Right hemisphere structural adaptation and changing language skills years after left hemisphere stroke

Hope, Thomas M.H.; Leff, Alexander; Prejawa, Susan; Bruce, Rachel; Haigh, Zula; Lim, Louise; Ramsden, Sue; Oberhuber, Marion; Ludersdorfer, Philipp; Crinion, Jenny; Seghier, Mohamed L.; Price, Cathy J.

doi:10.1093/brain/awx086

Cited by 83 publications

(80 citation statements)

References 45 publications

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“…Conceptually, regions can be considered a functional extension of each other, just like having a larger functional area which is composed of non-adjacent voxels. The process of functional compensation is thought to be one of the main mechanisms of cognitive recovery after stroke, and, consequently, the "AND" combination is potentially important for considering intervention and recovery mechanisms (R. H. Hope et al, 2017;Piai et al, 2017;Tracy et al, 2014;Turkeltaub et al, 2012) . From an information flow perspective, "AND" combinations can be considered as parallel pathways of information processing into different areas.…”

Section: Discussionmentioning

confidence: 99%

Improved accuracy of lesion to symptom mapping with multivariate sparse canonical correlations

Pustina

Avants

Faseyitan

et al. 2017

Preprint

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Lesion to symptom mapping (LSM) is a crucial tool for understanding the causality of brain-behavior relationships. The analyses are typically performed by applying statistical methods on individual brain voxels (VLSM), a method called the mass-univariate approach.Several authors have shown that VLSM suffers from limitations that may decrease the accuracy and reliability of the findings, and have proposed the use of multivariate methods to overcome these limitations. In this study, we propose a multivariate optimization technique known as sparse canonical correlation analysis for neuroimaging (SCCAN) for lesion to symptom mapping. To validate the method and compare it with mass-univariate results, we used data from 131 patients with chronic stroke lesions in the territory of the middle cerebral artery, and created synthetic behavioral scores based on the lesion load of 93 brain regions (putative functional units). LSM analyses were performed with univariate VLSM or SCCAN, and the accuracy of the two methods was compared in terms of both overlap and and displacement from the simulated functional areas. Overall, SCCAN produced more accurate results -higher dice overlap and smaller average displacement -compared to VLSM. This advantage persisted at different sample sizes (N=20-131) and different multiple comparison corrections (false discovery rate, FDR; Bonferroni; permutation-based family wise error rate, FWER). These findings were replicated with a fully automated SCCAN routine that relied on cross-validated predictive accuracy to find the optimal sparseness value. Simulations of one, two, and three brain regions 5, 2017; of aphasia severity (aphasia quotient and picture naming) showed that SCCAN could accurately identify known language-critical areas, while VLSM either produced diffuse maps (FDR correction) or few scattered voxels (FWER correction). Overall, this study shows that a multivariate method, such as, SCCAN, outperforms VLSM in a number of scenarios, including functional dependency on single or multiple areas, different sample sizes, different multi-area combinations, and different thresholding mechanisms (FWER, Bonferroni, FDR). These results support previous claims that multivariate methods are in general more accurate than mass-univariate approaches, and should be preferred over traditional VLSM approaches. All the methods described in this study are available in the newly developed LESYMAP package for R.peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/149260 doi: bioRxiv preprint first posted online Jul.

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Section: Discussionmentioning

confidence: 99%

Improved accuracy of lesion to symptom mapping with multivariate sparse canonical correlations

Pustina

Avants

Faseyitan

et al. 2017

Preprint

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“…A recent longitudinal study of natural (rather than treatment-induced) recovery in chronic aphasia used voxel based morphometry (VBM) to assess changes in right-hemisphere grey-matter density across two time-points, which in turn were correlated with changes in language functions. 21 Changes in naming accuracy were associated with both increased and decreased right-hemisphere grey matter density in the anterior temporal lobe and the precentral gyrus, respectively. A different, cross-sectional study also showed positive correlations between spoken word comprehension and grey-matter density from VBM in the right middle temporal gyrus and insula and between spoken word production and grey-matter density in the right supplementary motor area cortex and insula.…”

Section: Figurementioning

confidence: 97%

Neuroplasticity and aphasia treatments: new approaches for an old problem

Crosson

Rodriguez

Copland

et al. 2019

J Neurol Neurosurg Psychiatry

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Given the profound impact of language impairment after stroke (aphasia), neuroplasticity research is garnering considerable attention as means for eventually improving aphasia treatments and how they are delivered. Functional and structural neuroimaging studies indicate that aphasia treatments can recruit both residual and new neural mechanisms to improve language function and that neuroimaging modalities may hold promise in predicting treatment outcome. In relatively small clinical trials, both non-invasive brain stimulation and behavioural manipulations targeting activation or suppression of specific cortices can improve aphasia treatment outcomes. Recent language interventions that employ principles consistent with inducing neuroplasticity also are showing improved performance for both trained and novel items and contexts. While knowledge is rapidly accumulating, larger trials emphasising how to select optimal paradigms for individualised aphasia treatment are needed. Finally, a model of how to incorporate the growing knowledge into clinical practice could help to focus future research.

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“…For many years, researchers have tried to understand and predict whether and when stroke survivors will recover lost speech and language abilities ( Bang et al, 2005 ; Cloutman et al, 2009 ; Crinion and Price, 2005 ; Hope et al, 2017 ; Hope et al, 2013 ; Konig et al, 2008 ; Lazar et al, 2008 ; Lendrem and Lincoln, 1985 ; Marshall and Phillips, 1983 ; Payabvash et al, 2010 ; Pedersen et al, 1995 ; Seghier et al, 2016 ; Tilling et al, 2001 ; Ween et al, 2000 ). There is broad agreement that lesion location information should play some role in this work ( Plowman et al, 2012 ), but still no consensus on the best or right way to encode that information ( Forkel et al, 2014 ; Hope et al, 2013 ; Mah et al, 2014 ; Price et al, 2017 ; Zhang et al, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting language outcomes after stroke: Is structural disconnection a useful predictor?

Hope

Leff

Price

2018

NeuroImage: Clinical

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For many years, researchers have sought to understand whether and when stroke survivors with acquired language impairment (aphasia) will recover. There is broad agreement that lesion location information should play some role in these predictions, but still no consensus on the best or right way to encode that information. Here, we address the emerging emphasis on the structural connectome in this work – specifically the claim that disrupted white matter connectivity conveys important, unique prognostic information for stroke survivors with aphasia.Our sample included 818 stroke patients extracted from the PLORAS database, which associates structural MRI from stroke patients with language assessment scores from the Comprehensive Aphasia Test (CAT) and basic demographic. Patients were excluded when their lesions were too diffuse or small (<1 cm3) to be detected by the Automatic Lesion Identification toolbox, which we used to encode patients' lesions as binary lesion images in standard space. Lesions were encoded using the 116 regions defined by the Automatic Anatomical Labelling atlas. We examined prognostic models driven by both “lesion load” in these regions (i.e. the proportion of each region destroyed by each patient's lesion), and by the disconnection of the white matter connections between them which was calculated via the Network Modification toolbox. Using these data, we build a series of prognostic models to predict first one (“naming”), and then all of the language scores defined by the CAT.We found no consistent evidence that connectivity disruption data in these models improved our ability to predict any language score. This may be because the connectivity disruption variables are strongly correlated with the lesion load variables: correlations which we measure both between pairs of variables in their original form, and between principal components of both datasets. Our conclusion is that, while both types of structural brain data do convey useful, prognostic information in this domain, they also appear to convey largely the same variance. We conclude that connectivity disruption variables do not help us to predict patients' language skills more accurately than lesion location (load) data alone.

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Right hemisphere structural adaptation and changing language skills years after left hemisphere stroke

Cited by 83 publications

References 45 publications

Improved accuracy of lesion to symptom mapping with multivariate sparse canonical correlations

Improved accuracy of lesion to symptom mapping with multivariate sparse canonical correlations

Neuroplasticity and aphasia treatments: new approaches for an old problem

Predicting language outcomes after stroke: Is structural disconnection a useful predictor?

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