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      Reliable Analysis of Single-Unit Recordings from the Human Brain under Noisy Conditions: Tracking Neurons over Hours

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          Abstract

          Recording extracellulary from neurons in the brains of animals in vivo is among the most established experimental techniques in neuroscience, and has recently become feasible in humans. Many interesting scientific questions can be addressed only when extracellular recordings last several hours, and when individual neurons are tracked throughout the entire recording. Such questions regard, for example, neuronal mechanisms of learning and memory consolidation, and the generation of epileptic seizures. Several difficulties have so far limited the use of extracellular multi-hour recordings in neuroscience: Datasets become huge, and data are necessarily noisy in clinical recording environments. No methods for spike sorting of such recordings have been available. Spike sorting refers to the process of identifying the contributions of several neurons to the signal recorded in one electrode. To overcome these difficulties, we developed Combinato: a complete data-analysis framework for spike sorting in noisy recordings lasting twelve hours or more. Our framework includes software for artifact rejection, automatic spike sorting, manual optimization, and efficient visualization of results. Our completely automatic framework excels at two tasks: It outperforms existing methods when tested on simulated and real data, and it enables researchers to analyze multi-hour recordings. We evaluated our methods on both short and multi-hour simulated datasets. To evaluate the performance of our methods in an actual neuroscientific experiment, we used data from from neurosurgical patients, recorded in order to identify visually responsive neurons in the medial temporal lobe. These neurons responded to the semantic content, rather than to visual features, of a given stimulus. To test our methods with multi-hour recordings, we made use of neurons in the human medial temporal lobe that respond selectively to the same stimulus in the evening and next morning.

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          Most cited references22

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          Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering.

          This study introduces a new method for detecting and sorting spikes from multiunit recordings. The method combines the wavelet transform, which localizes distinctive spike features, with superparamagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions. Moreover, an improved method for setting amplitude thresholds for spike detection is proposed. We describe several criteria for implementation that render the algorithm unsupervised and fast. The algorithm is compared to other conventional methods using several simulated data sets whose characteristics closely resemble those of in vivo recordings. For these data sets, we found that the proposed algorithm outperformed conventional methods.
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            Invariant visual representation by single neurons in the human brain.

            It takes a fraction of a second to recognize a person or an object even when seen under strikingly different conditions. How such a robust, high-level representation is achieved by neurons in the human brain is still unclear. In monkeys, neurons in the upper stages of the ventral visual pathway respond to complex images such as faces and objects and show some degree of invariance to metric properties such as the stimulus size, position and viewing angle. We have previously shown that neurons in the human medial temporal lobe (MTL) fire selectively to images of faces, animals, objects or scenes. Here we report on a remarkable subset of MTL neurons that are selectively activated by strikingly different pictures of given individuals, landmarks or objects and in some cases even by letter strings with their names. These results suggest an invariant, sparse and explicit code, which might be important in the transformation of complex visual percepts into long-term and more abstract memories.
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              Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements.

              Simultaneous recording from large numbers of neurons is a prerequisite for understanding their cooperative behavior. Various recording techniques and spike separation methods are being used toward this goal. However, the error rates involved in spike separation have not yet been quantified. We studied the separation reliability of "tetrode" (4-wire electrode)-recorded spikes by monitoring simultaneously from the same cell intracellularly with a glass pipette and extracellularly with a tetrode. With manual spike sorting, we found a trade-off between Type I and Type II errors, with errors typically ranging from 0 to 30% depending on the amplitude and firing pattern of the cell, the similarity of the waveshapes of neighboring neurons, and the experience of the operator. Performance using only a single wire was markedly lower, indicating the advantages of multiple-site monitoring techniques over single-wire recordings. For tetrode recordings, error rates were increased by burst activity and during periods of cellular synchrony. The lowest possible separation error rates were estimated by a search for the best ellipsoidal cluster shape. Human operator performance was significantly below the estimated optimum. Investigation of error distributions indicated that suboptimal performance was caused by inability of the operators to mark cluster boundaries accurately in a high-dimensional feature space. We therefore hypothesized that automatic spike-sorting algorithms have the potential to significantly lower error rates. Implementation of a semi-automatic classification system confirms this suggestion, reducing errors close to the estimated optimum, in the range 0-8%.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2016
                8 December 2016
                : 11
                : 12
                : e0166598
                Affiliations
                [1 ]Department of Epileptology, University of Bonn, Bonn, Germany
                [2 ]Department of Neurosurgery, University of Bonn, Bonn, Germany
                Consejo Superior de Investigaciones Cientificas, SPAIN
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: JN FM.

                • Data curation: JN FM.

                • Formal analysis: JN.

                • Funding acquisition: FM CEE.

                • Investigation: JN FM JB.

                • Methodology: JN.

                • Project administration: FM.

                • Resources: CEE.

                • Software: JN.

                • Supervision: FM.

                • Validation: JN.

                • Visualization: JN FM.

                • Writing – original draft: JN.

                • Writing – review & editing: JN FM JB CEE.

                Author information
                http://orcid.org/0000-0003-3323-2986
                Article
                PONE-D-16-40557
                10.1371/journal.pone.0166598
                5145161
                27930664
                041f47fc-6485-4b5b-9444-49aca12691ed
                © 2016 Niediek et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 11 October 2016
                : 24 October 2016
                Page count
                Figures: 10, Tables: 2, Pages: 26
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001663, Volkswagen Foundation;
                Award ID: Lichtenberg 86 507
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: MO 930/4-1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: MO 930/4-1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: SFB 1089
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: SFB 1089
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100004963, Seventh Framework Programme;
                Award ID: 602102 EPITARGET
                Award Recipient :
                This work was supported by Volkswagen Foundation (Lichtenberg 86 507, https://www.volkswagenstiftung.de): FM; German Research Council (DFG MO 930/4-1, SFB 1089, http://www.dfg.de): FM, JN; and Seventh Framework Program (602102 EPITARGET, https://ec.europa.eu/research/fp7/index_en.cfm): FM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                Custom metadata
                Some restrictions apply to the dataset. The simulated neural data used in this study have been described in a study by Pedreira et al. (J. Neurosci. Methods, 2012, doi: 10.1016/j.jneumeth.2012.07.010) and are are available online ( http://bioweb.me/CPGJNM2012-dataset). The code used to generate multi-hour simulations is available upon request from the corresponding author of the present study. The source code of the software described in the present study is available online ( https://github.com/jniediek/combinato). The source code of the software described in the present study is available online ( https://github.com/jniediek/combinato). The code used to generate multi-hour simulations is available upon request from the corresponding author of the present study.

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