Effects of Different Analysis Strategies on Paired Associative Stimulation: A Pooled Data Analysis from Three Research Labs

Abstract

Paired associative stimulation (PAS) is a widely used transcranial magnetic stimulation (TMS) paradigm to non-invasively induce synaptic plasticity in the human brain in vivo. Altered PAS-induced plasticity has been demonstrated for several diseases. However, researchers are faced with a high inter- and intra- subject variability of the PAS response. Here, we pooled original data from nine PAS studies from three centers and analyzed the combined dataset of 190 healthy subjects with regard to age dependency, the role of stimulation parameters and the effect of different statistical methods. We observed no main effect of the PAS intervention over all studies (F(2;362) = 0.44; p = 0.644). The rate of subjects showing the expected increase of motor evoked potential (MEP) amplitudes was 53%. The PAS effect differed significantly between studies as shown by a significant interaction effect (F(16;362) = 1.77; p = 0.034) but post-hoc testing did not reveal significant effects after correction for multiple tests. There was a trend toward increased variability of the PAS effect in older subjects. Acquisition parameters differed across studies but without systematically influencing changes in MEP-size. The use of post/baseline quotients systematically indicated stronger PAS effects than post/baseline difference or the logarithm of the post/baseline quotient. The non-significant PAS effects across studies and a wide range of responder rates between studies indicate a high variability of this method. We were thus not able to replicate findings from a previous meta-analysis showing robust effects of PAS. No pattern emerged regarding acquisition parameters that at this point could guide future studies to reduce variability and help increase response rate. For future studies, we propose to report the responder rate and recommend the use of the logarithmized post/baseline quotient for further analyses to better address the possibility that results are driven by few extreme cases.