A data-driven approach to identifying and evaluating connectivity-based neural correlates of consciousness

A family of functional connectivity measures based on the barycenter—tracking the 'center of mass' between two neural signals—best decoded conscious vision from MEG data, outperforming other measures across brain regions central to both Integrated Information Theory and Global Neuronal Workspace Theory. Neural mass models showed that GNWT-based dynamics, featuring delayed ignition, better matched observed connectivity patterns than IIT-based highly synchronized sensory dynamics. The work introduces a generalizable approach for identifying and testing neural correlates of consciousness in an unbiased, data-driven way.

Study at a glance

Abstract

Identifying the neural correlates of consciousness 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 consciousness. 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 the GNWT-based model, featuring delayed ignition dynamics, better captured observed connectivity patterns than the IIT-based model, which relied on highly synchronized sensory dynamics. Beyond dataset-specific conclusions and limitations, we introduce a framework for systematically identifying and testing candidate neural correlates of consciousness in an unbiased and interpretable manner.