Comparison between diffusion MRI tractography and histological tract-tracing of cortico-cortical structural connectivity in the ferret brain
Author(s) -
Céline Delettre,
Arnaud Messé,
LeighAnne Dell,
Ophélie Foubet,
Katja Heuer,
Benoît Larrat,
Sébastien Mériaux,
JeanFrançois Mangin,
Isabel Reillo,
Camino de Juan Romero,
Vı́ctor Borrell,
Roberto Toro,
Claus C. Hilgetag
Publication year - 2019
Publication title -
network neuroscience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.128
H-Index - 18
ISSN - 2472-1751
DOI - 10.1162/netn_a_00098
Subject(s) - tractography , diffusion mri , tracing , neuroscience , psychology , magnetic resonance imaging , computer science , medicine , radiology , operating system
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.
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