A massive 7T fMRI dataset to bridge cognitive and computational neuroscience

Allen, Emily; St-Yves, Ghislain; Wu, Yihan; Breedlove, Jesse; Dowdle, Logan T.; Caron, Bradley; Pestilli, Franco; Charest, Ian; Hutchinson, J. Benjamin; Naselaris, Thomas; Kay, Kendrick

doi:10.1101/2021.02.22.432340

Cited by 33 publications

(52 citation statements)

References 129 publications

(119 reference statements)

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“…Another potential limitation is that our findings are based on a single fMRI and image dataset, so it is not clear how well they would generalize to a broader sample of images. Given the explosive growth of the deep learning field [ 59 ] and the ever increasing availability of open brain imaging data sets [ 60 , 61 ] we see a furtive ground for the application of our approach in the future.…”

Section: Discussionmentioning

confidence: 99%

Unveiling functions of the visual cortex using task-specific deep neural networks

et al. 2021

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The human visual cortex enables visual perception through a cascade of hierarchical computations in cortical regions with distinct functionalities. Here, we introduce an AI-driven approach to discover the functional mapping of the visual cortex. We related human brain responses to scene images measured with functional MRI (fMRI) systematically to a diverse set of deep neural networks (DNNs) optimized to perform different scene perception tasks. We found a structured mapping between DNN tasks and brain regions along the ventral and dorsal visual streams. Low-level visual tasks mapped onto early brain regions, 3-dimensional scene perception tasks mapped onto the dorsal stream, and semantic tasks mapped onto the ventral stream. This mapping was of high fidelity, with more than 60% of the explainable variance in nine key regions being explained. Together, our results provide a novel functional mapping of the human visual cortex and demonstrate the power of the computational approach.

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Section: Discussionmentioning

confidence: 99%

Unveiling functions of the visual cortex using task-specific deep neural networks

et al. 2021

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“…Although the organization of the dorsal third tier visual cortex remains to be further investigated in humans, the discontinuous representation of the anterior border of dorsal V3 has been seen in some individuals in published work from different labs (Allen et al, 2021;Van Essen and Glasser, 2018). Therefore, the ability of our model to predict the discontinuous representation of the lower vertical meridian at the anterior border of dorsal V3 suggests that this discontinuity is most likely a real structure-related variation, worthy of further investigation.…”

Section: Individual Variability In Retinotopic Mapsmentioning

confidence: 83%

Predicting the retinotopic organization of human visual cortex from anatomy using geometric deep learning

Ribeiro¹,

Bollmann²,

Puckett³

2020

Preprint

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17Whether it be in a single neuron or a more complex biological system like the human 18 brain, form and function are often directly related. The functional organization of 19 human visual cortex, for instance, is tightly coupled with the underlying anatomy. This 20 is seen in properties such as cortical magnification (i.e., there is more cortex dedicated 21 to processing foveal vs. peripheral information) as well as in the presence, placement, 22 and connectivity of multiple visual areaswhich is critical for the hierarchical 23 processing underpinning the rich experience of human vision. Here we developed a 24 geometric deep learning model capable of exploiting the actual structure of the cortex 25 to learn the complex relationship between brain function and anatomy in human visual 26 cortex. We show that our neural network was not only able to predict the functional 27 organization throughout the visual cortical hierarchy, but that it was also able to predict 28 nuanced variations across individuals. Although we demonstrate its utility for modeling 29 the relationship between structure and function in human visual cortex, geometric 30 deep learning is flexible and well-suited for a range of other applications involving data 31 structured in non-Euclidean spaces. 32Keywords 33 human connectome project, fMRI, 7T, vision, cortical surface, manifold, neural 34 network, machine learning, retinotopy, visual hierarchy 35

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“…Hlinka et al [37] only reported finding “subtle” nonlinear effects by, “testing across many pairs or even across many sessions.” In this study, we also found “subtle” yet significant nonlinear interactions by testing across a series of subjects. One potential avenue for future research might be to examine datasets comprising extensive scans of small numbers of participants [38]. Using many runs of single subject data and avoiding the averaging across subjects could increase the chances of identifying more subtle effects.…”

Section: Discussionmentioning

confidence: 99%

Computational Fingerprints: Modeling Interactions Between Brain Regions as Points in a Function Space

Poskanzer

Anzellotti

2021

Preprint

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In this paper we propose a novel technique to investigate the nonlinear interactions between brain regions that captures both the strength and the type of the functional relationship. Inspired by the field of functional analysis, we propose that the relationship between activity in two different brain areas can be viewed as a point in function space, identified by coordinates along an infinite set of basis functions. Using Hermite Polynomials as basis functions, we estimate from fMRI data a truncated set of coordinates that serve as a "computational fingerprint," characterizing the interaction between two brain areas. We provide a proof of the convergence of the estimates in the limit, and we validate the method with simulations in which the ground truth is known, additionally showing that computational fingerprints detect statistical dependence also when correlations ("functional connectivity") is near zero. We then use computational fingerprints to examine the neural interactions with a seed region of choice: the Fusiform Face Area (FFA). Using k-means clustering across each voxel's computational fingerprint, we illustrate that the addition of the nonlinear basis functions allows for the discrimination of inter-regional interactions that are otherwise grouped together when only linear dependence is used. Finally, we show that regions in V5 and medial occipital and temporal lobes exhibit significant nonlinear interactions with the FFA.

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A massive 7T fMRI dataset to bridge cognitive and computational neuroscience

Cited by 33 publications

References 129 publications

Unveiling functions of the visual cortex using task-specific deep neural networks

Unveiling functions of the visual cortex using task-specific deep neural networks

Predicting the retinotopic organization of human visual cortex from anatomy using geometric deep learning

Computational Fingerprints: Modeling Interactions Between Brain Regions as Points in a Function Space

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