217 papers
Understanding the human mind and treating mental health conditions requires a deep blend of biological insight and behavioral science. This category explores the cutting-edge research shaping how we diagnose and treat psychiatric disorders, from depression and anxiety to complex neurological conditions. Here, you will find the latest discoveries that bridge the gap between clinical observation and molecular mechanisms, offering fresh perspectives on patient care and therapeutic innovation.
Gist.Science brings these vital studies directly to you by monitoring medRxiv, the leading preprint server for health sciences. As soon as new manuscripts appear in this field, our team processes them to provide both accessible plain-language summaries and detailed technical overviews. This ensures that complex findings are understandable for everyone, from curious readers to busy professionals who need quick, accurate insights without wading through dense academic prose.
Below are the most recent psychiatry and clinical psychology preprints, curated and summarized to keep you informed on the evolving landscape of mental health research.
This study demonstrates that individuals at clinical high-risk for psychosis exhibit altered negative associations between corticolimbic glutamatergic metabolite levels and functional connectivity, a disruption not observed in healthy controls despite normal metabolite concentrations.
This study demonstrates that children with Specific Learning Disorder-Reading in the Malayalam alphasyllabary exhibit core phonemic awareness deficits that are most pronounced in linguistically complex tasks lacking lexical support, such as pseudoword blending, and show a distinct lack of age-related improvement compared to typically developing peers.
This study reveals that while autonomic arousal levels are similar across groups, the coupling between arousal and brain network switching—which predicts attentional performance in healthy individuals—is disrupted in schizophrenia spectrum disorders, and network switching rates specifically differentiate these patients from psychiatric controls.
This longitudinal study of the Dutch NeuroIMAGE cohort reveals that while ADHD symptoms are cross-sectionally associated with reduced brain surface area and subcortical volumes, clinical improvement into adulthood coincides with accelerated neural refinement, characterized by more pronounced cortical thinning and surface area reduction in prefrontal and occipital regions.
By integrating nationwide real-world clinical data with common and rare genetic variants in a large Danish cohort, this study employs deep learning to stratify schizophrenia spectrum disorder into ten distinct subgroups, revealing unique clinical profiles and specific genetic liabilities that advance biologically informed patient classification.
This study demonstrates that open-weight large language models can accurately extract clinically meaningful information from clinical interview transcripts to assess psychosis risk, offering a scalable and reliable tool to support early detection and preventive care.
This protocol outlines a parallel population-based target trial emulation study using linked health administrative data from British Columbia and Ontario (2010–2023) to compare the effectiveness of various pharmacological treatments for bipolar disorder among individuals with concurrent opioid use disorder, with outcomes including psychiatric acute-care visits, treatment discontinuation, and all-cause mortality.
This nationwide Danish cohort study of over 65,000 patients reveals that antipsychotic treatment exerts a time-dependent, biphasic effect on functional recovery, initially associated with reduced employment or education rates during the acute and consolidation phases (0–5 years) before showing a slight protective benefit in the long-term maintenance phase (5+ years).
This study demonstrates that wearable inertial sensors can objectively quantify distinct threat response movement patterns in young children, revealing that internalizing symptoms are linked to vigilance behaviors while comorbid externalizing symptoms shift these responses toward avoidance, thereby offering a promising tool for early mental health screening.
Using a fuzzy regression discontinuity design on data from over 43,000 primary care patients in Brazil, this study provides causal evidence that access to an AI-powered CBT chatbot significantly reduces anxiety and depressive symptoms, with the most pronounced benefits observed among rural, less-educated, and female populations.