Bipolar Disorder

Real-time estimation of lesion depth and control of radiofrequency ablation within ex vivo animal tissues using a neural network.

BACKGROUND: Radiofrequency ablation (RFA), a method of inducing thermal ablation (cell death), is often used to destroy tumours or potentially cancerous tissue. Current techniques for RFA estimation (electrical impedance tomography, Nakagami ultrasound, etc.) require long compute times (≥ 2 s) and measurement devices other than the RFA device. This study aims to determine if a neural network (NN) ...

Jobelyn attenuates inflammatory responses and neurobehavioural deficits associated with complete Freund-adjuvant-induced arthritis in mice.

Rheumatoid arthritis (RA) is a chronic inflammatory disease that affects the physical and psychosocial wellbeing of the patients and a major cause of work disability. Current drugs for its treatment only provide palliative effect, as cure for the disease still remains elusive. Jobelyn (JB), a potent anti-oxidant and anti-inflammatory dietary supplement obtained from Sorghum bicolor, has been claim...

Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network.

High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain reg...

Bipolar transurethral enucleation and resection of the prostate: Whether it is ready to supersede TURP?

OBJECTIVE: According to the EAU Guidelines, transurethral resection of the prostate (TURP) has so far still been considered as the gold standard for s...

Evaluating the Levels of Nesfatin-1 and Ghrelin Hormones in Patients with Moderate and Severe Major Depressive Disorders.

OBJECTIVE: The goal of this study was to evaluate the importance of nesfatin-1, acylated and des-acylated ghrelin, which are known as energy regulator...

Relevant Features Selection for Automatic Prediction of Preterm Deliveries from Pregnancy ElectroHysterograhic (EHG) records.

In this study, we proposed an approach able to predict whether a pregnant woman with contractions would give birth earlier than expected (i.e., before...

Influence of facial feedback during a cooperative human-robot task in schizophrenia.

Rapid progress in the area of humanoid robots offers tremendous possibilities for investigating and improving social competences in people with social...

Neurocognitive Graphs of First-Episode Schizophrenia and Major Depression Based on Cognitive Features.

Neurocognitive deficits are frequently observed in patients with schizophrenia and major depressive disorder (MDD). The relations between cognitive fe...

Predicting short term mood developments among depressed patients using adherence and ecological momentary assessment data.

Technology driven interventions provide us with an increasing amount of fine-grained data about the patient. This data includes regular ecological mom...

Estimation and evaluation of linear individualized treatment rules to guarantee performance.

In clinical practice, an informative and practically useful treatment rule should be simple and transparent. However, because simple rules are likely ...

A state-independent network of depressive, negative and positive symptoms in male patients with schizophrenia spectrum disorders.

Depressive symptoms occur frequently in patients with schizophrenia. Several factor analytical studies investigated the associations between positive,...

Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media.

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on soc...

The impact of machine learning techniques in the study of bipolar disorder: A systematic review.

Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing liter...

A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD).

Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and...

Using machine learning and surface reconstruction to accurately differentiate different trajectories of mood and energy dysregulation in youth.

Difficulty regulating positive mood and energy is a feature that cuts across different pediatric psychiatric disorders. Yet, little is known regarding...

Application of machine learning classification for structural brain MRI in mood disorders: Critical review from a clinical perspective.

Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches ...

BrainAGE score indicates accelerated brain aging in schizophrenia, but not bipolar disorder.

BrainAGE (brain age gap estimation) is a novel morphometric parameter providing a univariate score derived from multivariate voxel-wise analyses. It u...

Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis.

A relatively large number of studies have investigated the power of structural magnetic resonance imaging (sMRI) data to discriminate patients with sc...

Depressive Symptoms and Their Interactions With Emotions and Personality Traits Over Time: Interaction Networks in a Psychiatric Clinic.

OBJECTIVE: Associations between depression, personality traits, and emotions are complex and reciprocal. The aim of this study is to explore these int...

Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder.

Using MRI to diagnose mental disorders has been a long-term goal. Despite this, the vast majority of prior neuroimaging work has been descriptive rath...