Abstract
Extracellular recordings are the primary tool for extracting neuronal spike trains in-vivo. One of the crucial pre-processing stages of this signal is the high-pass filtration used to isolate neuronal spiking activity. Filters are characterized by changes in the magnitude and phase of different frequencies. While filters are typically chosen for their effect on magnitudes, little attention has been paid to the impact of these filters on the phase of each frequency. In this study we show that in the case of nonlinear phase shifts generated by most online and offline filters, the signal is severely distorted, resulting in an alteration of the spike waveform. This distortion leads to a shape that deviates from the original waveform as a function of its constituent frequencies, and a dramatic reduction in the SNR of the waveform that disrupts spike detectability. Currently, the vast majority of articles utilizing extracellular data are subject to these distortions since most commercial and academic hardware and software utilize nonlinear phase filters. We show that this severe problem can be avoided by recording wide-band signals followed by zero phase filtering, or alternatively corrected by reversed filtering of a narrow-band filtered, and in some cases even segmented signals. Implementation of either zero phase filtering or phase correction of the nonlinear phase filtering reproduces the original spike waveforms and increases the spike detection rates while reducing the number of false negative and positive errors. This process, in turn, helps eliminate subsequent errors in downstream analyses and misinterpretations of the results.
Highlights
Extracellular recordings of neuronal activity are one of the primary research tools in systems neuroscience [1,2,3,4,5]
Most of the online and offline, hardware and software used today in neurophysiology implement analog or digital NLP filters, such as the Butterworth filter, due to their relative efficiency, short time delays and ease of application. They impose nonlinear phase shifts that have a major impact on the temporal structure of the filtered signal
Whereas filtration using a ZP filter maintained the original shape of the action potentials, the spike shape was distorted when the signal was filtered with a NLP filter (Fig 1B)
Summary
Extracellular recordings of neuronal activity are one of the primary research tools in systems neuroscience [1,2,3,4,5]. High frequency changes in the extracellular signal stem primarily from the spiking activity of one or more neurons in close proximity to the recording electrode. Termed the local field potential (LFP), result primarily from sub-threshold and synaptic changes associated with larger neuronal populations [6,7]. Since each of these components dominates a typical range of frequencies, filtration is an efficient process of separating the raw signal into its constituents.
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