MULTIPLE FRAGMENT STATISTICAL-ANALYSIS OF POST-SPIKE EFFECTS IN SPIKE-TRIGGERED AVERAGES OF RECTIFIED EMG

Spike-triggered averaging of EMG is a useful experimental technique fo r revealing functional connectivity from central neurons to motoneuron s. Because EMG waveforms constitute time series, statistical analysis of spike-triggered averages is complicated. Empirical methods generall y have been employed to detect the presence of post-spike effects (PSE s), since, as we argue in this report, it is not feasible to develop a rigorous yet sensitive statistical test that detects PSEs in a single grand average of rectified EMG. We have developed a method of multipl e fragment statistical analysis (MESA) of PSEs, based on dividing an e xperimental record into a large numbers of non-overlapping fragments. The calculations necessary to obtain accurate P-values using the multi ple fragment method are simple and efficient, and therefore preliminar y results can be obtained while recording. In this report, we present the rationale for MESA, and give examples of its application. We found MESA to have considerable utility in accurately testing the significa nce of small PSEs, and in detecting PSEs in shorter recordings. Statis tical corrections that should be used when recording multiple channels simultaneously are discussed. MFSA could be implemented for statistic al analysis of other waveforms averaged, such as evoked potentials, mo vement-related cortical potentials, or event-related desychronizations . (C) 1998 Elsevier Science B.V. All rights reserved.