scholarly journals Low Frequency Oscillations drive EEG's complexity changes during wakefulness and sleep

2021 ◽  
Author(s):  
Joaquin Gonzalez ◽  
Diego M. Mateos ◽  
Matias Cavelli ◽  
Alejandra Mondino ◽  
Claudia Pascovich ◽  
...  
Keyword(s):  
Slow Wave Sleep ◽  
Low Frequency ◽  
Neuronal Population ◽  
Complexity Measures ◽  
Low Frequencies ◽  
New Approach ◽  
Frequency Bands ◽  
Cortical Oscillations ◽  
Brain Signals ◽  
Low Frequency Oscillations

Recently, the sleep-wake states have been analysed using novel complexity measures, complementing the classical analysis of EEGs by frequency bands. This new approach consistently shows a decrease in EEG's complexity during slow-wave sleep, yet it is unclear how cortical oscillations shape these complexity variations. In this work, we analyse how the frequency content of brain signals affects the complexity estimates in freely moving rats. We find that the low-frequency spectrum - including the Delta, Theta, and Sigma frequency bands - drives the complexity changes during the sleep-wake states. This happens because low-frequency oscillations emerge from neuronal population patterns, as we show by recovering the complexity variations during the sleep-wake cycle from micro, meso, and macroscopic recordings. Moreover, we find that the lower frequencies reveal synchronisation patterns across the neocortex, such as a sensory-motor decoupling that happens during REM sleep. Overall, our works shows that EEG's low frequencies are critical in shaping the sleep-wake states' complexity across cortical scales.

scholarly journals Low-Frequency Oscillations in the Cerebellar Cortex of the Tottering Mouse

2009 ◽  
Vol 101 (1) ◽  
pp. 234-245 ◽  
Author(s):  
Gang Chen ◽  
Laurentiu S. Popa ◽  
Xinming Wang ◽  
Wangcai Gao ◽  
Justin Barnes ◽  
...  
Keyword(s):  
Cerebellar Cortex ◽  
Ampa Receptors ◽  
Cortical Neurons ◽  
Low Frequency ◽  
Autosomal Recessive Disorder ◽  
Parallel Fiber ◽  
Emg Activity ◽  
Gene Encoding ◽  
Low Frequencies ◽  
Low Frequency Oscillations

The tottering mouse is an autosomal recessive disorder involving a missense mutation in the gene encoding P/Q-type voltage-gated Ca2+channels. The tottering mouse has a characteristic phenotype consisting of transient attacks of dystonia triggered by stress, caffeine, or ethanol. The neural events underlying these episodes of dystonia are unknown. Flavoprotein autofluorescence optical imaging revealed transient, low-frequency oscillations in the cerebellar cortex of anesthetized and awake tottering mice but not in wild-type mice. Analysis of the frequencies, spatial extent, and power were used to characterize the oscillations. In anesthetized mice, the dominant frequencies of the oscillations are between 0.039 and 0.078 Hz. The spontaneous oscillations in the tottering mouse organize into high power domains that propagate to neighboring cerebellar cortical regions. In the tottering mouse, the spontaneous firing of 83% (73/88) of cerebellar cortical neurons exhibit oscillations at the same low frequencies. The oscillations are reduced by removing extracellular Ca2+and blocking L-type Ca2+channels. The oscillations are likely generated intrinsically in the cerebellar cortex because they are not affected by blocking AMPA receptors or by electrical stimulation of the parallel fiber–Purkinje cell circuit. Furthermore, local application of an L-type Ca2+agonist in the tottering mouse generates oscillations with similar properties. The beam-like response evoked by parallel fiber stimulation is reduced in the tottering mouse. In the awake tottering mouse, transcranial flavoprotein imaging revealed low-frequency oscillations that are accentuated during caffeine-induced attacks of dystonia. During dystonia, oscillations are also present in the face and hindlimb electromyographic (EMG) activity that become significantly coherent with the oscillations in the cerebellar cortex. These low-frequency oscillations and associated cerebellar cortical dysfunction demonstrate a novel abnormality in the tottering mouse. These oscillations are hypothesized to be involved in the episodic movement disorder in this mouse model of episodic ataxia type 2.

scholarly journals Theta- and gamma-band oscillatory uncoupling in the macaque hippocampus

2022 ◽  
Author(s):  
Saman Abbaspoor ◽  
Ahmed Hussin ◽  
Kari L Hoffman
Keyword(s):  
Low Frequency ◽  
Temporal Coding ◽  
Theta Oscillations ◽  
Low Frequencies ◽  
Frequency Effects ◽  
Frequency Bands ◽  
Sharp Wave ◽  
Gamma Frequency ◽  
Active Exploration ◽  
Field Coherence

Nested hippocampal oscillations in the rodent gives rise to temporal coding that may underlie learning, memory, and decision making. Theta/gamma coupling in rodent CA1 occurs during exploration and sharp-wave ripples during quiescence. Whether these oscillatory regimes extend to primates is less clear. We therefore sought to identify correspondences in frequency bands, nesting, and behavioral coupling taken from macaque hippocampus. We found that, in contrast to the rodent, theta and gamma frequency bands in macaque CA1 were segregated by behavioral states. Beta/gamma (15-70Hz) had greater power during visual search while theta (7-10 Hz) dominated during quiescence. Moreover, delta/theta (3-8 Hz) amplitude was strongest when beta2/slow gamma (20-35 Hz) amplitude was weakest, though the low frequencies coupled with higher, ripple frequencies (60-150 Hz). The distribution of spike-field coherence revealed three peaks matching the 3-10 Hz, 20-30 Hz and 60-150 Hz bands; however, the low frequency effects were primarily due to sharp-wave ripples. Accordingly, no intrinsic theta spiking rhythmicity was apparent. These results support a role for beta2/slow gamma modulation in CA1 during active exploration in the primate that is decoupled from theta oscillations. These findings diverge from the rodent oscillatory canon and call for a shift in focus and frequency when considering the primate hippocampus.

scholarly journals Implementation of Power System Stabilizer Based on Conventional and Fuzzy Logic Controllers

2018 ◽  
Vol 24 (3) ◽  
pp. 97 ◽  
Author(s):  
Hanan Mikhael Habbi ◽  
Ahmed Alhamadani
Keyword(s):  
Fuzzy Logic ◽  
Power System ◽  
Fuzzy Logic Controller ◽  
Low Frequency ◽  
Power System Stabilizer ◽  
Local Mode ◽  
Electrical Power System ◽  
Low Frequencies ◽  
Low Frequency Oscillations ◽  
System Stabilizer

To damp the low-frequency oscillations which occurred due to the disturbances in the electrical power system, the generators are equipped with Power System Stabilizer (PSS) that provide supplementary feedback stabilizing signals. The low-frequency oscillations in power system are classified as local mode oscillations, intra-area mode oscillation, and interarea mode oscillations. A suitable PSS model was selected considering the low frequencies oscillation in the inter-area mode based on conventional PSS and Fuzzy Logic Controller. Two types of (FIS) Mamdani and suggeno were considered in this paper. The software of the methods was executed using MATLAB R2015a package.    

scholarly journals Low-frequency direct cortical stimulation of left superior frontal gyrus enhances working memory performance

2018 ◽  
Author(s):  
Sankaraleengam Alagapan ◽  
Caroline Lustenberger ◽  
Eldad Hadar ◽  
Hae Won Shin ◽  
Flavio Fröhlich
Keyword(s):  
Working Memory ◽  
Memory Performance ◽  
Reaction Times ◽  
Low Frequency ◽  
Cortical Stimulation ◽  
Superior Frontal Gyrus ◽  
Frequency Bands ◽  
Direct Cortical Stimulation ◽  
Cortical Oscillations

AbstractThe neural substrates of working memory are spread across prefrontal, parietal and cingulate cortices and are thought to be coordinated through low frequency cortical oscillations in the theta (3 – 8 Hz) and alpha (8 – 12 Hz) frequency bands. While the functional role of many subregions have been elucidated using neuroimaging studies, the role of superior frontal gyrus (SFG) is not yet clear. Here, we combined electrocorticography and direct cortical stimulation in three patients implanted with subdural electrodes to assess if superior frontal gyrus is indeed involved in working memory. We found left SFG exhibited task-related modulation of oscillations in the theta and alpha frequency bands specifically during the encoding epoch. Stimulation at the frequency matched to the endogenous oscillations resulted in reduced reaction times in all three participants. Our results support the causal role of SFG in working memory and suggest that SFG may coordinate working memory through low-frequency oscillations thus bolstering the feasibility of targeting oscillations for restoring cognitive function.

Modeling of Physical Subsystem Using an Example of Electromagnetic Exciter of Low-Frequency Oscillations

2015 ◽  
Vol 736 ◽  
pp. 97-102
Author(s):  
Issakul Tumanov ◽  
Seitzhan Orynbayev ◽  
Birzhan Baibutanov ◽  
Alexey Kruglikov ◽  
Piotr Kacejko
Keyword(s):  
Energy Conversion ◽  
Low Frequency ◽  
Electromechanical System ◽  
Reciprocating Motion ◽  
Low Frequencies ◽  
Conservation Of Energy ◽  
Resonance Circuit ◽  
Main Requirement ◽  
Low Frequency Oscillations

This work is devoted to modeling of physical subsystem, considering an electromagnetic exciter of low-frequency oscillations (EME LFO) as it, which is a classic example of an electromechanical system. For the simulation of electromagnetic exciter of low frequencies Simscape was chosen. Based on the principle of operation (reciprocating motion due to the configuration of the resonance circuit), and the structure of the apparatus, there is detected the operation character with several types of energy conversion: electrical to magnetic and magnetic to mechanical. And the main requirement to the developed model is the conservation of energy and power under the corresponding transformations.

scholarly journals Cortical Network Response to Acupuncture and the Effect of the Hegu Point: An fNIRS Study

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 394 ◽  
Author(s):  
Raul Fernandez Rojas ◽  
Mingyu Liao ◽  
Julio Romero ◽  
Xu Huang ◽  
Keng-Liang Ou
Keyword(s):  
Functional Connectivity ◽  
Analgesic Effect ◽  
Mixed Model ◽  
Low Frequency ◽  
Hemodynamic Response ◽  
The Body ◽  
Cortical Networks ◽  
Functional Near Infrared Spectroscopy ◽  
Frequency Bands ◽  
Low Frequency Oscillations

Acupuncture is a practice of treatment based on influencing specific points on the body by inserting needles. According to traditional Chinese medicine, the aim of acupuncture treatment for pain management is to use specific acupoints to relieve excess, activate qi (or vital energy), and improve blood circulation. In this context, the Hegu point is one of the most widely-used acupoints for this purpose, and it has been linked to having an analgesic effect. However, there exists considerable debate as to its scientific validity. In this pilot study, we aim to identify the functional connectivity related to the three main types of acupuncture manipulations and also identify an analgesic effect based on the hemodynamic response as measured by functional near-infrared spectroscopy (fNIRS). The cortical response of eleven healthy subjects was obtained using fNIRS during an acupuncture procedure. A multiscale analysis based on wavelet transform coherence was employed to assess the functional connectivity of corresponding channel pairs within the left and right somatosensory region. The wavelet analysis was focused on the very-low frequency oscillations (VLFO, 0.01–0.08 Hz) and the low frequency oscillations (LFO, 0.08–0.15 Hz). A mixed model analysis of variance was used to appraise statistical differences in the wavelet domain for the different acupuncture stimuli. The hemodynamic response after the acupuncture manipulations exhibited strong activations and distinctive cortical networks in each stimulus. The results of the statistical analysis showed significant differences ( p < 0.05 ) between the tasks in both frequency bands. These results suggest the existence of different stimuli-specific cortical networks in both frequency bands and the anaesthetic effect of the Hegu point as measured by fNIRS.

Vascular resistance and arterial pressure low-frequency oscillations in the anesthetized dog

1995 ◽  
Vol 268 (1) ◽  
pp. H7-H16 ◽  
Author(s):  
A. Cevese ◽  
R. Grasso ◽  
R. Poltronieri ◽  
F. Schena
Keyword(s):  
Heart Rate ◽  
Arterial Pressure ◽  
Vascular Resistance ◽  
Low Frequency ◽  
Peripheral Vascular Resistance ◽  
Common Mechanism ◽  
Peripheral Vascular ◽  
Frequency Range ◽  
Frequency Bands ◽  
Low Frequency Oscillations

The spontaneous variability of heart rate and arterial blood pressure was investigated in chloralose-anesthetized dogs with the left iliac vascular bed mechanically uncoupled from the central circulation. Electrocardiogram, mean arterial pressure (ABP), iliac perfusion and venous pressures, and flow (FLOW) were recorded for 5 min in steady state. Autoregressive spectral and cross-spectral analyses and digital filtering were performed. The variation coefficient (VC%), calculated from the overall variance of each signal, was 5–7%, with the exception of perfusion pressure (VC% = 1%). The frequency-related percentage of total variance was distributed among three frequency bands: two were < 0.20 Hz [lower (F1) and higher (F2; low-frequency range = F1 + F2)], and one was > 0.20 Hz (respiratory, F3). F3 was not always present in RR, which, however, oscillated also in F1 and F2, although with limited amplitude; ABP showed large respiratory and low-frequency oscillations; the FLOW oscillations were in the low-frequency range. Cross-spectral analysis showed high squared coherence in the relevant frequency bands between variables in the three couples: RR-ABP, RR-FLOW, and ABP-FLOW. Changes in RR preceded changes in ABP and in FLOW by > or = 3 s, whereas FLOW was approximately in phase opposition to ABP. It was concluded that, in the chloralose-anesthetized dog, 1) arterial pressure and heart rate oscillate with frequencies corresponding to those described in conscious humans, 2) low-frequency arterial pressure oscillations are due to changes in peripheral vascular resistance, and 3) peripheral vascular resistance does not display respiratory oscillations. Furthermore it was suggested that oscillations of vasomotor tone are generated by a rhythm of central origin and that F1 and F2 oscillations may recognize a common mechanism.

Rhythmic variations in R-R interval during sleep and wakefulness in puppies and dogs

1984 ◽  
Vol 247 (1) ◽  
pp. H67-H73
Author(s):  
G. G. Haddad ◽  
H. J. Jeng ◽  
S. H. Lee ◽  
T. L. Lai
Keyword(s):  
Low Frequency ◽  
Total Power ◽  
Short Term ◽  
Sleep State ◽  
Quiet Sleep ◽  
Low Frequencies ◽  
Low Frequency Oscillations ◽  
Percent Contribution ◽  
The Mean ◽  
Spectral Distributions

We studied the short-term oscillations in the R-R interval in five puppies at 4 wk of age and five adult dogs during sleep and wakefulness. The R-R interval was measured using an R-R preprocessor, and respiration was recorded using barometric plethysmography. Puppies showed much smaller fluctuations in the R-R interval (SD between 6 and 40 ms) than adult dogs (SD between 124 and 367 ms) in both rapid eye movement (REM) and quiet sleep. Spectral analysis demonstrated that these oscillations were primarily of low frequencies, and the contribution of respiratory sinus arrythmia (RSA) to total power was low. In contrast, in adult dogs during sleep, the spectral distributions were peaked in frequency bands corresponding to mean respiratory rate, and the percent contribution of low frequencies to power was small. Furthermore, the mean R-R interval was considerably larger during expiration than during inspiration in adult dogs (showing 20-140% increase), but not in puppies (showing only -0.4 to 4.4% increase). We conclude that 1) the mechanisms responsible for RSA mature postnatally in the dog; 2) the magnitude of RSA depends on the state of consciousness in the adult dog, being greater in sleep than during wakefulness; and 3) low-frequency oscillations, not related to breathing and independent of sleep state, characterize the variations in the R-R interval in early life but are insignificant in the adult dog.

scholarly journals Kurtosis and skewness of high-frequency brain signals are altered in paediatric epilepsy

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Jing Xiang ◽  
Ellen Maue ◽  
Yuyin Fan ◽  
Lei Qi ◽  
Francesco T Mangano ◽  
...  
Keyword(s):  
High Frequency ◽  
Intractable Epilepsy ◽  
Low Frequency ◽  
Brain Regions ◽  
Control Group ◽  
Frequency Bands ◽  
Non Invasive ◽  
Brain Signals ◽  
Paediatric Epilepsy ◽  
Virtual Electrode

Abstract Intracranial studies provide solid evidence that high-frequency brain signals are a new biomarker for epilepsy. Unfortunately, epileptic (pathological) high-frequency signals can be intermingled with physiological high-frequency signals making these signals difficult to differentiate. Recent success in non-invasive detection of high-frequency brain signals opens a new avenue for distinguishing pathological from physiological high-frequency signals. The objective of the present study is to characterize pathological and physiological high-frequency signals at source levels by using kurtosis and skewness analyses. Twenty-three children with medically intractable epilepsy and age-/gender-matched healthy controls were studied using magnetoencephalography. Magnetoencephalographic data in three frequency bands, which included 2–80 Hz (the conventional low-frequency signals), 80–250 Hz (ripples) and 250–600 Hz (fast ripples), were analysed. The kurtosis and skewness of virtual electrode signals in eight brain regions, which included left/right frontal, temporal, parietal and occipital cortices, were calculated and analysed. Differences between epilepsy and controls were quantitatively compared for each cerebral lobe in each frequency band in terms of kurtosis and skewness measurements. Virtual electrode signals from clinical epileptogenic zones and brain areas outside of the epileptogenic zones were also compared with kurtosis and skewness analyses. Compared to controls, patients with epilepsy showed significant elevation in kurtosis and skewness of virtual electrode signals. The spatial and frequency patterns of the kurtosis and skewness of virtual electrode signals among the eight cerebral lobes in three frequency bands were also significantly different from that of the controls (2–80 Hz, P &lt; 0.001; 80–250 Hz, P &lt; 0.00001; 250–600 Hz, P &lt; 0.0001). Compared to signals from non-epileptogenic zones, virtual electrode signals from epileptogenic zones showed significantly altered kurtosis and skewness (P &lt; 0.001). Compared to normative data from the control group, aberrant virtual electrode signals were, for each patient, more pronounced in the epileptogenic lobes than in other lobes(kurtosis analysis of virtual electrode signals in 250–600 Hz; odds ratio = 27.9; P &lt; 0.0001). The kurtosis values of virtual electrode signals in 80–250 and 250–600 Hz showed the highest sensitivity (88.23%) and specificity (89.09%) for revealing epileptogenic lobe, respectively. The combination of virtual electrode and kurtosis/skewness measurements provides a new quantitative approach to distinguishing pathological from physiological high-frequency signals for paediatric epilepsy. Non-invasive identification of pathological high-frequency signals may provide novel important information to guide clinical invasive recordings and direct surgical treatment of epilepsy.

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