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Reproducibility of the Structural Connectome Reconstruction across Diffusion Methods

Prčkovska V, Rodrigues P, Puigdellivol Sanchez A, Ramos M, Andorra M, Martinez-Heras E, Falcon C, Prats-Galino A, Villoslada P


Analysis of the structural connectomes can lead to powerful insights about the brain's organization and damage. However, the accuracy and reproducibility of constructing the structural connectome done with different acquisition and reconstruction techniques is not well defined. In this work, we evaluated the reproducibility of the structural connectome techniques by performing test-retest (same day) and longitudinal studies (after 1 month) as well as analyzing graph-based measures on the data acquired from 22 healthy volunteers (6 subjects were used for the longitudinal study). We compared connectivity matrices and tract reconstructions obtained with the most typical acquisition schemes used in clinical application: diffusion tensor imaging (DTI), high angular resolution diffusion imaging (HARDI), and diffusion spectrum imaging (DSI). We observed that all techniques showed high reproducibility in the test-retest analysis (correlation >.9). However, HARDI was the only technique with low variability (2%) in the longitudinal assessment (1-month interval). The intraclass coefficient analysis showed the highest reproducibility for the DTI connectome, however, with more sparse connections than HARDI and DSI. Qualitative (neuroanatomical) assessment of selected tracts confirmed the quantitative results showing that HARDI managed to detect most of the analyzed fiber groups and fanning fibers. In conclusion, we found that HARDI acquisition showed the most balanced trade-off between high reproducibility of the connectome, higher rate of path detection and of fanning fibers, and intermediate acquisition times (10-15 minutes), although at the cost of higher appearance of aberrant fibers.
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Prčkovska V, Rodrigues P, Puigdellivol Sanchez A, et al. Reproducibility of the Structural Connectome Reconstruction across Diffusion Methods. J Neuroimaging. 2016;26(1):46–57. doi: 10.1111/jon.12298