Bimodal coupling of ripples and slower oscillations during sleep in patients with focal epilepsy.

OBJECTIVE: Differentiating pathologic and physiologic high-frequency oscillations (HFOs) is challenging. In patients with focal epilepsy, HFOs occur during the transitional periods between the up and down state of slow waves. The preferred phase angles of this form of phase-event amplitude coupling are bimodally distributed, and the ripples (80-150 Hz) that occur during the up-down transition more often occur in the seizure-onset zone (SOZ). We investigated if bimodal ripple coupling was also evident for faster sleep oscillations, and could identify the SOZ. METHODS: Using an automated ripple detector, we identified ripple events in 40-60 min intracranial electroencephalography (iEEG) recordings from 23 patients with medically refractory mesial temporal lobe or neocortical epilepsy. The detector quantified epochs of sleep oscillations and computed instantaneous phase. We utilized a ripple phasor transform, ripple-triggered averaging, and circular statistics to investigate phase event-amplitude coupling. RESULTS: We found that at some individual recording sites, ripple event amplitude was coupled with the sleep oscillatory phase and the preferred phase angles exhibited two distinct clusters (p \u3c 0.05). The distribution of the pooled mean preferred phase angle, defined by combining the means from each cluster at each individual recording site, also exhibited two distinct clusters (p \u3c 0.05). Based on the range of preferred phase angles defined by these two clusters, we partitioned each ripple event at each recording site into two groups: depth iEEG peak-trough and trough-peak. The mean ripple rates of the two groups in the SOZ and non-SOZ (NSOZ) were compared. We found that in the frontal (spindle, p = 0.009; theta, p = 0.006, slow, p = 0.004) and parietal lobe (theta, p = 0.007, delta, p = 0.002, slow, p = 0.001) the SOZ incidence rate for the ripples occurring during the trough-peak transition was significantly increased. SIGNIFICANCE: Phase-event amplitude coupling between ripples and sleep oscillations may be useful to distinguish pathologic and physiologic events in patients with frontal and parietal SOZ

The human thalamus orchestrates neocortical oscillations during NREM sleep

A hallmark of non-rapid eye movement (NREM) sleep is the coordinated interplay of slow oscillations (SOs) and sleep spindles. Traditionally, a cortico-thalamo-cortical loop is suggested to coordinate these rhythms: neocortically-generated SOs trigger spindles in the thalamus that are projected back to neocortex. Here, we used direct intrathalamic recordings from human epilepsy patients to test this canonical interplay. We show that SOs in the anterior thalamus precede neocortical SOs, whereas concurrently-recorded SOs in the mediodorsal thalamus are led by neocortical SOs. Furthermore, sleep spindles, detected in both thalamic nuclei, preceded their neocortical counterparts and were initiated during early phases of thalamic SOs. Our findings indicate an active role of the anterior thalamus in organizing the cardinal sleep rhythms in the neocortex and highlight the functional diversity of specific thalamic nuclei in humans. The concurrent coordination of sleep oscillations by the thalamus could have broad implications for the mechanisms underlying memory consolidation

The human thalamus orchestrates neocortical oscillations during NREM sleep

A hallmark of non-rapid eye movement sleep is the coordinated interplay of slow oscillations (SOs) and sleep spindles. Traditionally, a cortico-thalamo-cortical loop is suggested to coordinate these rhythms: neocortically-generated SOs trigger spindles in the thalamus that are projected back to neocortex. Here, we used intrathalamic recordings from human epilepsy patients to test this canonical interplay. We show that SOs in the anterior thalamus precede neocortical SOs (peak −50 ms), whereas concurrently-recorded SOs in the mediodorsal thalamus are led by neocortical SOs (peak +50 ms). Sleep spindles, detected in both thalamic nuclei, preceded their neocortical counterparts (peak −100 ms) and were initiated during early phases of thalamic SOs. Our findings indicate an active role of the anterior thalamus in organizing sleep rhythms in the neocortex and highlight the functional diversity of thalamic nuclei in humans. The thalamic coordination of sleep oscillations could have broad implications for the mechanisms underlying memory consolidation

Heterogeneous profiles of coupled sleep oscillations in human hippocampus

Cross-frequency coupling of sleep oscillations is thought to mediate memory consolidation. While the hippocampus is deemed central to this process, detailed knowledge of which oscillatory rhythms interact in the sleeping human hippocampus is lacking. Combining intracranial hippocampal and non-invasive electroencephalography from twelve neurosurgical patients, we characterized spectral power and coupling during non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. Hippocampal coupling was extensive, with the majority of channels expressing spectral interactions. NREM consistently showed delta–ripple coupling, but ripples were also modulated by slow oscillations (SOs) and sleep spindles. SO–delta and SO–theta coupling, as well as interactions between delta/theta and spindle/beta frequencies also occurred. During REM, limited interactions between delta/theta and beta frequencies emerged. Moreover, oscillatory organization differed substantially between i) hippocampus and scalp, ii) sites along the anterior-posterior hippocampal axis, and iii) individuals. Overall, these results extend and refine our understanding of hippocampal sleep oscillations

Closed-loop modulation of local slow oscillations in human NREM sleep.

Slow-wave sleep is the deep non-rapid eye-movement (NREM) sleep stage that is most relevant for the recuperative function of sleep. Its defining property is the presence of slow oscillations (<2 Hz) in the scalp electroencephalogram (EEG). Slow oscillations are generated by a synchronous back and forth between highly active UP-states and silent DOWN-states in neocortical neurons. Growing evidence suggests that closed-loop sensory stimulation targeted at UP-states of EEG-defined slow oscillations can enhance the slow oscillatory activity, increase sleep depth, and boost sleep's recuperative functions. However, several studies failed to replicate such findings. Failed replications might be due to the use of conventional closed-loop stimulation algorithms that analyze the signal from one single electrode and thereby neglect the fact that slow oscillations vary with respect to their origins, distributions, and trajectories on the scalp. In particular, conventional algorithms nonspecifically target functionally heterogeneous UP-states of distinct origins. After all, slow oscillations at distinct sites of the scalp have been associated with distinct functions. Here we present a novel EEG-based closed-loop stimulation algorithm that allows targeting UP- and DOWN-states of distinct cerebral origins based on topographic analyses of the EEG: the topographic targeting of slow oscillations (TOPOSO) algorithm. We present evidence that the TOPOSO algorithm can detect and target local slow oscillations with specific, predefined voltage maps on the scalp in real-time. When compared to a more conventional, single-channel-based approach, TOPOSO leads to fewer but locally more specific stimulations in a simulation study. In a validation study with napping participants, TOPOSO targets auditory stimulation reliably at local UP-states over frontal, sensorimotor, and centro-parietal regions. Importantly, auditory stimulation temporarily enhanced the targeted local state. However, stimulation then elicited a standard frontal slow oscillation rather than local slow oscillations. The TOPOSO algorithm is suitable for the modulation and the study of the functions of local slow oscillations

Infraslow fluctuations and respiration are driving cortical neurophysiological oscillations during sleep

Abstract. Recently sleep has been linked to increased brain clearance through perivascular spaces from blood-brain barrier (BBB) externa limitans, facilitated by physiological pulsations such as cardiovascular and respiratory pulsations. Infraslow fluctuations (ISFs) characterize both fMRI BOLD signals and scalp EEG potentials. They are associated with both permeability fluctuations of BBB and the amplitude dynamics of faster (> 1Hz) neuronal oscillations. ISF together with respiration are though to synchronize with neural rhythms, however the directionality of these interactions has not been studied before. I used non-invasive measures which are necessary not to interfere the pressure sensitive CSF convection and BBB permeability combined with directional metrics to fully evaluate these relationships. I recorded full-band resting state EEG (fbEEG) during wakefulness and sleep and investigated whether recently shown increased brain clearance during sleep is followed by increased drive of neural amplitudes by the ISF and respiration phases. I show that ISF power increases during non-REM sleep, possibly reflecting altered BBB status. Furthermore, I show that ISF and respiration phase-amplitude couple and predict neuronal brain rhythms seen especially during sleep. These results pave the way for understanding the mechanisms how neuronal activity is modulated by the slow oscillations in human brain during wakefulness and sleep

Hippocampal Sleep Features: Relations to Human Memory Function

The recent spread of intracranial electroencephalographic (EEG) recording techniques for presurgical evaluation of drug-resistant epileptic patients is providing new information on the activity of different brain structures during both wakefulness and sleep. The interest has been mainly focused on the medial temporal lobe, and in particular the hippocampal formation, whose peculiar local sleep features have been recently described, providing support to the idea that sleep is not a spatially global phenomenon. The study of the hippocampal sleep electrophysiology is particularly interesting because of its central role in the declarative memory formation. Recent data indicate that sleep contributes to memory formation. Therefore, it is relevant to understand whether specific patterns of activity taking place during sleep are related to memory consolidation processes. Fascinating similarities between different states of consciousness (wakefulness, REM sleep, non-REM sleep) in some electrophysiological mechanisms underlying cognitive processes have been reported. For instance, large-scale synchrony in gamma activity is important for waking memory and perception processes, and its changes during sleep may be the neurophysiological substrate of sleep-related deficits of declarative memory. Hippocampal activity seems to specifically support memory consolidation during sleep, through specific coordinated neurophysiological events (slow waves, spindles, ripples) that would facilitate the integration of new information into the pre-existing cortical networks. A few studies indeed provided direct evidence that rhinal ripples as well as slow hippocampal oscillations are correlated with memory consolidation in humans. More detailed electrophysiological investigations assessing the specific relations between different types of memory consolidation and hippocampal EEG features are in order. These studies will add an important piece of knowledge to the elucidation of the ultimate sleep function

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

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

Functional brain networks: intra and inter-subject variability in healthy individuals and patients with neurological or neuropsychiatric diseases.

The projects of this thesis sits at the intersection between classical neuroscience and aspects related to engineering, signals’ and neuroimaging processing. Each of the three years has been dedicated to specific projects carried out on distinct datasets, groups of individuals/patients and methods, putting great emphasis on multidisciplinarity and international mobility. The studies carried out in Cagliari were based on EEG (electroencephalography), and the one conducted abroad was developed on functional magnetic resonance imaging (fMRI) data. The common thread of the project concerns variability and stability of individuals' features related primarily to functional connectivity and network, as well as to the periodic and aperiodic components of EEG power spectra, and their possible use for clinical purposes. In the first study (Fraschini et al., 2019) we aimed to investigate the impact of some of the most commonly used metrics to estimate functional connectivity on the ability to unveil personal distinctive patterns of inter-channel interaction. In the second study (Demuru et al., 2020) we performed a comparison between power spectral density and some widely used nodal network metrics, both at scalp and source level, with the aim of evaluating their possible association. The first first-authored study (Pani et al., 2020)was dedicated to investigate how the variability due to subject, session and task affects electroencephalogram(EEG) power, connectivity and network features estimated using source-reconstructed EEG time-series of healthy subjects. In the study carried out with the supervision of Prof. Fornito (https://doi.org/10.1016/j.pscychresns.2020.111202) during the experience at the Brain, Mind and Society Research Hub of Monash University, partial least square analysis has been applied on fMRI data of an healthy cohort to evaluate how different specific aspects of psychosis-like experiences related to functional connectivity. Due to the pandemic of Sars-Cov-2 it was impossible to continue recording the patients affected by neurological diseases (Parkinson’s, Diskynesia) involved in the study we planned for the third year, that should have replicated the design of the first first-authored one, with the aim of investigate how individual variability/stability of functional brain networks is affected by diseases. For the aforementioned reason, we carried out the last study on a dataset we finished to record in February 2020. The analysis has the aim of investigate whether it is possible by using 19 channels sleep scalp EEG to highlight differences in the brain of patients affected by non-rem parasomnias and sleep-related hypermotor epilepsy, when considering the periodic and aperiodic component of EEG power spectra