Dataset Information

Naming errors and dysfunctional tissue metrics predict language recovery after acute left hemisphere stroke.

ABSTRACT: Language recovery following acute left hemisphere (LH) stroke is notoriously difficult to predict. Global language measures (e.g., overall aphasia severity) and gross lesion metrics (e.g., size) provide incomplete recovery predictions. In this study, we test the hypothesis that the types of naming errors patients produce, combined with dysfunctional brain tissue metrics, can provide additional insight into recovery following acute LH stroke. One hundred forty-eight individuals who were hospitalized with a new LH stroke completed clinical neuroimaging and assessments of naming and global language skills. A subset of participants again completed language testing at subacute, early (5-7 months post-stroke), and late (?11 months post-stroke) chronic phases. At each time point, we coded naming errors into four types (semantic, phonological, mixed and unrelated) and determined error type totals and proportions. Dysfunctional tissue measures included the percentage of damage to language network regions and hypoperfusion in vascular territories. A higher proportion of semantic errors was associated with better acute naming, but higher proportions of other error types was related to poorer accuracy. Naming and global language skills significantly improved over time , but naming error profiles did not change. Fewer acute unrelated errors and less damage to left angular gyrus resulted in optimal naming and language recovery by the final testing time point, yet patients with more acute errors and damage to left middle temporal gyrus demonstrated the greatest increases in language over time. These results illustrate that naming error profiles, particularly unrelated errors, add power to predictions of language recovery after stroke.

SUBMITTER: Meier EL

PROVIDER: S-EPMC7546715 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Naming errors and dysfunctional tissue metrics predict language recovery after acute left hemisphere stroke.

Meier Erin L EL Sheppard Shannon M SM Goldberg Emily B EB Head Catherine R CR Ubellacker Delaney M DM Walker Alexandra A Hillis Argye E AE

Neuropsychologia 20201009

Language recovery following acute left hemisphere (LH) stroke is notoriously difficult to predict. Global language measures (e.g., overall aphasia severity) and gross lesion metrics (e.g., size) provide incomplete recovery predictions. In this study, we test the hypothesis that the types of naming errors patients produce, combined with dysfunctional brain tissue metrics, can provide additional insight into recovery following acute LH stroke. One hundred forty-eight individuals who were hospitali ...[more]

PMID: 33045231

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Project description:The neural mechanisms underlying recovery of language after left hemisphere stroke remain elusive. Although older evidence suggested that right hemisphere language homologues compensate for damage in left hemisphere language areas, the current prevailing theory suggests that right hemisphere engagement is ineffective or even maladaptive. Using a novel combination of support vector regression-based lesion-symptom mapping and voxel-based morphometry, we aimed to determine whether local grey matter volume in the right hemisphere independently contributes to aphasia outcomes after chronic left hemisphere stroke. Thirty-two left hemisphere stroke survivors with aphasia underwent language assessment with the Western Aphasia Battery-Revised and tests of other cognitive domains. High-resolution T1-weighted images were obtained in aphasia patients and 30 demographically matched healthy controls. Support vector regression-based multivariate lesion-symptom mapping was used to identify critical language areas in the left hemisphere and then to quantify each stroke survivor's lesion burden in these areas. After controlling for these direct effects of the stroke on language, voxel-based morphometry was then used to determine whether local grey matter volumes in the right hemisphere explained additional variance in language outcomes. In brain areas in which grey matter volumes related to language outcomes, we then compared grey matter volumes in patients and healthy controls to assess post-stroke plasticity. Lesion-symptom mapping showed that specific left hemisphere regions related to different language abilities. After controlling for lesion burden in these areas, lesion size, and demographic factors, grey matter volumes in parts of the right temporoparietal cortex positively related to spontaneous speech, naming, and repetition scores. Examining whether domain general cognitive functions might explain these relationships, partial correlations demonstrated that grey matter volumes in these clusters related to verbal working memory capacity, but not other cognitive functions. Further, grey matter volumes in these areas were greater in stroke survivors than healthy control subjects. To confirm this result, 10 chronic left hemisphere stroke survivors with no history of aphasia were identified. Grey matter volumes in right temporoparietal clusters were greater in stroke survivors with aphasia compared to those without history of aphasia. These findings suggest that the grey matter structure of right hemisphere posterior dorsal stream language homologues independently contributes to language production abilities in chronic left hemisphere stroke, and that these areas may undergo hypertrophy after a stroke causing aphasia.

Project description:In order to gain a better understanding of aphasia one must consider the complex combinations of language impairments along with the pattern of paraphasias. Despite the fact that both deficits and paraphasias feature in diagnostic criteria, most research has focused only on the lesion correlates of language deficits, with minimal attention on the pattern of patients' paraphasias. In this study, we used a data-driven approach (principal component analysis - PCA) to fuse patient impairments and their pattern of errors into one unified model of chronic post-stroke aphasia. This model was subsequently mapped onto the patients' lesion profiles to generate the triangulation of language-cognitive impairments, naming errors and their neural correlates. Specifically, we established the pattern of co-occurrence between fifteen error types, which avoids focussing on a subset of errors or the use of experimenter-derived methods to combine across error types. We obtained five principal components underlying the patients' errors: omission errors; semantically-related responses; phonologically-related responses; dysfluent responses; and a combination of circumlocutions with mixed errors. In the second step, we aligned these paraphasia-related principal components with the patients' performance on a detailed language and cognitive assessment battery, utilising an additional PCA. This omnibus PCA revealed seven unique fused impairment-paraphasia factors: output phonology; semantics; phonological working memory; speech quanta; executive-cognitive skill; phonological (input) discrimination; and the production of circumlocution errors. In doing so we were able to resolve the complex relationships between error types and impairments. Some are relatively straightforward: circumlocution errors formed their own independent factor; there was a one-to-one mapping for phonological errors with expressive phonological abilities and for dysfluent errors with speech fluency. In contrast, omission-type errors loaded across both semantic and phonological working memory factors, whilst semantically-related errors had the most complex relationship by loading across four factors (phonological ability, speech quanta, executive-cognitive skills and circumlocution-type errors). Three components had unique lesion correlates: phonological working memory with the primary auditory region; semantics with the anterior temporal region; and fluency with the pre-central gyrus, converging with existing literature. In conclusion, the data-driven approach allowed derivation of the triangulation of deficits, error types and lesion correlates in post-stroke aphasia.

Dataset Information

Naming errors and dysfunctional tissue metrics predict language recovery after acute left hemisphere stroke.

Publications

Naming errors and dysfunctional tissue metrics predict language recovery after acute left hemisphere stroke.

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