BACKGROUND: Currently available cardiovascular disease (CVD) risk prediction tools may underestimate the risk in individuals with schizophrenia. OBJECTIVE: To develop and externally validate 5-year CVD risk prediction models for people with schizophr...
OBJECTIVE: Neuropsychiatric disorders are characterized by high complexity and comorbidity, imposing a substantial burden on both patients and society. However, their elusive pathogenic mechanisms impede accurate clinical diagnosis and effective inte...
Schizophrenia is a complex psychiatric disorder that disrupts cognition, emotions, and social behavior. Timely and accurate diagnosis is essential for effective treatment. Traditional diagnostic methods relying on clinical assessments have limitation...
BACKGROUND: Lipids play a vital role in health and disease, but changes to their circulating levels and the link with schizophrenia remains poorly characterized. This study aimed to investigate the pathological lipid profiles in patients with first-e...
Schizophrenia (SZ) is increasingly recognized as a network disorder marked by abnormal functional connectivity, yet the clinical utility of fMRI remains limited. Electroencephalography (EEG) provides a more practical alternative, though conventional ...
International journal of neural systems
Nov 19, 2025
This research looks at the use of long-short-term memory (LSTM) networks to predict psychosis, in patients within the schizophrenia spectrum, based on Heart Rate Variability (HRV) data acquired from wearable devices. Our primary objective is to test ...
Neural network models for outcome prediction play a pivotal role in neurological disease research, particularly for baseline risk assessment. Schizophrenia, a complex and relatively rare neuropsychiatric disorder, presents significant diagnostic chal...
Schizophrenia is a heterogeneous disorder with diverse clinical presentations and neuroanatomical alterations. Despite recent advances, we still lack a working hypothesis for the pathophysiology of schizophrenia. One reason might be the heterogeneous...
The heterogeneity of psychotic disorders leads to instability in subjectively defined diagnoses. This study used a machine learning framework termed common orthogonal basis extraction (COBE) to decompose electroencephalography-based functional connec...
Schizophrenia is a complex neuropsychiatric disorder characterized by significant heterogeneity, posing a challenge for accurate classification using neuroimaging data. Graph convolutional networks (GCNs) have emerged as a promising approach for leve...
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