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 behaviour 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-behaviour relationship early in recovery is comparable to later in the recovery trajectory. To that end, we investigated two aims: (i) whether lesion data from acute and chronic patients yield similar results in region-based lesion-symptom mapping analyses and (ii) 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 data set 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 behaviour 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-behaviour predictions were almost equivalent when comparing within versus across stroke stage, despite differing lesion size/locations and distributions of aphasia severity between stroke timepoints. This suggests that research investigating the brain-behaviour relationship including subsets of patients from only one timepoint may also be applicable at other timepoints, 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 behaviour (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.

Keywords: acute; aphasia; chronic; lesion; stroke.

Graphical abstract

Figure 1

Lesion overlay map. (A) Acute participants and (B) chronic participants. Warmer colours indicate greater lesion overlap (i.e. more patients have damage in this region). The colour bar indicates the minimum number of people who have damage in that region.

Figure 2

Histograms of the distribution of aphasia severity and lesion volume. WAB-R AQ scores (A) and lesion volume (B) are shown for the acute data set (left) and chronic data set (right). Lesion volume is displayed as the number of voxels, divided by 1000. T-tests revealed that there was no significant difference between acute and chronic WAB-R AQ score [acute; M = 80.01, SD = 23.46, chronic; M = 62.95, SD = 24.68, t(179) = −0.451, P = 0.059]. Acute patients (M = 22 510 voxels, SD = 42 813 voxels) had significantly smaller lesions than chronic patients [M = 118 809 voxels, SD = 94 865 voxels, t(168) = −7.556, P < 0.001].

Figure 3

RLSM results. Regions which significantly predicted behavioural scores in the RLSM analysis. Orange is acute, green is chronic, and purple represents areas which overlap. (A) WAB-R AQ, (B) auditory verbal comprehension score, (C) naming and word-finding score, (D) repetition score and (E) spontaneous speech score.

Figure 4

Correlations between actual and predicted WAB-R AQ scores. Acute leave-one-out model (P < 0.001) and chronic leave-one-out model (P < 0.001). Size of datapoints are proportional to lesion volume for each participant.

Figure 5

Brain map of 10 lowest (most negative) beta maps for each analysis. (A) Acute leave-one-out model, (B) chronic leave-one-out model, (C) predicting acute scores from chronic data and (D) predicting chronic scores from acute data.