A multiscale symbolic approach to decoding delta and ripple oscillation bands as biomarkers for epileptiform discharges.

We use a multiscale symbolic approach to study the complex dynamics of temporal lobe refractory epilepsy employing high-resolution intracranial electroencephalogram (iEEG). We consider the basal and preictal phases and meticulously analyze the dynamics across frequency bands, focusing on high-frequency oscillations up to 240 Hz. Our results reveal significant periodicities and critical time scales within neural dynamics across frequency bands. By bandpass filtering neural signals into delta, theta, alpha, beta, gamma, and ripple high-frequency bands (HFO), each associated with specific neural processes, we examine the distinct nonlinear dynamics. Our method introduces a reliable approach to pinpoint intrinsic time lag scales τ within frequency bands of the basal and preictal signals, which are crucial for the study of refractory epilepsy. Using metrics such as permutation entropy (H), Fisher information (F), and complexity (C), we explore nonlinear patterns within iEEG signals. We reveal the intrinsic τmax that maximize complexity within each frequency band, unveiling the nonlinear subtle patterns of the temporal structures within the basal and preictal signal. Examining the H×F and C×F values allows us to identify differences in the delta band and a band between 200 and 220 Hz (HFO 6) when comparing basal and preictal signals. Differences in Fisher information in the delta and HFO 6 bands before seizures highlight their role in capturing important system dynamics. This offers new perspectives on the intricate relationship between delta oscillations and HFO waves in patients with focal epilepsy, highlighting the importance of these patterns and their potential as biomarkers.

Preoperative recovery sleep ameliorates postoperative cognitive dysfunction aggravated by sleep fragmentation in aged mice by enhancing EEG delta-wave activity and LFP theta oscillation in hippocampal CA1.

Sleep fragmentation (SF) is a common sleep problem experienced during the perioperative period by older adults, and is associated with postoperative cognitive dysfunction (POCD). Increasing evidence indicates that delta-wave activity during non-rapid eye movement (NREM) sleep is involved in sleep-dependent memory consolidation and that hippocampal theta oscillations are related to spatial exploratory memory. Recovery sleep (RS), a self-regulated state of sleep homeostasis, enhances delta-wave power and memory performance in sleep-deprived older mice. However, it remains unclear whether RS therapy has a positive effect on cognitive changes following SF in older mouse models. Therefore, this study aimed to explore whether preoperative RS can alleviate cognitive deficits in aged mice with SF. A model of preoperative 24-h SF combined with exploratory laparotomy-induced POCD was established in 18-month-old mice. Aged mice were treated with preoperative 6-h RS following SF and postoperative 6-h RS following surgery, respectively. The changes in hippocampus-dependent cognitive function were investigated using behavioral tests, electroencephalography (EEG), local field potential (LFP), magnetic resonance imaging, and neuromorphology. Mice that underwent 24-h SF combined with surgery exhibited severe spatial memory impairment; impaired cognitive performance could be alleviated by preoperative RS treatment. In addition, preoperative RS increased NREM sleep; enhanced EEG delta-wave activity and LFP theta oscillation in the hippocampal CA1; and improved hippocampal perfusion, microstructural integrity, and neuronal damage. Taken together, these results provide evidence that preoperative RS may ameliorate the severity of POCD aggravated by SF by enhancing delta slow-wave activity and hippocampal theta oscillation, and by ameliorating the reduction in regional cerebral blood flow and white matter microstructure integrity in the hippocampus.

Association of Disrupted Delta Wave Activity During Sleep With Long-Term Cardiovascular Disease and Mortality.

BACKGROUND: Delta wave activity is a prominent feature of deep sleep, which is significantly associated with sleep quality. OBJECTIVES: The authors hypothesized that delta wave activity disruption during sleep could predict long-term cardiovascular disease (CVD) and CVD mortality risk. METHODS: The authors used a comprehensive power spectral entropy-based method to assess delta wave activity during sleep based on overnight polysomnograms in 4,058 participants in the SHHS (Sleep Heart Health Study) and 2,193 participants in the MrOS (Osteoporotic Fractures in Men Study) Sleep study. RESULTS: During 11.0 ± 2.8 years of follow-up in SHHS, 729 participants had incident CVD and 192 participants died due to CVD. During 15.5 ± 4.4 years of follow-up in MrOS, 547 participants had incident CVD, and 391 died due to CVD. In multivariable Cox regression models, lower delta wave entropy during sleep was associated with higher risk of coronary heart disease (SHHS: HR: 1.46; 95% CI: 1.02-2.06; P = 0.03; MrOS: HR: 1.79; 95% CI: 1.17-2.73; P < 0.01), CVD (SHHS: HR: 1.60; 95% CI: 1.21-2.11; P < 0.01; MrOS: HR: 1.43; 95% CI: 1.00-2.05; P = 0.05), and CVD mortality (SHHS: HR: 1.94; 95% CI: 1.18-3.18; P < 0.01; MrOS: HR: 1.66; 95% CI: 1.12-2.47; P = 0.01) after adjusting for covariates. The Shapley Additive Explanations method indicates that low delta wave entropy was more predictive of coronary heart disease, CVD, and CVD mortality risks than conventional sleep parameters. CONCLUSIONS: The results suggest that delta wave activity disruption during sleep may be a useful metric to identify those at increased risk for CVD and CVD mortality.

Driver drowsiness estimation using EEG signals with a dynamical encoder-decoder modeling framework.

Drowsiness is a leading cause of accidents on the road as it negatively affects the driver's ability to safely operate a vehicle. Neural activity recorded by EEG electrodes is a widely used physiological correlate of driver drowsiness. This paper presents a novel dynamical modeling solution to estimate the instantaneous level of the driver drowsiness using EEG signals, where the PERcentage of eyelid CLOSure (PERCLOS) is employed as the ground truth of driver drowsiness. Applying our proposed modeling framework, we find neural features present in EEG data that encode PERCLOS. In the decoding phase, we use a Bayesian filtering solution to estimate the PERCLOS level over time. A data set that comprises 18 driving tests, conducted by 13 drivers, has been used to investigate the performance of the proposed framework. The modeling performance in estimation of PERCLOS provides robust and repeatable results in tests with manual and automated driving modes by an average RMSE of 0.117 (at a PERCLOS range of 0 to 1) and average High Probability Density percentage of 62.5%. We further hypothesized that there are biomarkers that encode the PERCLOS across different driving tests and participants. Using this solution, we identified possible biomarkers such as Theta and Delta powers. Results show that about 73% and 66% of the Theta and Delta powers which are selected as biomarkers are increasing as PERCLOS grows during the driving test. We argue that the proposed method is a robust and reliable solution to estimate drowsiness in real-time which opens the door in utilizing EEG-based measures in driver drowsiness detection systems.

Accelerated Aging Characterizes the Early Stage of Alzheimer's Disease.

For Alzheimer's disease (AD), aging is the main risk factor, but whether cognitive impairments due to aging resemble early AD deficits is not yet defined. When working with mouse models of AD, the situation is just as complicated, because only a few studies track the progression of the disease at different ages, and most ignore how the aging process affects control mice. In this work, we addressed this problem by comparing the aging process of PS2APP (AD) and wild-type (WT) mice at the level of spontaneous brain electrical activity under anesthesia. Using local field potential recordings, obtained with a linear probe that traverses the posterior parietal cortex and the entire hippocampus, we analyzed how multiple electrical parameters are modified by aging in AD and WT mice. With this approach, we highlighted AD specific features that appear in young AD mice prior to plaque deposition or that are delayed at 12 and 16 months of age. Furthermore, we identified aging characteristics present in WT mice but also occurring prematurely in young AD mice. In short, we found that reduction in the relative power of slow oscillations (SO) and Low/High power imbalance are linked to an AD phenotype at its onset. The loss of SO connectivity and cortico-hippocampal coupling between SO and higher frequencies as well as the increase in UP-state and burst durations are found in young AD and old WT mice. We show evidence that the aging process is accelerated by the mutant PS2 itself and discuss such changes in relation to amyloidosis and gliosis.

Brain, interrupted: alpha/delta EEG ratio in survivors of pre- and post-natal adversity.

RESULTS: Young adults born at extremely low birth weight (prenatal adversity; N = 64, Mage = 23.14 years, SDage = 1.26 years) had a lower alpha/delta ratio score compared to normal birth weight controls (N = 76, Mage = 23.60 years, SDage = 1.09 years), while youth exposed to child maltreatment (postnatal adversity; N = 39, Mage = 16.18 years, SDage = 1.15) had a higher alpha/delta ratio compared to controls (N = 23, Mage = 16.00 years, SDage = 1.50 years). CONCLUSIONS: Our results suggest that being exposed to pre- and post-natal adversity may have different long-term consequences on brain development. We speculate that these differences might be associated with some of the different functional outcomes known to characterize each type of adverse experience.

Delta- and theta-band cortical tracking and phase-amplitude coupling to sung speech by infants.

The amplitude envelope of speech carries crucial low-frequency acoustic information that assists linguistic decoding at multiple time scales. Neurophysiological signals are known to track the amplitude envelope of adult-directed speech (ADS), particularly in the theta-band. Acoustic analysis of infant-directed speech (IDS) has revealed significantly greater modulation energy than ADS in an amplitude-modulation (AM) band centred on ∼2 Hz. Accordingly, cortical tracking of IDS by delta-band neural signals may be key to language acquisition. Speech also contains acoustic information within its higher-frequency bands (beta, gamma). Adult EEG and MEG studies reveal an oscillatory hierarchy, whereby low-frequency (delta, theta) neural phase dynamics temporally organize the amplitude of high-frequency signals (phase amplitude coupling, PAC). Whilst consensus is growing around the role of PAC in the matured adult brain, its role in the development of speech processing is unexplored. Here, we examined the presence and maturation of low-frequency (<12 Hz) cortical speech tracking in infants by recording EEG longitudinally from 60 participants when aged 4-, 7- and 11- months as they listened to nursery rhymes. After establishing stimulus-related neural signals in delta and theta, cortical tracking at each age was assessed in the delta, theta and alpha [control] bands using a multivariate temporal response function (mTRF) method. Delta-beta, delta-gamma, theta-beta and theta-gamma phase-amplitude coupling (PAC) was also assessed. Significant delta and theta but not alpha tracking was found. Significant PAC was present at all ages, with both delta and theta -driven coupling observed.

Delta-modulated cortical alpha oscillations support new knowledge generation through memory integration.

The ability to generate new knowledge depends on integration of separate information. For example, in one episode an individual may learn that apple seeds are called pips. In a separate episode, the individual may then learn that pips contain cyanide. Integration of the related facts in memory may then support derivation of the new knowledge that apple seeds contain cyanide. Past studies show that adults form relational memories that represent the commonalities among discrete events, and that such integrated representation supports the ability to infer new knowledge. Although these integrated representations are thought to result from linking separate memories to the same neuronal ensemble, the neural mechanisms that underlie formation of such linkages are not well understood. Here we examined whether self-derivation of new, integrated knowledge was supported by oscillatory coherence, a means of linking discrete neuronal ensembles. Cortical alpha coherence was greater when adults encoded new facts that could be integrated with existing knowledge, relative to encoding unrelated facts, particularly in participants who showed better performance on the subsequent test of knowledge generation via fact integration. In high performers, posterior alpha amplitude was also modulated by delta phase, a form of cross-frequency coupling previously implicated in coordinating information stored widely throughout the cortex. Examination of the timing and topography of these respective signatures suggested that these oscillatory dynamics work in concert to encode and represent new knowledge with respect to prior knowledge that is reactivated, thus revealing fundamental mechanisms through which related memories are linked into integrated knowledge structures.

Timing-dependent differential effects of unexpected events on error processing reveal the interactive dynamics of surprise and error processing.

When unexpected events occur during goal-directed behavior, they automatically trigger an orienting-related cascade of psychological and neural processes through which they influence behavior and cognition. If the unexpected event was caused by an action error, additional error-specific, strategic-related processes have been proposed to follow the initial orienting period. Little is known about the neural interactions between action errors and unexpected perceptual events, two instantiations of unexpected events, in these two putative stages of post-error processing. Here, we aimed to address this by investigating the electrophysiological dynamics associated with action errors and unexpected perceptual events using scalp EEG with a focus on the frontal midline (FM) delta-to-theta oscillations (1-8 Hz) indicative of the performance-monitoring system. Specifically, we examined how the timing of unexpected sounds would influence behavior and neural oscillations after action errors, depending on the length of the intertrial interval (ITI). Our data showed that unexpected sounds aggravated post-error decreases in accuracy when they occurred (1) immediately after errors (i.e., post-error orienting period), regardless of ITI and (2) immediately after the post-error stimulus (i.e., post-error strategic period), at short ITIs. Meanwhile, action errors and unexpected sounds independently produced increased FM delta-to-theta power during the post-error orienting period, regardless of ITIs. However, when unexpected sounds occurred during the post-error strategic period, action errors produced lower FM delta-to-theta power than correct responses, at short ITIs. These differential effects of unexpected events on behavior and FM delta-to-theta dynamics support the notion of the two post-error periods during which different processes are implemented.

Excitation of Putative Glutamatergic Neurons in the Rat Parabrachial Nucleus Region Reduces Delta Power during Dexmedetomidine but not Ketamine Anesthesia.

BACKGROUND: Parabrachial nucleus excitation reduces cortical delta oscillation (0.5 to 4 Hz) power and recovery time associated with anesthetics that enhance γ-aminobutyric acid type A receptor action. The effects of parabrachial nucleus excitation on anesthetics with other molecular targets, such as dexmedetomidine and ketamine, remain unknown. The hypothesis was that parabrachial nucleus excitation would cause arousal during dexmedetomidine and ketamine anesthesia. METHODS: Designer Receptors Exclusively Activated by Designer Drugs were used to excite calcium/calmodulin-dependent protein kinase 2α-positive neurons in the parabrachial nucleus region of adult male rats without anesthesia (nine rats), with dexmedetomidine (low dose: 0.3 µg · kg-1 · min-1 for 45 min, eight rats; high dose: 4.5 µg · kg-1 · min-1 for 10 min, seven rats), or with ketamine (low dose: 2 mg · kg-1 · min-1 for 30 min, seven rats; high dose: 4 mg · kg-1 · min-1 for 15 min, eight rats). For control experiments (same rats and treatments), the Designer Receptors Exclusively Activated by Designer Drugs were not excited. The electroencephalogram and anesthesia recovery times were recorded and analyzed. RESULTS: Parabrachial nucleus excitation reduced delta power in the prefrontal electroencephalogram with low-dose dexmedetomidine for the 150-min analyzed period, excepting two brief periods (peak median bootstrapped difference [clozapine-N-oxide - saline] during dexmedetomidine infusion = -6.06 [99% CI = -12.36 to -1.48] dB, P = 0.007). However, parabrachial nucleus excitation was less effective at reducing delta power with high-dose dexmedetomidine and low- and high-dose ketamine (peak median bootstrapped differences during high-dose [dexmedetomidine, ketamine] infusions = [-1.93, -0.87] dB, 99% CI = [-4.16 to -0.56, -1.62 to -0.18] dB, P = [0.006, 0.019]; low-dose ketamine had no statistically significant decreases during the infusion). Recovery time differences with parabrachial nucleus excitation were not statistically significant for dexmedetomidine (median difference for [low, high] dose = [1.63, 11.01] min, 95% CI = [-20.06 to 14.14, -20.84 to 23.67] min, P = [0.945, 0.297]) nor low-dose ketamine (median difference = 12.82 [95% CI: -3.20 to 39.58] min, P = 0.109) but were significantly longer for high-dose ketamine (median difference = 11.38 [95% CI: 1.81 to 24.67] min, P = 0.016). CONCLUSIONS: These results suggest that the effectiveness of parabrachial nucleus excitation to change the neurophysiologic and behavioral effects of anesthesia depends on the anesthetic's molecular target.

A heuristic perspective on non-variational free energy modulation at the sleep-like edge.

BACKGROUND: The variational Free Energy Principle (FEP) establishes that a neural system minimizes a free energy function of their internal state through environmental sensing entailing beliefs about hidden states in their environment. PROBLEM: Because sensations are drastically reduced during sleep, it is still unclear how a self-organizing neural network can modulate free energy during sleep transitions. GOAL: To address this issue, we study how network's state-dependent changes in energy, entropy and free energy connect with changes at the synaptic level in the absence of sensing during a sleep-like transition. APPROACH: We use simulations of a physically plausible, environmentally isolated neuronal network that self-organize after inducing a thalamic input to show that the reduction of non-variational free energy depends sensitively upon thalamic input at a slow, rhythmic Poisson (delta) frequency due to spike timing dependent plasticity. METHODS: We define a non-variational free energy in terms of the relative difference between the energy and entropy of the network from the initial distribution (prior to activity dependent plasticity) to the nonequilibrium steady-state distribution (after plasticity). We repeated the analysis under different levels of thalamic drive - as defined by the number of cortical neurons in receipt of thalamic input. RESULTS: Entraining slow activity with thalamic input induces a transition from a gamma (awake-like state) to a delta (sleep-like state) mode of activity, which can be characterized through a modulation of network's energy and entropy (non-variational free energy) of the ensuing dynamics. The self-organizing response to low and high thalamic drive also showed characteristic differences in the spectrum of frequency content due to spike timing dependent plasticity. CONCLUSIONS: The modulation of this non-variational free energy in a network that self-organizes, seems to be an organizational network principle. This could open a window to new empirically testable hypotheses about state changes in a neural network.

Differential effect of sleep deprivation on place cell representations, sleep architecture, and memory in young and old mice.

Poor sleep quality is associated with age-related cognitive decline, and whether reversal of these alterations is possible is unknown. In this study, we report how sleep deprivation (SD) affects hippocampal representations, sleep patterns, and memory in young and old mice. After training in a hippocampus-dependent object-place recognition (OPR) task, control animals sleep ad libitum, although experimental animals undergo 5 h of SD, followed by recovery sleep. Young controls and old SD mice exhibit successful OPR memory, whereas young SD and old control mice are impaired. Successful performance is associated with two cellular phenotypes: (1) "context" cells, which remain stable throughout training and testing, and (2) "object configuration" cells, which remap when objects are introduced to the context and during testing. Additionally, effective memory correlates with spindle counts during non-rapid eye movement (NREM)/rapid eye movement (REM) sigma transitions. These results suggest SD may serve to ameliorate age-related memory deficits and allow hippocampal representations to adapt to changing environments.

Delta power robustly predicts cognitive function in Angelman syndrome.

OBJECTIVE: Angelman syndrome (AS) is a severe neurodevelopmental disorder caused by loss of function of the maternally inherited UBE3A gene in neurons. Promising disease-modifying treatments to reinstate UBE3A expression are under development and an early measure of treatment response is critical to their deployment in clinical trials. Increased delta power in EEG recordings, reflecting abnormal neuronal synchrony, occurs in AS across species and correlates with genotype. Whether delta power provides a reliable biomarker for clinical symptoms remains unknown. METHODS: We analyzed combined EEG recordings and developmental assessments in a large cohort of individuals with AS (N = 82 subjects, 133 combined EEG and cognitive assessments, 1.08-28.16 years; 32F) and evaluated delta power as a biomarker for cognitive function, as measured by the Bayley Cognitive Score. We examined the robustness of this biomarker to varying states of consciousness, recording techniques and analysis procedures. RESULTS: Delta power predicted the Bayley Scale cognitive score (P < 10-5 , R2  = 0.9374) after controlling for age (P < 10-24 ), genotype:age (P < 10-11 ), and repeat assessments (P < 10-8 ), with the excellent fit on cross validation (R2  = 0.95). There were no differences in model performance across states of consciousness or bipolar versus average montages (ΔAIC < 2). Models using raw data excluding frontal channels outperformed other models (ΔAIC > 4) and predicted performance in expressive (P = 0.0209) and receptive communication (P < 10-3 ) and fine motor skills (P < 10-4 ). INTERPRETATION: Delta power is a simple, direct measure of neuronal activity that reliably correlates with cognitive function in AS. This electrophysiological biomarker offers an objective, clinically relevant endpoint for treatment response in emerging clinical trials.

Mathematical Modeling of Brain Activity under Specific Auditory Stimulation.

Understanding the connection between different stimuli and the brain response represents a complex research area. However, the use of mathematical models for this purpose is relatively unexplored. The present study investigates the effects of three different auditory stimuli on cerebral biopotentials by means of mathematical functions. The effects of acoustic stimuli (S1, S2, and S3) on cerebral activity were evaluated by electroencephalographic (EEG) recording on 21 subjects for 20 minutes of stimulation, with a 5-minute period of silence before and after stimulation. For the construction of the mathematical models used for the study of the EEG rhythms, we used the Box-Jenkins methodology. Characteristic mathematical models were obtained for the main frequency bands and were expressed by 2 constant functions, 8 first-degree functions, a second-degree function, a fourth-degree function, 6 recursive functions, and 4 periodic functions. The values obtained for the variance estimator are low, demonstrating that the obtained models are correct. The resulting mathematical models allow us to objectively compare the EEG response to the three stimuli, both between the stimuli itself and between each stimulus and the period before stimulation.

Endogenous Oscillations Time-Constrain Linguistic Segmentation: Cycling the Garden Path.

Speech is transient. To comprehend entire sentences, segments consisting of multiple words need to be memorized for at least a while. However, it has been noted previously that we struggle to memorize segments longer than approximately 2.7 s. We hypothesized that electrophysiological processing cycles within the delta band (<4 Hz) underlie this time constraint. Participants' EEG was recorded while they listened to temporarily ambiguous sentences. By manipulating the speech rate, we aimed at biasing participants' interpretation: At a slow rate, segmentation after 2.7 s would trigger a correct interpretation. In contrast, at a fast rate, segmentation after 2.7 s would trigger a wrong interpretation and thus an error later in the sentence. In line with the suggested time constraint, the phase of the delta-band oscillation at the critical point in the sentence mirrored segmentation on the level of single trials, as indicated by the amplitude of the P600 event-related brain potential (ERP) later in the sentence. The correlation between upstream delta-band phase and downstream P600 amplitude implies that segmentation took place when an underlying neural oscillator had reached a specific angle within its cycle, determining comprehension. We conclude that delta-band oscillations set an endogenous time constraint on segmentation.

Delta-range coupling between prefrontal cortex and hippocampus supported by respiratory rhythmic input from the olfactory bulb in freely behaving rats.

Respiratory rhythm (RR) during sniffing is known to couple with hippocampal theta rhythm. However, outside of the short sniffing bouts, a more stable ~ 2 Hz RR was recently shown to rhythmically modulate non-olfactory cognitive processes, as well. The underlying RR coupling with wide-spread forebrain activity was confirmed using advanced techniques, creating solid premise for investigating how higher networks use this mechanism in their communication. Here we show essential differences in the way prefrontal cortex (PFC) and hippocampus (HC) process the RR signal from the olfactory bulb (OB) that may support dynamic, flexible PFC-HC coupling utilizing this input. We used inter-regional coherences and their correlations in rats, breathing at low rate (~ 2 Hz), outside of the short sniffing bouts. We found strong and stable OB-PFC coherence in wake states, contrasting OB-HC coherence which was low but highly variable. Importantly, this variability was essential for establishing PFC-HC synchrony at RR, whereas variations of RRO in OB and PFC had no significant effect. The findings help to understand the mechanism of rhythmic modulation of non-olfactory cognitive processes by the on-going regular respiration, reported in rodents as well as humans. These mechanisms may be impaired when nasal breathing is limited or in OB-pathology, including malfunctions of the olfactory epithelium due to infections, such as in Covid-19.

Delta/theta band EEG differentially tracks low and high frequency speech-derived envelopes.

The representation of speech in the brain is often examined by measuring the alignment of rhythmic brain activity to the speech envelope. To conveniently quantify this alignment (termed 'speech tracking') many studies consider the broadband speech envelope, which combines acoustic fluctuations across the spectral range. Using EEG recordings, we show that using this broadband envelope can provide a distorted picture on speech encoding. We systematically investigated the encoding of spectrally-limited speech-derived envelopes presented by individual and multiple noise carriers in the human brain. Tracking in the 1 to 6 Hz EEG bands differentially reflected low (0.2 - 0.83 kHz) and high (2.66 - 8 kHz) frequency speech-derived envelopes. This was independent of the specific carrier frequency but sensitive to attentional manipulations, and may reflect the context-dependent emphasis of information from distinct spectral ranges of the speech envelope in low frequency brain activity. As low and high frequency speech envelopes relate to distinct phonemic features, our results suggest that functionally distinct processes contribute to speech tracking in the same EEG bands, and are easily confounded when considering the broadband speech envelope.