Brain-Derived Neurotrophic Factor Polymorphism and Aphasia after Stroke

Stroke is one of the most deliberating causes of mortality and disability worldwide. Studies have implicated Val66Met polymorphism of the brain-derived neurotrophic factor (BDNF) gene as a genetic factor influencing stroke recovery. Still, the role of BDNF polymorphism in poststroke aphasia is relatively unclear. This review assesses the recent evidence on the association between the BDNF polymorphism and aphasia recovery in poststroke patients. The article highlights BNDF polymorphism characteristics, speech and language interventions delivered, and the influence of BNDF polymorphism on poststroke aphasia recovery. We conducted a literature search through PubMed and Google Scholar with the following terms: “brain derived-neurotrophic factor” and “aphasia” for original articles from January 2000 until June 2020. Out of 69 search results, a detailed selection process produced a total of 3 articles that met the eligibility criteria. All three studies included Val66Met polymorphism as the studied human BDNF gene. One of the studies demonstrated insufficient evidence to conclude that BDNF polymorphism plays a role in poststroke aphasia recovery. The remaining two studies have shown that Met allele genotype (either single or double nucleotides) was associated with poor aphasia recovery, in either acute or chronic stroke. Carriers of the Val66Met polymorphism of BDNF gave a poorer response to aphasia intervention and presented with more severe aphasia.

A comprehensive, interdisciplinary view of inner speech may be a unique lens through which we can better understand living with aphasia and aphasia recovery

Purpose: While behavioral aphasia therapy is beneficial (Brady et al., 2016), we do not fully understand factors that predict therapy response, or that contribute to extra-linguistic aspects of living with aphasia (e.g., psychosocial). The purpose of this Viewpoint is to postulate that inner speech – the ability to talk to oneself in one’s head – may be an important factor. However, prior work evaluating inner speech in aphasia has been limited in scope. Here, we innovatively draw from interdisciplinary evidence to discuss a more comprehensive view of inner speech and propose how evaluating a multidimensional inner speech may be meaningful in understanding living with aphasia and aphasia recovery. Methods: We give an interdisciplinary overview of inner speech, as it relates to aphasia. Results: Research with persons with aphasia shows that inner speech can be relatively spared in comparison to overt speech. However, this research has taken a narrow view of inner speech, defining inner speech as a covert ‘voice’ drawn upon during experimental tasks, such as object naming, rhyme decisions, or tongue twisters. Cross disciplinary research evaluating inner speech has identified its multidimensionality (specifically, dimensions of intentionality, condensation, and dialogality). Inner speech evaluated across these dimensions in neurotypical populations has shown that inner speech can be related to personal factors like self-awareness; retain phonetic features but also be like ‘thinking in pure pictures; and be both monologic and dialogic. Conclusions: Quantifying a multidimensional inner speech in aphasia will enable future work elaborating on factors related to extra-linguistic and linguistic processes of recovery, as well as living well with aphasia.

The Protective Influence of Bilingualism on the Recovery of Phonological Input Processing in Aphasia After Stroke

Language-related potentials are increasingly used to objectify (mal)adaptive neuroplasticity in stroke-related aphasia recovery. Using preattentive [mismatch negativity (MMN)] and attentive (P300) phonologically related paradigms, neuroplasticity in sensory memory and cognitive functioning underlying phonological processing can be investigated. In aphasic patients, MMN amplitudes are generally reduced for speech sounds with a topographic source distribution in the right hemisphere. For P300 amplitudes and latencies, both normal and abnormal results have been reported. The current study investigates the preattentive and attentive phonological discrimination ability in 17 aphasic patients (6 monolinguals and 11 bilinguals, aged 41–71 years) at two timepoints during aphasia recovery. Between the two timepoints, a significant improvement of behavioral language performance in both languages is observed in all patients with the MMN latency at timepoint 1 as a predictive factor for aphasia recovery. In contrast to monolinguals, bilingual aphasic patients have a higher probability to improve their processing speed during rehabilitation, resulting in a shortening of the MMN latency over time, which sometimes progresses toward the normative values.

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

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.