Martin Ingvar

While psychotic disorders such as schizophrenia represent a significant clinical burden for the individual and large costs for the society, not much is known about the underlying causes. The effort to explain psychotic disorders must include an information processing level since subjective experiences and thoughts are fundamentally altered. We have suggested that there is an unstable low-level information processing in psychosis. In order to compensate for such noise, overly strong top-down influence from prefrontal circuits are recruited and may cause psychotic symptoms like delusions. In the present project we test our hypotheses using a multi-imaging approach. We will perform dynamic network analyses, i.e. connectomics, in MRI-datasets on psychosis patients, and associated phenotypes, with a focus on alterations in prefrontal circuits. We will relate our results to longitudinal behavioral outcomes and treatment effects as well as genetic risk and synaptic density (measured with PET). The results will probe whether these measurements can be used in future precision medicine for clinical assessment of patients. We will also perform task-based functional imaging studies (fMRI and MEG) of psychosis patients to study belief formation dependent on prefrontal cortex, and test causality of these circuits with brain stimulation in non-patients. The project also supports core costs of several ongoing clinical projects such as Karolinska Schizophrenia Project (KaSP).

Purpose and goal: We will implement a general modular information architecture that supports the patient´s path in care and where structured data is generated in the same format regardless of application. This data can then be used in real time for record keeping, local quality work and operational follow-up, quality register reporting and clinical research. The architecture allows multiple use of health data in real time and ensures that power over the content of care remains at the clinical level (including patient influence) Expected results and effects: The project will lead to increased understanding of how health data can be used for multiple purposes in line with the GDPR as the pre-planning makes the principles of the GDPR easy to implement. Secure local storage of health data in a known structure allows the healthcare provider to manage the responsibility for the data. The use of outcome measures in the feedback to healthcare is an important part of improving the content of healthcare. Organized patient processes supported by a transaction engine will help reduce variability in healthcare. Approach and implementation: First, the common structure for process-oriented information support will be implemented. The process system that was previously tried will be modified and an implementation of a CDR (clinical data repository) must be developed on the basis of previous experience. Four comprehensive scenarios will demonstrate the new way of working: Support for patients with chronic disease, self-monitoring, app support for qualified medication in rare diseases and longitudinal secondary prevention. The effect of real-time access to quality data in care will be studied in particular.