11.2022 - 02.2026

Integrated technologies for brain connectomics

The main objective of this project is to integrate multi-modal imaging technologies for brain connectomics into medically interpretable AI models, starting from the use of connectivity-based machine learning (ML) tools to address neuronal plasticity in health and disease, to support the design of personalized treatments. In one of the task, the role of structure, function and metabolism across connectome layers is investigated with the express purpose to explain neuronal plasticity effects and cognitive, behavioral and motor control regulation in health and disease, using multi-modal neuroimaging data sets. For certain neurological diseases, such as Parkinson’s Disease (PD), the project also aims at linking molecular substrates to neuroimaging derived measurements and neurological symptoms, to correlate the serum biochemical profiling, as resulting from laboratory measurements to selected features of the human functional connectome. Novel connectome features are being introduced to addressing neuronal dynamics, connectivity and plasticity using neurostimulation, neuromodulation and neurorehabilitation technologies and the multi-modal portability of such features (e.g., between EEG and MRI) is also assessed.


Possibili applicazioni: Individual multi-scale characterization of the patient’s brain for monitoring pharmacological, physical and cognitive treatments in rehabilitation programs. Connectome-based predictive modelling of the disease course. 

Spoke 2 : Neural Plasticity and Connectivity