Lesion-symptom mapping in the study of spoken language understanding

Wilson, Stephen M.

doi:10.1080/23273798.2016.1248984

Cited by 38 publications

(29 citation statements)

References 88 publications

(129 reference statements)

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“…This method addresses limitations of the voxelwise FDR approach implemented previously in SVR-LSM, which treated each voxel as an independent comparison, resulting in potentially flawed corrections. Permutation-based multiple-comparisons correction methods are considered the gold standard for lesion symptom mapping because they account for the lesion autocorrelation structure inherent to the data sets (Kimberg et al, 2007;Mirman et al, 2018;Nichols & Holmes, 2002;Wilson, 2017). Second, we provide the ability to control for multiple covariates in the analysis design and provide more flexibility in how the covariates are handled via nuisance models.…”

Section: Zhang Et Al Have Made Their Implementation Of Svr-lsm Publiclymentioning

confidence: 99%

A multivariate lesion symptom mapping toolbox and examination of lesion‐volume biases and correction methods in lesion‐symptom mapping

DeMarco

Turkeltaub

2018

Human Brain Mapping

147

173

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Lesion-symptom mapping has become a cornerstone of neuroscience research seeking to localize cognitive function in the brain by examining the sequelae of brain lesions. Recently, multivariate lesion-symptom mapping methods have emerged, such as support vector regression, which simultaneously consider many voxels at once when determining whether damaged regions contribute to behavioral deficits (Zhang, Kimberg, Coslett, Schwartz, & Wang, ). Such multivariate approaches are capable of identifying complex dependences that traditional mass-univariate approach cannot. Here, we provide a new toolbox for support vector regression lesion-symptom mapping (SVR-LSM) that provides a graphical interface and enhances the flexibility and rigor of analyses that can be conducted using this method. Specifically, the toolbox provides cluster-level family-wise error correction via permutation testing, the capacity to incorporate arbitrary nuisance models for behavioral data and lesion data and makes available a range of lesion volume correction methods including a new approach that regresses lesion volume out of each voxel in the lesion maps. We demonstrate these new tools in a cohort of chronic left-hemisphere stroke survivors and examine the difference between results achieved with various lesion volume control methods. A strong bias was found toward brain wide lesion-deficit associations in both SVR-LSM and traditional mass-univariate voxel-based lesion symptom mapping when lesion volume was not adequately controlled. This bias was corrected using three different regression approaches; among these, regressing lesion volume out of both the behavioral score and the lesion maps provided the greatest sensitivity in analyses.

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Section: Zhang Et Al Have Made Their Implementation Of Svr-lsm Publiclymentioning

confidence: 99%

A multivariate lesion symptom mapping toolbox and examination of lesion‐volume biases and correction methods in lesion‐symptom mapping

DeMarco

Turkeltaub

2018

Human Brain Mapping

147

173

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“…To correct for multiple comparisons, a permutation approach was used to control the cluster-level family-wise error as follows: first, 10,000 permutations of behavioral scores were used to derive a map of voxelwise beta-values corresponding to a voxelwise P < 0.005 threshold; next, the largest cluster at a voxelwise P < 0.005 in each of the 10,000 permutation maps was recorded; finally, the cluster size threshold corresponding to a family-wise error controlled P=.05 was determined based on the distribution of maximum cluster sizes from the permutations. Permutation-based multiple-comparisons correction methods are considered the gold standard for lesion symptom mapping because they account for the lesion autocorrelation structure inherent to the datasets (Kimberg, Coslett, & Schwartz, 2007; Mirman, Landrigan, Kokolis, Verillo, & Ferrara, 2016; Nichols & Holmes, 2002; Wilson, 2017). …”

Section: Methodsmentioning

confidence: 99%

Phonotactic processing deficit following left-hemisphere stroke

Ghaleh

Skipper-Kallal

Xing

et al. 2018

Cortex

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The neural basis of speech processing is still a matter of great debate. Phonotactic knowledge-knowledge of the allowable sound combinations in a language-remains particularly understudied. The purpose of this study was to investigate the brain regions crucial to phonotactic knowledge in left-hemisphere stroke survivors. Results were compared to areas in which gray matter anatomy related to phonotactic knowledge in healthy controls. 44 patients with chronic left-hemisphere stroke, and 32 controls performed an English-likeness rating task on 60 auditory non-words of varying phonotactic regularities. They were asked to rate on a 1-5 scale, how close each non-word sounded to English. Patients' performance was compared to that of healthy controls, using mixed effects modeling. Multivariate lesion-symptom mapping and voxel-based morphometry were used to find the brain regions important for phonotactic processing in patients and controls respectively. The results showed that compared to controls, stroke survivors were less sensitive to phonotactic regularity differences. Lesion-symptom mapping demonstrated that a loss of sensitivity to phonotactic regularities was associated with lesions in left angular gyrus and posterior middle temporal gyrus. Voxel-based morphometry also revealed a positive correlation between gray matter density in left angular gyrus and sensitivity to phonotactic regularities in controls. We suggest that the angular gyrus is used to compare the incoming speech stream to internal predictions based on the frequency of sound sequences in the language derived from stored lexical representations in the posterior middle temporal gyrus.

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“…Furthermore, such variability renders the linking of anatomy back to cognitive processes logically flawed and, hence, potentially erroneous. These issues affect virtually all existing neuroimaging investigations of PWAs, including (i) voxel-based, lesion-symptom mapping analyses of anatomical data (Bates et al, 2003; Dronkers et al, 2004; Geva et al, 2011; Mesulam et al, 2015; Mirman et al, 2015; Wilson, 2016); (ii) group-level analyses of functional data in a common space, for identifying stereotactic coordinates that show an effect of interest across the sample (e.g., studies contrasting the recruitment of the two hemispheres during spontaneous recovery, cited above); (iii) group- and individual-level functional characterizations of particular brain regions that are chosen based on an independent, but group-based, criterion such as an independent task (Sharp, Turkheimer, Bose, Scott, & Wise, 2010) or data from neurologically healthy individuals (Bonner & Grossman, 2012; Fridriksson, Bonilha, Baker, Moser, & Rorden, 2010); and (iv) comparisons of fMRI data across a series of single cases on the basis of anatomical alignment. The implications, for anyone who regards neuroimaging as a valid research method in cognitive neuropsychology, are alarming.…”

Section: Struggling With Irreconcilable Desiderata In Neuroimaging Stmentioning

confidence: 99%

Can neuroimaging help aphasia researchers? Addressing generalizability, variability, and interpretability

Blank

Kıran

Fedorenko

2017

Cognitive Neuropsychology

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Cognitive neuropsychologists have long sought a method that would allow them to characterize the impaired performance of individuals with brain damage, in order to draw valid inferences about information-processing architectures and inform treatment design. Some have advocated group studies where data are averaged over individuals to ensure generalizability, whereas others have argued that high variability in symptoms across individuals prohibits such grouping and instead warrants single case studies that exhaustively characterize individual deficits. In studies that complement such behavioral investigations with neuroimaging data—for linking brain structure and activity to cognitive impairments, spontaneous recovery, or treatment outcomes—the validity of inferences rests on a critical assumption: that a given anatomical landmark corresponds to the same functional unit(s) across individuals. This assumption, however, is fallacious: the mapping of function onto macroanatomy is not consistent even across neurologically healthy individuals, especially in association cortices. Here, we discuss the severe implications of this issue, and argue for an approach that circumvents it by harnessing the complementary strengths of group-based and individual-subjects approaches: group-level analysis of data from subject-specific regions of interest that are localized functionally using fMRI. This method eschews the requirement for functional-anatomical correspondence across brains, allowing for inter-individual variability in the precise location of functional regions. Once localized, the response profile of these regions can be characterized using an array of other tasks, and the results can then be averaged across individuals even if there is little overlap in the location of a given functional region across the sample. This method thus guarantees generalizable results while taking inter-individual variability into account. Furthermore, it eliminates the need for post-hoc “reverse inference” from anatomical landmarks back to cognitive operations, leading to improved interpretability of the data.

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Lesion-symptom mapping in the study of spoken language understanding

Cited by 38 publications

References 88 publications

A multivariate lesion symptom mapping toolbox and examination of lesion‐volume biases and correction methods in lesion‐symptom mapping

A multivariate lesion symptom mapping toolbox and examination of lesion‐volume biases and correction methods in lesion‐symptom mapping

Phonotactic processing deficit following left-hemisphere stroke

Can neuroimaging help aphasia researchers? Addressing generalizability, variability, and interpretability

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