Bipolar Disorder

Residual Gaussian Splatting for Ultra Sparse-View CBCT Reconstruction

While 3D Gaussian splatting (3DGS) offers explicit and efficient scene representations for cone-beam computed tomography reconstruction, conventional photometric optimization inherently suffers from spectral bias under ultra sparse-view conditions, leading to over-smoothing and a loss of high-frequency anatomical details. Since wavelet transforms provide rich high-frequency information and have be...

Psychologically-Grounded Graph Modeling for Interpretable Depression Detection

Automatic depression detection from conversational interactions holds significant promise for scalable screening but remains hindered by severe data scarcity and a lack of clinical interpretability. Existing approaches typically rely on black-box deep learning architectures that struggle to model the subtle, temporal evolution of depressive symptoms or account for participant-specific heterogeneit...

Predicting Depressive Symptoms Among Reproductive-Aged Women in Bangladesh Using Bagging Ensemble Machine Learning on Imbalanced Bangladesh Demographic and Health Survey 2022 Data

Background Depressive symptoms among reproductive-aged women represent a major public health concern in low- and middle-income countries, yet systemat...

Improving clinical interpretability of linear neuroimaging models through feature whitening

Linear models are widely used in computational neuroimaging to identify biomarkers associated with brain pathologies. However, interpreting the learne...

Sustaining Control and Agency Under Threat: Computational Pathways to Persistence and Escape

Adaptive behavior requires deciding when to persist and when to disengage under uncertainty and partial outcome control. Avoidance has often been stud...

AQVolt26: High-Temperature r$^2$SCAN Halide Dataset for Universal ML Potentials and Solid-State Batteries

The demand for safe, high-energy-density batteries has spotlighted halide solid-state electrolytes, which offer the potential for enhanced ionic mobil...

How and why does deep ensemble coupled with transfer learning increase performance in bipolar disorder and schizophrenia classification?

Transfer learning (TL) and deep ensemble learning (DE) have recently been shown to outperform simple machine learning in classifying psychiatric disor...

MAMGL: A memory-augmented meta-graph learning framework for adolescent major depression disorder diagnosis

Adolescent major depressive disorder (AMDD) is a prevalent and heterogeneous psychiatric condition that emerges during a critical period of brain deve...

Data Diversity vs. Model Complexity in the Prediction of Pediatric Bipolar Disorder: Evidence from Academic and Community Clinical Samples

Pediatric bipolar disorder is challenging to diagnose accurately due to symptom heterogeneity. More standardized and data-driven approaches are needed...

SynForceNet: A Force-Driven Global-Local Latent Representation Framework for Lithium-Ion Battery Fault Diagnosis

Online safety fault diagnosis is essential for lithium-ion batteries in electric vehicles(EVs), particularly under complex and rare safety-critical co...

A widespread electrical brain network encodes anxiety in health and depressive states

In rodents, anxiety is characterized by heightened vigilance during low-threat and uncertain situations. Though activity in the frontal cortex and lim...

IDRL: An Individual-Aware Multimodal Depression-Related Representation Learning Framework for Depression Diagnosis

Depression is a severe mental disorder, and reliable identification plays a critical role in early intervention and treatment. Multimodal depression d...

World Model for Battery Degradation Prediction Under Non-Stationary Aging

Degradation prognosis for lithium-ion cells requires forecasting the state-of-health (SOH) trajectory over future cycles. Existing data-driven approac...

Precision stratification of risk for suicidal behavior in people with bipolar depression

Patients with bipolar depression are at the highest risk for suicidal behavior, comprising ~10% of all deaths. In the critical period preceding attemp...

Reproducible symptom subtypes of depression identified using unsupervised machine learning

Depression is a heterogeneous disorder, often diagnosed based on symptom co-occurrence. However, individuals may present with markedly different sympt...

Development and validation of neurological health score using machine learning algorithms

Neurological health score (NHS), indicating the health of brain and nervous system, helps in identifying high risk individuals, and in recommending li...

Data-Driven Multimodal Subtyping Reveals Differential Cognitive Risk and Treatment Effects in the All of Us Cohort

INTRODUCTION: Cognitively unimpaired (CU) adults show substantial variation in their risk of developing mild cognitive impairment (MCI), yet most subt...

When attention falters: brain, breathing, and behavioral signals of lapses in interoceptive attention

Mind-body practices like meditation and yoga, which are widely used to support mental health, involve paying attention to internal bodily sensations l...

Early Pregnancy DNA Methylation Signatures as Predictors of Antenatal Depressive Symptoms: A longitudinal study of DNA methylation changes

Background. Antenatal depressive symptoms (ADS) are common and underdiagnosed, particularly in low and middle income countries, and are associated wit...

Deep Lipidomic Phenotyping Identifies Ceramide-Centered Lipotoxicity and Depletion of Plasmalogen-Carnitine Pathways in Major Depressive Disorder: Implications for Precision Medicine

Background: Major depressive disorder (MDD) severely impairs individual health and creates heavy societal burdens. Diagnostic and therapeutic research...