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

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. •In acute stroke, a high proportion of semantic errors was related to better naming.•Higher proportions of other errors were related to lower acute naming skills.•Over time, naming and global language improved, but error patterns did not change.•The strongest recovery predictors were unrelated errors and temporoparietal integrity.