Role of Acute Lesion Topography in Initial Ischemic Stroke Severity and Long-Term Functional Outcomes

Background and Purpose— Acute infarct volume, often proposed as a biomarker for evaluating novel interventions for acute ischemic stroke, correlates only moderately with traditional clinical end points, such as the modified Rankin Scale. We hypothesized that the topography of acute stroke lesions on diffusion-weighted magnetic resonance imaging may provide further information with regard to presenting stroke severity and long-term functional outcomes. Methods— Data from a prospective stroke repository were limited to acute ischemic stroke subjects with magnetic resonance imaging completed within 48 hours from last known well, admission NIH Stroke Scale (NIHSS), and 3-to-6 months modified Rankin Scale scores. Using voxel-based lesion symptom mapping techniques, including age, sex, and diffusion-weighted magnetic resonance imaging lesion volume as covariates, statistical maps were calculated to determine the significance of lesion location for clinical outcome and admission stroke severity. Results— Four hundred ninety subjects were analyzed. Acute stroke lesions in the left hemisphere were associated with more severe NIHSS at admission and poor modified Rankin Scale at 3 to 6 months. Specifically, injury to white matter (corona radiata, internal and external capsules, superior longitudinal fasciculus, and uncinate fasciculus), postcentral gyrus, putamen, and operculum were implicated in poor modified Rankin Scale. More severe NIHSS involved these regions, as well as the amygdala, caudate, pallidum, inferior frontal gyrus, insula, and precentral gyrus. Conclusions— Acute lesion topography provides important insights into anatomic correlates of admission stroke severity and poststroke outcomes. Future models that account for infarct location in addition to diffusion-weighted magnetic resonance imaging volume may improve stroke outcome prediction and identify patients likely to benefit from aggressive acute intervention and personalized rehabilitation strategies.

[1]  Carlo Caltagirone,et al.  Combining Voxel-based Lesion-symptom Mapping (VLSM) With A-tDCS Language Treatment: Predicting Outcome of Recovery in Nonfluent Chronic Aphasia , 2015, Brain Stimulation.

[2]  J. Krakauer,et al.  The future of stroke treatment: bringing evaluation of behavior back to stroke neurology. , 2014, JAMA neurology.

[3]  C. Gerloff,et al.  Influence of Stroke Infarct Location on Functional Outcome Measured by the Modified Rankin Scale , 2014, Stroke.

[4]  C. Rorden,et al.  Damage to left anterior temporal cortex predicts impairment of complex syntactic processing: A lesion‐symptom mapping study , 2013, Human brain mapping.

[5]  Rico Laage,et al.  Initial Lesion Volume Is an Independent Predictor of Clinical Stroke Outcome at Day 90: An Analysis of the Virtual International Stroke Trials Archive (VISTA) Database , 2012, Stroke.

[6]  Leif Johannsen,et al.  The anatomy underlying acute versus chronic spatial neglect: a longitudinal study. , 2011, Brain : a journal of neurology.

[7]  Maria Luisa Gorno-Tempini,et al.  Connected speech production in three variants of primary progressive aphasia. , 2010, Brain : a journal of neurology.

[8]  Todd B. Parrish,et al.  Identification of critical areas for motor function recovery in chronic stroke subjects using voxel-based lesion symptom mapping , 2010, NeuroImage.

[9]  A Gregory Sorensen,et al.  The Real Estate Factor: Quantifying the Impact of Infarct Location on Stroke Severity , 2007, Stroke.

[10]  D. Louis Collins,et al.  Symmetric Atlasing and Model Based Segmentation: An Application to the Hippocampus in Older Adults , 2006, MICCAI.

[11]  N. Makris,et al.  Decreased volume of left and total anterior insular lobule in schizophrenia , 2006, Schizophrenia Research.

[12]  L. Caplan,et al.  Insular cortex infarction in acute middle cerebral artery territory stroke: predictor of stroke severity and vascular lesion. , 2005, Archives of neurology.

[13]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[14]  Peter B Barker,et al.  Change in Perfusion in Acute Nondominant Hemisphere Stroke May Be Better Estimated by Tests of Hemispatial Neglect Than by the National Institutes of Health Stroke Scale , 2003, Stroke.

[15]  F. Dick,et al.  Voxel-based lesion–symptom mapping , 2003, Nature Neuroscience.

[16]  J-M Beis,et al.  Sensitivity of clinical and behavioural tests of spatial neglect after right hemisphere stroke , 2002, Journal of neurology, neurosurgery, and psychiatry.

[17]  K. Muir Heterogeneity of Stroke Pathophysiology and Neuroprotective Clinical Trial Design , 2002, Stroke.

[18]  G. Schlaug,et al.  Is the Association of National Institutes of Health Stroke Scale Scores and Acute Magnetic Resonance Imaging Stroke Volume Equal for Patients With Right- and Left-Hemisphere Ischemic Stroke? , 2002, Stroke.

[19]  W Hacke,et al.  Stroke magnetic resonance imaging within 6 hours after onset of hyperacute cerebral ischemia , 2001, Annals of neurology.

[20]  T. L. Davis,et al.  Human acute cerebral ischemia: detection of changes in water diffusion anisotropy by using MR imaging. , 1999, Radiology.

[21]  D. Collins,et al.  Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space , 1994, Journal of computer assisted tomography.

[22]  A. Sorensen,et al.  Accuracy and execution speed of automatic voxel-based algorithms for segmenting stroke lesions in clinical DWI imaging , 2010 .

[23]  S. Wakana,et al.  Fiber tract-based atlas of human white matter anatomy. , 2004, Radiology.

[24]  L. Leemis Applied Linear Regression Models , 1991 .

[25]  R. Bloch,et al.  Interobserver agreement for the assessment of handicap in stroke patients. , 1988, Stroke.

[26]  J. Neter,et al.  Applied Linear Regression Models , 1983 .