The course provides an overview of the application of brain imaging methods to study human cognitive and sensorimotor processes and to identify neural architectures underlying normal and abnormal cognitive functioning. The main foundational and methodological issues, listed below, will be introduced and critically discussed during lectures, together with the presentation of typical findings and applications, with particular emphasis on the field of visual cognition: 1) Introduction: history of cognitive neuroscience and of neuroimaging techniques; basic conceptual foundations of cognitive neuroimaging. 2) Technological and physiological foundations of neuroimaging: cerebral metabolism, blood flow and oxygenation; positron emission tomography; (functional) magnetic resonance imaging; electro- and magneto-encephalography. 3) Experimental design in functional neuroimaging: cognitive subtraction; categorical, parametric, and factorial designs; block and event-related designs. 4) Data analysis in functional neuroimaging: preprocessing; general linear model; statistical inference; voxel- and ROI-based approaches; data-driven approaches. 5) Conceptual issues in neuroimaging: functional specialization vs. integration; relationship with computational models of mind functioning. 6) Structural neuroimaging: morphometry, structural connectivity, lesion-symptom mapping. 7) Advanced methods: adaptation and priming, pattern analysis, decoding, machine learning. 8) Connectomics: resting-state networks, psychophysiological interactions, dynamic causal modelling. The laboratory consists in guided practical experience in visualizing, manipulating, and analyzing brain images, using MATLAB and widely used tools such as SPM, FieldTrip, and MRIcro, and will include: analysis of sample PET data; complete preprocessing and analyzing of a true single-subject fMRI dataset; complete preprocessing and analyzing of a true single-subject EEG dataset; analysis of lesional, morphometric and functional connectivity data; writing simple MATLAB scripts to automate processing steps.

Teaching material is available on the course site on the Sapienza e-learning platform, and consists of slides, online resources, and scientific papers which either are publicly accessible or can be downloaded via the Sapienza digital library.