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      Frequency–specific network connectivity increases underlie accurate spatiotemporal memory retrieval

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          Abstract

          The medial temporal lobes, prefrontal cortex, and parts of parietal cortex form the neural underpinnings of episodic memory, which includes remembering both where and when an event occurred. Yet how these three key regions interact during retrieval of spatial and temporal context remains largely untested. Here, we employed simultaneous electrocorticographical recordings across multiple lobular regions, employing phase synchronization as a measure of network functional connectivity, while patients retrieved spatial and temporal context associated with an episode. Successful memory retrieval was characterized by greater global connectivity compared to incorrect retrieval, with the MTL acting as a convergence hub for these interactions. Spatial vs. temporal context retrieval resulted in prominent differences in both the spectral and temporal patterns of network interactions. These results emphasize dynamic network interactions as central to episodic memory retrieval, providing novel insight into how multiple contexts underlying a single event can be recreated within the same network.

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          Most cited references60

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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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            Complex brain networks: graph theoretical analysis of structural and functional systems.

            Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.
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              Episodic memory: from mind to brain.

              Episodic memory is a neurocognitive (brain/mind) system, uniquely different from other memory systems, that enables human beings to remember past experiences. The notion of episodic memory was first proposed some 30 years ago. At that time it was defined in terms of materials and tasks. It was subsequently refined and elaborated in terms of ideas such as self, subjective time, and autonoetic consciousness. This chapter provides a brief history of the concept of episodic memory, describes how it has changed (indeed greatly changed) since its inception, considers criticisms of it, and then discusses supporting evidence provided by (a) neuropsychological studies of patterns of memory impairment caused by brain damage, and (b) functional neuroimaging studies of patterns of brain activity of normal subjects engaged in various memory tasks. I also suggest that episodic memory is a true, even if as yet generally unappreciated, marvel of nature.
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                Author and article information

                Journal
                9809671
                21092
                Nat Neurosci
                Nat. Neurosci.
                Nature neuroscience
                1097-6256
                1546-1726
                13 February 2013
                27 January 2013
                March 2013
                01 September 2013
                : 16
                : 3
                : 349-356
                Affiliations
                [1 ]Neuroscience Graduate Group, University of California, Davis
                [2 ]Center for Neuroscience, University of California, Davis, 1544 Newton Court, Davis, CA
                [3 ]Department of Neurosurgery, University of Texas Medical School, Houston, Texas
                [4 ]Memorial Hermann Hospital – Texas Medical Center, Houston, Texas
                [5 ]Department of Psychology, University of California, Davis, CA
                Author notes
                CORRESPONDING AUTHOR: Dr. Arne Ekstrom, PhD, Center For Neuroscience, University of California, Davis, 1544 Newton Court, Davis, CA 95618, Phone: 530.757.8850, adekstrom@ 123456ucdavis.edu
                Article
                NIHMS431152
                10.1038/nn.3315
                3581758
                23354333
                ee66f20b-5485-443e-be5b-75e1111cd5ef

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

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                Categories
                Article

                Neurosciences
                human memory,ecog,network analysis,oscillations,phase synchronization,delta,theta
                Neurosciences
                human memory, ecog, network analysis, oscillations, phase synchronization, delta, theta

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