Transfer learning of deep neural network representations for fMRI decoding

Michele Svanera*, Mattia Savardi, Sergio Benini, Alberto Signoroni, Gal Raz, Talma Hendler, Lars Muckli, Rainer Goebel, Giancarlo Valente

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number108319
JournalJournal of Neuroscience Methods
StatePublished - 1 Dec 2019


FundersFunder number
European Union's Horizon 2020 Programme for Research and InnovationSGA2
Horizon 2020 Framework Programme785907


    • Brain decoding
    • Convolutional Neural Network
    • Deep learning
    • MultiVoxel Pattern Analysis
    • Transfer learning
    • fMRI


    Dive into the research topics of 'Transfer learning of deep neural network representations for fMRI decoding'. Together they form a unique fingerprint.

    Cite this