Bipolar Disorder

Euclidean Distance to Convex Polyhedra and Application to Class Representation in Spectral Images

With the aim of estimating the abundance map from observations only, linear unmixing approaches are not always suitable to spectral images, especially when the number of bands is too small or when the spectra of the observed data are too correlated. To address this issue in the general case, we present a novel approach which provides an adapted spatial density function based on any arbitrary lin...

Neural Constraints on Cognitive Experience and Mental Health

Understanding how neural dynamics shape cognitive experiences remains a central challenge in neuroscience and psychiatry. Here, we present a novel framework leveraging state-to-output controllability from dynamical systems theory to model the interplay between cognitive perturbations, neural activity, and subjective experience. We demonstrate that large-scale fMRI signals are constrained to low-...

Diagnostic-free onboard battery health assessment

Diverse usage patterns induce complex and variable aging behaviors in lithium-ion batteries, complicating accurate health diagnosis and prognosis. S...

A deep learning model for characterizing altered gyro-sulcal functional connectivity in abstinent males with methamphetamine use disorder and associated emotional symptoms.

Failure to manage emotional withdrawal symptoms can exacerbate relapse to methamphetamine use. Understanding the neuro-mechanisms underlying methamphe...

Efficient Fault Diagnosis in Lithium-Ion Battery Packs: A Structural Approach with Moving Horizon Estimation

Safe and reliable operation of lithium-ion battery packs depends on effective fault diagnosis. However, model-based approaches often encounter two m...

NNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High-Resolution Short-Echo-Time MR Spectroscopy Datasets.

Purpose To develop and evaluate the performance of NNFit, a self-supervised deep learning method for quantification of high-resolution short-echo-time...

Eeyore: Realistic Depression Simulation via Supervised and Preference Optimization

Large Language Models (LLMs) have been previously explored for mental healthcare training and therapy client simulation, but they still fall short i...

[Prediction of depression symptoms in seniors and analysis of influencing factors based on explainable machine learning].

This study aims to construct a machine learning model to predict depression symptoms in the elderly and analyze the key influencing factors of depres...

MDD-SSTNet: detecting major depressive disorder by exploring spectral-spatial-temporal information on resting-state electroencephalography data based on deep neural network.

Major depressive disorder (MDD) is a psychiatric disorder characterized by persistent lethargy that can lead to suicide in severe cases. Hence, timely...

Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities

Mental illness is a widespread and debilitating condition with substantial societal and personal costs. Traditional diagnostic and treatment approac...

LLM Assistance for Pediatric Depression

Traditional depression screening methods, such as the PHQ-9, are particularly challenging for children in pediatric primary care due to practical li...

DLinear-based Prediction of Remaining Useful Life of Lithium-Ion Batteries: Feature Engineering through Explainable Artificial Intelligence

Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for ensuring safety, reducing maintenance costs, and op...

Enhanced Large Language Models for Effective Screening of Depression and Anxiety

Depressive and anxiety disorders are widespread, necessitating timely identification and management. Recent advances in Large Language Models (LLMs)...

Investigating Large Language Models in Inferring Personality Traits from User Conversations

Large Language Models (LLMs) are demonstrating remarkable human like capabilities across diverse domains, including psychological assessment. This s...

Large Language Models for Mental Health Diagnostic Assessments: Exploring The Potential of Large Language Models for Assisting with Mental Health Diagnostic Assessments -- The Depression and Anxiety Case

Large language models (LLMs) are increasingly attracting the attention of healthcare professionals for their potential to assist in diagnostic asses...

Neural Timescale of Adolescents Major Depressive Disorder

Adolescent major depressive disorder (MDD) is characterized by heterogeneous symptomatology and complex neurodevelopmental underpinnings. Here, we inv...

Accurate and Interpretable Prediction of Antidepressant Treatment Response from Receptor-informed Neuroimaging

Conventional antidepressants show moderate efficacy in treating major depressive disorder. Psychedelic-assisted therapy holds promise, yet individual ...

Divergent Excitability of GABAergic Neurons Derived from Bipolar Disorder Patients Shapes Energy Shifts of Network Dynamics, possibly mimicking mania and depression

Bipolar disorder (BD) is characterized by fluctuating mood states, yet the cellular and circuit-level mechanisms distinguishing lithium-responsive (LR...

Dynamic Meta-Networking Identifies Distinct Network Correlates of Positive and Negative Formal Thought Disorder in Schizophrenia

Formal thought disorder (FTD) is a core symptom of schizophrenia, yet the neural network mechanisms underlying this phenotype remain poorly understood...

Resolving Heterogeneity in Major Depression: Overcoupling and Undercoupling Subtypes Exhibit Differential Treatment Response and Molecular Pathways

Major depressive disorder (MDD) exhibits significant heterogeneity whose neurobiological mechanisms remain elusive. Alterations in morphological-funct...