Comparing the brain–behaviour relationship in acute and chronic stroke aphasia

Abstract

In stroke aphasia, lesion volume is typically associated with aphasia severity. Although this relationship is likely present throughout recovery, different factors may affect lesion volume and behavior early into recovery (acute) and in the later stages of recovery (chronic). Therefore, studies typically separate patients into two groups (acute/chronic) and this is often accompanied with arguments for and against using data from acute stroke patients over chronic. However, no comprehensive studies have provided strong evidence of whether the lesion-behavior relationship early in recovery is comparable to later in the recovery trajectory. To that end, we investigated two aims; (1) whether lesion data from acute and chronic patients yield similar results in region-based lesion-symptom mapping analyses, and (2) if models based on one timepoint accurately predict the other. Lesions and aphasia severity scores from acute (N = 63) and chronic (N = 109) stroke survivors with aphasia were entered into separate univariate region-based lesion-symptom mapping analyses. A support vector regression model was trained on lesion data from either the acute or chronic dataset to give an estimate of aphasia severity. Four model-based analyses were conducted: trained on acute/chronic using leave-one-out, tested on left-out behavior, or trained on acute/chronic to predict the other timepoint. Region-based lesion-symptom mapping analyses identified similar but not identical regions in both timepoints. All four models revealed positive correlations between actual and predicted Western Aphasia Battery-Revised Aphasia-Quotient. scores. Lesion to behavior predictions were almost equivalent when comparing within vs. across stroke stage, despite differing lesion size/location and distribution of aphasia severity between stroke timepoints. This suggests that research investigating the brain-behavior relationship including subsets of patients from only one time point may also be applicable at other time points, although it is important to note that these comparable findings may only be seen using broad measures such as aphasia severity, rather than those aimed at identifying more specific deficits. Subtle differences found between timepoints may also be useful in understanding the nature of lesion volume and aphasia severity over time. Stronger correlations found when predicting acute behavior (e.g., predicting acute: r = .0.6888, p < 0.001, predicting chronic r = .0.5014, p < 0.001), suggest that the acute lesion/perfusion patterns more accurately capture the critical changes in underlying vascular territories. Differences in critical brain regions between timepoints may shed light on recovery patterns. Future studies could focus on a longitudinal design to compare acute and chronic patients in a more controlled manner.

Elucidating an implicit-statistical learning brain network: Coordinate-based meta-analyses and functional connectivity profiles of artificial grammar learning in healthy adults

“…However, little is known about how the damaged brain re -learns. There are favorable brain-based biomarkers of recovery from stroke (Basso, 1992; Busby et al, 2023; Fedorenko et al, 2013; Gilmore et al, 2018; Hartwigsen et al, 2013; Hope et al, 2017; Ramage et al, 2020; Saur et al, 2006), but not as much is known about how rehabilitation efforts lead to or enhance brain changes to result in favorable recovery of communication skills. Thus, we explore a return to the idea of Baddeley (1993), and posit that a model of learning is needed to inform how we approach rehabilitation.…”

“…There are favorable brain-based biomarkers of recovery from stroke (Basso, 1992;Busby et al, 2023;Fedorenko et al, 2013;Gilmore et al, 2018;Hartwigsen et al, 2013;Hope et al, 2017;Ramage et al, 2020;Saur et al, 2006), but not as much is known about how rehabilitation efforts lead to or enhance brain changes to result in favorable recovery of communication skills.…”