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The temporal dynamics of resting‐state networks may represent an intrinsic functional repertoire supporting cognitive control performance across the lifespan. However, little is known about brain dynamics during the preschool period, which is a sensitive time window for cognitive control development. The fast timescale of synchronization and switching characterizing cortical network functional organization gives rise to quasi‐stable patterns (i.e., brain states) that recur over time. These can be inferred at the whole‐brain level using hidden Markov models (HMMs), an unsupervised machine learning technique that allows the identification of rapid oscillatory patterns at the macroscale of cortical networks. The present study used an HMM technique to investigate dynamic neural reconfigurations and their associations with behavioral (i.e., parental questionnaires) and cognitive (i.e., neuropsychological tests) measures in typically developing preschoolers (4–6 years old). We used high‐density EEG to better capture the fast reconfiguration patterns of the HMM‐derived metrics (i.e., switching rates, entropy rates, transition probabilities and fractional occupancies). Our results revealed that the HMM‐derived metrics were reliable indices of individual neural variability and differed between boys and girls. However, only brain state transition patterns toward prefrontal and default‐mode brain states, predicted differences on parental‐report questionnaire scores. Overall, these findings support the importance of resting‐state brain dynamics as functional scaffolds for behavior and cognition. Brain state transitions may be crucial markers of individual differences in cognitive control development in preschoolers. Transitions toward prefrontal brain states (BS) predict better scores in questionnaires assessing executive functioning, behavioral and emotion regulation abilities. Conversely, higher probability of transitioning toward default‐mode BS predicts worse scores in the same measures. Therefore, BS transitions may be crucial markers of individual differences in preschoolers' cognitive control development.

The degree of response to subthalamic nucleus deep brain stimulation (STN‐DBS) is individual and hardly predictable. We hypothesized that DBS‐related changes in cortical network organization are related to the clinical effect. Network analysis based on graph theory was used to evaluate the high‐density electroencephalography (HDEEG) recorded during a visual three‐stimuli paradigm in 32 Parkinson's disease (PD) patients treated by STN‐DBS in stimulation “off” and “on” states. Preprocessed scalp data were reconstructed into the source space and correlated to the behavioral parameters. In the majority of patients (n = 26), STN‐DBS did not lead to changes in global network organization in large‐scale brain networks. In a subgroup of suboptimal responders (n = 6), identified according to reaction times (RT) and clinical parameters (lower Unified Parkinson's Disease Rating Scale [UPDRS] score improvement after DBS and worse performance in memory tests), decreased global connectivity in the 1–8 Hz frequency range and regional node strength in frontal areas were detected. The important role of the supplementary motor area for the optimal DBS response was demonstrated by the increased node strength and eigenvector centrality in good responders. This response was missing in the suboptimal responders. Cortical topologic architecture is modified by the response to STN‐DBS leading to a dysfunction of the large‐scale networks in suboptimal responders. Network analysis based on graph theory was used to evaluate the high‐density electroencephalography (HDEEG) recorded during a visual three‐stimuli paradigm in 32 Parkinson's disease (PD) patients treated by STN‐DBS in stimulation “off” and “on” states. Cortical topologic architecture is modified by the response to STN‐DBS leading to a dysfunction of the large‐scale networks in suboptimal responders.

Difficulty sleeping in a novel environment is a common phenomenon that is often described as the first night effect (FNE). Previous works have found FNE on sleep architecture and sleep power spectra parameters, especially during non-rapid eye movement (NREM) sleep. However, the impact of FNE on sleep parameters, including local differences in electroencephalographic (EEG) activity across nights, has not been systematically assessed. Here, we performed high-density EEG sleep recordings on 27 healthy individuals on two nights and examined differences in sleep architecture, NREM (stages 2 and 3) EEG power spectra, and NREM power topography across nights. We found higher wakefulness after sleep onset (WASO), reduced sleep efficiency, and less deep NREM sleep (stage 3), along with increased high-frequency NREM EEG power during the first night of sleep, corresponding to small to medium effect sizes (Cohen’s d ≤ 0.5). Furthermore, study individuals showed significantly lower slow-wave activity in right frontal/prefrontal regions as well as higher sigma and beta activities in medial and left frontal/prefrontal areas, yielding medium to large effect sizes (Cohen’s d ≥ 0.5). Altogether, these findings suggest the FNE is characterized by less efficient, more fragmented, shallower sleep that tends to affect especially certain brain regions. The magnitude and specificity of these effects should be considered when designing sleep studies aiming to compare across night effects.

IntroductionOcular artifact has long been viewed as an impediment to the interpretation of electroencephalogram (EEG) signals in basic and applied research. Today, the use of blind source separation (BSS) methods, including independent component analysis (ICA) and second-order blind identification (SOBI), is considered an essential step in improving the quality of neural signals. Recently, we introduced a method consisting of SOBI and a discriminant and similarity (DANS)-based identification method, capable of identifying and extracting eye movement–related components. These recovered components can be localized within ocular structures with a high goodness of fit (>95%). This raised the possibility that such EEG-derived SOBI components may be used to build predictive models for tracking gaze position.MethodsAs proof of this new concept, we designed an EEG-based virtual eye-tracker (EEG-VET) for tracking eye movement from EEG alone. The EEG-VET is composed of a SOBI algorithm for separating EEG signals into different components, a DANS algorithm for automatically identifying ocular components, and a linear model to transfer ocular components into gaze positions.ResultsThe prototype of EEG-VET achieved an accuracy of 0.920° and precision of 1.510° of a visual angle in the best participant, whereas an average accuracy of 1.008° ± 0.357° and a precision of 2.348° ± 0.580° of a visual angle across all participants (N = 18).ConclusionThis work offers a novel approach that readily co-registers eye movement and neural signals from a single-EEG recording, thus increasing the ease of studying neural mechanisms underlying natural cognition in the context of free eye movement.

Objective To evaluate whether scalp oscillatory patterns—particularly scalp fast oscillations (SFOs) (>30 Hz) detected using 256‐channel high‐density EEG (HD‐EEG)—are associated with and can predict surgical outcomes in patients with drug‐resistant temporal lobe epilepsy with hippocampal sclerosis (TLE‐HS) undergoing anterior temporal lobectomy (ATL). Method This prospective cohort study included 47 patients with drug‐resistant TLE‐HS who underwent HD‐EEG and subsequent ATL with at least 24 months of postoperative follow‐up. A total of 70 averaged interictal epileptiform discharges (IEDs) were analyzed using time–frequency methods. Scalp oscillation frequencies across anterior temporal, posterior temporal, and extratemporal regions were calculated, and cluster analysis was performed to identify significant patterns. Scalp fast oscillations (SFOs; >30 Hz) were separately analyzed. Associations with surgical outcomes were assessed using generalized linear mixed models, and diagnostic performance was evaluated. Results A total of 70 averaged IEDs from 47 patients were analyzed. Patients with seizure recurrence exhibited higher median scalp oscillation frequencies across anterior temporal (34.26 Hz), posterior temporal (29.47 Hz), and extratemporal regions (25.25 Hz) compared to seizure‐free patients (12.50, 7.56, and 9.17 Hz, respectively). Among 22 IEDs with SFOs (>30 Hz), cluster‐based analysis identified a specific frequency pattern significantly associated with surgical outcome (p = 0.031). This SFO‐based pattern independently predicted seizure recurrence (odds ratio = 12.60, 95% CI: 1.19–133.89, p = 0.036), with a sensitivity of 87.5%, specificity of 64.3%, and an area under the receiver operating characteristic curve of 0.759. Significance This study demonstrates that specific scalp oscillatory patterns, particularly SFOs detected via 256‐channel HD‐EEG, are predictive of surgical outcomes in TLE‐HS. These findings suggest that scalp oscillation analysis may serve as a valuable, noninvasive biomarker in the presurgical evaluation of epilepsy and could aid in delineating the extent of the epileptogenic network. Plain Language Summary In this study, we used advanced brainwave recordings (called high‐density EEG) to examine electrical patterns in the brains of people with epilepsy. We found that certain fast brainwave signals were linked to whether surgery could stop seizures. These signals may help doctors better plan epilepsy surgery in the future.

Objective Presurgical high‐density electric source imaging (hdESI) of interictal epileptic discharges (IEDs) is only used by few epilepsy centers. One obstacle is the time‐consuming workflow both for recording as well as for visual review. Therefore, we analyzed the effect of (a) an automated IED detection and (b) the number of IEDs on the accuracy of hdESI and time‐effectiveness. Methods In 22 patients with pharmacoresistant focal epilepsy receiving epilepsy surgery (Engel 1) we retrospectively detected IEDs both visually and semi‐automatically using the EEG analysis software Persyst in 256‐channel EEGs. The amount of IEDs, the Euclidean distance between hdESI maximum and resection zone, and the operator time were compared. Additionally, we evaluated the intra‐individual effect of IED quantity on the distance between hdESI maximum of all IEDs and hdESI maximum when only a reduced amount of IEDs were included. Results There was no significant difference in the number of IEDs between visually versus semi‐automatically marked IEDs (74 ± 56 IEDs/patient vs 116 ± 115 IEDs/patient). The detection method of the IEDs had no significant effect on the mean distances between resection zone and hdESI maximum (visual: 26.07 ± 31.12 mm vs semi‐automated: 33.6 ± 34.75 mm). However, the mean time needed to review the full datasets semi‐automatically was shorter by 275 ± 46 min (305 ± 72 min vs 30 ± 26 min, P < 0.001). The distance between hdESI of the full versus reduced amount of IEDs of the same patient was smaller than 1 cm when at least a mean of 33 IEDs were analyzed. There was a significantly shorter intraindividual distance between resection zone and hdESI maximum when 30 IEDs were analyzed as compared to the analysis of only 10 IEDs (P < 0.001). Significance Semi‐automatized processing and limiting the amount of IEDs analyzed (~30–40 IEDs per cluster) appear to be time‐saving clinical tools to increase the practicability of hdESI in the presurgical work‐up.

Previous work showed that two types of slow waves are temporally dissociated during the transition to sleep: widespread, large and steep slow waves predominate early in the falling asleep period ( ), while smaller, more circumscribed slow waves become more prevalent later ( ). Here, we studied the possible occurrence of these two types of slow waves in stable non-REM (NREM) sleep and explored potential differences in their regulation. A heuristic approach based on slow wave synchronization efficiency was developed and applied to high-density electroencephalographic (EEG) recordings collected during consolidated NREM sleep to identify the potential and slow waves. Slow waves with characteristics compatible with those previously described for and were identified in stable NREM sleep. Importantly, these slow waves underwent opposite changes across the night, with only slow waves displaying a clear homeostatic regulation. In addition, we showed that the occurrence of slow waves was often followed by larger , whereas the occurrence of slow waves was usually followed by smaller waves. Finally, slow waves were associated with a relative increase in spindle activity, while slow waves triggered periods of high-frequency activity. Our results provide evidence for the existence of two distinct slow wave synchronization processes that underlie two different types of slow waves. These slow waves may have different functional roles and mark partially distinct \"micro-states\" of the sleeping brain.

Abstract Study Objectives Sleep spindles are waxing and waning EEG waves exemplifying the main fast oscillatory activity occurring during NREM sleep. Several recent studies have established that sleep spindle abnormalities are present in schizophrenia spectrum disorders, including in early-course and first-episode patients, and those spindle deficits are associated with some of the cognitive impairments commonly observed in these patients. Cognitive deficits are often observed before the onset of psychosis and seem to predict poor functional outcomes in individuals at clinical high-risk for psychosis (CHR). Yet, the presence of spindle abnormalities and their relationship with cognitive dysfunction has not been investigated in CHR. Methods In this study, overnight high-density (hd)-EEG recordings were collected in 24 CHR and 24 healthy control (HC) subjects. Spindle density, duration, amplitude, and frequency were computed and compared between CHR and HC. Furthermore, WM was assessed for both HC and CHR, and its relationship with spindle parameters was examined. Results CHR had reduced spindle duration in centro-parietal and prefrontal regions, with the largest decrease in the right prefrontal area. Moderation analysis showed that the relation between spindle duration and spindle frequency was altered in CHR relative to HC. Furthermore, CHR had reduced WM performance compared to HC, which was predicted by spindle frequency, whereas in HC spindle frequency, duration, and density all predicted working memory performance. Conclusion Altogether, these findings indicate that sleep spindles are altered in CHR individuals, and spindle alterations are associated with their cognitive deficits, thus representing a sleep-specific putative neurophysiological biomarker of cognitive dysfunction in psychosis risk. Graphical Abstract Graphical Abstract

•Declarative memory odor cueing has been shown to improve memory consolidation.•Cueing increased centro-parietal sleep spindle rates and slow oscillation amplitudes.•Cueing further increased spindle duration over frontal regions.•Induced spindles were predominantly nested in slow oscillation up states.•Neural effects occurred mainly in the first seconds after each cueing onset. Long-term memories are formed by repeated reactivation of newly encoded information during sleep. This process can be enhanced by using memory-associated reminder cues like sounds and odors. While auditory cueing has been researched extensively, few electrophysiological studies have exploited the various benefits of olfactory cueing. We used high-density electroencephalography in an odor-cueing paradigm that was designed to isolate the neural responses specific to the cueing of declarative memories. We show widespread cueing-induced increases in the duration and rate of sleep spindles. Higher spindle rates were most prominent over centro-parietal areas and largely overlapping with a concurrent increase in the amplitude of slow oscillations (SOs). Interestingly, greater SO amplitudes were linked to a higher likelihood of coupling a spindle and coupled spindles expressed during cueing were more numerous in particular around SO up states. We thus identify temporally and spatially coordinated enhancements of sleep spindles and slow oscillations as a candidate mechanism behind cueing-induced memory processing. Our results further demonstrate the feasibility of studying neural activity patterns linked to such processing using olfactory cueing during sleep.

•Slow waves and spindles occur in a temporally coupled manner in infancy•Slow wave slope, fast spindle density, and slow wave-spindle coupling are not related to concurrent behavioral development•Fast spindle density at 6 months predicts behavioral status at 12 and 24 months, spindle frequency predicts behavioral status at 24 months•Slow wave slope and slow wave-spindle coupling are not predictive of behavioral development Infancy represents a critical period during which thalamocortical brain connections develop and mature. Deviations in the maturation of thalamocortical connectivity are linked to neurodevelopmental disorders. There is a lack of early biomarkers to detect and localize neuromaturational deviations, which can be overcome with mapping through high-density electroencephalography (hdEEG) assessed in sleep. Specifically, slow waves and spindles in non-rapid eye movement (NREM) sleep are generated by the thalamocortical system, and their characteristics, slow wave slope and spindle density, are closely related to neuroplasticity and learning. Spindles are often subdivided into slow (11.0-13.0 Hz) and fast (13.5-16.0 Hz) frequencies, for which not only different functions have been proposed, but for which also distinctive developmental trajectories have been reported across the first years of life. Recent studies further suggest that information processing during sleep underlying sleep-dependent learning is promoted by the temporal coupling of slow waves and spindles, yet slow wave-spindle coupling remains unexplored in infancy. Thus, we evaluated three potential biomarkers: 1) slow wave slope, 2) spindle density, and 3) the temporal coupling of slow waves with spindles. We use hdEEG to first examine the occurrence and spatial distribution of these three EEG features in healthy infants and second to evaluate a predictive relationship with later behavioral outcomes. We report four key findings: First, infants’ EEG features appear locally: slow wave slope is maximal in occipital and frontal areas, whereas slow and fast spindle density is most pronounced frontocentrally. Second, slow waves and spindles are temporally coupled in infancy, with maximal coupling strength in the occipital areas of the brain. Third, slow wave slope, fast spindle density, and slow wave-spindle coupling are not associated with concurrent behavioral status (6 months). Fourth, fast spindle density in central and frontocentral regions at age 6 months predicts overall developmental status at age 12 months, and motor skills at age 12 and 24 months. Neither slow wave slope nor slow wave-spindle coupling predict later behavioral development. We further identified spindle frequency as a determinant of slow and fast spindle density, which accordingly, also predicts motor skills at 24 months. Our results propose fast spindle density, or alternatively spindle frequency, as early EEG biomarker for identifying thalamocortical maturation, which can potentially be used for early diagnosis of neurodevelopmental disorders in infants. These findings are in support of a role of sleep spindles in sensorimotor microcircuitry development. A crucial next step will be to evaluate whether early therapeutic interventions may be effective to reverse deviations in identified individuals at risk.