White matter information flow mapping from diffusion MRI and EEG.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Medical Imaging

Background:

• The human brain functions as a complex network of specialized regions.
• Electroencephalography (EEG) measures brain activity, while diffusion MRI visualizes structural connectivity.
• Directly observing information flow between cortical regions via white matter has been a challenge.

Purpose of the Study:

• To develop a novel method for inferring white matter information flow by integrating diffusion MRI and EEG data.
• To estimate directed dynamical functional connectivity supported by structural connectivity.
• To visualize and analyze transient communication pathways within the brain's network.

Main Methods:

• A subject-specific Bayesian network was constructed, incorporating white matter connections from diffusion MRI tractography and their delays.
• EEG measurements were used as evidence to estimate directed dynamical functional connectivity.
• The method was validated using simulated data and tested on a visual-motor network during a reaching task.

Main Results:

• The study successfully estimated information flow, visualizing transient communication between cortical regions.
• The developed algorithm demonstrated robust performance under varying noise levels.
• Information transfer from visual stimuli to the primary motor cortex was successfully mapped.

Conclusions:

• The novel multi-modal approach provides a new window into dynamic brain connectivity.
• This technique allows for the visualization and animation of information pathways through white matter.
• The findings open avenues for understanding brain communication in health and disease.