Evaluating arcuate fasciculus laterality measurements across dataset and tractography pipelines

Jonathan S. Bain, Jason D. Yeatman, Roey Schurr, Ariel Rokem, Aviv A. Mezer*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


The arcuate fasciculi are white-matter pathways that connect frontal and temporal lobes in each hemisphere. The arcuate plays a key role in the language network and is believed to be left-lateralized, in line with left hemisphere dominance for language. Measuring the arcuate in vivo requires diffusion magnetic resonance imaging–based tractography, but asymmetry of the in vivo arcuate is not always reliably detected in previous studies. It is unknown how the choice of tractography algorithm, with each method's freedoms, constraints, and vulnerabilities to false-positive and -negative errors, impacts findings of arcuate asymmetry. Here, we identify the arcuate in two independent datasets using a number of tractography strategies and methodological constraints, and assess their impact on estimates of arcuate laterality. We test three tractography methods: a deterministic, a probabilistic, and a tractography-evaluation (LiFE) algorithm. We extract the arcuate from the whole-brain tractogram, and compare it to an arcuate bundle constrained even further by selecting only those streamlines that connect to anatomically relevant cortical regions. We test arcuate macrostructure laterality, and also evaluate microstructure profiles for properties such as fractional anisotropy and quantitative R1. We find that both tractography choice and implementing the cortical constraints substantially impact estimates of all indices of arcuate laterality. Together, these results emphasize the effect of the tractography pipeline on estimates of arcuate laterality in both macrostructure and microstructure.

Original languageAmerican English
Pages (from-to)3695-3711
Number of pages17
JournalHuman Brain Mapping
Issue number13
StatePublished - 2019

Bibliographical note

Funding Information:
This work was supported by the joint funding program between the National Science Foundation's Directorate of Social, Behavioral and Economics Sciences (2015608 to J.D.Y. and A.A.M.) and the United States-Israel Binational Science Foundation (BCS1551330 to A.A.M. and J.D.Y.); the Israel Science Foundation (0399306 to A.A.M.); the National Alliance for Research in Schizophrenia and Affective Disorders's Young Investigator Grant (to A.A.M.); the Gordon and Betty Moore Foundation (3835 to A.R.); the Alfred P. Sloan Foundation (2013-10-29 to A.R.); and ELSC graduate student scholarships (J.S.B. and R.S.). The authors thank B. Wandell for data collection, which was supported by the Weston Havens Foundation, the National Science Foundation (BCS1228397), and National Institutes of Health (EY015000); F. Pestilli and C. Caiafa for their assistance with LiFE; and S. Berman and A. Nachmani for their constructive comments and suggestions.

Funding Information:
Alfred P. Sloan Foundation, Grant/Award Number: 2013-10-29; Directorate for Social, Behavioral and Economic Sciences, Grant/ Award Number: 2015608; ELSC graduate student scholarships; Gordon and Betty Moore Foundation, Grant/Award Number: 3835; Israel Science Foundation, Grant/Award Number: 0399306; National Alliance for Research on Schizophrenia and Depression; National Institutes of Health, Grant/Award Number: EY015000; National Science Foundation, Grant/Award Number: BCS1228397; United States-Israel Binational Science Foundation, Grant/Award Number: BCS1551330; Weston Havens Foundation

Publisher Copyright:
© 2019 Wiley Periodicals, Inc.


  • asymmetry
  • diffusion MRI
  • microstructure
  • quantitative MRI
  • tractogram


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