Reliability of transcranial magnetic stimulation measures of afferent inhibition

Turco, Claudia V.; Pesevski, Angelina; McNicholas, Paul D.; Beaulieu, Louis‐David; Nelson, Aimee J.

doi:10.1016/j.brainres.2019.146394

Cited by 24 publications

(45 citation statements)

References 53 publications

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“…Trials were discarded if the EMG activity was >100 µV in the 100 ms preceding the stimulation artefacts, similar to previous work [26]. Normality was assessed with the Shapiro-Wilks test and a square-root or log transformation was applied in cases where data was not normally distributed.…”

Section: Statistical Analysesmentioning

confidence: 99%

The Impact of Glucose on Corticospinal and Intracortical Excitability

et al. 2019

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Neurotransmission is highly dependent on the availability of glucose-derived energy, although it is unclear how glucose availability modulates corticospinal and intracortical excitability as assessed via transcranial magnetic stimulation (TMS). In this double-blinded placebo-controlled study, we tested the effect of acute glucose intake on motor-evoked potential (MEP) recruitment curves, short-interval intracortical inhibition (SICI), short-latency afferent inhibition (SAI) and long-latency afferent inhibition (LAI). Eighteen healthy males participated in four sessions. Session 1 involved acquisition of an individualized blood glucose response curve. This allowed measurements to be time-locked to an individualized glucose peak after consuming one of three drinks during the subsequent three sessions. Participants were administered a 300 mL concealed solution containing 75 g of glucose, sucralose, or water in separate sessions. Dependent measures were assessed at baseline and twice after drinking the solution. Secondary measures included blood glucose and mean arterial pressure. Corticospinal excitability and blood pressure increased following the drink across all treatments. No changes were observed in SICI, SAI or LAI. There was no rise in corticospinal excitability that was specific to the glucose drink, suggesting that acute changes in glucose levels do not necessarily alter TMS measures of corticospinal or intracortical excitability.

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Section: Statistical Analysesmentioning

confidence: 99%

The Impact of Glucose on Corticospinal and Intracortical Excitability

et al. 2019

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“…Vertical dashed lines reflect the border between low, moderate, strong, and excellent reliability, from left to right. * Indicates a that the ICC and 95% confidence interval for Study F are referenced from Turco et al, [ 14 ]. A = Tsang et al 2014; B = Tsang et al 2015; C = Bailey et al 2016; D = Turco et al 2017; E = Turco et al 2018; F = Turco et al 2019; G = Toepp et al 2019.…”

Section: Resultsmentioning

confidence: 99%

“…A two-way random effects model ICC (2, k) was used, and 95% confidence intervals (CI) were reported with coefficients of variation (CV): CV = SD/mean×100. Since Study F included these analyses at ISIs of 24ms and N20+4 in the originally published report [ 14 ], those results are referenced in this work in lieu of repeating the analysis. The following cut-offs were used to interpret the ICCs in accordance with both Koo and Li [ 23 ] and Portney and Watkins, [ 24 ]; >0.9 indicates excellent relative reliability, 0.75–0.9 is strong, 0.5–0.75 is moderate, < 0.5 is low.…”

Section: Methodsmentioning

confidence: 99%

“…Efforts to resolve these questions and further advance the clinical utility of SAI and LAI have been hampered by their low-to-moderate relative reliability and high levels of measurement error [ 14 , 15 ]. Importantly, in our experience it is not uncommon for an experienced researcher to fail in eliciting SAI and/or LAI in a healthy test subject with no observable neurological abnormality.…”

Section: Introductionmentioning

confidence: 99%

“…Importantly, in our experience it is not uncommon for an experienced researcher to fail in eliciting SAI and/or LAI in a healthy test subject with no observable neurological abnormality. Further, study participants for whom inhibition is observed during one testing session may not express inhibition on a separate occasion despite no notable change in behavior or health status (e.g., Turco et al, [ 14 ]). It is therefore important to establish the reliability of these measures across larger sample sizes to properly interpret study results.…”

Section: Introductionmentioning

confidence: 99%

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The distribution and reliability of TMS-evoked short- and long-latency afferent interactions

et al. 2021

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Short-latency afferent inhibition (SAI) and long-latency afferent inhibition (LAI) occur when the motor evoked potential (MEP) elicited by transcranial magnetic stimulation (TMS) is reduced by the delivery of a preceding peripheral nerve stimulus. The intra-individual variability in SAI and LAI is considerable, and the influence of sample demographics (e.g., age and biological sex) and testing context (e.g., time of day) is not clear. There are also no established normative values for these measures, and their reliability varies from study-to-study. To address these issues and facilitate the interpretation of SAI and LAI research, we pooled data from studies published by our lab between 2014 and 2020 and performed several retrospective analyses. Patterns in the depth of inhibition with respect to age, biological sex and time of testing were investigated, and the relative reliability of measurements from studies with repeated baseline SAI and LAI assessments was examined. Normative SAI and LAI values with respect to the mean and standard deviation were also calculated. Our data show no relationship between the depth of inhibition for SAI and LAI with either time of day or age. Further, there was no significant difference in SAI or LAI between males and females. Intra-class correlation coefficients (ICC) for repeated measurements of SAI and LAI ranged from moderate (ICC = 0.526) to strong (ICC = 0.881). The mean value of SAI was 0.71 ± 0.27 and the mean value of LAI was 0.61 ± 0.34. This retrospective study provides normative values, reliability estimates, and an exploration of demographic and testing influences on these measures as assessed in our lab. To further facilitate the interpretation of SAI and LAI data, similar studies should be performed by other labs that use these measures.

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