Impairment of speech production predicted by lesion load of the left arcuate fasciculus

Abstract

Background and purpose: Previous studies have suggested that patients' potential for poststroke language recovery is related to lesion size; however, lesion location may also be of importance, particularly when fiber tracts that are critical to the sensorimotor mapping of sounds for articulation (eg, the arcuate fasciculus) have been damaged. In this study, we tested the hypothesis that lesion loads of the arcuate fasciculus (ie, volume of arcuate fasciculus that is affected by a patient's lesion) and of 2 other tracts involved in language processing (the extreme capsule and the uncinate fasciculus) are inversely related to the severity of speech production impairments in patients with stroke with aphasia.

Methods: Thirty patients with chronic stroke with residual impairments in speech production underwent high-resolution anatomic MRI and a battery of cognitive and language tests. Impairment was assessed using 3 functional measures of spontaneous speech (eg, rate, informativeness, and overall efficiency) as well as naming ability. To quantitatively analyze the relationship between impairment scores and lesion load along the 3 fiber tracts, we calculated tract-lesion overlap volumes for each patient using probabilistic maps of the tracts derived from diffusion tensor images of 10 age-matched healthy subjects.

Results: Regression analyses showed that arcuate fasciculus lesion load, but not extreme capsule or uncinate fasciculus lesion load or overall lesion size, significantly predicted rate, informativeness, and overall efficiency of speech as well as naming ability.

Conclusions: A new variable, arcuate fasciculus lesion load, complements established voxel-based lesion mapping techniques and, in the future, may potentially be used to estimate impairment and recovery potential after stroke and refine inclusion criteria for experimental rehabilitation programs.

Figure 1. Lesion Maps and Probabilistic Fiber Tracts

Shown here are probabilistic maps of the (A) AF, (B) EmC, and (C) UF. The sagittal slices shown correspond to x=−50, −36, and −36 in Talairach space; the axial slices shown correspond to z=−26, −4, and −6.

Figure 2. Regression Analyses

(A) Words/minute, (B) %CIUs, and (C) CIUs/minute are plotted as functions of lesion size (measured in cc) and AF-lesion load (represented as percentage of tract affected).

Figure 2. Regression Analyses

(A) Words/minute, (B) %CIUs, and (C) CIUs/minute are plotted as functions of lesion size (measured in cc) and AF-lesion load (represented as percentage of tract affected).

Figure 2. Regression Analyses

(A) Words/minute, (B) %CIUs, and (C) CIUs/minute are plotted as functions of lesion size (measured in cc) and AF-lesion load (represented as percentage of tract affected).

Figure 3. Lesion–DTI fiber tract overlap

Shown here are examples of three patients’ behavioral scores, lesion sizes, and AF-lesion loads, as well as their individual lesion maps (depicted in blue) overlaid onto the probabilistic AF map (depicted in red). Overlap between lesion and AF is displayed in purple. The axial slices depicted correspond to z=−10, −2, 8, 18, 26, 34, and 42 in Talairach space. Comparison of patients A and B shows how two patients can display comparable AF-lesion loads and behavioral scores despite drastically different overall lesion volumes. Similarly, comparison of patients B and C shows how a similar lesion size can produce two markedly different AF-lesion loads and, accordingly, result in very different levels of impairment.