Mapping Dynamic Changes in Ventricular Volume onto Baseline Cortical Surfaces in Normal Aging, MCI, and Alzheimer’s Disease

  • Sarah K. Madsen
  • Boris A. Gutman
  • Shantanu H. Joshi
  • Arthur W. Toga
  • Clifford R. JackJr.
  • Michael W. Weiner
  • Paul M. Thompson
  • for the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8159)


Ventricular volume (VV) is a powerful global indicator of brain tissue loss on MRI in normal aging and dementia. VV is used by radiologists in clinical practice and has one of the highest obtainable effect sizes for tracking brain change in clinical trials, but it is crucial to relate VV to structural alterations underlying clinical symptoms. Here we identify patterns of thinner cortical gray matter (GM) associated with dynamic changes in lateral VV at 1-year (N=677) and 2-year (N=536) intervals, in the ADNI cohort. People with faster VV loss had thinner baseline cortical GM in temporal, inferior frontal, inferior parietal, and occipital regions (controlling for age, sex, diagnosis). These findings show the patterns of relative cortical atrophy that predict later ventricular enlargement, further validating the use of ventricular segmentations as biomarkers. We may also infer specific patterns of regional cortical degeneration (and perhaps functional changes) that relate to VV expansion.


imaging biomarkers brain imaging magnetic resonance imaging quantitative image analysis statistical analysis temporal/longitudinal image series analysis 


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  1. 1.
    Ferrarini, L., et al.: Ventricular shape biomarkers for Alzheimer’s disease in clinical MR images. Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine 59(2), 260–267 (2008)CrossRefGoogle Scholar
  2. 2.
    Apostolova, L.G., et al.: Hippocampal atrophy and ventricular enlargement in normal aging, mild cognitive impairment (MCI), and Alzheimer Disease. Alzheimer Disease and Associated Disorders 26(1), 17–27 (2012)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Qiu, A., et al.: Regional shape abnormalities in mild cognitive impairment and Alzheimer’s disease. Neuroimage 45(3), 656–661 (2009)CrossRefGoogle Scholar
  4. 4.
    Cardenas, V.A., et al.: Comparison of methods for measuring longitudinal brain change in cognitive impairment and dementia. Neurobiology of Aging 24(4), 537–544 (2003)CrossRefGoogle Scholar
  5. 5.
    Nestor, S.M., et al.: Ventricular enlargement as a possible measure of Alzheimer’s disease progression validated using the Alzheimer’s Disease Neuroimaging Initiative database. Brain 131, 2443–2454 (2008)CrossRefGoogle Scholar
  6. 6.
    Jack Jr., C.R., et al.: Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD. Neurology 62(4), 591–600 (2004)CrossRefGoogle Scholar
  7. 7.
    Fjell, A.M., et al.: One-year brain atrophy evident in healthy aging. The Journal of Neuroscience 29(48), 15223–15231 (2009)CrossRefGoogle Scholar
  8. 8.
    Zhang, Y., et al.: Acceleration of hippocampal atrophy in a non-demented elderly population: the SNAC-K study. International Psychogeriatrics 22(1), 14–25 (2010)CrossRefGoogle Scholar
  9. 9.
    Chou, Y.Y., et al.: Automated ventricular mapping with multi-atlas fluid image alignment reveals genetic effects in Alzheimer’s disease. Neuroimage 40(2), 615–630 (2008)CrossRefGoogle Scholar
  10. 10.
    Chou, Y.Y., et al.: Mapping correlations between ventricular expansion and CSF amyloid and tau biomarkers in 240 subjects with Alzheimer’s disease, mild cognitive impairment and elderly controls. Neuroimage 46(2), 394–410 (2009)CrossRefGoogle Scholar
  11. 11.
    Fischl, B., Dale, A.M.: Measuring the thickness of the human cerebral cortex from magnetic resonance images. PNAS 97(20), 11050–11055 (2000)CrossRefGoogle Scholar
  12. 12.
    Ferrarini, L., et al.: Shape differences of the brain ventricles in Alzheimer’s disease. Neuroimage 32(3), 1060–1069 (2006)CrossRefGoogle Scholar
  13. 13.
    Hua, X., et al.: Optimizing power to track brain degeneration in Alzheimer’s disease and mild cognitive impairment with tensor-based morphometry: An ADNI study of 515 subjects. Neuroimage 48(4), 668–681 (2009)CrossRefGoogle Scholar
  14. 14.
    Paling, S.M., et al.: The application of serial MRI analysis techniques to the study of cerebral atrophy in late-onset dementia. Medical Image Analysis  8(1), 69–79 (2004)CrossRefGoogle Scholar
  15. 15.
    Gutman, B.A., et al.: Shape matching with medial curves and 1-D group-wise registration. 2012 9th IEEE International Symposium on Biomedical Imaging, 716–719 (2012)Google Scholar
  16. 16.
    Benjamini, Y., Hochberg, Y.: Controlling the False Discovery Rate - a Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological) 57(1), 289–300 (1995)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Braak, H., Braak, E.: Neuropathological stageing of Alzheimer-related changes. Acta Neuropathologica 82(4), 239–259 (1991)CrossRefGoogle Scholar
  18. 18.
    Braskie, M.N., et al.: Plaque and tangle imaging and cognition in normal aging and Alzheimer’s disease. Neurobiology of Aging 31(10), 1669–1678 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Sarah K. Madsen
    • 1
  • Boris A. Gutman
    • 1
  • Shantanu H. Joshi
    • 2
  • Arthur W. Toga
    • 1
  • Clifford R. JackJr.
    • 3
  • Michael W. Weiner
    • 4
    • 5
  • Paul M. Thompson
    • 1
  • for the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
    • 1
  1. 1.Imaging Genetics Center, Laboratory of Neuro ImagingUCLA School of MedicineLos AngelesUSA
  2. 2.Laboratory of Neuro ImagingUCLA School of MedicineLos AngelesUSA
  3. 3.Mayo ClinicRochesterUSA
  4. 4.Departments of Radiology, Medicine, PsychiatryUC San FranciscoSan FranciscoUSA
  5. 5.Department of Veterans Affairs Medical CenterSan FranciscoUSA

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