Generating surrogates for significance estimation of spatio-temporal spike patterns

Spatio-temporal spike patterns were suggested as indications of active cell assemblies. We developed the SPADE method to detect significant spatio-temporal patterns (STPs) with ms accuracy. STPs are defined as identically repeating spike patterns across neurons with temporal delays between the spikes. The significance of STPs is derived by comparison to the null-hypothesis of independence implemented by surrogate data. SPADE binarizes the spike trains and examines the data for STPs by counting repeated patterns using frequent itemset mining. The significance of STPs is evaluated by comparison to pattern counts derived from surrogate data, i.e., modifications of the original data with destroyed potential spike correlations but under conservation of the firing rate profiles. To avoid erroneous results, surrogate data are required to retain the statistical properties of the original data as much as possible. A classically chosen surrogate technique is Uniform Dithering (UD), which displaces each spike independently according to a uniform distribution. We find that binarized UD surrogates of our experimental data (motor cortex) contain fewer spikes than the binarized original data. As a consequence, false positives occur. Here, we identify the reason for the spike reduction, which is the lack of conservation of short ISIs. To overcome this problem, we study five alternative surrogate techniques and examine their statistical properties such as spike loss, ISI characteristics, effective movement of spikes, and arising false positives when applied to different ground truth data sets: first, on stationary point process models, and then on non-stationary point processes mimicking statistical properties of experimental data. We conclude that trial-shifting best preserves the features of the original data and has a low expected false-positive rate. Finally, the analysis of the experimental data provides consistent STPs across the alternative surrogates. Author summary The generation of surrogate data, i.e., the modification of original data to destroy a certain feature, is used for the implementation of a particular null-hypothesis if an analytical approach is not feasible. Thus, surrogate data generation has been extensively used to assess significance of spike correlations, e.g., by displacing spikes locally and uniformly (uniform dithering, UD). One of the challenges of surrogate methods is to properly construct the desired null-hypothesis distribution, i.e., to not bias the null-hypothesis through altering the spike train statistics. Here we compare five surrogate techniques to UD (two newly introduced) in the context of spatio-temporal spike pattern detection for their performance, first on controlled spike trains based on point process models, and second on modeled artificial data serving as ground truth to assess the pattern detection in a more complex and realistic setting. UD fails as an appropriate surrogate because it leads to a loss of spikes and thus to a large number of false-positive patterns. The other surrogates achieve a better performance in detecting precisely timed higher-order correlations. Based on these insights, we analyze experimental data from pre-/motor cortex of macaque monkeys during a reaching-and-grasping task for spatio-temporal spike patterns.