Spatial Covariance Modeling Analysis of Hypertension on Cognitive Aging

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 269)


It’s becoming increasingly clear that hypertension (HTN) is at the root of much cognitive decline previously attributed to aging. Long before patients in clinical stroke, HTN has caused a certain degree of damage on brain cognitive function. In this study, magnetic resonance images (MRI) of hypertensive and control group will be first collected, then processed by voxel based morphology (VBM), and further studied with spatial covariance modeling. Testing the effect of HTN on the expression of the age-related gray matter pattern revealed that hypertensive showed higher expression of age-related pattern than normotensives. HTN may have an accelerated effect on normal cognitive aging process.


Hypertension (HTN) Spatial covariance modeling Voxel-based morphometry (VBM) 


  1. 1.
    Vegard S, Elke L, Daniela W (2012) Variation in cognitive functioning as a refined approach to comparing aging across countries. Proc Natl Acad Sci 109(3):770–774CrossRefGoogle Scholar
  2. 2.
    Ritchie K, Artero S, Touchon J (2001) Classification criteria for mild cognitive impairment: a population-based validation study. Neurology 56:37–42CrossRefGoogle Scholar
  3. 3.
    Elias PK, Elias MF, Agostino RB et al (1997) NIDDM and blood pressure as risk factors for poor cognitive performance. Diabetes Care 20(9):1388–1395CrossRefGoogle Scholar
  4. 4.
    Raz N, Rodrigue KM, Acker JD (2003) Hypertension and the brain: vulnerability of the prefrontal regions and executive function. Behav Neurosci 117(6):1169–1180CrossRefGoogle Scholar
  5. 5.
    Reitz C, Tang M, Manly J et al (2007) Hypertension and the risk of mild cognitive impairment. Arch Neurol 64(12):1734–1740CrossRefGoogle Scholar
  6. 6.
    Glodzik L, Mosconi L, Tsui W et al (2012) Alzheimer’s disease markers, hypertension, and gray matter damage in normal elderly. Neurobiol Aging 33(7):1215–1227CrossRefGoogle Scholar
  7. 7.
    Moeller J, Strother S, Sidtis J et al (1987) Scaled subprofile model: a statistical approach to the analysis of functional patterns in positron emission tomographic data. J Cereb Blood Flow Metab 7(5):649–658CrossRefGoogle Scholar
  8. 8.
    Strother S, Anderson J, Schaper K et al (1995) Principal component analysis and the scaled subprofile model compared to intersubject averaging and statistical parametric mapping: I. “Functional connectivity” of the human motor system studied with [15O]water PET. J Cereb Blood Flow Metab 15(5):738–753CrossRefGoogle Scholar
  9. 9.
    Pereira JM, Xiong L, Acosta-Cabronero J et al (2010) Registration accuracy for VBM studies varies according to region and degenerative disease grouping. Neuroimage 49(3):2205–2215CrossRefGoogle Scholar
  10. 10.
    Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17(1):87–97CrossRefGoogle Scholar
  11. 11.
    Boyes RG, Gunter JL, Frost C et al (2008) Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils. Neuroimage 39(4):1752–1762CrossRefGoogle Scholar
  12. 12.
    Ashburner J, Friston KJ (2001) Why voxel-based morphometry should be used. Neuroimage 14(6):1238–1243CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Lan Lin
    • 1
  • Wei-wei Wu
    • 1
  • Shui-cai Wu
    • 1
  • Guang-yu Bin
    • 1
  1. 1.College of Life Science and BioengineeringBeijing University of TechnologyBeijingChina

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