Mapping sequences can bias population receptive field estimates

Infanti, Elisa; Schwarzkopf, D. Samuel

doi:10.1016/j.neuroimage.2020.116636

Cited by 14 publications

(12 citation statements)

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“…However, our study used a stimulus that continuously traveled the visual field or “face space.” Given the slow nature of the BOLD response, such a design likely enhances the power to detect smooth unimodal tuning at the expense of sensitivity for tuning curves with multiple or more narrow spikes. Moreover, recent results show that such orderly designs increase the susceptibility of spatial tuning fits to design choices, such as cycle duration ( Infanti and Schwarzkopf, 2020 ). Future studies could present face samples in a random order ( Kay et al, 2015 ) and explore tuning properties using model-free reverse correlation approaches ( Lee et al, 2013 ).…”

Section: Discussionmentioning

confidence: 99%

Inferior Occipital Gyrus Is Organized along Common Gradients of Spatial and Face-Part Selectivity

2021

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Section: Discussionmentioning

confidence: 99%

Inferior Occipital Gyrus Is Organized along Common Gradients of Spatial and Face-Part Selectivity

2021

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“…Otherwise, a randomized presentation may be preferred. Further, we learned that differences between parameter estimates from continuous and randomized presentations are to be expected based on the software methods and should not be attributed to psychological effects such as attention [23,36].…”

Section: Prf-validationmentioning

confidence: 99%

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

et al. 2020

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Neuroimaging software methods are complex, making it a near certainty that some implementations will contain errors. Modern computational techniques (i.e., public code and data repositories, continuous integration, containerization) enable the reproducibility of the analyses and reduce coding errors, but they do not guarantee the scientific validity of the results. It is difficult, nay impossible, for researchers to check the accuracy of software by reading the source code; ground truth test datasets are needed. Computational reproducibility means providing software so that for the same input anyone obtains the same result, right or wrong. Computational validity means obtaining the right result for the ground-truth test data. We describe a framework for validating and sharing software implementations, and we illustrate its usage with an example application: population receptive field (pRF) methods for functional MRI data. The framework is composed of three main components implemented with containerization methods to guarantee computational reproducibility. In our example pRF application, those components are: (1) synthesis of fMRI time series from ground-truth pRF parameters, (2) implementation of four public pRF analysis tools and standardization of inputs and outputs, and (3) report creation to compare the results with the ground truth parameters. The framework was useful in identifying realistic conditions that lead to imperfect parameter recovery in all four pRF implementations, that would remain undetected using classic validation methods. We provide means to mitigate these problems in future experiments. A computational validation framework supports scientific rigor and creativity, as opposed to the oft-repeated suggestion that investigators rely upon a few agreed upon packages. We hope that the framework will be helpful to validate other critical neuroimaging algorithms, as having a validation framework helps (1) developers to build new software, (2) research scientists to verify the software's accuracy, and (3) reviewers to evaluate the methods used in publications and grants.

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“…Although a previous study on the effect of stimulus configuration on VF map properties showed that this simultaneous wedge and ring combination resulted in the highest pRF fit reliability ( Alvarez et al, 2015 ), our present results suggest that the stimulus type should be carefully considered when aiming for fMRI-based VF reconstructions. In line with this, Infanti and Schwarzkopf (2020) showed that pRF mapping is sensitive to the protocol used and that the mapping sequence can bias its estimates. This higher sensitivity of MP, compared to the conventional pRF technique, favors the former over the latter for the reconstruction of the VFs.…”

Section: Discussionmentioning

confidence: 65%

Visual Field Reconstruction in Hemianopia Using fMRI Based Mapping Techniques

Bridge

Carvalho

Cornelissen

et al. 2021

Front. Hum. Neurosci.

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PurposeA stroke that includes the primary visual cortex unilaterally leads to a loss of visual field (VF) representation in the hemifield contralateral to the damage. While behavioral procedures for measuring the VF, such as perimetry, may indicate that a patient cannot see in a particular area, detailed psychophysical testing often detects the ability to perform detection or discrimination of visual stimuli (“blindsight”). The aim of this study was to determine whether functional magnetic resonance imaging (fMRI) could be used to determine whether perimetrically blind regions of the VF were still represented in VF maps reconstructed on the basis of visually evoked neural activity.MethodsThirteen patients with hemianopia and nine control participants were scanned using 3T MRI while presented with visual stimulation. Two runs of a dynamic “wedge and ring” mapping stimulus, totaling approximately 10 min, were performed while participants fixated centrally. Two different analysis approaches were taken: the conventional population receptive field (pRF) analysis and micro-probing (MP). The latter is a variant of the former that makes fewer assumptions when modeling the visually evoked neural activity. Both methods were used to reconstruct the VF by projecting modeled activity back onto the VF. Following a normalization step, these “coverage maps” can be compared to the VF sensitivity plots obtained using perimetry.ResultsWhile both fMRI-based approaches revealed regions of neural activity within the perimetrically “blind” sections of the VF, the MP approach uncovered more voxels in the lesioned hemisphere in which a modest degree of visual sensitivity was retained. Furthermore, MP-based analysis indicated that both early (V1/V2) and extrastriate visual areas contributed equally to the retained sensitivity in both patients and controls.ConclusionIn hemianopic patients, fMRI-based approaches for reconstructing the VF can pick up activity in perimetrically blind regions of the VF. Such regions of the VF may be particularly amenable for rehabilitation to regain visual function. Compared to conventional pRF modeling, MP reveals more voxels with retained visual sensitivity, suggesting it is a more sensitive approach for VF reconstruction.

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Mapping sequences can bias population receptive field estimates

Cited by 14 publications

References 42 publications

Inferior Occipital Gyrus Is Organized along Common Gradients of Spatial and Face-Part Selectivity

Inferior Occipital Gyrus Is Organized along Common Gradients of Spatial and Face-Part Selectivity

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

Visual Field Reconstruction in Hemianopia Using fMRI Based Mapping Techniques

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