Abstract
Functional MRI (fMRI) attracts huge interest for the machine learning community nowadays. In this work we propose a novel data augmentation procedure through analysing the inherent noise in fMRI. We then use the novel augmented dataset for the classification of subjects by age and gender, showing a significant improvement in the accuracy performance of Recurrent Neural Networks. We test the new data augmentation procedure in the fMRI dataset belonging to one international consortium of neuroimaging data for healthy controls: the Human Connectome Projects (HCP).
From the analysis of this dataset, we also show how the differences in acquisition habits and preprocessing pipelines require the development of representation learning tools. In the present paper we apply autoencoder deep learning architectures and we present their uses in resting state fMRI, using the novel data augmentation technique proposed.
This research field, appears to be unexpectedly undeveloped so far, and could potentially open new important and interesting directions for future analysis.
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Acknowledgements
G.M.D. is funded by the Engineering and Physical Sciences Research Council (EPSRC) with International Doctoral Scholarship [number 1649557]. P.L. would like to acknowledge funding from the European Union’s Horizon 2020 research and innovation programme PROPAGAGEING under grant agreement No. 634821.
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Kusztos, R., Dimitri, G.M., Lió, P. (2020). Neural Models for Brain Networks Connectivity Analysis. In: Raposo, M., Ribeiro, P., Sério, S., Staiano, A., Ciaramella, A. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2018. Lecture Notes in Computer Science(), vol 11925. Springer, Cham. https://doi.org/10.1007/978-3-030-34585-3_19
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