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DeepRF: Ultrafast population receptive field mapping with deep learning

View ORCID ProfileJordy Thielen, View ORCID ProfileUmut Güçlü, Yagmur Güçlütürk, View ORCID ProfileLuca Ambrogioni, View ORCID ProfileSander E. Bosch, View ORCID ProfileMarcel A. J. van Gerven
doi: https://doi.org/10.1101/732990
Jordy Thielen
Radboud University, Donders Centre for Cognition
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  • For correspondence: jordy.thielen@donders.ru.nl
Umut Güçlü
Radboud University, Donders Centre for Cognition
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Yagmur Güçlütürk
Radboud University, Donders Centre for Cognition
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Luca Ambrogioni
Radboud University, Donders Centre for Cognition
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Sander E. Bosch
Radboud University, Donders Centre for Cognition
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Marcel A. J. van Gerven
Radboud University, Donders Centre for Cognition
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Abstract

Population receptive field (pRF) mapping is an important asset for cognitive neuroscience. The pRF model is used for estimating retinotopy, defining functional localizers and to study a vast amount of cognitive tasks. In a classic pRF, the cartesian location and receptive field size are modeled as a 2D Gaussian kernel in visual space and are estimated by optimizing the fit between observed responses and predicted responses. In the standard framework this is achieved using an iterative gradient descent algorithm. This optimization is time consuming because the number of pRFs to fit (e.g., fMRI voxels) is typically large. This computation time increases further with the complexity of the pRF model (e.g., adding HRF parameters, surround suppression and uncertainty measures). Here, we introduce DeepRF, which uses deep convolutional neural networks to estimate pRFs. We compare the performance of DeepRF with that of the conventional method using a synthetic dataset for which the ground truth is known and an empirical dataset. We show that DeepRF achieves state-of-the-art performance while being more than 3 orders of magnitude faster than the conventional method. This enables easier and faster modeling of more complex pRF models, resolving an important limitation of the conventional approach.

Footnotes

  • jordy.thielen{at}donders.ru.nl, u.guclu{at}donders.ru.nl, y.gucluturk{at}donders.ru.nl, l.ambrogioni{at}donders.ru.nl, s.bosch{at}donders.ru.nl, m.vangerven{at}donders.ru.nl

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted August 13, 2019.
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DeepRF: Ultrafast population receptive field mapping with deep learning
Jordy Thielen, Umut Güçlü, Yagmur Güçlütürk, Luca Ambrogioni, Sander E. Bosch, Marcel A. J. van Gerven
bioRxiv 732990; doi: https://doi.org/10.1101/732990
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DeepRF: Ultrafast population receptive field mapping with deep learning
Jordy Thielen, Umut Güçlü, Yagmur Güçlütürk, Luca Ambrogioni, Sander E. Bosch, Marcel A. J. van Gerven
bioRxiv 732990; doi: https://doi.org/10.1101/732990

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