Odor cueing of declarative memories during sleep enhances coordinated spindles and slow oscillations.

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.

Electroencephalographic microstates as a potential neurophysiological marker differentiating bilateral from unilateral temporal lobe epilepsy.

OBJECTIVE: Electroencephalographic (EEG) microstate abnormalities have been documented in different neurological disorders. We aimed to assess whether EEG microstates are altered also in patients with temporal epilepsy (TLE) and whether they show different activations in patients with unilateral TLE (UTLE) and bilateral TLE (BTLE). METHODS: Nineteen patients with UTLE, 12 with BTLE, and 15 healthy controls were enrolled. Resting state high-density electroencephalography (128 channels) was recorded for 15 min with closed eyes. We obtained a set of stable scalp maps representing the EEG activity, named microstates, from which we acquired the following variables: global explained variance (GEV), mean duration (MD), time coverage (TC), and frequency of occurrence (FO). Two-way repeated measures analysis of variance was used to compare groups, and Spearman correlation was performed to study the maps in association with the clinical and neuropsychological data. RESULTS: Patients with BTLE and UTLE showed differences in most of the parameters (GEV, MD, TC, FO) of the four microstate maps (A-D) compared to controls. Patients with BTLE showed a significant increase in all parameters for the microstates in Map-A and a decrease in Map-D compared to UTLE and controls. We observed a correlation between Map-A, disease duration, and spatial short-term memory, whereas microstate Map-D was correlated with the global intelligence score and short-term memory performance. SIGNIFICANCE: A global alteration of the neural dynamics was observed in patients with TLE compared to controls. A different pattern of EEG microstate abnormalities was identified in BTLE compared to UTLE, which might represent a distinctive biomarker.

Microstates imbalance is associated with a functional dysregulation of the resting-state networks in obsessive-compulsive disorder: a high-density electrical neuroimaging study using the TESS method.

The dysfunctional patterns of microstates dynamics in obsessive-compulsive disorder (OCD) remain uncertain. Using high-density electrical neuroimaging (EEG) at rest, we explored microstates deterioration in OCD and whether abnormal microstates patterns are associated with a dysregulation of the resting-state networks interplay. We used EEG microstates analyses, TESS method for sources reconstruction, and General Linear Models to test for the effect of disease severity on neural responses. OCD patients exhibited an increased contribution and decreased duration of microstates C and D, respectively. Activity was decreased in the Salience Network (SN), associated with microstate C, but increased in the Default Mode Network (DMN) and Executive Control Network (ECN), respectively, associated with microstates E and D. The hyperactivity of the right angular gyrus in the ECN correlated with the symptoms severity. The imbalance between microstates C and D invalidates the hypothesis that this electrophysiological pattern is specific to psychosis. Demonstrating that the SN-ECN dysregulation manifests as abnormalities in microstates C and D, we confirm that the SN deterioration in OCD is accompanied by a failure of the DMN to deactivate and aberrant compensatory activation mechanisms in the ECN. These abnormalities explain typical OCD clinical features but also detachment from reality, shared with psychosis.

Use, experience and perspectives of high-density EEG among Italian epilepsy centers: a national survey.

INTRODUCTION: High-density EEG (hdEEG) is a validated tool in presurgical evaluation of people with epilepsy. The aim of this national survey is to estimate diffusion and knowledge of hdEEG to develop a network among Italian epilepsy centers. METHODS: A survey of 16 items (and 15 additional items) was distributed nationwide by email to all members of the Italian League Against Epilepsy and the Italian Society of Clinical Neurophysiology. The data obtained were analyzed using descriptive statistics. RESULTS: A total of 104 respondents were collected from 85 centers, 82% from the Centre-North of Italy; 27% of the respondents had a hdEEG. The main applications were for epileptogenic focus characterization in the pre-surgical evaluation (35%), biomarker research (35%) and scientific activity (30%). The greatest obstacles to hdEEG were economic resources (35%), acquisition of dedicated personnel (30%) and finding expertise (17%). Dissemination was limited by difficulties in finding expertise and dedicated personnel (74%) more than buying devices (9%); 43% of the respondents have already published hdEEG data, and 91% of centers were available to participate in multicenter hdEEG studies, helping in both pre-processing and analysis. Eighty-nine percent of respondents would be interested in referring patients to centers with established experience for clinical and research purposes. CONCLUSIONS: In Italy, hdEEG is mainly used in third-level epilepsy centers for research and clinical purposes. HdEEG diffusion is limited not only by costs but also by lack of trained personnel. Italian centers demonstrated a high interest in educational initiatives on hdEEG as well as in clinical and research collaborations.

High-Density Electroencephalogram Facilitates the Detection of Small Stimuli in Code-Modulated Visual Evoked Potential Brain-Computer Interfaces.

In recent years, there has been a considerable amount of research on visual evoked potential (VEP)-based brain-computer interfaces (BCIs). However, it remains a big challenge to detect VEPs elicited by small visual stimuli. To address this challenge, this study employed a 256-electrode high-density electroencephalogram (EEG) cap with 66 electrodes in the parietal and occipital lobes to record EEG signals. An online BCI system based on code-modulated VEP (C-VEP) was designed and implemented with thirty targets modulated by a time-shifted binary pseudo-random sequence. A task-discriminant component analysis (TDCA) algorithm was employed for feature extraction and classification. The offline and online experiments were designed to assess EEG responses and classification performance for comparison across four different stimulus sizes at visual angles of 0.5°, 1°, 2°, and 3°. By optimizing the data length for each subject in the online experiment, information transfer rates (ITRs) of 126.48 ± 14.14 bits/min, 221.73 ± 15.69 bits/min, 258.39 ± 9.28 bits/min, and 266.40 ± 6.52 bits/min were achieved for 0.5°, 1°, 2°, and 3°, respectively. This study further compared the EEG features and classification performance of the 66-electrode layout from the 256-electrode EEG cap, the 32-electrode layout from the 128-electrode EEG cap, and the 21-electrode layout from the 64-electrode EEG cap, elucidating the pivotal importance of a higher electrode density in enhancing the performance of C-VEP BCI systems using small stimuli.

An infant sleep electroencephalographic marker of thalamocortical connectivity predicts behavioral outcome in late infancy.

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.

Electrical Source Imaging of Somatosensory Evoked Potentials from Intracranial EEG Signals.

Stereoelectroencephalography (SEEG) records electrical brain activity with intracerebral electrodes. However, it has an inherently limited spatial coverage. Electrical source imaging (ESI) infers the position of the neural generators from the recorded electric potentials, and thus, could overcome this spatial undersampling problem. Here, we aimed to quantify the accuracy of SEEG ESI under clinical conditions. We measured the somatosensory evoked potential (SEP) in SEEG and in high-density EEG (HD-EEG) in 20 epilepsy surgery patients. To localize the source of the SEP, we employed standardized low resolution brain electromagnetic tomography (sLORETA) and equivalent current dipole (ECD) algorithms. Both sLORETA and ECD converged to similar solutions. Reflecting the large differences in the SEEG implantations, the localization error also varied in a wide range from 0.4 to 10 cm. The SEEG ESI localization error was linearly correlated with the distance from the putative neural source to the most activated contact. We show that it is possible to obtain reliable source reconstructions from SEEG under realistic clinical conditions, provided that the high signal fidelity recording contacts are sufficiently close to the source of the brain activity.

EEG Spatiotemporal Patterns Underlying Self-other Voice Discrimination.

There is growing evidence showing that the representation of the human "self" recruits special systems across different functions and modalities. Compared to self-face and self-body representations, few studies have investigated neural underpinnings specific to self-voice. Moreover, self-voice stimuli in those studies were consistently presented through air and lacking bone conduction, rendering the sound of self-voice stimuli different to the self-voice heard during natural speech. Here, we combined psychophysics, voice-morphing technology, and high-density EEG in order to identify the spatiotemporal patterns underlying self-other voice discrimination (SOVD) in a population of 26 healthy participants, both with air- and bone-conducted stimuli. We identified a self-voice-specific EEG topographic map occurring around 345 ms post-stimulus and activating a network involving insula, cingulate cortex, and medial temporal lobe structures. Occurrence of this map was modulated both with SOVD task performance and bone conduction. Specifically, the better participants performed at SOVD task, the less frequently they activated this network. In addition, the same network was recruited less frequently with bone conduction, which, accordingly, increased the SOVD task performance. This work could have an important clinical impact. Indeed, it reveals neural correlates of SOVD impairments, believed to account for auditory-verbal hallucinations, a common and highly distressing psychiatric symptom.

Spatio-temporal dynamics of large-scale electrophysiological networks during cognitive action control in healthy controls and Parkinson's disease patients.

Among the cognitive symptoms that are associated with Parkinson's disease (PD), alterations in cognitive action control (CAC) are commonly reported in patients. CAC enables the suppression of an automatic action, in favor of a goal-directed one. The implementation of CAC is time-resolved and arguably associated with dynamic changes in functional brain networks. However, the electrophysiological functional networks involved, their dynamic changes, and how these changes are affected by PD, still remain unknown. In this study, to address this gap of knowledge, 10 PD patients and 10 healthy controls (HC) underwent a Simon task while high-density electroencephalography (HD-EEG) was recorded. Source-level dynamic connectivity matrices were estimated using the phase-locking value in the beta (12-25 Hz) and gamma (30-45 Hz) frequency bands. Temporal independent component analyses were used as a dimension reduction tool to isolate the task-related brain network states. Typical microstate metrics were quantified to investigate the presence of these states at the subject-level. Our results first confirmed that PD patients experienced difficulties in inhibiting automatic responses during the task. At the group-level, we found three functional network states in the beta band that involved fronto-temporal, temporo-cingulate and fronto-frontal connections with typical CAC-related prefrontal and cingulate nodes (e.g., inferior frontal cortex). The presence of these networks did not differ between PD patients and HC when analyzing microstates metrics, and no robust correlations with behavior were found. In the gamma band, five networks were found, including one fronto-temporal network that was identical to the one found in the beta band. These networks also included CAC-related nodes previously identified in different neuroimaging modalities. Similarly to the beta networks, no subject-level differences were found between PD patients and HC. Interestingly, in both frequency bands, the dominant network at the subject-level was never the one that was the most durably modulated by the task. Altogether, this study identified the dynamic functional brain networks observed during CAC, but did not highlight PD-related changes in these networks that might explain behavioral changes. Although other new methods might be needed to investigate the presence of task-related networks at the subject-level, this study still highlights that task-based dynamic functional connectivity is a promising approach in understanding the cognitive dysfunctions observed in PD and beyond.

Cortical network organization reflects clinical response to subthalamic nucleus deep brain stimulation in Parkinson's disease.

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.

Development of motion speed perception from infancy to early adulthood: a high-density EEG study of simulated forward motion through optic flow.

This study investigated evoked and oscillatory brain activity in response to forward visual motion at three different ecologically valid speeds, simulated through an optic flow pattern consisting of a virtual road with moving poles at either side of it. Participants were prelocomotor infants at 4-5 months, crawling infants at 9-11 months, primary school children at 6 years, adolescents at 12 years, and young adults. N2 latencies for motion decreased significantly with age from around 400 ms in prelocomotor infants to 325 ms in crawling infants, and from 300 and 275 ms in 6- and 12-year-olds, respectively, to 250 ms in adults. Infants at 4-5 months displayed the longest latencies and appeared unable to differentiate between motion speeds. In contrast, crawling infants at 9-11 months and 6-year-old children differentiated between low, medium and high speeds, with shortest latency for low speed. Adolescents and adults displayed similar short latencies for the three motion speeds, indicating that they perceived them as equally easy to detect. Time-frequency analyses indicated that with increasing age, participants showed a progression from low- to high-frequency desynchronized oscillatory brain activity in response to visual motion. The developmental differences in motion speed perception are interpreted in terms of a combination of neurobiological development and increased experience with self-produced locomotion. Our findings suggest that motion speed perception is not fully developed until adolescence, which has implications for children's road traffic safety.

Memory retrieval in temporal lobe epilepsy is related to functional segregation of the mesiotemporal structures.

OBJECTIVE: We analyzed the impact of temporal lobe epilepsy (TLE) with hippocampal sclerosis (HS) on functional connectivity (FC) between mesiotemporal structures. Functional connectivity modifications related to word retrieval were investigated. METHODS: High-density EEG of 21 patients with TLE with HS (12 left TLE and 9 right TLE) and 10 healthy controls (HCs) were recorded during a verbal subsequent memory paradigm. Electroencephalography data were reconstructed into the source space and FC was calculated from the source activity of regions of interest. RESULTS: A significant decrease in FC between the right- and left-sided mesiotemporal structures in TLE was observed. The decrease was significant only with words that were correctly recognized. The decrease in interhemispheric FC between mesiotemporal structures was found in the 8- to 20-Hz frequency range in both left and right TLE. SIGNIFICANCE: The decreased FC between the mesiotemporal structures in TLE is a condition for successful performance of a memory retrieval task. The successful memory retrieval in TLE is related to functional segregation of lesional from nonlesional mesiotemporal structures. This decrease was absent in non-successful responses.

What's next? Neural correlates of emotional predictions: A high-density EEG investigation.

Emotions were recently reconsidered as predictions, constructed by the brain (generation stage) to prearrange action (implementation stage), and update internal models according to incoming stimuli (updating stage). However, it is unclear how emotional predictions are shaped by stimuli predictability. This study investigated the role of stimuli predictability on emotional predictions through high-density EEG. Twenty-six undergraduates underwent a S1-S2 paradigm, with emotional faces as S1s and emotional pictures as S2s. Stimuli predictability was manipulated across three blocks, in which S1 valence was predictive of S2 in the 100%, 75%, or 50% of trials. ERPs and brain sources were analyzed for each stage. During prediction generation, a larger N170/superior temporal sulcus activity emerged to fearful faces in blocks with full (100%) and medium (75%) predictive ratios. During implementation, the random block (50%) elicited a valence-independent pre-allocation of resources, reflected by a larger CNV and activation of a wide left network. In the updating stage, emotional pictures always elicited a larger LPP, while a larger P2 to neutral stimuli and a higher activity of the orbitofrontal cortex signaled early valence-dependent and late block-dependent prediction errors. These findings provide the first evidence of how stimuli predictability shape each neurocomputational stage of emotional predictions construction.

Scalp recorded spike ripples predict seizure risk in childhood epilepsy better than spikes.

In the past decade, brief bursts of fast oscillations in the ripple range have been identified in the scalp EEG as a promising non-invasive biomarker for epilepsy. However, investigation and clinical application of this biomarker have been limited because standard approaches to identify these brief, low amplitude events are difficult, time consuming, and subjective. Recent studies have demonstrated that ripples co-occurring with epileptiform discharges ('spike ripple events') are easier to detect than ripples alone and have greater pathological significance. Here, we used objective techniques to quantify spike ripples and test whether this biomarker predicts seizure risk in childhood epilepsy. We evaluated spike ripples in scalp EEG recordings from a prospective cohort of children with a self-limited epilepsy syndrome, benign epilepsy with centrotemporal spikes, and healthy control children. We compared the rate of spike ripples between children with epilepsy and healthy controls, and between children with epilepsy during periods of active disease (active, within 1 year of seizure) and after a period of sustained seizure-freedom (seizure-free, >1 year without seizure), using semi-automated and automated detection techniques. Spike ripple rate was higher in subjects with active epilepsy compared to healthy controls (P = 0.0018) or subjects with epilepsy who were seizure-free ON or OFF medication (P = 0.0018). Among epilepsy subjects with spike ripples, each month seizure-free decreased the odds of a spike ripple by a factor of 0.66 [95% confidence interval (0.47, 0.91), P = 0.021]. Comparing the diagnostic accuracy of the presence of at least one spike ripple versus a classic spike event to identify group, we found comparable sensitivity and negative predictive value, but greater specificity and positive predictive value of spike ripples compared to spikes (P = 0.016 and P = 0.006, respectively). We found qualitatively consistent results using a fully automated spike ripple detector, including comparison with an automated spike detector. We conclude that scalp spike ripple events identify disease and track with seizure risk in this epilepsy population, using both semi-automated and fully automated detection methods, and that this biomarker outperforms analysis of spikes alone in categorizing seizure risk. These data provide evidence that spike ripples are a specific non-invasive biomarker for seizure risk in benign epilepsy with centrotemporal spikes and support future work to evaluate the utility of this biomarker to guide medication trials and tapers in these children and predict seizure risk in other at-risk populations.

Cognitive task-related functional connectivity alterations in temporal lobe epilepsy.

OBJECTIVE: We investigated cognitive task-related functional connectivity (FC) in patients with temporal lobe epilepsy (TLE). Using a visual three-stimulus paradigm (VTSP), we studied cognitive large-scale networks and the impact of TLE on connectivity outside the temporal lobe. METHODS: High-density electroencephalography (EEG) was recorded during the paradigm from nineteen patients with epilepsy with hippocampal sclerosis (HS) and ten healthy controls (HCs). Scalp data were reconstructed into the source space, and FC was computed. Correlating with the neuropsychological data, possible compensatory mechanisms were investigated. RESULTS: Significant changes were found in the FC of regions outside the epileptogenic network, particularly in the attentional network. These changes were more widespread in left TLE (LTLE). There were no significant differences in task performance (accuracy, time response) in comparison with HCs, implying that there must be some mechanism reducing the impact of connectivity changes on brain functions. When correlated with neuropsychological data, we found stronger compensatory mechanisms in right TLE (RTLE). SIGNIFICANCE: Our findings confirm the hypothesis that LTLE is the more pervasive form of the disease. Even though the network alterations in TLE are severe, some mechanisms reduce the impact of epilepsy on cognitive functions; these mechanisms are more potent in RTLE. We also suggest that there are maladaptive mechanisms in LTLE.

Dreaming in NREM Sleep: A High-Density EEG Study of Slow Waves and Spindles.

Dreaming can occur in both rapid eye movement (REM) and non-REM (NREM) sleep. We recently showed that in both REM and NREM sleep, dreaming is associated with local decreases in slow wave activity (SWA) in posterior brain regions. To expand these findings, here we asked how specific features of slow waves and spindles, the hallmarks of NREM sleep, relate to dream experiences. Fourteen healthy human subjects (10 females) underwent nocturnal high-density EEG recordings combined with a serial awakening paradigm. Reports of dreaming, compared with reports of no experience, were preceded by fewer, smaller, and shallower slow waves, and faster spindles, especially in central and posterior cortical areas. We also identified a minority of very steep and large slow waves in frontal regions, which occurred on a background of reduced SWA and were associated with high-frequency power increases (local "microarousals") heralding the successful recall of dream content. These results suggest that the capacity of the brain to generate experiences during sleep is reduced in the presence of neuronal off-states in posterior and central brain regions, and that dream recall may be facilitated by the intermittent activation of arousal systems during NREM sleep.SIGNIFICANCE STATEMENT By combining high-density EEG recordings with a serial awakening paradigm in healthy subjects, we show that dreaming in non-rapid eye movement sleep occurs when slow waves in central and posterior regions are sparse, small, and shallow. We also identified a small subset of very large and steep frontal slow waves that are associated with high-frequency activity increases (local "microarousals") heralding successful recall of dream content. These results provide noninvasive measures that could represent a useful tool to infer the state of consciousness during sleep.

Diurnal changes in human brain glutamate + glutamine levels in the course of development and their relationship to sleep.

Sleep slow waves during non-rapid eye movement (NREM) sleep play a crucial role in maintaining cortical plasticity, a process that is especially important in the developing brain. Children show a considerably larger overnight decrease in slow wave activity (SWA; the power in the EEG frequency band between 1 and 4.5 â€‹Hz during NREM sleep), which constitutes the primary electrophysiological marker for the restorative function of sleep. We previously demonstrated in adults that this marker correlates with the overnight reduction in cortical glutamate â€‹+ â€‹glutamine (GLX) levels assessed by magnetic resonance spectroscopy (MRS), proposing GLX as a promising biomarker for the interplay between cortical plasticity and SWA. Here, we used a multimodal imaging approach of combined MRS and high-density EEG in a cross-sectional cohort of 46 subjects from 8 to 24 years of age in order to examine age-related changes in GLX and its relation to SWA. Gray matter volume, GLX levels and SWA showed the expected age-dependent decrease. Unexpectedly, the overnight changes in GLX followed opposite directions when comparing children to adults. These age-related changes could neither be explained by the overnight decrease in SWA nor by circadian factors.

An increase in sleep slow waves predicts better working memory performance in healthy individuals.

Sleep is imperative for brain health and well-being, and restorative sleep is associated with better cognitive functioning. Increasing evidence indicates that electrophysiological measures of sleep, especially slow wave activity (SWA), regulate the consolidation of motor and perceptual procedural memory. In contrast, the role of sleep EEG and SWA in modulating executive functions, including working memory (WM), has been far less characterized. Here, we investigated across-night changes in sleep EEG that may ameliorate WM performance. Participants (N = 25, M = 100%) underwent two consecutive nights with high-density EEG, along with N-back tasks, which were administered at three time points the day before and after the second night of sleep. Non-rapid eye movement sleep EEG power spectra, power topography, as well as several slow-wave parameters were computed and compared across nights. Improvers on the 1-back, but not non-improvers, showed a significant increase in SWA as well as in down slope and negative peak amplitude, in a fronto-parietal region, and these parameters increases predicted better WM performance. Overall, these findings show that slow-wave sleep has a beneficial effect on WM and that it can occur in the adult brain even after minimal training. This is especially relevant, when considering that WM and other executive function cognitive deficits are present in several neuropsychiatric disorders, and that slow-wave enhancing interventions can improve cognition, thus providing novel insights and treatment strategies for these patients.

A simple sleep EEG marker in childhood predicts brain myelin 3.5 years later.

Epidemiological research reveals that insufficient sleep in children has negative cognitive and emotional consequences; however, the physiological underpinnings of these observations remain understudied. We tested the hypothesis that the topographical distribution of deep sleep slow wave activity during the childhood predicts brain white matter microstructure (myelin) 3.5 y later. Healthy children underwent sleep high-density EEG at baseline (n = 13; ages 2.4-8.0 y) and follow-up (n = 14; ages 5.5-12.2 y). At follow-up, myelin (myelin water fraction) and cortical morphology were also quantified. Our investigation revealed 3 main findings. (1) The Frontal/Occipital (F/O)-ratio at baseline strongly predicted whole brain myelin at follow-up. (2) At follow-up, the F/O-ratio was only minimally (negatively) linked to brain myelin. (3) Cortical morphology was not related to the F/O-ratio, neither at baseline nor at follow-up. Our results support the hypothesis that during child development EEG markers during sleep longitudinally predict brain myelin content. Data extend previous findings reporting a link between EEG markers of sleep need and cortical morphology, by supporting the hypothesis that sleep is a necessary component to underlying processes of brain, and specifically myelin, maturation. In line with the overarching theory that sleep contributes to neurodevelopmental processes, it remains to be investigated whether chronic sleep loss negatively affects white matter myelin microstructure growth during sensitive periods of development.

Visual imagery and visual perception induce similar changes in occipital slow waves of sleep.

Previous studies have shown that regional slow-wave activity (SWA) during non-rapid eye movement (NREM) sleep is modulated by prior experience and learning. Although this effect has been convincingly demonstrated for the sensorimotor domain, attempts to extend these findings to the visual system have provided mixed results. In this study we asked whether depriving subjects of external visual stimuli during daytime would lead to regional changes in slow waves during sleep and whether the degree of "internal visual stimulation" (spontaneous imagery) would influence such changes. In two 8-h sessions spaced 1 wk apart, 12 healthy volunteers either were blindfolded while listening to audiobooks or watched movies (control condition), after which their sleep was recorded with high-density EEG. We found that during NREM sleep, the number of small, local slow waves in the occipital cortex decreased after listening with blindfolding relative to movie watching in a way that depended on the degree of visual imagery subjects reported during blindfolding: subjects with low visual imagery showed a significant reduction of occipital sleep slow waves, whereas those who reported a high degree of visual imagery did not. We also found a positive relationship between the reliance on visual imagery during blindfolding and audiobook listening and the degree of correlation in sleep SWA between visual areas and language-related areas. These preliminary results demonstrate that short-term alterations in visual experience may trigger slow-wave changes in cortical visual areas. Furthermore, they suggest that plasticity-related EEG changes during sleep may reflect externally induced ("bottom up") visual experiences, as well as internally generated ("top down") processes. NEW & NOTEWORTHY Previous work has shown that slow-wave activity, a marker of sleep depth, is linked to neural plasticity in the sensorimotor cortex. We show that after short-term visual deprivation, subjects who reported little visual imagery had a reduced incidence of occipital slow waves. This effect was absent in subjects who reported strong spontaneous visual imagery. These findings suggest that visual imagery may "substitute" for visual perception and induce similar changes in non-rapid eye movement slow waves.