Consciousness Science Needs Some Rest: How to Use Resting-State Paradigm to Improve Theories and Measures of Consciousness

Rest2Task: Modeling task-specific components in resting-state functional connectivity and applications

The networks observed in the brain during resting-state activity are not entirely "task-free." Instead, they hint at a hierarchical structure prepared for adaptive cognitive functions. Recent studies have increasingly demonstrated the potential of resting-state fMRI to predict local activations or global connectomes during task performance. However, uncertainties remain regarding the unique and shared task-specific components within resting-state brain networks, elucidating local activations and global connectome patterns. A coherent framework is also required to integrate these task-specific components to predict local activations and global connectome patterns. In this work, we introduce the Rest2Task model based on the partial least squares-based multivariate regression algorithm, which effectively integrates mappings from resting-state connectivity to local activations and global connectome patterns. By analyzing the coefficients of the regression model, we extracted task-specific resting-state components corresponding to brain local activation or global connectome of various tasks and applied them to the brain lateralization prediction and psychiatric disorders diagnostic. Our model effectively substitutes traditional whole-brain functional connectivity (FC) in predicting functional lateralization and diagnosing brain disorders. Our research represents the inaugural effort to quantify the contribution of patterns (components) within resting-state FC to different tasks, endowing these components with specific task-related contextual information. The task-specific resting-state components offer new insights into brain lateralization processing and disease diagnosis, potentially providing fresh perspectives on the adaptive transformation of brain networks in response to tasks.

What does it mean for consciousness to be multidimensional? A narrative review

A recent development in the psychological and neuroscientific study of consciousness has been the tendency to conceptualize consciousness as a multidimensional phenomenon. This narrative review elucidates the notion of dimensionality of consciousness and outlines the key concepts and disagreements on this topic through the viewpoints of several theoretical proposals. The reviewed literature is critically evaluated, and the main issues to be resolved by future theoretical and empirical work are identified: the problems of dimension selection and dimension aggregation, as well as some ethical considerations. This narrative review is seemingly the first to comprehensively overview this specific aspect of consciousness science.

How much consciousness is there in complexity?

The notion of complexity currently receives significant attention in neuroscience, mainly through the popularity of the Integrated Information Theory (IIT). It has proven successful in research centred on discriminating states of consciousness, while little theoretical and experimental effort was directed toward studying the content. In this paper, we argue that exploring the relationship between complexity and conscious content is necessary to understand the importance of information-theoretic measures for consciousness research properly. We outline how content could be experimentally operationalised and how rudimental testable hypotheses can be formulated without requiring IIT formalisms. This approach would not only allow for a better understanding of aspects of consciousness captured by complexity but could also facilitate comparison efforts for theories of consciousness.