Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98108
Title: AveLI : a robust lateralization index in functional magnetic resonance imaging using unbiased threshold-free computation
Authors: Matsuo, Kayako
Tseng, Wen-Yih Isaac
Chen, Annabel Shen-Hsing
Keywords: DRNTU::Social sciences
Issue Date: 2012
Source: Matsuo, K., Chen, S. H. A., & Tseng, W. Y. I. (2012). AveLI : a robust lateralization index in functional magnetic resonance imaging using unbiased threshold-free computation. Journal of Neuroscience Methods, 205(1), 119-129.
Series/Report no.: Journal of neuroscience methods
Abstract: The laterality index (LI) is often applied in functional magnetic resonance imaging (fMRI) studies to determine functional hemispheric lateralization. A difficulty in using conventional LI methods lies in ensuring a legitimate computing procedure with a clear rationale. Another problem with LI is dealing with outliers and noise. We propose a method called AveLI that follows a simple and unbiased computational principle using all voxel t-values within regions of interest (ROIs). This method first computes subordinate LIs (sub-LIs) using each of the task-related positive voxel t-values in the ROIs as the threshold as follows: sub-LI = (Lt − Rt)/(Lt + Rt), where Lt and Rt are the sums of the t-values at and above the threshold in the left and right ROIs, respectively. The AveLI is the average of those sub-LIs and indicates how consistently lateralized the performance of the subject is across the full range of voxel t-value thresholds. Its intrinsic weighting of higher t-value voxels in a data-driven manner helps to reduce noise effects. The resistance against outliers is demonstrated using a simulation. We applied the AveLI as well as other “non-thresholding” and “thresholding” LI methods to two language tasks using participants with right- and left-hand preferences. The AveLI showed a moderate index value among 10 examined indices. The rank orders of the participants did not vary between indices. AveLI provides an index that is not only comprehensible but also highly resistant to outliers and to noise, and it has a high reproducibility between tasks and the ability to categorize functional lateralization.
URI: https://hdl.handle.net/10356/98108
http://hdl.handle.net/10220/17333
ISSN: 0165-0270
DOI: http://dx.doi.org/10.1016/j.jneumeth.2011.12.020
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:HSS Journal Articles

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