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Information-Theoretic Clustering of Neuroimaging Metrics Related to Cognitive Decline in the Elderly

  • Madelaine Daianu
  • Greg Ver Steeg
  • Adam Mezher
  • Neda Jahanshad
  • Talia M. Nir
  • Xiaoran Yan
  • Gautam Prasad
  • Kristina Lerman
  • Aram Galstyan
  • Paul M. Thompson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9601)

Abstract

As Alzheimer’s disease progresses, there are changes in metrics of brain atrophy and network breakdown derived from anatomical or diffusion MRI. Neuroimaging biomarkers of cognitive decline are crucial to identify, but few studies have investigated how sets of biomarkers cluster in terms of the information they provide. Here, we evaluated more than 700 frequently studied diffusion and anatomical measures in 247 elderly participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We used a novel unsupervised machine learning technique - CorEx - to identify groups of measures with high multivariate mutual information; we computed latent factors to explain correlations among them. We visualized groups of measures discovered by CorEx in a hierarchical structure and determined how well they predict cognitive decline. Clusters of variables significantly predicted cognitive decline, including measures of cortical gray matter, and correlated measures of brain networks derived from graph theory and spectral graph theory.

Keywords

Machine learning Diffusion weighted imaging Brain connectivity Spectral graph theory Gray matter 

Notes

Acknowledgments

Algorithm development and image analysis for this study was funded, in part, by grants to PT from the NIBIB (R01 EB008281, R01 EB008432) and by the NIA, NIBIB, NIMH, the National Library of Medicine, and the National Center for Research Resources (AG016570, AG040060, EB01651, MH097268, LM05639, RR019771 to PT). Data collection and sharing for this project was funded by ADNI (NIH Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through contributions from the following: Abbott; Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Amorfix Life Sciences Ltd.; AstraZeneca; Bayer HealthCare; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health. The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. This research was also supported by NIH grants P30 AG010129 and K01 AG030514 from the National Institute of General Medical Sciences; and by a Consortium grant (U54 EB020403) from the NIH Institutes contributing to the Big Data to Knowledge (BD2 K) Initiative, including the NIBIB and NCI.

References

  1. 1.
    Daianu, M., Jahanshad, N., Nir, T.M., Leonardo, C.D., Clifford, J.R.J., Weiner, M.W., Bernstein, M.A., Thompson, P.M.: Algebraic connectivity of brain networks shows patterns of segregation leading to reduced network robustness in Alzheimer’s disease. In: O’Donnell, L., Nedjati-Gilani, G., Rathi, Y., Reisert, M., Schneider, T. (eds.) Medical Image Computing and Computer Assisted Intervention (MICCAI), Computational Diffusion MRI, pp. 55–64. Springer, Switzerland (2014)Google Scholar
  2. 2.
    Daianu, M., Mezher, A., Jahanshad, N., Hibar, D.P., Nir, T.M., Jack, C.R., Weiner, M.W., Bernstein, M.A., Thompson, P.M.: Spectral graph theory and graph energy metrics show evidence for the Alzheimer’s disease disconnection syndrome in APOE-4 gene carriers. In: IEEE International Symposium of Biomedical Imaging (ISBI), pp. 458–461 (2015)Google Scholar
  3. 3.
    Ver Steeg, G., Galstyan, A.: Maximally informative hierarchical representations of high-dimensional data. In: Artificial Intelligence and Statistics Conference (2014)Google Scholar
  4. 4.
    Ver Steeg, G., Galstyan, A.: Discovering structure in high-dimensional data through correlation explanation. In: Advances in Neural Information Processing Systems (2014)Google Scholar
  5. 5.
    Madsen, S.K., Ver Steeg, G., Daianu, M., Mezher, A., Jahanshad, N., Nir, T.M., Hua, X., Gutman, B.A., Galstyan, A., Thompson, P.M.: Relative value of diverse brain MRI and blood-based biomarkers for predicting cognitive decline in the elderly. In: The International Society for Optics and Photonics (SPIE), Medical Imaging 2016: Image Processing (2015, in Press)Google Scholar
  6. 6.
    Daianu, M., Jahanshad, N., Nir, T.M., Jack Jr., C.R., Weiner, M.W., Bernstein, M.A., Thompson, P.M., Alzheimer’s Disease Neuroimaging, I.: Rich club analysis in the Alzheimer’s disease connectome reveals a relatively undisturbed structural core network. Hum. Brain Mapp. 36, 3087–3103 (2015)Google Scholar
  7. 7.
    Daianu, M., Jahanshad, N., Nir, T.M., Toga, A.W., Jack Jr., C.R., Weine, M.W., Thompson, P.M., Alzheimer’s Disease Neuroimaging, I.: Breakdown of brain connectivity between normal aging and Alzheimer’s disease: a structural k-core network analysis. Brain Connectivity 3, 407–422 (2013)Google Scholar
  8. 8.
    Daianu, M., Dennis, E.L., Jahanshad, N., Nir, T.M., Toga, A.W., Jack, C.R., Weiner, M.W., Thompson, P.M.: Alzheimer’s disease disrupts rich club organization in brain connectivity networks. In: IEEE International Symposium of Biomedical Imaging (ISBI), pp. 266–269 (2013)Google Scholar
  9. 9.
    Mori, S., Oishi, K., Jiang, H., Jiang, L., Li, X., Akhter, K., Hua, K., Faria, A.V., Mahmood, A., Woods, R., Toga, A.W., Pike, G.B., Neto, P.R., Evans, A., Zhang, J., Huang, H., Miller, M.I., van Zijl, P., Mazziotta, J.: Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. NeuroImage 40, 570–582 (2008)CrossRefGoogle Scholar
  10. 10.
    Leow, A., Huang, S.-C., Geng, A., Becker, J., Davis, S., Toga, A.W., Thompson, P.: Inverse consistent mapping in 3D deformable image registration: its construction and statistical properties. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 493–503. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Fischl, B.: Automatically parcellating the human cerebral cortex. Cereb. Cortex 14, 11–22 (2004)CrossRefGoogle Scholar
  12. 12.
    Desikan, R.S., Segonne, 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, 968–980 (2006)CrossRefGoogle Scholar
  13. 13.
    Sporns, O.: The human connectome: a complex network. Ann. N. Y. Acad. Sci. 1224, 109–125 (2011)CrossRefGoogle Scholar
  14. 14.
    Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52, 1059–1069 (2010)CrossRefGoogle Scholar
  15. 15.
    Honey, C.J., Kotter, R., Breakspear, M., Sporns, O.: Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc. Natl. Acad. Sci. U.S.A. 104, 10240–10245 (2007)CrossRefGoogle Scholar
  16. 16.
    Brandes, U.: A faster algorithm for betweenness centrality. J. Math. Sociol. 25, 163–177 (2001)CrossRefMATHGoogle Scholar
  17. 17.
    Ghosh, R., Lerman, K., Teng, S.H., Yan, X.: The interplay between dynamics and networks: centrality, communities, and cheeger inequality. Soc. Inf. Netw. (2014)Google Scholar
  18. 18.
    Roussotte, F.F., Daianu, M., Jahanshad, N., Leonardo, C.D., Thompson, P.M.: Neuroimaging and genetic risk for Alzheimer’s disease and addiction-related degenerative brain disorders. Brain Imaging Behav. 8, 217–233 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Madelaine Daianu
    • 1
    • 2
  • Greg Ver Steeg
    • 3
  • Adam Mezher
    • 1
  • Neda Jahanshad
    • 1
  • Talia M. Nir
    • 1
  • Xiaoran Yan
    • 2
  • Gautam Prasad
    • 1
  • Kristina Lerman
    • 3
  • Aram Galstyan
    • 3
  • Paul M. Thompson
    • 1
    • 2
    • 4
  1. 1.Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and InformaticsUniversity of Southern CaliforniaMarina del ReyUSA
  2. 2.Department of NeurologyUCLA School of MedicineLos AngelesUSA
  3. 3.USC Information Sciences InstituteMarina del ReyUSA
  4. 4.Departments of Neurology, Psychiatry, Radiology, Engineering, Pediatrics, and OphthalmologyUniversity of Southern CaliforniaLos AngelesUSA

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