ArticleFrontiers in public health2014
Early Recognition of High Risk of Bipolar Disorder and Psychosis: An Overview of the ZInEP "Early Recognition" Study.
Article in Frontiers in public health, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Establishing a clinical service to prevent psychosis: What, how and when? Systematic review.Translational psychiatry · 2021Pooled it
- Psychosocial Interventions, Recovery, and Mediating Mechanisms in Schizophrenia-Spectrum Disorders: A Systematic Review and Meta-Analysis of Longitudinal Studies.Brain sciences · 2026Review
- Shift in sex and age of individuals at a clinical high risk (CHR) for psychosis: relation to differences in recruitment methods and effect on sample characteristics.Schizophrenia (Heidelberg, Germany) · 2025Article
- Mismatch negativity generation in subjects at risk for psychosis: source analysis is more sensitive than surface electrodes in risk prediction.Frontiers in psychiatry · 2023Article
- Polygenic risk scores across the extended psychosis spectrum.Translational psychiatry · 2021Article
- Cortical Volume Differences in Subjects at Risk for Psychosis Are Driven by Surface Area.Schizophrenia bulletin · 2020Article
- Frontal brain activity in individuals at risk for schizophrenic psychosis and bipolar disorder during the emotional Stroop task - an fNIRS study.NeuroImage. Clinical · 2020Article
- Triple Network Model Dynamically Revisited: Lower Salience Network State Switching in Pre-psychosis.Frontiers in physiology · 2020Article
- Evaluation of the Combined Treatment Approach "Robin" (Standardized Manual and Smartphone App) for Adolescents at Clinical High Risk for Psychosis.Frontiers in psychiatry · 2019Article
- Borderline Personality Pathology in an At Risk Mental State Sample.Frontiers in psychiatry · 2019Article
- Early Somatosensory Processing Over Time in Individuals at Risk to Develop Psychosis.Frontiers in psychiatry · 2019Article
- Prediction Analysis for Transition to Schizophrenia in Individuals at Clinical High Risk for Psychosis: The Relationship ofFrontiers in psychiatry · 2017Article
- Self-labelling and stigma as predictors of attitudes towards help-seeking among people at risk of psychosis: 1-year follow-up.European archives of psychiatry and clinical neuroscience · 2016Article
- Alterations in the hippocampus and thalamus in individuals at high risk for psychosis.NPJ schizophrenia · 2016Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early detection of persons with first signs of emerging psychosis is regarded as a promising strategy to reduce the burden of the disease. In recent years, there has been increasing interest in early detection of psychosis and bipolar disorders, with a clear need for sufficient sample sizes in prospective research. The underlying brain network disturbances in individuals at risk or with a prodrome are complex and yet not well known. This paper provides the rationale and design of a prospective longitudinal study focused on at-risk states of psychosis and bipolar disorder. The study is carried out within the context of the Zurich Program for Sustainable Development of Mental Health services (Zürcher Impulsprogramm zur Nachhaltigen Entwicklung der Psychiatrie). Persons at risk for psychosis or bipolar disorder between 13 and 35 years of age are examined by using a multi-level-approach (psychopathology, neuropsychology, genetics, electrophysiology, sociophysiology, magnetic resonance imaging, near-infrared spectroscopy). The included adolescents and young adults have four follow-ups at 6, 12, 24, and 36 months. This approach provides data for a better understanding of the relevant mechanisms involved in the onset of psychosis and bipolar disorder, which can serve as targets for future interventions. But for daily clinical practice a practicable "early recognition" approach is required. The results of this study will be useful to identify the strongest predictors and to delineate a prediction model.
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