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      Individual Differences in Frequency and Topography of Slow and Fast Sleep Spindles

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          Abstract

          Sleep spindles are transient oscillatory waveforms that occur during non-rapid eye movement (NREM) sleep across widespread cortical areas. In humans, spindles can be classified as either slow or fast, but large individual differences in spindle frequency as well as methodological difficulties have hindered progress towards understanding their function. Using two nights of high-density electroencephalography recordings from 28 healthy individuals, we first characterize the individual variability of NREM spectra and demonstrate the difficulty of determining subject-specific spindle frequencies. We then introduce a novel spatial filtering approach that can reliably separate subject-specific spindle activity into slow and fast components that are stable across nights and across N2 and N3 sleep. We then proceed to provide detailed analyses of the topographical expression of individualized slow and fast spindle activity. Group-level analyses conform to known spatial properties of spindles, but also uncover novel differences between sleep stages and spindle classes. Moreover, subject-specific examinations reveal that individual topographies show considerable variability that is stable across nights. Finally, we demonstrate that topographical maps depend nontrivially on the spindle metric employed. In sum, our findings indicate that group-level approaches mask substantial individual variability of spindle dynamics, in both the spectral and spatial domains. We suggest that leveraging, rather than ignoring, such differences may prove useful to further our understanding of the physiology and functional role of sleep spindles.

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

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            Blind separation of auditory event-related brain responses into independent components.

            Averaged event-related potential (ERP) data recorded from the human scalp reveal electroencephalographic (EEG) activity that is reliably time-locked and phase-locked to experimental events. We report here the application of a method based on information theory that decomposes one or more ERPs recorded at multiple scalp sensors into a sum of components with fixed scalp distributions and sparsely activated, maximally independent time courses. Independent component analysis (ICA) decomposes ERP data into a number of components equal to the number of sensors. The derived components have distinct but not necessarily orthogonal scalp projections. Unlike dipole-fitting methods, the algorithm does not model the locations of their generators in the head. Unlike methods that remove second-order correlations, such as principal component analysis (PCA), ICA also minimizes higher-order dependencies. Applied to detected-and undetected-target ERPs from an auditory vigilance experiment, the algorithm derived ten components that decomposed each of the major response peaks into one or more ICA components with relatively simple scalp distributions. Three of these components were active only when the subject detected the targets, three other components only when the target went undetected, and one in both cases. Three additional components accounted for the steady-state brain response to a 39-Hz background click train. Major features of the decomposition proved robust across sessions and changes in sensor number and placement. This method of ERP analysis can be used to compare responses from multiple stimuli, task conditions, and subject states.
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              Fast and slow spindles during the sleep slow oscillation: disparate coalescence and engagement in memory processing.

              Thalamo-cortical spindles driven by the up-state of neocortical slow (< 1 Hz) oscillations (SOs) represent a candidate mechanism of memory consolidation during sleep. We examined interactions between SOs and spindles in human slow wave sleep, focusing on the presumed existence of 2 kinds of spindles, i.e., slow frontocortical and fast centro-parietal spindles. Two experiments were performed in healthy humans (24.5 ± 0.9 y) investigating undisturbed sleep (Experiment I) and the effects of prior learning (word paired associates) vs. non-learning (Experiment II) on multichannel EEG recordings during sleep. Only fast spindles (12-15 Hz) were synchronized to the depolarizing SO up-state. Slow spindles (9-12 Hz) occurred preferentially at the transition into the SO down-state, i.e., during waning depolarization. Slow spindles also revealed a higher probability to follow rather than precede fast spindles. For sequences of individual SOs, fast spindle activity was largest for "initial" SOs, whereas SO amplitude and slow spindle activity were largest for succeeding SOs. Prior learning enhanced this pattern. The finding that fast and slow spindles occur at different times of the SO cycle points to disparate generating mechanisms for the 2 kinds of spindles. The reported temporal relationships during SO sequences suggest that fast spindles, driven by the SO up-state feed back to enhance the likelihood of succeeding SOs together with slow spindles. By enforcing such SO-spindle cycles, particularly after prior learning, fast spindles possibly play a key role in sleep-dependent memory processing.
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                Author and article information

                Contributors
                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                05 September 2017
                2017
                : 11
                : 433
                Affiliations
                [1] 1Department of Psychiatry, Beth Israel Deaconess Medical Center Boston, MA, United States
                [2] 2Department of Psychiatry, Harvard Medical School Boston, MA, United States
                [3] 3Department of Psychiatry, Massachusetts General Hospital Charlestown, MA, United States
                [4] 4Athinoula A. Martinos Center for Biomedical Imaging Charlestown, MA, United States
                Author notes

                Edited by: Juliana Yordanova, Institute of Neurobiology (BAS), Bulgaria

                Reviewed by: Luigi De Gennaro, Sapienza Università di Roma, Italy; Christian O’Reilly, École Polytechnique Fédérale de Lausanne, Switzerland

                *Correspondence: Roy Cox roycox.roycox@ 123456gmail.com
                Article
                10.3389/fnhum.2017.00433
                5591792
                28928647
                c2cf7f4d-0954-44f8-841e-04f628973931
                Copyright © 2017 Cox, Schapiro, Manoach and Stickgold.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 08 June 2017
                : 15 August 2017
                Page count
                Figures: 7, Tables: 4, Equations: 0, References: 82, Pages: 22, Words: 17341
                Funding
                Funded by: Nederlandse Organisatie voor Wetenschappelijk Onderzoek 10.13039/501100003246
                Award ID: 446-14-009
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: F32-NS093901, MH048832, K24MH099421, MH092638
                Funded by: Harvard Catalyst 10.13039/100007299
                Award ID: TR001102
                Categories
                Neuroscience
                Original Research

                Neurosciences
                sleep spindles,individual differences,spatial filter,generalized eigendecomposition,eeg

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