4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Restricting Visual Exploration Directly Impedes Neural Activity, Functional Connectivity, and Memory

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          We move our eyes to explore the visual world, extract information, and create memories. The number of gaze fixations—the stops that the eyes make—has been shown to correlate with activity in the hippocampus, a region critical for memory, and with later recognition memory. Here, we combined eyetracking with fMRI to provide direct evidence for the relationships between gaze fixations, neural activity, and memory during scene viewing. Compared to free viewing, fixating a single location reduced: 1) subsequent memory, 2) neural activity along the ventral visual stream into the hippocampus, 3) neural similarity between effects of subsequent memory and visual exploration, and 4) functional connectivity among the hippocampus, parahippocampal place area, and other cortical regions. Gaze fixations were uniquely related to hippocampal activity, even after controlling for neural effects due to subsequent memory. Therefore, this study provides key causal evidence supporting the notion that the oculomotor and memory systems are intrinsically related at both the behavioral and neural level. Individual gaze fixations may provide the basic unit of information on which memory binding processes operate.

          Related collections

          Most cited references59

          • Record: found
          • Abstract: found
          • Article: not found

          Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

          An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks.

              Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn ( www.nitrc.org/projects/conn ) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method allows for interpretation of anticorrelations as there is no regression of the global signal. The toolbox implements fcMRI measures, such as estimation of seed-to-voxel and region of interest (ROI)-to-ROI functional correlations, as well as semipartial correlation and bivariate/multivariate regression analysis for multiple ROI sources, graph theoretical analysis, and novel voxel-to-voxel analysis of functional connectivity. We describe the methods implemented in the Conn toolbox for the analysis of fcMRI data, together with examples of use and interscan reliability estimates of all the implemented fcMRI measures. The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fcMRI measures.
                Bookmark

                Author and article information

                Contributors
                Journal
                Cereb Cortex Commun
                Cereb Cortex Commun
                cercorcomms
                Cerebral Cortex Communications
                Oxford University Press
                2632-7376
                2020
                25 August 2020
                25 August 2020
                : 1
                : 1
                : tgaa054
                Affiliations
                Department of Behavioral Sciences , University of Michigan-Dearborn , Dearborn, Michigan 48128, USA
                Rotman Research Institute , Baycrest Health Sciences, Toronto, ON M6A 2E1, Canada
                Centre for Vision Research and Vision: Science to Applications (VISTA) Program , York University , Toronto, ON M3J 1P3, Canada
                Rotman Research Institute , Baycrest Health Sciences, Toronto, ON M6A 2E1, Canada
                Centre for Vision Research and Vision: Science to Applications (VISTA) Program , York University , Toronto, ON M3J 1P3, Canada
                Department of Psychology , York University , Toronto, ON M3J 1P3, Canada
                Rotman Research Institute , Baycrest Health Sciences, Toronto, ON M6A 2E1, Canada
                Centre for Vision Research and Vision: Science to Applications (VISTA) Program , York University , Toronto, ON M3J 1P3, Canada
                Departments of Psychology , Psychiatry, University of Toronto , Toronto, ON M5S 3G3, Canada
                Author notes
                Address correspondence to Zhong-Xu Liu, 4053 CASL Building, Department of Behavioral Sciences, College of Arts, Sciences & Letters, University of Michigan-Dearborn, 4901 Evergreen Road, Dearborn, Michigan 48128, USA. Email: zhongxu@ 123456umich.edu .
                Author information
                http://orcid.org/0000-0002-0304-3271
                Article
                tgaa054
                10.1093/texcom/tgaa054
                7595095
                dd2210b9-d290-4df4-a620-aa852368829c
                © The Author(s) 2020. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 27 May 2020
                : 28 July 2020
                : 12 August 2020
                Page count
                Pages: 15
                Funding
                Funded by: Canada First Research Excellence Fund, DOI 10.13039/501100010785;
                Funded by: Natural Sciences and Engineering Research Council of Canada, DOI 10.13039/501100000038;
                Award ID: RGPIN-2018-06399
                Award ID: RGPIN-04238-2015
                Funded by: Canadian Institutes of Health Research, DOI 10.13039/501100000024;
                Award ID: MOP126003
                Categories
                Original Article

                functional connectivity/similarity,gaze fixations,hippocampus,neuroimaging,visual exploration

                Comments

                Comment on this article