Long-range fibre damage in small vessel brain disease affects aphasia severity.

We sought to determine the underlying pathophysiology relating white matter hyperintensities to chronic aphasia severity. We hypothesized that: (i) white matter hyperintensities are associated with damage to fibres of any length, but to a higher percentage of long-range compared to mid- and short-range intracerebral white matter fibres; and (ii) the number of long-range fibres mediates the relationship between white matter hyperintensities and chronic post-stroke aphasia severity. We measured the severity of periventricular and deep white matter hyperintensities and calculated the number and percentages of short-, mid- and long-range white matter fibres in 48 individuals with chronic post-stroke aphasia. Correlation and mediation analyses were performed to assess the relationship between white matter hyperintensities, connectome fibre-length measures and aphasia severity as measured with the aphasia quotient of the Western Aphasia Battery-Revised (WAB-AQ). We found that more severe periventricular and deep white matter hyperintensities correlated with a lower proportion of long-range fibres (r = -0.423, P = 0.003 and r = -0.315, P = 0.029, respectively), counterbalanced by a higher proportion of short-range fibres (r = 0.427, P = 0.002 and r = 0.285, P = 0.050, respectively). More severe periventricular white matter hyperintensities also correlated with a lower proportion of mid-range fibres (r = -0.334, P = 0.020), while deep white matter hyperintensities did not correlate with mid-range fibres (r = -0.169, P = 0.250). Mediation analyses revealed: (i) a significant total effect of periventricular white matter hyperintensities on WAB-AQ (standardized beta = -0.348, P = 0.008); (ii) a non-significant direct effect of periventricular white matter hyperintensities on WAB-AQ (P > 0.05); (iii) significant indirect effects of more severe periventricular white matter hyperintensities on worse aphasia severity mediated in parallel by fewer long-range fibres (effect = -6.23, bootstrapping: standard error = 2.64, 95%CI: -11.82 to -1.56) and more short-range fibres (effect = 4.50, bootstrapping: standard error = 2.59, 95%CI: 0.16 to 10.29). We conclude that small vessel brain disease seems to affect chronic aphasia severity through a change of the proportions of long- and short-range fibres. This observation provides insight into the pathophysiology of small vessel brain disease, and its relationship with brain health and chronic aphasia severity.

[1]  José M. García,et al.  Accelerating Fibre Orientation Estimation from Diffusion Weighted Magnetic Resonance Imaging Using GPUs , 2012, PDP.

[2]  H. Rolf Jäger,et al.  Enantiomorphic normalization of focally lesioned brains , 2008, NeuroImage.

[3]  J. Garcìa,et al.  Pathogenesis of leukoaraiosis: a review. , 1997, Stroke.

[4]  J. Helenius,et al.  Extensive Leukoaraiosis Is Associated With High Early Risk of Recurrence After Ischemic Stroke , 2014, Stroke.

[5]  O. Sporns.  Contributions and challenges for network models in cognitive neuroscience , 2014, Nature Neuroscience.

[6]  A. Hillis,et al.  Leukoaraiosis is independently associated with naming outcome in poststroke aphasia , 2018, Neurology.

[7]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[8]  G. Buzsáki.  Rhythms of the brain , 2006 .

[9]  Dorothee Saur,et al.  Neuroimaging of stroke recovery from aphasia – Insights into plasticity of the human language network , 2017, NeuroImage.

[10]  Eric E. Smith,et al.  Severity of leukoaraiosis correlates with clinical outcome after ischemic stroke , 2009, Neurology.

[11]  M. van Buchem,et al.  Decline in total cerebral blood flow is linked with increase in periventricular but not deep white matter hyperintensities. , 2007, Radiology.

[12]  H. Moon,et al.  Periventricular White Matter Lesions as a Prognostic Factor of Swallowing Function in Older Patients with Mild Stroke , 2017, Dysphagia.

[13]  Hsiu-Fen Lin,et al.  Cerebral White Matter Hyperintensities Predict Functional Stroke Outcome , 2009, Cerebrovascular Diseases.

[14]  C. Rorden,et al.  Success of Anomia Treatment in Aphasia Is Associated With Preserved Architecture of Global and Left Temporal Lobe Structural Networks , 2016, Neurorehabilitation and neural repair.

[15]  Stefan Skare,et al.  How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging , 2003, NeuroImage.

[16]  Nick C Fox,et al.  Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration , 2013, The Lancet Neurology.

[17]  B. Mazoyer,et al.  AICHA: An atlas of intrinsic connectivity of homotopic areas , 2015, Journal of Neuroscience Methods.

[18]  Robert Leech,et al.  Network dysfunction predicts speech production after left hemisphere stroke , 2016, Neurology.

[19]  J F Toole,et al.  Presence and severity of cerebral white matter lesions and hypertension, its treatment, and its control. The ARIC Study. Atherosclerosis Risk in Communities Study. , 1996, Stroke.

[20]  W. M. van der Flier,et al.  Heterogeneity of small vessel disease: a systematic review of MRI and histopathology correlations , 2010, Journal of Neurology, Neurosurgery & Psychiatry.

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

[22]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[23]  Michael L. Hines,et al.  Cable energy function of cortical axons , 2016, Scientific Reports.

[24]  Andrea R. Ashbaugh,et al.  Simple and parallel mediation: {A} tutorial exploring anxiety sensitivity, sensation seeking, and gender , 2017 .

[25]  A. Hofman,et al.  Periventricular cerebral white matter lesions predict rate of cognitive decline , 2002, Annals of neurology.

[26]  Stamatios N. Sotiropoulos,et al.  An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging , 2016, NeuroImage.

[27]  Eng H. Lo,et al.  Neurological diseases: Mechanisms, challenges and opportunities in stroke , 2003, Nature Reviews Neuroscience.

[28]  Mark W. Woolrich,et al.  Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? , 2007, NeuroImage.

[29]  A G Sorensen,et al.  Severity of leukoaraiosis determines clinical phenotype after brain infarction , 2011, Neurology.

[30]  C. Ayers,et al.  Effect of Normal Aging Versus Hypertension, Abnormal Body Mass Index, and Diabetes Mellitus on White Matter Hyperintensity Volume , 2014, Stroke.

[31]  Basil Grueter,et al.  Age-related cerebral white matter disease (leukoaraiosis): a review , 2011, Postgraduate Medical Journal.

[32]  A. Alavi,et al.  MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. , 1987, AJR. American journal of roentgenology.

[33]  L. Pantoni.  Leukoaraiosis: from an ancient term to an actual marker of poor prognosis. , 2008, Stroke.

[34]  Wei Wen,et al.  Extent and Distribution of White Matter Hyperintensities in Stroke Patients: The Sydney Stroke Study , 2004, Stroke.

[35]  G. Schlaug.  Even when right is all that's left: There are still more options for recovery from aphasia , 2018, Annals of neurology.

[36]  A Hofman,et al.  Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study , 2001, Journal of neurology, neurosurgery, and psychiatry.

[37]  Chris Rorden,et al.  Chronic post-stroke aphasia severity is determined by fragmentation of residual white matter networks , 2017, Scientific Reports.

[38]  Karl J. Friston,et al.  Unified segmentation , 2005, NeuroImage.

[39]  D. Leys,et al.  Leukoaraiosis More Than Dementia Is a Predictor of Stroke Recurrence , 2003, Stroke.

[40]  T. Keith.  Multiple Regression and Beyond , 2005, Principles & Methods of Statistical Analysis.

[41]  Timothy Edward John Behrens,et al.  Accelerating Fibre Orientation Estimation from Diffusion Weighted Magnetic Resonance Imaging Using GPUs , 2012, 2012 20th Euromicro International Conference on Parallel, Distributed and Network-based Processing.

[42]  W. Mali,et al.  High total cerebral blood flow is associated with a decrease of white matter lesions , 2004, Journal of Neurology.

[43]  Chris Rorden,et al.  Anatomy of aphasia revisited , 2018, Brain : a journal of neurology.

[44]  H. Markus,et al.  Longitudinal patterns of leukoaraiosis and brain atrophy in symptomatic small vessel disease , 2016, Brain : a journal of neurology.

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