Psychiatry

From Big to Small: Emerging Methods for Enhancing Precision Psychiatry Through Transfer Learning.

Identifying reliable links between individual differences in neurobiological features and differences in symptom profiles or treatment outcomes is a primary goal of precision psychiatry. In this context, brain-behavior predictive modeling has emerged as a powerful approach for elucidating the neural mechanisms underlying both basic cognitive functions and complex clinical phenomena. However, the w...

From Likert Scales to Large Language Models: Validating a Computational Approach to Psychological Assessment of Future Self-Continuity.

Recent advances in Large Language Models (LLMs) offer new assessment approaches that can help overcome the limitations of traditional Likert-item scales in measuring complex, subjective constructs. To demonstrate this, we introduce and validate a novel LLM-based methodology for psychological assessment by applying it to Future Self-Continuity (FSC), the perceived connection, including similarity, ...

Navigating artificial intelligence's role in suicide prevention: balancing innovation with ethical vigilance.

Integrating structural and functional brain features to classify major depressive disorder: a multi modal approach.

Major Depressive Disorder (MDD) is a common mental disorder that markedly impairs psychosocial functioning and quality of life. Multi-modal fusion met...

Phase-sensitive individualized pharmacotherapy for alcohol use disorder.

Alcohol use disorder (AUD) is a prevalent psychiatric disorder that continually causes significant suffering. Although pharmacotherapies are available...

Screening for depression risk in asthma patients: Development and external validation of a machine learning-based predictive model.

BACKGROUND: Depression is prevalent among asthma patients, negatively impacting their quality of life, treatment adherence, and prognosis. This study ...

Biased minds, sensitive hearts: Divergent neural signatures of interpersonal sensitivity in atypical and non-atypical depression.

BACKGROUND: Atypical depression (AD) is a distinct subtype of depression, with interpersonal sensitivity as one of its core characteristics. However, ...

Association between triglyceride-glucose index and depression and the mediating role of hypertension: a cross-sectional study of obese adults.

BACKGROUND: Recent findings indicate a positive correlation between the TyG (triglyceride-glucose) index and the incidence of depression. However, the...

Interpretable machine learning models for predicting depression status and remission three years later in older adults in China: A longitudinal study.

BACKGROUND: Depression significantly impacts older adults, making it valuable to use machine learning to predict their future depressive status and as...

Primary-care-focused interpretable machine learning model for depression screening in geriatrics: A comparative study of multiple algorithms.

BACKGROUND: The prevalence of depression among older adults places a considerable strain on healthcare systems due to a shortage of psychiatrists for ...

Development and validation of a machine learning model for predicting the risk of current depression in physically inactive adults in the United States.

Depression is a major global public health concern, with physical inactivity recognized as a key modifiable risk factor. However, tools for predicting...

Uncovering predictors of bipolar II conversion to bipolar I: A machine learning analysis of national health records in Taiwan.

BACKGROUND: Bipolar II disorder (BD-II) can progress to bipolar I disorder (BD-I), carrying profound psychosocial consequences. However, limited resea...

AI as a Novel Digital Stressor in Adolescent Psychosis: Clinical and Ethical Implications.

ObjectiveWith the rapid adoption of artificial intelligence (AI) technologies by adolescents, the impact on their mental health is of critical concern...

Modeling homosynaptic and heterosynaptic plasticity with a single neuromemristive synapse.

INTRODUCTION: Efforts are being made to design a brain-like intelligence due to its robustness, synaptic modification (i.e., learning and memory), ana...

A Window Into Vulnerability and Support: At-Risk Mothers' Perinatal Experiences and Perspectives on mHealth-Based Care.

AIM: To examine the perinatal experiences of at-risk mothers and their engagement with mobile-health-based care. DESIGN: A qualitative descriptive stu...

Beyond Wealth: The Impact of Robot Adoption on Chronic Diseases.

While the labor market effects of industrial robots have been extensively studied, their broader health implications, particularly on chronic diseases...

Anxiety Promotes Occlusal Interference-Induced Myalgia via the Mesolimbic System.

Acute occlusal interference may induce chronic masticatory myalgia in some individuals. The potential factors and underlying mechanisms that predispos...

Machine Learning to Detect Vocal Stereotypy: Improving Duration-Based Measures.

Direct observation is a process central to behavior science, but its implementation may be challenging in some contexts (e.g., classrooms, homes). One...

Neurophysiological mechanisms and predictive modeling of SSRI treatment response in depression disorder based on multidimensional EEG features.

BACKGROUND: Depression exhibits significant heterogeneity in antidepressant treatment response. This study aimed to develop an Electroencephalography ...

Sleep disturbance recorded via wearable sensors predicts depression severity 9 years later.

BACKGROUND: Major depressive disorder (MDD) is prevalent and poses major public health implications. Autonomic nervous system (ANS) dysregulation and ...