Neuroimaging (NI) has enormous potential to improve the clinical care of patients with psychiatric disorders, but has yet to deliver. The PSYSCAN project will address this issue directly by developing a NI-based tool that will help clinicians resolve key clinical issues in the management of patients with psychotic disorders. Clinicians will use the tool to assess patients with a standardised set of NI and complementary demographic, clinical, cognitive, and genetic measures. The clinician will enter data on to an iPad, and these data, along with NI data will be electronically transferred to a central facility for analysis. Key features of the analysis include the assessment of NI data at a network level, the integration of NI and non-NI data, and the use of machine learning methods to make predictions specific to the patient being assessed. The results will be delivered to the clinician’s iPad and will indicate the likelihood of a given clinical or functional outcome. The tool will have 3 clinical applications. PSYSCAN-Predict will facilitate prediction of the onset of psychosis in high risk subjects. PSYSCAN-Stratify will aid early diagnosis and the stratification of patients with first episode psychosis according to future course and outcome. PSYSCAN-Monitor will allow clinicians to measure progression of the disorder over time. The tool will be developed and validated in 2 large scale naturalistic studies using the consortium’s extensive network of centres. The validated tool will then be disseminated to clinical centres across the EU. The PSYSCAN project involves a world-class consortium of experts on NI and psychiatry that unites academic centres, SME’s with image processing and computerised testing expertise, a large medical device provider, and the pharmaceutical industry. The consortium is thus ideally suited to translating expertise and knowledge in NI to build a tool that can be used to improve the care of patients with psychiatric disorders.
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Régimen de financiaciónCP-FP - Small or medium-scale focused research project
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CB25 9TU Cambridge