diagnosis

New Study Reveals: Childhood Brain Morphometry Predicts Future Risk of Psychosis, Depression, and Anxiety

Background
Gray matter morphometry studies have lent seminal insights into the etiology of mental illness. Existing research has primarily focused on adults and then, typically on a single disorder. Examining brain characteristics in late childhood, when the brain is preparing to undergo significant adolescent reorganization and various forms of serious psychopathology are just first emerging, may allow for a unique and highly important perspective of overlapping and unique pathogenesis.

Methods
A total of 8645 youth were recruited as part of the Adolescent Brain and Cognitive Development study. Magnetic resonance imaging scans were collected, and psychotic-like experiences (PLEs), depressive, and anxiety symptoms were assessed three times over a 2-year period. Cortical thickness, surface area, and subcortical volume were used to predict baseline symptomatology and symptom progression over time.

Results
Some features could possibly signal common vulnerability, predicting progression across forms of psychopathology (e.g. superior frontal and middle temporal regions). However, there was a specific predictive value for emerging PLEs (lateral occipital and precentral thickness), anxiety (parietal thickness/area and cingulate), and depression (e.g. parahippocampal and inferior temporal).

Conclusion
Findings indicate common and distinct patterns of vulnerability for varying forms of psychopathology are present during late childhood, before the adolescent reorganization, and have direct relevance for informing novel conceptual models along with early prevention and intervention efforts.

New Study Reveals: Childhood Brain Morphometry Predicts Future Risk of Psychosis, Depression, and Anxiety Read More »

Virtually screening adults for depression, anxiety, and suicide risk using machine learning and language from an open-ended interview

Current screening techniques for depression, anxiety, and suicide rely on retrospective patient reports to standardized scales. But combining a natural language processing (NLP) and machine learning (ML) approach with qualitative screening shows promise. This new approach detects depression, anxiety, and suicide risk by analyzing a patient’s language during a 5-to-10-min open-ended interview. This recent study

Virtually screening adults for depression, anxiety, and suicide risk using machine learning and language from an open-ended interview Read More »

How Machine Learning Can Detect Social Anxiety Disorder? Using Connectivity and Graph Theory Measures Effectively!

Social anxiety disorder (SAD) is a serious concern for medical practitioners worldwide. Identifying its severity level (severe, moderate, mild, or none) can be challenging, which is why this paper proposes a solution. The researchers developed a method that classifies SAD severity levels using the patterns of brain information flow and graphical network structures, analyzed via

How Machine Learning Can Detect Social Anxiety Disorder? Using Connectivity and Graph Theory Measures Effectively! Read More »

anxiety panic attack fear treatment help OCD phobia psychotherapy psychiatry Dr Jonathan Haverkampf

An Overview of Substance Use, Mood, and Anxiety Disorders in ADHD and Non-ADHD Adults

Publication date: Available online 5 May 2023Source: Neuroscience & Biobehavioral ReviewsAuthor(s): Catharina A. Hartman, Qi Chen, Berit Skretting Solberg, Ebba Du Rietz, Kari Klungsøyr, Samuele Cortese, Søren Dalsgaard, Jan Haavik, Marta Ribasés, Jeanette C. Mostert, Berit Libutzki, Sarah Kittel-Schneider, Bru Cormand, Melissa Vos, Henrik Larsson, Andreas Reif, Stephen V. Faraone, Alessio Bellato

An Overview of Substance Use, Mood, and Anxiety Disorders in ADHD and Non-ADHD Adults Read More »

A New Model Predicts Depression and Anxiety Using Artificial Intelligence and Social Media

Utilizing data from Twitter and applying natural language processing artificial intelligence algorithms, researchers created a new, accurate prediction model for depression and anxiety.

A New Model Predicts Depression and Anxiety Using Artificial Intelligence and Social Media Read More »

St Patrick’s – The Anxiety Disorders Programme

Anxiety Anxiety is the body and mind’s natural reaction to threat or danger. In certain cases, high levels of anxiety are considered normal and helpful if they prompt an escape from danger. In situations such as interviews and exams, anxiety can enhance performance. When anxiety becomes excessive or debilitating, however, it is then considered an

St Patrick’s – The Anxiety Disorders Programme Read More »