White matter information flow mapping from diffusion MRI and EEG

• Published in: NeuroImage (Orlando, Fla.) Vol. 201; p. 116017
• Main Authors: Deslauriers-Gauthier, Samuel, Lina, Jean-Marc, Butler, Russell, Whittingstall, Kevin, Gilbert, Guillaume, Bernier, Pierre-Michel, Deriche, Rachid, Descoteaux, Maxime
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.11.2019; Elsevier Limited; Elsevier
• Subjects: Algorithms; Bayesian analysis; Brain - physiology; Brain mapping; Brain Mapping - methods; Brain networks; Brain research; Cognitive science; Computer Science; Cortex (motor); Diffusion Magnetic Resonance Imaging - methods; Diffusion MRI; Dynamic connectivity; EEG; Electroencephalography; Electroencephalography - methods; Entropy; Expected values; Functional connectivity; Humans; Inverse problems; Joint use; Magnetic resonance imaging; Medical Imaging; MEG; Models, Neurological; Neural networks; Neuroimaging; Neuroscience; NMR; Nuclear magnetic resonance; Sensorimotor integration; Structural connectivity; Substantia alba; Visual cortex; Visual stimuli; White Matter - physiology; Functional connectivity; Structural connectivity; Brain networks; EEG; Diffusion MRI; Dynamic connectivity; MEG; functional connectivity; dynamic connectivity; structural connectivity; diffusion MRI; brain networks
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2019.116017

The human brain can be described as a network of specialized and spatially distributed regions. The activity of individual regions can be estimated using electroencephalography and the structure of the network can be measured using diffusion magnetic resonance imaging. However, the communication between the different cortical regions occurring through the white matter, coined information flow, cannot be observed by either modalities independently. Here, we present a new method to infer information flow in the white matter of the brain from joint diffusion MRI and EEG measurements. This is made possible by the millisecond resolution of EEG which makes the transfer of information from one region to another observable. A subject specific Bayesian network is built which captures the possible interactions between brain regions at different times. This network encodes the connections between brain regions detected using diffusion MRI tractography derived white matter bundles and their associated delays. By injecting the EEG measurements as evidence into this model, we are able to estimate the directed dynamical functional connectivity whose delays are supported by the diffusion MRI derived structural connectivity. We present our results in the form of information flow diagrams that trace transient communication between cortical regions over a functional data window. The performance of our algorithm under different noise levels is assessed using receiver operating characteristic curves on simulated data. In addition, using the well-characterized visual motor network as grounds to test our model, we present the information flow obtained during a reaching task following left or right visual stimuli. These promising results present the transfer of information from the eyes to the primary motor cortex. The information flow obtained using our technique can also be projected back to the anatomy and animated to produce videos of the information path through the white matter, opening a new window into multi-modal dynamic brain connectivity. •Diffusion MRI and M/EEG can be combined to map information flow in the white matter.•The information flow is directed and supported by structural connectivity.•The temporal resolution of the information flow is on the order of the millisecond.•Results on lateralized visual stimulus show the expected lateralization.•Our novel information flow opens a new window into visualization of brain dynamics.