A data-driven approach to identifying and evaluating connectivity-based neural correlates of conscious visual perception.

A family of functional connectivity measures based on tracking the 'center of mass' between two brain signals outperforms other measures at decoding conscious visual perception from magnetoencephalography data. These measures generalize across brain regions central to both Integrated Information Theory and Global Neuronal Workspace Theory. Neural mass models simulating each theory's hypothesized dynamics showed that both the GNWT-based model (featuring delayed ignition dynamics) and the IIT-based model (relying on synchronous sensory dynamics) captured the observed connectivity patterns. However, the presence of ignition dynamics independent of task-demand conditions contradicts IIT predictions, lending tentative support to GNWT. The work introduces a framework for systematically identifying and testing neural correlates of conscious vision.

Study at a glance

Abstract

Identifying the neural correlates of conscious visual perception remains a major challenge in neuroscience, requiring theories that bridge between subjective experience and measurable neural correlates. However, theoretical interpretation of empirical evidence is often post hoc and susceptible to confirmation bias. Building upon the adversarial collaboration mediated by the COGITATE Consortium, we present a generalizable approach for the data-driven identification, evaluation, and theoretical modeling of connectivity-based neural correlates of conscious visual perception. Using the same magnetoencephalography (MEG) dataset and accompanying pre-registered hypotheses from the COGITATE Consortium, we systematically compared 246 functional connectivity (FC) measures between regions predicted to underlie conscious vision by Integrated Information Theory (IIT) and/or Global Neuronal Workspace Theory (GNWT). We identified a family of FC measures based on the barycenter-tracking the 'center of mass' between two signals-as the top-performing stimulus decoding measures that generalize across regions central to predictions of both IIT and GNWT. To interpret these findings within a theoretical framework, we developed neural mass models that recapitulate the neural dynamics hypothesized to underlie conscious perception by each theory. Comparing simulated barycenter values from these models against empirically measured MEG data revealed that both the GNWT-based model, featuring delayed ignition dynamics, and the IIT-based model, which relied on synchronous sensory dynamics, captured the observed connectivity patterns. These results lend tentative support to GNWT, as the presence of ignition dynamics independent of task-demand conditions contradicts the predictions of IIT. Beyond dataset-specific conclusions and limitations, we introduce a framework for systematically identifying and testing candidate neural correlates of conscious visual perception in an unbiased and interpretable manner.