scholarly journals Bayesian analysis of retinotopic maps

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Noah C Benson ◽  
Jonathan Winawer
Keyword(s):  
Natural Variation ◽  
Subjective Assessment ◽  
Measurement Noise ◽  
Fmri Data ◽  
Cortical Surface ◽  
Validation Data ◽  
Visual Neuroscience ◽  
Retinotopic Mapping ◽  
Retinotopic Maps ◽  
Bayesian Mapping

Human visual cortex is organized into multiple retinotopic maps. Characterizing the arrangement of these maps on the cortical surface is essential to many visual neuroscience studies. Typically, maps are obtained by voxel-wise analysis of fMRI data. This method, while useful, maps only a portion of the visual field and is limited by measurement noise and subjective assessment of boundaries. We developed a novel Bayesian mapping approach which combines observation–a subject’s retinotopic measurements from small amounts of fMRI time–with a prior–a learned retinotopic atlas. This process automatically draws areal boundaries, corrects discontinuities in the measured maps, and predicts validation data more accurately than an atlas alone or independent datasets alone. This new method can be used to improve the accuracy of retinotopic mapping, to analyze large fMRI datasets automatically, and to quantify differences in map properties as a function of health, development and natural variation between individuals.

scholarly journals Bayesian Analysis of Retinotopic Maps

2018 ◽  
Author(s):  
Noah C. Benson ◽  
Jonathan Winawer
Keyword(s):  
Natural Variation ◽  
Subjective Assessment ◽  
Measurement Noise ◽  
Fmri Data ◽  
Cortical Surface ◽  
Validation Data ◽  
Visual Neuroscience ◽  
Retinotopic Mapping ◽  
Retinotopic Maps ◽  
Bayesian Mapping

AbstractHuman visual cortex is organized into multiple retinotopic maps. Characterizing the arrangement of these maps on the cortical surface is essential to many visual neuroscience studies. Typically, maps are obtained by voxel-wise analysis of fMRI data. This method, while useful, maps only a portion of the visual field and is limited by measurement noise and subjective assessment of boundaries. We developed a novel Bayesian mapping approach which combines observation–a subject’s retinotopic measurements from small amounts of fMRI time–with a prior–a learned retinotopic atlas. This process automatically draws areal boundaries, corrects discontinuities in the measured maps, and predicts validation data more accurately than an atlas alone or independent datasets alone. This new method can be used to improve the accuracy of retinotopic mapping, to analyze large fMRI datasets automatically, and to quantify differences in map properties as a function of health, development and natural variation between individuals.

scholarly journals Topology-preserving smoothing of retinotopic maps

2021 ◽  
Vol 17 (8) ◽  
pp. e1009216
Author(s):  
Yanshuai Tu ◽  
Duyan Ta ◽  
Zhong-Lin Lu ◽  
Yalin Wang
Keyword(s):  
Signal To Noise Ratio ◽  
Accurate Analysis ◽  
Visual Neuroscience ◽  
Retinotopic Mapping ◽  
Retinotopic Maps ◽  
Topological Condition ◽  
Visual Inputs ◽  
Human Connectome Project ◽  
Cortical Responses ◽  
Cortical Regions

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.

scholarly journals Diffeomorphic Registration for Retinotopic Maps of Multiple Visual Regions

2021 ◽  
Author(s):  
Yanshuai Tu ◽  
Zhong-Lin Lu ◽  
Yalin Wang
Keyword(s):  
Surface Registration ◽  
Clinical Settings ◽  
Cortical Surface ◽  
Vision Science ◽  
Visual Neuroscience ◽  
Diffeomorphic Registration ◽  
Retinotopic Maps ◽  
Evaluation Protocol ◽  
Visual Inputs

Abstract 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.

scholarly journals Scale-specific analysis of fMRI data on the irregular cortical surface

NeuroImage ◽  
2018 ◽  
Vol 181 ◽  
pp. 370-381
Author(s):  
Yi Chen ◽  
Radoslaw Martin Cichy ◽  
Wilhelm Stannat ◽  
John-Dylan Haynes
Keyword(s):  
Fmri Data ◽  
Cortical Surface ◽  
Specific Analysis

Anatomically informed interpolation of fMRI data on the cortical surface

NeuroImage ◽  
2006 ◽  
Vol 31 (4) ◽  
pp. 1475-1486 ◽  
Author(s):  
C. Grova ◽  
S. Makni ◽  
G. Flandin ◽  
P. Ciuciu ◽  
J. Gotman ◽  
...  
Keyword(s):  
Fmri Data ◽  
Cortical Surface

scholarly journals Topographic connectivity reveals task-dependent retinotopic processing throughout the human brain.

2020 ◽  
Author(s):  
Tomas Knapen
Keyword(s):  
Visual System ◽  
Spatial Organization ◽  
Brain Regions ◽  
Retinotopic Mapping ◽  
Visual Spatial ◽  
Retinotopic Maps ◽  
Sensory Predictions ◽  
Visual Organization ◽  
The Brain

The human visual system is organized as a hierarchy of maps that share the retina's topography. Although retinotopic maps have been identified throughout the brain, how much of the brain is visually organized remains unknown. Here we demonstrate widespread stable visual organization beyond the traditional visual system by analyzing topographic connectivity with primary visual cortex during moviewatching, rest, and retinotopic mapping. Detailed visual-spatial organization derived from retinotopic connectivity is modulated by experimental condition. Specifically, traditionally visual regions alternate with default mode network and hippocampus in preferentially representing the center of the visual field. This visual role of hippocampus would allow it to implement sensory predictions by interfacing between abstract memories and concrete perceptions. These results indicate that pervasive sensory coding facilitates the communication between far-flung brain regions.

scholarly journals Biological variation in the sizes, shapes and locations of visual cortical areas in the mouse

2018 ◽  
Author(s):  
Jack Waters ◽  
Eric Lee ◽  
Nathalie Gaudreault ◽  
Fiona Griffin ◽  
Jerome Lecoq ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Visual Information ◽  
Biological Variation ◽  
Measurement Noise ◽  
Cortical Areas ◽  
Retinotopic Maps ◽  
Visual Areas ◽  
Visual Cortical ◽  
Cortical Visual Areas ◽  
Processing Of Visual Information

ABSTRACTVisual cortex is organized into discrete sub-regions or areas that are arranged into a hierarchy and serve different functions in the processing of visual information. In our previous work, we noted that retinotopic maps of cortical visual areas differed between mice, but did not quantify these differences or determine the relative contributions of biological variation and measurement noise. Here we quantify the biological variation in the size, shape and locations of 11 visual areas in the mouse. We find that there is substantial biological variation in the sizes of visual areas, with some visual areas varying in size by two-fold across the population of mice.

Volumetric retinotopic mapping without cortical surface reconstruction

NeuroImage ◽  
2000 ◽  
Vol 11 (5) ◽  
pp. S613 ◽  
Author(s):  
Serge O. Domoulin ◽  
Rick D. Hoge ◽  
Rebecca L. Achtman ◽  
Curtis L. Baker ◽  
Robert F. Hess ◽  
...  
Keyword(s):  
Surface Reconstruction ◽  
Cortical Surface ◽  
Retinotopic Mapping

scholarly journals Rapid and Precise Retinotopic Mapping of the Visual Cortex Obtained by Voltage-Sensitive Dye Imaging in the Behaving Monkey

2007 ◽  
Vol 98 (2) ◽  
pp. 1002-1014 ◽  
Author(s):  
Zhiyong Yang ◽  
David J. Heeger ◽  
Eyal Seidemann
Keyword(s):  
Visual Cortex ◽  
Traveling Wave ◽  
High Speed ◽  
Visual Space ◽  
Submillimeter Range ◽  
Population Activity ◽  
Voltage Sensitive Dye ◽  
Retinotopic Mapping ◽  
Retinotopic Maps ◽  
Voltage Sensitive Dye Imaging

Retinotopy is a fundamental organizing principle of the visual cortex. Over the years, a variety of techniques have been used to examine it. None of these techniques, however, provides a way to rapidly characterize retinotopy, at the submillimeter range, in alert, behaving subjects. Voltage-sensitive dye imaging (VSDI) can be used to monitor neuronal population activity at high spatial and temporal resolutions. Here we present a VSDI protocol for rapid and precise retinotopic mapping in the behaving monkey. Two monkeys performed a fixation task while thin visual stimuli swept periodically at a high speed in one of two possible directions through a small region of visual space. Because visual space is represented systematically across the cortical surface, each moving stimulus produced a traveling wave of activity in the cortex that could be precisely measured with VSDI. The time at which the peak of the traveling wave reached each location in the cortex linked this location with its retinotopic representation. We obtained detailed retinotopic maps from a region of about 1 cm2 over the dorsal portion of areas V1 and V2. Retinotopy obtained during <4 min of imaging had a spatial precision of 0.11–0.19 mm, was consistent across experiments, and reliably predicted the locations of the response to small localized stimuli. The ability to rapidly obtain precise retinotopic maps in behaving monkeys opens the door for detailed analysis of the relationship between spatiotemporal dynamics of population responses in the visual cortex and perceptually guided behavior.

scholarly journals Assessing dynamic functional connectivity in heterogeneous samples

2017 ◽  
Author(s):  
B. C. L. Lehmann ◽  
S. R. White ◽  
R. N. Henson ◽  
Cam-CAN ◽  
L. Geerligs
Keyword(s):  
Functional Connectivity ◽  
Functional Organization ◽  
Measurement Noise ◽  
Fmri Data ◽  
Group Differences ◽  
Dynamic Features ◽  
Dynamic Functional Connectivity ◽  
Heterogeneous Samples ◽  
Alternative Sources ◽  
Connectivity Strength

AbstractSeveral methods have been developed to measure dynamic functional connectivity (dFC) in fMRI data. These methods are often based on a sliding-window analysis, which aims to capture how the brain’s functional organization varies over the course of a scan. The aim of many studies is to compare dFC across groups, such as younger versus older people. However, spurious group differences in measured dFC may be caused by other sources of heterogeneity between people. For example, the shape of the haemodynamic response function (HRF) and levels of measurement noise have been found to vary with age. We use a generic simulation framework for fMRI data to investigate the effect of such heterogeneity on estimates of dFC. Our findings show that, despite no differences in true dFC, individual differences in measured dFC can result from other (non-dynamic) features of the data, such as differences in neural autocorrelation, HRF shape, connectivity strength and measurement noise. We also find that common dFC methods such as k-means and multilayer modularity approaches can detect spurious group differences in dynamic connectivity due to inappropriate setting of their hyperparameters. fMRI studies therefore need to consider alternative sources of heterogeneity across individuals before concluding differences in dFC.

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