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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Notes
- 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.
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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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