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Updated: May 12, 2026
A Versatile Murine Model of Subcortical White Matter Stroke for the Study of Axonal Degeneration and White Matter Neurobiology
Published on: March 17, 2016
Impact of white matter hyperintensity location on outcome in acute ischemic stroke patients: a lesion-symptom mapping
Yuling Peng1, Dan Luo1, Peng Zeng1
1Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
Background:
Studies on the impact of white matter hyperintensity (WMH) on function outcome have primarily concentrated on WMH volume, overlooking the potential significance of WMH location. This study aimed to investigate the relationship between WMH location and outcome in patients with their first-ever acute ischemic stroke (AIS).
Methods:
Patients who underwent their first AIS between September 2021 and September 2022 were recruited. Function outcome was assessed using the 90-day modified Rankin Scale (mRS). The association between the location of WMH and functional outcome was examined at the voxel level and subsequently at the region of interest tract-based level.
Results:
A total of 134 patients were included (mean age, 66.28 years ± 12.48; 90 male [67.16%]). The median mRS was 2 (IQR, 1-3). The median total WMH volume was 3.80 cm3 (IQR, 2.07-6.78). WMH volume was significantly correlated with mRS (r = 0.28, p = 0.001). WMH in the splenium of corpus callosum, the left superior corona radiata, the left posterior corona radiata, and the bilateral posterior thalamic radiation were associated with poor mRS. The strategic WMH score (OR, 1.18; 95% CI, 1.06-1.32; p = 0.003), derived from these five specific tracts, was an independent predictor of mRS after accounting for the effects of total WMH volume (OR, 1.02; 95% CI, 0.90-1.16; p = 0.771) and infarct lesion volume (OR, 1.26; 95% CI, 1.08-1.48; p = 0.004).
Conclusion:
Our findings indicated that the impact of WMH on function outcome is location-dependent, mainly involving five strategic tracts. Evaluating WMH location may help to more accurately predict the functional outcome of AIS.
