Connectome-based lesion-symptom mapping (CLSM): A novel approach to map neurological function☆

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Abstract

Lesion-symptom mapping is a key tool in understanding the relationship between structure and function in neuroscience as it can provide objective evidence about which regions are crucial for a given process. Initial limitations with this approach were largely overcome by voxel-based lesion-symptom mapping (VLSM), a method introduced in the early 2000s, which allows for a whole-brain approach to study the association between damaged areas and behavioral impairment by applying an independent statistical test at every voxel. By doing so, this technique eliminated the need to predefine regions of interest or classify patients into groups based on arbitrary cutoff scores. VLSM has nonetheless its own limitations; chiefly, a bias towards recognizing cortical necrosis/gliosis but with poor sensitivity for detecting injury along long white matter tracts, thus ignoring cortical disconnection, which can per se lead to behavioral impairment. Here, we propose a complementary method that, instead, establishes a statistical relationship between the strength of connections between all brain regions of the brain (as defined by a standard brain atlas) and the array of behavioral performance seen in patients with brain injury: connectome-based lesion-symptom mapping (CLSM). Whole-brain CLSM therefore has the potential to identify key connections for behavior independently of a priori assumptions with applicability across a broad spectrum of neurological and psychiatric diseases. We propose that this approach can further our understanding of brain-structure relationships and is worth exploring in clinical and theoretical contexts.

Highlights

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Lesion-symptom mapping has been crucial to understand brain-function relations
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VLSM eliminated the need to predefine regions of interest or biased patient groups.
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Main limitations of VLSM relate cortical necrosis/gliosis and white matter tracts
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CLSM can identify key connections for behavior independently of a priori assumptions
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CLSM has applicability across several neurological and psychiatric diseases

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Most cited references 41

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Prior investigations of functional specialization have focused on the response profiles of particular brain regions. Given the growing emphasis on regional covariation, we propose to reframe these questions in terms of brain 'networks' (collections of regions jointly engaged by some mental process). Despite the challenges that investigations of the language network face, a network approach may prove useful in understanding the cognitive architecture of language. We propose that a language network plausibly includes a functionally specialized 'core' (brain regions that coactivate with each other during language processing) and a domain-general 'periphery' (a set of brain regions that may coactivate with the language core regions at some times but with other specialized systems at other times, depending on task demands). Framing the debate around network properties such as this may prove to be a more fruitful way to advance our understanding of the neurobiology of language. Copyright © 2013 Elsevier Ltd. All rights reserved.

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Author and article information

Contributors

Ezequiel Gleichgerrcht

Leonardo Bonilha

Journal

Journal ID (nlm-ta): Neuroimage Clin

Journal ID (iso-abbrev): Neuroimage Clin

Title: NeuroImage : Clinical

Publisher: Elsevier

ISSN (Electronic): 2213-1582

Publication date PMC-release: 24 August 2017

Publication date Collection: 2017

Publication date (Electronic): 24 August 2017

Volume: 16

Pages: 461-467

Affiliations

[a ]Department of Neurology, Medical University of South Carolina, Charleston, SC, USA

[b ]Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA

[c ]Department of Psychology, University of South Carolina, Columbia, SC, USA

Author notes

[* ]Corresponding authors. gleichge@ 123456musc.edu bonilha@ 123456musc.edu

Article

Publisher ID: S2213-1582(17)30208-5

DOI: 10.1016/j.nicl.2017.08.018

PMC ID: 5581860

PubMed ID: 28884073

SO-VID: 8fcf6bc4-55f5-4e57-adf2-576d9e0dc0a9

License:

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

History

Date received : 3 April 2017

Date revision received : 23 July 2017

Date accepted : 23 August 2017

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