Processing and model design of the gamma oscillation activity based on FitzHugh-Nagumo model and its interaction with slow rhythms in the brain

The brain is processing information 24 hours a day. There are millions of processes proceeding in it accompanied by various spectra of rhythms. This paper tests the hypothesis that the slow delta rhythm excites the gamma rhythm oscillations. Unlike other papers, we determine the slow rhythm spectrum not at the hypothesis stage but during the experiment. We design algorithms of filtering, envelope extraction, and correlation coefficient calculation for signal processing. Moreover, we examine the data on all electroencephalogram channels, which allows us to make a more reasonable conclusion. We confirm that a slow delta rhythm excites a fast gamma rhythm with an amplitude-phase type of interaction and calculate a delay between these two signals equal to about half a second.

Wavelet-Based Biphase Analysis of Brain Rhythms in Automated Wake–Sleep Classification

Many studies in the field of sleep have focused on connectivity and coherence. Still, the nonstationary nature of electroencephalography (EEG) makes many of the previous methods unsuitable for automatic sleep detection. Time-frequency representations and high-order spectra are applied to nonstationary signal analysis and nonlinearity investigation, respectively. Therefore, combining wavelet and bispectrum, wavelet-based bi-phase (Wbiph) was proposed and used as a novel feature for sleep–wake classification. The results of the statistical analysis with emphasis on the importance of the gamma rhythm in sleep detection show that the Wbiph is more potent than coherence in the wake–sleep classification. The Wbiph has not been used in sleep studies before. However, the results and inherent advantages, such as the use of wavelet and bispectrum in its definition, suggest it as an excellent alternative to coherence. In the next part of this paper, a convolutional neural network (CNN) classifier was applied for the sleep–wake classification by Wbiph. The classification accuracy was 97.17% in nonLOSO and 95.48% in LOSO cross-validation, which is the best among previous studies on sleep–wake classification.

Synchronization through uncorrelated noise in excitatory-inhibitory networks

Gamma rhythms play a major role in many different processes in the brain, such as attention, working memory and sensory processing. While typically considered detrimental, counterintuitively noise can sometimes have beneficial effects on communication and information transfer. Recently, Meng and Riecke showed that synchronization of interacting networks of inhibitory neurons increases while synchronization within these networks decreases when neurons are subject to uncorrelated noise. However, experimental and modelling studies point towards an important role of the pyramidal-interneuronal network gamma (PING) mechanism in the cortex. Therefore, we investigated the effect of uncorrelated noise on the communication between excitatory-inhibitory networks producing gamma oscillations via a PING mechanism. Our results suggest that synaptic noise can have a supporting role in facilitating inter-regional communication and that noise-induced synchronization between networks is generated via a different mechanism than when synchronization is mediated by strong synaptic coupling. Noise-induced synchronization is achieved by lowering synchronization within networks which allows the respective other network to impose its own gamma rhythm resulting in synchronization between networks.

Impaired spike-gamma coupling of area CA3 fast-spiking interneurons as the earliest functional impairment in the AppNL-G-F mouse model of Alzheimer’s disease

In Alzheimer’s disease (AD) the accumulation of amyloid-β (Aβ) correlates with degradation of cognition-relevant gamma oscillations. The gamma rhythm relies on proper neuronal spike-gamma coupling, specifically of fast-spiking interneurons (FSN). Here we tested the hypothesis that decrease in gamma power and FSN synchrony precede amyloid plaque deposition and cognitive impairment in AppNL-G-F knock-in mice (AppNL-G-F). The aim of the study was to evaluate the amyloidogenic pathology progression in the novel AppNL-G-F mouse model using in vitro electrophysiological network analysis. Using patch clamp of FSNs and pyramidal cells (PCs) with simultaneous gamma oscillation recordings, we compared the activity of the hippocampal network of wild-type mice (WT) and the AppNL-G-F mice at four disease stages (1, 2, 4, and 6 months of age). We found a severe degradation of gamma oscillation power that is independent of, and precedes Aβ plaque formation, and the cognitive impairment reported previously in this animal model. The degradation correlates with increased Aβ1-42 concentration in the brain. Analysis on the cellular level showed an impaired spike-gamma coupling of FSN from 2 months of age that correlates with the degradation of gamma oscillations. From 6 months of age PC firing becomes desynchronized also, correlating with reports in the literature of robust Aβ plaque pathology and cognitive impairment in the AppNL-G-F mice. This study provides evidence that impaired FSN spike-gamma coupling is one of the earliest functional impairment caused by the amyloidogenic pathology progression likely is the main cause for the degradation of gamma oscillations and consequent cognitive impairment. Our data suggests that therapeutic approaches should be aimed at restoring normal FSN spike-gamma coupling and not just removal of Aβ.

Altered low frequency brain rhythms precede changes in gamma power during tauopathy

Alzheimer's disease and other dementias are associated with disruptions of electrophysiological brain activity, including low frequency and gamma rhythms. Many of these dementias are also associated with the malfunction of the membrane associated protein tau. Tauopathy disrupts neuronal function and the stability of synapses and is a key driver of neurodegeneration. Here we ask how brain rhythms are affected by tauopathy, at different stages of its progression. We performed local field potential recordings from visual cortex of rTg4510 and control animals at early stages of neurodegeneration (5 months) and at a more advanced stage where pathology is evident (8 months). We measured brain activity in the presence or absence of external visual stimulation, and while monitoring pupil diameter and locomotion to establish animal behavioural states. At 5 months, before substantial pathology, we found an increase in low frequency rhythms during resting state in tauopathic animals. This was because tauopathic animals entered intermittent periods of increased neural synchronisation, where activity across a wide band of low frequencies was strongly correlated. At 8 months, when the degeneration was more advanced, the increased synchronisation and low frequency power was accompanied by a reduction in power in the gamma range, with diverse effects across different components of the gamma rhythm. Our results indicate that slower rhythms are impaired earlier than gamma rhythms in tauopathy, suggesting that electrophysiological measurements can indicate both the presence and progression of tauopathic degeneration.

EXPERIMENTAL RESEARCH ON FORECASTING INDEX OF BIOLOGICAL IMAGE AEROBIC EXERCISE ANALYSIS OF ARTIFICIAL NEURAL NETWORK

ABSTRACT Objective: The paper uses artificial neural network images to explore the effects of aerobic exercise on the gamma rhythm of theta period in the awake hippocampal CA1 area of APP/PS1/tau mice and the low-frequency gamma rhythm of the sleep state hippocampal CA1 area SWR period. Methods: Clean grade 6-month-old APP/PS1/tau mice were randomly divided into quiet group (AS) and exercise group (AE), C57BL/6J control group mice were randomly divided into quiet group (CS) and exercise group (CE). The AE group and the CE group performed 12-week treadmill exercise, 5d/week, 60min/d, the first 10min exercise load was 12m/min, the last 50min was 15m/min treadmill slope was 0°. Eight-arm maze detection of behavioral changes in mice; multi-channel in vivo recording technology to record the electrical signals of the awake state and sleep state in the hippocampal CA1 area, MATLAB extracts the awake state theta period and sleep state SWR period, multi-window spectrum estimation method Perform time-frequency analysis and power spectral density analysis. Results: 12 weeks of aerobic exercise can significantly improve the working memory and reference memory of the AS group, increase the gamma energy in theta period of the awake hippocampus CA1 area and the low-frequency gamma energy in the sleep state CA1 area SWR period. Conclusions: Aerobic exercise can improve the neural network state of the AD model and increase the gamma energy in theta period of the hippocampus CA1 area, and the low-frequency gamma energy in the SWR period is one of the neural network mechanisms for its overall behavioral improvement. Level of evidence II; Therapeutic studies - investigation of treatment results.

Can the causal role of brain oscillations be studied through rhythmic brain stimulation?

Many studies have investigated the causal relevance of brain-oscillations using rhythmic stimulation, either through direct-brain-stimulation or sensory stimulation. Yet, how intrinsic rhythms interact with the externally generated rhythm is largely unknown. We either presented a flickered visual grating or its correspondent unflickered stimulus in a psychophysical change-detection-task to humans, during simultaneous MEG-recordings, to test the effect of visual entrainment on induced gamma- oscillations. Notably, we generally observed a co-existence of the broadband induced gamma-rhythm with the entrained flicker-rhythm (reliably measured in each participant), with the peak frequency of the induced response remaining unaltered in approximately half of participants - relatively independently of their native frequency. However, flicker increased broadband induced-gamma-power, and this was stronger in participants with a native frequency closer to the flicker-frequency (resonance), and led to strong phase-entrainment. Presence of flicker did not change behaviour itself, but profoundly altered brain-behaviour correlates across the sample: whilst broadband induced gamma-oscillations correlated with reaction-times for unflickered stimuli (as known previously), for the flicker, the amplitude of the entrained flicker-rhythm (but no more the induced oscillation) correlated with reaction-times. This, however, strongly depended on whether a participants peak frequency shifted to the entrained rhythm. Our results suggests that rhythmic brain-stimulation leads to a coexistence of two partially independent oscillations with heterogeneous effects across participants on the downstream relevance of these rhythms for behaviour. This may explain the inconsistency of findings related to external entrainment of brain-oscillations and poses further questions towards causal manipulations of brain-oscillations in general.

Orientation and color tuning of the human visual gamma rhythm

Narrowband gamma oscillations (NBG: ~20-60Hz) in visual cortex reflect rhythmic fluctuations in population activity generated by underlying circuits tuned for stimulus location, orientation, and color. Consequently, the amplitude and frequency of induced NBG activity is highly sensitive to these stimulus features. For example, in the non-human primate, NBG displays biases in orientation and color tuning at the population level. Such biases may relate to recent reports describing the large-scale organization of single-cell orientation and color tuning in visual cortex, thus providing a potential bridge between measurements made at different scales. Similar biases in NBG population tuning have been predicted to exist in the human visual cortex, but this has yet to be fully examined. Using intracranial recordings from human visual cortex, we investigated the tuning of NBG to orientation and color, both independently and in conjunction. NBG was shown to display a cardinal orientation bias (horizontal) and also an end- and mid-spectral color bias (red/blue and green). When jointly probed, the cardinal bias for orientation was attenuated and an end-spectral preference for red and blue predominated. These data both elaborate on the close, yet complex, link between the population dynamics driving NBG oscillations and known feature selectivity biases in visual cortex, adding to a growing set of stimulus dependencies associated with the genesis of NBG. Together, these two factors may provide a fruitful testing ground for examining multi-scale models of brain activity, and impose new constraints on the functional significance of the visual gamma rhythm.