Cognitive computational neuroscience

The creation of this unit within the area of the Dementia Research Center was prompted by the massive digital revolution that the biomedical field is currently undergoing. The events of 2020 have highlighted the very real need to rapidly translate advances in scientific knowledge into solutions for the benefit of individuals and society as a whole. The recent rush of biomedical research on technology transfer has changed the way in which clinical research is carried out and medical decisions taken, especially in the field of chronic diseases and in the management of frail older adults.

Against this background, the Cognitive Computational Neuroscience (CNN) Research Unit aims to become, for the national and international scientific and biomedical community, a benchmark of technology transfer in the field of cognitive neuroscience, thanks to collaborations with historic university institutions and scientific clinics and, equally importantly, partnerships with innovative companies operating in the biomedical field. 

The Unit adopts an integrated approach to cognitive neuroscience, embracing all aspects, from experimental research to clinical applications, and focusing, in particular, on the immediate technological transfer of research ideas. The aim is to ensure that, through an innovative and effective approach, the results of research carried out by the Unit are rapidly applied within the medical and healthcare field.

The CNN Research Unit has the following main aims:

– to develop and validate new digital screening, monitoring and rehabilitation tools for cognitive disorders, so as to encourage better use of limited financial resources and move diagnostic and therapeutic pathways away from the hospital setting directly to the patient’s home.

– to develop and validate models for interpreting cognitive-behavioral profiles in subjective cognitive decline, since such models, based on neuropsychological data, might make it possible to predict possible negative medium- and long-term outcomes and thus allow early identification of healthy at-risk populations that could be referred for preventive programs; accordingly, they could also allow cost savings and healthcare promotion.

– to develop and validate models for predicting the risk of negative cognitive and psychosocial outcomes in frail patients with chronic disease, since such models, based on psychosocial fragility and vulnerability indices, might make it possible to monitor the bio-psycho-social profiles of individual patients, identifying their needs and referring them for targeted care pathways with the ultimate aim of increasing the well-being of society as a whole.