Purpose There is a rapid growth of telepractice in both clinical and research settings; however, the literature validating translation of traditional methods of assessments and interventions to valid remote videoconference administrations is limited. This is especially true in the field of speech-language pathology where assessments of language and communication can be easily conducted via remote administration. The aim of this study was to validate videoconference administration of the Western Aphasia Battery–Revised (WAB-R). Method Twenty adults with chronic aphasia completed the assessment both in person and via videoconference with the order counterbalanced across administrations. Specific modifications to select WAB-R subtests were made to accommodate interaction by computer and Internet. Results Results revealed that the two methods of administration were highly correlated and showed no difference in domain scores. Additionally, most participants endorsed being mostly or very satisfied with the videoconference administration. Conclusion These findings suggest that administration of the WAB-R in person and via videoconference may be used interchangeably in this patient population. Modifications and guidelines are provided to ensure reproducibility and access to other clinicians and scientists interested in remote administration of the WAB-R. Supplemental Material https://doi.org/10.23641/asha.11977857

No abstract

No abstract

Background: Poststroke recovery depends on multiple factors and varies greatly across individuals. Using machine learning models, this study investigated the independent and complementary prognostic role of different patient-related factors in predicting response to language rehabilitation after a stroke. Methods: Fifty-five individuals with chronic poststroke aphasia underwent a battery of standardized assessments and structural and functional magnetic resonance imaging scans, and received 12 weeks of language treatment. Support vector machine and random forest models were constructed to predict responsiveness to treatment using pretreatment behavioral, demographic, and structural and functional neuroimaging data. Results: The best prediction performance was achieved by a support vector machine model trained on aphasia severity, demographics, measures of anatomic integrity and resting-state functional connectivity (F1=0.94). This model resulted in a significantly superior prediction performance compared with support vector machine models trained on all feature sets (F1=0.82, P <0.001) or a single feature set (F1 range=0.68–0.84, P <0.001). Across random forest models, training on resting-state functional magnetic resonance imaging connectivity data yielded the best F1 score (F1=0.87). Conclusions: While behavioral, multimodal neuroimaging data and demographic information carry complementary information in predicting response to rehabilitation in chronic poststroke aphasia, functional connectivity of the brain at rest after stroke is a particularly important predictor of responsiveness to treatment, both alone and combined with other patient-related factors.

No abstract