Beyond Pairwise Connections in Complex Systems: Insights into the Human Multiscale Psychotic Brain

Area of Science:

• Neuroscience
• Network Science
• Information Theory

Background:

• Complex biological systems, like the brain, feature multiway and multiscale interactions crucial for emergent behaviors.
• Psychiatric disorders involve higher-order neural interactions beyond simple pairwise connectivity.
• Traditional brain network studies focusing on pairwise links are insufficient for understanding complex neural dysfunction.

Purpose of the Study:

• To develop and apply a novel framework for analyzing higher-order interactions in brain networks.
• To move beyond pairwise connectivity analysis in neuroscience and psychiatry.
• To investigate complex multiway interaction patterns in human brain activity using functional magnetic resonance imaging (fMRI) and independent component analysis (ICA).

Main Methods:

• Application of a matrix-based entropy functional to estimate total correlation, a multivariate information measure.
• Utilizing fMRI-ICA-derived multiscale brain networks for analysis.
• Employing tensor decomposition to examine triple interactions and latent factors in intrinsic brain connectivity networks.

Main Results:

• Demonstrated a novel approach to estimate total correlation, capturing interactions beyond pairwise relationships.
• Successfully applied the framework to fMRI-ICA data, revealing higher-order brain dynamics.
• Identified complex multiway interaction patterns in neural signals, offering a new perspective on brain connectivity.

Conclusions:

• The developed framework provides a powerful tool for investigating beyond pairwise brain network interactions.
• This approach enhances the understanding of complex brain functions and offers new avenues for psychiatric research.
• The methodology holds potential for informing targeted diagnostic and therapeutic strategies for mental disorders and can be applied to broader signal analysis.