Restorative neurology and neuroscience · 2018
Relation of white matter hyperintensities and motor deficits in chronic stroke.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод пока не подготовлен
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Hicks Jarrod M, Taub Edward, Womble Brent, Barghi Ameen, Rickards Tyler, Mark Victor W, Uswatte Gitendra
Аннотация
BACKGROUND: Infarct size and location account for only a relatively small portion of post-stroke motor impairment, suggesting that other less obvious factors may be involved.
OBJECTIVE: Examine the relationship between white matter hyperintensity (WMH) load among other factors and upper extremity motor deficit in patients with mild to moderate chronic stroke.
METHODS: The magnetic resonance images of 28 patients were studied. WMH load was assessed as total WMH volume and WMH overlap with the corticospinal tract in the centrum semiovale. Hemiparetic arm function was measured using the Motor Activity Log (MAL) and Wolf Motor Function Test (WMFT).
RESULTS: Hierarchical multiple regression models found WMH volume predicted motor deficits in both real-world arm use (MAL;ΔR2 = 0.12, F(1, 22) = 4.73, p = 0.04) and in arm motor capacity as measured by a laboratory motor function test (WMFT;ΔR2 = 0.18, F(1, 22) = 6.32, p = 0.02) over and above age and lesion characteristics. However, these models accounted for less than half of the variance in post-stroke motor deficits.
CONCLUSION: The results suggest that WMH may be an important factor to consider in stroke-related upper extremity motor impairment. Nonetheless, the basis of the largest part of the post-stroke motor deficit remains unaccounted for by structural CNS factors. This component may be behavioral or learned, involving learned nonuse.
OBJECTIVE: Examine the relationship between white matter hyperintensity (WMH) load among other factors and upper extremity motor deficit in patients with mild to moderate chronic stroke.
METHODS: The magnetic resonance images of 28 patients were studied. WMH load was assessed as total WMH volume and WMH overlap with the corticospinal tract in the centrum semiovale. Hemiparetic arm function was measured using the Motor Activity Log (MAL) and Wolf Motor Function Test (WMFT).
RESULTS: Hierarchical multiple regression models found WMH volume predicted motor deficits in both real-world arm use (MAL;ΔR2 = 0.12, F(1, 22) = 4.73, p = 0.04) and in arm motor capacity as measured by a laboratory motor function test (WMFT;ΔR2 = 0.18, F(1, 22) = 6.32, p = 0.02) over and above age and lesion characteristics. However, these models accounted for less than half of the variance in post-stroke motor deficits.
CONCLUSION: The results suggest that WMH may be an important factor to consider in stroke-related upper extremity motor impairment. Nonetheless, the basis of the largest part of the post-stroke motor deficit remains unaccounted for by structural CNS factors. This component may be behavioral or learned, involving learned nonuse.