scholarly journals SCALP HIGH FREQUENCY OSCILLATION RATE DEPENDS ON SLEEP STAGE AND DECREASES WITH TIME SPENT IN SLEEP

2021 ◽  
Author(s):  
Dorottya Cserpan ◽  
Richard Rosch ◽  
Santo Pietro Lo Biundo ◽  
Johannes Sarnthein ◽  
Georgia Ramantani
Keyword(s):  
High Frequency ◽  
Time Window ◽  
Sleep Stage ◽  
Nrem Sleep ◽  
Interval Length ◽  
High Frequency Oscillation ◽  
Sleep Cycle ◽  
Sleep Stages ◽  
Pediatric Epilepsy ◽  
Scalp Eeg

High frequency oscillations (HFO) in scalp EEG are a new and promising epilepsy biomarker. HFO analysis is typically restricted to random and relatively brief sleep segments. However, considerable fluctuations of HFO rates have been observed over the recording nights, particularly in relation to sleep stages and cycles. Here, we identify the timing within the sleep period and the minimal data interval length that allow for sensitive and reproducible detection of scalp HFO. We selected 16 seizure-free whole-night scalp EEG recordings of children and adolescents with focal lesional epilepsy (median age 7.6 y, range 2.2-17.4 y). We used an automated and clinically validated HFO detector to determine HFO rates (80-250 Hz) in bipolar channels. To identify significant variability over different NREM sleep stages and over time spent in sleep, we modelled HFO rate as a Poisson process. We analysed the test-retest reliability to evaluate the reproducibility of HFO detection across recording intervals. Scalp HFO rates were higher in N3 than in N2 sleep and highest in the first sleep cycle, decreasing with time spent in sleep. In N3 sleep, the median reliability of HFO detection increased from 67% to 79% to 100% for 5-, 10-, and 15-min data intervals, improving significantly (p=0.004) from 5 to 10 min but not from 10 to 15 min. In this analysis of whole-night scalp EEG, we identified the first N3 sleep stage as the most sensitive time window for HFO rate detection. N3 data intervals of 10 min duration are required and sufficient for reliable measurements of HFO rates. Our study provides a robust and reliable framework for implementing scalp HFO as an EEG biomarker in pediatric epilepsy.

scholarly journals Detectability of the somatosensory evoked high frequency oscillation (HFO) co-recorded by scalp EEG and ECoG under propofol

2016 ◽  
Vol 10 ◽  
pp. 318-325 ◽  
Author(s):  
Sergey Burnos ◽  
Tommaso Fedele ◽  
Olivier Schmid ◽  
Niklaus Krayenbühl ◽  
Johannes Sarnthein
Keyword(s):  
High Frequency ◽  
High Frequency Oscillation ◽  
Frequency Oscillation ◽  
Scalp Eeg

scholarly journals Stable high frequency background EEG activity distinguishes epileptic from healthy brain regions

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Annika Minthe ◽  
Wibke G Janzarik ◽  
Daniel Lachner-Piza ◽  
Peter Reinacher ◽  
Andreas Schulze-Bonhage ◽  
...  
Keyword(s):  
High Frequency ◽  
Background Activity ◽  
Sleep Stage ◽  
Strong Association ◽  
Brain Regions ◽  
High Frequency Oscillation ◽  
Seizure Onset ◽  
High Frequency Oscillations ◽  
Seizure Onset Zone ◽  
Low Entropy

Abstract High-frequency oscillations are markers of epileptic tissue. Recently, different patterns of EEG background activity were described from which high-frequency oscillations occur: high-frequency oscillations with continuously oscillating background were found to be primarily physiological, those from quiet background were linked to epileptic tissue. It is unclear, whether these interactions remain stable over several days and during different sleep-wake stages. High-frequency oscillation patterns (oscillatory vs. quiet background) were analysed in 23 patients implanted with depth and subdural grid electrodes. Pattern scoring was performed on every channel in 10 s intervals in three separate day- and night-time EEG segments. An entropy value, measuring variability of patterns per channel, was calculated. A low entropy value indicated a stable occurrence of the same pattern in one channel, whereas a high value indicated pattern instability. Differences in pattern distribution and entropy were analysed for 143 280 10 s intervals with allocated patterns from inside and outside the seizure onset zone, different electrode types and brain regions. We found a strong association between high-frequency oscillations out of quiet background activity, and channels of the seizure onset zone (35.2% inside versus 9.7% outside the seizure onset zone, P < 0.001), no association was found for high-frequency oscillations from continuous oscillatory background (P = 0.563). The type of background activity remained stable over the same brain region over several days and was independent of sleep stage and recording technique. Stability of background activity was significantly higher in channels of the seizure onset zone (entropy mean value 0.56 ± 0.39 versus 0.64 ± 0.41; P < 0.001). This was especially true for the presumed epileptic high-frequency oscillations out of quiet background (0.57 ± 0.39 inside versus 0.72 ± 0.37 outside the seizure onset zone; P < 0.001). In contrast, presumed physiological high-frequency oscillations from continuous oscillatory backgrounds were significantly more stable outside the seizure onset zone (0.72 ± 0.45 versus 0.48 ± 0.53; P < 0.001). The overall low entropy values suggest that interactions between high-frequency oscillations and background activity are a stable phenomenon specific to the function of brain regions. High-frequency oscillations occurring from a quiet background are strongly linked to the seizure onset zone whereas high-frequency oscillations from an oscillatory background are not. Pattern stability suggests distinct underlying mechanisms. Analysing short time segments of high-frequency oscillations and background activity could help distinguishing epileptic from physiologically active brain regions.

Nocturnal Psychophysiological Correlates of Somatic Conditions and Sleep Disorders

1975 ◽  
Vol 6 (1-2) ◽  
pp. 43-62 ◽  
Author(s):  
Joyce D. Kales ◽  
Anthony Kales
Keyword(s):  
Sleep Disorders ◽  
Sleep Stage ◽  
Symptomatic Relief ◽  
Nrem Sleep ◽  
Normal Subjects ◽  
Sleep Stages ◽  
Sleep Laboratory ◽  
Drug Induced ◽  
Night Terrors ◽  
Psychophysiological Correlates

Modern sleep research studies have provided the practicing physician with considerable new information concerning the basic psychophysiology of sleep, the effects of medical conditions on sleep and the role of maturational and emotional factors in producing certain sleep disorders. Medical and psychiatric disorders, sleep disorders and drug-induced sleep stage alterations are studied in the sleep laboratory using the same techniques developed to analyze sleep patterns in normal subjects. After initial sleep laboratory adaptation, a profile of the sleep characteristics of various clinical conditions is obtained. This profile can be compared to sleep profiles of normal subjects as well as to the effects on sleep of subsequent experimental or therapeutic procedures. Various studies have shown that coronary artery, duodenal ulcer and nocturnal headache patients experience angina, increased gastric acid secretion and migraine or cluster headaches, respectively during REM sleep. Adult nocturnal asthmatic episodes occur out of all sleep stages while attacks of dyspnea in asthmatic children occur in all stages except stage 4 sleep. Hypothyroid patients show decreases in stages 3 and 4 sleep, while in hyperthyroid patients the percentage of time spent in stages 3 and 4 sleep is markedly increased. Enuretic episodes occur predominantly in non-rapid eye movement (NREM) sleep. Sleepwalking and night terror episodes occur exclusively out of NREM sleep, particularly from stages 3 and 4 sleep. Most child somnambulists and children with night terrors “outgrow” this disorder, suggesting a delayed maturation of the central nervous system. Stimulant drugs are effective in the treatment of the sleep attacks of narcolepsy and in treating certain cases of hypersomnia, while imipramine is an effective treatment for the auxiliary symptoms of narcolepsy. Psychological disturbances are frequent in adult somnambulism and night terrors as well as in hypersomnia and insomnia. Proper pharmacologic treatment to provide symptomatic relief for insomnia is recommended to enhance the psychotherapeutic process.

Human Sleep EEG Analysis Using the Correlation Dimension

2001 ◽  
Vol 32 (3) ◽  
pp. 112-118 ◽  
Author(s):  
Toshio Kobayashi ◽  
Shigeki Madokoro ◽  
Yuji Wada ◽  
Kiwamu Misaki ◽  
Hiroki Nakagawa
Keyword(s):  
Rem Sleep ◽  
Correlation Dimension ◽  
Degrees Of Freedom ◽  
Slow Wave Sleep ◽  
Sleep Stage ◽  
Sleep Eeg ◽  
Sleep Cycle ◽  
Sleep Stages ◽  
The Mean ◽  
Male Subjects

Sleep electroencephalograms (EEG) were analyzed by non-linear analysis. Polysomnography (PSG) of nine healthy male subjects was analyzed and the correlation dimension (D2) was calculated. The D2 characterizes the dynamics of the sleep EEG, estimates the degrees of freedom, and describes the complexity of the signal. The mean D2 decreased from the awake stage to stages 1,2,3 and 4 and increased during rapid eye movement (REM) sleep. The D2 during each REM sleep stage were high and those during each slow wave sleep stage were low, respectively, for each sleep cycle. The mean D2 of the sleep EEG in the second half of the night was significantly higher than those in the first half of the night. Significant changes were also observed during sleep stage 2, but were not seen during REM sleep and sleep stages 3 and 4. The D2 may be a useful method in the analysis of the entire sleep EEG.

scholarly journals Scalp high-frequency oscillation rates are higher in younger children

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Dorottya Cserpan ◽  
Ece Boran ◽  
Santo Pietro Lo Biundo ◽  
Richard Rosch ◽  
Johannes Sarnthein ◽  
...  
Keyword(s):  
High Frequency ◽  
Signal To Noise Ratio ◽  
Paediatric Population ◽  
High Frequency Oscillation ◽  
Older Children ◽  
Frequency Oscillation ◽  
Scalp Eeg ◽  
High Frequency Oscillations ◽  
Median Test ◽  
Generalized Epilepsy

Abstract High-frequency oscillations in scalp EEG are promising non-invasive biomarkers of epileptogenicity. However, it is unclear how high-frequency oscillations are impacted by age in the paediatric population. We prospectively recorded whole-night scalp EEG in 30 children and adolescents with focal or generalized epilepsy. We used an automated and clinically validated high-frequency oscillation detector to determine ripple rates (80–250 Hz) in bipolar channels. Children < 7 years had higher high-frequency oscillation rates (P = 0.021) when compared with older children. The median test−retest reliability of high-frequency oscillation rates reached 100% (iqr 50) for a data interval duration of 10 min. Scalp high-frequency oscillation frequency decreased with age (r = −0.558, P = 0.002), whereas scalp high-frequency oscillation duration and amplitude were unaffected. The signal-to-noise ratio improved with age (r = 0.37, P = 0.048), and the background ripple band activity decreased with age (r = −0.463, P = 0.011). We characterize the relationship of scalp high-frequency oscillation features and age in paediatric patients. EEG intervals of ≥10 min duration are required for reliable measurements of high-frequency oscillation rates. This study is a further step towards establishing scalp high-frequency oscillations as a valid epileptogenicity biomarker in this vulnerable age group.

scholarly journals Towards automatic classification of pathological epileptic tissue with interictal high frequency oscillations

2020 ◽  
Author(s):  
Michael D. Nunez ◽  
Krit Charupanit ◽  
Indranil Sen-Gupta ◽  
Beth A. Lopour ◽  
Jack J. Lin
Keyword(s):  
High Frequency ◽  
Negative Binomial ◽  
Slow Wave Sleep ◽  
Nrem Sleep ◽  
Oscillatory Dynamics ◽  
Scalp Eeg ◽  
High Frequency Oscillations ◽  
Binomial Mixture ◽  
Brain States

AbstractHigh frequency oscillations (HFOs) recorded by intracranial electrodes have generated excitement for their potential to help localize epileptic tissue for surgical resection (Frauscher et al., 2017). However, previous research has shown that the number of HFOs per minute (i.e. the HFO “rate”) is not stable over the duration of intracranial recordings. The rate of HFOs increases during periods of slow-wave sleep (von Ellenrieder et al., 2017), and HFOs that are predictive of epileptic tissue may occur in oscillatory patterns (Motoi et al., 2018). We sought to further understand how between-seizure (i.e. “interictal”) HFO dynamics predict the seizure onset zone (SOZ). Using long-term intracranial EEG from 16 subjects, we fit Poisson and Negative Binomial mixture models that describe HFO dynamics and include the ability to switch between two discrete brain states. Oscillatory dynamics of HFO occurrences were found to be predictive of SOZ and were more consistently predictive than HFO rate. Using concurrent scalp-EEG in two patients, we show that the model-found brain states corresponded to (1) non-REM (NREM) sleep and (2) awake and rapid eye movement (REM) sleep. This work suggests that unsupervised approaches for classification of epileptic tissue without sleep-staging can be developed using mixture modeling of HFO dynamics.

scholarly journals Ketamine and sleep modulate neural complexity dynamics in cats

2021 ◽  
Author(s):  
Claudia Pascovich ◽  
Santiago Castro-Zaballa ◽  
Pedro A.M. Mediano ◽  
Daniel Bor ◽  
Andrés Canales-Johnson ◽  
...  
Keyword(s):  
Response Curve ◽  
Sleep Stage ◽  
Nrem Sleep ◽  
Conscious State ◽  
Sleep Stages ◽  
Highly Sensitive ◽  
Level Of Consciousness ◽  
State Changes ◽  
Intracranial Electrodes ◽  
Different Levels

There is increasing evidence that level of consciousness can be captured by neural informational complexity: for instance, complexity, as measured by the Lempel Ziv (LZ) compression algorithm, decreases during anesthesia and non-rapid eye movement (NREM) sleep in humans and rats, when compared to LZ in awake and REM sleep. In contrast, LZ is higher in humans under the effect of psychedelics, including subanesthetic doses of ketamine. However, it is both unclear how this result would be modulated by varying ketamine doses, and whether it would extend to other species. Here we studied LZ with and without auditory stimulation during wakefulness and different sleep stages in 5 cats implanted with intracranial electrodes, as well as under subanesthetic doses of ketamine (5, 10, and 15 mg/kg i.m.). In line with previous results, LZ was lowest in NREM sleep, but similar in REM and wakefulness. Furthermore, we found an inverted U-shaped curve following different levels of ketamine doses in a subset of electrodes, primarily in prefrontal cortex. However, it is worth noting that the variability in the ketamine dose-response curve across cats and cortices was larger than that in the sleep-stage data, highlighting the differential local dynamics created by two different ways of modulating conscious state. These results replicate previous findings, both in humans and other species, demonstrating that neural complexity is highly sensitive to capture state changes between wake and sleep stages while adding a local cortical description. Finally, this study describes the differential effects of ketamine doses, replicating a rise in complexity for low doses, and further fall as doses approach anesthetic levels in a differential manner depending on the cortex.

Increased Chin Muscle Tone during All Sleep Stages in Children Taking SSRI Antidepressants and in Children with Narcolepsy Type 1

SLEEP ◽  
2021 ◽  
Author(s):  
Raffaele Ferri ◽  
Maria P Mogavero ◽  
Oliviero Bruni ◽  
Giuseppe Plazzi ◽  
Carlos H Schenck ◽  
...  
Keyword(s):  
Rem Sleep ◽  
Serotonin Reuptake ◽  
Treatment Duration ◽  
Muscle Tone ◽  
Sleep Stage ◽  
Nrem Sleep ◽  
Sleep Stages ◽  
Pharmacological Properties ◽  
Age And Sex

Abstract Study Objectives To assess if selective serotonin reuptake inhibitor (SSRI) antidepressants are able to modify the chin EMG tone during sleep also in children. Methods Twenty-three children and adolescents (12 girls, mean age 14.1 years, SD 2.94) under therapy with antidepressant for their mood disorder were consecutively recruited and had a PSG recording. Twenty-one were taking were taking SSRI and treatment duration was 2-12 months. An age- and sex matched group of 33 control children (17 girls, mean age 14.2 years, SD 2.83) and 24 children with narcolepsy type 1 (12 girls, mean age 13.7 years, SD 2.80) were also included. The Atonia Index was then computed for each NREM sleep stage and for REM sleep, also all EMG activations were counted. Results Atonia Index in all sleep stages was found to be significantly reduced in children with narcolepsy followed by the group taking SSRI antidepressants and the number of EMG activations was also increased in both groups. Fluoxetine, in particular, was found to be significantly associated with reduced Atonia index during NREM sleep stages N1, N2, and N3, and with increased number of EMG activations/hour during sleep stage N3. Conclusions Similarly to adults, SSRI antidepressants are able to modify the chin EMG tone also in children during REM sleep, as well as during NREM sleep stages. Different pharmacological properties of the different SSRI might explain the differential effect on chin tone during sleep found in this study.

NREM Sleep and Antiepileptic Medications Modulate Epileptiform Activity by Altering Cortical Synchrony

2018 ◽  
Vol 49 (6) ◽  
pp. 417-424 ◽  
Author(s):  
Chetan S. Nayak ◽  
N. Mariyappa ◽  
Kaushik K. Majumdar ◽  
G. S. Ravi ◽  
Pradeep D. Prasad ◽  
...  
Keyword(s):  
Phase Synchronization ◽  
Focal Epilepsy ◽  
Sleep Stage ◽  
Epileptiform Activity ◽  
Nrem Sleep ◽  
Sleep Stages ◽  
Epileptiform Discharges ◽  
Drug Naïve ◽  
Antiepileptic Medications

Introduction. The activating role of non–rapid eye movement (NREM) sleep on epileptic cortex and conversely, the seizure remission brought about by antiepileptic medications, has been attributed to their effects on neuronal synchrony. This study aims to understand the role of neural synchrony of NREM sleep in promoting interictal epileptiform discharges (IEDs) in patients with epilepsy (PWE) by assessing the peri-IED phase synchrony during awake and sleep states. It also studies the role played by antiepileptic drugs (AEDs) on EEG desynchronization in the above cohort. Methods. A total of 120 PWE divided into 3 groups (each n = 40; juvenile myoclonic epilepsy [JME], temporal lobe epilepsy [TLE]. and extratemporal lobe epilepsy [Ex-TLE]) were subjected to overnight polysomnography. Each patient group was subdivided into drug-naive and on treatment (Each n = 20). EEG phase synchronization analysis was performed to compare peri-IED phase synchronization indices (SI) during awake and sleep stages and between drug naïve and on treatment groups in 4 frequency bands, namely delta, theta, alpha, and beta. The mean ± SD of peri-IED SI among various subgroups was compared employing a multilevel mixed effects modeling approach. Results. Patients with JME had increased peri-IED cortical synchrony in N3 sleep stage, whereas patients with partial epilepsy had increased IED cortical synchrony in N1 sleep stage. On the other hand, peri-IED synchrony was lower during wake and REM sleep. We also found that peri-IED synchronization in patients with JME was higher in drug-naive patients compared with those on sodium valproate monotherapy in theta, alpha, and beta bands. Conclusion. The findings of this study suggest that sleep stages can alter cortical synchrony in patients with JME and focal epilepsy, with NREM IEDs being more synchronized and wake/REM IEDs being less synchronized. Furthermore, it also suggests that AEDs alleviate seizures in PWE by inhibiting cortical synchrony.

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