Dottorato in NEUROSCIENZE DEL COMPORTAMENTO

Titolo della tesi: Deep sleep and its cognitive effects - Slow wave activity and the learning and sleep cycle

Sleep is definitely an essential cerebral state for all living beings. The occurrence of sleep passed through the evolutionary sieve and is widespread in animal species. Sleep is known to be beneficial to cognitive and mnemonic tasks, while chronic sleep deprivation is detrimental. Indeed, young humans pass most of their time sleeping, and younger are the subjects that have to learn at faster rates. In adults, sleep deprivation is detrimental for cognition and it is one of the worst tortures that can be inflicted. Despite the importance of the phenomenon in relation with the cognitive effects, a complete understanding of its functions and underlying mechanisms is still lacking. Recent studies focused the attention on a specific phase of sleep in relation to cognition and learning: the deepest of physiological non-REM sleep stage in which specific phenomena like Slow Oscillations (SO) are the default emergent activity of the cortical network and are observed as an alternation between Down states (characterized by nearly silent neurons) and Up states (with intense neuronal activity). Among the contribution of this work, there is the building of fundamental instruments for the study of this phenomenon and of its interplay with learning, also thanks to the experimental context in which I am involved: the European Human Brain Project (HBP) and The Motor Control and Cognition Lab (MCC_lab) directed by Prof. Stefano Ferraina at Sapienza University. Basically, we developed an analysis tool for the extraction of salient feature (velocity, direction, planarity of the slow waves and other specific features) from the experimental recordings in vivo acquired with different methodologies (electrophysiology, calcium imaging, etc.), and simulations tools able to reproduce the behaviour of large portion of the cerebral cortex. To complement this activity, we built theoretical models and simulation tools able to reproduce the SWA and their effect on learning, demonstrating that such a cerebral state has a fundamental role in memory consolidation. The simulated outputs of these theoretical models are then analyzed with the same instruments used to analyze in vivo recorded data, allowing a faster and simpler comparison between experimental and synthetic data. In the end, I also had the chance to analyze signals recorded in vivo on monkeys during learning tasks, more specifically during the countermanding and the transitive inference tasks. I had not the possibility to verify the effect of sleep on these tasks, due to the impossibility of setting-up new experiments on animals in the last period, but the activity done, other then reporting interesting results, was preparatory to the study of the role of sleep on learning process and memory consolidation. Moreover, it gave me the possibility to work on data directly acquired in an experimental lab, that I consider a complementary activity essential for my scientific background.