Abstract
In this paper, we propose a novel unsupervised pre-training method to learn the brain dynamics using a deep learning architecture named residual D-net. As it is often the case in medical research, in contrast to typical deep learning tasks, the size of the resting-state functional Magnetic Resonance Image (rs-fMRI) datasets for training is limited. Thus, the available data should be very efficiently used to learn the complex patterns underneath the brain connectivity dynamics. To address this issue, we use residual connections to alleviate the training complexity through recurrent multi-scale representation and pre-training the architecture unsupervised way. We conduct two classification tasks to differentiate early and late stage Mild Cognitive Impairment (MCI) from Normal healthy Control (NC) subjects. The experiments verify that our proposed residual D-net indeed learns the brain connectivity dynamics, leading to significantly higher classification accuracy compared to previously published techniques.
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- 1.
Availiable at http://adni.loni.usc.edu/.
- 2.
DPARSF: Available at http://rfmri.org/DPARSF.
- 3.
SPM 12: Available at http://www.fil.ion.ucl.ac.uk/spm.
- 4.
AAL documentation available at http://www.gin.cnrs.fr/en/tools/aal-aal2/.
- 5.
The code and pre-processed data is available at https://github.com/youngjoo-epfl/residualDnet.
References
Aisen, P.S., et al.: Clinical core of the Alzheimer’s disease neuroimaging initiative: progress and plans. Alzheimer’s Dement. J. Alzheimer’s Assoc. 6(3), 239–246 (2010)
Allen, E.A., Damaraju, E., Plis, S.M., Erhardt, E.B., Eichele, T., Calhoun, V.D.: Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 24(3), 663–676 (2014)
Alom, M.Z., Hasan, M., Yakopcic, C., Taha, T.M., Asari, V.K.: Recurrent residual convolutional neural network based on u-net (R2U-Net) for medical image segmentation. arXiv preprint. arXiv:1802.06955 (2018)
Alzheimer’s Association: 2016 Alzheimer’s disease facts and figures. Alzheimer’s Dement. 12(4), 459–509 (2016)
Barker, W.W., et al.: Relative frequencies of Alzheimer disease, lewy body, vascular and frontotemporal dementia, and hippocampal sclerosis in the state of Florida brain bank. Alzheimer Dis. Assoc. Disord. 16(4), 203–212 (2002)
Brown, C.J., Hamarneh, G.: Machine learning on human connectome data from MRI. arXiv preprint. arXiv:1611.08699 (2016)
Buckner, R.L., Kelley, W.M., Petersen, S.E.: Frontal cortex contributes to human memory formation. Nat. Neurosci. 2(4), 311–314 (1999)
Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUs). arXiv preprint. arXiv:1511.07289 (2015)
Friston, K.J., Frith, C.D., Frackowiak, R.S., Turner, R.: Characterizing dynamic brain responses with fMRI: a multivariate approach. NeuroImage 2(2, Part A), 166–172 (1995)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Jack, C.R., et al.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27(4), 685–691 (2008)
Khazaee, A., Ebrahimzadeh, A., Babajani-Feremi, A.: Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer’s disease. Brain Imaging Behav. 10(3), 799–817 (2016)
Kim, J., El-Khamy, M., Lee, J.: Residual LSTM: design of a deep recurrent architecture for distant speech recognition. arXiv preprint. arXiv:1701.03360 (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint. arXiv:1412.6980 (2014)
Leonardi, N., et al.: Principal components of functional connectivity: a new approach to study dynamic brain connectivity during rest. NeuroImage 83, 937–950 (2013)
Middleton, F.A., Strick, P.L.: Basal ganglia and cerebellar loops: motor and cognitive circuits. Brain Res. Rev. 31(2–3), 236–250 (2000)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Squire, L.R.: Memory and the hippocampus: a synthesis from findings with rats, monkeys, and humans. Psychol. Rev. 99(2), 195 (1992)
Squire, L.R., Stark, C.E.L., Clark, R.E.: The medial temporal lobe. Ann. Rev. Neurosci. 27(1), 279–306 (2004)
Srivastava, N., Mansimov, E., Salakhudinov, R.: Unsupervised learning of video representations using LSTMs. In: International Conference on Machine Learning, pp. 843–852 (2015)
Suk, H.I., Wee, C.Y., Lee, S.W., Shen, D.: State-space model with deep learning for functional dynamics estimation in resting-state fMRI. NeuroImage 129, 292–307 (2016)
Wang, Y., Tian, F.: Recurrent residual learning for sequence classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 938–943 (2016)
Ward, A., Tardiff, S., Dye, C., Arrighi, H.M.: Rate of conversion from prodromal alzheimer’s disease to alzheimer’s dementia: a systematic review of the literature. Dement. Geriatr. Cogn. Disord. Extra 3(1), 320–332 (2013)
Weston, J., Mukherjee, S., Chapelle, O., Pontil, M., Poggio, T., Vapnik, V.: Feature selection for SVMs. In: Advances in Neural Information Processing Systems, pp. 668–674 (2001)
Wilson, R.S., Segawa, E., Boyle, P.A., Anagnos, S.E., Hizel, L.P., Bennett, D.A.: The natural history of cognitive decline in Alzheimer’s disease. Psychol. Aging 27(4), 1008–1017 (2012)
Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, pp. 802–810 (2015)
Zhang, X., Hu, B., Ma, X., Xu, L.: Resting-state whole-brain functional connectivity networks for MCI classification using L2-regularized logistic regression. IEEE Trans. NanoBiosci. 14(2), 237–247 (2015)
Acknowledgment
This research was supported in part by the European Union’s H2020 Framework Programme (H2020-MSCA-ITN-2014) under grant No. 642685 MacSeNet.
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Seo, Y., Morante, M., Kopsinis, Y., Theodoridis, S. (2019). Unsupervised Pre-training of the Brain Connectivity Dynamic Using Residual D-Net. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11955. Springer, Cham. https://doi.org/10.1007/978-3-030-36718-3_51
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