Bayesian analysis of retinotopic maps

  1. Noah C Benson  Is a corresponding author
  2. Jonathan Winawer
  1. New York University, United States

Abstract

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.

Data availability

All data generated or analyzed in this study have been made public on an Open Science Foundation website: https://osf.io/knb5g/Preprocessed MRI data as well as analyses and source code for reproducing figures and performing additional analyses can be found on the Open Science Foundation website https://osf.io/knb5g/.Performing Bayesian inference using your own retinotopic maps.To perform Bayesian inference on a FreeSurfer subject, one can use the neuropythy Python library (https://github.com/noahbenson/neuropythy). For convenience, this library has also been packaged into a Docker container that is freely available on Docker Hub (https://hub.docker.com/r/nben/neuropythy).The following command will provide an explanation of how to use the Docker:> docker run -it --rm nben/neuropythy:v0.5.0 register_retinotopy --helpDetailed instructions on how to use the tools documented in this paper are included in the Open Science Foundation website mentioned above.

The following data sets were generated

Article and author information

Author details

  1. Noah C Benson

    Department of Psychology, New York University, New York, United States
    For correspondence
    nben@nyu.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2365-8265
  2. Jonathan Winawer

    Department of Psychology, New York University, New York, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7475-5586

Funding

National Eye Institute (R01 EY027401)

  • Jonathan Winawer

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: This study was conducted with the approval of the New York University Institutional Review Board (IRB-FY2016-363) and in accordance with the Declaration of Helsinki. Informed consent was obtained for all subjects.

Reviewing Editor

  1. Mark Schira, University of Wollongong, Australia

Publication history

  1. Received: July 18, 2018
  2. Accepted: November 29, 2018
  3. Accepted Manuscript published: December 6, 2018 (version 1)
  4. Version of Record published: January 21, 2019 (version 2)

Copyright

© 2018, Benson & Winawer

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 4,109
    Page views
  • 457
    Downloads
  • 54
    Citations

Article citation count generated by polling the highest count across the following sources: Scopus, Crossref, PubMed Central.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Noah C Benson
  2. Jonathan Winawer
(2018)
Bayesian analysis of retinotopic maps
eLife 7:e40224.
https://doi.org/10.7554/eLife.40224

Further reading

    1. Neuroscience
    Brian D Mueller, Sean A Merrill ... Erik M Jorgensen
    Research Article Updated

    Activation of voltage-gated calcium channels at presynaptic terminals leads to local increases in calcium and the fusion of synaptic vesicles containing neurotransmitter. Presynaptic output is a function of the density of calcium channels, the dynamic properties of the channel, the distance to docked vesicles, and the release probability at the docking site. We demonstrate that at Caenorhabditis elegans neuromuscular junctions two different classes of voltage-gated calcium channels, CaV2 and CaV1, mediate the release of distinct pools of synaptic vesicles. CaV2 channels are concentrated in densely packed clusters ~250 nm in diameter with the active zone proteins Neurexin, α-Liprin, SYDE, ELKS/CAST, RIM-BP, α-Catulin, and MAGI1. CaV2 channels are colocalized with the priming protein UNC-13L and mediate the fusion of vesicles docked within 33 nm of the dense projection. CaV2 activity is amplified by ryanodine receptor release of calcium from internal stores, triggering fusion up to 165 nm from the dense projection. By contrast, CaV1 channels are dispersed in the synaptic varicosity, and are colocalized with UNC-13S. CaV1 and ryanodine receptors are separated by just 40 nm, and vesicle fusion mediated by CaV1 is completely dependent on the ryanodine receptor. Distinct synaptic vesicle pools, released by different calcium channels, could be used to tune the speed, voltage-dependence, and quantal content of neurotransmitter release.

    1. Cell Biology
    2. Neuroscience
    Yu Wang, Meghan Lee Arnold ... Barth D Grant
    Research Article Updated

    Caenorhabditis elegans neurons under stress can produce giant vesicles, several microns in diameter, called exophers. Current models suggest that exophers are neuroprotective, providing a mechanism for stressed neurons to eject toxic protein aggregates and organelles. However, little is known of the fate of the exopher once it leaves the neuron. We found that exophers produced by mechanosensory neurons in C. elegans are engulfed by surrounding hypodermal skin cells and are then broken up into numerous smaller vesicles that acquire hypodermal phagosome maturation markers, with vesicular contents gradually degraded by hypodermal lysosomes. Consistent with the hypodermis acting as an exopher phagocyte, we found that exopher removal requires hypodermal actin and Arp2/3, and the hypodermal plasma membrane adjacent to newly formed exophers accumulates dynamic F-actin during budding. Efficient fission of engulfed exopher-phagosomes to produce smaller vesicles and degrade their contents requires phagosome maturation factors SAND-1/Mon1, GTPase RAB-35, the CNT-1 ARF-GAP, and microtubule motor-associated GTPase ARL-8, suggesting a close coupling of phagosome fission and phagosome maturation. Lysosome activity was required to degrade exopher contents in the hypodermis but not for exopher-phagosome resolution into smaller vesicles. Importantly, we found that GTPase ARF-6 and effector SEC-10/exocyst activity in the hypodermis, along with the CED-1 phagocytic receptor, is required for efficient production of exophers by the neuron. Our results indicate that the neuron requires specific interaction with the phagocyte for an efficient exopher response, a mechanistic feature potentially conserved with mammalian exophergenesis, and similar to neuronal pruning by phagocytic glia that influences neurodegenerative disease.