Emergence of sensory patterns during sleep highlights differential dynamics of REM and non-REM sleep stages

Annotated interictal discharges in intracranial EEG sleep data and related machine learning detection scheme

Interictal epileptiform discharges (IEDs) such as spikes and sharp waves represent pathological electrophysiological activities occurring in epilepsy patients between seizures. IEDs occur preferentially during non-rapid eye movement (NREM) sleep and are associated with impaired memory and cognition. Despite growing interest, most studies involving IED detections rely on visual annotations or employ simple amplitude threshold approaches. Alternatively, advanced computerized detection methods are not standardized or publicly available. To address this gap, we introduce a novel dataset comprising multichannel intracranial electroencephalography (iEEG) data recorded at two medical centers during overnight sleep with IED annotations performed by expert neurologists. Utilizing these annotations to train machine learning models via a gradient-boosting algorithm, we demonstrate automated IED detection with high precision (94.4%) and sensitivity (94.3%) that can generalize across individuals and surpass performance of a leading commercial software. The dataset featuring multi-channel annotations with sub-second resolution including hippocampus and medial temporal lobe (MTL) regions is made publicly available, together with the detection algorithm, to advance research on detection methodology, epilepsy, sleep, and cognition.

DO NOT LOSE SLEEP OVER IT: IMPLANTED BRAIN-COMPUTER INTERFACE FUNCTIONALITY DURING NIGHTTIME IN LATE-STAGE AMYOTROPHIC LATERAL SCLEROSIS

Background and objectives

Brain-computer interfaces (BCIs) hold promise as augmentative and alternative communication technology for people with severe motor and speech impairment (locked-in syndrome) due to neural disease or injury. Although such BCIs should be available 24/7, to enable communication at all times, feasibility of nocturnal BCI use has not been investigated. Here, we addressed this question using data from an individual with amyotrophic lateral sclerosis (ALS) who was implanted with an electrocorticography-based BCI that enabled the generation of click-commands for spelling words and call-caregiver signals.

Methods

We investigated nocturnal dynamics of neural signal features used for BCI control, namely low (LFB: 10-30Hz) and high frequency band power (HFB: 65-95Hz). Additionally, we assessed the nocturnal performance of a BCI decoder that was trained on daytime data by quantifying the number of unintentional BCI activations at night. Finally, we developed and implemented a nightmode decoder that allowed the participant to call a caregiver at night, and assessed its performance.

Results

Power and variance in HFB and LFB were significantly higher at night than during the day in the majority of the nights, with HFB variance being higher in 88% of nights. Daytime decoders caused 245 unintended selection-clicks and 13 unintended caregiver-calls per hour when applied to night data. The developed nightmode decoder functioned error-free in 79% of nights over a period of ±1.5 years, allowing the user to reliably call the caregiver, with unintended activations occurring only once every 12 nights.

Discussion

Reliable nighttime use of a BCI requires decoders that are adjusted to sleep-related signal changes. This demonstration of a reliable BCI nightmode and its long-term use by an individual with advanced ALS underscores the importance of 24/7 BCI reliability.

Trial registration

This trial is registered in clinicaltrials.gov under number NCT02224469 (https://clinicaltrials.gov/study/NCT02224469?term=NCT02224469&rank=1). Date of submission to registry: August 21, 2014. Enrollment of first participant: September 7, 2015.

Augmenting hippocampal-prefrontal neuronal synchrony during sleep enhances memory consolidation in humans

Memory consolidation during sleep is thought to depend on the coordinated interplay between cortical slow waves, thalamocortical sleep spindles and hippocampal ripples, but direct evidence is lacking. Here, we implemented real-time closed-loop deep brain stimulation in human prefrontal cortex during sleep and tested its effects on sleep electrophysiology and on overnight consolidation of declarative memory. Synchronizing the stimulation to the active phases of endogenous slow waves in the medial temporal lobe (MTL) enhanced sleep spindles, boosted locking of brain-wide neural spiking activity to MTL slow waves, and improved coupling between MTL ripples and thalamocortical oscillations. Furthermore, synchronized stimulation enhanced the accuracy of recognition memory. By contrast, identical stimulation without this precise time-locking was not associated with, and sometimes even degraded, these electrophysiological and behavioral effects. Notably, individual changes in memory accuracy were highly correlated with electrophysiological effects. Our results indicate that hippocampo-thalamocortical synchronization during sleep causally supports human memory consolidation.

Closed-loop neuromodulation for studying spontaneous activity and causality

Having established that spontaneous brain activity follows meaningful coactivation patterns and correlates with behavior, researchers have turned their attention to understanding its function and behavioral significance. We suggest closed-loop neuromodulation as a neural perturbation tool uniquely well suited for this task. Closed-loop neuromodulation has primarily been viewed as an interventionist tool to teach subjects to directly control their own brain activity. We examine an alternative operant conditioning model of closed-loop neuromodulation which, through implicit feedback, can manipulate spontaneous activity at the network level, without violating the spontaneous or endogenous nature of the signal, thereby providing a direct test of network causality.

Spontaneously Emerging Patterns in Human Visual Cortex Reflect Responses to Naturalistic Sensory Stimuli

In the absence of stimulus or task, the cortex spontaneously generates rich and consistent functional connectivity patterns (termed resting state networks) which are evident even within individual cortical areas. We and others have previously hypothesized that habitual cortical network activations during daily life contribute to the shaping of these connectivity patterns. Here we tested this hypothesis by comparing, using blood oxygen level-dependent-functional magnetic resonance imaging, the connectivity patterns that spontaneously emerge during rest in retinotopic visual areas to the patterns generated by naturalistic visual stimuli (repeated movie segments). These were then compared with connectivity patterns produced by more standard retinotopic mapping stimuli (polar and eccentricity mapping). Our results reveal that the movie-driven patterns were significantly more similar to the spontaneously emerging patterns, compared with the connectivity patterns of either eccentricity or polar mapping stimuli. Intentional visual imagery of naturalistic stimuli was unlikely to underlie these results, since they were duplicated when participants were engaged in an auditory task. Our results suggest that the connectivity patterns that appear during rest better reflect naturalistic activations rather than controlled, artificially designed stimuli. The results are compatible with the hypothesis that the spontaneous connectivity patterns in human retinotopic areas reflect the statistics of cortical coactivations during natural vision.

Sleep- and Wake-Like States in Small Networks In Vivo and In Vitro

Wakefulness and sleep are highly complex and heterogeneous processes, involving multiple neurotransmitter systems and a sophisticated interplay between global and local networks of neurons and non-neuronal cells. Macroscopic approaches applied at the level of the whole organism, view sleep as a global behaviour and allow for investigation into aspects such as the effects of insufficient or disrupted sleep on cognitive function, metabolism, thermoregulation and sensory processing. While significant progress has been achieved using such large-scale approaches, the inherent complexity of sleep-wake regulation has necessitated the development of methods which tackle specific aspects of sleep in isolation. One way this may be achieved is by investigating specific cellular or molecular phenomena in the whole organism in situ, either during spontaneous or induced sleep-wake states. This approach has greatly advanced our knowledge about the electrophysiology and pharmacology of ion channels, specific receptors, intracellular pathways and the small networks implicated in the control and regulation of the sleep-wake cycle. Importantly though, there are a variety of external and internal factors that influence global behavioural states which are difficult to control for using these approaches. For this reason, over the last few decades, ex vivo experimental models have become increasingly popular and have greatly advanced our understanding of many fundamental aspects of sleep, including the neuroanatomy and neurochemistry of sleep states, sleep regulation, the origin and dynamics of specific sleep oscillations, network homeostasis as well as the functional roles of sleep. This chapter will focus on the use of small neuronal networks as experimental models and will highlight the most significant and novel insights these approaches have provided.

Replay of large-scale spatio-temporal patterns from waking during subsequent NREM sleep in human cortex

Animal studies support the hypothesis that in slow-wave sleep, replay of waking neocortical activity under hippocampal guidance leads to memory consolidation. However, no intracranial electrophysiological evidence for replay exists in humans. We identified consistent sequences of population firing peaks across widespread cortical regions during complete waking periods. The occurrence of these “Motifs” were compared between sleeps preceding the waking period (“Sleep-Pre”) when the Motifs were identified, and those following (“Sleep-Post”). In all subjects, the majority of waking Motifs (most of which were novel) had more matches in Sleep-Post than in Sleep-Pre. In rodents, hippocampal replay occurs during local sharp-wave ripples, and the associated neocortical replay tends to occur during local sleep spindles and down-to-up transitions. These waves may facilitate consolidation by sequencing cell-firing and encouraging plasticity. Similarly, we found that Motifs were coupled to neocortical spindles, down-to-up transitions, theta bursts, and hippocampal sharp-wave ripples. While Motifs occurring during cognitive task performance were more likely to have more matches in subsequent sleep, our studies provide no direct demonstration that the replay of Motifs contributes to consolidation. Nonetheless, these results confirm a core prediction of the dominant neurobiological theory of human memory consolidation.

Just a thought: How mind-wandering is represented in dynamic brain connectivity

The neuroscience of mind-wandering has begun to flourish, with roles of brain regions and networks being defined for various components of spontaneous thought. However, most of brain activity does not represent immediately occurring thoughts. Instead, spontaneous, organized network activity largely reflects "intrinsic" functions that are unrelated to the current experience. There remains no consensus on how brain networks represent mind-wandering in parallel to functioning in other ongoing, predominantly unconscious processes. Commonly, in network analysis of functional neuroimaging data, functional connectivity (FC; correlated time series) between remote brain regions is considered over several minutes or longer. In contrast, dynamic functional connectivity (dFC) is a new, promising approach to characterizing spontaneous changes in neural network communication on the faster time-scale at which intra-individual fluctuations in thought contents may occur. Here I describe how a potential relationship between mind-wandering and FC has traditionally been considered in the literature, and I review methods and results pertaining to the study of the dFC-mind-wandering relationship. While acknowledging challenges to the dFC approach and to behaviorally capturing fluctuations in inner experiences, I describe a framework for describing spontaneous thoughts in terms of brain-network activity patterns that are comprised of connections weighted by time-varying relevance to conscious and unconscious processing. This perspective suggests preferential roles of certain anatomical communication avenues (e.g., via the default mode network) in mind-wandering, while also implying that a region's connectivity fluctuates over time in its immediate degree of relevance to conscious contents, ultimately allowing novelty and diversity of thought.

Diminished Auditory Responses during NREM Sleep Correlate with the Hierarchy of Language Processing

Natural sleep provides a powerful model system for studying the neuronal correlates of awareness and state changes in the human brain. To quantitatively map the nature of sleep-induced modulations in sensory responses we presented participants with auditory stimuli possessing different levels of linguistic complexity. Ten participants were scanned using functional magnetic resonance imaging (fMRI) during the waking state and after falling asleep. Sleep staging was based on heart rate measures validated independently on 20 participants using concurrent EEG and heart rate measurements and the results were confirmed using permutation analysis. Participants were exposed to three types of auditory stimuli: scrambled sounds, meaningless word sentences and comprehensible sentences. During non-rapid eye movement (NREM) sleep, we found diminishing brain activation along the hierarchy of language processing, more pronounced in higher processing regions. Specifically, the auditory thalamus showed similar activation levels during sleep and waking states, primary auditory cortex remained activated but showed a significant reduction in auditory responses during sleep, and the high order language-related representation in inferior frontal gyrus (IFG) cortex showed a complete abolishment of responses during NREM sleep. In addition to an overall activation decrease in language processing regions in superior temporal gyrus and IFG, those areas manifested a loss of semantic selectivity during NREM sleep. Our results suggest that the decreased awareness to linguistic auditory stimuli during NREM sleep is linked to diminished activity in high order processing stations.

Covert neurofeedback without awareness shapes cortical network spontaneous connectivity

Recent advances in blood oxygen level-dependent-functional MRI (BOLD-fMRI)-based neurofeedback reveal that participants can modulate neuronal properties. However, it is unknown whether such training effects can be introduced in the absence of participants' awareness that they are being trained. Here, we show unconscious neurofeedback training, which consequently produced changes in functional connectivity, introduced in participants who received positive and negative rewards that were covertly coupled to activity in two category-selective visual cortex regions. The results indicate that brain networks can be modified even in the complete absence of intention and awareness of the learning situation, raising intriguing possibilities for clinical interventions.

Spontaneous Neural Dynamics and Multi-scale Network Organization

Spontaneous neural activity has historically been viewed as task-irrelevant noise that should be controlled for via experimental design, and removed through data analysis. However, electrophysiology and functional MRI studies of spontaneous activity patterns, which have greatly increased in number over the past decade, have revealed a close correspondence between these intrinsic patterns and the structural network architecture of functional brain circuits. In particular, by analyzing the large-scale covariation of spontaneous hemodynamics, researchers are able to reliably identify functional networks in the human brain. Subsequent work has sought to identify the corresponding neural signatures via electrophysiological measurements, as this would elucidate the neural origin of spontaneous hemodynamics and would reveal the temporal dynamics of these processes across slower and faster timescales. Here we survey common approaches to quantifying spontaneous neural activity, reviewing their empirical success, and their correspondence with the findings of neuroimaging. We emphasize invasive electrophysiological measurements, which are amenable to amplitude- and phase-based analyses, and which can report variations in connectivity with high spatiotemporal precision. After summarizing key findings from the human brain, we survey work in animal models that display similar multi-scale properties. We highlight that, across many spatiotemporal scales, the covariance structure of spontaneous neural activity reflects structural properties of neural networks and dynamically tracks their functional repertoire.

Optimizing the Detection of Wakeful and Sleep-Like States for Future Electrocorticographic Brain Computer Interface Applications

Previous studies suggest stable and robust control of a brain-computer interface (BCI) can be achieved using electrocorticography (ECoG). Translation of this technology from the laboratory to the real world requires additional methods that allow users operate their ECoG-based BCI autonomously. In such an environment, users must be able to perform all tasks currently performed by the experimenter, including manually switching the BCI system on/off. Although a simple task, it can be challenging for target users (e.g., individuals with tetraplegia) due to severe motor disability. In this study, we present an automated and practical strategy to switch a BCI system on or off based on the cognitive state of the user. Using a logistic regression, we built probabilistic models that utilized sub-dural ECoG signals from humans to estimate in pseudo real-time whether a person is awake or in a sleep-like state, and subsequently, whether to turn a BCI system on or off. Furthermore, we constrained these models to identify the optimal anatomical and spectral parameters for delineating states. Other methods exist to differentiate wake and sleep states using ECoG, but none account for practical requirements of BCI application, such as minimizing the size of an ECoG implant and predicting states in real time. Our results demonstrate that, across 4 individuals, wakeful and sleep-like states can be classified with over 80% accuracy (up to 92%) in pseudo real-time using high gamma (70-110 Hz) band limited power from only 5 electrodes (platinum discs with a diameter of 2.3 mm) located above the precentral and posterior superior temporal gyrus.

Intrinsic and task-dependent coupling of neuronal population activity in human parietal cortex

Human neuroimaging studies have suggested that subregions of the medial and lateral parietal cortex form key nodes of a larger brain network supporting episodic memory retrieval. To explore the electrophysiological correlates of functional connectivity between these subregions, we recorded simultaneously from medial and lateral parietal cortex using intracranial electrodes in three human subjects. We observed electrophysiological co-activation of retrosplenial/posterior cingulate cortex (RSC/PCC) and angular gyrus (AG) in the high-frequency broadband (HFB, or high-gamma) range, for conditions that required episodic retrieval. During resting and sleeping states, slow fluctuations (<1 Hz) of HFB activity were highly correlated between these task-co-activated neuronal populations. Furthermore, intrinsic electrophysiological connectivity patterns matched those obtained with resting-state fMRI from the same subjects. Our findings quantify the spatiotemporal dynamics of parietal cortex during episodic memory retrieval and provide clear neurophysiological correlates of intrinsic and task-dependent functional connectivity in the human brain.

Neurocognitive biases and the patterns of spontaneous correlations in the human cortex

When the brain is 'at rest', spatiotemporal activity patterns emerge spontaneously, that is, in the absence of an overt task. However, what these patterns reveal about cortical function remains elusive. In this article, we put forward the hypothesis that the correlation patterns among these spontaneous fluctuations (SPs) reflect the profile of individual a priori cognitive biases, coded as synaptic efficacies in cortical networks. Thus, SPs offer a new means for mapping personal traits in both neurotypical and atypical cases. Three sets of observations and related empirical evidence provide support for this hypothesis. First, SPs correspond to activation patterns that occur during typical task performance. Second, individual differences in SPs reflect individual biases and abnormalities. Finally, SPs can be actively remodeled in a long-term manner by focused and intense cortical training.