Comparison between diffusion MRI tractography and histological tract-tracing of cortico-cortical structural connectivity in the ferret brain.

Céline Delettre,Leigh-Anne Dell,Claus C Hilgetag,Roberto Toro,Ophélie Foubet,Victor Borrell,Isabel Reillo,Arnaud Messé,Katja Heuer,Jean-Francois Mangin,Benoit Larrat,Sebastien Meriaux,Camino De Juan Romero

doi:10.1162/netn_a_00098

Abstract

The anatomical wiring of the brain is a central focus in network neuroscience. Diffusion MRI tractography offers the unique opportunity to investigate the brain fiber architecture in vivo and noninvasively. However, its reliability is still highly debated. Here, we explored the ability of diffusion MRI tractography to match invasive anatomical tract-tracing connectivity data of the ferret brain. We also investigated the influence of several state-of-the-art tractography algorithms on this match to ground truth connectivity data. Tract-tracing connectivity data were obtained from retrograde tracer injections into the occipital, parietal, and temporal cortices of adult ferrets. We found that the relative densities of projections identified from the anatomical experiments were highly correlated with the estimates from all the studied diffusion tractography algorithms (Spearman’s rho ranging from 0.67 to 0.91), while only small, nonsignificant variations appeared across the tractography algorithms. These results are comparable to findings reported in mouse and monkey, increasing the confidence in diffusion MRI tractography results. Moreover, our results provide insights into the variations of sensitivity and specificity of the tractography algorithms, and hence into the influence of choosing one algorithm over another.

Highlights

Brain function emerges from the communication of spatially distributed large-scale networks via the underlying structural connectivity architecture (Engel, Gerloff, Hilgetag, & Nolte, 2013; Kandel, Schwartz, Jessell, Siegelbaum, & Hudspeth, 2012; Park & Friston, 2013; Varela, Lachaux, Rodriguez, & Martinerie, 2001)
Our results showed that diffusion MRI tractography provides statistically significant estimates of ferret brain structural connectivity, the different tractography algorithms presented variations in terms of sensitivity and specificity
We considered three local models: (a) the diffusion tensor (DT) model; (b) fiber orientation distribution (FOD) estimated with a constrained spherical deconvolution (CSD) using the tournier algorithm (Tournier, Calamante, & Connelly, 2013); and (c) Fiber orientation distribution (FOD) estimated with the multishell multitissue Constrained spherical deconvolution (CSD) using the dhollander algorithm, which provides an unsupervised estimation of tissue-specific response functions

Summary

Introduction

Brain function emerges from the communication of spatially distributed large-scale networks via the underlying structural connectivity architecture (Engel, Gerloff, Hilgetag, & Nolte, 2013; Kandel, Schwartz, Jessell, Siegelbaum, & Hudspeth, 2012; Park & Friston, 2013; Varela, Lachaux, Rodriguez, & Martinerie, 2001). Reliable estimates of brain structural connectivity are essential for advancing our understanding of the network basis of brain function. Diffusion MRI tractography is an indirect approach for inferring brain structural connectivity from the Brownian motion of water molecules constrained by the axonal fiber architecture (Jeurissen, Descoteaux, Mori, & Leemans, 2017). The reliability of diffusion MRI tractography for properly mapping structural connections remains highly debated (Jones, Knösche, & Turner, 2013; Thomas et al, 2014). Validation appears as a key step in evaluating current methodologies and identifying new perspectives of improvement (Dyrby, Innocenti, Bech, & Lundell, 2018)

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Discussion

Conclusion