Background:Motor adaptation relies on error-based learning for accurate movements in changing environ-ments.However,the neurophysiological mechanisms driving individual differences in performance are unclear.Transcran...Background:Motor adaptation relies on error-based learning for accurate movements in changing environ-ments.However,the neurophysiological mechanisms driving individual differences in performance are unclear.Transcranial magnetic stimulation(TMS)-evoked potential can provide a direct measure of cortical excitability.Objective:To investigate cortical excitability as a predictor of motor learning and motor adaptation in a robot-mediated forcefield.Methods:A group of 15 right-handed healthy participants(mean age 23 years)performed a robot-mediated forcefield perturbation task.There were two conditions:unperturbed non-adaptation and perturbed adapta-tion.TMS was applied in the resting state at baseline and following motor adaptation over the contralateral primary motor cortex(left M1).Electroencephalographic(EEG)activity was continuously recorded,and cortical excitability was measured by TMS-evoked potential(TEP).Motor learning was quantified by the motor learning index.Results:Larger error-related negativity(ERN)in fronto-central regions was associated with improved motor per-formance as measured by a reduction in trajectory errors.Baseline TEP N100 peak amplitude predicted motor learning(P=0.005),which was significantly attenuated relative to baseline(P=0.0018)following motor adap-tation.Conclusions:ERN reflected the formation of a predictive internal model adapted to the forcefield perturbation.Attenuation in TEP N100 amplitude reflected an increase in cortical excitability with motor adaptation reflecting neuroplastic changes in the sensorimotor cortex.TEP N100 is a potential biomarker for predicting the outcome in robot-mediated therapy and a mechanism to investigate psychomotor abnormalities in depression.展开更多
基金supported by a University of East London Excellence PhD scholarship to MT and in part from a Medical Research Council grant to CF(grant number G0802594).
文摘Background:Motor adaptation relies on error-based learning for accurate movements in changing environ-ments.However,the neurophysiological mechanisms driving individual differences in performance are unclear.Transcranial magnetic stimulation(TMS)-evoked potential can provide a direct measure of cortical excitability.Objective:To investigate cortical excitability as a predictor of motor learning and motor adaptation in a robot-mediated forcefield.Methods:A group of 15 right-handed healthy participants(mean age 23 years)performed a robot-mediated forcefield perturbation task.There were two conditions:unperturbed non-adaptation and perturbed adapta-tion.TMS was applied in the resting state at baseline and following motor adaptation over the contralateral primary motor cortex(left M1).Electroencephalographic(EEG)activity was continuously recorded,and cortical excitability was measured by TMS-evoked potential(TEP).Motor learning was quantified by the motor learning index.Results:Larger error-related negativity(ERN)in fronto-central regions was associated with improved motor per-formance as measured by a reduction in trajectory errors.Baseline TEP N100 peak amplitude predicted motor learning(P=0.005),which was significantly attenuated relative to baseline(P=0.0018)following motor adap-tation.Conclusions:ERN reflected the formation of a predictive internal model adapted to the forcefield perturbation.Attenuation in TEP N100 amplitude reflected an increase in cortical excitability with motor adaptation reflecting neuroplastic changes in the sensorimotor cortex.TEP N100 is a potential biomarker for predicting the outcome in robot-mediated therapy and a mechanism to investigate psychomotor abnormalities in depression.
