Diffeomorphic Smoothing for Retinotopic Mapping

et al. 2021

PLoS Comput Biol

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Retinotopic mapping, i.e., the mapping between visual inputs on the retina and neuronal activations in cortical visual areas, is one of the central topics in visual neuroscience. For human observers, the mapping is obtained by analyzing functional magnetic resonance imaging (fMRI) signals of cortical responses to slowly moving visual stimuli on the retina. Although it is well known from neurophysiology that the mapping is topological (i.e., the topology of neighborhood connectivity is preserved) within each visual area, retinotopic maps derived from the state-of-the-art methods are often not topological because of the low signal-to-noise ratio and spatial resolution of fMRI. The violation of topological condition is most severe in cortical regions corresponding to the neighborhood of the fovea (e.g., < 1 degree eccentricity in the Human Connectome Project (HCP) dataset), significantly impeding accurate analysis of retinotopic maps. This study aims to directly model the topological condition and generate topology-preserving and smooth retinotopic maps. Specifically, we adopted the Beltrami coefficient, a metric of quasiconformal mapping, to define the topological condition, developed a mathematical model to quantify topological smoothing as a constrained optimization problem, and elaborated an efficient numerical method to solve the problem. The method was then applied to V1, V2, and V3 simultaneously in the HCP dataset. Experiments with both simulated and real retinotopy data demonstrated that the proposed method could generate topological and smooth retinotopic maps.

Section: Fig 1 Illustration Of Topological and Non-topological Maps (A)mentioning

confidence: 99%

“…Our previous work on topology correction [29] was only applicable to V1; it could not be used to improve boundary delineation. In this work, by introducing extended polar angles, we…”

Section: Benefits Of the Extended Polar Anglesmentioning

confidence: 99%

Section: Plos Computational Biologymentioning

confidence: 99%

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Topology-preserving smoothing of retinotopic maps

et al. 2021

PLoS Comput Biol

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“…We developed Diffeomorphic Registration for Retinotopic Map (DRRM) to align retinotopic maps in multiple visual areas under the diffeomorphic condition. Diffeomorphic registration is a feasible way to ensure the topological condition in retinotopic mapping (Tu et al 2020b): nearby neurons have receptive fields at nearby locations on the retina (Wandell et al 2007). The raw pRF results cannot ensure such condition.…”

Section: Introductionmentioning

confidence: 99%

Diffeomorphic registration for retinotopic maps of multiple visual regions

et al. 2022

Brain Struct Funct

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Retinotopic map, the mapping between visual inputs on the retina and neuronal responses on cortical surface, is one of the central topics in vision science. Typically, human retinotopic maps are constructed by analyzing functional magnetic resonance responses to designed visual stimuli on cortical surface.Although it is widely used in visual neuroscience, retinotopic maps are limited by measurement noise and resolution. One promising approach to improve the quality of retinotopic maps is to register individual subject's retinotopic maps to a retinotopic template or atlas. However, none of the existing retinotopic registration methods has explicitly quantified the diffeomorphic condition, that is, retinotopic maps can be aligned by stretching/compressing but without tearing up. Here, we developed Diffeomorphic Registration for Retinotopic Maps (DRRM) to simultaneously align retinotopic maps in multiple visual regions under the diffeomorphic condition. Specifically, we used the Beltrami coefficient to model the diffeomorphic condition and performed surface registration based on retinotopic coordinates. The overall framework is simple and elegant and preserves topological condition defined in the atlas. We further developed a unique performance evaluation protocol and compared the performance of the new method with several existing image intensity-based registration methods on both synthetic and real datasets. The results showed that DRRM is superior to the existing methods in achieving diffeomorphic mappings in synthetic and empirical data from 3T and 7T magnets. DRRM may improve the interpretation of low-quality retinotopic maps and facilitate adoption of retinotopic maps in clinical settings.

“…A variety of methods were developed to reduce topological violations in the post-processing of pRF results [15][16][17][18][19][20]. For instance, the model fitting [15] is widely used to fit the decoded parameters with an algebraic model for V1-V3.…”

Section: Introductionmentioning

confidence: 99%

Topological Receptive Field Model for Human Retinotopic Mapping

Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

et al. 2021

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The mapping between visual inputs on the retina and neuronal activations in the visual cortex, i.e., retinotopic map, is an essential topic in vision science and neuroscience. Human retinotopic maps can be revealed by analyzing the functional magnetic resonance imaging (fMRI) signal responses to designed visual stimuli in vivo. Neurophysiology studies summarized that visual areas are topological (i.e., nearby neurons have receptive fields at nearby locations in the image). However, conventional fMRI-based analyses frequently generate non-topological results because they process fMRI signals on a voxel-wise basis, without considering the neighbor relations on the surface. Here we propose a topological receptive field (tRF) model which imposes the topological condition when decoding retinotopic fMRI signals. More specifically, we parametrized the cortical surface to a unit disk, characterized the topological condition by tRF, and employed an efficient scheme to solve the tRF model. We tested our framework on both synthetic and human fMRI data. Experimental results showed that the tRF model could remove the topological violations, improve model explaining power, and generate biologically plausible retinotopic maps. The proposed framework is general and can be applied to other sensory maps.