Generalized Flash Suppression Research Articles

Reduced alpha amplitudes predict perceptual suppression

The amplitude of prestimulus alpha oscillations over parieto-occipital cortex has been shown to predict visual detection of masked and threshold-level stimuli. Whether alpha activity similarly predicts target visibility in perceptual suppression paradigms, another type of illusion commonly used to investigate visual awareness, is presently unclear. Here, we examined prestimulus alpha activity in the electroencephalogram (EEG) of healthy participants in the context of a generalized flash suppression (GFS) task during which salient target stimuli are rendered subjectively invisible in a subset of trials following the onset of a full-field motion stimulus. Unlike for masking or threshold paradigms, alpha (8–12 Hz) amplitude prior to motion onset was significantly higher when targets remained subjectively visible compared to trials during which the targets became perceptually suppressed. Furthermore, individual prestimulus alpha amplitudes strongly correlated with the individual trial-to-trial variability quenching following motion stimulus onset, indicating that variability quenching in visual cortex is closely linked to prestimulus alpha activity. We conclude that predictive correlates of conscious perception derived from perceptual suppression paradigms differ substantially from those obtained with “near threshold paradigms”, possibly reflecting the effectiveness of the suppressor stimulus.

PERCEPTUAL SUPPRESSION REVEALED BY ADAPTIVE MULTI-SCALE ENTROPY ANALYSIS OF LOCAL FIELD POTENTIAL IN MONKEY VISUAL CORTEX

Generalized flash suppression (GFS), in which a salient visual stimulus can be rendered invisible despite continuous retinal input, provides a rare opportunity to directly study the neural mechanism of visual perception. Previous work based on linear methods, such as spectral analysis, on local field potential (LFP) during GFS has shown that the LFP power at distinctive frequency bands are differentially modulated by perceptual suppression. Yet, the linear method alone may be insufficient for the full assessment of neural dynamic due to the fundamentally nonlinear nature of neural signals. In this study, we set forth to analyze the LFP data collected from multiple visual areas in V1, V2 and V4 of macaque monkeys while performing the GFS task using a nonlinear method - adaptive multi-scale entropy (AME) - to reveal the neural dynamic of perceptual suppression. In addition, we propose a new cross-entropy measure at multiple scales, namely adaptive multi-scale cross-entropy (AMCE), to assess the nonlinear functional connectivity between two cortical areas. We show that: (1) multi-scale entropy exhibits percept-related changes in all three areas, with higher entropy observed during perceptual suppression; (2) the magnitude of the perception-related entropy changes increases systematically over successive hierarchical stages (i.e. from lower areas V1 to V2, up to higher area V4); and (3) cross-entropy between any two cortical areas reveals higher degree of asynchrony or dissimilarity during perceptual suppression, indicating a decreased functional connectivity between cortical areas. These results, taken together, suggest that perceptual suppression is related to a reduced functional connectivity and increased uncertainty of neural responses, and the modulation of perceptual suppression is more effective at higher visual cortical areas. AME is demonstrated to be a useful technique in revealing the underlying dynamic of nonlinear/nonstationary neural signal.

Noise-assisted instantaneous coherence analysis of brain connectivity.

Characterizing brain connectivity between neural signals is key to understanding brain function. Current measures such as coherence heavily rely on Fourier or wavelet transform, which inevitably assume the signal stationarity and place severe limits on its time-frequency resolution. Here we addressed these issues by introducing a noise-assisted instantaneous coherence (NAIC) measure based on multivariate mode empirical decomposition (MEMD) coupled with Hilbert transform to achieve high-resolution time frequency representation of neural coherence. In our method, fully data-driven MEMD, together with Hilbert transform, is first employed to provide time-frequency power spectra for neural data. Such power spectra are typically sparse and of high resolution, that is, there usually exist many zero values, which result in numerical problems for directly computing coherence. Hence, we propose to add random noise onto the spectra, making coherence calculation feasible. Furthermore, a statistical randomization procedure is designed to cancel out the effect of the added noise. Computer simulations are first performed to verify the effectiveness of NAIC. Local field potentials collected from visual cortex of macaque monkey while performing a generalized flash suppression task are then used to demonstrate the usefulness of our NAIC method to provide highresolution time-frequency coherence measure for connectivity analysis of neural data.

Variance Entropy: A Method for Characterizing Perceptual Awareness of Visual Stimulus

Entropy, as a complexity measure, is a fundamental concept for time series analysis. Among many methods, sample entropy (SampEn) has emerged as a robust, powerful measure for quantifying complexity of time series due to its insensitivity to data length and its immunity to noise. Despite its popular use, SampEn is based on the standardized data where the variance is routinely discarded, which may nonetheless provide additional information for discriminant analysis. Here we designed a simple, yet efficient, complexity measure, namely variance entropy (VarEn), to integrate SampEn with variance to achieve effective discriminant analysis. We applied VarEn to analyze local field potential (LFP) collected from visual cortex of macaque monkey while performing a generalized flash suppression task, in which a visual stimulus was dissociated from perceptual experience, to study neural complexity of perceptual awareness. We evaluated the performance of VarEn in comparison with SampEn on LFP, at both single and multiple scales, in discriminating different perceptual conditions. Our results showed that perceptual visibility could be differentiated by VarEn, with significantly better discriminative performance than SampEn. Our findings demonstrate that VarEn is a sensitive measure of perceptual visibility, and thus can be used to probe perceptual awareness of a stimulus.

Adaptive Multiscale Entropy Analysis of Multivariate Neural Data

Multiscale entropy (MSE) has been widely used to quantify a system's complexity by taking into account the multiple time scales inherent in physiologic time series. The method, however, is biased toward the coarse scale, i.e., low-frequency components due to the progressive smoothing operations. In addition, the algorithm for extracting the different scales is not well adapted to nonlinear/nonstationary signals. In this letter, we introduce adaptive multiscale entropy (AME) measures in which the scales are adaptively derived directly from the data by virtue of recently developed multivariate empirical mode decomposition. Depending on the consecutive removal of low-frequency or high-frequency components, our AME can be estimated at either coarse-to-fine or fine-to-coarse scales over which the sample entropy is performed. Computer simulations are performed to verify the effectiveness of AME for analysis of the highly nonstationary data. Local field potentials collected from the visual cortex of macaque monkey while performing a generalized flash suppression task are used as an example to demonstrate the usefulness of our AME approach to reveal the underlying dynamics in complex neural data.

Intrinsic mode entropy based on multivariate empirical mode decomposition and its application to neural data analysis

Entropy, a measure of the regularity of a time series, has long been used to quantify the complexity of brain dynamics. Given the multiple spatiotemporal scales inherent in the brain, traditional entropy analysis based on a single scale is not adequate to accurately describe the underlying nonlinear dynamics. Intrinsic mode entropy (IMEn) is a recent development with appealing properties to estimate entropy over multiple time scales. It is a multiscale entropy measure that computes sample entropy (SampEn) over different scales of intrinsic mode functions extracted by empirical mode decomposition (EMD) method. However, it suffers from both mode-misalignment and mode-mixing problems when applied to multivariate time series data. In this paper, we address these two problems by employing the recently introduced multivariate empirical mode decomposition (MEMD). First, we extend the MEMD to multi-channel multi-trial neural data to ensure the IMEn matched at different scales. Second, for the discriminant analysis of IMEn, we propose to improve the discriminative ability by including variance that has not been used before in entropy analysis. Finally, we apply the proposed approach to the multi-electrode local field potentials (LFPs) simultaneously collected from visual cortical areas of macaque monkeys while performing a generalized flash suppression task. The results have shown that the entropy of LFP is indeed scale-dependent and is closely related to the perceptual conditions. The discriminative results of the perceptual conditions, revealed by support vector machine, show that the accuracy based on IMEn and variance reaches 83.05%, higher than that only by IMEn (76.27%). These results suggest that our approach is sensitive to capture the complex dynamics of neural data.

Extraction of microsaccade-related signal from single-trial local field potential by ICA with reference

During visual fixation, we unconsciously make tiny, involuntary eye movements or ‘microsaccades’, which have been shown to have a crucial influence on analysis and perception of our visual environment. Given the small size and high irregularity of microsaccades, it is a significant challenge to detect and extract the microsaccade-related neural activities. In this work, we present a novel application of the independent component analysis with reference algorithm to extract microsaccade-related neural activity from single-trial local field potential (LFP). We showed via extensive computer simulations that our approach can be used to reliably extract microsaccade-related activity. We then applied our method to real cortical LFP data collected from multiple visual areas of monkeys performing a generalized flash suppression task and demonstrated that our approach has excellent performance in extracting microsaccade-related signal from single-trial LFP data.

Suppression wave dynamics: Visual field anisotropies and inducer strength

We used binocular rivalry and generalized flash suppression to identify several new shared properties of traveling suppression waves. A strong relationship was found between suppression wave speed and induction pulse strength: increasing the contrast or dot density of the induction pulse led to an increase in wave speed. Evidence of visual field anisotropies in wave propagation speeds were also seen, with suppression waves decelerating as they travel towards the fovea. This deceleration could not be accounted for by cortical magnification in lower level brain areas, suggesting an important role for other, yet to be identified, factors.

Switching of the transmitters that mediate the depolarization wave in the embryonic chick CNS

We used binocular rivalry and generalized flash suppression to identify several new shared properties of traveling suppression waves. A strong relationship was found between suppression wave speed and induction pulse strength: increasing the contrast or dot density of the induction pulse led to an increase in wave speed. Evidence of visual field anisotropies in wave propagation speeds were also seen, with suppression waves decelerating as they travel towards the fovea. This deceleration could not be accounted for by cortical magnification in lower level brain areas, suggesting an important role for other, yet to be identified, factors.

Visibility states modulate microsaccade rate and direction

We investigated how the perceptual visibility of a target influences the pattern of microsaccadic eye movements expressed during generalized flash suppression. We found that the microsaccade rate was highly dependent on the reported visibility of the target. In the visible trials, the microsaccade rate promptly rebounded to the pre-onset level, whereas on the invisible trials the rate remained low, reaching pre-onset levels hundreds of milliseconds later. In addition, the directional distributions of microsaccades were biased to the target positions in the visible condition. The present findings indicate that the microsaccade behavior is highly correlated with the perceptual state of target visibility, and suggest that the measured microsaccade rate and direction are reliable indicators of the perception.