Within-subject Consistency of Paired Associative Stimulation as Assessed by Linear Mixed Models

Paired associative stimulation (PAS) is a frequently used TMS paradigm that induces long-term potentiation in the human cortex. However, little is known about the within-subject consistency of PAS-induced effects. We determined PAS-induced effects and their consistency in healthy volunteers between two PAS sessions. Additionally, we assessed the benefit of applying linear mixed models (LMMs) to PAS data. Thirty-eight healthy volunteers underwent two identical PAS sessions with a gt;1 week interval. During each session, motor evoked potentials (MEPs) were assessed once before PAS induction and 3 times after at 30 min intervals. We did not detect any significant potentiation of MEP size after PAS induction. However, MEP size during PAS induction showed significant potentiation over time in both sessions (LR(1)=13.36, plt;0.001). Nevertheless, there was poor within-subject consistency of PAS-induced effects both during (ICC=0.15) and after induction (ICC=0.04-0.09). Additionally, statistical model selection procedures demonstrate that a LMM with an unstructured covariance matrix better estimated PAS-induced effects than one with a conventional compound symmetry matrix (LR(34)=214.73, plt;0.001). While our results are supportive of a high intra-individual variability of PAS-induced effects, the generalizability of our results is unclear, as we were only partially successful in replicating results from previous PAS studies typically showing potentiation of MEPs during and after PAS induction. We do, however, demonstrate that linear mixed models can improve the reliability of PAS-induced effects estimation.