Non-invasive inference of information flow using diffusion MRI, functional MRI, and MEG

Area of Science:

• Neuroscience
• Brain Imaging
• Computational Neuroscience

Background:

• Estimating white matter information flow is crucial for understanding brain interactions.
• Current methods are often invasive, limiting non-invasive exploration.
• Integrating multimodal neuroimaging data offers a promising avenue for advancing this field.

Purpose of the Study:

• To develop a non-invasive method for inferring brain information flow and cortical activity.
• To recover white matter connections and activity without manual selection.
• To enable a deeper understanding of communication between cortical regions.

Main Methods:

• A Bayesian network was constructed using functional MRI (fMRI), diffusion MRI (dMRI), and magnetoencephalography (MEG).
• dMRI identified potential white matter connections between cortical regions.
• fMRI pruned connections and informed region activity likelihood, while MEG data served as evidence for network analysis.

Main Results:

• The method successfully identified connections related to a sensory-motor task.
• A Bayesian network was built without manual intervention in connection selection.
• MEG data revealed known brain regions involved in visuomotor tasks and recovered information flow along white matter pathways.

Conclusions:

• The proposed non-invasive method effectively infers brain information flow and cortical activity.
• This approach overcomes limitations of invasive techniques, opening new avenues for research.
• The integration of fMRI, dMRI, and M/EEG provides a powerful tool for exploring brain connectivity.