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Post-stroke outcomes predicted from multivariate lesion-behaviour and lesion network mapping
Journal article   Open access   Peer reviewed

Post-stroke outcomes predicted from multivariate lesion-behaviour and lesion network mapping

Mark Bowren, Joel Bruss, Kenneth Manzel, Dylan Edwards, Charles Liu, Maurizio Corbetta, Daniel Tranel and Aaron D Boes
Brain (London, England : 1878), Vol.145(4), pp.1338-1353
01/13/2022
DOI: 10.1093/brain/awac010
PMCID: PMC9630711
PMID: 35025994
url
https://doi.org/10.1093/brain/awac010View
Published (Version of record) Open Access

Abstract

Clinicians and scientists alike have long sought to predict the course and severity of chronic post-stroke cognitive and motor outcomes, as the ability to do so would inform treatment and rehabilitation strategies. However, it remains difficult to make accurate predictions about chronic post-stroke outcomes due, in large part, to high inter-individual variability in recovery and a reliance on clinical heuristics rather than empirical methods. The neuroanatomical location of a stroke is a key variable associated with long-term outcomes, and because lesion location can be derived from routinely collected clinical neuroimaging data there is an opportunity to use this information to make empirically based predictions about post-stroke deficits. For example, lesion location can be compared to statistically weighted multivariate lesion-behavior maps (LBMs) of neuroanatomical regions that, when damaged, are associated with specific deficits based on aggregated outcome data from large cohorts. Here, our goal was to evaluate whether we can leverage LBMs based on data from two large cohorts of individuals with focal brain lesions to make predictions of 12-month cognitive and motor outcomes in an independent sample of stroke patients. Further, we evaluated whether we could augment these predictions by estimating the structural and functional networks disrupted in association with each LBM through the use of structural and functional lesion network mapping (sLNM & fLNM, respectively), which use normative structural and functional connectivity data from neurologically healthy individuals to elucidate lesion-associated networks. We derived these brain network maps using the anatomical regions with the strongest association with impairment for each cognitive and motor outcome based on LBM results. These peak regional findings became the 'seeds' to generate networks, an approach that offers potentially greater precision compared to previously used single-lesion approaches. Next, in an independent sample, we quantified the overlap of each lesion location with the LBM, sLNM, and fLNM and evaluated how much variance each could explain in 12-month behavioral outcomes using a latent growth curve statistical model. We found that each lesion-deficit mapping modality was able to predict a statistically significant amount of variance in cognitive and motor outcomes. Both fLNM and sLNM were able to predict variance in 12-month outcomes beyond LBM. fLNM performed best for the prediction of language deficits, and sLNM performed best for the prediction of motor deficits. Altogether, these results support the notion that lesion location and lesion network mapping can be combined to improve the prediction of post-stroke deficits at 12-months.
 Stroke lesion network mapping functional connectivity lesion-behavior mapping brain networks

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