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Spontaneous network activity <35 Hz accounts for variability in stimulus-induced gamma responses.

ABSTRACT: Gamma activity is thought to serve several cognitive processes, including attention and memory. Even for the simplest stimulus, the occurrence of gamma activity is highly variable, both within and between individuals. The sources of this variability, however, are largely unknown. In this paper, we address one possible cause: the cross-frequency influence of spontaneous, whole-brain network activity on visual stimulus processing. By applying Hidden Markov modelling to MEG data, we reveal that the trial-averaged gamma response to a moving grating depends on the individual network dynamics, inferred from slower brain activity (<35 Hz) in the absence of stimulation (resting-state and task baseline). In addition, we demonstrate that modulations of network activity in task baseline influence the gamma response on the level of trials. In summary, our results reveal a cross-frequency and cross-session association between gamma responses induced by visual stimulation and spontaneous network activity. These findings underline the dependency of visual stimulus processing on the individual, functional network architecture.

SUBMITTER: Hirschmann J

PROVIDER: S-EPMC8111242 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Spontaneous network activity <35 Hz accounts for variability in stimulus-induced gamma responses.

Hirschmann Jan J Baillet Sylvain S Woolrich Mark M Schnitzler Alfons A Vidaurre Diego D Florin Esther E

NeuroImage 20191120

Gamma activity is thought to serve several cognitive processes, including attention and memory. Even for the simplest stimulus, the occurrence of gamma activity is highly variable, both within and between individuals. The sources of this variability, however, are largely unknown. In this paper, we address one possible cause: the cross-frequency influence of spontaneous, whole-brain network activity on visual stimulus processing. By applying Hidden Markov modelling to MEG data, we reveal that the ...[more]

PMID: 31759115

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Project description:ImportanceA major goal of translational neuroscience is to identify neural circuit abnormalities in neuropsychiatric disorders that can be studied in animal models to facilitate the development of new treatments. Oscillations in the gamma band (30-100 Hz) of the electroencephalogram have received considerable interest as the basic mechanisms underlying these oscillations are understood, and gamma abnormalities have been found in schizophrenia (SZ). Animal models of SZ based on hypofunction of the N-methyl-d-aspartate receptor (NMDAR) demonstrate increased spontaneous broadband gamma power, but this phenomenon has not been identified clearly in patients with SZ.ObjectiveTo examine spontaneous gamma power and its relationship to evoked gamma oscillations in the auditory cortex of patients with SZ.Design, setting, and participantsWe performed a cross-sectional study including 24 patients with chronic SZ and 24 matched healthy control participants at the Veterans Affairs Boston Healthcare System from January 1, 2009, through December 31, 2012. Electroencephalograms were obtained during auditory steady-state stimulation at multiple frequencies (20, 30, and 40 Hz) and during a resting state in 18 participants in each group.Main outcomes and measuresElectroencephalographic activity in the auditory cortex was estimated using dipole source localization. Auditory steady-state response (ASSR) measures included the phase-locking factor and evoked power. Spontaneous gamma power was measured as induced (non-phase-locked) gamma power in the ASSR data and as total gamma power in the resting-state data.ResultsThe ASSR phase-locking factor was reduced significantly in patients with SZ compared with controls for the 40-Hz stimulation (mean [SD], 0.075 [0.028] vs 0.113 [0.065]; F1,46 = 6.79 [P = .012]) but not the 20- or the 30-Hz stimulation (0.042 [0.038] vs 0.043 [0.034]; F1,46 = 0.006 [P = .938] and 0.084 [0.040] vs 0.098 [0.050]; F1,46 = 1.605 [P = .212], respectively), repeating previous findings. The mean [SD] broadband-induced (30-100 Hz) gamma power was increased in patients with SZ compared with controls during steady-state stimulation (6.579 [3.783] vs 3.984 [1.843]; F1,46 = 9.128 [P = .004]; d = 0.87) but not during rest (0.006 [0.003] vs 0.005 [0.002]; F1,34 = 1.067 [P = .309]; d = 0.35). Induced gamma power in the left hemisphere of the patients with SZ during the 40-Hz stimulation was positively correlated with auditory hallucination symptoms (tangential, ρ = 0.587 [P = .031]; radial, ρ = 0.593 [P = .024]) and negatively correlated with the ASSR phase-locking factor (baseline: ρ = -0.572 [P = .024]; ASSR: ρ = -0.568 [P = .032]).Conclusions and relevanceSpontaneous gamma activity is increased during auditory steady-state stimulation in SZ, reflecting a disruption in the normal balance of excitation and inhibition. This phenomenon interacts with evoked oscillations, possibly contributing to the gamma ASSR deficit found in SZ. The similarity of increased spontaneous gamma power in SZ to the findings of increased spontaneous gamma power in animal models of NMDAR hypofunction suggests that spontaneous gamma power could serve as a biomarker for the integrity of NMDARs on parvalbumin-expressing inhibitory interneurons in humans and in animal models of neuropsychiatric disorders.

Project description:When stimulated, neural populations in the visual cortex exhibit fast rhythmic activity with frequencies in the gamma band (30-80 Hz). The gamma rhythm manifests as a broad resonance peak in the power-spectrum of recorded local field potentials, which exhibits various stimulus dependencies. In particular, in macaque primary visual cortex (V1), the gamma peak frequency increases with increasing stimulus contrast. Moreover, this contrast dependence is local: when contrast varies smoothly over visual space, the gamma peak frequency in each cortical column is controlled by the local contrast in that column's receptive field. No parsimonious mechanistic explanation for these contrast dependencies of V1 gamma oscillations has been proposed. The stabilized supralinear network (SSN) is a mechanistic model of cortical circuits that has accounted for a range of visual cortical response nonlinearities and contextual modulations, as well as their contrast dependence. Here, we begin by showing that a reduced SSN model without retinotopy robustly captures the contrast dependence of gamma peak frequency, and provides a mechanistic explanation for this effect based on the observed non-saturating and supralinear input-output function of V1 neurons. Given this result, the local dependence on contrast can trivially be captured in a retinotopic SSN which however lacks horizontal synaptic connections between its cortical columns. However, long-range horizontal connections in V1 are in fact strong, and underlie contextual modulation effects such as surround suppression. We thus explored whether a retinotopically organized SSN model of V1 with strong excitatory horizontal connections can exhibit both surround suppression and the local contrast dependence of gamma peak frequency. We found that retinotopic SSNs can account for both effects, but only when the horizontal excitatory projections are composed of two components with different patterns of spatial fall-off with distance: a short-range component that only targets the source column, combined with a long-range component that targets columns neighboring the source column. We thus make a specific qualitative prediction for the spatial structure of horizontal connections in macaque V1, consistent with the columnar structure of cortex.

Project description:Even in the absence of sensory stimulation the brain is spontaneously active. This background "noise" seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network's spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network's behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms.

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Spontaneous network activity <35 Hz accounts for variability in stimulus-induced gamma responses.

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Spontaneous network activity <35 Hz accounts for variability in stimulus-induced gamma responses.

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Spontaneous network activity <35 ​Hz accounts for variability in stimulus-induced gamma responses.

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Spontaneous network activity <35 ​Hz accounts for variability in stimulus-induced gamma responses.

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OmicsDI is part of the ELIXIR infrastructure

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Spontaneous network activity <35 Hz accounts for variability in stimulus-induced gamma responses.

Spontaneous network activity <35 Hz accounts for variability in stimulus-induced gamma responses.