AIMC Topic: Schizophrenia
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Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection.
Scientific reports
Jul 1, 2025
Schizophrenia is a mental disorder characterized by hallucinations, delusions, disorganized thinking and behavior, and inappropriate affect. Early and accurate diagnosis of schizophrenia remains a challenge due to the disorder's complex nature and th...
Schizophrenia detection from electroencephalogram signals using image encoding and wrapper-based deep feature selection approach.
Scientific reports
Jul 1, 2025
Schizophrenia is a persistent and serious mental illness that leads to distortions in cognition, perception, emotions, speech, self-awareness, and actions. Affecting about 1% of people worldwide, schizophrenia usually emerges in late adolescence or e...
Prognostic predictions in psychosis: exploring the complementary role of machine learning models.
BMJ mental health
Jun 26, 2025
BACKGROUND: Predicting outcomes in schizophrenia spectrum disorders is challenging due to the variability of individual trajectories. While machine learning (ML) shows promise in outcome prediction, it has not yet been integrated into clinical practi...
Providing context: Extracting non-linear and dynamic temporal motifs from brain activity.
PloS one
Jun 12, 2025
Approaches studying the dynamics of resting-state functional magnetic resonance imaging (rs-fMRI) activity often focus on time-resolved functional connectivity (tr-FC). While many tr-FC approaches have been proposed, most are linear approaches, e.g. ...
A scientometric analysis of machine learning in schizophrenia neuroimaging: Trends and insights (2012-2024).
Journal of affective disorders
May 27, 2025
Machine learning applications in schizophrenia neuroimaging research have undergone significant evolution since 2012. However, a comprehensive scientometric analysis of this field has not yet been conducted. This study analyzed 315 original research ...
The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy.
BMC psychiatry
May 24, 2025
Motor activity alterations are key symptoms of psychiatric disorders like schizophrenia. Actigraphy, a non-invasive monitoring method, shows promise in early identification. This study characterizes Positive Schizotypy Factor (PSF) and Chronic Schizo...
Visualizing functional network connectivity differences using an explainable machine-learning method.
Physiological measurement
May 2, 2025
. Functional network connectivity (FNC) estimated from resting-state functional magnetic resonance imaging showed great information about the neural mechanism in different brain disorders. But previous research has mainly focused on standard statisti...
Ge-SAND: an explainable deep learning-driven framework for disease risk prediction by uncovering complex genetic interactions in parallel.
BMC genomics
May 1, 2025
BACKGROUND: Accurate genetic risk prediction and understanding the mechanisms underlying complex diseases are essential for effective intervention and precision medicine. However, current methods often struggle to capture the intricate and subtle gen...
Biological age prediction in schizophrenia using brain MRI, gut microbiome and blood data.
Brain research bulletin
Apr 27, 2025
The study of biological age prediction using various biological data has been widely explored. However, single biological data may offer limited insights into the pathological process of aging and diseases. Here we evaluated the performance of machin...
Brain circuits that regulate social behavior.
Molecular psychiatry
Apr 26, 2025
Social interactions are essential for the survival of individuals and the reproduction of populations. Social stressors, such as social defeat and isolation, can lead to emotional disorders and cognitive impairments. Furthermore, dysfunctional social...
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