Higher-order functional connectivity analysis of resting-state functional magnetic resonance imaging data using...

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Network Science

Background:

• Blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is a primary tool for studying brain functional connectivity.
• Current research predominantly focuses on pairwise (second-order) connectivity, neglecting complex, multi-region interactions.
• Existing methods struggle to differentiate between second-order and genuine higher-order brain connectivity.

Purpose of the Study:

• To introduce and validate a novel method for quantifying genuine higher-order functional connectivity in fMRI data.
• To differentiate higher-order connectivity from pairwise connectivity.
• To assess the utility of this method in clinical and cognitive neuroscience applications.

Main Methods:

• Development and application of multivariate cumulants to estimate higher-order connectivity.
• Utilizing block bootstrapping for robust statistical inference.
• Formulating a generative model for fMRI signals to evaluate method performance (bias, standard errors, detection probabilities).

Main Results:

• Demonstrated that multivariate cumulants reliably quantify genuine higher-order connectivity, distinct from second-order networks.
• Analysis of resting-state fMRI data revealed spontaneous signals organized into unique higher-order networks.
• Successfully classified multiple sclerosis patient groups and explained behavioral variability using the developed cumulant-based approach.

Conclusions:

• Multivariate cumulants offer a reliable framework for estimating genuine higher-order functional connectivity in fMRI.
• This approach enables the construction of hyperedges, advancing network analysis.
• The method shows promise for applications in neuropsychiatric disease research and cognitive neuroscience.