Latency correction in sparse neuronal spike trains.

BACKGROUND: In neurophysiological data, latency refers to a global shift of spikes from one spike train to the next, either caused by response onset fluctuations or by finite propagation speed. Such systematic shifts in spike timing lead to a spurious decrease in synchrony which needs to be corrected. NEW METHOD: We propose a new algorithm of multivariate latency correction suitable for sparse data for which the relevant information is not primarily in the rate but in the timing of each individual spike. The algorithm is designed to correct systematic delays while maintaining all other kinds of noisy disturbances. It consists of two steps, spike matching and distance minimization between the matched spikes using simulated annealing. RESULTS: We show its effectiveness on simulated and real data: cortical propagation patterns recorded via calcium imaging from mice before and after stroke. Using simulations of these data we also establish criteria that can be evaluated beforehand in order to anticipate whether our algorithm is likely to yield a considerable improvement for a given dataset. COMPARISON WITH EXISTING METHOD(S): Existing methods of latency correction rely on adjusting peaks in rate profiles, an approach that is not feasible for spike trains with low firing in which the timing of individual spikes contains essential information. CONCLUSIONS: For any given dataset the criterion for applicability of the algorithm can be evaluated quickly and in case of a positive outcome the latency correction can be applied easily since the source codes of the algorithm are publicly available.

Reconstructing ERP amplitude effects after compensating for trial-to-trial latency jitter: A solution based on a novel application of residue iteration decomposition.

Stimulus-locked averaged event-related potentials (ERPs) are among the most frequently used signals in Cognitive Neuroscience. However, the late, cognitive or endogenous ERP components are often variable in latency from trial to trial in a component-specific way, compromising the stability assumption underlying the averaging scheme. Here we show that trial-to-trial latency variability of ERP components not only blurs the average ERP waveforms, but may also attenuate existing or artificially induce condition effects in amplitude. Hitherto this problem has not been well investigated. To tackle this problem, a method to measure and compensate component-specific trial-to-trial latency variability is required. Here we first systematically analyze the problem of single trial latency variability for condition effects based on simulation. Then, we introduce a solution by applying residue iteration decomposition (RIDE) to experimental data. RIDE separates different clusters of ERP components according to their time-locking to stimulus onsets, response times, or neither, based on an algorithm of iterative subtraction. We suggest to reconstruct ERPs by re-aligning the component clusters to their most probable single trial latencies. We demonstrate that RIDE-reconstructed ERPs may recover amplitude effects that are diminished or exaggerated in conventional averages by trial-to-trial latency jitter. Hence, RIDE-corrected ERPs may be a valuable tool in conditions where ERP effects may be compromised by latency variability.