Transfer learning of deep neural network representations for fMRI decoding

Svanera, M. , Savardi, M., Benini, S., Signoroni, A., Raz, G., Hendler, T., Muckli, L. , Goebel, R. and Valente, G. (2019) Transfer learning of deep neural network representations for fMRI decoding. Journal of Neuroscience Methods, 328, 108319. (doi: 10.1016/j.jneumeth.2019.108319) (PMID:31585315)

Abstract

Background: Deep neural networks have revolutionised machine learning, with unparalleled performance in object classification. However, in brain imaging (e.g., fMRI), the direct application of Convolutional Neural Networks (CNN) to decoding subject states or perception from imaging data seems impractical given the scarcity of available data. New method: In this work we propose a robust method to transfer information from deep learning (DL) features to brain fMRI data with the goal of decoding. By adopting Reduced Rank Regression with Ridge Regularisation we establish a multivariate link between imaging data and the fully connected layer (fc7) of a CNN. We exploit the reconstructed fc7 features by performing an object image classification task on two datasets: one of the largest fMRI databases, taken from different scanners from more than two hundred subjects watching different movie clips, and another with fMRI data taken while watching static images. Results: The fc7 features could be significantly reconstructed from the imaging data, and led to significant decoding performance. Comparison with existing methods: The decoding based on reconstructed fc7 outperformed the decoding based on imaging data alone. Conclusion: In this work we show how to improve fMRI-based decoding benefiting from the mapping between functional data and CNN features. The potential advantage of the proposed method is twofold: the extraction of stimuli representations by means of an automatic procedure (unsupervised) and the embedding of high-dimensional neuroimaging data onto a space designed for visual object discrimination, leading to a more manageable space from dimensionality point of view.

• Item Type: Articles
• Additional Information: This project has received funding from the European Union's Horizon 2020 Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2) awarded to LM and RG.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Muckli, Professor Lars and Svanera, Dr Michele
• Authors: Svanera, M., Savardi, M., Benini, S., Signoroni, A., Raz, G., Hendler, T., Muckli, L., Goebel, R., and Valente, G.
• College/School: College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
• Journal Name: Journal of Neuroscience Methods
• Publisher: Elsevier
• ISSN: 0165-0270
• ISSN (Online): 0165-0270
• Published Online: 01 October 2019
• Copyright Holders: Copyright © 2019 The Authors
• First Published: First published in Journal of Neuroscience Methods 328:108319
• Publisher Policy: Reproduced under a Creative Commons license