posted on 2020-08-05, 23:11authored byAmy E. Ramage, Semra Aytur, Kirrie J. Ballard
Purpose: Brain imaging has provided puzzle pieces in the understanding of language. In neurologically healthy populations, the structure of certain brain regions is associated with particular language functions (e.g., semantics, phonology). In studies on focal brain damage, certain brain regions or connections are considered sufficient or necessary for a given language function. However, few of these account for the effects of lesioned tissue on the “functional” dynamics of the brain for language processing. Here, functional connectivity (FC) among semantic–phonological regions of interest (ROIs) is assessed to fill a gap in our understanding about the neural substrates of impaired language and whether connectivity strength can predict language performance on a clinical tool in individuals with aphasia.
Method: Clinical assessment of language, using the Western Aphasia Battery–Revised, and resting-state functional magnetic resonance imaging data were obtained for 30 individuals with chronic aphasia secondary to left-hemisphere stroke and 18 age-matched healthy controls. FC between bilateral ROIs was contrasted by group and used to predict Western Aphasia Battery–Revised scores.
Results: Network coherence was observed in healthy controls and participants with stroke. The left–right premotor cortex connection was stronger in healthy controls, as reported by New et al. (2015) in the same data set. FC of (a) connections between temporal regions, in the left hemisphere and bilaterally, predicted lexical–semantic processing for auditory comprehension and (b) ipsilateral connections between temporal and frontal regions in both hemispheres predicted access to semantic–phonological representations and processing for verbal production.
Conclusions: Network connectivity of brain regions associated with semantic–phonological processing is predictive of language performance in poststroke aphasia. The most predictive connections involved right-hemisphere ROIs—particularly those for which structural adaptions are known to associate with recovered word retrieval performance. Predictions may be made, based on these findings, about which connections have potential as targets for neuroplastic functional changes with intervention in aphasia.
Supplemental Material S1. Region of interest (ROI) maps in the lateral (top left), coronal (bottom left), and axial (right) views.
Supplemental Material S2. Partial correlations controlled for lesion volume, months post onset of stroke, and sex.
Supplemental Material S3. Means and standard deviations (SD) for the connectivity strength of each ROI-ROI connection.
Supplemental Material S4. Generalized linear model results for the first proposed model, based on the use of univariate linear models to identify potential predictors. Significant predictors are in bold. Model fit statistics (Akaike’s Information Criterion [AIC] and Bayesian Information Criterion [BIC]) are provided for each model. Indicators of whether the functional connection was significant in both groups or the PWA group only, are provided in the column left of each potential predictor connection. The normal distribution produced the best fit model for all analyses.
Supplemental Material S5. Generalized linear model results for the best fit models. Significant predictors are in bold. Model fit statistics (Akaike’s Information Criterion [AIC] and Bayesian Information Criterion [BIC]) are provided for each model. The normal distribution produced the best fit model for all analyses.
Ramage, A. E., Aytur, S., & Ballard, K. J. (2020). Resting-state functional magnetic resonance imaging connectivity between semantic and phonological regions of interest may inform language targets in aphasia. Journal of Speech, Language, and Hearing Research. Advance online publication. https://doi.org/10.1044/2020_JSLHR-19-00117
This work was supported by National Health and Medical Research Council Project Grant 632763 and Australian Research Council Future Fellowship FT120100355 (Principal Investigator: Kirrie J. Ballard).