Spontaneous variability in gamma dynamics described by a damped harmonic oscillator driven by noise

Spyropoulos, Georgios; Saponati, Matteo; Dowdall, Jarrod Robert; Schölvinck, Marieke L.; Bosman, Conrado A.; Lima, Bruss; Peter, Alina; Onorato, Irene; Klon-Lipok, Johanna; Roese, Rasmus; Neuenschwander, Sergio; Vinck, Martin

doi:10.1038/s41467-022-29674-x

Cited by 34 publications

(43 citation statements)

References 69 publications

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“…Oscillations are ubiquitous in the emergent macroscopic activity of the brain, yet only those in the damped regime predominated among the optimal equations for the local dynamics. This result agrees with empirical results as well as with the dynamical repertoire of multiple models of large-scale brain activity, which feature transitions towards stable spirals through different bifurcations 22,38-40 . Finally, in the case of oscillatory dynamics (stable limit cycle), the presence of anharmonicities influenced the goodness of fit metrics, with departures from sinusoidal waveforms benefiting the reproduction of empirical FCD.…”

Section: Discussionsupporting

confidence: 89%

Data-driven discovery of canonical large-scale brain dynamics

Piccinini

Deco

Kringelbach

et al. 2022

Preprint

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Human behavior and cognitive function correlate with complex patterns of spatio-temporal brain dynamics, which can be simulated using computational models with different degrees of biophysical realism. We used a data-driven optimization algorithm to determine and classify the types of local dynamics that enable the reproduction of different observables derived from functional magnetic resonance recordings. The phase space analysis of the resulting equations revealed a predominance of stable spiral attractors, which optimized the similarity with the empirical data in terms of the synchronization, metastability, and functional connectivity dynamics. For stable limit cycles, departures from harmonic oscillations improved the fit in terms of functional connectivity dynamics. Eigenvalue analyses showed that the proximity to Hopf bifurcations improved the accuracy of the simulation for wakefulness, while deep sleep was associated with increased stability. Our results provide testable predictions that constrain the landscape of suitable biophysical models, while supporting noise-driven dynamics close to a bifurcation as a canonical mechanism underlying the complex fluctuations that characterize endogenous brain activity.

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Section: Discussionsupporting

confidence: 89%

Data-driven discovery of canonical large-scale brain dynamics

Piccinini

Deco

Kringelbach

et al. 2022

Preprint

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“…firing variability) during states of strong gamma (Vinck and Bosman, 2016). The stochastic nature of the oscillations in this regime, as observed by Burns et al (2011); Spyropoulos et al (2022a), is critical because too strong synchronization is detrimental for coding (Chalk et al, 2016). Following the empirical observations of Uran et al (2022), we hypothesize that when inputs into the network become higher dimensional, the reconstruction error increases and the optimum shifts leftwards.…”

Section: Box 4: Measures Of Inter-areal Interactionsmentioning

confidence: 87%

The neural dynamics of feedforward and feedback interactions in predictive processing

Vinck¹,

Uran²,

Canales‐Johnson³

2022

Preprint

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Cortical areas are reciprocally coupled via feedforward (FF) and feedback (FB) connections that have distinct laminar profiles. Recurrent interactions between FF and FB streams may underlie context-dependent, flexible processing of sensory stimuli and the formation of predictions. Hierarchical predictive coding theories postulate that the communication of FF sensory input signals and FB predictions uses distinct frequency channels, namely gamma and alpha/beta-frequencies. Our review calls for an update of this dual-frequency theory: Empirical evidence suggests that both gamma and beta rhythms emerge during sensory and motor states characterized by high spatial and temporal predictability, in which case horizontal and top-down FB can explain away FF inputs. By contrast, broadband increases in gamma power for unpredicted stimuli likely result from increased firing rates. Recentevidence further indicates that distinct rhythms emerge in specific networks and are not consistently associated with either FF or FB influences, or with corresponding laminar compartments. Accordingly, we discuss potential functions and mechanisms of rhythms in the local circuit related to efficient coding and synaptic plasticity, rather than the inter-areal communication of FF error and FB prediction signals.

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“…We found that while visual stimulation reduces the frequency peak of alpha power (Figure 3D), high arousal states show consistently higher alpha peaks than low arousal states. Previous studies focused on gamma oscillations have found that this peak variability might reflect rapid cyclic changes in synaptic excitation (and a proportional inhibitory counterbalance) within a cortical microcircuit (Atallah and Scanziani 2009; Spyropoulos et al 2022). Our results show that this variability can also affect low frequency oscillatory components of the LFP.…”

Section: Discussionmentioning

confidence: 99%

Spontaneous Variations in Arousal Modulate Subsequent Visual Processing and Local Field Potential Dynamics in the Ferret during Quiet Wakefulness

Klaver

Brinkhof

Sikkens

et al. 2022

Preprint

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Behavioral states affect neuronal responses throughout the cortex and influence visual processing. Quiet wakefulness (QW) is a behavioral state during which subjects are quiescent, but awake and connected to the environment. Here, we examined the effects of pre-stimulus arousal variability on post-stimulus neural activity in primary visual cortex (V1) and posterior parietal cortex (PPc) in awake ferrets, using the pupil diameter as an indicator of arousal. We observed that during low arousal, low- frequency power increases during visual stimulation, and that the peak alpha frequency shifted depending on the arousal state. High arousal increased gamma power as well as low-frequency inter- and intra-areal coherence. Using a simplified model of laminar circuits, we show that this connectivity pattern is compatible with feedback signals targeting infragranular layers in area PPc and supragranular layers in V1. Neurons in V1 displayed higher firing rates at their preferred orientations on high-arousal trials. Broad-spiking cells in V1 entrained to high-frequency oscillations (>80 Hz), whereas narrow-spiking neurons phase-locked to low (12-18 Hz) and high-frequency (>80 Hz) rhythms. These results indicate that the variability and sensitivity of post-stimulus cortical responses and coherence depend on the pre-stimulus behavioral state and account for the neuronal response variability observed during repeated stimulation.

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Spontaneous variability in gamma dynamics described by a damped harmonic oscillator driven by noise

Cited by 34 publications

References 69 publications

Data-driven discovery of canonical large-scale brain dynamics

Data-driven discovery of canonical large-scale brain dynamics

The neural dynamics of feedforward and feedback interactions in predictive processing

Spontaneous Variations in Arousal Modulate Subsequent Visual Processing and Local Field Potential Dynamics in the Ferret during Quiet Wakefulness

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