Brenton Hordacre1, Mitchell R Goldsworthy2, Ann-Maree Vallence3, Sam Darvishi4, Bahar Moezzi5, Masashi Hamada6, John C Rothwell7, Michael C Ridding8. 1. The Robinson Research Institute, School of Medicine, The University of Adelaide, Adelaide 5005, Australia. 2. The Robinson Research Institute, School of Medicine, The University of Adelaide, Adelaide 5005, Australia; Discipline of Psychiatry, School of Medicine, The University of Adelaide, Adelaide, Australia. 3. School of Psychology and Exercise Science, Murdoch University, Murdoch 6150, Australia. 4. School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide 5005, Australia. 5. Computational and Theoretical Neuroscience Laboratory, School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes 5095, Australia. 6. Department of Neurology, Graduate School of Medicine, University of Tokyo, Tokyo 113-8655, Japan. 7. Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, United Kingdom. 8. The Robinson Research Institute, School of Medicine, The University of Adelaide, Adelaide 5005, Australia. Electronic address: michael.ridding@adelaide.edu.au.
Abstract
BACKGROUND: The potential of non-invasive brain stimulation (NIBS) for both probing human neuroplasticity and the induction of functionally relevant neuroplastic change has received significant interest. However, at present the utility of NIBS is limited due to high response variability. One reason for this response variability is that NIBS targets a diffuse cortical population and the net outcome to stimulation depends on the relative levels of excitability in each population. There is evidence that the relative excitability of complex oligosynaptic circuits (late I-wave circuits) as assessed by transcranial magnetic stimulation (TMS) is useful in predicting NIBS response. OBJECTIVE: Here we examined whether an additional marker of cortical excitability, MEP amplitude variability, could provide additional insights into response variability following application of the continuous theta burst stimulation (cTBS) NIBS protocol. Additionally we investigated whether I-wave recruitment was associated with MEP variability. METHODS: Thirty-four healthy subjects (15 male, aged 18-35 years) participated in two experiments. Experiment 1 investigated baseline MEP variability and cTBS response. Experiment 2 determined if I-wave recruitment was associated with MEP variability. RESULTS: Data show that both baseline MEP variability and late I-wave recruitment are associated with cTBS response, but were independent of each other; together, these variables predict 31% of the variability in cTBS response. CONCLUSIONS: This study provides insight into the physiological mechanisms underpinning NIBS plasticity responses and may facilitate development of more reliable NIBS protocols.
BACKGROUND: The potential of non-invasive brain stimulation (NIBS) for both probing human neuroplasticity and the induction of functionally relevant neuroplastic change has received significant interest. However, at present the utility of NIBS is limited due to high response variability. One reason for this response variability is that NIBS targets a diffuse cortical population and the net outcome to stimulation depends on the relative levels of excitability in each population. There is evidence that the relative excitability of complex oligosynaptic circuits (late I-wave circuits) as assessed by transcranial magnetic stimulation (TMS) is useful in predicting NIBS response. OBJECTIVE: Here we examined whether an additional marker of cortical excitability, MEP amplitude variability, could provide additional insights into response variability following application of the continuous theta burst stimulation (cTBS) NIBS protocol. Additionally we investigated whether I-wave recruitment was associated with MEP variability. METHODS: Thirty-four healthy subjects (15 male, aged 18-35 years) participated in two experiments. Experiment 1 investigated baseline MEP variability and cTBS response. Experiment 2 determined if I-wave recruitment was associated with MEP variability. RESULTS: Data show that both baseline MEP variability and late I-wave recruitment are associated with cTBS response, but were independent of each other; together, these variables predict 31% of the variability in cTBS response. CONCLUSIONS: This study provides insight into the physiological mechanisms underpinning NIBS plasticity responses and may facilitate development of more reliable NIBS protocols.
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