Psychotic disorders have a wide variety of outcomes, as exemplified by the influential 1972 Bleuler’s study on the long-term course of schizophrenia. The biological heterogeneity within psychotic disorders, which can characterize the disease phenotypes and clinical courses, has also drawn attention. Recent evidence revealed that early detection and intervention could lead to better clinical and functional outcomes in patients with schizophrenia, possibly by preventing or ameliorating active brain changes during the early phases of psychotic disorders. Identifying prognostic biomarkers in patients with psychotic disorders (especially in the early stages) may contribute to improving their long-term outcome, but no clinically reliable biomarkers have been identified yet.
This Research Topic aims to examine the association of biological characteristics at baseline with clinical, cognitive, and functional variables at follow-up in psychotic disorders. Elucidating such relationships in the prodromal state for psychotic disorder, such as at-risk mental state and adolescents with subclinical psychotic experiences (even if they do not subsequently develop into psychotic disorder), is also clinically useful. We may be able to achieve precision medicine in psychotic disorder, by collecting many candidates for prognostic biomarkers and creating a highly accurate classifier using these biological features.
Topics of interest include;
• The target medical condition is the early stage of psychotic disorders including schizophrenia, genetic/clinical high-risk state for psychotic disorder, and adolescents with subclinical psychotic experiences.
• Extraction of baseline biological characteristics using structural/functional MRI, EEG/MEG, MRS, PET, etc. Multimodal analyses may be preferred.
• Longitudinal studies are also welcome.
• Including clinical indicators such as positive/negative symptom severity, self-disorder, relapse, treatment response/resistance, cognitive performance, and social functioning, etc., which have an impact on functional outcomes would be preferred.
• Based on the results, discriminant analyses using machine learning systems are also desirable.
Psychotic disorders have a wide variety of outcomes, as exemplified by the influential 1972 Bleuler’s study on the long-term course of schizophrenia. The biological heterogeneity within psychotic disorders, which can characterize the disease phenotypes and clinical courses, has also drawn attention. Recent evidence revealed that early detection and intervention could lead to better clinical and functional outcomes in patients with schizophrenia, possibly by preventing or ameliorating active brain changes during the early phases of psychotic disorders. Identifying prognostic biomarkers in patients with psychotic disorders (especially in the early stages) may contribute to improving their long-term outcome, but no clinically reliable biomarkers have been identified yet.
This Research Topic aims to examine the association of biological characteristics at baseline with clinical, cognitive, and functional variables at follow-up in psychotic disorders. Elucidating such relationships in the prodromal state for psychotic disorder, such as at-risk mental state and adolescents with subclinical psychotic experiences (even if they do not subsequently develop into psychotic disorder), is also clinically useful. We may be able to achieve precision medicine in psychotic disorder, by collecting many candidates for prognostic biomarkers and creating a highly accurate classifier using these biological features.
Topics of interest include;
• The target medical condition is the early stage of psychotic disorders including schizophrenia, genetic/clinical high-risk state for psychotic disorder, and adolescents with subclinical psychotic experiences.
• Extraction of baseline biological characteristics using structural/functional MRI, EEG/MEG, MRS, PET, etc. Multimodal analyses may be preferred.
• Longitudinal studies are also welcome.
• Including clinical indicators such as positive/negative symptom severity, self-disorder, relapse, treatment response/resistance, cognitive performance, and social functioning, etc., which have an impact on functional outcomes would be preferred.
• Based on the results, discriminant analyses using machine learning systems are also desirable.
