26
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Hippocampal and cortical mechanisms at retrieval explain variability in episodic remembering in older adults

      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

          Age-related episodic memory decline is characterized by striking heterogeneity across individuals. Hippocampal pattern completion is a fundamental process supporting episodic memory. Yet, the degree to which this mechanism is impaired with age, and contributes to variability in episodic memory, remains unclear. We combine univariate and multivariate analyses of fMRI data from a large cohort of cognitively normal older adults (N=100) to measure hippocampal activity and cortical reinstatement during retrieval of trial-unique associations. Trial-wise analyses revealed that (a) hippocampal activity scaled with reinstatement strength, (b) cortical reinstatement partially mediated the relationship between hippocampal activity and associative retrieval, (c) older age weakened cortical reinstatement and its relationship to memory behaviour. Moreover, individual differences in the strength of hippocampal activity and cortical reinstatement explained unique variance in performance across multiple assays of episodic memory. These results indicate that fMRI indices of hippocampal pattern completion explain within- and across-individual memory variability in older adults.

          Related collections

          Most cited references57

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

          Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python

          Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient, and optimal use of neuroimaging analysis approaches: (1) No uniform access to neuroimaging analysis software and usage information; (2) No framework for comparative algorithm development and dissemination; (3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; (4) Neuroimaging software packages do not address computational efficiency; and (5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package, and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is Berkeley Software Distribution licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Machine learning for neuroimaging with scikit-learn

            Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g., resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Requirement for hippocampal CA3 NMDA receptors in associative memory recall.

              Pattern completion, the ability to retrieve complete memories on the basis of incomplete sets of cues, is a crucial function of biological memory systems. The extensive recurrent connectivity of the CA3 area of hippocampus has led to suggestions that it might provide this function. We have tested this hypothesis by generating and analyzing a genetically engineered mouse strain in which the N-methyl-D-asparate (NMDA) receptor gene is ablated specifically in the CA3 pyramidal cells of adult mice. The mutant mice normally acquired and retrieved spatial reference memory in the Morris water maze, but they were impaired in retrieving this memory when presented with a fraction of the original cues. Similarly, hippocampal CA1 pyramidal cells in mutant mice displayed normal place-related activity in a full-cue environment but showed a reduction in activity upon partial cue removal. These results provide direct evidence for CA3 NMDA receptor involvement in associative memory recall.
                Bookmark

                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                29 May 2020
                2020
                : 9
                : e55335
                Affiliations
                [1 ]Department of Psychology, Stanford University StanfordUnited States
                [2 ]Department of Neurology & Neurological Sciences, Stanford University StanfordUnited States
                [3 ]Department of Radiology & Radiological Sciences, Stanford University StanfordUnited States
                University of Toronto Canada
                University of Texas at Austin United States
                University of Toronto Canada
                Umeå University Sweden
                Author notes
                [†]

                San Jose State University, San Jose, United States.

                [‡]

                Columbia University, New York, United States.

                [§]

                University of Oregon, Eugene, United States.

                [#]

                University of Texas at Austin, Austin, United States.

                [¶]

                Johns Hopkins University, Baltimore, United States.

                [**]

                UC San Diego Medical School, San Diego, United States.

                [††]

                University of Iowa, Iowa City, United States.

                [‡‡]

                Yale University, New Haven, United States.

                Author information
                https://orcid.org/0000-0003-2837-8753
                http://orcid.org/0000-0003-0624-4543
                Article
                55335
                10.7554/eLife.55335
                7259949
                32469308
                430d21a1-60a1-47dc-b96f-3bf2bc0ae4e8
                © 2020, Trelle 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
                : 21 January 2020
                : 05 May 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000049, National Institute on Aging;
                Award ID: R01AG048076
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000049, National Institute on Aging;
                Award ID: R21AG058111
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000049, National Institute on Aging;
                Award ID: R21AG058859
                Award Recipient :
                Funded by: Stanford Center for Precision Health and Integrated Diagnostics;
                Award ID: PHIND
                Award Recipient :
                Funded by: Stanford Wu Tsai Neuroscience Institute;
                Award ID: Seed Grant
                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
                The strength of pattern completion during memory retrieval, indexed by hippocampal activity and cortical reinstatement, explains within- and across-individual variability in episodic memory in older adults.

                Life sciences
                episodic memory,aging,hippocampus,cortical reinstatement,pattern completion,functional magnetic resonance imaging,human

                Comments

                Comment on this article