Robust Automated White Matter Pathway Reconstruction for Large Studies
Automated probabilistic reconstruction of white matter pathways facilitates tractography in large studies. TRACULA (TRActs Constrained by UnderLying Anatomy) follows a Markov-chain Monte Carlo (MCMC) approach that is compute-intensive. TRACULA is available on our Neuroscience Gateway (NSG), a user-friendly environment for fully automated data processing on grid computing resources. Despite the robustness of TRACULA, our users and others have reported incidents of partially reconstructed tracts. Investigation revealed that in these situations the MCMC algorithm is caught in local minima. We developed a method that detects unsuccessful tract reconstructions and iteratively repeats the sampling procedure while maintaining the anatomical priors to reduce computation time. The anatomical priors are recomputed only after several unsuccessful iterations. Our method detects affected tract reconstructions by analyzing the dependency between samples produced by the MCMC algorithm. We extensively validated the original and the modified methods by performing five repeated reconstructions on a dataset of 74 HIV-positive patients and 47 healthy controls. Our method increased the rate of successful reconstruction in the two most prominently affected tracts (forceps major and minor) on average from 74% to 99%. In these tracts, no group difference in FA and MD was found, while a significant association with age could be confirmed.
KeywordsFractional Anisotropy Markov Chain Monte Carlo Markov Chain Monte Carlo Algorithm Uncinate Fasciculus Successful Reconstruction
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- 6.Mori, S., Oishi, K., Jiang, H., Jiang, L., Li, X., Akhter, K., Hua, K., Faria, A.V., Mahmood, A., Woods, R., Toga, A., Pike, G., Neto, P., Evans, A., Zhang, J., Huang, H., Miller, M., van Zijl, P., Mazziotta, J.: Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. NeuroImage 40(2), 570–582 (2008)CrossRefGoogle Scholar
- 7.Shahand, S., Benabdelkader, A., Jaghoori, M.M., al Mourabit, M., Huguet, J., Caan, M.W.A., van Kampen, A.H.C., Olabarriaga, S.D.: A Data-Centric Neuroscience Gateway: Design, Implementation, and Experiences. Concurrency and Computation: Practice and Experience 27(2), 489–506 (2015)CrossRefGoogle Scholar
- 8.Woolrich, M.W., Jbabdi, S., Patenaude, B., Chappell, M., Makni, S., Behrens, T., Beckmann, C., Jenkinson, M., Smith, S.M.: Bayesian analysis of neuroimaging data in FSL. NeuroImage 45(1), S173–S186 (2009)Google Scholar
- 9.Yendiki, A., Panneck, P., Srinivasan, P., Stevens, A., Zöllei, L., Augustinack, J., Wang, R., Salat, D., Ehrlich, S., Behrens, T., Jbabdi, S., Gollub, R., Fischl, B.: Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy. Frontiers in Neuroinformatics 5(23), 1–12 (2011)Google Scholar