A validation framework for neuroimaging software: The case of population receptive fields

Lerma-Usabiaga, Garikoitz; Benson, Noah C.; Winawer, Jonathan; Wandell, Brian A.

doi:10.1371/journal.pcbi.1007924

Cited by 41 publications

(62 citation statements)

References 39 publications

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“…While our results on the validity of the estimates are in line with the results reported previously ( Lerma-Usabiaga et al, 2020 ), here we focused on understanding how the interaction between the pRF model (e.g., Gaussian) and cost function underlie the previously reported variability of the pRF size. In this respect, while interesting, we did not consider the effect that the hemodynamic response function has on the estimates (which we considered fixed and equal across methods).…”

Section: Resultssupporting

confidence: 85%

“…Note that different software implementations of the same algorithm are available and they may differ in computational efficiency, number of hyperparameters and programming language. For an exhaustive comparison between software tools for estimating the pRF see (Lerma-Usabiaga et al, 2020).…”

Section: Prf Estimation Methodsmentioning

confidence: 99%

“…These algorithmic alternatives come with multiple software implementations which complicates the validation of the pRF estimation. With the aim of evaluating the reproducibility of these software alternatives a computational framework has been recently developed (Lerma-Usabiaga et al, 2020). Following a similar but complementary aim, this article focuses on the algorithmic principles rather than the software alternatives for estimating the pRF.…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Reliability of Population Receptive Field Size Estimates Using fMRI

et al. 2020

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In functional MRI (fMRI), population receptive field (pRF) models allow a quantitative description of the response as a function of the features of the stimuli that are relevant for each voxel. The most popular pRF model used in fMRI assumes a Gaussian shape in the features space (e.g., the visual field) reducing the description of the voxel's pRF to the Gaussian mean (the pRF preferred feature) and standard deviation (the pRF size). The estimation of the pRF mean has been proven to be highly reliable. However, the estimate of the pRF size has been shown not to be consistent within and between subjects. While this issue has been noted experimentally, here we use an optimization theory perspective to describe how the inconsistency in estimating the pRF size is linked to an inherent property of the Gaussian pRF model. When fitting such models, the goodness of fit is less sensitive to variations in the pRF size than to variations in the pRF mean. We also show how the same issue can be considered from a bias-variance perspective. We compare different estimation procedures in terms of the reliability of their estimates using simulated and real fMRI data in the visual (using the Human Connectome Project database) and auditory domain. We show that, the reliability of the estimate of the pRF size can be improved considering a linear combination of those pRF models with similar goodness of fit or a permutation based approach. This increase in reliability of the pRF size estimate does not affect the reliability of the estimate of the pRF mean and the prediction accuracy.

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Section: Resultssupporting

confidence: 85%

Section: Prf Estimation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigating the Reliability of Population Receptive Field Size Estimates Using fMRI

et al. 2020

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“…One non-neural factor that has a large effect on the estimated pRF size (and less so for pRF position) is the mismatch between the assumed and actual underlying hemodynamic response function (HRF). This mismatch can cause both over- and underestimation of pRF sizes, depending on the experimental design or whether the spatial or temporal component of the assumed HRF is inaccurate (Dumoulin & Wandell, 2008; Lerma-Usabiaga, Benson, Winawer, & Wandell, 2020). Since our fMRI session used stimuli that swept across the visual field in both directions for a given orientation, we believe that our experimental design minimized any bias in the estimated pRF size caused by the sluggish HRF.…”

Section: Discussionmentioning

confidence: 99%

“…We also did not model the spatial component of the HRF. However, our presentation time of sweeping bars was relatively long (31s/bar sweep), which largely reduces the impact of pRF size biases caused by the HRF mismatch (Lerma-Usabiaga et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

A Population Receptive Field Model of the Magnetoencephalography Response

Kupers

Edadan

Benson

et al. 2020

Preprint

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1AbstractComputational models which predict the neurophysiological response from experimental stimuli have played an important role in human neuroimaging. One type of computational model, the population receptive field (pRF), has been used to describe cortical responses at the millimeter scale using functional magnetic resonance imaging (fMRI) and electrocorticography (ECoG). However, pRF models are not widely used for non-invasive electromagnetic field measurements (EEG/MEG), because individual sensors pool responses originating from several centimeter of cortex, containing neural populations with widely varying spatial tuning. Here, we introduce a forward-modeling approach in which pRFs estimated from fMRI data are used to predict MEG sensor responses. Subjects viewed contrast-reversing bar stimuli sweeping across the visual field in separate fMRI and MEG sessions. Individual subject’s pRFs were modeled on the cortical surface at the millimeter scale using the fMRI data. We then predicted cortical time series and projected these predictions to MEG sensors using a biophysical MEG forward model, accounting for the pooling across cortex. We compared the predicted MEG responses to observed visually evoked steady-state responses measured in the MEG session. We found that pRF parameters estimated by fMRI could explain a substantial fraction of the variance in steady-state MEG sensor responses (up to 60% in individual sensors). Control analyses in which we artificially perturbed either pRF size or pRF position reduced MEG prediction accuracy, indicate that MEG data are sensitive to pRF properties derived from fMRI. Our model provides a quantitative approach to link fMRI and MEG measurements, thereby enabling advances in our understanding of spatiotemporal dynamics in human visual field maps.

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Sub‐bundle based analysis reveals the role of human optic radiation in visual working memory

Wang,

et al. 2024

Human Brain Mapping

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White matter (WM) functional activity has been reliably detected through functional magnetic resonance imaging (fMRI). Previous studies have primarily examined WM bundles as unified entities, thereby obscuring the functional heterogeneity inherent within these bundles. Here, for the first time, we investigate the function of sub‐bundles of a prototypical visual WM tract—the optic radiation (OR). We use the 7T retinotopy dataset from the Human Connectome Project (HCP) to reconstruct OR and further subdivide the OR into sub‐bundles based on the fiber's termination in the primary visual cortex (V1). The population receptive field (pRF) model is then applied to evaluate the retinotopic properties of these sub‐bundles, and the consistency of the pRF properties of sub‐bundles with those of V1 subfields is evaluated. Furthermore, we utilize the HCP working memory dataset to evaluate the activations of the foveal and peripheral OR sub‐bundles, along with LGN and V1 subfields, during 0‐back and 2‐back tasks. We then evaluate differences in 2bk‐0bk contrast between foveal and peripheral sub‐bundles (or subfields), and further examine potential relationships between 2bk‐0bk contrast and 2‐back task d‐prime. The results show that the pRF properties of OR sub‐bundles exhibit standard retinotopic properties and are typically similar to the properties of V1 subfields. Notably, activations during the 2‐back task consistently surpass those under the 0‐back task across foveal and peripheral OR sub‐bundles, as well as LGN and V1 subfields. The foveal V1 displays significantly higher 2bk‐0bk contrast than peripheral V1. The 2‐back task d‐prime shows strong correlations with 2bk‐0bk contrast for foveal and peripheral OR fibers. These findings demonstrate that the blood oxygen level‐dependent (BOLD) signals of OR sub‐bundles encode high‐fidelity visual information, underscoring the feasibility of assessing WM functional activity at the sub‐bundle level. Additionally, the study highlights the role of OR in the top‐down processes of visual working memory beyond the bottom‐up processes for visual information transmission. Conclusively, this study innovatively proposes a novel paradigm for analyzing WM fiber tracts at the individual sub‐bundle level and expands understanding of OR function.

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A validation framework for neuroimaging software: The case of population receptive fields

Cited by 41 publications

References 39 publications

Investigating the Reliability of Population Receptive Field Size Estimates Using fMRI

Investigating the Reliability of Population Receptive Field Size Estimates Using fMRI

A Population Receptive Field Model of the Magnetoencephalography Response

Sub‐bundle based analysis reveals the role of human optic radiation in visual working memory

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