Population Receptive Field Shapes in Early Visual Cortex Are Nearly Circular

First published February 2, 2021.The visual field region where a stimulus evokes a neural response is called the receptive field (RF). Analytical tools combined with functional MRI (fMRI) can estimate the RF of the population of neurons within a voxel. Circular population RF (pRF) methods accurately specify the central position of the pRF and provide some information about the spatial extent (diameter) of the RF. A number of investigators developed methods to further estimate the shape of the pRF, for example, whether the shape is more circular or elliptical. There is a report that there are many pRFs with highly elliptical pRFs in early visual cortex (V1–V3; Silson et al., 2018). Large aspect ratios (.2) are difficult to reconcile with the spatial scale of orientation columns or visual field map properties in early visual cortex. We started to replicate the experiments and found that the software used in the publication does not accurately estimate RF shape: it produces elliptical fits to circular ground-truth data. We analyzed an independent data set with a different software package that was validated over a specific range of measurement conditions, to show that in early visual cortex the aspect ratios are ,2. Furthermore, current empirical and theoretical methods do not have enough precision to discriminate ellipses with aspect ratios of 1.5 from circles. Through simulation we identify methods for improving sensitivity that may estimate ellipses with smaller aspect ratios. The results we present are quantitatively consistent with prior assessments using other methodologies.This work was supported by the European Union’s Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant 795807 (to G.L.-U.) and by National Institutes of Health Grants EY027401, EY027964, and MH111417 (to J.W.). We thank E. Silson, C. Baker, and R. Reynolds. We also thank R. Reynolds for help with the AFNI softwar

Common mechanisms for the representation of real, implied, and imagined visual motion

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2007.Includes bibliographical references (p. 118-130).Perceptual systems are specialized for transducing and interpreting information from the environment. But perceptual systems can also be used for processing information that arises from other sources, such as mental imagery and cued associations. Here we ask how a particular sensory property, visual motion, is represented when it is not directly perceived but only imagined or inferred from other cues. In a series of experiments, a motion adaptation paradigm is used to assess directional properties of the responses to mental imagery of motion and viewing photographs that depict motion. The results show that both imagining motion and inferring motion from pictures can cause direction-specific adaptation of perceptual motion mechanisms, thus producing a motion aftereffect when a subsequent real motion stimulus is viewed. The transfer of adaptation from implied and imagined motion to real motion indicates that shared mechanisms are used for the perception, inference and imagination of visual motion.by Jonathan Winawer.Ph.D

Socializing Vacancy: An Architectural Thesis

A large portion of office space has been left vacant, and thus provides no beneficial program to its remaining occupants or the local urbanity it is surrounded by. When considering what can be done with this vacant space, the primary motivation should be to integrate a program which does the opposite: a program which positively disrupts its existing context to hybridize and improve the current outdated programmatic arrangement. To insert a residential program into an existing office tower both disrupts and enhances the rigorous flows of our working and our domestic lives. The predefined universal concept of the ‘working-day’ is no longer applicable to the way we work and live: the boundaries of our time and priorities no longer fall strictly within a daily 9-5 schedule. As the relationship between time spent working and living is blurred, so too should its environment

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

Published: June 25, 2020Neuroimaging 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.Supported by a Marie Sklodowska-Curie (https://ec.europa.eu/programmes/horizon2020/ en/h2020-section/marie-sklodowska-curie-actions) grant to G.L.-U. (H2020-MSCA-IF-2017-795807- ReCiModel) and National Institutes of Health (https://www.nih.gov/) grants supporting N.C.B. and J.W. (EY027401, EY027964, MH111417). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Automated Delineation of Visual Area Boundaries and Eccentricities by a CNN Using Functional, Anatomical, and Diffusion-weighted MRI Data

Delineating visual field maps and iso-eccentricities from fMRI data is an important but time-consuming task for many neuroimaging studies on the human visual cortex because the traditional methods of doing so using retinotopic mapping experiments require substantial expertise as well as scanner, computer, and human time. Automated methods based on gray-matter anatomy or a combination of anatomy and functional mapping can reduce these requirements but are less accurate than experts. Convolutional Neural Networks (CNNs) are powerful tools for automated medical image segmentation. We hypothesize that CNNs can define visual area boundaries with high accuracy. We trained U-Net CNNs with ResNet18 backbones to predict either V1, V2, and V3 boundaries or 5 regions of iso-eccentricity using human-labeled maps. Separate CNNs were trained to predict these regions using different combinations of the following input data: (1) anatomical data from a T1-weighted image only, (2) anatomical data from T1-weighted and T2*-weighted images, (3) white-matter tract endpoints from diffusion-weighted imaging, (4) functional data from retinotopic mapping. All CNNs using functional data had cross-validated accuracy that was statistically indistinguishable from the inter-rater reliability of the training dataset (dice coefficient of 92%) while the CNNs lacking functional data had lower but similar accuracies (~75%). Existing models that do not use CNNs had accuracies lower than any of the CNNs. These results demonstrate that with current methods and data quality, CNNs can replace the time and effort of human experts in manually defining early retinotopic maps, but cannot yet replace the acquisition of functional data

Targeting Chemoprevention of Colorectal Cancer to Those Who Are Likely to Respond

In the past four decades, chemoprevention of colorectal cancer (CRC) has been the subject of many epidemiologic and intervention trials of naturally occurring or pharmacologic agents. Recently, the positioning of cyclooxygenase 2 inhibitors as a viable option in this context was a major breakthrough; however, it was hampered by adverse cardiovascular events. This review questions whether chemopreventive measures for CRC are ready to be used in mass or individual applications, standing alone or in combination with other CRC-preventive measures. It also discusses steps that may be undertaken to explore this field further