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

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          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 nonhuman 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 blood-oxygen-level-dependent (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.

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          FreeSurfer.

          FreeSurfer is a suite of tools for the analysis of neuroimaging data that provides an array of algorithms to quantify the functional, connectional and structural properties of the human brain. It has evolved from a package primarily aimed at generating surface representations of the cerebral cortex into one that automatically creates models of most macroscopically visible structures in the human brain given any reasonable T1-weighted input image. It is freely available, runs on a wide variety of hardware and software platforms, and is open source. Copyright © 2012 Elsevier Inc. All rights reserved.
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            Improved optimization for the robust and accurate linear registration and motion correction of brain images.

            Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods.
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              Neuronal oscillations in cortical networks.

              G Buzsáki (2004)
              Clocks tick, bridges and skyscrapers vibrate, neuronal networks oscillate. Are neuronal oscillations an inevitable by-product, similar to bridge vibrations, or an essential part of the brain's design? Mammalian cortical neurons form behavior-dependent oscillating networks of various sizes, which span five orders of magnitude in frequency. These oscillations are phylogenetically preserved, suggesting that they are functionally relevant. Recent findings indicate that network oscillations bias input selection, temporally link neurons into assemblies, and facilitate synaptic plasticity, mechanisms that cooperatively support temporal representation and long-term consolidation of information.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                03 November 2021
                2021
                : 10
                : e67304
                Affiliations
                [1 ] Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences Amsterdam Netherlands
                [2 ] Psychiatry Department, Amsterdam UMC Amsterdam Netherlands
                [3 ] Laboratory for Neuro- and Psychophysiology, Department of Neurosciences, KU Leuven Medical School Leuven Belgium
                [4 ] Massachusetts General Hospital, Martinos Ctr. for Biomedical Imaging Charlestown United States
                [5 ] Leuven Brain Institute, KU Leuven Leuven Belgium
                [6 ] Harvard Medical School Boston United States
                [7 ] Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, VU University Amsterdam Netherlands
                University of Oxford United Kingdom
                University of Oxford United Kingdom
                University of Oxford United Kingdom
                University of Oxford United Kingdom
                University of Oxford United Kingdom
                Author information
                https://orcid.org/0000-0002-6784-7842
                https://orcid.org/0000-0002-3589-1750
                https://orcid.org/0000-0002-1625-0034
                Article
                67304
                10.7554/eLife.67304
                8641953
                34730515
                c3bc8a7f-5c90-436c-96c5-3a1ac253acff
                © 2021, Klink et al

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

                History
                : 07 February 2021
                : 24 October 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100003246, Nederlandse Organisatie voor Wetenschappelijk Onderzoek;
                Award ID: VENI 451.13.023
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003246, Nederlandse Organisatie voor Wetenschappelijk Onderzoek;
                Award ID: STW-Perspectief P15-42 "NESTOR"
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100011199, FP7 Ideas: European Research Council;
                Award ID: ERC 339490 "Cortic_al_gorithms"
                Award Recipient :
                Funded by: Human Brain Project;
                Award ID: Agreements 720270 and 785907 "Human Brain Project SGA1 and SGA2"
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003246, Nederlandse Organisatie voor Wetenschappelijk Onderzoek;
                Award ID: Crossover Program 17619 "INTENSE"
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Article
                Neuroscience
                Custom metadata
                Within-species comparison of population receptive fields determined with fMRI and electrophysiology in nonhuman primates reveals the neuronal basis of blood-oxygen-level-dependent-based retinotopy.

                Life sciences
                population receptive field,vision,nonhuman primate,neuroimaging,neurophysiology,rhesus macaque

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