Population receptive fields in non-human primates from whole-brain fMRI and large-scale neurophysiology in visual cortex in visual cortex

  1. Peter Christiaan Klink  Is a corresponding author
  2. Xing Chen
  3. Vim Vanduffel
  4. Pieter Roelfsema
  1. Netherlands Institute for Neuroscience, Netherlands
  2. KU Leuven Medical School, Belgium

Abstract

Population receptive field (pRF) modeling is a popular fMRI method to map the retinotopic organization of the human brain. While fMRI-based pRF-maps are qualitatively similar to invasively recorded single-cell receptive fields in animals, it remains unclear what neuronal signal they represent. We addressed this question in awake non-human primates comparing whole-brain fMRI and large-scale neurophysiological recordings in areas V1 and V4 of the visual cortex. We examined the fits of several pRF-models based on the fMRI BOLD-signal, multi-unit spiking activity (MUA) and local field potential (LFP) power in different frequency bands. We found that pRFs derived from BOLD-fMRI were most similar to MUA-pRFs in V1 and V4, while pRFs based on LFP gamma power also gave a good approximation. FMRI-based pRFs thus reliably reflect neuronal receptive field properties in the primate brain. In addition to our results in V1 and V4, the whole-brain fMRI measurements revealed retinotopic tuning in many other cortical and subcortical areas with a consistent increase in pRF-size with increasing eccentricity, as well as a retinotopically specific deactivation of default-mode network nodes similar to previous observations in humans.

Data availability

- All data and code are available on GIN: https://doi.org/10.12751/g-node.p8ypgv- Unthresholded fMRI model fitting results are available on Neurovault: https://identifiers.org/neurovault.collection:8082

Article and author information

Author details

  1. Peter Christiaan Klink

    Vision and Cognition, Netherlands Institute for Neuroscience, Amsterdam, Netherlands
    For correspondence
    c.klink@nin.knaw.nl
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6784-7842
  2. Xing Chen

    Vision and Cognition, Netherlands Institute for Neuroscience, Amsterdam, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3589-1750
  3. Vim Vanduffel

    KU Leuven Medical School, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  4. Pieter Roelfsema

    Vision and Cognition, Netherlands Institute for Neuroscience, Amsterdam, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1625-0034

Funding

Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VENI 451.13.023)

  • Peter Christiaan Klink

Nederlandse Organisatie voor Wetenschappelijk Onderzoek (STW-Perspectief P15-42 NESTOR"")

  • Xing Chen
  • Pieter Roelfsema

FP7 Ideas: European Research Council (ERC 339490 Cortic_al_gorithms"")

  • Pieter Roelfsema

Human Brain Project ((agreements 720270 and 785907,Human Brain Project SGA1 and SGA2"")

  • Vim Vanduffel
  • Pieter Roelfsema

Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Crossover Program 17619 INTENSE"")

  • Peter Christiaan Klink
  • Pieter Roelfsema

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

Ethics

Animal experimentation: Animal care and experimental procedures were in accordance with the ILAR's Guide for the Care and Use of Laboratory Animals, the European legislation (Directive 2010/63/EU) and approved by the institutional animal care and use committee of the Royal Netherlands Academy of Arts and Sciences and the Central Authority for Scientific Procedures on Animals (CCD) in the Netherlands (License numbers AVD8010020173789 and AVD8010020171046).

Copyright

© 2021, Klink et al.

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

  • 2,857
    views
  • 396
    downloads
  • 43
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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. Peter Christiaan Klink
  2. Xing Chen
  3. Vim Vanduffel
  4. Pieter Roelfsema
(2021)
Population receptive fields in non-human primates from whole-brain fMRI and large-scale neurophysiology in visual cortex in visual cortex
eLife 10:e67304.
https://doi.org/10.7554/eLife.67304

Share this article

https://doi.org/10.7554/eLife.67304

Further reading

    1. Neuroscience
    Simonas Griesius, Amy Richardson, Dimitri Michael Kullmann
    Research Article

    Non-linear summation of synaptic inputs to the dendrites of pyramidal neurons has been proposed to increase the computation capacity of neurons through coincidence detection, signal amplification, and additional logic operations such as XOR. Supralinear dendritic integration has been documented extensively in principal neurons, mediated by several voltage-dependent conductances. It has also been reported in parvalbumin-positive hippocampal basket cells, in dendrites innervated by feedback excitatory synapses. Whether other interneurons, which support feed-forward or feedback inhibition of principal neuron dendrites, also exhibit local non-linear integration of synaptic excitation is not known. Here, we use patch-clamp electrophysiology, and two-photon calcium imaging and glutamate uncaging, to show that supralinear dendritic integration of near-synchronous spatially clustered glutamate-receptor mediated depolarization occurs in NDNF-positive neurogliaform cells and oriens-lacunosum moleculare interneurons in the mouse hippocampus. Supralinear summation was detected via recordings of somatic depolarizations elicited by uncaging of glutamate on dendritic fragments, and, in neurogliaform cells, by concurrent imaging of dendritic calcium transients. Supralinearity was abolished by blocking NMDA receptors (NMDARs) but resisted blockade of voltage-gated sodium channels. Blocking L-type calcium channels abolished supralinear calcium signalling but only had a minor effect on voltage supralinearity. Dendritic boosting of spatially clustered synaptic signals argues for previously unappreciated computational complexity in dendrite-projecting inhibitory cells of the hippocampus.

    1. Neuroscience
    Christine Ahrends, Mark W Woolrich, Diego Vidaurre
    Tools and Resources

    Predicting an individual’s cognitive traits or clinical condition using brain signals is a central goal in modern neuroscience. This is commonly done using either structural aspects, such as structural connectivity or cortical thickness, or aggregated measures of brain activity that average over time. But these approaches are missing a central aspect of brain function: the unique ways in which an individual’s brain activity unfolds over time. One reason why these dynamic patterns are not usually considered is that they have to be described by complex, high-dimensional models; and it is unclear how best to use these models for prediction. We here propose an approach that describes dynamic functional connectivity and amplitude patterns using a Hidden Markov model (HMM) and combines it with the Fisher kernel, which can be used to predict individual traits. The Fisher kernel is constructed from the HMM in a mathematically principled manner, thereby preserving the structure of the underlying model. We show here, in fMRI data, that the HMM-Fisher kernel approach is accurate and reliable. We compare the Fisher kernel to other prediction methods, both time-varying and time-averaged functional connectivity-based models. Our approach leverages information about an individual’s time-varying amplitude and functional connectivity for prediction and has broad applications in cognitive neuroscience and personalised medicine.