Bipolar Disorder

Machine learning for classification of pediatric bipolar disorder with and without psychotic symptoms based on thalamic subregional structural volume.

BACKGROUND: The thalamus plays a crucial role in sensory processing, emotional regulation, and cognitive functions, and its dysregulation may be implicated in psychosis. The aim of the present study was to examine the differences in thalamic subregional volumes between pediatric bipolar disorder patients with (P-PBD) and without psychotic symptoms (NP-PBD).

Inflammatory biomarkers as predictors for unlocking antidepressant efficacy: Assessing predictive value and risk stratification in major depressive disorder in a prospective longitudinal study.

BACKGROUND: Major depressive disorder (MDD) is characterized by significant heterogeneity in treatment response, with inflammation hypothesized to play a role in its pathophysiology. Peripheral inflammatory biomarkers, such as the neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP), may predict antidepressant efficacy. This study investigated the association between baseline inflamma...

Contrastive functional connectivity defines neurophysiology-informed symptom dimensions in major depression.

Major depressive disorder (MDD) is highly heterogeneous, posing challenges for effective treatment due to complex interactions between clinical sympto...

Brain stimulation outcome prediction in Major Depressive Disorder by deep learning models using EEG representations.

Major Depressive Disorder (MDD) is known as a widespread illness and needs a timely treatment. The treatment procedure is currently based on the trial...

Physics-based inverse modeling of battery degradation with Bayesian methods.

To further improve Lithium-ion batteries (LiBs), a profound understanding of complex battery processes is crucial. Physical models offer understanding...

Predicting rTMS treatment response in depression: use of machine learning models to identify the roles of metabolic and clinical factors.

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression in patients with major depressive disorder (M...

Detecting Tardive Dyskinesia Using Video-Based Artificial Intelligence.

Tardive dyskinesia (TD) is a late-onset adverse effect of dopamine receptor-blocking medications, characterized by involuntary movements primarily af...

Functional connectome-based predictive modeling of suicidal ideation.

Suicide represents an egregious threat to society despite major advancements in medicine, in part due to limited knowledge of the biological mechanism...

Automated analysis of clinical interviews indicates altered head movements during social interactions in youth at clinical high-risk for psychosis.

Alterations in social functioning are commonly observed in youth at clinical high risk (CHR) for psychosis. Previous research has focused on perceptio...

Preschool and preadolescent antecedents of externalizing problems: a machine learning approach.

This study used a data-driven approach to elucidate the relative importance of child, maternal, and mother-child dyadic characteristics in predicting ...

Sensors Innovations for Smart Lithium-Based Batteries: Advancements, Opportunities, and Potential Challenges.

Lithium-based batteries (LiBs) are integral components in operating electric vehicles to renewable energy systems and portable electronic devices, tha...

Predicting treatment response in individuals with major depressive disorder using structural MRI-based similarity features.

BACKGROUND: Major Depressive Disorder (MDD) is a prevalent mental health condition with significant societal impact. Structural magnetic resonance ima...

A data-centric and interpretable EEG framework for depression severity grading using SHAP-based insights.

BACKGROUND: Major Depressive Disorder is a leading cause of disability worldwide. An accurate assessment of depression severity is critical for diagno...

SOC estimation for a lithium-ion pouch cell using machine learning under different load profiles.

Estimating the state of charge of lithium-ion battery systems is important for efficient battery management systems. This work conducts a thorough eva...

Using Digital Phenotyping to Discriminate Unipolar Depression and Bipolar Disorder: Systematic Review.

BACKGROUND: Differentiating bipolar disorder (BD) from unipolar depression (UD) is essential, as these conditions differ greatly in their progression ...

Automated depression detection via cloud based EEG analysis with transfer learning and synchrosqueezed wavelet transform.

Post-COVID-19, depression rates have risen sharply, increasing the need for early diagnosis using electroencephalogram (EEG) and deep learning. To tac...

Cortical myelin mapping in antipsychotic medication-naïve, first-episode psychosis patients.

While white matter myelin primarily functions to accelerate conduction velocity and has been extensively studied in schizophrenia-spectrum disorders (...

[Psilocybin in the setting of treatment-resistant unipolar depression: A case report].

BACKGROUND: Current antidepressants have shown certain limitations in the treatment of unipolar depression. Their long onset of action, interactions, ...

Integrating Knowledge: The Power of Ontologies in Psychiatric Research and Clinical Informatics.

Ontologies are structured frameworks for representing knowledge by systematically defining concepts, categories, and their relationships. While widely...

Treatment journey clustering with a novel kernel k-means machine learning algorithm: a retrospective analysis of insurance claims in bipolar I disorder.

In real-world psychiatric practice, patients may experience complex treatment journeys, including various diagnoses and lines of therapy. Insurance cl...