Psychiatry

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 perception and interpretation of social stimuli. Assessments of social behavior have been limited and have typically been conducted using time-consuming, manual, and not always reliable methods. The current study aimed to characterize patterns of head movemen...

The Role of Generative Artificial Intelligence in Evaluating Adherence to Responsible Press Media Reports on Suicide: A Multi-Site, Three-Language Study.

Artificial intelligence (AI) in psychotherapy: A challenging frontier.

ObjectiveArtificial intelligence (AI) chatbots have emerged as a potential tool to revolutionise mental health care, offering innovative solutions for...

Age-Related Differences in Risk Factors for Long-Term Care Certification in Japan: A Decision Tree Analysis Spanning 3 Years.

Ensuring healthy aging and extending health span is critical, particularly in aging societies. This study analyzed patterns of risk for long-term care...

Modeling Brain Aging with Explainable Triamese ViT: Towards Deeper Insights into Autism Disorder.

Machine learning, particularly through advanced imaging techniques such as three-dimensional Magnetic Resonance Imaging (MRI), has significantly impro...

Depression is associated with treatment response trajectories in adults with Prolonged Grief Disorder: A machine learning analysis.

BACKGROUND: Although evidence-based Prolonged Grief Disorder treatments (PGDT) exist, pretreatment characteristics associated with differential improv...

Multiband EEG signatures decoded using machine learning for predicting rTMS treatment response in MDD.

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is a promising treatment for major depression disorder (MDD), particularly for treatme...

Prediction of attention deficit hyperactivity disorder using the comprehensive attention test: a large-scale machine learning approach.

BACKGROUND: The diagnosis of attention deficit hyperactivity disorder (ADHD) relies on comprehensive approaches, including clinical interviews, scales...

EEG Connectivity as Predictor of ICAN ADHD Children's Improvement After Completion of Theta Beta Ratio Neurofeedback: Machine Learning Analyses.

Attention deficit hyperactivity disorder is a prevalent syndrome that costs billions of dollars annually. Finding meaningful interventions based upon ...

Large-scale examination of early-age sex differences in neurotypical toddlers and those with autism spectrum disorder or other developmental conditions.

Autism spectrum disorder (ASD) is clinically heterogeneous, with ongoing debates about phenotypic differences between boys and girls. Understanding th...

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...

The influence of mental state attributions on trust in large language models.

Rapid advances in artificial intelligence (AI) have led users to believe that systems such as large language models (LLMs) have mental states, includi...

Identifying key risk factors for intentional self-harm, including suicide, among a cohort of people prescribed opioid agonist treatment: A predictive modelling study.

BACKGROUND AND AIMS: People with opioid use disorder are at increased risk of intentional self-harm and suicide. Although risk factors are well known,...

Exploring voice as a digital phenotype in adults with ADHD.

Current diagnostic procedures for attention deficit hyperactivity disorder (ADHD) are mainly subjective and prone to bias. While research on potential...

Deep learning algorithms reveal genomic markers for anxiety disorder in a large cohort of children with down syndrome.

Despite a significant burden of neurobehavioral and psychiatric comorbidities in children with Down syndrome (DS), and the general increased risk for ...

The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy.

Motor activity alterations are key symptoms of psychiatric disorders like schizophrenia. Actigraphy, a non-invasive monitoring method, shows promise i...

Needs and Preferences of Swedish Young Adults for a Digital App Promoting Mental Health Literacy, Occupational Balance, and Peer Support: Qualitative Interview Study.

BACKGROUND: Young adults experience stressors in their transition to adulthood and are at increased risk of mental ill-health. This risk is compounded...

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 ...

Unlocking the Potential of Wear Time of a Wearable Device to Enhance Postpartum Depression Screening and Detection: Cross-Sectional Study.

BACKGROUND: Postpartum depression (PPD) is a mood disorder affecting 1 in 7 women after childbirth that is often underscreened and underdetected. If n...