Abstract
Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging technique with pivotal importance due to its scientific and clinical applications. As with any widely used imaging modality, there is a need to ensure the quality of the same, with missing values being highly frequent due to the presence of artifacts or sub-optimal imaging resolutions. Our work focus on missing values imputation on multivariate signal data. To do so, a new imputation method is proposed consisting on two major steps: spatial-dependent signal imputation and time-dependent regularization of the imputed signal. A novel layer, to be used in deep learning architectures, is proposed in this work, bringing back the concept of chained equations for multiple imputation [26]. Finally, a recurrent layer is applied to tune the signal, such that it captures its true patterns. Both operations yield an improved robustness against state-of-the-art alternatives. The code is made available on Github.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
\(\psi , \mu , \varphi \) and \(\iota \) values selected for simplicity sake, not necessarily resembling fMRI values.
References
Birn, R.M.: The role of physiological noise in resting-state functional connectivity. Neuroimage (2012)
Cao, W., Wang, D., Li, J., Zhou, H., Li, L., Li, Y.: Bidirectional recurrent imputation for time series. In: NIPS, Brits (2018)
Che, Z., Purushotham, S., Cho, K., Sontag, D., Liu, Y.: Recurrent neural networks for multivariate time series with missing values. Scientific reports (2018)
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv (2014)
Conroy, B.R., Walz, J.M., Sajda, P.: Fast bootstrapping and permutation testing for assessing reproducibility and interpretability of multivariate fMRI decoding models. PLoS ONE (2013)
Deligianni, F., Carmichael, D.W., Zhang, G.H., Clark, C.A., Clayden, J.D.: Noddi and tensor-based microstructural indices as predictors of functional connectivity. PLoS ONE (2016)
Deligianni, F., Centeno, M., Carmichael, D.W., Clayden, J.D.: Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands. Front. Neurosci. (2014)
Fortuin, V., Baranchuk, D., Rätsch, G., Mandt, S.: GP-VAE: deep probabilistic time series imputation. arXiv (2019)
Goodfellow, I., et al.: Generative adversarial nets. In NIPS, Sherjil Ozair (2014)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2001)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv (2014)
Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv (2013)
Lu, R., Duan, Z.: Bidirectional GRU for sound event detection. Detection and Classification of Acoustic Scenes and Events (2017)
Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. In: NIPS (2018)
Luo, Y., Cai, X., Zhang, Y., Xu, J., Xiaojie, Y.: Multivariate time series imputation with generative adversarial networks. In: NIPS (2018)
Pan, J.-Y., Yang, H.-J., Faloutsos, C., Duygulu, P.: Automatic multimedia cross-modal correlation discovery. In: ACM SIGKDD (2004)
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: CVPR (2016)
Petitjean, F., Ketterlin, A., Gançarski, P.: A global averaging method for dynamic time warping, with applications to clustering. Pattern Recogn. (2011)
Śmieja, M., Struski, Ł., Tabor, J., Zieliński, B., Spurek, P.: Processing of missing data by neural networks. In: NIPS (2018)
Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: NIPS (2012)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. JMLR (2014)
Tran, L., Liu, X., Zhou, J., Jin, R.: Missing modalities imputation via cascaded residual autoencoder. In: CVPR (2017)
Walz, J.M., Goldman, R.I., Carapezza, M., Muraskin, J., Brown, T.R., Sajda, P.: Simultaneous EEG-fMRI reveals temporal evolution of coupling between supramodal cortical attention networks and the brainstem. J. Neurosci. (2013)
Walz, J.M., Goldman, R.I., Carapezza,, M., Muraskin, J., Brown, T.R., Sajda, P.: Simultaneous eeg-fmri reveals a temporal cascade of task-related and default-mode activations during a simple target detection task. Neuroimage (2014)
Wehrl, H.F., et al.: Simultaneous pet-MRI reveals brain function in activated and resting state on metabolic, hemodynamic and multiple temporal scales. Nature Med. (2013)
White, I., Royston, P., Wood, A.: Multiple imputation using chained equations: issues and guidance for practice. Stat. Med. (2011)
Acknowledgments
This work was supported by national funds through Fundação para a Ciência e Tecnologia (FCT), for the Ph.D. Grant DFA/BD/5762/2020, ILU project DSAIPA/DS/0111/2018 and INESC-ID pluriannual UIDB/50021/2020.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Calhas, D., Henriques, R. (2021). fMRI Multiple Missing Values Imputation Regularized by a Recurrent Denoiser. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P., Riaño, D. (eds) Artificial Intelligence in Medicine. AIME 2021. Lecture Notes in Computer Science(), vol 12721. Springer, Cham. https://doi.org/10.1007/978-3-030-77211-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-77211-6_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77210-9
Online ISBN: 978-3-030-77211-6
eBook Packages: Computer ScienceComputer Science (R0)