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Comparative Study
. 2010 May;41(5):910-5.
doi: 10.1161/STROKEAHA.109.577023. Epub 2010 Apr 8.

Lesion load of the corticospinal tract predicts motor impairment in chronic stroke

Lin L Zhu  1 , Robert Lindenberg, Michael P Alexander, Gottfried Schlaug
Affiliations
Comparative Study

Lesion load of the corticospinal tract predicts motor impairment in chronic stroke

Lin L Zhu et al. Stroke. 2010 May.

Abstract

Background and purpose: Previous studies have shown motor impairment after a stroke relates to lesion size and location, but unexplained variability in recovery still exists. In this study, we used lesion-mapping techniques in combination with diffusion tensor imaging to quantitatively test the hypothesis that motor recovery in patients with chronic stroke is inversely related to the proportion of the corticospinal tract (CST) affected by the lesion.

Methods: We studied 50 patients with chronic stroke, all of whom presented with moderate to severe motor impairments in the acute stage, using high-resolution anatomic MRI. We evaluated the degree of motor impairment with the Upper Extremity module of the Fugl-Meyer Assessment. To analyze the relationship between CST damage and impairment scores, we calculated a CST-lesion load for each patient by overlaying the patient's lesion map with a probabilistic tract derived from diffusion tensor images of age-matched healthy subjects.

Results: CST-lesion load was a significant predictor of motor deficit. Infarct size, despite correlating with motor scores, did not significantly predict impairment.

Conclusions: Our results show the degree of functional motor deficit after a stroke is highly dependent on the overlap of the lesion with the CST and not lesion size per se. In the future, automated calculation of CST-lesion load may allow more precise prediction of motor impairment after stroke.

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Figures

Figure 1
Figure 1. Probabilistic CST Fiber Map
(A) The coronal slices shown correspond to y=−20 and −18 and (B) the axial slices correspond to z=6 and 4 in Talairach space.
Figure 1
Figure 1. Probabilistic CST Fiber Map
(A) The coronal slices shown correspond to y=−20 and −18 and (B) the axial slices correspond to z=6 and 4 in Talairach space.
Figure 2
Figure 2. Regression Analyses
UE-FM scores are plotted as functions of (A) raw CST-lesion load, (B) weighted CST-lesion load, and (C) lesion size.
Figure 2
Figure 2. Regression Analyses
UE-FM scores are plotted as functions of (A) raw CST-lesion load, (B) weighted CST-lesion load, and (C) lesion size.
Figure 2
Figure 2. Regression Analyses
UE-FM scores are plotted as functions of (A) raw CST-lesion load, (B) weighted CST-lesion load, and (C) lesion size.
Figure 3
Figure 3. CST-Lesion Load vs. Lesion Size
Shown here are examples of three patients’ motor impairment scores, lesion sizes, and lesion load volumes, as well as their individual lesion maps (depicted in blue) overlaid onto the probabilistic fiber map (depicted in red). Overlap between lesion and CST is displayed in purple. The axial slices depicted correspond to z=0, 4, 8, 10, 20, and 28 in Talairach space. Comparison of patients A and B shows how two similarly-sized lesions can produce markedly different CST-lesion loads and, accordingly, result in very different levels of motor impairment. Similarly, comparison of patients A and C shows how two patients can display comparable CST-lesion loads and functional ability despite drastically different overall lesion volumes.
See this image and copyright information in PMC

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