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      Partially overlapping spatial environments trigger reinstatement in hippocampus and schema representations in prefrontal cortex

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          Abstract

          When we remember a city that we have visited, we retrieve places related to finding our goal but also non-target locations within this environment. Yet, understanding how the human brain implements the neural computations underlying holistic retrieval remains unsolved, particularly for shared aspects of environments. Here, human participants learned and retrieved details from three partially overlapping environments while undergoing high-resolution functional magnetic resonance imaging (fMRI). Our findings show reinstatement of stores even when they are not related to a specific trial probe, providing evidence for holistic environmental retrieval. For stores shared between cities, we find evidence for pattern separation (representational orthogonalization) in hippocampal subfield CA2/3/DG and repulsion in CA1 (differentiation beyond orthogonalization). Additionally, our findings demonstrate that medial prefrontal cortex (mPFC) stores representations of the common spatial structure, termed schema, across environments. Together, our findings suggest how unique and common elements of multiple spatial environments are accessed computationally and neurally.

          Abstract

          The authors examine how we differentiate highly similar places from each other. They provide evidence for complementary neural mechanisms in the human hippocampus and prefrontal cortex involved in processing interfering and common elements important to remembering places that we have visited.

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          LIBSVM: A library for support vector machines

          LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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            Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.

            Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements. Copyright © 2011 Elsevier Inc. All rights reserved.
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              Information-based functional brain mapping.

              The development of high-resolution neuroimaging and multielectrode electrophysiological recording provides neuroscientists with huge amounts of multivariate data. The complexity of the data creates a need for statistical summary, but the local averaging standardly applied to this end may obscure the effects of greatest neuroscientific interest. In neuroimaging, for example, brain mapping analysis has focused on the discovery of activation, i.e., of extended brain regions whose average activity changes across experimental conditions. Here we propose to ask a more general question of the data: Where in the brain does the activity pattern contain information about the experimental condition? To address this question, we propose scanning the imaged volume with a "searchlight," whose contents are analyzed multivariately at each location in the brain.
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                Author and article information

                Contributors
                adekstrom@email.arizona.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                28 October 2021
                28 October 2021
                2021
                : 12
                : 6231
                Affiliations
                [1 ]GRID grid.134563.6, ISNI 0000 0001 2168 186X, Department of Psychology, , University of Arizona, ; 1503 E. University Blvd., Tucson, AZ 85721 USA
                [2 ]GRID grid.134563.6, ISNI 0000 0001 2168 186X, Evelyn McKnight Brain Institute, , University of Arizona, ; 1503 E. University Blvd., Tucson, AZ 85721 USA
                [3 ]GRID grid.5685.e, ISNI 0000 0004 1936 9668, Department of Psychology, , University of York, ; Heslington, York YO10 5DD UK
                Author information
                http://orcid.org/0000-0002-9358-4353
                http://orcid.org/0000-0002-8909-8096
                http://orcid.org/0000-0002-6812-2368
                Article
                26560
                10.1038/s41467-021-26560-w
                8553856
                34711830
                ffa79c4f-72b8-499c-8788-1cf148314b9c
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 6 February 2021
                : 11 October 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000065, U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS);
                Award ID: NS076856
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                cognitive neuroscience,hippocampus,spatial memory
                Uncategorized
                cognitive neuroscience, hippocampus, spatial memory

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