NeuroGen: Activation optimized image synthesis for discovery neuroscience

Zijin Gu, Keith Wakefield Jamison, Meenakshi Khosla, Emily J Allen, Yihan Wu, Thomas Naselaris, Kendrick Kay, Mert R. Sabuncu, Amy Kuceyeski

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number118812
JournalNeuroImage
Volume247
DOIs
StatePublished - Feb 15 2022

Bibliographical note

Funding Information:
This work was funded by the following grants: R01 NS102646 (AK), RF1 MH123232 (AK), R21 NS104634 (AK), R01 LM012719 (MS), R01 AG053949 (MS), NSF CAREER 1748377 (MS), and NSF NeuroNex Grant 1707312 (MS). The NSD data were collected under the NSF CRCNS grants IIS-1822683 (KK) and IIS-1822929 (TN).

Funding Information:
This work was funded by the following grants: R01 NS102646 (AK), RF1 MH123232 (AK), R21 NS104634 (AK), R01 LM012719 (MS), R01 AG053949 (MS), NSF CAREER 1748377 (MS), and NSF NeuroNex Grant 1707312 (MS). The NSD data were collected under the NSF CRCNS grants IIS-1822683 (KK) and IIS-1822929 (TN).

Publisher Copyright:
© 2021

Keywords

  • Deep learning
  • Function MRI
  • Image synthesis
  • Neural encoding

Center for Magnetic Resonance Research (CMRR) tags

  • BFC

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

Fingerprint

Dive into the research topics of 'NeuroGen: Activation optimized image synthesis for discovery neuroscience'. Together they form a unique fingerprint.

Cite this