic resonance imaging (MRI...

The Virtual Operative Assistant: An explainable artificial intelligence tool for simulation-based training in surgery and medicine.

Simulation-based training is increasingly being used for assessment and training of psychomotor skills involved in medicine. The application of artifi...