Deep Learning Approach for Classification of Mild Cognitive Impairment Subtypes

Upul Senanayake, Arcot Sowmya, Laughlin Dawes, Nicole A. Kochan, Wei Wen, Perminder Sachdev

2017

Abstract

Timely intervention in individuals at risk of dementia is often emphasized, and Mild Cognitive Impairment (MCI) is considered to be an effective precursor to Alzheimers disease (AD), which can be used as an intervention criterion. This paper attempts to use deep learning techniques to recognise MCI in the elderly. Deep learning has recently come to attention with its superior expressive power and performance over conventional machine learning algorithms. The current study uses variations of auto-encoders trained on neuropsychological test scores to discriminate between cognitively normal individuals and those with MCI in a cohort of community dwelling individuals aged 70-90 years. The performance of the auto-encoder classifier is further optimized by creating an ensemble of such classifiers, thereby improving the generalizability as well. In addition to comparable results to those of conventional machine learning algorithms, the auto-encoder based classifiers also eliminate the need for separate feature extraction and selection while also allowing seamless integration of features from multiple modalities.

References

  1. Alexander, A. L., Lee, J. E., Lazar, M., and Field, A. S. (2007). Diffusion tensor imaging of the brain. Neurotherapeutics, 4(3):316-329. 17599699[pmid].
  2. Baldi, P. (2012). Autoencoders, Unsupervised Learning, and Deep Architectures. ICML Unsupervised and Transfer Learning, pages 37-50.
  3. Chételat, G., Landeau, B., Eustache, F., Mézenge, F., Viader, F., de la Sayette, V., Desgranges, B., and Baron, J.-C. (2005). Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal MRI study. NeuroImage, 27(4):934-46.
  4. Chua, T. C., Wen, W., Chen, X., Kochan, N., Slavin, M. J., Trollor, J. N., Brodaty, H., and Sachdev, P. S. (2009). Diffusion tensor imaging of the posterior cingulate is a useful biomarker of mild cognitive impairment. The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry, 17(July):602-613.
  5. Chua, T. C., Wen, W., Slavin, M. J., and Sachdev, P. S. (2008). Diffusion tensor imaging in mild cognitive impairment and Alzheimer s disease : a review. Current Opinions in Neurology.
  6. Cui, Y., Sachdev, P. S., Lipnicki, D. M., Jin, J. S., Luo, S., Zhu, W., Kochan, N. a., Reppermund, S., Liu, T., Trollor, J. N., Brodaty, H., and Wen, W. (2012a). Predicting the development of mild cognitive impairment: A new use of pattern recognition. NeuroImage, 60(2):894-901.
  7. Cui, Y., Wen, W., Lipnicki, D. M., Beg, M. F., Jin, J. S., Luo, S., Zhu, W., Kochan, N. a., Reppermund, S., Zhuang, L., Raamana, R., Liu, T., Trollor, J. N., Wang, L., Brodaty, H., and Sachdev, P. S. (2012b). Automated detection of amnestic mild cognitive impairment in community-dwelling elderly adults: A combined spatial atrophy and white matter alteration approach. NeuroImage, 59(2):1209-1217.
  8. Gauthier, S., Reisberg, B., Zaudig, M., Petersen, R. C., Ritchie, K., Broich, K., Belleville, S., Brodaty, H., Bennett, D., Chertkow, H., Cummings, J. L., de Leon, M., Feldman, H., Ganguli, M., Hampel, H., Scheltens, P., Tierney, M. C., Whitehouse, P., and Winblad, B. (2006). Mild cognitive impairment. The Lancet, 367(9518):1262 - 1270.
  9. Haller, S., Missonnier, P., Herrmann, F. R., Rodriguez, C., Deiber, M.-P., Nguyen, D., Gold, G., Lovblad, K.-O., and Giannakopoulos, P. (2013). Individual classification of mild cognitive impairment subtypes by support vector machine analysis of white matter DTI. AJNR. American journal of neuroradiology, 34(2):283-91.
  10. Hedden, T. and Gabrieli, J. D. E. (2004). Insights into the ageing mind: a view from cognitive neuroscience. Nat Rev Neurosci, 5(2):87-96.
  11. Hindmarch, I., Lehfeld, H., de Jongh, P., and Erzigkeit, H. (1998). The bayer activities of daily living scale (badl). Dementia and Geriatric Cognitive Disorders, 9(suppl 2)(Suppl. 2):20-26.
  12. Hinrichs, C., Singh, V., Xu, G., and Johnson, S. C. (2011).
  13. Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population. NeuroImage, 55(2):574-589.
  14. Hinton, G. E., Osindero, S., and Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural computation, 18(7):1527-1554.
  15. Kallenberg, M., Petersen, K., Nielsen, M., Ng, A. Y., Diao, P., Igel, C., Vachon, C. M., Holland, K., Winkel, R. R., Karssemeijer, N., and Lillholm, M. (2016). Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring. IEEE Transactions on Medical Imaging, 35(5):1322-1331.
  16. Kochan, N. A., Slavin, M. J., Brodaty, H., Crawford, J. D., Trollor, J. N., Draper, B., and Sachdev, P. S. (2010). Effect of Different Impairment Criteria on Prevalence of “Objective” Mild Cognitive Impairment in a Community Sample. The American Journal of Geriatric Psychiatry, 18(8):711-722.
  17. Lemos, L., Silva, D., Guerreiro, M., Santana, I., de Mendona, A., Toms, P., and Madeira, S. C. (2012). Discriminating alzheimers disease from mild cognitive impairment using neuropsychological data. KDD 2012.
  18. Li, F., Tran, L., Thung, K.-H., Ji, S., Shen, D., and Li, J. (2014). Robust Deep Learning for Improved Classification of AD/MCI Patients. Machine Learning in Medical Imaging, 8679:240-247.
  19. Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., and Feng, D. (2014). Early diagnosis of Alzheimer's disease with deep learning. 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pages 1015- 1018.
  20. Mitchell, A. J. and Shiri-Feshki, M. (2009). Rate of progression of mild cognitive impairment to dementia meta-analysis of 41 robust inception cohort studies. Acta Psychiatrica Scandinavica, 119(4):252-265.
  21. Petersen, R. C., Knopman, D. S., Boeve, B. F., Geda, Y. E., Ivnik, R. J., Smith, G. E., Roberts, R. O., and Jack, C. R. (2009). Mild Cognitive Impairment: Ten Years Later. Archives of neurology, 66(12):1447-1455.
  22. Raamana, P. R., Wen, W., Kochan, N. a., Brodaty, H., Sachdev, P. S., Wang, L., and Beg, M. F. (2014). The sub-classification of amnestic mild cognitive impairment using MRI-based cortical thickness measures. Frontiers in Neurology, pages 1-10.
  23. Reddy, P., Kochan, N., Brodaty, H., Sachdev, P., Wang, L., Beg, M. F., and Wen, W. (2013). Novel ThickNet features for the discrimination of amnestic MCI subtypes. NeuroImage Clinical, 6:284-295.
  24. Reppermund, S., Zhuang, L., Wen, W., Slavin, M. J., Trollor, J. N., Brodaty, H., and Sachdev, P. S. (2014). White matter integrity and late-life depression in community-dwelling individuals: diffusion tensor imaging study using tract-based spatial statistics. The British Journal of Psychiatry, 205:315-320.
  25. Sachdev, P. S., Brodaty, H., Reppermund, S., Kochan, N. A., Trollor, J. N., Draper, B., Slavin, M. J., Crawford, J., Kang, K., Broe, G. A., Mather, K. A., and Lux, O. (2010). The sydney memory and ageing study (mas): methodology and baseline medical and neuropsychiatric characteristics of an elderly epidemiological non-demented cohort of australians aged 7090 years. International Psychogeriatrics, 22:1248-1264.
  26. Sachdev, P. S., Lipnicki, D. M., Crawford, J., Reppermund, S., Kochan, N. a., Trollor, J. N., Wen, W., Draper, B., Slavin, M. J., Kang, K., Lux, O., Mather, K. a., Brodaty, H., and Team, A. S. (2013a). Factors Predicting Reversion from Mild Cognitive Impairment to Normal Cognitive Functioning: A Population-Based Study. PLoS ONE, 8(3):1-10.
  27. Sachdev, P. S., Zhuang, L., Braidy, N., and Wen, W. (2013b). Is Alzheimer's a disease of the white matter? Curr Opin Psychiatry, 26(3):244-251.
  28. Schmidhuber, J. (2014). Deep Learning in Neural Networks: An Overview. pages 1-88.
  29. Senanayake, U., Sowmya, A., Dawes, L., Kochan, N. A., Wen, W., and Sachdev, P. (2016). Classification of mild cognitive impairment subtypes using neuropsychological data. In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods, pages 620-629.
  30. Suk, H. I. and Shen, D. (2013). Deep learning-based feature representation for AD/MCI classification.Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8150 LNCS(0 2):583-590.
  31. Thillainadesan, S., Wen, W., Zhuang, L., Crawford, J., Kochan, N., Reppermund, S., Slavin, M., Trollor, J., Brodaty, H., and Sachdev, P. (2012). Changes in mild cognitive impairment and its subtypes as seen on diffusion tensor imaging. International Psychogeriatrics, 24:1483-1493.
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Paper Citation


in Harvard Style

Senanayake U., Sowmya A., Dawes L., Kochan N., Wen W. and Sachdev P. (2017). Deep Learning Approach for Classification of Mild Cognitive Impairment Subtypes . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 655-662. DOI: 10.5220/0006246306550662


in Bibtex Style

@conference{icpram17,
author={Upul Senanayake and Arcot Sowmya and Laughlin Dawes and Nicole A. Kochan and Wei Wen and Perminder Sachdev},
title={Deep Learning Approach for Classification of Mild Cognitive Impairment Subtypes},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={655-662},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006246306550662},
isbn={978-989-758-222-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Deep Learning Approach for Classification of Mild Cognitive Impairment Subtypes
SN - 978-989-758-222-6
AU - Senanayake U.
AU - Sowmya A.
AU - Dawes L.
AU - Kochan N.
AU - Wen W.
AU - Sachdev P.
PY - 2017
SP - 655
EP - 662
DO - 10.5220/0006246306550662