Publications

Electroencephalographic (EEG) source reconstruction is a powerful approach that allows anatomical localization of electrophysiological brain activity. Algorithms used to estimate cortical sources require an anatomical model of the head and the brain, generally reconstructed using magnetic resonance imaging (MRI). When such scans are unavailable, a population average can be used for adults, but no average surface template is available for cortical source imaging in infants. To address this issue, we introduce a new series of 13 anatomical models for subjects between zero and 24 months of age. These templates are built from MRI averages and boundary element method (BEM) segmentation of head tissues available as part of the Neurodevelopmental MRI Database. Surfaces separating the pia mater, the gray matter, and the white matter were estimated using the Infant FreeSurfer pipeline. The surface of the skin as well as the outer and inner skull surfaces were extracted using a cube marching algorithm followed by Laplacian smoothing and mesh decimation. We post-processed these meshes to correct topological errors and ensure watertight meshes. Source reconstruction with these templates is demonstrated and validated using 100 high-density EEG recordings from 7-month-old infants. Hopefully, these templates will support future studies on EEG-based neuroimaging and functional connectivity in healthy infants as well as in clinical pediatric populations.

Whether neuronal populations exhibit zero-lag (in-phase or in-antiphase) functional connectivity is a fundamental question when conceptualizing communication between cell assemblies. It also has profound implications on how we assess such interactions. Given that the brain is a delayed network due to the finite conduction velocity of the electrical impulses traveling across its fibers, the existence of long-distance zero-lag functional connectivity may be considered improbable. However, in this study, using human intracranial recordings we demonstrate that most interhemispheric connectivity between homotopic cerebral regions is zero-lagged and that this type of connectivity is ubiquitous. Volume conduction can be safely discarded as a confounding factor since it is known to drop almost completely within short interelectrode distances (<20 mm) in intracranial recordings. This finding should guide future electrophysiological connectivity studies and highlight the importance of considering the role of zero-lag connectivity in our understanding of communication between cell assemblies.

As our understanding of the thalamocortical system deepens, the questions we face become more complex. Their investigation requires the adoption of novel experimental approaches complemented with increasingly sophisticated computational modeling. In this review, we take stock of current data and knowledge about the circuitry of the somatosensory thalamocortical loop in rodents, discussing common principles across modalities and species whenever appropriate. We review the different levels of organization, including the cells, synapses, neuroanatomy, and network connectivity. We provide a complete overview of this system that should be accessible for newcomers to this field while nevertheless being comprehensive enough to serve as a reference for seasoned neuroscientists and computational modelers studying the thalamocortical system. We further highlight key gaps in data and knowledge that constitute pressing targets for future experimental work. Filling these gaps would provide invaluable information for systematically unveiling how this system supports behavioral and cognitive processes.

OBJECTIVES: Sleepwalkers have consistently shown N3 sleep discontinuity, especially after sleep deprivation. In healthy subjects, sleep spindles activity has been positively correlated to sleep stability. We aimed to compare spindles density during N3 sleep between sleepwalkers and healthy controls.

METHODS: Two cohorts of 10 and 21 adult sleepwalkers respectively controlled with 10 and 18 healthy volunteers underwent one baseline and one recovery sleep recording after 38h (cohort 1) and 25h (cohort 2) of sleep deprivation. For the two recordings, we performed an automatic detection of spindles (11-16Hz) from EEG signal during N3 sleep, restricted to the first sleep cycle and repeated for all cycles. For better interpretation of results, we extended the analysis to N2 sleep and we also measured the density of slow waves oscillation (SWO) (0.5-4Hz) during the same periods.

RESULTS: Compared to controls, sleepwalkers showed significantly lower spindle densities during N3 sleep considering the first sleep cycle (both cohorts) or all cycles (cohort 1). SWO densities did not differ (cohort 1) or were lower (cohort 2) for sleepwalkers. The effect of sleep deprivation did not interact with the effect of group on spindles and SWO densities.

CONCLUSION: This work suggests that the instability of N3 sleep inherent to sleepwalkers may be underpinned by a specific alteration of spindles activity.