Background Speech entrainment (SE), the online mimicking of an audio-visual speech model, has been shown to increase speech fluency in individuals with non-fluent aphasia. One theory that may explain why SE improves speech output is that it synchronizes functional connectivity between anterior and posterior language regions to be more similar to that of neurotypical speakers. Objectives The present study tested this by measuring functional connectivity between 2 regions shown to be necessary for speech production, and their right hemisphere homologues, in 24 persons with aphasia compared to 20 controls during both free (spontaneous) speech and SE. Methods Regional functional connectivity in participants with aphasia were normalized to the control data. Two analyses were then carried out: (1) normalized functional connectivity was compared between persons with aphasia and controls during free speech and SE and (2) stepwise linear models with leave-one-out cross-validation including normed functional connectivity during both tasks and proportion damage to the left hemisphere as independent variables were created for each language score. Results Left anterior-posterior functional connectivity and left posterior to right anterior functional connectivity were significantly more similar to connectivity of the control group during SE compared to free speech. Additionally, connectivity during free speech was more associated with language measures than connectivity during SE. Conclusions Overall, these results suggest that SE promotes normalization of functional connectivity (i.e., return to patterns observed in neurotypical controls), which may explain why individuals with non-fluent aphasia produce more fluent speech during SE compared to spontaneous speech.

The association between age and language recovery in stroke remains unclear. Here, we used neuroimaging data to estimate brain age, a measure of structural integrity, and examined the extent to which brain age at stroke onset is associated with (i) cross-sectional language performance, and (ii) longitudinal recovery of language function, beyond chronological age alone. A total of 49 participants (age: 65.2 ± 12.2 years, 25 female) underwent routine clinical neuroimaging (T1) and a bedside evaluation of language performance (Bedside Evaluation Screening Test-2) at onset of left hemisphere stroke. Brain age was estimated from enantiomorphically reconstructed brain scans using a machine learning algorithm trained on a large sample of healthy adults. A subsample of 30 participants returned for follow-up language assessments at least 2 years after stroke onset. To account for variability in age at stroke, we calculated proportional brain age difference, i.e. the proportional difference between brain age and chronological age. Multiple regression models were constructed to test the effects of proportional brain age difference on language outcomes. Lesion volume and chronological age were included as covariates in all models. Accelerated brain age compared with age was associated with worse overall aphasia severity (F(1, 48) = 5.65, P = 0.022), naming (F(1, 48) = 5.13, P = 0.028), and speech repetition (F(1, 48) = 8.49, P = 0.006) at stroke onset. Follow-up assessments were carried out ≥2 years after onset; decelerated brain age relative to age was significantly associated with reduced overall aphasia severity (F(1, 26) = 5.45, P = 0.028) and marginally failed to reach statistical significance for auditory comprehension (F(1, 26) = 2.87, P = 0.103). Proportional brain age difference was not found to be associated with changes in naming (F(1, 26) = 0.23, P = 0.880) and speech repetition (F(1, 26) = 0.00, P = 0.978). Chronological age was only associated with naming performance at stroke onset (F(1, 48) = 4.18, P = 0.047). These results indicate that brain age as estimated based on routine clinical brain scans may be a strong biomarker for language function and recovery after stroke.

Left-hemisphere brain damage commonly affects patients' abilities to produce and comprehend syntactic structures, a condition typically referred to as “agrammatism.” The neural correlates of agrammatism remain disputed in the literature, and distributed areas have been implicated as important predictors of performance, for example, Broca's area, anterior temporal areas, and temporo-parietal areas. We examined the association between damage to specific language-related ROIs and impaired syntactic processing in acute aphasia. We hypothesized that damage to the posterior middle temporal gyrus, and not Broca's area, would predict syntactic processing abilities. One hundred four individuals with acute aphasia (<20 days poststroke) were included in the study. Structural MRI scans were obtained, and all participants completed a 45-item sentence–picture matching task. We performed an ROI-based stepwise regression analyses to examine the relation between cortical brain damage and impaired comprehension of canonical and noncanonical sentences. Damage to the posterior middle temporal gyrus was the strongest predictor for overall task performance and performance on noncanonical sentences. Damage to the angular gyrus was the strongest predictor for performance on canonical sentences, and damage to the posterior superior temporal gyrus predicted noncanonical scores when performance on canonical sentences was included as a cofactor. Overall, our models showed that damage to temporo-parietal and posterior temporal areas was associated with impaired syntactic comprehension. Our results indicate that the temporo-parietal area is crucially implicated in complex syntactic processing, whereas the role of Broca's area may be complementary.

Background: Perfusion Computerized Axial Tomography (pCT) is a modality gaining popularity for acute stroke management decisions. We investigated the role of automated pCT in prediction of deficits measured by NIH Stroke Scale (NIHSS). Method: Acute stroke patients underwent pCT and NIHSS. Digital Imaging and Communications in Medicine ( DICOM ) images were assessed for maximum intensity (MI) and cerebral blood flow (CBF). MI scans (where there is little acute abnormality) were used to transform each scan into standard space. The CBF images were the continuous measure for abnormality. The regional CBF was estimated for 150 regions of interest. Statistical analyses used 4000 permutations to correct for multiple comparisons. Additionally support vector machines (SVM) were used to assess the ability of machine learning to classify the deficit severity. Results: PCT scans for 83 patients where analyzed (mean age ± SD=64.2 ±14.8, 63% males). Traditional analyses revealed (p < 0.01) that reduced blood flow in the large regions of the right hemisphere were associated with left motor impairment, while regional reductions in the left hemisphere were associated with motor impairment on the right side as well as language impairments. We were accurately able to predict diagnosis based purely on perfusion. Reliable detection (all p < 0.001) of aphasia (74.7% accuracy or ACC, 86.1% specificity or SP, 56.8% sensitivity or SN), left upper motor (69.3% ACC, 87.9% SP, 42% SN), right upper motor (74.6% ACC, 84.4% SP, 57.3% SN), left lower (68.9% ACC, 87.5% SP, 43.7% SN) and right lower (71.2% ACC, 87.5% SP, 51.1% SN) impairments were accomplished. Conclusion: We describe a reliable automated pCT method to predict neurological deficit in acute stroke patients. The methodology may be useful in patients in who neurological assessment is not possible (e.g. unconscious or intubated patients) or assessment may be at risk of observer bias (e.g. in clinical trials).

Extensive research has related specific lesion locations to language impairment in aphasia. However, far less work has focused on the patterns of brain damage that predict prognosis in aphasia. The current study examined brain damage as a predictor of language recovery in acute patients with aphasia caused by stroke. Damage to the left posterior middle temporal gyrus (MTG) and left pars triangularis predicted poor recovery of speech production and MTG damage predicted less recovery of speech comprehension. These findings suggest that brain changes associated with language recovery rely on preservation and recruitment of the aforementioned areas in the left hemisphere.