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      Stable long-term chronic brain mapping at the single-neuron level

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          Abstract

          Stable in vivo mapping and modulation of the same neurons and brain circuits over extended periods is critical to both neuroscience and medicine. Current electrical implants offer single-neuron spatiotemporal resolution but are limited by such factors as relative shear motion and chronic immune responses during long-term recording. To overcome these limitations, we developed a chronic in vivo recording and stimulation platform based on flexible mesh electronics, and we demonstrated stable multiplexed local field potentials and single-unit recordings in mouse brains for at least 8 months without probe repositioning. Properties of acquired signals suggest robust tracking of the same neurons over this period. This recording and stimulation platform allowed us to evoke stable single-neuron responses to chronic electrical stimulation and to carry out longitudinal studies of brain aging in freely behaving mice. Such advantages could open up future studies in mapping and modulating changes associated with learning, aging and neurodegenerative diseases.

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          Most cited references41

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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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            The variable discharge of cortical neurons: implications for connectivity, computation, and information coding.

            Cortical neurons exhibit tremendous variability in the number and temporal distribution of spikes in their discharge patterns. Furthermore, this variability appears to be conserved over large regions of the cerebral cortex, suggesting that it is neither reduced nor expanded from stage to stage within a processing pathway. To investigate the principles underlying such statistical homogeneity, we have analyzed a model of synaptic integration incorporating a highly simplified integrate and fire mechanism with decay. We analyzed a "high-input regime" in which neurons receive hundreds of excitatory synaptic inputs during each interspike interval. To produce a graded response in this regime, the neuron must balance excitation with inhibition. We find that a simple integrate and fire mechanism with balanced excitation and inhibition produces a highly variable interspike interval, consistent with experimental data. Detailed information about the temporal pattern of synaptic inputs cannot be recovered from the pattern of output spikes, and we infer that cortical neurons are unlikely to transmit information in the temporal pattern of spike discharge. Rather, we suggest that quantities are represented as rate codes in ensembles of 50-100 neurons. These column-like ensembles tolerate large fractions of common synaptic input and yet covary only weakly in their spike discharge. We find that an ensemble of 100 neurons provides a reliable estimate of rate in just one interspike interval (10-50 msec). Finally, we derived an expression for the variance of the neural spike count that leads to a stable propagation of signal and noise in networks of neurons-that is, conditions that do not impose an accumulation or diminution of noise. The solution implies that single neurons perform simple algebra resembling averaging, and that more sophisticated computations arise by virtue of the anatomical convergence of novel combinations of inputs to the cortical column from external sources.
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              Response of brain tissue to chronically implanted neural electrodes.

              Chronically implanted recording electrode arrays linked to prosthetics have the potential to make positive impacts on patients suffering from full or partial paralysis. Such arrays are implanted into the patient's cortical tissue and record extracellular potentials from nearby neurons, allowing the information encoded by the neuronal discharges to control external devices. While such systems perform well during acute recordings, they often fail to function reliably in clinically relevant chronic settings. Available evidence suggests that a major failure mode of electrode arrays is the brain tissue reaction against these implants, making the biocompatibility of implanted electrodes a primary concern in device design. This review presents the biological components and time course of the acute and chronic tissue reaction in brain tissue, analyses the brain tissue response of current electrode systems, and comments on the various material science and bioactive strategies undertaken by electrode designers to enhance electrode performance.
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                Author and article information

                Journal
                Nature Methods
                Nat Methods
                Springer Science and Business Media LLC
                1548-7091
                1548-7105
                October 2016
                August 29 2016
                October 2016
                : 13
                : 10
                : 875-882
                Article
                10.1038/nmeth.3969
                27571550
                8d3ec54a-455d-4328-b763-cdb5d11b2fa6
                © 2016

                http://www.springer.com/tdm

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