AIMC Topic: Psychotic Disorders
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Power spectral density-based resting-state EEG classification of first-episode psychosis.
Scientific reports
Jul 2, 2024
Historically, the analysis of stimulus-dependent time-frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic disorders during sensory and c...
Glycocalyx shedding patterns identifies antipsychotic-naïve patients with first-episode psychosis.
Psychiatry research
Jun 21, 2024
Psychotic disorders have been linked to immune-system abnormalities, increased inflammatory markers, and subtle neuroinflammation. Studies further suggest a dysfunctional blood brain barrier (BBB). The endothelial Glycocalyx (GLX) functions as a prot...
Dynamic and Transdiagnostic Risk Calculator Based on Natural Language Processing for the Prediction of Psychosis in Secondary Mental Health Care: Development and Internal-External Validation Cohort Study.
Biological psychiatry
Jun 7, 2024
BACKGROUND: Automatic transdiagnostic risk calculators can improve the detection of individuals at risk of psychosis. However, they rely on assessment at a single point in time and can be refined with dynamic modeling techniques that account for chan...
Examining the Most Important Risk Factors for Predicting Youth Persistent and Distressing Psychotic-Like Experiences.
Biological psychiatry. Cognitive neuroscience and neuroimaging
Jun 5, 2024
BACKGROUND: Persistence and distress distinguish more clinically significant psychotic-like experiences (PLEs) from those that are less likely to be associated with impairment and/or need for care. Identifying risk factors that identify clinically re...
Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.
Molecular psychiatry
Feb 9, 2024
Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psy...
Ecological momentary assessment (EMA) combined with unsupervised machine learning shows sensitivity to identify individuals in potential need for psychiatric assessment.
European archives of psychiatry and clinical neuroscience
Sep 16, 2023
Ecological momentary assessment (EMA), a structured diary assessment technique, has shown feasibility to capture psychotic(-like) symptoms across different study groups. We investigated whether EMA combined with unsupervised machine learning can dist...
Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification.
Human brain mapping
Mar 15, 2023
The validity and reliability of diagnoses in psychiatry is a challenging topic in mental health. The current mental health categorization is based primarily on symptoms and clinical course and is not biologically validated. Among multiple ongoing eff...
Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment.
Current psychiatry reports
Nov 18, 2022
PURPOSE OF REVIEW: This review will cover the most relevant findings on the use of machine learning (ML) techniques in the field of non-affective psychosis, by summarizing the studies published in the last three years focusing on illness detection an...
Identification of texture MRI brain abnormalities on first-episode psychosis and clinical high-risk subjects using explainable artificial intelligence.
Translational psychiatry
Nov 16, 2022
Structural MRI studies in first-episode psychosis and the clinical high-risk state have consistently shown volumetric abnormalities. Aim of the present study was to introduce radiomics texture features in identification of psychosis. Radiomics textur...
Data augmentation with Mixup: Enhancing performance of a functional neuroimaging-based prognostic deep learning classifier in recent onset psychosis.
NeuroImage. Clinical
Sep 29, 2022
Although deep learning holds great promise as a prognostic tool in psychiatry, a limitation of the method is that it requires large training sample sizes to achieve replicable accuracy. This is problematic for fMRI datasets as they are typically smal...
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