Slow brain oscillations of sleep, resting state, and vigilance

Sex differences in amygdalohippocampal oscillations and neuronal activation in a rodent anxiety model and in response to infralimbic deep brain stimulation

Introduction

Depression and anxiety are highly comorbid mental disorders with marked sex differences. Both disorders show altered activity in the amygdala, hippocampus, and prefrontal cortex. Infralimbic deep brain stimulation (DBS-IL) has anxiolytic and antidepressant effects, but the underlying mechanisms remain unclear. We aimed to contribute to understanding sex differences in the neurobiology of these disorders.

Methods

In male and female rats, we recorded neural oscillations along the dorsoventral axis of the hippocampus and the amygdala in response to an anxiogenic drug, FG-7142. Following this, we applied DBS-IL.

Results

Surprisingly, in females, the anxiogenic drug failed to induce most of the changes observed in males. We found sex differences in slow, delta, theta, and beta oscillations, and the amygdalo-hippocampal communication in response to FG-7142, with modest changes in females. Females had a more prominent basal gamma, and the drug altered this band only in males. We also analyzed c-Fos expression in both sexes in stress-related structures in response to FG-7142, DBS-IL, and combined interventions. With the anxiogenic drug, females showed reduced expression in the nucleus incertus, amygdala, septohippocampal network, and neocortical levels. In both experiments, the DBS-IL reversed FG-7142-induced effects, with a more substantial effect in males than females.

Discussion

Here, we show a reduced response in female rats which contrasts with the higher prevalence of anxiety in women but is consistent with other studies in rodents. Our results open compelling questions about sex differences in the neurobiology of anxiety and depression and their study in animal models.

Impact of DC-Coupled Electrophysiological Recordings for Translational Neuroscience: Case Study of Tracking Neural Dynamics in Rodent Models of Seizures

We propose that to fully understand biological mechanisms underlying pathological brain activity with transitions (e.g., into and out of seizures), wide-bandwidth electrophysiological recordings are important. We demonstrate the importance of ultraslow potential shifts and infraslow oscillations for reliable tracking of synaptic physiology, within a neural mass model, from brain recordings that undergo pathological phase transitions. We use wide-bandwidth data (direct current (DC) to high-frequency activity), recorded using epidural and penetrating graphene micro-transistor arrays in a rodent model of acute seizures. Using this technological approach, we capture the dynamics of infraslow changes that contribute to seizure initiation (active pre-seizure DC shifts) and progression (passive DC shifts). By employing a continuous-discrete unscented Kalman filter, we track biological mechanisms from full-bandwidth data with and without active pre-seizure DC shifts during paroxysmal transitions. We then apply the same methodological approach for tracking the same parameters after application of high-pass-filtering >0.3Hz to both data sets. This approach reveals that ultraslow potential shifts play a fundamental role in the transition to seizure, and the use of high-pass-filtered data results in the loss of key information in regard to seizure onset and termination dynamics.

Visual exploration dynamics are low-dimensional and driven by intrinsic factors

When looking at visual images, the eyes move to the most salient and behaviourally relevant objects. Saliency and semantic information significantly explain where people look. Less is known about the spatiotemporal properties of eye movements (i.e., how people look). We show that three latent variables explain 60% of eye movement dynamics of more than a hundred observers looking at hundreds of different natural images. The first component explaining 30% of variability loads on fixation duration, and it does not relate to image saliency or semantics; it approximates a power-law distribution of gaze steps, an intrinsic dynamic measure, and identifies observers with two viewing styles: static and dynamic. Notably, these viewing styles were also identified when observers look at a blank screen. These results support the importance of endogenous processes such as intrinsic dynamics to explain eye movement spatiotemporal properties.

Sleep Deprivation, a Link Between Post-Traumatic Stress Disorder and Alzheimer's Disease

An estimated 5 million Americans are living with Alzheimer's disease (AD), and there is also a significant impact on caregivers, with an additional 16 million Americans providing unpaid care for individuals with AD and other dementias. These numbers are projected to increase in the coming years. While AD is still without a cure, continued research efforts have led to better understanding of pathology and potential risk factors that could be exploited to slow disease progression. A bidirectional relationship between sleep deprivation and AD has been suggested and is well supported by both human and animal studies. Even brief episodes of inadequate sleep have been shown to cause an increase in amyloidβ and tau proteins, both well-established contributors toAD pathology. Sleep deprivation is also the most common consequence of post-traumatic stress disorder (PTSD). Patients with PTSD frequently present with sleep disturbances and also develop dementia at twice the rate of the general population accounting for a disproportionate representation of AD among U.S. Veterans. The goal of this review is to highlight the relationship triad between sleep deprivation, AD, and PTSD as well as their impact on molecular mechanisms driving AD pathology.

Memory Reactivation and Its Effect on Exercise Performance and Heart Rate

Neuronal ensemble and brain plasticity both play an important role in memory consolidation and subsequently memory reactivation. To date, many studies have been designed to study the effect of exercise, heart-rate variability, and other factors on brain plasticity and memory. Here, we present a case study in which we have demonstrated the effect of neuronal ensemble and memory formed during High-intensity aerobic training (VO2 max) and Target Heart-Rate (THR) training and the effect of reactivation of same memory on THR and performance. Of note is the fact that the reactivation and recreation of memory stimulus learned and formed during High-intensity training, such as place, time, odor, and other conditions, can elevate the THR to the same previous peak zone even at low intensity. This demonstrates that reactivation of previously acquired memory or using the stimulation from the neuronal ensemble of consolidated memory during the specific event of training may exert similar physiological effects on exercise or the body to those that are learned during the memory acquisition phase. Hence, as exercise has an effect on memory, the memories may have an effect on exercise performances.

Coupled electrophysiological, hemodynamic, and cerebrospinal fluid oscillations in human sleep

Sleep is essential for both cognition and maintenance of healthy brain function. Slow waves in neural activity contribute to memory consolidation, whereas cerebrospinal fluid (CSF) clears metabolic waste products from the brain. Whether these two processes are related is not known. We used accelerated neuroimaging to measure physiological and neural dynamics in the human brain. We discovered a coherent pattern of oscillating electrophysiological, hemodynamic, and CSF dynamics that appears during non-rapid eye movement sleep. Neural slow waves are followed by hemodynamic oscillations, which in turn are coupled to CSF flow. These results demonstrate that the sleeping brain exhibits waves of CSF flow on a macroscopic scale, and these CSF dynamics are interlinked with neural and hemodynamic rhythms.

Neural oscillations in the infralimbic cortex after electrical stimulation of the amygdala. Relevance to acute stress processing

The stress system coordinates the adaptive reactions of the organism to stressors. Therefore, dysfunctions in this circuit may correlate to anxiety-related disorders, including depression. Comprehending the dynamics of this network may lead to a better understanding of the mechanisms that underlie these diseases. The central nucleus of the amygdala (CeA) activates the hypothalamic-pituitary-adrenal axis and brainstem nodes by triggering endocrine, autonomic and behavioral stress responses. The medial prefrontal cortex plays a significant role in regulating reactions to stressors, and is specifically important for limiting fear responses. Brain oscillations reflect neural systems activity. Synchronous neuronal assemblies facilitate communication and synaptic plasticity, mechanisms that cooperatively support the temporal representation and long-term consolidation of information. The purpose of this article was to delve into the interactions between these structures in stress contexts by evaluating changes in oscillatory activity. We particularly analyzed the local field potential in the infralimbic region of the medial prefrontal cortex (IL) in urethane-anesthetized rats after the electrical activation of the central nucleus of the amygdala by mimicking firing rates induced by acute stress. Electrical CeA activation induced a delayed, but significant, change in the IL, with prominent slow waves accompanied by an increase in the theta and gamma activities, and spindles. The phase-amplitude coupling of both slow waves and theta oscillations significantly increased with faster oscillations, including theta-gamma coupling and the nesting of spindles, theta and gamma oscillations in the slow wave cycle. These results are further discussed in neural processing terms of the stress response and memory formation.

Functional brain abnormalities in major depressive disorder using the Hilbert-Huang transform

Major depressive disorder is a common disease worldwide, which is characterized by significant and persistent depression. Non-invasive accessory diagnosis of depression can be performed by resting-state functional magnetic resonance imaging (rs-fMRI). However, the fMRI signal may not satisfy linearity and stationarity. The Hilbert-Huang transform (HHT) is an adaptive time-frequency localization analysis method suitable for nonlinear and non-stationary signals. The objective of this study was to apply the HHT to rs-fMRI to find the abnormal brain areas of patients with depression. A total of 35 patients with depression and 37 healthy controls were subjected to rs-fMRI. The HHT was performed to extract the Hilbert-weighted mean frequency of the rs-fMRI signals, and multivariate receiver operating characteristic analysis was applied to find the abnormal brain regions with high sensitivity and specificity. We observed differences in Hilbert-weighted mean frequency between the patients and healthy controls mainly in the right hippocampus, right parahippocampal gyrus, left amygdala, and left and right caudate nucleus. Subsequently, the above-mentioned regions were included in the results obtained from the compared region homogeneity and the fractional amplitude of low frequency fluctuation method. We found brain regions with differences in the Hilbert-weighted mean frequency, and examined their sensitivity and specificity, which suggested a potential neuroimaging biomarker to distinguish between patients with depression and healthy controls. We further clarified the pathophysiological abnormality of these regions for the population with major depressive disorder.

Dysfunction of GABAergic neurons in the parafacial zone mediates sleep disturbances in a streptozotocin-induced rat model of sporadic Alzheimer's disease

Sleep disturbances are prevalent among patients with Alzheimer's disease (AD) and often precede the onset and progression of dementia. However, there are no reliable animal models for investigating sleep disturbances in patients with sporadic AD (sAD), which accounts for more than 90% of all AD cases. In the present study, we characterize the sleep/wake cycles and explore a potential mechanism underlying sleep disturbance in a rat model of sAD induced via intracerebroventricular (icv) injection of streptozotocin (STZ). STZ-icv rats exhibited progressive decreases in slow wave sleep (SWS) during the light phase and throughout the light/dark cycle beginning from 7 days after STZ-icv. Additionally, increased wakefulness and decreased rapid-eye-movement (REM) and non-REM (NREM) sleep were observed from 14 days after STZ-icv. Beginning on day 7, STZ-icv rats exhibited significant decreases in delta (0.5-4.0 Hz) power accompanied by increased power in the beta (12-30 Hz) and low gamma bands (30-50 Hz) during NREM sleep, resembling deficits in sleep quality observed in patients with AD. Immunohistochemical staining revealed a significant reduction in the ratio of c-Fos-positive GABAergic neurons in the parafacial zone (PZ) beginning from day 7 after STZ-icv. These results suggest that the STZ-icv rat model is useful for evaluating sleep disturbances associated with AD, and implicate the dysregulation of GABAergic neuronal activity in the PZ is associated with sleep disturbance induced by STZ.

The effect of preprocessing pipelines in subject classification and detection of abnormal resting state functional network connectivity using group ICA

Resting state functional network connectivity (rsFNC) derived from functional magnetic resonance (fMRI) imaging is emerging as a possible biomarker to identify several brain disorders. Recently it has been pointed out that methods used to preprocess head motion variance might not fully remove all unwanted effects in the data. Proposed processing pipelines locate the treatment of head motion effects either close to the beginning or as one of the final steps. In this work, we assess several preprocessing pipelines applied in group independent component analysis (gICA) methods to study the rsFNC of the brain. The evaluation method utilizes patient/control classification performance based on linear support vector machines and leave-one-out cross validation. In addition, we explored group tests and correlation with severity measures in the patient population. We also tested the effect of removing high frequencies via filtering. Two real data cohorts were used: one consisting of 48 mTBI and one composed of 21 smokers, both with their corresponding matched controls. A simulation procedure was designed to test the classification power of each pipeline. Results show that data preprocessing can change the classification performance. In real data, regressing motion variance before gICA produced clearer group differences and stronger correlation with nicotine dependence.

Large Scale Cortical Functional Networks Associated with Slow-Wave and Spindle-Burst-Related Spontaneous Activity

Cortical sensory systems are active with rich patterns of activity during sleep and under light anesthesia. Remarkably, this activity shares many characteristics with those present when the awake brain responds to sensory stimuli. We review two specific forms of such activity: slow-wave activity (SWA) in the adult brain and spindle bursts in developing brain. SWA is composed of 0.5-4 Hz resting potential fluctuations. Although these fluctuations synchronize wide regions of cortex, recent large-scale imaging has shown spatial details of their distribution that reflect underlying cortical structural projections and networks. These networks are regulated, as prior awake experiences alter both the spatial and temporal features of SWA in subsequent sleep. Activity patterns of the immature brain, however, are very different from those of the adult. SWA is absent, and the dominant pattern is spindle bursts, intermittent high frequency oscillations superimposed on slower depolarizations within sensory cortices. These bursts are driven by intrinsic brain activity, which act to generate peripheral inputs, for example via limb twitches. They are present within developing sensory cortex before they are mature enough to exhibit directed movements and respond to external stimuli. Like in the adult, these patterns resemble those evoked by sensory stimulation when awake. It is suggested that spindle-burst activity is generated purposefully by the developing nervous system as a proxy for true external stimuli. While the sleep-related functions of both slow-wave and spindle-burst activity may not be entirely clear, they reflect robust regulated phenomena which can engage select wide-spread cortical circuits. These circuits are similar to those activated during sensory processing and volitional events. We highlight these two patterns of brain activity because both are prominent and well-studied forms of spontaneous activity that will yield valuable insights into brain function in the coming years.

Change in Mean Frequency of Resting-State Electroencephalography after Transcranial Direct Current Stimulation

Transcranial direct current stimulation (tDCS) is proposed as a tool to investigate cognitive functioning in healthy people and as a treatment for various neuropathological disorders. However, the underlying cortical mechanisms remain poorly understood. We aim to investigate whether resting-state electroencephalography (EEG) can be used to monitor the effects of tDCS on cortical activity. To this end we tested whether the spectral content of ongoing EEG activity is significantly different after a single session of active tDCS compared to sham stimulation. Twenty participants were tested in a sham-controlled, randomized, crossover design. Resting-state EEG was acquired before, during and after active tDCS to the left dorsolateral prefrontal cortex (15 min of 2 mA tDCS) and sham stimulation. Electrodes with a diameter of 3.14 cm² were used for EEG and tDCS. Partial least squares (PLS) analysis was used to examine differences in power spectral density (PSD) and the EEG mean frequency to quantify the slowing of EEG activity after stimulation. PLS revealed a significant increase in spectral power at frequencies below 15 Hz and a decrease at frequencies above 15 Hz after active tDCS (P = 0.001). The EEG mean frequency was significantly reduced after both active tDCS (P < 0.0005) and sham tDCS (P = 0.001), though the decrease in mean frequency was smaller after sham tDCS than after active tDCS (P = 0.073). Anodal tDCS of the left DLPFC using a high current density bi-frontal electrode montage resulted in general slowing of resting-state EEG. The similar findings observed following sham stimulation question whether the standard sham protocol is an appropriate control condition for tDCS.

Distinguishing cognitive state with multifractal complexity of hippocampal interspike interval sequences

Fractality, represented as self-similar repeating patterns, is ubiquitous in nature and the brain. Dynamic patterns of hippocampal spike trains are known to exhibit multifractal properties during working memory processing; however, it is unclear whether the multifractal properties inherent to hippocampal spike trains reflect active cognitive processing. To examine this possibility, hippocampal neuronal ensembles were recorded from rats before, during and after a spatial working memory task following administration of tetrahydrocannabinol (THC), a memory-impairing component of cannabis. Multifractal detrended fluctuation analysis was performed on hippocampal interspike interval sequences to determine characteristics of monofractal long-range temporal correlations (LRTCs), quantified by the Hurst exponent, and the degree/magnitude of multifractal complexity, quantified by the width of the singularity spectrum. Our results demonstrate that multifractal firing patterns of hippocampal spike trains are a marker of functional memory processing, as they are more complex during the working memory task and significantly reduced following administration of memory impairing THC doses. Conversely, LRTCs are largest during resting state recordings, therefore reflecting different information compared to multifractality. In order to deepen conceptual understanding of multifractal complexity and LRTCs, these measures were compared to classical methods using hippocampal frequency content and firing variability measures. These results showed that LRTCs, multifractality, and theta rhythm represent independent processes, while delta rhythm correlated with multifractality. Taken together, these results provide a novel perspective on memory function by demonstrating that the multifractal nature of spike trains reflects hippocampal microcircuit activity that can be used to detect and quantify cognitive, physiological, and pathological states.

Multidimensional frequency domain analysis of full-volume fMRI reveals significant effects of age, gender, and mental illness on the spatiotemporal organization of resting-state brain activity

Clinical research employing functional magnetic resonance imaging (fMRI) is often conducted within the connectionist paradigm, focusing on patterns of connectivity between voxels, regions of interest (ROIs) or spatially distributed functional networks. Connectivity-based analyses are concerned with pairwise correlations of the temporal activation associated with restrictions of the whole-brain hemodynamic signal to locations of a priori interest. There is a more abstract question however that such spatially granular correlation-based approaches do not elucidate: Are the broad spatiotemporal organizing principles of brains in certain populations distinguishable from those of others? Global patterns (in space and time) of hemodynamic activation are rarely scrutinized for features that might characterize complex psychiatric conditions, aging effects or gender—among other variables of potential interest to researchers. We introduce a canonical, transparent technique for characterizing the role in overall brain activation of spatially scaled periodic patterns with given temporal recurrence rates. A core feature of our technique is the spatiotemporal spectral profile (STSP), a readily interpretable 2D reduction of the native four-dimensional brain × time frequency domain that is still “big enough” to capture important group differences in globally patterned brain activation. Its power to distinguish populations of interest is demonstrated on a large balanced multi-site resting fMRI dataset with nearly equal numbers of schizophrenia patients and healthy controls. Our analysis reveals striking differences in the spatiotemporal organization of brain activity that correlate with the presence of diagnosed schizophrenia, as well as with gender and age. To the best of our knowledge, this is the first demonstration that a 4D frequency domain analysis of full volume fMRI data exposes clinically or demographically relevant differences in resting-state brain function.

Annual research review: Growth connectomics--the organization and reorganization of brain networks during normal and abnormal development

BACKGROUND

We first give a brief introduction to graph theoretical analysis and its application to the study of brain network topology or connectomics. Within this framework, we review the existing empirical data on developmental changes in brain network organization across a range of experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans).

SYNTHESIS

We discuss preliminary evidence and current hypotheses for how the emergence of network properties correlates with concomitant cognitive and behavioural changes associated with development. We highlight some of the technical and conceptual challenges to be addressed by future developments in this rapidly moving field. Given the parallels previously discovered between neural systems across species and over a range of spatial scales, we also review some recent advances in developmental network studies at the cellular scale. We highlight the opportunities presented by such studies and how they may complement neuroimaging in advancing our understanding of brain development. Finally, we note that many brain and mind disorders are thought to be neurodevelopmental in origin and that charting the trajectory of brain network changes associated with healthy development also sets the stage for understanding abnormal network development.

CONCLUSIONS

We therefore briefly review the clinical relevance of network metrics as potential diagnostic markers and some recent efforts in computational modelling of brain networks which might contribute to a more mechanistic understanding of neurodevelopmental disorders in future.

Slow cortical potentials and "inner time consciousness" - A neuro-phenomenal hypothesis about the "width of present"

William James postulated a "stream of consciousness" that presupposes temporal continuity. The neuronal mechanisms underlying the construction of such temporal continuity remain unclear, however, in my contribution, I propose a neuro-phenomenal hypothesis that is based on slow cortical potentials and their extension of the present moment as described in the phenomenal term of "width of present". More specifically, I focus on the way the brain's neural activity needs to be encoded in order to make possible the "stream of consciousness." This leads us again to the low-frequency fluctuations of the brain's neural activity and more specifically to slow cortical potentials (SCPs). Due to their long phase duration as low-frequency fluctuations, SCPs can integrate different stimuli and their associated neural activity from different regions in one converging region. Such integration may be central for consciousness to occur, as it was recently postulated by He and Raichle. They leave open, however, the question of the exact neuronal mechanisms, like the encoding strategy, that make possible the association of the otherwise purely neuronal SCP with consciousness and its phenomenal features. I hypothesize that SCPs allow for linking and connecting different discrete points in physical time by encoding their statistically based temporal differences rather than the single discrete time points by themselves. This presupposes difference-based coding rather than stimulus-based coding. The encoding of such statistically based temporal differences makes it possible to "go beyond" the merely physical features of the stimuli; that is, their single discrete time points and their conduction delays (as related to their neural processing in the brain). This, in turn, makes possible the constitution of "local temporal continuity" of neural activity in one particular region. The concept of "local temporal continuity" signifies the linkage and integration of different discrete time points into one neural activity in a particular region. How does such local temporal continuity predispose the experience of time in consciousness? For that, I turn to phenomenological philosopher Edmund Husserl and his description of what he calls "inner time consciousness" (Husserl and Brough, 1990). One hallmark of humans' "inner time consciousness" is that we experience events and objects in succession and duration in our consciousness; according to Husserl, this amounts to what he calls the "width of [the] present." The concept of the width of present describes the extension of the present beyond the single discrete time point, such as, for instance, when we perceive different tones as a melody. I now hypothesize the degree of the width of present to be directly dependent upon and thus predisposed by the degree of the temporal differences between two (or more) discrete time points as they are encoded into neural activity. I therefore conclude that the SCPs and their encoding of neural activity in terms of temporal differences must be regarded a neural predisposition of consciousness (NPC) as distinguished from a neural correlate of consciousness (NCC).

Large-scale probabilistic functional modes from resting state fMRI

It is well established that it is possible to observe spontaneous, highly structured, fluctuations in human brain activity from functional magnetic resonance imaging (fMRI) when the subject is ‘at rest’. However, characterising this activity in an interpretable manner is still a very open problem.

In this paper, we introduce a method for identifying modes of coherent activity from resting state fMRI (rfMRI) data. Our model characterises a mode as the outer product of a spatial map and a time course, constrained by the nature of both the between-subject variation and the effect of the haemodynamic response function. This is presented as a probabilistic generative model within a variational framework that allows Bayesian inference, even on voxelwise rfMRI data. Furthermore, using this approach it becomes possible to infer distinct extended modes that are correlated with each other in space and time, a property which we believe is neuroscientifically desirable.

We assess the performance of our model on both simulated data and high quality rfMRI data from the Human Connectome Project, and contrast its properties with those of both spatial and temporal independent component analysis (ICA). We show that our method is able to stably infer sets of modes with complex spatio-temporal interactions and spatial differences between subjects.

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We introduce a probabilistic model for modes in resting state fMRI.

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Our hierarchical model captures subject variability and haemodynamic effects.

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We illustrate its performance on simulated data and rfMRI data from 200 subjects.

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We demonstrate the ability of our method to infer spatio-temporally interacting modes.

Voxel-wise motion artifacts in population-level whole-brain connectivity analysis of resting-state FMRI

Functional Magnetic Resonance Imaging (fMRI) based brain connectivity analysis maps the functional networks of the brain by estimating the degree of synchronous neuronal activity between brain regions. Recent studies have demonstrated that "resting-state" fMRI-based brain connectivity conclusions may be erroneous when motion artifacts have a differential effect on fMRI BOLD signals for between group comparisons. A potential explanation could be that in-scanner displacement, due to rotational components, is not spatially constant in the whole brain. However, this localized nature of motion artifacts is poorly understood and is rarely considered in brain connectivity studies. In this study, we initially demonstrate the local correspondence between head displacement and the changes in the resting-state fMRI BOLD signal. Than, we investigate how connectivity strength is affected by the population-level variation in the spatial pattern of regional displacement. We introduce Regional Displacement Interaction (RDI), a new covariate parameter set for second-level connectivity analysis and demonstrate its effectiveness in reducing motion related confounds in comparisons of groups with different voxel-vise displacement pattern and preprocessed using various nuisance regression methods. The effect of using RDI as second-level covariate is than demonstrated in autism-related group comparisons. The relationship between the proposed method and some of the prevailing subject-level nuisance regression techniques is evaluated. Our results show that, depending on experimental design, treating in-scanner head motion as a global confound may not be appropriate. The degree of displacement is highly variable among various brain regions, both within and between subjects. These regional differences bias correlation-based measures of brain connectivity. The inclusion of the proposed second-level covariate into the analysis successfully reduces artifactual motion-related group differences and preserves real neuronal differences, as demonstrated by the autism-related comparisons.

Transcranial oscillatory direct current stimulation during sleep improves declarative memory consolidation in children with attention-deficit/hyperactivity disorder to a level comparable to healthy controls

BACKGROUND

Slow oscillations (<1 Hz) during slow wave sleep (SWS) promote the consolidation of declarative memory. Children with attention-deficit/hyperactivity disorder (ADHD) have been shown to display deficits in sleep-dependent consolidation of declarative memory supposedly due to dysfunctional slow brain rhythms during SWS.

OBJECTIVE

Using transcranial oscillating direct current stimulation (toDCS) at 0.75 Hz, we investigated whether an externally triggered increase in slow oscillations during early SWS elevates memory performance in children with ADHD.

METHODS

12 children with ADHD underwent a toDCS and a sham condition in a double-blind crossover study design conducted in a sleep laboratory. Memory was tested using a 2D object-location task. In addition, 12 healthy children performed the same memory task in their home environment.

RESULTS

Stimulation enhanced slow oscillation power in children with ADHD and boosted memory performance to the same level as in healthy children.

CONCLUSION

These data indicate that increasing slow oscillation power during sleep by toDCS can alleviate declarative memory deficits in children with ADHD.

Sisyphus effect in pulse-coupled excitatory neural networks with spike-timing-dependent plasticity

The collective dynamics of excitatory pulse-coupled neural networks with spike-timing-dependent plasticity (STDP) is studied. Depending on the model parameters stationary states characterized by high or low synchronization can be observed. In particular, at the transition between these two regimes, persistent irregular low frequency oscillations between strongly and weakly synchronized states are observable, which can be identified as infraslow oscillations with frequencies ≃0.02-0.03 Hz. Their emergence can be explained in terms of the Sisyphus effect, a mechanism caused by a continuous feedback between the evolution of the coherent population activity and of the average synaptic weight. Due to this effect, the synaptic weights have oscillating equilibrium values, which prevents the neuronal population from relaxing into a stationary macroscopic state.

Increased neuronal firing in resting and sleep in areas of the macaque medial prefrontal cortex

The medial prefrontal cortex (mPFC) of humans and macaques is an integral part of the default mode network and is a brain region that shows increased activation in the resting state. A previous paper from our laboratory reported significantly increased firing rates of neurons in the macaque subgenual cingulate cortex, Brodmann area (BA) 25, during disengagement from a task and also during slow wave sleep [E.T. Rolls et al. (2003) J. Neurophysiology, 90, 134-142]. Here we report the finding that there are neurons in other areas of mPFC that also increase their firing rates during disengagement from a task, drowsiness and eye-closure. During the neurophysiological recording of single mPFC cells (n = 249) in BAs 9, 10, 13 m, 14c, 24b and especially pregenual area 32, populations of neurons were identified whose firing rates altered significantly with eye-closure compared with eye-opening. Three types of neuron were identified: Type 1 cells (28.1% of the total population) significantly increased (mean + 329%; P ≪ 0.01) their average firing rate with eye-closure, from 3.1 spikes/s when awake to 10.2 spikes/s when asleep; Type 2 cells (6.0%) significantly decreased (mean -68%; P < 0.05) their firing rate on eye-closure; and Type 3 cells (65.9%) were unaffected. Thus, in many areas of mPFC, implicated in the anterior default mode network, there is a substantial population of neurons that significantly increase their firing rates during periods of eye-closure. Such neurons may be part of an interconnected network of distributed brain regions that are more active during periods of relaxed wakefulness than during attention-demanding tasks.

Brain functional networks in syndromic and non-syndromic autism: a graph theoretical study of EEG connectivity

BACKGROUND

Graph theory has been recently introduced to characterize complex brain networks, making it highly suitable to investigate altered connectivity in neurologic disorders. A current model proposes autism spectrum disorder (ASD) as a developmental disconnection syndrome, supported by converging evidence in both non-syndromic and syndromic ASD. However, the effects of abnormal connectivity on network properties have not been well studied, particularly in syndromic ASD. To close this gap, brain functional networks of electroencephalographic (EEG) connectivity were studied through graph measures in patients with Tuberous Sclerosis Complex (TSC), a disorder with a high prevalence of ASD, as well as in patients with non-syndromic ASD.

METHODS

EEG data were collected from TSC patients with ASD (n = 14) and without ASD (n = 29), from patients with non-syndromic ASD (n = 16), and from controls (n = 46). First, EEG connectivity was characterized by the mean coherence, the ratio of inter- over intra-hemispheric coherence and the ratio of long- over short-range coherence. Next, graph measures of the functional networks were computed and a resilience analysis was conducted. To distinguish effects related to ASD from those related to TSC, a two-way analysis of covariance (ANCOVA) was applied, using age as a covariate.

RESULTS

Analysis of network properties revealed differences specific to TSC and ASD, and these differences were very consistent across subgroups. In TSC, both with and without a concurrent diagnosis of ASD, mean coherence, global efficiency, and clustering coefficient were decreased and the average path length was increased. These findings indicate an altered network topology. In ASD, both with and without a concurrent diagnosis of TSC, decreased long- over short-range coherence and markedly increased network resilience were found.

CONCLUSIONS

The altered network topology in TSC represents a functional correlate of structural abnormalities and may play a role in the pathogenesis of neurological deficits. The increased resilience in ASD may reflect an excessively degenerate network with local overconnection and decreased functional specialization. This joint study of TSC and ASD networks provides a unique window to common neurobiological mechanisms in autism.

Modulation of γ and spindle-range power by slow oscillations in scalp sleep EEG of children

Deep sleep is characterized by slow waves of electrical activity in the cerebral cortex. They represent alternating down states and up states of, respectively, hyperpolarization with accompanying neuronal silence and depolarization during which neuronal firing resumes. The up states give rise to faster oscillations, notably spindles and gamma activity which appear to be of major importance to the role of sleep in brain function and cognition. Unfortunately, while spindles are easily detectable, gamma oscillations are of very small amplitude. No previous sleep study has succeeded in demonstrating modulations of gamma power along the time course of slow waves in human scalp EEG. As a consequence, progress in our understanding of the functional role of gamma modulation during sleep has been limited to animal studies and exceptional human studies, notably those of intracranial recordings in epileptic patients. Because high synaptic density, which peaks some time before puberty depending on the brain region (Huttenlocher and Dabholkar, 1997), generates oscillations of larger amplitude, we considered that the best chance to demonstrate a modulation of gamma power by slow wave phase in regular scalp sleep EEG would be in school-aged children. Sleep EEG was recorded in 30 healthy children (aged 10.7 ± 0.8 years; mean ± s.d.). Time-frequency analysis was applied to evaluate the time course of spectral power along the development of a slow wave. Moreover, we attempted to modify sleep architecture and sleep characteristics through automated acoustic stimulation coupled to the occurrence of slow waves in one subset of the children. Gamma power increased on the rising slope and positive peak of the slow wave. Gamma and spindle activity is strongly suppressed during the negative peak. There were no differences between the groups who received and did not receive acoustic stimulation in the sleep parameters and slow wave-locked time-frequency analysis. Our findings show, for the first time in scalp EEG in humans, that gamma activity is associated with the up-going slope and peak of the slow wave. We propose that studies in children provide a uniquely feasible opportunity to conduct investigations into the role of gamma during sleep.