White matter information flow mapping from diffusion MRI and EEG

Abstract

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.

A unified framework for multimodal structure–function mapping based on eigenmodes

“…While the relationship is not local or one to one (Mišić et al, 2016), structural and functional connectomes are related by analytical operations (Saggio et al, 2016) and simple linear models may be sufficient to characterize resting state dynamics (Deco et al, 2013). The joint analysis of structure and function has also been considered to identify function-specific brain circuits (Chu et al, 2018) or map information flow in the white matter (Deslauriers-Gauthier et al, 2019). All of these results support the hypothesis that the structural network acts as a substrate on which functional connectivity emerges (Honey et al, 2007(Honey et al, , 2010.…”

Dynamic functional networks in idiopathic normal pressure hydrocephalus: Alterations and reversibility byCSFtap test

“…This hypothesis should be tested in future research using multimodal analyses. New methods, including diffusion weighted‐functional MRI imprinting, spatiotemporal networks and graph spectral approaches, have recently been proposed to map the white matter circuitries associated with specific (transient) functional patterns (Calamante, Smith, Liang, Zalesky, & Connelly, 2017; Deslauriers‐Gauthier et al, 2019; Griffa et al, 2017; Huang et al, 2018; Tarun et al, 2020). In particular, a study on healthy young adults has identified the WM connectivity maps corresponding to distinct precuneus CAPs (Tarun et al, 2020), with direct possible extensions to our results.…”

Integrating TMS, EEG, and MRI as an Approach for Studying Brain Connectivity

“…Modeling information processing among a large number of elements is challenging (Tadić and others 2019). The difficulty in explaining the flow of information in the brain relies on the nature of the electrophysiological brain signal (Deslauriers-Gauthier and others 2019). Different components of the signal may encode different information, which may follow several different routes, such that the overall information flow is spread across all the routes of each signal component.…”