Network Localisation of White Matter Damage in Cerebral Small Vessel Disease

Cerebral small vessel disease (CSVD) is a widespread condition associated to stroke, dementia and depression. To shed light on its opaque pathophysiology, we conducted a neuroimaging study aiming to assess the location of CSVD-induced damage in the human brain network. Structural connectomes of 930 subjects of the Hamburg City Health Study were reconstructed from diffusion weighted imaging. The connectome edges were partitioned into groups according to specific schemes: (1) connection to grey matter regions, (2) course and length of underlying streamlines. Peak-width of skeletonised mean diffusivity (PSMD) - a surrogate marker for CSVD - was related to each edge group’s connectivity in a linear regression analysis allowing localisation of CSVD-induced effects. PSMD was associated with statistically significant decreases in connectivity of most investigated edge groups except those involved in connecting limbic, insular, temporal or cerebellar regions. Connectivity of interhemispheric and long intrahemispheric edges as well as edges connecting subcortical and frontal brain regions decreased most severely with increasing PSMD. In conclusion, MRI findings of CSVD are associated with widespread impairment of structural brain network connectivity, which supports the understanding of CSVD as a global brain disease. The pattern of regional preference might provide a link to clinical phenotypes of CSVD.

[1]  Sandra E. Black,et al.  Associations between amyloid β and white matter hyperintensities: A systematic review , 2017, Alzheimer's & Dementia.

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

[3]  Andrew Zalesky,et al.  Building connectomes using diffusion MRI: why, how and but , 2017, NMR in biomedicine.

[4]  Michael Wagner,et al.  A peripheral epigenetic signature of immune system genes is linked to neocortical thickness and memory , 2017, Nature Communications.

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

[6]  P. Sachdev,et al.  Are the brain's vascular and Alzheimer pathologies additive or interactive? , 2017, Current opinion in psychiatry.

[7]  Owen Carmichael,et al.  White Matter Hyperintensity Penumbra , 2011, Stroke.

[8]  F. de Leeuw,et al.  A Novel Imaging Marker for Small Vessel Disease Based on Skeletonization of White Matter Tracts and Diffusion Histograms , 2016, Annals of neurology.

[9]  M. Delgado-Rodríguez,et al.  Systematic review and meta-analysis. , 2017, Medicina intensiva.

[10]  Edward T. Bullmore,et al.  Connectomics: A new paradigm for understanding brain disease , 2015, European Neuropsychopharmacology.

[11]  David G. Norris,et al.  Relationship Between White Matter Hyperintensities, Cortical Thickness, and Cognition , 2015, Stroke.

[12]  Alan Connelly,et al.  The effects of SIFT on the reproducibility and biological accuracy of the structural connectome , 2015, NeuroImage.

[13]  D. Bennett,et al.  White matter changes: neurobehavioral manifestations of Binswanger's disease and clinical correlates in Alzheimer's disease. , 1994, Dementia.

[14]  R. G. Lacsamana,et al.  Where do we go from here? , 1986, The Journal of the Florida Medical Association.

[15]  Alan Connelly,et al.  Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution , 2004, NeuroImage.

[16]  Alan Connelly,et al.  SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography , 2015, NeuroImage.

[17]  David G Norris,et al.  Diffusion tensor imaging and cognition in cerebral small vessel disease: the RUN DMC study. , 2012, Biochimica et biophysica acta.

[18]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[19]  Johan Svensson,et al.  Update on Vascular Cognitive Impairment Associated with Subcortical Small-Vessel Disease2 , 2018, Journal of Alzheimer's disease : JAD.

[20]  H. Chui,et al.  Subcortical ischaemic vascular dementia , 2002, The Lancet Neurology.

[21]  B. Cheng,et al.  Characterization of White Matter Hyperintensities in Large-Scale MRI-Studies , 2019, Front. Neurol..

[22]  Stephen M. Smith,et al.  A Bayesian model of shape and appearance for subcortical brain segmentation , 2011, NeuroImage.

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

[24]  Stephen T. C. Wong,et al.  Cortical and frontal atrophy are associated with cognitive impairment in age-related confluent white-matter lesion , 2010, Journal of Neurology, Neurosurgery & Psychiatry.

[25]  Alan Connelly,et al.  Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information , 2012, NeuroImage.

[26]  E. Bullmore,et al.  Impaired long distance functional connectivity and weighted network architecture in Alzheimer's disease. , 2014, Cerebral cortex.

[27]  A. Wallin The Overlap between Alzheimer’s Disease and Vascular Dementia: The Role of White Matter Changes , 1998, Dementia and Geriatric Cognitive Disorders.

[28]  D. Norris,et al.  Disruption of rich club organisation in cerebral small vessel disease , 2016, Human brain mapping.

[29]  R. Kahn,et al.  Efficiency of Functional Brain Networks and Intellectual Performance , 2009, The Journal of Neuroscience.

[30]  Chun-Hung Yeh,et al.  Is removal of weak connections necessary for graph-theoretical analysis of dense weighted structural connectomes from diffusion MRI? , 2019, NeuroImage.

[31]  K. Jellinger,et al.  The overlap between vascular disease and Alzheimer’s disease - lessons from pathology , 2014, BMC Medicine.

[32]  Bastian Cheng,et al.  Altered topology of large-scale structural brain networks in chronic stroke , 2019, Brain communications.

[33]  C. Büchel,et al.  Rationale and Design of the Hamburg City Health Study , 2019, European Journal of Epidemiology.

[34]  H. Markus,et al.  The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis , 2010, BMJ : British Medical Journal.

[35]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[36]  H. C. Chui,et al.  White matter lesions impair frontal lobe function regardless of their location , 2004, Neurology.

[37]  Bibek Dhital,et al.  Gibbs‐ringing artifact removal based on local subvoxel‐shifts , 2015, Magnetic resonance in medicine.

[38]  Alan Connelly,et al.  MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation , 2019, NeuroImage.

[39]  Chun-Hung Yeh,et al.  Is removal of weak connections necessary for graph-theoretical analysis of dense weighted structural connectomes? , 2019, bioRxiv.

[40]  Bastian Cheng,et al.  Cortical atrophy and transcallosal diaschisis following isolated subcortical stroke , 2020, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[41]  Daniel Rueckert,et al.  Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.

[42]  Danielle S Bassett,et al.  Cognitive fitness of cost-efficient brain functional networks , 2009, Proceedings of the National Academy of Sciences.

[43]  F.‐E. Leeuw,et al.  Cerebral small vessel disease: from a focal to a global perspective , 2018, Nature Reviews Neurology.

[44]  Brian B. Avants,et al.  Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..

[45]  Heidi Johansen-Berg,et al.  Tractography: Where Do We Go from Here? , 2011, Brain Connect..

[46]  Andrew Simmons,et al.  Beyond cortical localization in clinico-anatomical correlation , 2012, Cortex.

[47]  L. Pantoni Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges , 2010, The Lancet Neurology.

[48]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[49]  Lenore J. Launer,et al.  Cerebral small vessel disease and risk of incident stroke, dementia and depression, and all-cause mortality: A systematic review and meta-analysis , 2018, Neuroscience & Biobehavioral Reviews.

[50]  A. W. Chung,et al.  Structural network efficiency is associated with cognitive impairment in small-vessel disease , 2014, Neurology.

[51]  Reinhold Schmidt,et al.  Strategic white matter tracts for processing speed deficits in age-related small vessel disease , 2014, Neurology.

[52]  Chun-Hung Yeh,et al.  MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation , 2019, NeuroImage.

[53]  E. Mohammadi,et al.  Barriers and facilitators related to the implementation of a physiological track and trigger system: A systematic review of the qualitative evidence , 2017, International journal for quality in health care : journal of the International Society for Quality in Health Care.

[54]  Benjamin S. Aribisala,et al.  White matter hyperintensities and normal-appearing white matter integrity in the aging brain , 2015, Neurobiology of Aging.

[55]  Wim Fias,et al.  Brain networks under attack: robustness properties and the impact of lesions. , 2016, Brain : a journal of neurology.

[56]  Ludovica Griffanti,et al.  BIANCA (Brain Intensity AbNormality Classification Algorithm): A new tool for automated segmentation of white matter hyperintensities , 2016, NeuroImage.

[57]  P. Matthews,et al.  White matter lesion progression, brain atrophy, and cognitive decline: The Austrian stroke prevention study , 2005, Annals of neurology.

[58]  A. Connelly,et al.  Improved probabilistic streamlines tractography by 2 nd order integration over fibre orientation distributions , 2009 .

[59]  Peter F. Neher,et al.  The challenge of mapping the human connectome based on diffusion tractography , 2017, Nature Communications.

[60]  Seth Love,et al.  Cerebrovascular disease in ageing and Alzheimer’s disease , 2015, Acta Neuropathologica.

[61]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[62]  Jan Sijbers,et al.  Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data , 2014, NeuroImage.

[63]  Jan Sijbers,et al.  Denoising of diffusion MRI using random matrix theory , 2016, NeuroImage.

[64]  Justin P. Haldar,et al.  Co-registration and distortion correction of diffusion and anatomical images based on inverse contrast normalization , 2015, NeuroImage.