Temporal Trajectory and Progression Score Estimation from Voxelwise Longitudinal Imaging Measures: Application to Amyloid Imaging

  • Murat BilgelEmail author
  • Bruno Jedynak
  • Dean F. Wong
  • Susan M. Resnick
  • Jerry L. Prince
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9123)


Cortical \(\beta \)-amyloid deposition begins in Alzheimer’s disease (AD) years before the onset of any clinical symptoms. It is therefore important to determine the temporal trajectories of amyloid deposition in these earliest stages in order to better understand their associations with progression to AD. A method for estimating the temporal trajectories of voxelwise amyloid as measured using longitudinal positron emission tomography (PET) imaging is presented. The method involves the estimation of a score for each subject visit based on the PET data that reflects their amyloid progression. This amyloid progression score allows subjects with similar progressions to be aligned and analyzed together. The estimation of the progression scores and the amyloid trajectory parameters are performed using an expectation-maximization algorithm. The correlations among the voxel measures of amyloid are modeled to reflect the spatial nature of PET images. Simulation results show that model parameters are captured well at a variety of noise and spatial correlation levels. The method is applied to longitudinal amyloid imaging data considering each cerebral hemisphere separately. The results are consistent across the hemispheres and agree with a global index of brain amyloid known as mean cortical DVR. Unlike mean cortical DVR, which depends on a priori defined regions, the progression score extracted by the method is data-driven and does not make assumptions about regional longitudinal changes. Compared to regressing on age at each voxel, the longitudinal trajectory slopes estimated using the proposed method show better localized longitudinal changes.


Progression score Amyloid Pittsburgh compound B PiB Longitudinal image analysis 



This research was supported in part by the Intramural Research Program of the National Institutes of Health.


  1. 1.
    Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26–41 (2008)CrossRefGoogle Scholar
  2. 2.
    Avants, B.B., Yushkevich, P., Pluta, J., Minkoff, D., Korczykowski, M., Detre, J., Gee, J.C.: The optimal template effect in hippocampus studies of diseased populations. NeuroImage 49(3), 2457–2466 (2010)CrossRefGoogle Scholar
  3. 3.
    Bilgel, M., An, Y., Lang, A., Prince, J., Ferrucci, L., Jedynak, B., Resnick, S.M.: Trajectories of Alzheimer disease-related cognitive measures in a longitudinal sample. Alzheimer’s Dement. 10(6), 735–742 (2014)CrossRefGoogle Scholar
  4. 4.
    Bilgel, M., Carass, A., Resnick, S.M., Wong, D.F., Prince, J.L.: Deformation field correction for spatial normalization of PET images using a population-derived partial least squares model. In: Wu, G., Zhang, D., Zhou, L. (eds.) MLMI 2014. LNCS, vol. 8679, pp. 198–206. Springer, Heidelberg (2014) Google Scholar
  5. 5.
    Cressie, N., Hawkins, D.M.: Robust estimation of the variogram. J. Int. Assoc. Math. Geol. 12(2), 115–125 (1980)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Dale, A., Fischl, B., Sereno, M.: Cortical surface-based analysis: I. segmentation and surface reconstruction. NeuroImage 194, 179–194 (1999)CrossRefGoogle Scholar
  7. 7.
    Desikan, R.S., Ségonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., Buckner, R.L., Dale, A.M., Maguire, R.P., Hyman, B.T., Albert, M.S., Killiany, R.J.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31(3), 968–980 (2006)CrossRefGoogle Scholar
  8. 8.
    Jack, C.R., Knopman, D.S., Jagust, W.J., Petersen, R.C., Weiner, M.W., Aisen, P.S., Shaw, L.M., Vemuri, P., Wiste, H.J., Weigand, S.D., Lesnick, T.G., Pankratz, V.S., Donohue, M.C., Trojanowski, J.Q.: Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 12(2), 207–216 (2013)CrossRefGoogle Scholar
  9. 9.
    Jedynak, B.M., Lang, A., Liu, B., Katz, E., Zhang, Y., Wyman, B.T., Raunig, D., Jedynak, C.P., Caffo, B., Prince, J.L.: A computational neurodegenerative disease progression score: method and results with the Alzheimer’s disease neuroimaging initiative cohort. NeuroImage 63(3), 1478–1486 (2012)CrossRefGoogle Scholar
  10. 10.
    Jenkinson, M., Bannister, P., Brady, M., Smith, S.: Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17(2), 825–841 (2002)CrossRefGoogle Scholar
  11. 11.
    Resnick, S.M., Goldszal, A.F., Davatzikos, C., Golski, S., Kraut, M.A., Metter, E.J., Bryan, R.N., Zonderman, A.B.: One-year age changes in MRI brain volumes in older adults. Cereb. Cortex 10(5), 464–472 (2000)CrossRefGoogle Scholar
  12. 12.
    Shock, N.W., Greulich, R.C., Andres, R., Arenberg, D., Costa Jr., P.T., Lakatta, E.G., Tobin, J.D.: Normal human aging: The Baltimore Longitudinal Study of Aging. Technical report, U.S. Government Printing Office, Washington, DC (1984)Google Scholar
  13. 13.
    Younes, L., Albert, M., Miller, M.I.: Inferring changepoint times of medial temporal lobe morphometric change in preclinical Alzheimer’s disease. NeuroImage Clin. 5, 178–187 (2014)CrossRefGoogle Scholar
  14. 14.
    Young, A.L., Oxtoby, N.P., Daga, P., Cash, D.M., Fox, N.C., Ourselin, S., Schott, J.M., Alexander, D.C.: A data-driven model of biomarker changes in sporadic Alzheimer’s disease. Brain 137, 2564–2577 (2014)CrossRefGoogle Scholar
  15. 15.
    Zhou, Y., Endres, C.J., Brašić, J.R., Huang, S.C., Wong, D.F.: Linear regression with spatial constraint to generate parametric images of ligand-receptor dynamic PET studies with a simplified reference tissue model. NeuroImage 18(4), 975–989 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Murat Bilgel
    • 1
    • 2
    Email author
  • Bruno Jedynak
    • 3
  • Dean F. Wong
    • 4
  • Susan M. Resnick
    • 2
  • Jerry L. Prince
    • 1
    • 3
    • 4
    • 5
  1. 1.Department of Biomedical Engineering, School of EngineeringJohns Hopkins UniversityBaltimoreUSA
  2. 2.Laboratory of Behavioral NeuroscienceNational Institute on Aging, NIHBaltimoreUSA
  3. 3.Department of Applied Mathematics and Statistics, School of EngineeringJohns Hopkins UniversityBaltimoreUSA
  4. 4.Department of Radiology, School of MedicineJohns Hopkins UniversityBaltimoreUSA
  5. 5.Department of Electrical and Computer Engineering, School of EngineeringJohns Hopkins UniversityBaltimoreUSA

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