Early Alzheimer’s Disease Progression Detection using Multi-subnetworks of the Brain

Jaroslav Rokicki, Hiyoshi Kazuko, Francois-Benoit Vialatte, Andrius Ušinskas, Andrzej Cichocki

2012

Abstract

Alzheimer’s disease is neurodegenerative disorder believed to affect 24.3 million people worldwide. Proposed MRI based disease progression markers have shown ability to perform the classification between the Alzheimer’s Disease (AD), Mild Cognitive Impariment (MCI) and Normal Cognitive (NC) subjects. We exploited two approaches, first one is to use single sub-network volume as a feature, second to use a network of most discriminative sub-networks. Multi-feature approach showed improvement by 4.5% in AD/NC classification case, and 1.5 % in MCI/NC case. Study was summarized for 48 AD, 119 MCI and 66 NC subjects.

References

  1. Braak, H. and Braak, E. (1997). Staging of alzheimerrelated cortical destruction. Int Psychogeriatry, 9 Suppl 1.
  2. Chupin, M., Gerardin, E., Cuingnet, R., Boutet, C., Lemieux, L., Lehericy, S., Benali, H., Garnero, L., and Colliot, O. (2009). Fully automatic hippocampus segmentation and classification in alzheimer's disease and mild cognitive impairment applied on data from adni. Hippocampus, 19(6):579-587.
  3. Neurobiology of Aging, 29(4):514-523.
  4. Desikan, R. S., Cabral, H. J., Hess, C. P., Dillon, W. P., Glastonbury, C. M., Weiner, M. W., Schmansky, N. J., Greve, D. N., Salat, D. H., Buckner, R. L., Fischl, B., and Initiative, A. D. N. (2009). Automated mri measures identify individuals with mild cognitive impairment and alzheimer's disease. Brain, 132(8):2048- 2057.
  5. Du, A. T., Schuff, N., Amend, D., Laakso, M. P., Hsu, Y. Y., Jagust, W. J., Yaffe, K., Kramer, J. H., Reed, B., Norman, D., Chui, H. C., and Weiner, M. W. (2001). Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and alzheimer's disease. Journal of Neurology, Neurosurgery & Psychiatry, 71(4):441-447.
  6. Fan, Y., Batmanghelich, N., Clark, C. M., and Davatzikos, C. (2008). Spatial patterns of brain atrophy in mci patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. NeuroImage, 39(4):1731-1743.
  7. Fennema-Notestine, C., Hagler, D. J., McEvoy, L. K., Fleisher, A. S., Wu, E. H., Karow, D. S., and Dale, A. M. (2009). Structural mri biomarkers for preclinical and mild alzheimer's disease. Human Brain Mapping, 30(10):3238-3253.
  8. Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., and Dale, A. M. (2002). Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3):341- 355.
  9. Gerardin, E., Chetelat, G., Chupin, M., Cuingnet, R., Desgranges, B., Kim, H.-S., Niethammer, M., Dubois, B., Lehericy, S., Garnero, L., Eustache, F., and Colliot, O. (2009). Multidimensional classification of hippocampal shape features discriminates alzheimer's disease and mild cognitive impairment from normal aging. NeuroImage, 47(4):1476-1486.
  10. Goldszal, A. F., Davatzikos, C., Pham, D. L., Yan, M. X., Bryan, R. N., and Resnick, S. M. (1998). An imageprocessing system for qualitative and quantitative volumetric analysis of brain images. Journal Of Computer Assisted Tomography, 22(5):827-837.
  11. Juottonen, K., Laakso, M., Insausti, R., Lehtovirta, M., Pitknen, A., Partanen, K., and Soininen, H. (1998). Volumes of the entorhinal and perirhinal cortices in alzheimers disease. Neurobiology of Aging, 19(1):15- 22.
  12. Kloppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J. D., Fox, N. C., Jack, C. R., Ashburner, J., and Frackowiak, R. S. J. (2008). Automatic classification of mr scans in alzheimer's disease. Brain, 131(3):681-689.
  13. Lotjonen, J., Wolz, R., Koikkalainen, J., Julkunen, V., Thurfjell, L., Lundqvist, R., Waldemar, G., Soininen, H., and Rueckert, D. (2011). Fast and robust extraction of hippocampus from mr images for diagnostics of alzheimer's disease. NeuroImage, 56(1):185-196.
  14. Magnin, B., Mesrob, L., Kinkingnhun, S., Plgrini-Issac, M., Colliot, O., Sarazin, M., Dubois, B., Lehricy, S., and Benali, H. (2009). Support vector machine-based classification of alzheimers disease from whole-brain anatomical mri. Neuroradiology, 51:73-83.
  15. Pennanen, C., Kivipelto, M., Tuomainen, S., Hartikainen, P., Hnninen, T., Laakso, M. P., Hallikainen, M., Vanhanen, M., Nissinen, A., Helkala, E.-L., Vainio, P., Vanninen, R., Partanen, K., and Soininen, H. (2004). Hippocampus and entorhinal cortex in mild cognitive impairment and early ad. Neurobiology of Aging, 25(3):303-310.
  16. Tohka, J., Zijdenbos, A., and Evans, A. (2004). Fast and robust parameter estimation for statistical partial volume models in brain mri. NeuroImage, 23(1):84-97.
  17. Vemuri, P., Gunter, J. L., Senjem, M. L., Whitwell, J. L., Kantarci, K., Knopman, D. S., Boeve, B. F., Petersen, R. C., and Jr., C. R. J. (2008). Alzheimer's disease diagnosis in individual subjects using structural mr images: Validation studies. NeuroImage, 39(3):1186- 1197.
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Paper Citation


in Harvard Style

Rokicki J., Kazuko H., Vialatte F., Ušinskas A. and Cichocki A. (2012). Early Alzheimer’s Disease Progression Detection using Multi-subnetworks of the Brain . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: SSCN, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 684-691. DOI: 10.5220/0004182806840691


in Bibtex Style

@conference{sscn12,
author={Jaroslav Rokicki and Hiyoshi Kazuko and Francois-Benoit Vialatte and Andrius Ušinskas and Andrzej Cichocki},
title={Early Alzheimer’s Disease Progression Detection using Multi-subnetworks of the Brain},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: SSCN, (IJCCI 2012)},
year={2012},
pages={684-691},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004182806840691},
isbn={978-989-8565-33-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: SSCN, (IJCCI 2012)
TI - Early Alzheimer’s Disease Progression Detection using Multi-subnetworks of the Brain
SN - 978-989-8565-33-4
AU - Rokicki J.
AU - Kazuko H.
AU - Vialatte F.
AU - Ušinskas A.
AU - Cichocki A.
PY - 2012
SP - 684
EP - 691
DO - 10.5220/0004182806840691