The Additional Contribution of White Matter Hyperintensity Location to Post-stroke Cognitive Impairment: Insights From a Multiple-Lesion Symptom Mapping Study

White matter hyperintensities (WMH) are common in acute ischemic stroke patients. Although WMH volume has been reported to influence post-stroke cognition, it is still not clear whether WMH location, independent of acute ischemic lesion (AIL) volume and location, contributes to cognitive impairment after stroke. Here, we proposed a multiple-lesion symptom mapping model that considers both the presence of WMH and AIL to measure the additional contribution of WMH locations to post-stroke cognitive impairment. Seventy-six first-ever stroke patients with AILs in the left hemisphere were examined by Montreal Cognitive Assessment (MoCA) at baseline and 1 year after stroke. The association between the location of AIL and WMH and global cognition was investigated by a multiple-lesion symptom mapping (MLSM) model based on support vector regression (SVR). To explore the relative merits of MLSM over the existing lesion-symptom mapping approaches with only AIL considered (mass-univariate VLSM and SVR-LSM), we measured the contribution of the significant AIL and/or WMH clusters from these models to post-stroke cognitive impairment. In addition, we compared the significant WMH locations identified by the optimal SVR-MLSM model for cognitive impairment at baseline and 1 year post stroke. The identified strategic locations of WMH significantly contributed to the prediction of MoCA at baseline (short-term) and 1 year (long-term) after stroke independent of the strategic locations of AIL. The significant clusters of WMH for short-term and long-term post-stroke cognitive impairment were mainly in the corpus callosum, corona radiata, and posterior thalamic radiation. We noted that in some regions, the AIL clusters that were significant for short-term outcome were no longer significant for long-term outcome, and interestingly more WMH clusters in these regions became significant for long-term outcome compared to short-term outcome. This indicated that there are some regions where local WMH burden has larger impact than AIL burden on the long-term post-stroke cognitive impairment. In consequence, SVR-MLSM was effective in identifying the WMH locations that have additional impact on post-stroke cognition on top of AIL locations. Such a method can also be applied to other lesion-behavior studies where multiple types of lesions may have potential contributions to a specific behavior.

[1]  P. Scheltens,et al.  White matter hyperintensities, cognitive impairment and dementia: an update , 2015, Nature Reviews Neurology.

[2]  M. Schwartz,et al.  Multivariate lesion‐symptom mapping using support vector regression , 2014, Human brain mapping.

[3]  A. Hofman,et al.  Cerebral white matter lesions and the risk of dementia. , 2004, Archives of neurology.

[4]  C. Almli,et al.  Unbiased nonlinear average age-appropriate brain templates from birth to adulthood , 2009, NeuroImage.

[5]  Hans-Otto Karnath,et al.  The next step in modern brain lesion analysis: multivariate pattern analysis. , 2014, Brain : a journal of neurology.

[6]  Chris Rorden,et al.  The anatomy of spatial neglect based on voxelwise statistical analysis: a study of 140 patients. , 2004, Cerebral cortex.

[7]  H. Karnath,et al.  On the validity of lesion-behaviour mapping methods , 2017, Neuropsychologia.

[8]  T. Mulder,et al.  Cognitive recovery after stroke: a 2-year follow-up. , 2003, Archives of physical medicine and rehabilitation.

[9]  T. Shallice,et al.  Left- and right-hemisphere forms of phonological alexia , 2012, Cognitive neuropsychology.

[10]  V. Mok,et al.  The Validity, Reliability and Clinical Utility of the Hong Kong Montreal Cognitive Assessment (HK-MoCA) in Patients with Cerebral Small Vessel Disease , 2009, Dementia and Geriatric Cognitive Disorders.

[11]  N. Henninger,et al.  White Matter Hyperintensity–Adjusted Critical Infarct Thresholds to Predict a Favorable 90-Day Outcome , 2016, Stroke.

[12]  Lesley K. Fellows,et al.  Are core component processes of executive function dissociable within the frontal lobes? Evidence from humans with focal prefrontal damage , 2013, Cortex.

[13]  Chris Rorden,et al.  Important considerations in lesion‐symptom mapping: Illustrations from studies of word comprehension , 2017, Human brain mapping.

[14]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.

[15]  Nagaendran Kandiah,et al.  Frontal subcortical ischemia is crucial for post stroke cognitive impairment , 2011, Journal of the Neurological Sciences.

[16]  Nick A. Weaver,et al.  Lesion location and cognitive impact of cerebral small vessel disease. , 2017, Clinical science.

[17]  A. Postma,et al.  Distinct anatomical correlates of discriminability and criterion setting in verbal recognition memory revealed by lesion‐symptom mapping , 2015, Human brain mapping.

[18]  GuidoChiti,et al.  Use of Montreal Cognitive Assessment in Patients With Stroke , 2014 .

[19]  V. Mok,et al.  Risk factors for incident dementia after stroke and transient ischemic attack , 2015, Alzheimer's & Dementia.

[20]  A. Postma,et al.  Shared and distinct anatomical correlates of semantic and phonemic fluency revealed by lesion-symptom mapping in patients with ischemic stroke , 2015, Brain Structure and Function.

[21]  Sayan Mukherjee,et al.  Feature Selection for SVMs , 2000, NIPS.

[22]  C. Guttmann,et al.  Stroke Location Is an Independent Predictor of Cognitive Outcome , 2016, Stroke.

[23]  Hans-Otto Karnath,et al.  Topography of acute stroke in a sample of 439 right brain damaged patients , 2015, NeuroImage: Clinical.

[24]  R. Stewart,et al.  White Matter Hyperintensities and Functional Outcomes at 2 Weeks and 1 Year after Stroke , 2012, Cerebrovascular Diseases.

[25]  V. Mok,et al.  Effect of White Matter Changes on Cognitive Impairment in Patients With Lacunar Infarcts , 2004, Stroke.

[26]  C. Hilgetag,et al.  Multiclass Support Vector Machine-Based Lesion Mapping Predicts Functional Outcome in Ischemic Stroke Patients , 2015, PloS one.

[27]  M. Kaste,et al.  White matter hyperintensities as a predictor of neuropsychological deficits post-stroke , 2005, Journal of Neurology, Neurosurgery & Psychiatry.

[28]  V. Mok,et al.  Strategic infarct location for post-stroke cognitive impairment: A multivariate lesion-symptom mapping study , 2017, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[29]  Frederik Barkhof,et al.  Infratentorial Abnormalities in Vascular Dementia , 2006, Stroke.

[30]  N. Bornstein,et al.  Cognitive State following Stroke: The Predominant Role of Preexisting White Matter Lesions , 2014, PloS one.

[31]  C. Beaulieu,et al.  White matter hyperintensity volume predicts persistent cognitive impairment in transient ischemic attack and minor stroke , 2017, International journal of stroke : official journal of the International Stroke Society.

[32]  M. Jenkinson,et al.  White Matter Imaging Correlates of Early Cognitive Impairment Detected by the Montreal Cognitive Assessment After Transient Ischemic Attack and Minor Stroke , 2017, Stroke.

[33]  Winnie C.W. Chu,et al.  Automated quantification of white matter lesion in magnetic resonance imaging of patients with acute infarction , 2013, Journal of Neuroscience Methods.

[34]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[35]  Chris Rorden,et al.  Multivariate Connectome-Based Symptom Mapping in Post-Stroke Patients: Networks Supporting Language and Speech , 2016, The Journal of Neuroscience.