Structural network alterations and neurological dysfunction in cerebral amyloid angiopathy.

Cerebral amyloid angiopathy is a common form of small-vessel disease and an important risk factor for cognitive impairment. The mechanisms linking small-vessel disease to cognitive impairment are not well understood. We hypothesized that in patients with cerebral amyloid angiopathy, multiple small spatially distributed lesions affect cognition through disruption of brain connectivity. We therefore compared the structural brain network in patients with cerebral amyloid angiopathy to healthy control subjects and examined the relationship between markers of cerebral amyloid angiopathy-related brain injury, network efficiency, and potential clinical consequences. Structural brain networks were reconstructed from diffusion-weighted magnetic resonance imaging in 38 non-demented patients with probable cerebral amyloid angiopathy (69 ± 10 years) and 29 similar aged control participants. The efficiency of the brain network was characterized using graph theory and brain amyloid deposition was quantified by Pittsburgh compound B retention on positron emission tomography imaging. Global efficiency of the brain network was reduced in patients compared to controls (0.187 ± 0.018 and 0.201 ± 0.015, respectively, P < 0.001). Network disturbances were most pronounced in the occipital, parietal, and posterior temporal lobes. Among patients, lower global network efficiency was related to higher cortical amyloid load (r = -0.52; P = 0.004), and to magnetic resonance imaging markers of small-vessel disease including increased white matter hyperintensity volume (P < 0.001), lower total brain volume (P = 0.02), and number of microbleeds (trend P = 0.06). Lower global network efficiency was also related to worse performance on tests of processing speed (r = 0.58, P < 0.001), executive functioning (r = 0.54, P = 0.001), gait velocity (r = 0.41, P = 0.02), but not memory. Correlations with cognition were independent of age, sex, education level, and other magnetic resonance imaging markers of small-vessel disease. These findings suggest that reduced structural brain network efficiency might mediate the relationship between advanced cerebral amyloid angiopathy and neurologic dysfunction and that such large-scale brain network measures may represent useful outcome markers for tracking disease progression.

[1]  G. Savva,et al.  Epidemiological Studies of the Effect of Stroke on Incident Dementia: A Systematic Review , 2010, Stroke.

[2]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[3]  Alexander Leemans,et al.  The B‐matrix must be rotated when correcting for subject motion in DTI data , 2009, Magnetic resonance in medicine.

[4]  Carole Dufouil,et al.  Distribution of white matter hyperintensity in cerebral hemorrhage and healthy aging , 2012, Journal of Neurology.

[5]  Arthur W. Toga,et al.  Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template , 2008, NeuroImage.

[6]  David W Fardo,et al.  APOE-ε2 and APOE-ε4 correlate with increased amyloid accumulation in cerebral vasculature. , 2013, Journal of neuropathology and experimental neurology.

[7]  Olaf Sporns,et al.  Modeling the Impact of Lesions in the Human Brain , 2009, PLoS Comput. Biol..

[8]  David A. Bennett,et al.  Mixed brain pathologies account for most dementia cases in community-dwelling older persons , 2007, Neurology.

[9]  Eduard Kraft,et al.  MRI detection of new hemorrhages: Potential marker of progression in cerebral amyloid angiopathy , 1999, Neurology.

[10]  L. Wilkins.  Characteristic distributions of intracerebral hemorrhage–associated diffusion-weighted lesions , 2013, Neurology.

[11]  L. Sudarsky,et al.  Balance and gait problems in the elderly. , 2012, Handbook of clinical neurology.

[12]  Keith A. Johnson,et al.  Predicting sites of new hemorrhage with amyloid imaging in cerebral amyloid angiopathy , 2012, Neurology.

[13]  R. Gross.  Spotlight on the July 1 Issue , 2014, Neurology.

[14]  J. Brandt.  The Hopkins Verbal Learning Test: Development of a new memory test with six equivalent forms. , 1991 .

[15]  J. Baron,et al.  Acute ischaemic brain lesions in intracerebral haemorrhage: multicentre cross-sectional magnetic resonance imaging study. , 2011, Brain : a journal of neurology.

[16]  Yong He,et al.  Diffusion Tensor Tractography Reveals Abnormal Topological Organization in Structural Cortical Networks in Alzheimer's Disease , 2010, The Journal of Neuroscience.

[17]  Eric E. Smith,et al.  Cerebral microinfarcts: the invisible lesions , 2012, The Lancet Neurology.

[18]  Benjamin J. Shannon,et al.  Molecular, Structural, and Functional Characterization of Alzheimer's Disease: Evidence for a Relationship between Default Activity, Amyloid, and Memory , 2005, The Journal of Neuroscience.

[19]  S. Greenberg,et al.  Posterior white matter disease distribution as a predictor of amyloid angiopathy , 2014, Neurology.

[20]  O. Sporns,et al.  High-cost, high-capacity backbone for global brain communication , 2012, Proceedings of the National Academy of Sciences.

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

[22]  T. Tombaugh,et al.  Normative data stratified by age and education for two measures of verbal fluency: FAS and animal naming. , 1999, Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists.

[23]  Michael Breakspear,et al.  Graph analysis of the human connectome: Promise, progress, and pitfalls , 2013, NeuroImage.

[24]  S. Black,et al.  National Institute of Neurological Disorders and Stroke–Canadian Stroke Network Vascular Cognitive Impairment Harmonization Standards , 2006, Stroke.

[25]  J. Corrigan,et al.  Relationships between parts A and B of the Trail Making Test. , 1987, Journal of clinical psychology.

[26]  Eric E. Smith,et al.  Spatial clustering of hemorrhages in probable cerebral amyloid angiopathy , 2005, Annals of neurology.

[27]  O. Sporns,et al.  Rich-Club Organization of the Human Connectome , 2011, The Journal of Neuroscience.

[28]  Eric E. Smith,et al.  Plasma β-amyloid and white matter lesions in AD, MCI, and cerebral amyloid angiopathy , 2006, Neurology.

[29]  B. Jeurissen,et al.  Improved Sensitivity to Cerebral White Matter Abnormalities in Alzheimer’s Disease with Spherical Deconvolution Based Tractography , 2012, PloS one.

[30]  Anand Viswanathan,et al.  Topography of dilated perivascular spaces in subjects from a memory clinic cohort , 2013, Neurology.

[31]  A. W. Chung,et al.  Cerebral Microbleeds and Cognition in Patients With Symptomatic Small Vessel Disease , 2013, Stroke.

[32]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[33]  D. Wechsler.  Wechsler Adult Intelligence Scale , 2021, Encyclopedia of Evolutionary Psychological Science.

[34]  Andrew Dumas,et al.  Functional magnetic resonance imaging detection of vascular reactivity in cerebral amyloid angiopathy , 2012, Annals of neurology.

[35]  Anand Viswanathan,et al.  Cerebral amyloid angiopathy in the elderly , 2011, Annals of neurology.

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

[37]  Niels D Prins,et al.  Cerebral small-vessel disease and decline in information processing speed, executive function and memory. , 2005, Brain : a journal of neurology.

[38]  J. J. Ryan,et al.  Wechsler Adult Intelligence Scale-III , 2001 .

[39]  Nadim Joni Shah,et al.  Human cortical connectome reconstruction from diffusion weighted MRI: The effect of tractography algorithm , 2012, NeuroImage.

[40]  Yong He,et al.  Discrete Neuroanatomical Networks Are Associated with Specific Cognitive Abilities in Old Age , 2011, The Journal of Neuroscience.

[41]  Alexander Leemans,et al.  Disruption of cerebral networks and cognitive impairment in Alzheimer disease , 2013, Neurology.

[42]  Jan Sijbers,et al.  Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution , 2011, Human brain mapping.

[43]  J. Growdon,et al.  Plasma beta-amyloid and white matter lesions in AD, MCI, and cerebral amyloid angiopathy. , 2006, Neurology.

[44]  Eric E. Smith,et al.  White Matter Alterations in Cerebral Amyloid Angiopathy Measured by Diffusion Tensor Imaging , 2006, Stroke.

[45]  A. Verma,et al.  Imaging of Amyloid Burden and Distribution in Cerebral Amyloid Angiopathy , 2008 .

[46]  Keith A. Johnson,et al.  Cerebral amyloid angiopathy burden associated with leukoaraiosis: A positron emission tomography/magnetic resonance imaging study , 2013, Annals of neurology.

[47]  M. Woodbury,et al.  Density profiles of Alzheimer disease regional brain pathology for the Huddinge brain bank: pattern recognition emulates and expands upon Braak staging , 2000, Experimental Gerontology.

[48]  Alexander Leemans,et al.  Disruption of the Cerebral White Matter Network Is Related to Slowing of Information Processing Speed in Patients With Type 2 Diabetes , 2013, Diabetes.

[49]  A M Dale,et al.  Measuring the thickness of the human cerebral cortex from magnetic resonance images. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[50]  Matthew P. Frosch,et al.  Detection of isolated cerebrovascular β‐amyloid with pittsburgh compound B , 2008, Annals of neurology.

[51]  S. Greenberg,et al.  Clinical diagnosis of cerebral amyloid angiopathy: Validation of the Boston Criteria , 2003, Current atherosclerosis reports.

[52]  D. Werring,et al.  Outcome markers for clinical trials in cerebral amyloid angiopathy , 2001, The Lancet Neurology.

[53]  S. Leurgans,et al.  Microinfarct Pathology, Dementia, and Cognitive Systems , 2011, Stroke.

[54]  S. Greenberg,et al.  Microbleeds do not affect rate of cognitive decline in Alzheimer disease , 2013, Neurology.

[55]  Derek K. Jones,et al.  Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging , 2013, Human brain mapping.

[56]  Diane Podsiadlo,et al.  The Timed “Up & Go”: A Test of Basic Functional Mobility for Frail Elderly Persons , 1991, Journal of the American Geriatrics Society.

[57]  P. Nederkoorn,et al.  Presence and progression of white matter hyperintensities and cognition , 2014, Neurology.

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

[59]  Carlo Pierpaoli,et al.  Informed RESTORE: A method for robust estimation of diffusion tensor from low redundancy datasets in the presence of physiological noise artifacts , 2012, Magnetic resonance in medicine.

[60]  O. Wu,et al.  Microinfarct disruption of white matter structure , 2014, Neurology.

[61]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[62]  He Li,et al.  Disrupted topological organization in white matter structural networks in amnestic mild cognitive impairment: relationship to subtype. , 2012, Radiology.