Regional staging of white matter signal abnormalities in aging and Alzheimer's disease

White matter lesions, quantified as ‘white matter signal abnormalities’ (WMSA) on neuroimaging, are common incidental findings on brain images of older adults. This tissue damage is linked to cerebrovascular dysfunction and is associated with cognitive decline. The regional distribution of WMSA throughout the cerebral white matter has been described at a gross scale; however, to date no prior study has described regional patterns relative to cortical gyral landmarks which may be important for understanding functional impact. Additionally, no prior study has described how regional WMSA volume scales with total global WMSA. Such information could be used in the creation of a pathologic ‘staging’ of WMSA through a detailed regional characterization at the individual level. Magnetic resonance imaging data from 97 cognitively-healthy older individuals (OC) aged 52–90 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study were processed using a novel WMSA labeling procedure described in our prior work. WMSA were quantified regionally using a procedure that segments the cerebral white matter into 35 bilateral units based on proximity to landmarks in the cerebral cortex. An initial staging was performed by quantifying the regional WMSA volume in four groups based on quartiles of total WMSA volume (quartiles I–IV). A consistent spatial pattern of WMSA accumulation was observed with increasing quartile. A clustering procedure was then used to distinguish regions based on patterns of scaling of regional WMSA to global WMSA. Three patterns were extracted that showed high, medium, and non-scaling with global WMSA. Regions in the high-scaling cluster included periventricular, caudal and rostral middle frontal, inferior and superior parietal, supramarginal, and precuneus white matter. A data-driven staging procedure was then created based on patterns of WMSA scaling and specific regional cut-off values from the quartile analyses. Individuals with Alzheimer's disease (AD) and mild cognitive impairment (MCI) were then additionally staged, and significant differences in the percent of each diagnostic group in Stages I and IV were observed, with more AD individuals residing in Stage IV and more OC and MCI individuals residing in Stage I. These data demonstrate a consistent regional scaling relationship between global and regional WMSA that can be used to classify individuals into one of four stages of white matter disease. White matter staging could play an important role in a better understanding and the treatment of cerebrovascular contributions to brain aging and dementia.

[1]  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.

[2]  M. Dichgans,et al.  Mechanisms of sporadic cerebral small vessel disease: insights from neuroimaging , 2013, The Lancet Neurology.

[3]  A. Gatherer,et al.  Sarcoma of the Larynx , 1958, The Journal of Laryngology & Otology.

[4]  M. Bondi,et al.  Heterogeneity in mild cognitive impairment: Differences in neuropsychological profile and associated white matter lesion pathology , 2009, Journal of the International Neuropsychological Society.

[5]  Anders M. Dale,et al.  Reliability of MRI-derived measurements of human cerebral cortical thickness: The effects of field strength, scanner upgrade and manufacturer , 2006, NeuroImage.

[6]  Maja A. A. Binnewijzend,et al.  Brain volume and white matter hyperintensities as determinants of cerebral blood flow in Alzheimer's disease , 2014, Neurobiology of Aging.

[7]  Owen Carmichael,et al.  White Matter Hyperintensities and Their Penumbra Lie Along a Continuum of Injury in the Aging Brain , 2014, Stroke.

[8]  A. Dale,et al.  High‐resolution intersubject averaging and a coordinate system for the cortical surface , 1999, Human brain mapping.

[9]  Nikos Makris,et al.  Automatically parcellating the human cerebral cortex. , 2004, Cerebral cortex.

[10]  Frederik Barkhof,et al.  Visual Rating Scales for Age-Related White Matter Changes (Leukoaraiosis): Can the Heterogeneity Be Reduced? , 2002, Stroke.

[11]  C. Iadecola,et al.  The Pathobiology of Vascular Dementia , 2013, Neuron.

[12]  J. V. van Swieten,et al.  Periventricular lesions in the white matter on magnetic resonance imaging in the elderly. A morphometric correlation with arteriolosclerosis and dilated perivascular spaces. , 1991, Brain : a journal of neurology.

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

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

[15]  B. Fischl,et al.  Entorhinal verrucae geometry is coincident and correlates with Alzheimer’s lesions: a combined neuropathology and high-resolution ex vivo MRI analysis , 2011, Acta Neuropathologica.

[16]  M. A. Bell,et al.  Features of the cerebral vascular pattern that predict vulnerability to perfusion or oxygenation deficiency: an anatomic study. , 1990, AJNR. American journal of neuroradiology.

[17]  Carl D Langefeld,et al.  Quantification of afferent vessels shows reduced brain vascular density in subjects with leukoaraiosis. , 2004, Radiology.

[18]  H Lechner,et al.  White matter signal abnormalities in normal individuals: correlation with carotid ultrasonography, cerebral blood flow measurements, and cerebrovascular risk factors. , 1988, Stroke.

[19]  E Auffray,et al.  Longitudinal study of blood pressure and white matter hyperintensities , 2001, Neurology.

[20]  H. Damasio,et al.  A computed tomographic guide to the identification of cerebral vascular territories. , 1983, Archives of neurology.

[21]  H. Braak,et al.  Neuropathological stageing of Alzheimer-related changes , 2004, Acta Neuropathologica.

[22]  David H. Salat,et al.  The Relationship between Cortical Blood Flow and Sub-Cortical White-Matter Health across the Adult Age Span , 2013, PloS one.

[23]  Nora Mattek,et al.  Neuropathologic basis of white matter hyperintensity accumulation with advanced age , 2013, Neurology.

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

[25]  Anders M. Dale,et al.  Sequence-independent segmentation of magnetic resonance images , 2004, NeuroImage.

[26]  K. Zou,et al.  Quantitative analysis of MRI signal abnormalities of brain white matter with high reproducibility and accuracy , 2002, Journal of magnetic resonance imaging : JMRI.

[27]  W. Longstreth Brain abnormalities in the elderly: frequency and predictors in the United States (the Cardiovascular Health Study). Cardiovascular Health Study Collaborative Research Group. , 1998, Journal of neural transmission. Supplementum.

[28]  L A Saint-Louis,et al.  Periventricular hyperintensity as seen by magnetic resonance: prevalence and significance. , 1986, AJR. American journal of roentgenology.

[29]  B. Zlokovic Neurovascular pathways to neurodegeneration in Alzheimer's disease and other disorders , 2011, Nature Reviews Neuroscience.

[30]  D. Harvey,et al.  Measures of brain morphology and infarction in the framingham heart study: establishing what is normal , 2005, Neurobiology of Aging.

[31]  Massimo Filippi,et al.  Assessment of white matter tract damage in mild cognitive impairment and Alzheimer's disease , 2010, Human brain mapping.

[32]  Yaakov Stern,et al.  Reduction in cerebral blood flow in areas appearing as white matter hyperintensities on magnetic resonance imaging , 2009, Psychiatry Research: Neuroimaging.

[33]  M. O’Sullivan,et al.  Patterns of cerebral blood flow reduction in patients with ischemic leukoaraiosis , 2002, Neurology.

[34]  R Stewart,et al.  Cerebral white matter lesions and subjective cognitive dysfunction: The Rotterdam Scan Study , 2001, Neurology.

[35]  Neuroanatomy—An Atlas of Structures, Sections, and Systems , 1983 .

[36]  Anders M. Dale,et al.  Cortical Surface-Based Analysis I. Segmentation and Surface Reconstruction , 1999, NeuroImage.

[37]  Nick C Fox,et al.  The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods , 2008, Journal of magnetic resonance imaging : JMRI.

[38]  D. Harvey,et al.  Extent and distribution of white matter hyperintensities in normal aging, MCI, and AD , 2006, Neurology.

[39]  Eric E. Smith,et al.  Reduced Blood Flow in Normal White Matter Predicts Development of Leukoaraiosis , 2015, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[40]  G. V. Van Hoesen,et al.  The topographical and neuroanatomical distribution of neurofibrillary tangles and neuritic plaques in the cerebral cortex of patients with Alzheimer's disease. , 1991, Cerebral cortex.

[41]  M. van Buchem,et al.  Not all age-related white matter hyperintensities are the same: a magnetization transfer imaging study. , 2006, AJNR. American journal of neuroradiology.

[42]  Jordan Muraskin,et al.  Structural neuroimaging in Alzheimer's disease: do white matter hyperintensities matter? , 2009, Dialogues in clinical neuroscience.

[43]  A. Dale,et al.  Improved Localizadon of Cortical Activity by Combining EEG and MEG with MRI Cortical Surface Reconstruction: A Linear Approach , 1993, Journal of Cognitive Neuroscience.

[44]  Eric E. Smith,et al.  White matter signal abnormality quality differentiates mild cognitive impairment that converts to Alzheimer's disease from nonconverters , 2015, Neurobiology of Aging.

[45]  P. Scheltens,et al.  A semiquantative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging , 1993, Journal of the Neurological Sciences.

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

[47]  Anders M. Dale,et al.  Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex , 2001, IEEE Transactions on Medical Imaging.

[48]  Massimo Filippi,et al.  The effect of white matter lesions on cognition in the elderly—small but detectable , 2007, Nature Clinical Practice Neurology.

[49]  David H. Salat,et al.  Age-associated reductions in cerebral blood flow are independent from regional atrophy , 2011, NeuroImage.

[50]  Bruce Fischl,et al.  Regional white matter volume differences in nondemented aging and Alzheimer's disease , 2009, NeuroImage.

[51]  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.

[52]  J M Wardlaw,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.

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

[54]  Anders M. Dale,et al.  Reliability in multi-site structural MRI studies: Effects of gradient non-linearity correction on phantom and human data , 2006, NeuroImage.

[55]  Jordan Muraskin,et al.  White matter hyperintensities and cerebral amyloidosis: necessary and sufficient for clinical expression of Alzheimer disease? , 2013, JAMA neurology.