AIMC Topic: Schizophrenia
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Neurofind: using deep learning to make individualised inferences in brain-based disorders.
Translational psychiatry
Feb 27, 2025
Within precision psychiatry, there is a growing interest in normative models given their ability to parse heterogeneity. While they are intuitive and informative, the technical expertise and resources required to develop normative models may not be a...
Identifying periphery biomarkers of first-episode drug-naïve patients with schizophrenia using machine-learning-based strategies.
Progress in neuro-psychopharmacology & biological psychiatry
Feb 25, 2025
Schizophrenia is a complex mental disorder. Accurate diagnosis and classification of schizophrenia has always been a major challenge in clinic due to the lack of biomarkers. Therefore, identifying molecular biomarkers, particularly in the peripheral ...
Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia.
Cell genomics
Feb 21, 2025
DNA methylation (DNAm) is a key epigenetic mark with essential roles in gene regulation, mammalian development, and human diseases. Single-cell technologies enable profiling DNAm at cytosines in individual cells, but they often suffer from low covera...
Multi-feature fusion method combining brain functional connectivity and graph theory for schizophrenia classification and neuroimaging markers screening.
Journal of psychiatric research
Feb 21, 2025
BACKGROUND: The abnormalities in brain functional connectivity (FC) and graph topology (GT) in patients with schizophrenia (SZ) are unclear. Researchers proposed machine learning algorithms by combining FC or GT to identify SZ from healthy controls. ...
Predicting mental health disparities using machine learning for African Americans in Southeastern Virginia.
Scientific reports
Feb 18, 2025
This study examined mental health disparities among African Americans using AI and machine learning for outcome prediction. Analyzing data from African American adults (18-85) in Southeastern Virginia (2016-2020), we found Mood Affective Disorders we...
Efficient Neural Network Classification of Parkinson's Disease and Schizophrenia Using Resting-State EEG Data.
Brain topography
Feb 17, 2025
Timely identification of Parkinson's disease and schizophrenia is crucial for the effective management and enhancement of patients' quality of life. The utilization of electroencephalogram (EEG) monitoring applications has proven instrumental in diag...
Predicting antipsychotic responsiveness using a machine learning classifier trained on plasma levels of inflammatory markers in schizophrenia.
Translational psychiatry
Feb 14, 2025
We apply machine learning techniques to navigate the multifaceted landscape of schizophrenia. Our method entails the development of predictive models, emphasizing peripheral inflammatory biomarkers, which are classified into treatment response subgro...
Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network.
Scientific reports
Feb 3, 2025
Previous deep learning-based brain network research has made significant progress in understanding the pathophysiology of schizophrenia. However, it ignores the three-dimensional spatial characteristics of EEG signals and cannot dynamically learn the...
Estimating the Prevalence of Schizophrenia in the General Population of Japan Using an Artificial Neural Network-Based Schizophrenia Classifier: Web-Based Cross-Sectional Survey.
JMIR formative research
Jan 29, 2025
BACKGROUND: Estimating the prevalence of schizophrenia in the general population remains a challenge worldwide, as well as in Japan. Few studies have estimated schizophrenia prevalence in the Japanese population and have often relied on reports from ...
A comparative machine learning study of schizophrenia biomarkers derived from functional connectivity.
Scientific reports
Jan 22, 2025
Functional connectivity holds promise as a biomarker of schizophrenia. Yet, the high dimensionality of predictive models trained on functional connectomes, combined with small sample sizes in clinical research, increases the risk of overfitting. Rece...
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