Probing Electrophysiological Indices of Perceptual Awareness across Unisensory and Multisensory Modalities

Abstract

The neural underpinnings of perceptual awareness have been extensively studied using unisensory (e.g., visual alone) stimuli. However, perception is generally multisensory, and it is unclear whether the neural architecture uncovered in these studies directly translates to the multisensory domain. Here, we use EEG to examine brain responses associated with the processing of visual, auditory, and audiovisual stimuli presented near threshold levels of detectability, with the aim of deciphering similarities and differences in the neural signals indexing the transition into perceptual awareness across vision, audition, and combined visual-auditory (multisensory) processing. More specifically, we examine (1) the presence of late evoked potentials (∼>300 msec), (2) the across-trial reproducibility, and (3) the evoked complexity associated with perceived versus nonperceived stimuli. Results reveal that, although perceived stimuli are associated with the presence of late evoked potentials across each of the examined sensory modalities, between-trial variability and EEG complexity differed for unisensory versus multisensory conditions. Whereas across-trial variability and complexity differed for perceived versus nonperceived stimuli in the visual and auditory conditions, this was not the case for the multisensory condition. Taken together, these results suggest that there are fundamental differences in the neural correlates of perceptual awareness for unisensory versus multisensory stimuli. Specifically, the work argues that the presence of late evoked potentials, as opposed to neural reproducibility or complexity, most closely tracks perceptual awareness regardless of the nature of the sensory stimulus. In addition, the current findings suggest a greater similarity between the neural correlates of perceptual awareness of unisensory (visual and auditory) stimuli when compared with multisensory stimuli.

Leveraging Nonhuman Primate Multisensory Neurons and Circuits in Assessing Consciousness Theory

“…This shuffled LZ time-series represents a theoretical upper bound (i.e., random data has a higher LZ) and was used to normalize the calculated LZ from the non-shuffled data. Hence, a normalized LZ of 1 indicates "as complex as random noise", whereas lower values indicate the presence of structure in the data (Andrillon et al, 2016;Noel et al, 2018). Statistical analysis were conducted on non-corrected values to examine the basal complexity in spiking activity as a function of consciousness and whether neurons were categorized as convergent or integrative.…”

“…Of note, the strongest evidence for the utility of Lempel-Ziv Complexity (LZc) in dissociating levels of consciousness comes from transcranial magnetic stimulation (TMS) evoked responses, which spatiotemporal pattern (i.e., time-series and EEG channels projected into voxels) is compressed and measured (Casali et al, 2013). Differently, here evoked activity is driven by sensory stimulation (Noel et al, 2018), and given the difficulty in knowing the precise location of neurons during extra-cellular recordings, we apply a temporal version of LZc (i.e., where there is no concatenation across space). This temporal-only version of LZc is that which has been applied to single units previously (Amigó et al, 2004), and has been shown to be elevated during psychedelic experiences (denoted LZs by Schartner et al, 2017).…”

“…This empirical measure was directly derived from the IIT (Tononi et al, 2016) and has been shown to successfully differentiate between distinct levels of consciousness (Casali et al, 2013;Sarasso et al, 2015). In more simple applications, LZ has also been applied to resting state (Schartner et al, 2015(Schartner et al, , 2017 and stimulus evoked (Andrillon et al, 2016;Noel et al, 2018) neural activity (most commonly in scalp EEG datasets), and similar to PCI, has been shown capable of differentiating between levels of consciousness (Schartner et al, 2015(Schartner et al, , 2017. Here we first characterize time-resolved LZ complexity in spike trains as a function of consciousness state and modality of stimulation.…”

From multisensory integration in peripersonal space to bodily self‐consciousness: from statistical regularities to statistical inference

“…Finally, we are not passive observers of events occurring in the outside world, rather we act upon the world to achieve specific behavioral goals, and in doing so, we further modify both the way our sensory systems operate (e.g., sampling different spatial locations or privileging specific sensory processes; i.e., active sensing) and the external world itself. Indeed, as multisensory inquiry moved from the feline SC to the primate cortex and incorporated more complex and ecologically valid scenarios, the principles of multisensory integration only appeared to apply moderately . Furthermore, the focus shifted from explaining how single neurons integrate information to how populations of neurons effect this process depending on the different behavioral needs an organism may encounter.…”

Rapid Recalibration of Peri-Personal Space; Psychophysical, Electrophysiological, and Neural Network Modeling Evidence

“…Lastly, Noel and colleagues (2018b) showed that audio-visual responses are facilitated near the boundary of PPS, and only evident ~300ms post-stimulus onset. Together, this pattern of results is evocative of a temporal cascade, wherein visuo-tactile near space is differentiated from the far space first (~130ms), then audio-tactile space is (~200ms), and finally perhaps a space-selective audio-visual processing (Van der Stoep et al, 2016;Noel et al, 2018d) is scaffolded upon this encoding of PPS. This speculation, however, needs to be confirmed by further studies directly comparing visuo-tactile, audio-tactile, and audio-visual integration as a function of distance.…”