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How distributed processing produces false negatives in voxel-based lesion-deficit analyses

Gajardo-Vidal, Andrea, Lorca-Puls, Diego L., Crinion, Jennifer T., White, Jitrachote, Seghier, Mohamed L., Leff, Alex P., Hope, Thomas M.H., Ludersdorfer, Philipp, Green, David W., Bowman, Howard, and others. (2018) How distributed processing produces false negatives in voxel-based lesion-deficit analyses. Neuropsychologia, . ISSN 0028-3932. (doi:10.1016/j.neuropsychologia.2018.02.025)

Abstract

In this study, we hypothesized that if the same deficit can be caused by damage to one or another part of a distributed neural system, then voxel-based analyses might miss critical lesion sites because preservation of each site will not be consistently associated with preserved function. The first part of our investigation used voxelbased multiple regression analyses of data from 359 right-handed stroke survivors to identify brain regions where lesion load is associated with picture naming abilities after factoring out variance related to object recognition, semantics and speech articulation so as to focus on deficits arising at the word retrieval level. A highly significant lesion-deficit relationship was identified in left temporal and frontal/premotor regions. Post-hoc analyses showed that damage to either of these sites caused the deficit of interest in less than half the affected patients (76/162 = 47%). After excluding all patients with damage to one or both of the identified regions, our second analysis revealed a new region, in the anterior part of the left putamen, which had not been previously detected because many patients had the deficit of interest after temporal or frontal damage that preserved the left putamen. The results illustrate how (i) false negative results arise when the same deficit can be caused by different lesion sites; (ii) some of the missed effects can be unveiled by adopting an iterative approach that systematically excludes patients with lesions to the areas identified in previous analyses, (iii) statistically significant voxel-based lesion-deficit mappings can be driven by a subset of patients; (iv) focal lesions to the identified regions are needed to determine whether the deficit of interest is the consequence of focal damage or much more extensive damage that includes the identified region; and, finally, (v) univariate voxel-based lesiondeficit mappings cannot, in isolation, be used to predict outcome in other patients.

Item Type: Article
DOI/Identification number: 10.1016/j.neuropsychologia.2018.02.025
Uncontrolled keywords: Voxel-based lesion-deficit mapping, Voxel-based morphometry, Voxel-based lesion-symptom mapping, stroke, Anomia, Word-finding difficulties
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Howard Bowman
Date Deposited: 29 Mar 2018 11:07 UTC
Last Modified: 09 Jul 2019 09:05 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/66586 (The current URI for this page, for reference purposes)
Bowman, Howard: https://orcid.org/0000-0003-4736-1869
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