The Speakers

 Signals, Graphs, and Brains: Quantifying the Structure-Function Relationship

State-of-the-art neuroimaging such as magnetic resonance imaging (MRI) provides unprecedented opportunities to non-invasively measure human brain structure (anatomy) and function (physiology). To fully exploit the rich spatiotemporal structure of these data and gain insights into brain function in health and disorder, novel signal processing and modeling approaches are needed, instilled by domain knowledge from neuroscience and instrumentation. I will highlight one of our research avenues that leverages graph signal processing by integrating a brain graph (i.e., the structural connectome determined by diffusion-weighted MRI and tractography) and graph signals (i.e., the spatial activity patterns obtained by fMRI). The latter are decomposed onto a graph harmonic basis defined through the eigendecomposition of the graph Laplacian. Spectral filtering operations are then designed to separate brain activity into its structurally aligned and liberal parts, respectively, which allows quantifying of how strongly function is shaped by the underlying structure. The structure-function strength throughout the brain uncovers a behaviorally-relevant spatial gradient from uni- to transmodal regions, which is also informative about task conditions or identifying individuals. Several promising ongoing and future research directions will also be featured.

 Multi-scalar, Dynamic Intrinsic Brain Activity

The brain has all of the hallmarks of a complex system, with meaningful activity occurring at a wide range of spatial and temporal scales. When measured with resting state fMRI, all of this activity is compressed into a single measurement of the resulting hemodynamic response for each voxel at each time point. However, using multimodal preclinical neuroimaging techniques and leveraging the spatial, temporal and spectral properties of different types of activity, we may be able to identify signatures in the rs-fMRI signal. In this talk, I will describe some of the types of activity that we expect to contribute to the rs-fMRI signal, features that might allow us to selectively extract them for use in research or the clinic, and open questions about how to characterize and understand systems-level intrinsic activity in the brain.

Real-time Human Factors Assessment during Flight Operations and Training: A Neuroscience Perspective 

The aviation industry is moving away from an hours-based to a competency-based training approach. Neuroscience is nowadays able to provide objective insights regarding human factors behind those core competencies that a pilots should overcome, such as mental workload, stress, team cooperation. With respect to the standard methods to evaluate human factors, such as subjective measures (e.g. questionnaires), neuroscience-based ones have several advantages, allowing an objective, non-intrusive, and even real-time measure. Decoding real time information of the human’s states covertly, i.e., without asking anything to the user, could even make surrounding environment able to automatically adapt itself to offer the best conformable or safe condition, even before the operator becomes aware of the need to change something (e.g., lack of attention, or increasing of stress). 

The use of neuroscience during flight operations and training could offer then an interesting aid to the trainer for the real-time evaluation of the different human factors involved in the evaluation of a single trainee as well as a couple of trainees in the cockpit.

In the speech it will be reported the results from 5 years of experiments in the aeronautic domains performed within 5 european Horizon2020 projects. As a result of these projects, several neurophysiologic variables linked to the internal states of “Attention”, “Cognitive Workload” and even “Cooperation between pilots” have been employed and integrated in the Mindtooth technology. 

Such technology has been already demonstrated in many “out-of-the-lab” contexts (i.e. military and commercial pilots, air traffic controllers, car drivers, surgeons), by giving easy access to a large number of neural indicators related to different processes going on in the brain. This technology is fully compatible with the aviation instruments (e.g. headphones), and does not induce any discomfort during the operational activities, as confirmed by a recent validation performed on 22 pilots at Urbe aero training centre (Rome, Italy). Thus it can be easily integrated into training school services to further improve the trainee’s assessment and training program management by providing the instructors with the real-time evaluation of those human factors directly linked to operator pilots competences.