Predicting language recovery in post-stroke aphasia using behavior and functional MRI

Michael Iorga, James Higgins, David Caplan, Richard Zinbarg, Swathi Kiran, Cynthia K Thompson, Brenda Rapp, Todd B Parrish, Michael Iorga, James Higgins, David Caplan, Richard Zinbarg, Swathi Kiran, Cynthia K Thompson, Brenda Rapp, Todd B Parrish

Abstract

Language outcomes after speech and language therapy in post-stroke aphasia are challenging to predict. This study examines behavioral language measures and resting state fMRI (rsfMRI) as predictors of treatment outcome. Fifty-seven patients with chronic aphasia were recruited and treated for one of three aphasia impairments: anomia, agrammatism, or dysgraphia. Treatment effect was measured by performance on a treatment-specific language measure, assessed before and after three months of language therapy. Each patient also underwent an additional 27 language assessments and a rsfMRI scan at baseline. Patient scans were decomposed into 20 components by group independent component analysis, and the fractional amplitude of low-frequency fluctuations (fALFF) was calculated for each component time series. Post-treatment performance was modelled with elastic net regression, using pre-treatment performance and either behavioral language measures or fALFF imaging predictors. Analysis showed strong performance for behavioral measures in anomia (R2 = 0.948, n = 28) and for fALFF predictors in agrammatism (R2 = 0.876, n = 11) and dysgraphia (R2 = 0.822, n = 18). Models of language outcomes after treatment trained using rsfMRI features may outperform models trained using behavioral language measures in some patient populations. This suggests that rsfMRI may have prognostic value for aphasia therapy outcomes.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Multicollinearity across Behavioral Measures. (A) A shaded color-plot of the correlation matrix across all 27 behavioral measures is shown. Due to imbalance in sample sizes, correlations were first calculated within each impairment group, and then averaged. Box colors correspond to pairwise Kendall’s Tau-b values (red is positive, blue is negative correlation). Only pairwise complete observations were used (no imputation). (B) An association dendrogram of behavioral measures is shown. Correlation distance is one minus the absolute pairwise Kendall’s Tau correlation. The dendrogram was created by analyzing correlation distances using hierarchical clustering (Unweighted Pair Group Method with Arithmetic Mean).
Figure 2
Figure 2
Predicting scores on the post-treatment TSM with behavioral measures. Linear models which predict the TSM after therapy were constructed for each aphasia impairment: anomia, agrammatism, and dysgraphia. The dashed line represents a perfect prediction (predicted score = actual score). Black circles show the median predicted score for each patient across all 1000 imputations during LOOCV.
Figure 3
Figure 3
Predicting scores on the post-treatment TSM with GICA fALFF and pre-treatment TSM. Linear models which predict the TSM after therapy were constructed for each aphasia impairment (anomia, agrammatism, and dysgraphia) using a combination of the pre-treatment TSM and the fALFF for each GICA component. The dashed line represents a perfect prediction (predicted score = actual score). Black circles show the predicted score for each patient using LOOCV. For the agrammatism model, the circles represent median values across 1000 imputations.

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