Design of MRI Structured Spiking Neural Networks and Learning Algorithms for Personalized Modelling, Analysis, and Prediction of EEG Signals

Abstract This paper proposes a novel method and algorithms for the design of MRI structured personalized 3D spiking neural network models (MRI-SNN) for a better analysis, modeling, and prediction of EEG signals. It proposes a novel gradient-descent learning algorithm integrated with a spike-time-dependent-plasticity algorithm. The models capture informative personal patterns of interaction between EEG channels, contrary to single EEG signal modeling methods or to spike-based approaches which do not use personal MRI data to pre-structure a model. The proposed models can not only learn and model accurately measured EEG data, but they can also predict signals at 3D model locations that correspond to non-monitored brain areas, e.g. other EEG channels, from where data has not been collected. This is the first study in this respect. As an illustration of the method, personalized MRI-SNN models are created and tested on EEG data from two subjects. The models result in better prediction accuracy and a better understanding of the personalized EEG signals than traditional methods due to the MRI and EEG information integration. The models are interpretable and facilitate a better understanding of related brain processes. This approach can be applied for personalized modeling, analysis, and prediction of EEG signals across brain studies such as the study and prediction of epilepsy, peri-perceptual brain activities, brain-computer interfaces, and others

The sleep-deprived human brain

How does a lack of sleep affect our brains? In contrast to the benefits of sleep, frameworks exploring the impact of sleep loss are relatively lacking. Importantly, the effects of sleep deprivation (SD) do not simply reflect the absence of sleep and the benefits attributed to it; rather, they reflect the consequences of several additional factors, including extended wakefulness. With a focus on neuroimaging studies, we review the consequences of SD on attention and working memory, positive and negative emotion, and hippocampal learning. We explore how this evidence informs our mechanistic understanding of the known changes in cognition and emotion associated with SD, and the insights it provides regarding clinical conditions associated with sleep disruption

The role of sleep in emotional processing

Abstract In this chapter, we have reviewed an extensive literature supporting the 4 critical role of sleep for several aspects of emotional processing and regulation. In the first part, we discussed the main behavioral and psychophysiological studies that examined how sleep influences the processes of encoding and consolidation of emotional memory. In addition, we examined how sleep modulates emotion regulation, emotional reactivity, and empathy. Further, we discussed the implication of sleep in fear conditioning memory, threat generalization, and extinction memory. In the second part, we discussed evidence specifically suggesting the implication of REM sleep in the consolidation of emotional memory and in the modulation of emotional reactivity. In particular, we will focus on the specific physiological REM features that contributed to suggest its critical involvement in emotional processing. In the third part, we overviewed the functional neuroimaging studies on the brain mechanisms that underlie the relations between sleep and emotions. Finally, we focused on the most important psychiatric disorders that express abnormalities of sleep and emotional alterations, briefly reviewing our knowledge about the relationships between sleep disturbances and mood in major depression, anxiety disorders, and post-traumatic stress disorder. We showed that sleep helps in the formation of emotional memories at every stage of this process. On the contrary, sleep loss induces deficit in encoding of emotional information, leading to a disruptive interference with emotional memory consolidation. The reviewed literatures clearly suggest that sleep loss significantly influences emotional reactivity. Whether sleep acts to protect, potentiate, or de-potentiate emotional reactivity is, however, still debatable. Future studies will have to elucidate, at the behavioral level, the specific direction of the sleep-dependent emotional modulation. Sleep seems to be crucial also for our ability to correctly process emotional information that allows us to understand the others’ feelings and to be empathic with them, as well as for our ability to encode and consolidate fear conditioning and extinction learning. As far as the role of REM sleep is concerned, it seems to be crucial for the consolidation of emotional memory, while its specific contribution on next-day emotional reactivity is less clear. In fact, REM sleep could act to potentiate or, conversely, de-potentiate the emotional charge associated to a memory along with its consolidation. This topic could be also relevant for its implications in clinical settings. Indeed, further explaining how sleep influences the next-day emotional brain functioning will be crucial to open a new perspective for the understanding and treatment of affective or anxiety disturbances in patients with disturbed sleep