Abstract
ObjectiveTo characterise the effect of altering transcranial magnetic stimulation parameters on the magnitude of interhemispheric inhibition (IHI) from dorsal premotor (PMd) to primary motor cortex (M1). MethodWe used a fully automated adaptive threshold hunting paradigm to quantify PMd-M1 IHI across a range of conditioning stimulus (CS) intensities (90%, 110%, 130% of resting motor threshold, rMT) and interstimulus intervals (ISIs) (8, 10, 40 ms). M1-M1 IHI was examined with CS intensities of 110%, 120%, and 130% rMT and ISIs of 10 and 40 ms. Two test coil orientations (inducing posterior-anterior or anterior-posterior current) were used. ResultsPMd-M1 IHI was obtained consistently with posterior-anterior (but not anterior-posterior) test stimuli and increased with CS intensity. M1-M1 IHI was expressed across all conditions and increased with CS intensity when posterior-anterior but not anterior-posterior induced current was used. ConclusionsThe expression of PMd-M1 IHI is contingent on test coil orientation (requiring posterior-anterior induced current) and increases as a function of CS intensity. The expression of M1-M1 IHI is not dependent on test coil orientation. SignificanceWe defined a range of parameters that elicit reliable PMd-M1 IHI. This (threshold hunting) methodology may provide a means to quantify premotor-motor pathology and reveal novel quantitative biomarkers.
Highlights
A robust rank-based non-parametric model with conditioning stimulus (CS) intensity and interstimulus intervals (ISIs) as fixed effects revealed a main effect of CS intensity (F (1.83, 1) = 5.49, p = 0.005)
We found that the magnitude of PMd-M1 IHI elicited with a CS intensity of 130% Resting motor thresholds (rMT) was significantly greater than that elicited by a CS intensity of 90% rMT [t (13) = 3.00, pcorrected = 0.031, dR = 0.84 (0.40, 1.64)]
A robust rank-based non-parametric model with CS intensity and ISI as fixed effects revealed a main effect of CS intensity (F (1.89, 1) = 4.55, p = 0.012)
Summary
Callosal pathology has been implicated in a number of neurodegenerative conditions, such as amyotrophic lateral sclerosis (Bede and Hardiman, 2017; Dukic et al, 2019), classical progressive supranuclear palsy (Lenka et al, 2017), and both pre- and postsymptomatic Huntington’s disease (Phillips et al, 2013). In respect of these conditions, there exists an urgent need to develop quanti-.
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