A Bayesian Clustering Method for Tracking Neural Signals Over Successive Intervals

Wolf, Michael; Burdick, Joel W.

doi:10.1109/tbme.2009.2027604

Cited by 16 publications

(10 citation statements)

References 27 publications

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“…Because of this, correlations in waveform shape (or a difference-of-waveform norm) could not be used as the sole indicator of cluster identity. Consequently, although automated tracking of clusters across discrete recording positions would have considerably improved the efficacy of our dipole localization procedure, existing automated cluster linking methods (Emondi et al 2004;Wolf and Burdick 2009) would not have sufficed. Additionally, the candidate set of clusters to be linked had to pass a test concerning the noise covariance; the noise covariance (across the 4 channels) had to be similar at each tetrode position.…”

Section: Spike Preprocessingmentioning

confidence: 99%

Three-dimensional localization of neurons in cortical tetrode recordings

Mechler

Victor

Ohiorhenuan

et al. 2011

Journal of Neurophysiology

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The recording radius and spatial selectivity of an extracellular probe are important for interpreting neurophysiological recordings but are rarely measured. Moreover, an analysis of the recording biophysics of multisite probes (e.g., tetrodes) can provide for source characterization and localization of spiking single units, but this capability has remained largely unexploited. Here we address both issues quantitatively. Advancing a tetrode (Ϸ40-m contact separation, tetrahedral geometry) in 5-to 10-m steps, we repeatedly recorded extracellular action potentials (EAPs) of single neurons in the visual cortex. Using measured spatial variation of EAPs, the tetrodes' measured geometry, and a volume conductor model of the cortical tissue, we solved the inverse problem of estimating the location and the size of the equivalent dipole model of the spike generator associated with each neuron. Half of the 61 visual neurons were localized within a radius of Ϸ100 m and 95% within Ϸ130 m around the tetrode tip (i.e., a large fraction was much further than previously thought). Because of the combined angular sensitivity of the tetrode's leads, location uncertainty was less than one-half the cell's distance. We quantified the spatial dependence of the probability of cell isolation, the isolated fraction, and the dependence of the recording radius on probe size and equivalent dipole size. We also reconstructed the spatial configuration of sets of simultaneously recorded neurons to demonstrate the potential use of 3D dipole localization for functional anatomy. Finally, we found that the dipole moment vector, surprisingly, tended to point toward the probe, leading to the interpretation that the equivalent dipole represents a "local lobe" of the dendritic arbor. recording radius; extracellular action potential; multisite recording; inverse problem; equivalent dipole; lead field theory MULTICONTACT RECORDINGS HAVE HIGH YIELD (Blanche et al. 2005;Csicsvari et al. 2003;Eckhorn and Thomas 1993;Gray et al. 1995;McNaughton et al. 1983) (Buzsaki and Kandel 1998;Drake et al. 1988;Henze et al. 2000) show, the shape and size of the extracellular action potential (EAP) waveform depends on the relative position of cell and probe. Thus, these recordings carry spatial information about spike sources that is not available in single electrode records. However, this spatial information is typically not exploited, because extracting it requires solution of an inverse problem, deducing the position and the size of the current source of a spiking neuron from measurements of its EAP at multiple locations.Here, we solve this inverse problem by modeling the spiking neuron with a single current dipole. The choice of a dipole model rests on reasoning presented in detail in a companion paper and its supplemental material (Mechler and Victor 2011). Briefly, although the membrane currents of a spiking neuron constitute a genuinely distributed current source, the resulting extracellular field is well approximated by that of a dipole beyond a minimum distance a...

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Section: Spike Preprocessingmentioning

confidence: 99%

Three-dimensional localization of neurons in cortical tetrode recordings

Mechler

Victor

Ohiorhenuan

et al. 2011

Journal of Neurophysiology

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“…[12-15]). Nevertheless, such analytical processes largely relate peak identification to their regular frequency.…”

Section: Introductionmentioning

confidence: 99%

Automated analysis of calcium spiking profiles with CaSA software: two case studies from root-microbe symbioses

et al. 2013

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BackgroundRepeated oscillations in intracellular calcium (Ca2+) concentration, known as Ca2+ spiking signals, have been described in plants for a limited number of cellular responses to biotic or abiotic stimuli and most notably the common symbiotic signaling pathway (CSSP) which mediates the recognition by their plant hosts of two endosymbiotic microbes, arbuscular mycorrhizal (AM) fungi and nitrogen fixing rhizobia. The detailed analysis of the complexity and variability of the Ca2+ spiking patterns which have been revealed in recent studies requires both extensive datasets and sophisticated statistical tools.ResultsAs a contribution, we have developed automated Ca2+ spiking analysis (CaSA) software that performs i) automated peak detection, ii) statistical analyses based on the detected peaks, iii) autocorrelation analysis of peak-to-peak intervals to highlight major traits in the spiking pattern.We have evaluated CaSA in two experimental studies. In the first, CaSA highlighted unpredicted differences in the spiking patterns induced in Medicago truncatula root epidermal cells by exudates of the AM fungus Gigaspora margarita as a function of the phosphate concentration in the growth medium of both host and fungus. In the second study we compared the spiking patterns triggered by either AM fungal or rhizobial symbiotic signals. CaSA revealed the existence of different patterns in signal periodicity, which are thought to contribute to the so-called Ca2+ signature.ConclusionsWe therefore propose CaSA as a useful tool for characterizing oscillatory biological phenomena such as Ca2+ spiking.

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“…A common approach is to break the recording into chunks, perform spike sorting on each chunk independently, and finally link the clusters across time (Bar-Hillel et al, 2006; Tolias et al, 2007; Wolf & Burdick, 2009; Shalchyan & Farina, 2014; Dhawale et al, 2015). …”

Section: Resultsmentioning

confidence: 99%

Model-based spike sorting with a mixture of drifting t -distributions

Shan

Lubenov

Siapas

2017

Journal of Neuroscience Methods

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Background Chronic extracellular recordings are a powerful tool for systems neuroscience, but spike sorting remains a challenge. A common approach is to fit a generative model, such as a mixture of Gaussians, to the observed spike data. Even if non-parametric methods are used for spike sorting, such generative models provide a quantitative measure of unit isolation quality, which is crucial for subsequent interpretation of the sorted spike trains. New method We present a spike sorting strategy that models the data as a mixture of drifting t-distributions. This model captures two important features of chronic extracellular recordings—cluster drift over time and heavy tails in the distribution of spikes—and offers improved robustness to outliers. Results We evaluate this model on several thousand hours of chronic tetrode recordings and show that it fits the empirical data substantially better than a mixture of Gaussians. We also provide a software implementation that can re-fit long datasets in a few seconds, enabling interactive clustering of chronic recordings. Comparison with existing methods We identify three common failure modes of spike sorting methods that assume stationarity and evaluate their impact given the empirically-observed cluster drift in chronic recordings. Using hybrid ground truth datasets, we also demonstrate that our model-based estimate of misclassification error is more accurate than previous unit isolation metrics. Conclusions The mixture of drifting t-distributions model enables efficient spike sorting of long datasets and provides an accurate measure of unit isolation quality over a wide range of conditions.

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A Bayesian Clustering Method for Tracking Neural Signals Over Successive Intervals

Cited by 16 publications

References 27 publications

Three-dimensional localization of neurons in cortical tetrode recordings

Three-dimensional localization of neurons in cortical tetrode recordings

Automated analysis of calcium spiking profiles with CaSA software: two case studies from root-microbe symbioses

Model-based spike sorting with a mixture of drifting t -distributions

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