Topology, cross-frequency, and same-frequency band interactions shape the generation of phase-amplitude coupling in a neural mass model of a cortical column

Sotero, Roberto C.

doi:10.1101/023291

Cited by 6 publications

(5 citation statements)

References 85 publications

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“…First, ␣ and ␤ generating networks appear, to a certain extent, intertwined during a volitional task engaging no sensory stimulation (i.e., self-generated endogenous timing). Second, that higher precision may arise from the maintenance of ␤-encoded information by the phase of ␣ rhythm is in line with recent simulations using a four-layer neuronal mass model (Sotero, 2015(Sotero, , 2016. The phase of ␣ oscillations may provide an optimal window for the reactivation of ␤-driven activity, consistent with a global ␣ network regulating local ␤ activity (Lee et al, 2013).…”

Section: Specificity Of ␣-␤ Coupling To the Precision Of Motor Timingsupporting

confidence: 81%

The Strength of Alpha–Beta Oscillatory Coupling Predicts Motor Timing Precision

et al. 2019

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Precise timing makes the difference between harmony and cacophony, but how the brain achieves precision during timing is unknown. In this study, human participants (7 females, 5 males) generated a time interval while being recorded with magnetoencephalography. Building on the proposal that the coupling of neural oscillations provides a temporal code for information processing in the brain, we tested whether the strength of oscillatory coupling was sensitive to self-generated temporal precision. On a per individual basis, we show the presence of alpha-beta phase-amplitude coupling whose strength was associated with the temporal precision of self-generated time intervals, not with their absolute duration. Our results provide evidence that active oscillatory coupling engages ␣ oscillations in maintaining the precision of an endogenous temporal motor goal encoded in ␤ power; the when of self-timed actions. We propose that oscillatory coupling indexes the variance of neuronal computations, which translates into the precision of an individual's behavioral performance.Which neural mechanisms enable precise volitional timing in the brain is unknown, yet accurate and precise timing is essential in every realm of life. In this study, we build on the hypothesis that neural oscillations, and their coupling across time scales, are essential for the coding and for the transmission of information in the brain. We show the presence of alpha-beta phaseamplitude coupling (␣-␤ PAC) whose strength was associated with the temporal precision of self-generated time intervals, not with their absolute duration. ␣-␤ PAC indexes the temporal precision with which information is represented in an individual's brain. Our results link large-scale neuronal variability on the one hand, and individuals' timing precision, on the other.

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Section: Specificity Of ␣-␤ Coupling To the Precision Of Motor Timingsupporting

confidence: 81%

The Strength of Alpha–Beta Oscillatory Coupling Predicts Motor Timing Precision

et al. 2019

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“…The most significant influence on PAC was exerted by a group of non-hubs, which connected with high probability to high degree nodes ( figure 4). This facilitated the generation of PAC (information flow from low to high frequencies 13 ) since low and high degree nodes were generally associated with low and high frequencies,…”

Section: Discussionmentioning

confidence: 99%

Cross-Frequency Interactions During Information Flow in Complex Brain Networks Are Facilitated by Scale-Free Properties

et al. 2019

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We studied the interactions between different temporal scales of diffusion processes on complex networks and found them to be stronger in scale-free (SF) than in Erdos-Renyi (ER) networks, especially for the case of phase-amplitude coupling (PAC)-the phenomenon where the phase of an oscillatory mode modulates the amplitude of another oscillation. We found that SF networks facilitate PAC between slow and fast frequency components of the diffusion process, whereas ER networks enable PAC between slow-frequency components. Nodes contributing the most to the generation of PAC in SF networks were non-hubs that connected with high probability to hubs.Additionally, brain networks from healthy controls (HC) and Alzheimer's disease (AD) patients presented a weaker PAC between slow and fast frequencies than SF, but higher than ER. We found that PAC decreased in AD compared to HC and was more strongly correlated to the scores of two different cognitive tests than what the strength of functional connectivity was, suggesting a link between cognitive impairment and multi-scale information flow in the brain.interactions correspond to the phenomenon known as cross-frequency coupling (CFC) 13 . We focus on three types of CFC: phase-amplitude coupling (PAC), the phenomenon where the instantaneous phase of a low frequency oscillation modulates the instantaneous amplitude of a higher frequency oscillation 14 15 ; amplitude-amplitude coupling (AAC), which measures the co-modulation of the instantaneous amplitudes of two oscillations 16 ; and phase-phase coupling (PPC), which corresponds to the synchronization between two instantaneous phases 17 . Results Diffusion of simulated ER and SF networksWe start by considering an unweighted network consisting of nodes. We place a large number ( ≫ ) of random walkers onto this network. At each time step, the walkers move randomly between the nodes that are directly linked to each other. We allow the walkers to perform time steps. As a walker visits a node, we record the fraction of walkers present at it, which we term node activity. Thus, after time steps, we obtain time series reflecting different realizations of the flow of information in the network.Two types of simulated complex networks are considered here, ER and SF networks. An ER network is a random graph where each possible edge has the same probability of existing. The degree of a node i ( ) is defined as the number of connections it has to other nodes. The degree distribution ( ) of an ER network is a binomial distribution, which decays exponentially for large degrees , allowing only very small degree fluctuations 18 . On the other hand, SF networks

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“…Neural mass and neural field models are able to reproduce a range of dynamical behaviors that are observed in EEG, like oscillations in typical EEG frequency bands (David and Friston 2003), phase-amplitude-coupling (Onslow et al 2014;Sotero 2016), evoked responses (Jansen et al 1993;Jansen and Rit 1995;David et al 2005), and power spectra (Lopes Da Silva et al 1974;Robinson et al 2001b;David and Friston 2003;Bojak and Liley 2005;Moran et al 2007). Power spectra are of particular interest in EEG because on the one hand, they can be precisely measured due to the high temporal resolution, and on the other, they can be thought of as a low-dimensional representation of steady-state dynamics.…”

Section: Computational Models For Eeg On the Level Of Neural Masses Amentioning

confidence: 99%

Computational Models in Electroencephalography

et al. 2021

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Computational models lie at the intersection of basic neuroscience and healthcare applications because they allow researchers to test hypotheses in silico and predict the outcome of experiments and interactions that are very hard to test in reality. Yet, what is meant by “computational model” is understood in many different ways by researchers in different fields of neuroscience and psychology, hindering communication and collaboration. In this review, we point out the state of the art of computational modeling in Electroencephalography (EEG) and outline how these models can be used to integrate findings from electrophysiology, network-level models, and behavior. On the one hand, computational models serve to investigate the mechanisms that generate brain activity, for example measured with EEG, such as the transient emergence of oscillations at different frequency bands and/or with different spatial topographies. On the other hand, computational models serve to design experiments and test hypotheses in silico. The final purpose of computational models of EEG is to obtain a comprehensive understanding of the mechanisms that underlie the EEG signal. This is crucial for an accurate interpretation of EEG measurements that may ultimately serve in the development of novel clinical applications.

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Topology, cross-frequency, and same-frequency band interactions shape the generation of phase-amplitude coupling in a neural mass model of a cortical column

Cited by 6 publications

References 85 publications

The Strength of Alpha–Beta Oscillatory Coupling Predicts Motor Timing Precision

The Strength of Alpha–Beta Oscillatory Coupling Predicts Motor Timing Precision

Cross-Frequency Interactions During Information Flow in Complex Brain Networks Are Facilitated by Scale-Free Properties

Computational Models in Electroencephalography

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