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      Volitional learning promotes theta phase coding in the human hippocampus

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          Significance

          While behavioral evidence shows that volitionally controlled learning benefits human memory, little is known about the neural mechanisms underlying this effect. Insights from spatial navigation research in rodents point to the relevance of hippocampal theta oscillations. However, the mechanisms through which theta might support the beneficial effects of active learning in humans are currently unknown. Here, we demonstrate hippocampal theta oscillations increase during volitional learning, promoting a segregation of task-relevant representational signals according to their semantic content. Our results constitute a direct link to the animal literature on hippocampal theta oscillations and its relation to volition and memory processes.

          Abstract

          Electrophysiological studies in rodents show that active navigation enhances hippocampal theta oscillations (4–12 Hz), providing a temporal framework for stimulus-related neural codes. Here we show that active learning promotes a similar phase coding regime in humans, although in a lower frequency range (3–8 Hz). We analyzed intracranial electroencephalography (iEEG) from epilepsy patients who studied images under either volitional or passive learning conditions. Active learning increased memory performance and hippocampal theta oscillations and promoted a more accurate reactivation of stimulus-specific information during memory retrieval. Representational signals were clustered to opposite phases of the theta cycle during encoding and retrieval. Critically, during active but not passive learning, the temporal structure of intracycle reactivations in theta reflected the semantic similarity of stimuli, segregating conceptually similar items into more distant theta phases. Taken together, these results demonstrate a multilayered mechanism by which active learning improves memory via a phylogenetically old phase coding scheme.

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          EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

          We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
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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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              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

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                09 March 2021
                05 March 2021
                05 March 2021
                : 118
                : 10
                : e2021238118
                Affiliations
                [1] aDepartment of Neuropsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum , 44801 Bochum, Germany;
                [2] bLaboratory of Synthetic Perceptive, Emotive and Cognitive Systems, Institute for Bioengineering of Catalonia, Barcelona Institute of Science and Technology , 08028 Barcelona, Spain;
                [3] cDepartment of Information and Communications Technologies, Universitat Pompeu Fabra , 08018 Barcelona, Spain;
                [4] dEpilepsy Monitoring Unit, Department of Neurology, Hospital del Mar , 08003 Barcelona, Spain;
                [5] eHospital del Mar Medical Research Institute , 08003 Barcelona, Spain;
                [6] fFaculty of Health and Life Sciences, Universitat Pompeu Fabra , 08003 Barcelona, Spain;
                [7] gInstitució Catalana de Recerca i Estudis Avançats , 08010 Barcelona, Spain
                Author notes
                2To whom correspondence may be addressed. Email: paul.verschure@ 123456specs-lab.com .

                Edited by Edward T. Bullmore, University of Cambridge, Cambridge, United Kingdom, and accepted by Editorial Board Member Michael S. Gazzaniga January 28, 2021 (received for review October 16, 2020)

                Author contributions: D.P.E. and P.F.M.J.V. designed research; D.P.E., R.Z., X.A. A.P., H.Z., and R.R. performed research; D.P.E., N.A., and P.F.M.J.V. analyzed data; and D.P.E., N.A., and P.F.M.J.V. wrote the paper.

                1N.A. and P.F.M.J.V. contributed equally to this work.

                Author information
                https://orcid.org/0000-0003-2253-5172
                https://orcid.org/0000-0003-4808-6010
                https://orcid.org/0000-0003-1485-1853
                https://orcid.org/0000-0001-7345-4300
                https://orcid.org/0000-0003-3643-9544
                Article
                202021238
                10.1073/pnas.2021238118
                7958181
                33674388
                f040cb75-acab-4f80-ab67-efbc468cff96
                Copyright © 2021 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 12
                Categories
                424
                Biological Sciences
                Neuroscience

                active learning,intracranial eeg,theta oscillations,neural phase coding,hippocampus

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