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1.
Neurobiol Dis ; 153: 105319, 2021 06.
Article in English | MEDLINE | ID: covidwho-1108562

ABSTRACT

Visual recognition of facial expression modulates our social interactions. Compelling experimental evidence indicates that face conveys plenty of information that are fundamental for humans to interact. These are encoded at neural level in specific cortical and subcortical brain regions through activity- and experience-dependent synaptic plasticity processes. The current pandemic, due to the spread of SARS-CoV-2 infection, is causing relevant social and psychological detrimental effects. The institutional recommendations on physical distancing, namely social distancing and wearing of facemasks are effective in reducing the rate of viral spread. However, by impacting social interaction, facemasks might impair the neural responses to recognition of facial cues that are overall critical to our behaviors. In this survey, we briefly review the current knowledge on the neurobiological substrate of facial recognition and discuss how the lack of salient stimuli might impact the ability to retain and consolidate learning and memory phenomena underlying face recognition. Such an "abnormal" visual experience raises the intriguing possibility of a "reset" mechanism, a renewed ability of adult brain to undergo synaptic plasticity adaptations.


Subject(s)
Brain/physiology , COVID-19/prevention & control , Facial Recognition/physiology , Masks , Neuronal Plasticity/physiology , Communicable Disease Control , Humans , Occipital Lobe/physiology , Prefrontal Cortex/physiology , SARS-CoV-2 , Social Perception , Temporal Lobe/physiology , Visual Pathways/physiology
2.
Clin Neurophysiol ; 132(3): 730-736, 2021 03.
Article in English | MEDLINE | ID: covidwho-1039319

ABSTRACT

OBJECTIVE: To study if limited frontotemporal electroencephalogram (EEG) can guide sedation changes in highly infectious novel coronavirus disease 2019 (COVID-19) patients receiving neuromuscular blocking agent. METHODS: 98 days of continuous frontotemporal EEG from 11 consecutive patients was evaluated daily by an epileptologist to recommend reduction or maintenance of the sedative level. We evaluated the need to increase sedation in the 6 h following this recommendation. Post-hoc analysis of the quantitative EEG was correlated with the level of sedation using a machine learning algorithm. RESULTS: Eleven patients were studied for a total of ninety-eight sedation days. EEG was consistent with excessive sedation on 57 (58%) and adequate sedation on 41 days (42%). Recommendations were followed by the team on 59% (N = 58; 19 to reduce and 39 to keep the sedation level). In the 6 h following reduction in sedation, increases of sedation were needed in 7 (12%). Automatized classification of EEG sedation levels reached 80% (±17%) accuracy. CONCLUSIONS: Visual inspection of a limited EEG helped sedation depth guidance. In a secondary analysis, our data supported that this determination may be automated using quantitative EEG analysis. SIGNIFICANCE: Our results support the use of frontotemporal EEG for guiding sedation in patients with COVID-19.


Subject(s)
COVID-19 Drug Treatment , Electroencephalography/methods , Frontal Lobe/physiology , Hypnotics and Sedatives/administration & dosage , Machine Learning , Temporal Lobe/physiology , Aged , Anesthesia/methods , COVID-19/diagnosis , COVID-19/physiopathology , Cohort Studies , Electroencephalography/drug effects , Female , Humans , Intensive Care Units , Male , Middle Aged
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