Research Pillars

WP1 aims to develop and validate multimodal predictive models for the early identification, classification, and monitoring of neurodegenerative diseases by integrating neuroimaging, clinical, genetic, and functional data. By leveraging advanced connectomics, artificial intelligence, and musculoskeletal imaging tools, it intends to deliver high-TRL diagnostic and prognostic tools to support clinical digital twins.

WP2 aims to develop, integrate, and validate wearable, implantable, and neurorehabilitation technologies designed for continuous monitoring and therapeutic intervention in patients affected by neurodegenerative diseases. The technologies developed in WP2 aim to achieve TRL 6, aligned with the objective of providing solutions ready for clinical trials and subsequent integration into the multifactorial digital twins planned in WP4.

WP3 aims to develop and bring advanced imaging and electrophysiological recording technologies to TRL 6, generating multifunctional and dynamic datasets from cellular and animal models to be used for the training and validation of the biological and biomechanical digital twins in WP4. The collected data will provide the quantitative, biologically relevant interface between experimental and computational models, contributing to the development of predictive, personalized tools for the study, diagnosis, and treatment of neurodegenerative diseases. The WP is organized into two complementary tasks focusing respectively on 3D cellular models and animal models, united by the goal of generating integrated, scalable measurements to describe the functional state of neuronal circuits under physiological and pathological conditions.

WP4 aims to develop biological and biomechanical digital twins capable of providing a realistic, personalized, and dynamic representation of the health status of patients affected by central nervous system disorders—particularly neurodegenerative conditions such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis, as well as genetic encephalopathies, developmental epilepsy, and blood-brain barrier dysfunctions. These digital twins will be generated by integrating clinical, biological, environmental, and sensory data obtained from in vitro and in vivo experimental models, advanced imaging, wearable technologies, environmental sensors, and digital platforms. The ultimate goal is to model the individual across multiple parameters, anticipate disease progression, and personalize diagnosis, treatments, and rehabilitation.