Many studies have shown widespread but subtle pathological changes in gray matter in patients with schizophrenia. Some of these studies have related specific alterations to the genesis of auditory hallucinations, particularly in the left superior temporal gyrus, but none has analysed the relationship between morphometric data and a specific scale for auditory hallucinations. The present study aims to define the presence and characteristics of structural abnormalities in relation with the intensity and phenomenology of auditory hallucinations by means of magnetic resonance voxel-based morphometry (MR-VBM) method applied on a highly homogeneous group of 18 persistent hallucinatory patients meeting DSM-IV criteria for schizophrenia compared to 19 healthy matched controls. Patients were evaluated using the PSYRATS scale for auditory hallucinations. Reductions of gray matter concentration in patients to controls were observed in bilateral insula, bilateral superior temporal gyri and left amygdala. In addition, specific relationships between left inferior frontal and right postcentral gyri reductions and the severity of auditory hallucinations were observed. All these areas might be implicated in the genesis and/or persistence of auditory hallucinations through specific mechanisms. Precise morphological abnormalities may help to define reliable MR-VBM biomarkers for the genesis and persistence of auditory hallucinations.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.pnpbp.2007.07.014 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
AI-Powered Neurogenetics: Supporting Patient's Evaluation with Chatbot.
Genes (Basel)
December 2024
Genomic Medicine Laboratory UILDM, IRCCS Santa Lucia Foundation, 00179 Rome, Italy.
Background/objectives: Artificial intelligence and large language models like ChatGPT and Google's Gemini are promising tools with remarkable potential to assist healthcare professionals. This study explores ChatGPT and Gemini's potential utility in assisting clinicians during the first evaluation of patients with suspected neurogenetic disorders.
Methods: By analyzing the model's performance in identifying relevant clinical features, suggesting differential diagnoses, and providing insights into possible genetic testing, this research seeks to determine whether these AI tools could serve as a valuable adjunct in neurogenetic assessments.
Adjunctive High-Definition Transcranial Direct Current Stimulation in Treatment of Resistant Auditory Verbal Hallucinations in Schizophrenia: A Randomized Sham-Controlled Trial.
J ECT
January 2025
Department of Psychiatry, Central Institute of Psychiatry, Kanke, Ranchi, Jharkhand, India.
Background: Resistant auditory verbal hallucination (AVH) remains a disabling symptom in schizophrenia. Transcranial direct current stimulation (tDCS) and its more targeted variant, high-definition tDCS (HD-tDCS), have shown promising results in reducing AVH. We aimed to determine the effects of adjunctive HD-tDCS on various dimensions of AVH in patients with schizophrenia.
View Article and Find Full Text PDFIntervention Programmes for First-Episode Psychosis: A Scoping Review.
Nurs Rep
January 2025
RISE-Health, Nursing School of Porto, 4200-450 Porto, Portugal.
The aim of this scoping review was to map intervention programmes for first-episode psychosis by identifying their characteristics, participants, and specific contexts of implementation. It seems reasonable to suggest that early intervention may be beneficial in improving recovery outcomes and reducing the duration of untreated psychosis (DUP). Despite the expansion of these programmes, there are still some significant variations and barriers to access that need to be addressed.
View Article and Find Full Text PDFAutomated Audit and Self-Correction Algorithm for Seg-Hallucination Using MeshCNN-Based On-Demand Generative AI.
Bioengineering (Basel)
January 2025
Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea.
Recent advancements in deep learning have significantly improved medical image segmentation. However, the generalization performance and potential risks of data-driven models remain insufficiently validated. Specifically, unrealistic segmentation predictions deviating from actual anatomical structures, known as a Seg-Hallucination, often occur in deep learning-based models.
View Article and Find Full Text PDFAI-pocalypse now: Automating the systematic literature review with SPARK (Systematic processing and automated review Kit) - gathering, organising, filtering, and scaffolding.
MethodsX
June 2025
School of Social Sciences, University of Tasmania, Tasmania 7005, Australia.
Researchers today face significant challenges reshaping the landscape of academic, government, and industry research due to the exponential growth of global research outputs and the advent of Generative Artificial Intelligence (GenAI). The annual increase in published works has made it difficult for traditional literature review and data analysis methods to keep pace, often rendering reviews outdated by the time of publication. In response, this methods article introduces a suite of new tools designed to automate a number of stages for systematic literature reviews.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!