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1.
Neuroimage ; 247: 118812, 2022 02 15.
Article in English | MEDLINE | ID: mdl-34936922

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.


Subject(s)
Deep Learning , Image Processing, Computer-Assisted/methods , Magnetic Resonance Imaging/methods , Visual Cortex/diagnostic imaging , Visual Cortex/physiology , Datasets as Topic , Humans , Image Enhancement/methods
2.
Nat Commun ; 12(1): 4894, 2021 08 12.
Article in English | MEDLINE | ID: mdl-34385454

ABSTRACT

White matter structural connections are likely to support flow of functional activation or functional connectivity. While the relationship between structural and functional connectivity profiles, here called SC-FC coupling, has been studied on a whole-brain, global level, few studies have investigated this relationship at a regional scale. Here we quantify regional SC-FC coupling in healthy young adults using diffusion-weighted MRI and resting-state functional MRI data from the Human Connectome Project and study how SC-FC coupling may be heritable and varies between individuals. We show that regional SC-FC coupling strength varies widely across brain regions, but was strongest in highly structurally connected visual and subcortical areas. We also show interindividual regional differences based on age, sex and composite cognitive scores, and that SC-FC coupling was highly heritable within certain networks. These results suggest regional structure-function coupling is an idiosyncratic feature of brain organisation that may be influenced by genetic factors.


Subject(s)
Algorithms , Brain/physiology , Connectome/methods , Models, Neurological , Nerve Net/physiology , Adult , Brain/anatomy & histology , Brain/diagnostic imaging , Diffusion Magnetic Resonance Imaging/methods , Female , Humans , Magnetic Resonance Imaging/methods , Male , Nerve Net/anatomy & histology , Nerve Net/diagnostic imaging , Young Adult
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