Brain age predicts long-term recovery in post-stroke aphasia

The effect of head motion on brain age prediction using deep convolutional neural networks

Deep learning can be used effectively to predict participants' age from brain magnetic resonance imaging (MRI) data, and a growing body of evidence suggests that the difference between predicted and chronological age-referred to as brain-predicted age difference (brain-PAD)-is related to various neurological and neuropsychiatric disease states. A crucial aspect of the applicability of brain-PAD as a biomarker of individual brain health is whether and how brain-predicted age is affected by MR image artifacts commonly encountered in clinical settings. To investigate this issue, we trained and validated two different 3D convolutional neural network architectures (CNNs) from scratch and tested the models on a separate dataset consisting of motion-free and motion-corrupted T1-weighted MRI scans from the same participants, the quality of which were rated by neuroradiologists from a clinical diagnostic point of view. Our results revealed a systematic increase in brain-PAD with worsening image quality for both models. This effect was also observed for images that were deemed usable from a clinical perspective, with brains appearing older in medium than in good quality images. These findings were also supported by significant associations found between the brain-PAD and standard image quality metrics indicating larger brain-PAD for lower-quality images. Our results demonstrate a spurious effect of advanced brain aging as a result of head motion and underline the importance of controlling for image quality when using brain-predicted age based on structural neuroimaging data as a proxy measure for brain health.

A Review of Poststroke Aphasia Recovery and Treatment Options

Poststroke aphasia, which impacts expressive and receptive communication, can have detrimental effects on the psychosocial well-being and the quality of life of those affected. Aphasia recovery is multidimensional and can be influenced by several baseline, stroke-related, and treatment-related factors, including preexisting cerebrovascular conditions, stroke size and location, and amount of therapy received. Importantly, aphasia recovery can continue for many years after aphasia onset. Behavioral speech and language therapy with a speech-language pathologist is the most common form of aphasia therapy. In this review, the authors also discuss augmentative treatment methodologies, collaborative goal setting frameworks, and recommendations for future research.

Discourse- and lesion-based aphasia quotient estimation using machine learning

Discourse is a fundamentally important aspect of communication, and discourse production provides a wealth of information about linguistic ability. Aphasia commonly affects, in multiple ways, the ability to produce discourse. Comprehensive aphasia assessments such as the Western Aphasia Battery-Revised (WAB-R) are time- and resource-intensive. We examined whether discourse measures can be used to estimate WAB-R Aphasia Quotient (AQ), and whether this can serve as an ecologically valid, less resource-intensive measure. We used features extracted from discourse tasks using three AphasiaBank prompts involving expositional (picture description), story narrative, and procedural discourse. These features were used to train a machine learning model to predict the WAB-R AQ. We also compared and supplemented the model with lesion location information from structural neuroimaging. We found that discourse-based models could estimate AQ well, and that they outperformed models based on lesion features. Addition of lesion features to the discourse features did not improve the performance of the discourse model substantially. Inspection of the most informative discourse features revealed that different prompt types taxed different aspects of language. These findings suggest that discourse can be used to estimate aphasia severity, and provide insight into the linguistic content elicited by different types of discourse prompts.

The volume and the distribution of premorbid white matter hyperintensities: Impact on post-stroke aphasia

White matter hyperintensities (WMH) are a radiological manifestation of progressive white matter integrity loss. The total volume and distribution of WMH within the corpus callosum have been associated with pathological cognitive ageing processes but have not been considered in relation to post-stroke aphasia outcomes. We investigated the contribution of both the total volume of WMH, and the extent of WMH lesion load in the corpus callosum to the recovery of language after first-ever stroke. Behavioural and neuroimaging data from individuals (N = 37) with a left-hemisphere stroke were included at the early subacute stage of recovery. Spoken language comprehension and production abilities were assessed using word and sentence-level tasks. Neuroimaging data was used to derive stroke lesion variables (volume and lesion load to language critical regions) and WMH variables (WMH volume and lesion load to three callosal segments). WMH volume did not predict variance in language measures, when considered together with stroke lesion and demographic variables. However, WMH lesion load in the forceps minor segment of the corpus callosum explained variance in early subacute comprehension abilities (t = -2.59, p = .01) together with corrected stroke lesion volume and socio-demographic variables. Premorbid WMH lesions in the forceps minor were negatively associated with early subacute language comprehension after aphasic stroke. This negative impact of callosal WMH on language is consistent with converging evidence from pathological ageing suggesting that callosal WMH disrupt the neural networks supporting a range of cognitive functions.

Insights gained over 60 years on factors shaping post-stroke aphasia recovery: A commentary on Vignolo (1964)

Aphasia is an acquired language disorder resulting from brain injury, including strokes which is the most common etiology, neurodegenerative diseases, tumors, traumatic brain injury, and resective surgery. Aphasia affects a significant portion of stroke survivors, with approximately one third experiencing its debilitating effects in the long term. Despite its challenges, there is growing evidence that recovery from aphasia is possible, even in the chronic phase of stroke. Sixty years ago, Vignolo (1964) outlined the primary challenges confronted by researchers in this field. These challenges encompassed the absence of an objective evaluation of language difficulties, the scarcity of evidence regarding spontaneous aphasia recovery, and the presence of numerous variables that could potentially influence the process of aphasia recovery. In this paper, we discuss the remarkable progress that has been made in the assessment of language and communication in aphasia as well as in understanding the factors influencing post-stroke aphasia recovery.

Intersectional sociodemographic and neurological relationships in the naming ability of persons with post-stroke aphasia

INTRODUCTION

Significant attention has been given to the role of brain function and disruption in determining performance on naming tasks among individuals with aphasia. However, scholarly pursuit of a neurological explanation has overlooked the fundamental cornerstone of individual health-the underlying social, economic, and environmental factors that shape how they live, work, and age, also known as the social determinants of health (SDOH). This study examines the correlation between naming performance and these underlying factors.

METHODS

Individual level data from the 2010 Moss Aphasia Psycholinguistic Project Database (MAPPD) was matched with the 2009-2011 Medical Expenditure Panel Survey (MEPS) using a propensity score algorithm based on functional, health, and demographic characteristics. Multilevel, generalized, nonlinear regression models were applied to the resulting data set to assess the correlation between the Boston Naming Test (BNT) percentile score and age, income, sex, race, household size, marital status, aphasia type, and region of residence. Poisson regression models with bootstrapped standard errors were used to estimate these relationships RESULTS: Discrete dependent variable estimation with non-normal prior specification included individual level (age, marital status, years of education), socioeconomic (family income), health (aphasia type), household (family size), and environmental (region of residence) characteristics. Regression results indicated that, relative to individuals with Wernicke's, individuals with Anomic (0.74, SE = 0.0008) and Conduction (0.42, SE = 0.0009) aphasia performed better on the BNT. While age at the time of testing was not significantly correlated, higher income level (0.15, SE = 0.0003) and larger family size (0.002, SE = 0.002) was associated with higher BNT score percentiles. Finally, Black persons with aphasia (PWA) (-0.0124, SE = 0.0007) had lower average score percentiles when other factors were held constant.

CONCLUSIONS

The findings reported here suggest higher income and larger family size are associated with better outcomes. As expected, aphasia type was significantly associated with naming outcomes. However, poorer performance by Black PWA and individuals with low income suggests that SDOH can play a critical role (positive and negative) in naming impairment in some populations with aphasia.

Dynamic network properties of the superior temporal gyrus mediate the impact of brain age gap on chronic aphasia severity

Brain structure deteriorates with aging and predisposes an individual to more severe language impairments (aphasia) after a stroke. However, the underlying mechanisms of this relation are not well understood. Here we use an approach to model brain network properties outside the stroke lesion, network controllability, to investigate relations among individualized structural brain connections, brain age, and aphasia severity in 93 participants with chronic post-stroke aphasia. Controlling for the stroke lesion size, we observe that lower average controllability of the posterior superior temporal gyrus (STG) mediates the relation between advanced brain aging and aphasia severity. Lower controllability of the left posterior STG signifies that activity in the left posterior STG is less likely to yield a response in other brain regions due to the topological properties of the structural brain networks. These results indicate that advanced brain aging among individuals with post-stroke aphasia is associated with disruption of dynamic properties of a critical language-related area, the STG, which contributes to worse aphasic symptoms. Because brain aging is variable among individuals with aphasia, our results provide further insight into the mechanisms underlying the variance in clinical trajectories in post-stroke aphasia.

Distinct brain morphometry patterns revealed by deep learning improve prediction of aphasia severity

Emerging evidence suggests that post-stroke aphasia severity depends on the integrity of the brain beyond the stroke lesion. While measures of lesion anatomy and brain integrity combine synergistically to explain aphasic symptoms, significant interindividual variability remains unaccounted for. A possible explanatory factor may be the spatial distribution of brain atrophy beyond the lesion. This includes not just the specific brain areas showing atrophy, but also distinct three-dimensional patterns of atrophy. Here, we tested whether deep learning with Convolutional Neural Networks (CNN) on whole brain morphometry (i.e., segmented tissue volumes) and lesion anatomy can better predict which individuals with chronic stroke (N=231) have severe aphasia, and whether encoding spatial dependencies in the data might be capable of improving predictions by identifying unique individualized spatial patterns. We observed that CNN achieves significantly higher accuracy and F1 scores than Support Vector Machine (SVM), even when the SVM is nonlinear or integrates linear and nonlinear dimensionality reduction techniques. Performance parity was only achieved when the SVM was directly trained on the latent features learned by the CNN. Saliency maps demonstrated that the CNN leveraged widely distributed patterns of brain atrophy predictive of aphasia severity, whereas the SVM focused almost exclusively on the area around the lesion. Ensemble clustering of CNN saliency maps revealed distinct morphometry patterns that were unrelated to lesion size, highly consistent across individuals, and implicated unique brain networks associated with different cognitive processes as measured by the wider neuroimaging literature. Individualized predictions of severity depended on both ipsilateral and contralateral features outside of the location of stroke. Our findings illustrate that three-dimensional network distributions of atrophy in individuals with aphasia are directly associated with aphasia severity, underscoring the potential for deep learning to improve prognostication of behavioral outcomes from neuroimaging data, and highlighting the prospective benefits of interrogating spatial dependence at different scales in multivariate feature space.

Diabetes, brain health, and treatment gains in post-stroke aphasia

In post-stroke aphasia, language improvements following speech therapy are variable and can only be partially explained by the lesion. Brain tissue integrity beyond the lesion (brain health) may influence language recovery and can be impacted by cardiovascular risk factors, notably diabetes. We examined the impact of diabetes on structural network integrity and language recovery. Seventy-eight participants with chronic post-stroke aphasia underwent six weeks of semantic and phonological language therapy. To quantify structural network integrity, we evaluated the ratio of long-to-short-range white matter fibers within each participant's whole brain connectome, as long-range fibers are more susceptible to vascular injury and have been linked to high level cognitive processing. We found that diabetes moderated the relationship between structural network integrity and naming improvement at 1 month post treatment. For participants without diabetes (n = 59), there was a positive relationship between structural network integrity and naming improvement (t = 2.19, p = 0.032). Among individuals with diabetes (n = 19), there were fewer treatment gains and virtually no association between structural network integrity and naming improvement. Our results indicate that structural network integrity is associated with treatment gains in aphasia for those without diabetes. These results highlight the importance of post-stroke structural white matter architectural integrity in aphasia recovery.

Pharmacotherapy for post-stroke aphasia: what are the options?

INTRODUCTION

Aphasia is a common, long-lasting aftermath of stroke lesions. There is an increased integration of pharmacotherapy as an adjunctive strategy to speech and language therapy (SLT) for post-stroke aphasia (PSA). Nevertheless, more research in pharmacotherapy for acute and chronic PSA is necessary, including the election of drugs that target different neurotransmitter systems and deficits in specific language domains.

AREAS COVERED

This article updates the role of pharmacotherapy for PSA, focusing the spotlight on some already investigated drugs and candidate agents deserving of future research. Refining the precision of drug election would require using multimodal biomarkers to develop personalized treatment approaches. There is a solid need to devise feasible randomized controlled trials adapted to the particularities of the PSA population. The emergent role of multimodal interventions combining one or two drugs with noninvasive brain stimulation to augment SLT is emphasized.

EXPERT OPINION

Pharmacotherapy can improve language deficits not fully alleviated by SLT. In addition, the 'drug-only' approach can also be adopted when administering SLT is not possible. The primary goal of pharmacotherapy is reducing the overall aphasia severity, although targeting language-specific deficits (i.e. naming, spoken output) also contributes to improving functional communication. Unfortunately, there is still little information for recommending a drug for specific language deficits.