Methodological approach.
Investigating multiple levels of brain complexities by combining two complementary approaches:
- Theory-driven approaches which use computational models that instantiate explicit hypotheses about brain functions at multiple levels of analysis (from circuit to behavioral level).
- Data-driven approaches which apply artificial intelligence and machine learning methods to high dimensional data to improve classification of disease, predict treatment outcomes or improve treatment selection.
These modelling approaches are integrated with empirical results of behavioral and neurophysiological experiments as well as with clinical and imaging data. Advanced neuroimaging analyses (MVPA, DCM, DTI) could be also used to test theoretical predictions.
Lab network facilities.
Imaging techniques (e.g., Policlinico Umberto I, Fondazione Santa Lucia), motion capture systems (e.g., University of Genova), neurophysiology (e.g., Campus Bio-Medico).
https://ctnlab.it/
Coordinators
Daniele Caligiore
Massimo Silvetti
Members
Pierandrea Mirino (PhD student)
Chiara Ponte (PhD student)
Adriano Capirchio (Postgraduate trainee)
Flora Giocondo (Postgraduate trainee)
Francesco Montedori (Postgraduate trainee)
