Low frequency oscillations drive EEG’s complexity changes during wakefulness and sleep

González, Joaquín; Mateos, Diego M.; Cavelli, Matías; Mondino, Alejandra; Pascovich, Claudia; Torterolo, Pablo; Rubido, Nicolás

doi:10.1016/j.neuroscience.2022.04.025

Cited by 18 publications

(20 citation statements)

References 54 publications

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“…Our finding that neural complexity decreases from wake to NREM sleep in both pre-and post-auditory stimulation intervals is corroborated by previous results in spontaneous EEG (Aamodt et al, 2021;Andrillon et al, 2016;Burioka et al, 2005;Gonz alez et al, 2022;Schartner et al, 2017). This finding is likely due to slow wave activity, leading to less variation in the EEG signal (Gonz alez et al, 2022).…”

Section: Neural Complexity Differentiates Sleep Stages In Pre-and Pos...supporting

confidence: 91%

“…In spontaneous activity, neural complexity tends to decrease when conscious processing is reduced, such as during propofol anaesthesia (Medel et al, 2022; Schartner et al, 2015; Varley et al, 2021), deep sleep (Abásolo et al, 2015; Andrillon et al, 2016; Mateos et al, 2018; Nicolaou & Georgiou, 2011), and epileptic seizures (Mateos et al, 2018). In sleep in particular, complexity of neural activity decreases compared to wakefulness (Abásolo et al, 2015; Andrillon et al, 2016; González et al, 2022; Mateos et al, 2018; Nicolaou & Georgiou, 2011), and it is lower in non‐rapid eye movement (NREM) than in rapid eye movement (REM) sleep (Abásolo et al, 2015; Andrillon et al, 2016; Mateos et al, 2018; Nicolaou & Georgiou, 2011). The reduction in complexity of spontaneous EEG signals during NREM sleep relative to wakefulness or REM may be due to slow wave activity (0.5–4 Hz), which exhibits strong stereotypical patterns (Adamantidis et al, 2019; González et al, 2022; Massimini et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…In sleep in particular, complexity of neural activity decreases compared to wakefulness (Abásolo et al, 2015; Andrillon et al, 2016; González et al, 2022; Mateos et al, 2018; Nicolaou & Georgiou, 2011), and it is lower in non‐rapid eye movement (NREM) than in rapid eye movement (REM) sleep (Abásolo et al, 2015; Andrillon et al, 2016; Mateos et al, 2018; Nicolaou & Georgiou, 2011). The reduction in complexity of spontaneous EEG signals during NREM sleep relative to wakefulness or REM may be due to slow wave activity (0.5–4 Hz), which exhibits strong stereotypical patterns (Adamantidis et al, 2019; González et al, 2022; Massimini et al, 2004). Despite the plethora of studies quantifying complexity measures in spontaneous neural activity during sleep, the investigations of these measures following auditory stimulation are sparse (Andrillon et al, 2016; Gyurkovics et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Neural complexity and the spectral slope characterise auditory processing in wakefulness and sleep

Alnes,

Bächlin,

Schindler

et al. 2023

Eur J of Neuroscience

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Auditory processing and the complexity of neural activity can both indicate residual consciousness levels and differentiate states of arousal. However, how measures of neural signal complexity manifest in neural activity following environmental stimulation and, more generally, how the electrophysiological characteristics of auditory responses change in states of reduced consciousness remain under‐explored. Here, we tested the hypothesis that measures of neural complexity and the spectral slope would discriminate stages of sleep and wakefulness not only in baseline electroencephalography (EEG) activity but also in EEG signals following auditory stimulation. High‐density EEG was recorded in 21 participants to determine the spatial relationship between these measures and between EEG recorded pre‐ and post‐auditory stimulation. Results showed that the complexity and the spectral slope in the 2–20 Hz range discriminated between sleep stages and had a high correlation in sleep. In wakefulness, complexity was strongly correlated to the 20–40 Hz spectral slope. Auditory stimulation resulted in reduced complexity in sleep compared to the pre‐stimulation EEG activity and modulated the spectral slope in wakefulness. These findings confirm our hypothesis that electrophysiological markers of arousal are sensitive to sleep/wake states in EEG activity during baseline and following auditory stimulation. Our results have direct applications to studies using auditory stimulation to probe neural functions in states of reduced consciousness.

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Section: Neural Complexity Differentiates Sleep Stages In Pre-and Pos...supporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural complexity and the spectral slope characterise auditory processing in wakefulness and sleep

Alnes,

Bächlin,

Schindler

et al. 2023

Eur J of Neuroscience

View full text Add to dashboard Cite

show abstract

“…Kolmogorov, Lempel-Ziv or multiscale entropy) (Cafaro & Ali, 2021; Wang, 2012)). Information-theoretic approaches have not distinguished aperiodic and periodic contributions to the EEG spectra (Al-Nuaimi et al, 2018; González et al, 2022; Pappalettera et al, 2023; Sun et al, 2020), thus future work might identify differences between information-theoretic and geometric approaches to EEG spectral data. We utilized a geometric approach because of increasing evidence that variability in brain activity can be characterized by dynamic evolution in manifold structure.…”

Section: Discussionmentioning

confidence: 99%

EEG spectral attractors identify a geometric core of resting brain activity

Pourdavood,

Jacob

2023

Preprint

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Spectral analysis of electroencephalographic (EEG) data simplifies the characterization of periodic band parameters but can obscure underlying dynamics. By contrast, reconstruction of neural activity in state-space preserves geometric complexity in the form of a multidimensional, global attractor. Here we combine these perspectives, inferring complexity and shared dynamics from eigen-time-delay embedding of periodic and aperiodic spectral parameters to yield unique dynamical attractors for each EEG parameter. We find that resting-state alpha and aperiodic attractors show low geometric complexity and shared dynamics with all other frequency bands, what we refer to as geometric cross-parameter coupling. Further, the geometric signatures of alpha and aperiodic attractors dominate spectral dynamics, identifying a geometric core of brain activity. Non-core attractors demonstrate higher complexity but retain traces of this low-dimensional signal, supporting a hypothesis that frequency specific information differentiates out of an integrative, dynamic core. Older adults show lower geometric complexity but greater geometric coupling, resulting from dedifferentiation of gamma band activity. The form and content of resting-state thoughts were further associated with the complexity of core dynamics. Thus, the hallmarks of resting-state EEG in the frequency domain, the alpha peak and the aperiodic backbone, reflect a dynamic, geometric core of resting-state brain activity. This evidence for a geometric core in EEG complements evidence for a regionally defined dynamic core from fMRI-based neuroimaging, further supporting the utility of geometric approaches to the analysis of neural data.

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“…These bands are also grouped into two major groups based on low and high frequency. Low frequency bands consist of delta, theta, and alpha which often activated during inattentive and sleep-wake state, while the higher frequency bands consist of beta and gamma that have been mainly associated with higher cognitive functioning [13]. Brainwave activity distributed unilaterally or bilaterally according to variety of tasks, such as visuospatial processing, selective attention, language ability, spatial skills, emotional recognition, memory tasks, and information processing [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Cognitive analysis based on EEG measurements on children with stunting-indicated in East Nusa Tenggara

Syahidah,

Fitri,

Kusumawardani

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Children with stunting have been associated with delayed brain development and poor cognitive performance by a lack of attentional control. The aim of the present study was to determine characteristics of stunting children based on theta (4-8 Hz), alpha (8-12 Hz) and beta (12-30 Hz) oscillation at anterofrontal (AF) and temporoparietal (TP). This research involved two groups: Stunting Group (SG, N=14) and Control Group (C; N=8) from East Nusa Tenggara. EEG was recorded during an eyes-open condition at baseline and puzzle task. Our result revealed alteration of theta oscillation in SG AF8 and TP10 during puzzle task, supporting role of theta oscillation in higher working memory loads although it was not accompanied by proper TP connectivity. Higher alpha and beta AF7 activity in SG compared to control group implying decreasing attentional processing and higher arousal. According to Laterality Index (LI), we revealed alteration in temporoparietal SG during puzzle task. These findings provide new insights about theta, alpha, and beta oscillation in stunting children may reflect that declining attentional functioning during the puzzle task leads to poor cognitive performance.

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Low frequency oscillations drive EEG’s complexity changes during wakefulness and sleep

Cited by 18 publications

References 54 publications

Neural complexity and the spectral slope characterise auditory processing in wakefulness and sleep

Neural complexity and the spectral slope characterise auditory processing in wakefulness and sleep

EEG spectral attractors identify a geometric core of resting brain activity

Cognitive analysis based on EEG measurements on children with stunting-indicated in East Nusa Tenggara

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