Computational Neuroscience Approach to Psychiatry: A Review on Theory-driven Approaches

Translating progress in neuroscience into clinical benefits for patients with psychiatric disorders is challenging because it involves the brain as the most complex organ and its interaction with a complex environment and condition. Dealing with such complexity requires powerful techniques. Computational neuroscience approach to psychiatry integrates multiple levels and types of simulation, analysis and computation according to the different types of computational models to enhance comprehending, prediction and treatment of psychiatric disorder. This approach comprises two approaches: theory-driven and data-driven. In this review, we focus on recent advances in theory-driven approaches that mathematically and mechanistically examine the relationships between disorder-related changes and behavior at different level of brain organization. We discuss recent progresses in computational neuroscience models that relate to psychiatry and show how principles of neural computational modeling can be employed to explain psychopathology.

IHE, solution for integration of information systems and PACS

PACS is used as a way to store images and matches well with the workflow in the radiology department and can spread to other parts of hospital. Integration with other PACS and other hospital systems like radiology information system [RIS], hospital information system [HIS], and electronic patient records has been completely done, but there are still problems. PACS also provide good conditions for setting up Tele-radiology. The next step for PACS is where hospitals and health care organizations share photos in integrated electronic patient record. Among the different ways for sharing photos between different hospitals, IHE [integrating the health care enterprise] standard indexes the cross-enterprise document sharing profile [XDS] and allows sharing photos from various hospitals even if their PACS has different brands and different vendors. Application of XDS is useful for sharing images between health care organizations without duplicating them in a central archive. Images need to be indexed in a central registry. In the XDS profile, IHE defines an indexing mechanism for printing and indexing images in the central document registry. IHE also defines mechanisms to be used by each hospital to retrieve images, regardless of storing them in hospital PACS