Measures to Predict The Individual Variability of Corticospinal Responses Following Transcranial Direct Current Stimulation

Abstract

Individual responses to transcranial direct current stimulation (tDCS) are varied and therefore potentially limit its application. There is evidence that this variability is related to the contributions of Indirect waves (I-waves) recruited in the cortex. The latency of motor-evoked potentials (MEPs) can be measured through transcranial magnetic stimulation (TMS), allowing an individual’s responsiveness to tDCS to be determined. However, this single-pulse method requires several different orientations of the TMS coil, potentially affecting its reliability. Instead, we propose a paired-pulse TMS paradigm targeting I-waves as an alternative method. This method uses one orientation that reduces inter- and intra-trial variability. It was hypothesized that the paired-pulse method would correlate more highly to tDCS responses than the single-pulse method. In a randomized, double blinded, cross-over design, 30 healthy participants completed two sessions, receiving 20 min of either anodal (2 mA) or sham tDCS. TMS was used to quantify Short interval intracortical facilitation (SICF) at Inter stimulus intervals (ISIs) of 1.5, 3.5 and 4.5 ms. Latency was determined in the posterior-anterior (PA), anterior-posterior (AP) and latero-medial (LM) coil orientations. The relationship between latency, SICF measures and the change in suprathreshold MEP amplitude size following tDCS were determined with Pearson’s correlations. TMS measures, SICI and SICF were also used to determine responses to Anodal-tDCS (a-tDCS). Neither of the latency differences nor the SICF measures correlated to the change in MEP amplitude from pre-post tDCS (all P > 0.05). Overall, there was no significant response to tDCS in this cohort. This study highlights the need for testing the effects of various tDCS protocols on the different I-waves. Further research into SICF and whether it is a viable measure of I-wave facilitation is warranted.

A Data-Driven Approach to Responder Subgroup Identification after Paired Continuous Theta Burst Stimulation

“…Moreover, the problem of high individual response variability is not limited to TMS. It also concerns other NIBS techniques such as TCDS (Nuzum et al, 2016 ). Systematic reviews therefore concluded that the reliability of TMS as a tool for changing and measuring cortical excitability may be low and seems to be vulnerable to methodological and other confounders (Beaulieu et al, 2017 ).…”

Response variability of different anodal transcranial direct current stimulation intensities across multiple sessions

“…Besides reported inter-group variability (i.e., inconsistent responses across different groups of individuals to the application of an identical tDCS protocol [24]), other studies described variable responses within the same experimental group resulting in noticeable inter-individual variability [7, 25]. Indeed, many studies have shown that 20 – 60% of a group of individuals experience the classical excitability increase induced by a single atDCS session, whereas the rest have no change or even the opposite effect compared to baseline values [26–32]. With this in mind, another key question is whether individuals respond in a consistent and predictable manner to repeated tDCS sessions.…”

Influence of Concurrent Finger Movements on Transcranial Direct Current Stimulation (tDCS)-Induced Aftereffects

“…Inter-individual variabilities in the aftereffects of tDCS have been repeatedly reported (López-Alonso et al, 2014 ; Wiethoff et al, 2014 ; Chew et al, 2015 ; Nuzum et al, 2016 ; Strube et al, 2016 ). Such variabilities could have resulted in different tDCS effects in combination with different motor tasks, since not all the participants were involved in multiple tasks.…”

“…Furthermore, they can differ simply due to the inter-individual variability among participants (López-Alonso et al, 2014 ; Wiethoff et al, 2014 ; Chew et al, 2015 ; Strube et al, 2016 ), for which anatomical, physiological, genetic and other characteristics of the individuals have been proposed to play different roles (Antal et al, 2010 ; Wiethoff et al, 2014 ; Laakso et al, 2015 ; Opitz et al, 2015 ). Because of the increasing use of this technique in research as well as for clinical purposes it is important to determine the sources or predictors of such variability (see e.g., Nuzum et al, 2016 ) that can alter the aftereffects in a systematic way (Brunoni et al, 2012 ; Hashemirad et al, 2016 ; Kang et al, 2016 ).…”