Journal Article

NeuroGen: Activation optimized image synthesis for discovery neuroscience

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

2022

Overview

•We propose a state-of-the-art generative framework, called NeuroGen, which allows synthesis of images that are optimized to achieve specific, predetermined brain activation responses in the human brain.•We apply this framework as a discovery architecture to amplify differences in regional and individual brain response patterns to visual stimuli, which we then verify using several thousand observed image responses measured with fMRI.•We demonstrated that NeuroGen has the capacity of generating images predicted to achieve activation levels that are more extreme than the predicted activations of the best natural images.•NeuroGen extends the utility of brain encoding models and opens up a new avenue for exploring, and possibly precisely controlling, the human visual system. 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.

• Publisher: Elsevier Inc,Elsevier Limited,Elsevier
• Subject: Brain / Brain mapping / Computational neuroscience / Datasets / Datasets as Topic / Deep Learning / Function MRI / Functional magnetic resonance imaging / Humans / Image Enhancement - methods / Image Processing, Computer-Assisted - methods / Image synthesis / Magnetic Resonance Imaging - methods / Nervous system / Neural coding / Neural encoding / Neurons / Neurosciences / Noise reduction / Regions / Semantics / Vision / Visual cortex / Visual Cortex - diagnostic imaging / Visual Cortex - physiology / Visual stimuli / Visual system