Failure in identification of overlapping spikes from multiple neuron activity causes artificial correlations
Introduction
Extracellular recording of neuronal activity is a major tool in neurophysiological studies of the brain. Microelectrodes are used for recording local currents deriving from both spiking activity and local field potentials. A single electrode can potentially pick up signals from multiple cells within a local area (Abeles, 1974, Asanuma, 1989). Many types of studies of the nervous system function demand a separation of the recorded signal into spikes originating from different cells. The spikes recorded from different neurons usually differ in size and shape, thereby enabling their sorting into the different sources (Lewicki, 1998, Harris et al., 2000). Recording and separation of multiple neurons from a single electrode allows examination of the behavior of a population of neurons that are usually much closer than multiple neurons recorded by different electrodes. Such studies are therefore mandatory for understanding local neural networks (Eggermont, 1990, Abeles, 1991).
There are plenty of methods for decomposing the output of single electrodes into parallel spike trains (see review of spike sorting methods in Lewicki, 1998). The methods differ in the algorithms used for sorting (ranging from amplitude discrimination to principal and independent component analysis of the spike shape), the working time frame (real time and offline methods) and the verification methods. In general, all methods suffer from problems common in classical signal detection methods, e.g. false positives (noise in the signal is classified as real spikes) and false negatives (real spikes are rejected as noise). However, when using spike-sorting methods, additional errors may occur. These errors include false match (a spike generated by one unit is classified to a different unit) and double match (a single waveform is classified as belonging to more than one class). The number of such errors can be reduced by better signal to noise recording conditions, and by more careful and elaborate sorting methods.
When the errors in either sorting or identification are systematic, they might cause effects, which seem to derive from the properties of the neurons rather than from the classification procedure (Quirk and Wilson, 1999). Such a systematic classification error occurs due to the spike overlap problem. All sorting methods perform quite well when the spikes recorded from the electrode are sufficiently separated in time. However, when multiple spikes appear closely, causing an overlap of their effects on the recorded signal, all sorting methods perform significantly worse. This overlap may result in several consequences: none of the spikes is identified (complete false negative); only one of the spikes is identified (partial false negative); or the overlapped signal is identified as another different spike (false match). The overlapping problem is usually not handled, although some methods have been developed for reducing the misidentification cause by the overlap (Lewicki, 1998). These methods include identification by neural networks (Chandra and Optican, 1997) and overlap decomposition (Atiya, 1992, Lewicki, 1994, Zouridakis and Tam, 1997). However, whichever methods are used, overlapping spikes are identified significantly worse than well-separated spikes. In this manuscript we show that the auto and cross-correlation functions of simultaneously recorded units (especially in brain areas with high firing rates and synchronized discharge) are significantly affected by the sorting limits. Thus, short and long term synchronization might appear in the cross-correlograms due to the sorting problems in spite of the fact that the neurons fire independently. Finally, we describe methods for estimating these artifacts, and thus enabling better understanding of the firing patterns and synchronization of neighboring neurons in the central nervous system.
Section snippets
Behavioral and recording methods
Real data was taken from electrophysiological recordings of multiple spike trains from the globus pallidus of behaving monkeys. Details of the behavior of the monkeys and animal care are described elsewhere (Bar-Gad et al., 2000). During the recording sessions, eight glass-coated tungsten microelectrodes confined within a cylindrical guide (2.2 mm outer diameter) were advanced to the target. Neuronal activity from each electrode was amplified (*10 000), bandpass filtered (300–6000 Hz, four
Electrophysiological recordings
Neurons recorded in the globus pallidus display common characteristics. The spontaneous firing rate of the cells is generally high compared to other brain areas (40–100 Hz), with a refractory period of several milliseconds (4–10 ms). The short time scale autocorrelation functions display a typical peak in the autocorrelation function following the refractory period (Fig. 1a,c). These peaks derive from the refractory period of the cells and not from an increased firing probability (Bar-Gad et
Discussion
The major points mentioned in the article are:
- 1.
Cells recorded simultaneously from the same electrode and sorted using current sorting methods change each other's observed firing pattern. These changes in the auto and cross correlation functions are due to the ‘shadowing effect’. This effect may exist even if a spike is not detected by the recording system.
- 2.
The mutual effects of the cells are different, varying with the cells characteristics such as rate and shape of the original autocorrelation
Acknowledgements
This study was supported in part by the Israeli Academy of Science and the US-Israel Bi-national Science Foundation. We thank Moshe Abeles, Yoram Ben-Shaul and Genella Morris for their critical reading and helpful suggestions. We thank Gali Havazelet-Heimer, Joshua A. Goldberg and Sharon Maraton for sharing their data with us.
References (28)
- et al.
Functional properties and interactions of neuron pairs simultaneously recorded in the medial geniculate body of the cat
Hear. Res.
(1987) - et al.
Slow rhythms and correlations in spike trains from midbrain neurons
Exp. Neurol.
(1975) - et al.
The stereotrode: a new technique for simultaneous isolation of several single units in the central nervous system from multiple unit records
J. Neurosci. Methods
(1983) - et al.
Interaction between spike waveform classification and temporal sequence detection
J. Neurosci. Methods
(1999) - et al.
Statistical properties of neuronal spike trains in the substantia nigra: cell types and their interactions
Brain Res.
(1977) - et al.
Multi-unit spike discrimination using wavelet transforms
Comput. Biol. Med.
(1997) A journey into the brain
Corticonics — Neural Circuits of the Cerebral Cortex
(1991)- et al.
Multispike train analysis
IEEE Trans. Biomed. Eng.
(1977)
Recognition of multiunit neural signals
IEEE Trans. Biomed. Eng.
Detection, classification, and superposition resolution of action potentials in multiunit single-channel recordings by an on-line real-time neural network
IEEE Trans. Biomed. Eng.
Activity of pallidal neurons during movement
J. Neurophysiol.
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