Evaluation and statistical inference for human connectomes

Franco Pestilli, Jason D. Yeatman, Ariel Rokem, Kendrick N. Kay, Brian A. Wandell

Research output: Contribution to journalArticlepeer-review

146 Scopus citations

Abstract

Diffusion-weighted imaging coupled with tractography is currently the only method for in vivo mapping of human white-matter fascicles. Tractography takes diffusion measurements as input and produces the connectome, a large collection of white-matter fascicles, as output. We introduce a method to evaluate the evidence supporting connectomes. Linear fascicle evaluation (LiFE) takes any connectome as input and predicts diffusion measurements as output, using the difference between the measured and predicted diffusion signals to quantify the prediction error. We use the prediction error to evaluate the evidence that supports the properties of the connectome, to compare tractography algorithms and to test hypotheses about tracts and connections.

Original languageEnglish (US)
Pages (from-to)1058-1063
Number of pages6
JournalNature Methods
Volume11
Issue number10
DOIs
StatePublished - Jan 1 2014

Bibliographical note

Funding Information:
We thank M.L. Perry for assistance with collecting and preprocessing the data and M. Ben-Shachar, R. Dougherty, J. Gardner, S. Ling, A. Mezer, A. Sherbondy, J. Winawer, H. Takemura, J. Solomon, L. Guibas, A. Butscher, C.Y. Zheng, R. Tibshirani and T. Hastie for useful comments and discussions. This work was funded by NEI F32 EY022294 from the US National Institutes of Health (NIH) to A.R. and by US National Science Foundation grant BCS1228397 to B.A.W. Data were provided in part by the Human Connectome Project, WU-Minn Consortium (D. Van Essen and K. Ugurbil, 1U54MH091657 NIH).

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

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