4.7 Article

NeuroGen: Activation optimized image synthesis for discovery neuroscience

期刊

NEUROIMAGE
卷 247, 期 -, 页码 -

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2021.118812

关键词

Function MRI; Neural encoding; Image synthesis; Deep learning

资金

  1. NSF CAREER [1748377]
  2. NSF NeuroNex Grant [1707312]
  3. NSF CRCNS [IIS-1822683, IIS-1822929]
  4. [R01 NS102646]
  5. [RF1 MH123232]
  6. [R21 NS104634]
  7. [R01 LM012719]
  8. [R01 AG053949]

向作者/读者索取更多资源

NeuroGen is a novel computational strategy that combines an fMRI-trained neural encoding model with a deep generative network to synthesize images predicted to achieve a target brain activation pattern. By reducing noise and creating high-fidelity images, NeuroGen enables robust discovery in visual neuroscience. It can detect and amplify differences in regional and individual brain response patterns, and even create synthetic images with response patterns not achievable by natural images.
Functional MRI (fMRI) is a powerful technique that has allowed us to characterize visual cortex responses to stimuli, yet such experiments are by nature constructed based on a priori hypotheses, limited to the set of images presented to the individual while they are in the scanner, are subject to noise in the observed brain responses, and may vary widely across individuals. In this work, we propose a novel computational strategy, which we call NeuroGen, to overcome these limitations and develop a powerful tool for human vision neuroscience discovery. NeuroGen combines an fMRI-trained neural encoding model of human vision with a deep generative network to synthesize images predicted to achieve a target pattern of macro-scale brain activation. We demonstrate that the reduction of noise that the encoding model provides, coupled with the generative network's ability to produce images of high fidelity, results in a robust discovery architecture for visual neuroscience. By using only a small number of synthetic images created by NeuroGen, we demonstrate that we can detect and amplify differences in regional and individual human brain response patterns to visual stimuli. We then verify that these discoveries are reflected in the several thousand observed image responses measured with fMRI. We further demonstrate that NeuroGen can create synthetic images predicted to achieve regional response patterns not achievable by the best-matching natural images. The NeuroGen framework extends the utility of brain encoding models and opens up a new avenue for exploring, and possibly precisely controlling, the human visual system.

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