Modelling the Anatomic Distribution of Neurologic Events in Patients with COVID-19: A Systematic Review of MRI Findings.

BACKGROUND: Neurologic events have been reported in patients with coronavirus disease 2019 (COVID-19). However, a model-based evaluation of the spatial distribution of these events is lacking. PURPOSE: Our aim was to quantitatively evaluate whether a network diffusion model can explain the spread of small neurologic events. DATA SOURCES: The MEDLINE, EMBASE, Scopus, and LitCovid data bases were searched from January 1, 2020, to July 19, 2020. STUDY SELECTION: Thirty-five case series and case studies reported 317 small neurologic events in 123 unique patients with COVID-19. DATA ANALYSIS: Neurologic events were localized to gray or white matter regions of the Illinois Institute of Technology (gray-matter and white matter) Human Brain Atlas using radiologic images and descriptions. The total proportion of events was calculated for each region. A network diffusion model was implemented, and any brain regions showing a significant association (P < .05, family-wise error-corrected) between predicted and measured events were considered epicenters. DATA SYNTHESIS: Within gray matter, neurologic events were widely distributed, with the largest number of events (∼10%) observed in the bilateral superior temporal, precentral, and lateral occipital cortices, respectively. Network diffusion modeling showed a significant association between predicted and measured gray matter events when the spread of pathology was seeded from the bilateral cerebellum (r = 0.51, P < .001, corrected) and putamen (r = 0.4, P = .02, corrected). In white matter, most events (∼26%) were observed within the bilateral corticospinal tracts. LIMITATIONS: The risk of bias was not considered because all studies were either case series or case studies. CONCLUSIONS: Transconnectome diffusion of pathology via the structural network of the brain may contribute to the spread of neurologic events in patients with COVID-19.

Time-varying functional connectivity for understanding the neural basis of behavioral microsleeps.

Episodes of complete failure to respond during attentive tasks--lapses of responsiveness ('lapses')--accompanied by behavioral signs of sleep such as slow-eye-closure are known as behavioral microsleeps (BMs). The occurrence of BMs can have serious/fatal consequences, particularly in the transport sectors, and therefore further investigations on neurophysiological correlates of BMs are highly desirable. In this paper we propose a combination of High Resolution EEG techniques and an advanced method for time-varying functional connectivity estimation for reconstructing the temporal evolution of causal relations between cortical regions of BMs occurring during a visuomotor tracking task. The preliminary results highlight connectivity patterns involving parietal and fronto-parietal areas both preceding and following the onset of a BM.