Moment-by-moment tracking of naturalistic learning and its underlying hippocampo-cortical interactions – ScienceOpen
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      Moment-by-moment tracking of naturalistic learning and its underlying hippocampo-cortical interactions

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          Abstract

          Humans form lasting memories of stimuli that were only encountered once. This naturally occurs when listening to a story, however it remains unclear how and when memories are stored and retrieved during story-listening. Here, we first confirm in behavioral experiments that participants can learn about the structure of a story after a single exposure and are able to recall upcoming words when the story is presented again. We then track mnemonic information in high frequency activity (70–200 Hz) as patients undergoing electrocorticographic recordings listen twice to the same story. We demonstrate predictive recall of upcoming information through neural responses in auditory processing regions. This neural measure correlates with behavioral measures of event segmentation and learning. Event boundaries are linked to information flow from cortex to hippocampus. When listening for a second time, information flow from hippocampus to cortex precedes moments of predictive recall. These results provide insight on a fine-grained temporal scale into how episodic memory encoding and retrieval work under naturalistic conditions.

          Abstract

          When listening to a story, humans learn about its structure and content. Here the authors reveal the neural processes behind episodic memory and predictive recall at a fine temporal scale in this naturalistic setting

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            The organization of the human cerebral cortex estimated by intrinsic functional connectivity.

            Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.
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              FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data

              This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.
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                Author and article information

                Contributors
                s.michelmann@princeton.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                13 September 2021
                13 September 2021
                2021
                : 12
                : 5394
                Affiliations
                [1 ]GRID grid.16750.35, ISNI 0000 0001 2097 5006, Department of Psychology, , Princeton University, ; Princeton, NJ USA
                [2 ]GRID grid.16750.35, ISNI 0000 0001 2097 5006, Princeton Neuroscience Institute, , Princeton University, ; Princeton, NJ USA
                [3 ]GRID grid.137628.9, ISNI 0000 0004 1936 8753, School of Medicine, , New York University, ; New York, NY USA
                Author information
                http://orcid.org/0000-0002-5717-586X
                http://orcid.org/0000-0002-3560-6294
                http://orcid.org/0000-0002-7119-3810
                http://orcid.org/0000-0003-1068-1797
                http://orcid.org/0000-0003-0044-4632
                http://orcid.org/0000-0002-3599-7168
                http://orcid.org/0000-0002-5887-9682
                Article
                25376
                10.1038/s41467-021-25376-y
                8438040
                34518520
                52f8fe2d-ce3d-4a73-800d-8c88dae43e67
                © 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
                : 26 October 2020
                : 2 August 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);
                Award ID: 437219953
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100007321, Finding A Cure for Epilepsy and Seizures (FACES);
                Funded by: FundRef https://doi.org/10.13039/100000002, U.S. Department of Health & Human Services | National Institutes of Health (NIH);
                Award ID: MH112357
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                cognitive neuroscience,psychology
                Uncategorized
                cognitive neuroscience, psychology

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