Predicting Language Recovery in Post-Stroke Aphasia using Behavior and Functional MRI

Abstract

Background: 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 prognostics for response to language therapy. Methods: Seventy patients with chronic aphasia were recruited and treated for one of three deficits: 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 an fMRI scan at baseline. Patient scans were decomposed into 20 components by group independent component analysis, and each component time series was summarized by its fractional amplitude of low-frequency fluctuations (fALFF). Results: Treatment effects were modelled with elastic net regression, using clinical language measures and fALFF imaging predictors independently. Correlation analyses showed high performance for language measures in anomia (r = 0.958, n = 30) and for fALFF predictors in agrammatism (r = 0.940, n = 11) and dysgraphia (r = 0.925, n = 18). These models are state-of-the-art for aphasia recovery prediction. Conclusion: Predicting aphasia recovery with rsfMRI features may outperform predictions from clinical language measures in some patient populations. This suggests rsfMRI may have prognostic value for chronic aphasia patients undergoing language therapy. Differentiating patients who respond to therapy from those who do not is a first step towards personalized treatment in post-stroke aphasia.

Multimodal Neural and Behavioral Data Predict Response to Rehabilitation in Chronic Poststroke Aphasia

“…Previous studies from our group on a subset of these data showed that brain function data, 15,36 brain structure information, 9,14 and language and cognitive performance 11,36 independently predict treatment-related outcomes. This study is the first, to our knowledge, to investigate the cumulative importance of patient-related information using a comprehensive data set including behavioral, demographic, and multimodal neuroimaging information to predict rehabilitation-induced language recovery in individuals with chronic poststroke aphasia.…”

“…Imaging protocols were harmonized across sites to ensure similar quality and timing and these protocols have been reported in previous papers. 35–37 Structural imaging included a T1-weighted sagittal sequence (voxel size=1×1×1 mm 3 ), and a high-resolution whole-brain cardiac-gated diffusion-weighted imaging sequence (voxel size=1.983×1.983×2.000 mm 3 , 72 interleaved slices with 60 gradient directions and 10 nondiffusion weighted ( b =0) volumes, b value=1500 s/mm 2 ). Whole-brain functional images were collected using a gradient-echo T2*-weighted sequence (voxel size=1.72×1.72×3 mm 3 ).…”

Edge-centric analysis of stroke patients: An alternative approach for biomarkers of lesion recovery

“…Recent literature has developed strategies to combine different data modalities (anatomical, functional, behavior, etc.) of stroke patients ( Iorga et al, 2021 , Kristinsson et al, 2021 , Pustina et al, 2017 ). While this study does not include anatomical information of the patients, future multimodal studies could adapt the presented methods to include the structural constraints of each individual subject.…”

Dissociation of white matter bundles in different recovery measures in post-stroke aphasia

“…Recent reviews agree that lesion-related factors, such as initial aphasia severity, lesion size, and affected structures (3)(4)(5) are more relevant predictors of outcomes than demographic factors (e.g., age, education). Along with improvement of neuroimaging techniques and statistical approaches (6)(7)(8)(9), modeling of PSA recovery has become an important goal in aphasia research. Two aspects of recovery have increasingly received attention: how to measure the recovery phenomenon and what brain regions and structures are associated with this recovery.…”