A multimodal approach for determining brain networks by jointly modeling functional and structural connectivity

Area of Science:

• Neuroimaging
• Computational Neuroscience
• Network Science

Background:

• Neuroimaging advances enable investigation of human brain connectivity via anatomical and functional relationships.
• Current statistical methods often analyze functional connectivity (FC) or structural linkages separately.
• Existing approaches lack a unified framework integrating both functional and structural brain data.

Purpose of the Study:

• To present a unified Bayesian framework for analyzing functional connectivity (FC) by incorporating structural connectivity (SC) information.
• To extend previous methods by integrating data from functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI).
• To develop novel measures for characterizing brain network hierarchy and topology.

Main Methods:

• Developed a Bayesian framework to analyze FC using SC information from diffusion tensor imaging (DTI).
• Introduced an FC measure based on functional coherence from functional magnetic resonance imaging (fMRI) data.
• Formulated a prior distribution for FC dependent on SC probabilities, linking fMRI and DTI data.
• Defined an ascendancy measure for functional hierarchy and applied graph theoretic analyses for network topology.

Main Results:

• Demonstrated the Bayesian model's utility with fMRI and DTI data from an auditory processing study.
• Showcased advantages of the integrated functional-structural approach compared to methods using only functional data.
• The unified framework provides a more comprehensive analysis of brain network organization.

Conclusions:

• The proposed Bayesian framework offers a powerful, integrated approach to analyzing brain connectivity.
• Combining functional and structural neuroimaging data provides deeper insights into brain network organization.
• This method advances the study of brain networks, particularly in auditory processing and potentially other cognitive functions.