About this Research Topic
The description of "at risk mental state" for psychosis, Alzheimer disease and depression has been a highly productive change of research paradigm over the last 30 years. Mild cognitive impairment, ultra high-risk for psychosis or subsyndromal depression represented the first attempts to move from clinically overt pathologies to transition stages. This new way to conceptualize the old debate on prediction in psychiatry implied the need for identifying the determinants of individual trajectories instead of group differences. New challenges and difficulties are of course obvious. The limitations of current diagnostic systems and risk identification approaches, the diffuse and unstable symptom patterns in early stages, and their pluripotent, transdiagnostic trajectories are some of them. Several studies attempted to reach prediction of at risk mental state evolution combining neuroimaging, genetic and epigenetic diathesis and psychological parameters including personality. Environmental parameters such as nutrition, cardiovascular risk factors, and life events are also considered not only as risk or protective factors but also as part of a more complex puzzle that could define the vulnerability to mental illness at an individual level. This research gave the first and very promising results in the field of Alzheimer disease with the biomarker-based definition of its preclinical stages. Similar trends are now seen for patients with sub-threshold symptoms of psychosis. A holistic strategy can be developed that applies these new prediction approaches, as well as machine learning and iterative probabilistic multimodal models, to a blend of neuroimaging, genetic and psychological data, acquired through longitudinal assessments.
This Research Topic of Frontiers in Psychiatry welcomes the submission of manuscripts either describing original research or reviewing the scientific literature, including systematic reviews and meta-analyses.
Keywords: Gene-environement interactions, Mental Disorders, Predictive Biomarkers, Prodromal States, Neuroimaging
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