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| Home > Publications database > Surrogate techniques to evaluate significance of spike patterns |
| Poster (After Call) | FZJ-2025-02384 |
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2022
Abstract: Introduction / MotivationSurrogate spike train data is used to generate the null hypothesis in the context of the significance analysis of spike train correlations and spatio-temporal spike patterns. In our work, we compare five different surrogate techniques against the classical technique called Uniform Dithering (UD [1]). In particular, we discuss the use of the surrogates to generate the null-hypothesis distribution in the statistical test of the SPADE method (Spike PAttern Detection and Evaluation [2,3]), which detects spatio-temporal spike patterns in parallel spike trains. In SPADE, both spike trains and surrogate realizations are discretized into 0-1 sequences (binarization) before the pattern detection. We discover that binarized surrogates have a lower spike count than the original data, due to the change in the surrogate’s inter-spike interval (ISI) distribution caused by the UD algorithm. The spike count mismatch between the original data and the surrogates is predominant in the case of high firing rates, spiking regularity, and presence of a dead time (minimal temporal distance between spikes typically induced by spike sorting). We prove that spike count reduction leads to false positive (FP) detection, motivating us to explore alternative surrogate techniques to UD.MethodsSurrogate Techniques (Figure 1)Uniform Dithering (UD)[1] displaces each spike individually according to a uniform distribution.To account for the dead-time present in experimental data, Uniform Dithering with Dead-Time (UDD) limits the displacement of each spike such that the dead-times are conserved.(Joint)-ISI dithering [4] displaces each spike individually preserving the (joint-)ISI distribution.Window Shuffling shuffles binarized spike trains inside of short windows. Trial Shifting consists in shifting entire segments of a spike train according to a uniform distribution, independently trial by trial, and neuron by neuron [1, 5].SPADESPADE detects spike patterns at a millisecond resolution, allowing for temporal delays between the spikes. The spike trains are first discretized and patterns are then mined and counted. To assess the significance of these patterns, the same procedure is performed on multiple realizations of surrogate spike trains, resulting in a p-value spectrum [3]. Significant patterns have p-values lower than the (corrected) significance threshold.Artificial data generationIn order to evaluate the effect of the different surrogates on SPADE, we create artificial spike trains modeled according to the statistics of experimental data from the pre-/motor cortex of macaque monkeys [6], including non-stationary firing rate profiles. The dead time and regularity of the data are modeled by simulating Poisson processes with dead-time (PPD) and Gamma spike trains, respectively. In particular, using a Gamma process the coefficient of variation can be adjusted explicitly, thus allowing for the generation of regular and bursty processes.Figure 1. Surrogate Techniques. A) Uniform Dithering (UD) displaces each spike according to a uniform distribution centered on the spike. B) Uniform Dithering with dead-time (UDD) is based on uniform dithering, but spikes are constrained not to be closer to each other than a dead-time C) Joint Inter-Spike Interval Dithering (JISI-D) displaces each spike according to the J-ISI distribution of the neuron. D) Inter-Spike Interval Dithering (ISI-D) displaces each spike according to the ISI distribution of the neuron. E) Trial Shifting (TR-SHIFT) shifts each trial according to a uniform distribution. F) Window Shuffling (WIN-SHUFF) shuffles binned spike trains within windows.Results and DiscussionWe observe that UD surrogates modify the ISI distribution of PPD and Gamma spike trains (approximately) into an exponential distribution. As a consequence, spike counts are reduced after binarization. By applying SPADE on the artificially generated data, we observe a high number of false positives when employing UD (see Figure 2). Thus, we conclude that UD is not a suitable surrogate technique for spike train data that either contains a dead-time or is regular. The alternative surrogate methods, instead, yield a consistently low number of false-positive patterns; between 8 and 15 FPs in 48 analyzed datasets (except for UDD on Gamma spike trains). In conclusion, since trial shifting is the simplest method among the best-performing ones, we recommend it as the method of choice.Figure 2. False-positive patterns in artificial data. The figure shows the number of false positives (FPs) detected per surrogate techniques (color-coded) normalized over the 48 data sets analyzed (y-axis), left for PPD and right for Gamma process data analyses. Numbers at the x-axis indicate the total number of FPs over all data sets per surrogate technique.Key Wordsspike patterns, surrogate techniques, motor cortexReferences[1] Louis S, Borgelt C, Grün S. Generation and Selection of Surrogate Methods for Correlation Analysis. In: Rotter S, Grün S, editors. Analysis of Parallel Spike Trains. Berlin: Springer; 2010. p. 359–382.[2] Quaglio P, Yegenoglu A, Torre E, Endres DM, Grün S. Detection and evaluation of spatio-temporal spike patterns in massively parallel spike train data with SPADE. Frontiers in computational neuroscience. 2017;11:41.[3] Stella A, Quaglio P, Torre E, Grün S. 3d-SPADE: Significance evaluation of spatio-temporal patterns of various temporal extents. Biosystems. 2019;185:104022. doi:10.1016/j.biosystems.2019.104022. [4] Gerstein GL. Searching for significance in spatio-temporal firing patterns. Acta Neurobiol Exp. 2004;64:203–207.[5] Pipa, G., Wheeler, D. W., Singer, W., and Nikolie, D. (2008). NeuroXidence: reliable and efficient analysis of an excess or deficiency of joint-spike events. J. Comput. Neurosci. 25, 64–88. doi: 10.1007/s10827-007-0065-3[6] Brochier T, Zehl L, Hao Y, Duret M, Sprenger J, Denker M, et al. Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task. Scientific Data. 2018;5:180055. doi:10.1038/sdata.2018.55.
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