Abstract
fMRI has been a popular way for encoding and decoding human visual cortex activity. A previous research reconstructed binary image using a sparse logistic regression (SLR) with fMRI activity patterns as its input. In this article, based on SLR, we propose a new sparse logistic regression with a tunable regularization parameter (SLR-T), which includes the SLR and maximum likelihood regression (MLR) as two special cases. By choosing a proper regularization parameter in SLR-T, it may yield a better performance than both SLR and MLR. An fMRI visual image reconstruction experiment is carried out to verify the performance of SLR-T.
This work was supported by 973 Program (No. 2015CB351703).
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Vu, V.Q., Ravikumar, P., Naselaris, T., Kay, K.N., Gallant, J.L., Yu, B.: Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models. Ann. Appl. Stat. 5, 1159–1182 (2011)
Engel, S.A., Glover, G.H., Wandell, B.A.: Retinotopic organization in human visual cortex and the spatial precision of functional MRI. Cereb. Cortex. 7, 181–192 (1997)
Wandell, B.A., Dumoulin, S.O., Brewer, A.A.: Visual Field Maps in Human Cortex. Neuron 56, 366–383 (2007)
Naselaris, T., Olman, C.A., Stansbury, D.E., Ugurbil, K., Gallant, J.L.: A voxel-wise encoding model for early visual areas decodes mental images of remembered scenes. NeuroImage 105, 215–228 (2015)
Brouwer, G.J., Heeger, D.J.: Decoding and Reconstructing Color from Responses in Human Visual Cortex. J. Neurosci. 29, 13992–14003 (2009)
Nishimoto, S., Vu, A.T., Naselaris, T., Benjamini, Y., Yu, B., Gallant, J.L.: Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies. Curr. Biol. 21, 1641–1646 (2011)
Bannert, M.M., Bartels, A.: Decoding the Yellow of a Gray Banana. Curr. Biol. 23, 2268–2272 (2013)
Goncalves, N.R., Ban, H., Sánchez-Panchuelo, R.M., Francis, S.T., Schluppeck, D., Welchman, A.E.: 7 Tesla fMRI Reveals Systematic Functional Organization for Binocular Disparity in Dorsal Visual Cortex. J. Neurosci. 35, 3056–3072 (2015)
Kay, K.N., Naselaris, T., Prenger, R.J., Gallant, J.L.: Identifying natural images from human brain activity. Nature 452, 352–355 (2008)
Miyawaki, Y., Uchida, H., Yamashita, O., Sato, M., Morito, Y., Tanabe, H.C., Sadato, N., Kamitani, Y.: Visual Image Reconstruction from Human Brain Activity using a Combination of Multiscale Local Image Decoders. Neuron 60, 915–929 (2008)
Yamashita, O., Sato, M., Yoshioka, T., Tong, F., Kamitani, Y.: Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns. NeuroImage 42, 1414–1429 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Wu, H., Wang, J., Chen, B., Zheng, N. (2015). fMRI Visual Image Reconstruction Using Sparse Logistic Regression with a Tunable Regularization Parameter. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_77
Download citation
DOI: https://doi.org/10.1007/978-3-319-25159-2_77
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25158-5
Online ISBN: 978-3-319-25159-2
eBook Packages: Computer ScienceComputer Science (R0)