Circadian Rhythms in Fractal Features of EEG Signals

Croce, Pierpaolo; Quercia, Angelica; Costa, Sérgio Hofmeister de Almeida Martins; Zappasodi, Filippo

doi:10.3389/fphys.2018.01567

Cited by 34 publications

(35 citation statements)

References 82 publications

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“…Consequently, our results strongly support and extend studies in human that carried out PeEn analyses and other complexity measures in standard EEG recordings. [27][28][29][30][31]35 We also showed that PeEn profile during W and sleep did not change according to the cortical recording site, reflecting a common micro-structure motif and a dynamical behavior which are independent from the origin of the cortical signal. The average randomness of these micro-structure patterns distinguished sleep from W, regardless of the changes in the embedding dimensions employed in PeEn analysis (Fig.…”

Section: Discussionmentioning

confidence: 95%

Decreased electrocortical temporal complexity distinguishes sleep from wakefulness

González

Cavelli

Mondino

et al. 2019

Preprint

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In most mammals, the sleep-wake cycle is constituted by three behavioral states: wakefulness (W), non-NREM (NREM) sleep, and REM sleep. These states are associated with drastic changes in cognitive capacities, mostly determined by the function of the thalamo-cortical system. The intra-cranial electroencephalogram or electocorticogram (ECoG), is an important tool for measuring the changes in the thalamo-cortical activity during W and sleep. In the present study we analyzed broad-band ECoG recordings of the rat by means of a time-series complexity measure that is easy to implement and robust to noise: the Permutation Entropy (PeEn). We found that PeEn is maximal during W and decreases during sleep. These results bring to light the different thalamo-cortical dynamics emerging during sleep-wake states, which are associated with the well-known spectral changes that occur when passing from W to sleep. Moreover, the PeEn analysis allows to determine behavioral states independently of the electrodes' cortical location, which points to an underlying global pattern in the signal that differs among the cycle states that is missed by classical methods. Consequently, our data suggest that PeEn analysis of a single EEG channel could allow for cheap, easy, and efficient sleep monitoring. 10/11

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Section: Discussionmentioning

confidence: 95%

Decreased electrocortical temporal complexity distinguishes sleep from wakefulness

González

Cavelli

Mondino

et al. 2019

Preprint

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“…Furthermore, this effect might also contribute to a reduction in mixed states, hypomania, and autonomic hyperactivations that can occur in BD chronotherapy due to overapplication of light stimuli and medication. Additionally, methods for measuring the daily variation of frontal neural activity are now under development, with a focus on EEG approaches (Croce et al, 2018 ; González et al, 2019 ). These methods might contribute to the realization of a form of chronotherapy modulated by RRO feedback.…”

Section: Discussionmentioning

confidence: 99%

“…Second, we used competition between excitatory and inhibitory neurons in this study to describe long-term neural dynamics in the frontal cortex. However, the questions of what reflects the long-term dynamics of brain activity and what mechanism produces it are currently controversial (Croce et al, 2018 ; González et al, 2019 ). Therefore, it is important that our proposed method be verified with models based on other neural mechanisms for producing long-term neural dynamics in the frontal cortex.…”

Section: Discussionmentioning

confidence: 99%

“…However, to stabilize more challenging forms of chaotic behavior, other candidate chaos control methods must be considered; we plan to research these points in the future. In addition to model-based studies, the methods of measuring the daily-timescale variation in the frontal neural activity that have recently been proposed (Croce et al, 2018 ; González et al, 2019 ) will be crucial for applications and will aid in the estimation of the controlling parameters required by RRO feedback methods.…”

Section: Discussionmentioning

confidence: 99%

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Transition of Neural Activity From the Chaotic Bipolar-Disorder State to the Periodic Healthy State Using External Feedback Signals

Doho

Nobukawa

Nishimura

et al. 2020

Front. Comput. Neurosci.

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Chronotherapy is a treatment for mood disorders, including major depressive disorder, mania, and bipolar disorder (BD). Neurotransmitters associated with the pathology of mood disorders exhibit circadian rhythms. A functional deficit in the neural circuits related to mood disorders disturbs the circadian rhythm; chronotherapy is an intervention that helps resynchronize the patient's biological clock with the periodic daily cycle, leading to amelioration of symptoms. In previous reports, Hadaeghi et al. proposed a non-linear dynamic model composed of the frontal and sensory cortical neural networks and the hypothalamus to explain the relationship between deficits in neural function in the frontal cortex and the disturbed circadian rhythm/mood transitions in BD (hereinafter referred to as the Hadaeghi model). In this model, neural activity in the frontal and sensory lobes exhibits periodic behavior in the healthy state; while in BD, this neural activity is in a state of chaos-chaos intermittency; this temporal departure from the healthy periodic state disturbs the circadian pacemaker in the hypothalamus. In this study, we propose an intervention based on a feedback method called the “reduced region of orbit” (RRO) method to facilitate the transition of the disturbed frontal cortical neural activity underlying BD to healthy periodic activity. Our simulation was based on the Hadaeghi model. We used an RRO feedback signal based on the return-map structure of the simulated frontal and sensory lobes to induce synchronization with a relatively weak periodic signal corresponding to the healthy condition by applying feedback of appropriate strength. The RRO feedback signal induces chaotic resonance, which facilitates the transition to healthy, periodic frontal neural activity, although this synchronization is restricted to a relatively low frequency of the periodic input signal. Additionally, applying an appropriate strength of the RRO feedback signal lowered the amplitude of the periodic input signal required to induce a synchronous state compared with the periodic signal applied alone. In conclusion, through a chaotic-resonance effect induced by the RRO feedback method, the state of the disturbed frontal neural activity characteristic of BD was transformed into a state close to healthy periodic activity by relatively weak periodic perturbations. Thus, RRO feedback-modulated chronotherapy might be an innovative new type of minimally invasive chronotherapy.

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“…Based on the associations observed in Figure 6 and the literature (Gomolka et al, 2018), a random motion (white noise) was found when α DFA ≃ 0.5 and D HG ≃ 2.0 and a more predictable, strongly auto-correlated, time series when 0.5 < α DFA ≤ 1.0 and 1.8 ≤ D HG < 2.0. However, it is worth mentioning that either α DFA or D HG might be restricted to specific walking conditions (Dierick et al, 2017), and evaluating the properties of both fractal analysis methods may provide additional information (Figures 4C,D) (Dierick et al, 2017;Croce et al, 2018).…”

Section: Scaling Exponent and Fractal Dimensionmentioning

confidence: 99%

Fractal Analysis of Human Gait Variability via Stride Interval Time Series

2020

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Fractal analysis of stride interval time series is a useful tool in human gait research which could be used as a marker for gait adaptability, gait disorder, and fall risk among patients with movement disorders. This study is designed to systematically and comprehensively investigate two practical aspects of fractal analysis which significantly affect the outcome: the series length and the parameters used in the algorithm. The Hurst exponent, scaling exponent, and/or fractal dimension are computed from both simulated and experimental data using three fractal methods, namely detrended fluctuation analysis, box-counting dimension, and Higuchi's fractal dimension. The advantages and drawbacks of each method are discussed, in terms of biases and variability. The results demonstrate that a careful selection of fractal analysis methods and their parameters is required, which is dependent on the aim of study (either analyzing differences between experimental groups or estimating an accurate determination of fractal features). A set of guidelines for the selection of the fractal methods and the length of stride interval time series is provided, along with the optimal parameters for a robust implementation for each method.

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Circadian Rhythms in Fractal Features of EEG Signals

Cited by 34 publications

References 82 publications

Decreased electrocortical temporal complexity distinguishes sleep from wakefulness

Decreased electrocortical temporal complexity distinguishes sleep from wakefulness

Transition of Neural Activity From the Chaotic Bipolar-Disorder State to the Periodic Healthy State Using External Feedback Signals

Fractal Analysis of Human Gait Variability via Stride Interval Time Series

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