Function-specific and Enhanced Brain Structural Connectivity Mapping via Joint Modeling of Diffusion and Functional MRI

Shu-Hsien Chu,Christophe Lenglet,Keshab K Parhi

doi:10.1038/s41598-018-23051-9

Shu-Hsien Chu, Christophe Lenglet + Show 1 more

Open Access

https://doi.org/10.1038/s41598-018-23051-9

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Abstract

A joint structural-functional brain network model is presented, which enables the discovery of function-specific brain circuits, and recovers structural connections that are under-estimated by diffusion MRI (dMRI). Incorporating information from functional MRI (fMRI) into diffusion MRI to estimate brain circuits is a challenging task. Usually, seed regions for tractography are selected from fMRI activation maps to extract the white matter pathways of interest. The proposed method jointly analyzes whole brain dMRI and fMRI data, allowing the estimation of complete function-specific structural networks instead of interactively investigating the connectivity of individual cortical/sub-cortical areas. Additionally, tractography techniques are prone to limitations, which can result in erroneous pathways. The proposed framework explicitly models the interactions between structural and functional connectivity measures thereby improving anatomical circuit estimation. Results on Human Connectome Project (HCP) data demonstrate the benefits of the approach by successfully identifying function-specific anatomical circuits, such as the language and resting-state networks. In contrast to correlation-based or independent component analysis (ICA) functional connectivity mapping, detailed anatomical connectivity patterns are revealed for each functional module. Results on a phantom (Fibercup) also indicate improvements in structural connectivity mapping by rejecting false-positive connections with insufficient support from fMRI, and enhancing under-estimated connectivity with strong functional correlation.

Highlights

Functional MRI data was generated via realistic simulations, using the SimTB Toolbox[88], so that nodes belonging to the same sub-network share a common activity pattern
We focus first on the enhancement of structural connectivity and emphasize that, since structural connectivity values are different between deterministic and probabilistic tractography, it is not meaningful to directly compare link widths
We have introduced a joint structural-functional brain network model using the concept of information flow, which integrates diffusion MRI and functional MRI data to enable the identification of function-specific anatomical circuits from whole brain networks

Summary

Introduction

Functional MRI data was generated via realistic simulations, using the SimTB Toolbox[88], so that nodes belonging to the same sub-network share a common activity pattern (see Section 4). The correlation-based functional network, illustrated, successfully captures the sub-networks (identified by the wider edges). Within a given sub-network, all nodes are strongly connected and it is impossible to extract the underlying structural circuits. Partial correlations across sub-networks (e.g., between the red and blue sub-networks) can make it challenging to identify those circuits

Methods

Results

Discussion

Conclusion