The distinctive sleep pattern of the human calcarine cortex: a stereo-electroencephalographic study

Gradients in signal complexity of sleep-wake intracerebral EEG

Spatial variation in the morphology of the electroencephalogram (EEG) over the head is classically described. Ultimately, location-dependent variation in EEG must arise from the cytoarchitectural and network structure of the portion of cortex sensed. In previous work, we demonstrated that over the lateral frontal lobe, sample entropy (SE) of intracerebral EEG (iEEG) over a subdural recording contact was predictive of that contact's connectivity to other contacts. In this work, we used a publicly available repository (the Montreal Neurological Institute Atlas; MNIA) of whole-brain normative iEEG to calculate SE over the entire cortical surface. SE was averaged region-wise and classified by the state of arousal (awake, N2, N3 and REM). SE averages were transformed to a linear scale between zero and unity, mapped to continuous color scale and overlaid on segmented cortical surface models, one for each sleep-wake state. Wake SE followed a rostro-caudal gradient (RCG), with high values anteriorly and a global minimum in the posterior cortex. Superimposed on the RCG were other gradients radiating away from primary somatic sensorimotor, visual and auditory regions to their association areas. All gradients were attenuated in deep (N3) sleep. In REM, the majority of the cortex exhibited wake-like SE, with the prominent exception of primary cortical sensory and motor areas. Normative human intracerebral EEG exhibits rich spatial structure - cortical gradients - in the distribution of SE. SE in the wake state tracks temporal processing hierarchies in cerebral cortex, concordant to the distribution of several other cortical attributes of structure (e.g., cortical thickness, myelin content). Sleep disrupts these gradients, with REM sleep bringing out unusual discordances between primary sensory and their association areas. Our results deepen the interpretation of EEG from conventional descriptors such as Berger bands to a spatial perspective related to cortical biology.

Altered cerebral gray matter volume and functional connectivity in patients with residual dizziness of benign paroxysmal positional vertigo

AIM

To provide a theoretical basis for the study of the pathogenesis of residual dizziness (RD) from the perspective of imaging.

MATERIALS AND METHODS

The general clinical data of the RD group and healthy control (HC) group were statistically analysed by two independent sample t tests, rank sum tests or chi-square tests. The imaging data of the two groups of people were preprocessed and statistically analysed by using the data processing and analysis for brain imaging (DPABI) software package.

RESULTS

Compared with the HC group, the grey matter volume (GMV) in the left medial superior frontal gyrus, the left superior temporal gyrus, the right cerebellum crus1 area, and the right calcarine were significantly reduced in the RD group; the functional connectivity (FC) between the ventromedial prefrontal cortex (vmPFC) and the post insula in the RD group was enhanced; The FC between the vmPFC and the occipital lobe, between the temporal lobe and the inferior parietal lobe, between the mid insula and the mid insula, between the post cingulate gyrus and the post cingulate gyrus was weakened.

CONCLUSION

1. The GMV of many brain areas processing vestibular information of RD patients is reduced, the FC between them is weakened, which may be an important cause of RD. 2. The FC between many brain areas dealing with emotional information in RD patients is abnormal, which may be the adaptive response of them caused by emotional factors.

Spatiotemporal EEG dynamics of the sleep onset process in preadolescence

BACKGROUND

During preadolescence the sleep electroencephalography undergoes massive qualitative and quantitative modifications. Despite these relevant age-related peculiarities, the specific EEG pattern of the wake-sleep transition in preadolescence has not been exhaustively described.

METHODS

The aim of the present study is to characterize regional and temporal electrophysiological features of the sleep onset (SO) process in a group of 23 preadolescents (9-14 years) and to compare the topographical pattern of slow wave activity and delta/beta ratio of preadolescents with the EEG pattern of young adults.

RESULTS

Results showed in preadolescence the same dynamics known for adults, but with peculiarities in the delta and beta activity, likely associated with developmental cerebral modifications: the delta power showed a widespread increase during the SO with central maxima, and the lower bins of the beta activity showed a power increase after SO. Compared to adults, preadolescents during the SO exhibited higher delta power only in the slowest bins of the band: before SO slow delta activity was higher in prefrontal, frontal and occipital areas in preadolescents, and, after SO the younger group had higher slow delta activity in occipital areas. In preadolescents delta/beta ratio was higher in more posterior areas both before and after the wake-sleep transition and, after SO, preadolescents showed also a lower delta/beta ratio in frontal areas, compared to adults.

CONCLUSION

Results point to a general higher homeostatic drive for the developing areas, consistently with plastic-related maturational modifications, that physiologically occur during preadolescence.

Spotlight on Sleep Stage Classification Based on EEG

The recommendations for identifying sleep stages based on the interpretation of electrophysiological signals (electroencephalography [EEG], electro-oculography [EOG], and electromyography [EMG]), derived from the Rechtschaffen and Kales manual, were published in 2007 at the initiative of the American Academy of Sleep Medicine, and regularly updated over years. They offer an important tool to assess objective markers in different types of sleep/wake subjective complaints. With the aims and advantages of simplicity, reproducibility and standardization of practices in research and, most of all, in sleep medicine, they have overall changed little in the way they describe sleep. However, our knowledge on sleep/wake physiology and sleep disorders has evolved since then. High-density electroencephalography and intracranial electroencephalography studies have highlighted local regulation of sleep mechanisms, with spatio-temporal heterogeneity in vigilance states. Progress in the understanding of sleep disorders has allowed the identification of electrophysiological biomarkers better correlated with clinical symptoms and outcomes than standard sleep parameters. Finally, the huge development of sleep medicine, with a demand for explorations far exceeding the supply, has led to the development of alternative studies, which can be carried out at home, based on a smaller number of electrophysiological signals and on their automatic analysis. In this perspective article, we aim to examine how our description of sleep has been constructed, has evolved, and may still be reshaped in the light of advances in knowledge of sleep physiology and the development of technical recording and analysis tools. After presenting the strengths and limitations of the classification of sleep stages, we propose to challenge the "EEG-EOG-EMG" paradigm by discussing the physiological signals required for sleep stages identification, provide an overview of new tools and automatic analysis methods and propose avenues for the development of new approaches to describe and understand sleep/wake states.

Regional variability in intracerebral properties of NREM to REM sleep transitions in humans

Transitions between wake and sleep states show a progressive pattern underpinned by local sleep regulation. In contrast, little evidence is available on non-rapid eye movement (NREM) to rapid eye movement (REM) sleep boundaries, considered as mainly reflecting subcortical regulation. Using polysomnography (PSG) combined with stereoelectroencephalography (SEEG) in humans undergoing epilepsy presurgical evaluation, we explored the dynamics of NREM-to-REM transitions. PSG was used to visually score transitions and identify REM sleep features. SEEG-based local transitions were determined automatically with a machine learning algorithm using features validated for automatic intra-cranial sleep scoring (10.5281/zenodo.7410501). We analyzed 2988 channel-transitions from 29 patients. The average transition time from all intracerebral channels to the first visually marked REM sleep epoch was 8 s ± 1 min 58 s, with a great heterogeneity between brain areas. Transitions were observed first in the lateral occipital cortex, preceding scalp transition by 1 min 57 s ± 2 min 14 s (d = -0.83), and close to the first sawtooth wave marker. Regions with late transitions were the inferior frontal and orbital gyri (1 min 1 s ± 2 min 1 s, d = 0.43, and 1 min 1 s ± 2 min 5 s, d = 0.43, after scalp transition). Intracranial transitions were earlier than scalp transitions as the night advanced (last sleep cycle, d = -0.81). We show a reproducible gradual pattern of REM sleep initiation, suggesting the involvement of cortical mechanisms of regulation. This provides clues for understanding oneiric experiences occurring at the NREM/REM boundary.

Local neurodynamics as a signature of cortical areas: new insights from sleep

Sleep crucial for the animal survival is accompanied by huge changes in neuronal electrical activity over time, the neurodynamics. Here, drawing on intracranial stereo-electroencephalographic (sEEG) recordings from the Montreal Neurological Institute (MNI), we analyzed local neurodynamics in the waking state at rest and during the N2, N3, and rapid eye movement (REM) sleep phases. Higuchi fractal dimension (HFD)-a measure of signal complexity-was studied as a feature of the local neurodynamics of the primary motor (M1), somatosensory (S1), and auditory (A1) cortices. The key working hypothesis, that the relationships between local neurodynamics preserve in all sleep phases despite the neurodynamics complexity reduces in sleep compared with wakefulness, was supported by the results. In fact, while HFD awake > REM > N2 > N3 (P < 0.001 consistently), HFD in M1 > S1 > A1 in awake and all sleep stages (P < 0.05 consistently). Also power spectral density was studied for consistency with previous investigations. Meaningfully, we found a local specificity of neurodynamics, well quantified by the fractal dimension, expressed in wakefulness and during sleep. We reinforce the idea that neurodynamic may become a new criterion for cortical parcellation, prospectively improving the understanding and ability of compensatory interventions for behavioral disorders.