Data-Driven, Visual Framework for the Characterization of Aphasias Across Stroke, Post-resective, and Neurodegenerative Disorders Over Time

Abstract

Aphasia classifications and specialized language batteries differ across the fields of neurodegenerative disorders and lesional brain injuries, resulting in difficult comparisons of language deficits across etiologies. In this study, we present a simplified framework, in which a widely-used aphasia battery captures clinical clusters across disease etiologies and provides a quantitative and visual method to characterize and track patients over time. The framework is used to evaluate populations representing three disease etiologies: stroke, primary progressive aphasia (PPA), and post-operative aphasia. A total of 330 patients across three populations with cerebral injury leading to aphasia were investigated, including 76 patients with stroke, 107 patients meeting criteria for PPA, and 147 patients following left hemispheric resective surgery. Western Aphasia Battery (WAB) measures (Information Content, Fluency, answering Yes/No questions, Auditory Word Recognition, Sequential Commands, and Repetition) were collected across the three populations and analyzed to develop a multi-dimensional aphasia model using dimensionality reduction techniques. Two orthogonal dimensions were found to explain 87% of the variance across aphasia phenotypes and three disease etiologies. The first dimension reflects shared weighting across aphasia subscores and correlated with aphasia severity. The second dimension incorporates fluency and comprehension, thereby separating Wernicke's from Broca's aphasia, and the non-fluent/agrammatic from semantic PPA variants. Clusters representing clinical classifications, including late PPA presentations, were preserved within the two-dimensional space. Early PPA presentations were not classifiable, as specialized batteries are needed for phenotyping. Longitudinal data was further used to visualize the trajectory of aphasias during recovery or disease progression, including the rapid recovery of post-operative aphasic patients. This method has implications for the conceptualization of aphasia as a spectrum disorder across different disease etiology and may serve as a framework to track the trajectories of aphasia progression and recovery.

Mapping behavioural, cognitive and affective transdiagnostic dimensions in frontotemporal dementia

“…This pursuit is perfectly reasonable, provided there is sufficient intra-category homogeneity and inter-category separation that is reliably detected across studies, cohorts, and techniques. In clinical practice and across independent bodies of evidence, 8,12,32 however, graded performance variations within and between mutually exclusive categorisations are evident. Much of this variation is closely tied to individual differences in underlying neural degeneration, pathological processes, and disease progression.…”

“…[25][26][27][28] Recent studies employing these methods in dementia syndromes demonstrate considerable success in explaining symptomatic heterogeneity in terms of coherent variations along orthogonal dimensions of clinical and cognitive changes. 8,12,[29][30][31][32] Across syndromes, such phenotypic variations further closely relate to unique patterns of neural network degeneration, 8,12,30 metabolic brain changes, 33 and categorically-distinct neuropathological drivers of underlying disease. 34 Applying transdiagnostic approaches in bvFTD and SD, therefore, holds immense promise in understanding clinical heterogeneity of these syndromes and associated neurocognitive mechanisms.…”

Behavioural and neural structure of fluent speech production deficits in aphasia

“…A large-scale study of 330 participants with aphasia across three different aetiologies (post-stroke aphasia, primary progressive aphasia, and post-operative aphasia) found that severity was the primary dimension of variability, explaining 75% of the variance in aphasia battery sub-scores. 33 A more focused examination of 226 participants with post-stroke aphasia found that a ‘mild aphasia’ cluster was behaviourally and neuroanatomically distinct from the other two clusters, which corresponded to the semantic deficit and phonological deficit. 34 …”