Ricerc@Sapienza

Human tissues own conductive properties, and the electrical activity produced by human organs can propagate throughout the body due to neuro transmitters and electrolytes. Therefore, it might be reasonable to hypothesize correlations and similarities between electrical activities among different parts of the body.

The human capacity to simultaneously perform several tasks depends on the quantity and the mode of mentally processing the information imposed by the tasks. Since operational environments are highly dynamic, priorities across tasks will be expected to change as the mission evolves, thus the capability to reallocate the mental resources dynamically depending on such changes is very important. The resources required in very complex situations, such as air traffic management (ATM), can exceed the user's available resources leading to increased workload and performance impairments.

Stress is a word used to describe human reactions to emotionally, cognitively and physically challenging experiences. A hallmark of the stress response is the activation of the autonomic nervous system, resulting in the "fight-freeze-flight" response to a threat from a dangerous situation. Consequently, the capability to objectively assess and track a controller's stress level while dealing with air traffic control (ATC) activities would make it possible to better tailor the work shift and maintain high safety levels, as well as to preserve the operator's health.

An important feature of complex networks that can help to understand their internal organization is community structure. Recognize such structures in brain networks could be crucial, as the brain functioning is thought to be based on modular organization. Moreover, brain networks are intrinsically multilayer, which essentially means they can vary in time, in frequency or other domains depending on the topology of the network. In the last decades, some multilayer clustering algorithm has been developed with the aim to identify communities in dynamic networks.

Motifs are small recurring meso-scale structures characterizing real networks. Studies in literature investigated network motifs focusing only on their frequency spectrum. In the present work, we propose to study functional brain networks by means of motifs analysis, going beyond the identification of brain network-characterizing motifs. Indeed, we defined and implemented new indices based on motifs analysis and we applied this approach to better understand the architecture of brain networks after stroke.

Several non-invasive imaging methods have contributed to shed light on the brain mechanisms underlying working memory (WM). The aim of the present study was to depict the topology of the relevant EEG-derived brain networks associated to distinct operations of WM function elicited by the Sternberg Item Recognition Task (SIRT) such as encoding, storage, and retrieval in healthy, middle age (46 ± 5 years) adults. High density EEG recordings were performed in 17 participants whilst attending a visual SIRT.

We review the theory and algorithms of electrophysiological brain connectivity analysis. This tutorial is aimed at providing an introduction to brain functional connectivity from electrophysiological signals, including electroencephalography, magnetoencephalography, electrocorticography, and stereoelectroencephalography. Various connectivity estimators are discussed, and algorithms introduced. Important issues for estimating and mapping brain functional connectivity with electrophysiology are discussed.

Two-person neuroscience (2 ​PN) is a recently introduced conceptual and methodological framework used to investigate the neural basis of human social interaction from simultaneous neuroimaging of two or more subjects (hyperscanning). In this study, we adopted a 2 ​PN approach and a multiple-brain connectivity model to investigate the neural basis of a form of cooperation called joint action.