Connectivity network breakdown predicts imminent volumetric atrophy in early mild cognitive impairment

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7509 LNCS, Issue , 2012, Pages 41-50

  Nir, Talia M ^a   Jahanshad, Neda ^a   Toga, Arthur W ^a   Jack, Clifford R ^b   Weiner, Michael W ^c   Thompson, Paul M ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b MAYO CLINIC   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

ADNI; brain networks; connectivity; DTI; Graph theory; small worldness; TBM; tractography; white matter

Indexed keywords

ADNI; BRAIN NETWORKS; CONNECTIVITY; DTI; SMALL WORLDNESS; TBM; TRACTOGRAPHY; WHITE MATTER;

GRAPH THEORY; IMAGE ANALYSIS;

BRAIN;

EID: 84868222646     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33530-3_4     Document Type: Conference Paper

References (22)

1. Zhang, Y.-Z., et al.: Comparison of diffusion tensor image study in associa-tion fiber tracts among normal, amnestic mild cognitive impairment, and Alzheimer's patients. Neurol. India 59, 168-173 (2011)
2. Liu, Y., et al.: Diffusion tensor imaging and tract-based spatial statistics in Alzheimer's disease and mild cognitive impairment. Neurobiol. Aging 32, 1558-1571 (2011)
3. Hilgetag, C.C., et al.: Anatomical connectivity defines the organization of clusters of cortical areas in the macaque and the cat. Phil. Trans. R. Soc. Lond. 355, 91-110 (2000)
4. He, Y., et al.: Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer's disease. J. Neurosci. 28, 4756-4766 (2008)
5. Stam, C.J., et al.: Small-world networks and functional connectivity in Alzheimer's disease. Cereb. Cortex 17, 92-99 (2007)
6. Sanz-Arigita, E.J., et al.: Loss of 'small-world' networks in Alzheimer's disease: graph analysis of FMRI resting-state functional connectivity. PLoS One 5, e13788 (2010)
7. Iglesias, J.E., et al.: Robust brain extraction across datasets and compari-son with publicly available methods. IEEE Trans. on Medical Imaging 30(9), 1617-1634 (2011)
8. Holmes, C.J., et al.: Enhancement of MR images using registration for signal averaging. JCAT 22(2), 324-333 (1998)
9. Jenkinson, M., et al.: Improved optimisation for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17(2), 825-841 (2002)
10. Smith, S.M.: Fast robust automated brain extraction. Hum. Brain Mapp. 17(3), 143-155 (2002)
11. Leow, A.D., et al.: Statistical properties of Jacobian maps and the realiza-tion of unbiased large-deformation nonlinear image registration. IEEE Trans. on Medical Imaging 26(6), 822-832 (2007)
12. Aganj, I., et al.: Reconstruction of the orientation distribution function in single- and multiple-shell q-ball imaging within constant solid angle. Magn. Reson. Med. 64(2), 554-566 (2010)
13. Aganj, I., et al.: A Hough transform global probabilistic approach to mul-tiple-subject diffusion MRI tractography. Med. Image Analysis 15, 414-425 (2011)
14. Fischl, B., et al.: Automatically parcellating the human cerebral cortex. Cereb. Cortex 14, 11-22 (2004)
15. Jahanshad, N., et al.: Sex differences in the Human Connectome: 4-Tesla high angular resolution diffusion tensor imaging (HARDI) tractography in 234 young adult twins. In: ISBI 2011, pp. 939-943 (2011)
16. Rubinov, M., Sporns, O.: Complex network measures of brain connec-tivity: Uses and interpretations. NeuroImage 3, 1059-1069 (2010)
17. Leporé, N., Brun, C.A., Pennec, X., Chou, Y.-Y., Lopez, O.L., Aizenstein, H.J., Becker, J.T., Toga, A.W., Thompson, P.M.: Mean Template for Tensor-Based Morphometry Using Deformation Tensors. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 826-833. Springer, Heidelberg (2007)
18. Gutman, B., et al.: Creating unbiased minimal deformation templates for brain volume registration. In: OHBM 2010 (2010)
19. Yanovsky, I., et al.: Topology preserving log-unbiased nonlinear image registration: theory and implementation. In: IEEE CVPR 2007, pp. 1-8 (2007)
20. Langers, D.R., et al.: Enhanced signal detection in neuroimaging by means of regional control of the global false discovery rate. NeuroImage 38, 43-56 (2007)
21. Nir, T.M., et al.: Small world network measures predict white matter de-generation in patients with early-stage mild cognitive impairment. In: ISBI 2012 (2012)
22. Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Rev. Neurosci. 10, 186-198 (2009)