Variability in neural excitability and plasticity induction in the human cortex: A brain stimulation study

Accepted Manuscript Variability in Neural Excitability and Plasticity Induction in the Human Cortex: A Brain Stimulation Study Brenton Hordacre, Mitchell R. Goldsworthy, Ann-Maree Vallence, Sam Darvishi, Bahar Moezzi, Masashi Hamada, John C. Rothwell, Michael C. Ridding PII:

S1935-861X(16)30383-7

DOI:

10.1016/j.brs.2016.12.001

Reference:

BRS 987

To appear in:

Brain Stimulation

Received Date: 1 August 2016 Revised Date:

9 November 2016

Accepted Date: 3 December 2016

Please cite this article as: Hordacre B, Goldsworthy MR, Vallence A-M, Darvishi S, Moezzi B, Hamada M, Rothwell JC, Ridding MC, Variability in Neural Excitability and Plasticity Induction in the Human Cortex: A Brain Stimulation Study, Brain Stimulation (2017), doi: 10.1016/j.brs.2016.12.001. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Variability in Neural Excitability and Plasticity Induction in the Human Cortex: A Brain

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Stimulation Study Abbreviated Title: Excitation and Plasticity in the Human Cortex

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Authors and Affiliations: Brenton Hordacre1, Mitchell R. Goldsworthy1,2, Ann-Maree Vallence3,

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Sam Darvishi4, Bahar Moezzi5, Masashi Hamada6, John C. Rothwell7, Michael C. Ridding1

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5005, Australia.

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Australia.

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and Mathematical Sciences, University of South Australia, Mawson Lakes 5095, Australia.

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Japan.

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Neurology, Queen Square, London WC1N 3BG, United Kingdom.

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The Robinson Research Institute, School of Medicine, The University of Adelaide, Adelaide

Discipline of Psychiatry, School of Medicine, The University of Adelaide, Adelaide, Australia. School of Psychology and Exercise Science, Murdoch University, Murdoch 6150, Australia.

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School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide 5005,

Computational and Theoretical Neuroscience Laboratory, School of Information Technology

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Department of Neurology, Graduate School of Medicine, University of Tokyo, Tokyo, 113-8655,

Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of

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Corresponding Author: Michael C Ridding, The Robinson Research Institute, School of

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Medicine, The University of Adelaide, 77 King William St, North Adelaide, 5006, Australia. Email:

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[email protected]

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Abstract

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Background: The potential of non-invasive brain stimulation (NIBS) for both probing human

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neuroplasticity and the induction of functionally relevant neuroplastic change has received

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significant interest. However, at present the utility of NIBS is limited due to high response

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variability. One reason for this response variability is that NIBS targets a diffuse cortical

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population and the net outcome to stimulation depends on the relative levels of excitability in

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each population. There is evidence that the relative excitability of complex oligosynaptic circuits

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(late I-wave circuits) as assessed by transcranial magnetic stimulation (TMS) is useful in

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predicting NIBS response.

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Objective: Here we examined whether an additional marker of cortical excitability, MEP

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amplitude variability, could provide additional insights into response variability following

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application of the continuous theta burst stimulation (cTBS) NIBS protocol. Additionally we

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investigated whether I-wave recruitment was associated with MEP variability.

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Methods: Thirty-four healthy subjects (15 male, aged 18-35 years) participated in two

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experiments. Experiment 1 investigated baseline MEP variability and cTBS response.

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Experiment 2 determined if I-wave recruitment was associated with MEP variability.

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Results: Data show that both baseline MEP variability and late I-wave recruitment are

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associated with cTBS response, but were independent of each other; together, these variables

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predict 31% of the variability in cTBS response.

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Conclusions: This study provides insight into the physiological mechanisms underpinning NIBS

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plasticity responses and may facilitate development of more reliable NIBS protocols.

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Key Words: Transcranial magnetic stimulation, Theta burst stimulation, Motor evoked

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potential, Variability, Motor cortex

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Introduction

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Non-invasive brain stimulation (NIBS) can induce neuroplasticity in the human cortex that has

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similar characteristics to activity-dependent long-term potentiation (LTP) and long-term

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depression (LTD) [1,2]. NIBS-induced neuroplasticity outlasts the stimulation [3-5], is bi-

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directional based on pattern of stimulation [3-5], and is abolished following administration of

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NMDA antagonists [6]. Importantly, there are behavioural effects following NIBS. For example,

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inhibitory NIBS protocols applied to the motor cortex (M1) can degrade motor control [7], and

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facilitatory NIBS can increase the rate of learning on a ballistic motor task [8]. Inducing LTP- or

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LTD-like plasticity in the human motor cortex and modifying behaviour would be of clinical

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value for a range of neurological conditions. However, at present the effects of various NIBS

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protocols are highly variable [9-14]. This response variability limits the behavioural and clinical

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usefulness of NIBS.

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Several factors contribute to NIBS response variability including age, time of day, attention,

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history of physical activity and genetics [15]. Additionally, inter-individual differences in the

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cortical network activated by NIBS can influence the response. The descending volley evoked by

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single pulse transcranial magnetic stimulation (TMS) consists of a series of components. The

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earliest of these probably reflects direct activation of the corticospinal output cells and is

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known as the “direct (D)-wave”. The later components have been termed “indirect (I)-waves”.

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The early I-waves likely reflect monosynaptic input to corticospinal neurons from layer II/III

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interneurons, whereas more complex oligosynaptic circuits generate the late I-waves [16].

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Individuals in whom TMS is more likely to recruit late I-waves respond more strongly to several

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forms of NIBS [13,17]. The reason for this is unclear but Hamada and colleagues (2013)

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suggested that the late I-wave generating circuit might be more sensitive to NIBS than the early

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I-wave generating circuit. Here, we were interested in examining whether variability in baseline

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motor evoked potential (MEP) amplitude could serve as an indicator of likely neuroplastic

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response to a NIBS protocol (continuous theta burst stimulation: cTBS). Our reasoning was as

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follows: the amplitude of MEPs evoked in individuals in whom TMS was more likely to recruit

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late I-wave generating circuits would be more variable due to the involvement of more complex

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networks than in individuals in whom TMS was more likely to recruit less complex early I-wave

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generating circuitry [18]. To explore mechanisms underpinning MEP variability we used

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multiple TMS coil orientations to examine I-wave recruitment [13]. In summary, the aims of this

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study were to (1) investigate the relationship between MEP variability and NIBS (cTBS)

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response, and (2) explore whether I-wave recruitment profile might influence MEP variability.

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Material and Methods

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Subjects

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A total of 34 healthy subjects (15 male) aged 18-35 years (mean age, 25.0 ± 4.9 years)

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participated in two experiments. Potential subjects with contraindications for TMS, including

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metallic implants, a history of seizures and medications known to alter CNS excitability were

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excluded [19]. Ethical approval was provided by the University of Adelaide Human Research

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Ethics Committee, and all participants provided written informed consent in accordance with

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the Declaration of Helsinki.

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Electromyography

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For both experiments, surface EMG was recorded from the right first dorsal interosseous (FDI)

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muscle using Ag/AgCl electrodes (Ambu, Ballerup, Denmark) with electrodes positioned in a

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belly-tendon montage. Signals were sampled at 5 kHz (Cambridge Electronic Design 1401,

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Cambridge, UK), amplified with a gain of 1000, band-pass filtered (20-1000 Hz) (Cambridge

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Electronic Design 1902 amplifier, Cambridge, UK) and stored for offline analysis (Signal v4.09,

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Cambridge Electronic Design, Cambridge, UK).

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Transcranial Magnetic Stimulation

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Single-pulse TMS was applied with a monophasic waveform using a figure-of-eight coil (external

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wing diameter 90mm) connected to a Magstim 200 stimulator (Magstim Company, Dyfed, UK).

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For Experiment 1, the coil was positioned tangentially over the left M1, with the handle rotated

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posterior-laterally approximately 45° to the sagittal plane to induce a posterior-anterior current

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flow across the hand M1. The optimal coil position for evoking a MEP in the right FDI muscle at

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rest was located and marked on the scalp using a water-soluble felt tip marker. Rest motor

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threshold (RMT) for the right FDI was defined as the minimum stimulus intensity required to

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evoke an MEP with peak-to-peak amplitude ≥50µV in at least five out of 10 consecutive trials in

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the relaxed FDI.

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For Experiment 2, MEPs were evoked using three different directions of current flow across the

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left M1 hand area. Previous studies have demonstrated that by modifying the direction of

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current flow it is possible to target specific populations of neurons using single pulse TMS.

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Posterior-anterior (PA) currents preferentially recruit early I-waves, anterior-posterior (AP)

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currents recruit late I-waves and lateral-medial (LM) currents at high stimulus intensities evoke

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D-waves [18,20-23]. In this experiment we evoked MEPs using three different coil orientations

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to preferentially induce current flow across the hand M1 to investigate late I-waves, early I-

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waves and D-waves. PA currents were elicited with the handle of the figure-of-eight coil rotated

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posterior-laterally, approximately 45° to the sagittal plane. AP currents were elicited by placing

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the coil 180° to the PA current coil position. LM currents were elicited with the handle rotated

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laterally to a position 90° to the midsagittal line. Active motor threshold (AMT) was measured

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for PA, AP and LM currents while stimulating at the hotspot determined by PA currents, as

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previous studies have determined that direction of the current does not influence the position

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of the hotspot [22,24]. AMT was defined as the lowest intensity to evoke an MEP of ≥ 200µV in

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at least five out of 10 consecutive trials whilst maintaining a 5-10% maximal voluntary

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contraction of the FDI. Muscle contraction was monitored visually using a digital oscilloscope

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with participants able to monitor and adjust muscle contraction to maintain the required 5-10%

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MVC.

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Continuous theta burst stimulation

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In Experiment 1, an air-cooled figure-of-eight coil connected to a Magstim Super Rapid

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stimulator (Magstim Company, Dyfed, UK) was used to apply cTBS with a biphasic pulse

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waveform (current direction PA-AP) to the optimal site for stimulating the right FDI. The cTBS

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protocol consisted of 600 pulses applied in bursts of three pulses at 50Hz, repeated at 5Hz for a

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total of 40 seconds [3]. The intensity of stimulation was set to 70% RMT [25,26], assessed prior

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to cTBS application using the rTMS coil.

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Experimental Protocol

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For Experiment 1, subjects attended an afternoon experimental session to determine the

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relationship between baseline MEP variability and the response to cTBS. Subjects were seated

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in a comfortable chair with their right upper limb in a relaxed position. At baseline, a total of

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225 MEPs were evoked over two blocks separated by a short, 2 minute rest interval. Three

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stimulation intensities were used to examine whether the relationship between MEP variability

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and cTBS response was influenced by MEP amplitude; the intensities were 120% RMT, 150%

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RMT and a stimulus intensity set to produce a 1mV MEP (SI1mV). The 120% RMT and SI1mV

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intensities were selected as they are commonly used to evoke test MEPs prior to plasticity

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induction protocols [27-29]. The 150% intensity was used to explore the relationship between

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baseline MEP amplitude variability and plasticity response at larger mean MEP amplitudes. At

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baseline, a total of 75 TMS pulses at each of the three intensities were delivered randomly with

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an inter-stimulus interval of 6 sec ± 10%. Following cTBS, 50 TMS pulses at each of the three

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intensities were delivered randomly (with an inter-stimulus interval of 6 sec ± 10%) from 0-15

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minutes following cTBS, and again at 20-35 minutes following cTBS; therefore, a total of 300

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MEPs (100 MEPs for each intensity) were obtained following cTBS (and we grouped these into

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5-minute blocks: 0-, 5-, 10-, 20-, 25-, 30-min post cTBS). The same stimulation intensities and

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inter-stimulus intervals were used at baseline and following cTBS.

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Experiment 2 was conducted in the afternoon, >7 days following experiment 1. The purpose of

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Experiment 2 was to determine if differences between individuals in I-wave recruitment were

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associated with different levels of MEP variability. Subjects were seated in a comfortable chair

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with the lateral aspect of the distal phalanx of the right index finger positioned against a force

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transducer. For both AP and PA coil orientations, 20 MEPs were evoked at 110% AMT with

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subjects asked to relax their hand every 10 trials to avoid fatigue. For LM coil orientation, 10

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MEPs were evoked at 150% AMT or 50% of maximum stimulator output (MSO), whichever was

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greater. Higher stimulus intensities were used for LM currents to increase the likelihood of

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evoking a D-wave [21]. For all three coil orientations, MEPs were evoked with an inter-stimulus

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interval of 6 sec ± 10%. MEP onset latency for each trial (for all three coil orientations) was

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determined automatically with a custom made script to avoid assessor bias (Signal v4.09,

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Cambridge Electronic Design, Cambridge, UK). In each trial, the onset latency was defined as

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the time point where the rectified EMG signals exceeded an average plus two standard

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deviations of the pre-stimulus EMG level 100ms prior to the TMS pulse. AP, PA and LM onset

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latencies were averaged across trials for each subject. Consistent with the study of Hamada and

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colleagues (2013), the latency difference between AP and LM evoked MEPs was used as a

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measure of the relative likelihood of recruiting late I-wave input to corticospinal neurons [13].

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Data analysis

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Normality of data were tested with the Shapiro-Wilk test and where required, logarithmic

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transformations were performed. Descriptive statistics were used to report experimental

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variables. To characterise the response to cTBS in Experiment 1, MEP amplitudes were

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averaged for each stimulus intensity, subject and time point. Trials contaminated with

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background EMG activity during 100ms prior to the TMS pulse were excluded from the analysis.

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A 2 x 2 repeated measures ANOVA (factor of ‘TIME’: baseline 1, baseline 2, Post 0 min, Post 20

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min; factor of ‘INTENSITY’: 120% RMT, 150% RMT, SI1mV) was used to investigate the effect of

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cTBS on absolute (raw) MEP amplitude. The association between baseline MEP variability

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(coefficient of variation, CV) and cTBS response (quantified as the grand average of post-cTBS

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time points normalised to the average baseline) was investigated with Pearson correlations for

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MEPs evoked at 120% RMT, 150% RMT and SI1mV. To investigate whether MEP amplitude or

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RMT were associated with baseline MEP variability, we performed Pearson correlations for

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MEPs evoked at 120% RMT, 150% RMT and SI1mV. To investigate whether the distribution of

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MEP amplitudes changed following cTBS we analysed the skewness and kurtosis of MEPs

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evoked at 120% RMT and SI1mV before and after cTBS, split into high and low MEP variability

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groups (median split), and tested for differences using paired t-tests. For Experiment 2, the

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association between AP-LM and PA-LM latency differences, and both cTBS response and

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variability of baseline MEPs was investigated with Pearson correlations for MEPs evoked at

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120% RMT, 150% RMT and SI1mV. Since it could be argued that the range of MEP latencies

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evoked by AP currents is due to variation of MEP amplitudes evoked with this coil orientation,

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we correlated AP MEP amplitude and AP MEP latency. The significance level was set at p ≤ 0.05,

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and SPSS software was used for all statistical analyses (IBM Corp., Released 2011, IBM SPSS

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Statistics for Windows, Version 20.0, Armonk, NY, USA).

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Results

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Experiment 1 – Baseline MEP variability and cTBS response

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The average RMT was 41.3% MSO (SD 8.3). Baseline neurophysiological measures for each

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stimulus intensity are reported in Table 1. As expected, there was a main effect of INTENSITY

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(F(2,66) = 52.3, p < 0.001) with MEPs evoked at 150% RMT larger than those evoked at 120% RMT

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(95%CI; 1.2-1.8, p < 0.001) and SI1mV (95%CI; 1.2-2.4, p < 0.001). However, there was no main

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effect of TIME (F(3,99) = 0.86, p = 0.47) or TIME x INTENSITY interaction (F(6,198) = 0.29, p = 0.94)

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suggesting cTBS did not significantly change MEP amplitude (see figure 1; individual subject

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cTBS response profiles provided in Supplementary figure 1; tabulated data provided in

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Supplementary table 1). As there were no significant differences in MEP amplitude across post-

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cTBS time points, we calculated a grand average cTBS response value for each intensity: MEP

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amplitudes were normalised to baseline (percentage of the average baseline MEP amplitude),

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and then averaged across all post-cTBS time-points. There were significant correlations

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between baseline MEP variability and cTBS response for 120% RMT (r = -0.44, p = 0.01) and

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SI1mV (r = -0.37, p = 0.03), but not 150 %RMT (p = 0.59) (see figure 2); higher variability in

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baseline MEP amplitude (at 120%RMT and SI1mV) was associated with a stronger inhibitory

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response to cTBS. Following correction for multiple comparisons (Bonferroni correction,

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adjusted level of significance p < 0.017), the association between baseline MEP variability and

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cTBS response remained significant for 120% RMT. Further investigation of the relationship

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between baseline MEP variability (120% RMT) and cTBS response revealed that participants

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with higher baseline MEP variability (median split of baseline MEP variability) had a significant

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suppression of MEP amplitude following cTBS (post cTBS MEPs 84% of baseline, t(16) = 2.19, p =

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0.04). However, participants with low baseline MEP variability did not have a significant

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facilitation of MEP amplitude following cTBS (post cTBS MEPs 112% of baseline, t(16) = 1.75, p =

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0.10). Although there were significant associations between the variability and amplitude of

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MEPs recorded at baseline (120%RMT; r = -0.47, p = 0.01; 150% RMT; r = -0.45, p = 0.01; SI1mV; r

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= -0.43, p = 0.01), the relationship between baseline MEP variability and cTBS response

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remained when controlling for baseline MEP amplitude (120%RMT; r = -0.43, p = 0.01; SI1mV; r =

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-0.35, p = 0.03). As previous, the association between MEP variability and cTBS response

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remained significant for 120% RMT survived correction for multiple comparisons. There were

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no significant relationships between RMT and baseline MEP variability for MEPs evoked at all

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intensities (all p > 0.16). There was no significant difference in distribution of MEP amplitudes

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from baseline to post cTBS for MEPs evoked at 120% RMT (p = 0.44) or SI1mV (p = 0.42) in both

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participants with high or low MEP variability (see figure 3).

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Experiment 2 – I-wave recruitment and MEP variability

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Mean AMT, MEP amplitude and onset latencies for PA, AP and LM coil orientations are

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reported in table 2. The mean AP-LM latency difference was 3.85±1.25 ms and PA-LM latency

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difference was 1.82±1.12 ms (see figure 4). There was a significant correlation between AP-LM

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latency difference and grand average cTBS response for MEPs evoked at 120% RMT (r = -0.37, p

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= 0.03) (figure 4), but not 150% RMT (p = 0.40) or SI1mV (p = 0.27), however the relationship with

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AP-LM latency difference and cTBS response for MEPs evoked at 120% RMT did not survive

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correction for multiple comparisons. There were no significant associations between PA-LM

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latency difference and cTBS response for MEPs evoked at any stimulation intensity (p > 0.54).

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There were no significant associations between AP-LM or PA-LM latency difference and

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baseline MEP variability for MEPs evoked at any intensity (all p > 0.33). There was no

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association between MEP amplitude evoked by AP currents and AP latency (p = 0.83). Since

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both MEP variability and late I-wave recruitment appear to be independent, but important,

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factors associated with the cTBS response measured at 120% RMT we investigated the

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relationship with a multiple regression. The regression model reached significance (R2 = 0.31, p

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= 0.004), indicating that MEP variability and late I-wave recruitment predict 31% of the variance

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in cTBS response.

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Discussion

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In this study we report significant inter-subject variability in the response to cTBS, which is

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consistent with recent reports that demonstrate highly variable response profiles following

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cTBS [13,30]. Indeed, even though care was taken in controlling factors know to influence cTBS

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response (e.g. pre-activation and time of day) there was no significant group level cTBS

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response. While progress has been made in understanding the causes of response variability

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(for review see Ridding and Ziemann [15]), a large component of this variability remains

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unexplained and this may have contributed to the non-significant group response to cTBS

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observed in this study. Identifying additional factors contributing to response variability is

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important to both improve understanding of the physiology underpinning responses to cTBS

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(and other NIBS protocols) and to direct development and targeting of more robust stimulation

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protocols. Here, we provide some novel insights into additional factors contributing to cTBS

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response variability.

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Associations between I-wave recruitment, MEP variability, and cTBS response

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By using different coil orientations (PA/AP) it is possible to preferentially recruit early and late I-

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waves. Using this approach, Hamada and colleagues [13] demonstrated a stronger cTBS

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response in individuals in whom TMS pulses preferentially recruited late I-waves. The current

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results replicate the findings of Hamada et al. [13], showing that inter-individual differences in

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late I-wave recruitment is associated with cTBS response. Hamada and colleagues [13]

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suggested that the association between I-wave recruitment and cTBS-induced plasticity could

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be due to a greater sensitivity of the late I-wave generating circuit than the early I-wave

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generating circuit to cTBS.

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Given the proposed differential sensitivity of the late and early I-wave generating circuits, we

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examined the relationship between the likelihood of the TMS pulse recruiting late I-waves (with

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AP-LM latency difference acting as a marker) and MEP variability; we hypothesised that MEPs

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evoked in individuals in whom TMS was more likely to recruit late I-wave circuits would be

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more variable than MEPs in individuals in which TMS was more likely to recruit less complex

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early I-wave circuitry. However, surprisingly, we found no relationship between I-wave

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recruitment and MEP variability. This suggests that MEP variability is not highly dependent

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upon engagement of late I-wave circuits.

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While there was no significant association between I-wave recruitment and MEP variability,

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there was a significant association between MEP variability and cTBS response; greater MEP

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variability at baseline was associated with a stronger inhibitory cTBS response. This relationship

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was significant for MEPs evoked at 120% RMT and was supported by data recorded at the 1mV

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intensity where the correlation between variability and response approached statistical

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significance. One possible explanation for this is that high MEP variability is associated with a

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wider distribution of MEP amplitudes; for example, in individuals with high variability, TMS

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might evoke more very large MEPs that are closer to the ceiling of the testable range than in

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individuals with low variability. This could be important because it has been reported that cTBS

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is more likely to inhibit near-maximal MEPs [31]. Thus it could be that, on average, individuals

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with high variability are more likely to respond to cTBS because they have more near-maximal

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MEPs. If this were the case, then we might expect these large MEPs to be inhibited following

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cTBS, and hence the distribution of MEP amplitudes would change. However, we show that the

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distribution of MEPs evoked at 120% RMT and SI1mV intensities did not change following cTBS,

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irrespective of whether individuals demonstrated high or low baseline variability. There was no

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relationship between baseline variability in MEPs evoked at an intensity of 150% RMT and cTBS

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response. We suggest this likely reflects the reduced MEP variability when tested at this high

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intensity.

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While the data does show that high variability is associated with a strong inhibitory response to

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cTBS it also suggests that low variability is associated with a facilitatory response. To explore

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this further we examined the response to cTBS by splitting the participant data into two groups,

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namely high and low variability groups. We were able to show that the higher baseline MEP

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variability group demonstrated a significant suppression of MEP amplitudes following cTBS. In

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contrast, the low baseline variability group had a non-significant trend towards a facilitatory

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response to cTBS. This result suggests that the relationship between variability and the

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response to cTBS might be complex and not one merely based upon the strength of the

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response. Indeed, differing levels of MEP variability may be associated with the engagement of

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different mechanisms (LTD/LTD) by the cTBS stimulus. However, this is highly speculative and

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we suggest this issue requires further investigation, perhaps, by using a facilitatory (LTP-like)

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non-invasive brain stimulation protocol. Nevertheless, our results suggest late I-wave

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recruitment and MEP variability are independently associated with cTBS response. When

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combined, MEP variability and late I-wave recruitment predicted 31% of the cTBS response

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variability in the current study. Given the extensive range of factors that contribute to

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variability in NIBS response [15], these two factors account for a major component of cTBS

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response variability. However, we acknowledge that this estimate, and that reported for several

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other factors, is likely to be an overestimation given likely interdependence between multiple

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factors. Further work is required to investigate the interdependence between factors that have

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been identified as contributing to NIBS response variability.

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We hypothesised that MEP variability would be greatest in individuals in which late I-waves

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were more likely to be recruited. However, as described above, we were unable to provide

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support for this hypothesis. What then is the mechanism underpinning MEP variability?

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Previous studies have demonstrated that MEP amplitude is influenced by intrinsic oscillatory

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activities recorded using electroencephalography (EEG). For example, for TMS applied near

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resting motor threshold, MEPs are more likely to be evoked when preceded by low pre-stimulus

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alpha power [32]. Similarly, larger MEPs have been observed with lower pre-stimulus beta

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power [33-37]. Furthermore, oscillatory phase of both alpha and beta activities can influence

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MEP amplitude [37-39]. Therefore, we suggest intrinsic fluctuations in ongoing oscillatory

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activity are a likely substrate to explain at least some of the MEP variability. How such changes

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might explain the variability in cTBS response is unclear.

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Similarity to the relationships between movement variability and motor learning

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Interestingly, there are some parallels between the current results and several recent reports

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examining motor learning. Greater task related baseline movement variability predicts faster

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motor learning [40]. Also, Teo and colleagues [8] reported that facilitatory intermittent theta

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burst stimulation increased movement variability on a subsequent ballistic motor learning task,

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and that this increase in variability correlated with improved learning. Movement variability

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allows the individual to explore motor command space and identify optimal motor patterns

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resulting in greater efficiency during learning. While multiple factors are likely to contribute to

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variability of movement output, movement execution contributes a large proportion of this

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variability [41]. The corticospinal system plays a key role in movement execution [42] and

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variability in movement likely reflects both cortical and spinal influences. Therefore, the MEP

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variability described here may reflect to some degree the variability seen in movements during

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learning. While the output measures of behavioural (learning) and neurophysiological studies

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(cTBS response) are clearly quite different, there might be involvement of a common

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physiological mechanism, namely activity dependent changes in synaptic strength.

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When considering these results, it is important to acknowledge some limitations. First, we only

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studied a population of healthy adults using one common NIBS plasticity-inducing paradigm

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(cTBS). It is unclear whether these findings are generalizable to wider populations or alternative

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NIBS paradigms. Second, we have only investigated one potential contributor to MEP

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variability. It is likely multiple, interacting factors contribute to MEP variability and further

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studies should seek to provide greater understanding of contributions to this variability. Third,

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MEP variability is likely due to both cortical and spinal effects [32,43,44]. We did not investigate

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spinal influences and so the association between MEP variability and the cortically generated

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cTBS response might be over or under estimated. Finally, although we took care to minimise

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coil movement during data collection, it is possible that random small coil movements may

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have contributed to the overall MEP variability. However, we consider it unlikely that this

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contributed to the reported association between variability and cTBS response and, in fact, may

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have weakened the relationship by introducing noise. The use of stereotactic navigation

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techniques may strengthen the findings reported in this study.

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In summary, we provide evidence that MEP variability is an important influence on the cTBS

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response in healthy adults. Traditionally considered an unwanted characteristic of stochastic

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nervous system function driven by multiple intrinsic and extrinsic contributions, MEP variability

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may in fact be an important physiological characteristic to enhance our understanding of

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cortical network excitability, motor learning and NIBS response. Our results may suggest

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avenues for developments that improve the reliability of NIBS, for example by employing

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behavioural or external priming procedures to modulate variability in the excitability of the

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corticospinal system prior to plasticity induction.

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Funding: MRG is supported by an NHMRC-ARC Dementia Research Development Fellowship

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(1102272). AMV is supported by a NHMRC Biomedical Training Fellowship (GNT1088295).

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Table and Figure Legends

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Table 1: Baseline neurophysiological measures for each stimulus intensity

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RMT, resting motor threshold; MSO, maximum stimulator output; mV, millivolts; CV, coefficient

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of variation; MEP, motor evoked potential.

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Table 2: AMT, MEP amplitude and onset latency for MEPs evoked using PA, AP and LM coil

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orientations. AMT, active motor threshold; MEP, motor evoked potential; MSO, maximal

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stimulator output; PA, posterior-anterior current direction; AP, anterior-posterior current

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direction; LM, lateral-medial current direction.

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Figure 1: Group average cTBS response for MEPs evoked at 120% RMT, 150% RMT, and SI1mV for

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both raw MEP amplitudes (top) and MEPs normalised to the mean baseline (bottom). CTBS did

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not affect MEP amplitude for any stimulation intensity.

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Figure 2: Correlation between MEP variability at baseline for MEPs evoked at A) 120% RMT, B)

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150% RMT and C) SI1mV and cTBS response averaged across post cTBS time points. Greater MEP

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variability was associated with a stronger inhibitory response to cTBS for MEPs evoked at 120%

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RMT and SI1mV intensities.

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Figure 3: Distribution of MEPs evoked at baseline (top) and following cTBS (bottom) for subjects

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with low variability (left) and high variability (right) for MEPs evoked at SI1mV. MEP amplitude

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was normalised to the mean baseline amplitude for each subject. There was no change in

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distribution of MEPs following cTBS suggesting that cTBS does not target specific networks

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responsible for either large or small MEPs.

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Figure 4: PA-LM and AP-LM latency differences for A) individual participants and B) presented

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as a histogram in 0.5ms bins. The AP-LM latency difference was longer than the PA-LM latency

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difference. The AP-LM latency difference also appeared more variable across participants

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compared to the PA-LM latency difference. Figure C) presents the correlation between AP-LM

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latency difference as a measure of late I-wave efficiency and cTBS response averaged across

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post cTBS time points for MEPs evoked at 120% RMT. A stronger response to cTBS was

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associated with a greater AP-LM latency difference.

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Tables

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Table 1

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Stimulus Intensity 150%RMT

SI1mV

Stimulation Intensity (%MSO, mean (SD))

49.7 (9.6)

62.3 (11.8)

50.9 (11.7)

MEP amplitude (mV, mean (SD))

1.48 (0.9)

3.05 (1.5)

1.18 (0.4)

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Table 2

35.6 (13.3)

66.5 (24.9)

Current Direction

AMT (%MSO)

MEP amplitude (mV)

Latency (ms)

PA

32.7 (7.4)

0.83 (0.41)

22.6 (1.7)

AP

45.1 (10.7)

0.68 (0.38)

24.5 (2.1)

LM

36.1 (8.7)

5.89 (2.41)

20.7 (1.6)

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64.4 (20.7)

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MEP variability (CV, %, mean (SD))

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Highlights

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Non-invasive brain stimulation (NIBS) offers significant opportunities to examine plasticity in the human brain. There is some evidence that NIBS might be useful for inducing functionally beneficial change in a variety of neurological/psychiatric conditions. However, at present the utility is NIBS is somewhat limited due to high response variability. Key to harnessing the potential of NIBS is to understand more fully the factors contributing to this variability. This variability might, at least in part, be determined by the relative excitability of different cortical networks activated by NIBS. This study provides new insights into the influence of cortical excitability on the response to a commonly employed NIBS protocol and may open up avenues to improve response reliability.

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