Abstract
We present a deep neural network method that enables learning of a personal representation from samples acquired while subjects are performing a self neuro-feedback task, guided by functional MRI (fMRI). The neurofeedback task (watch vs. regulate) provides the subjects with continuous feedback, contingent on the down-regulation of their Amygdala signal. The representation is learned by a self-supervised recurrent neural network that predicts the Amygdala activity in the next fMRI frame given recent fMRI frames and is conditioned on the learned individual representation. We show that our personal representation, learned solely using fMRI images, improves the next-frame prediction considerably and, more importantly, yields superior performance in linear prediction of psychiatric traits, compared to performing such predictions based on clinical data and personality tests. Our code is attached as supplementary and the data would be shared subject to ethical approvals.
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Acknowledgments
This project has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant ERC CoG 725974), the BRAINTRAIN consortium, 7th Framework Programme, under Grant Agreement no. 602186, and US Department of Defense grant agreement no. W81XWH-11–2–0008.
We thank Shira Reznik-Balter for insightful discussions on the analytic approach and helpful comments on the manuscript.
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Osin, J. et al. (2020). Learning Personal Representations from fMRI by Predicting Neurofeedback Performance. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12267. Springer, Cham. https://doi.org/10.1007/978-3-030-59728-3_46
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DOI: https://doi.org/10.1007/978-3-030-59728-3_46
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