Motor deficits are common outcomes of neurological conditions such as stroke. In order to design personalised motor rehabilitation programmes such as robot-assisted therapy, it would be advantageous to predict how a patient might respond to such treatment. Spontaneous neural activity has been observed to predict differences in the ability to learn a new motor behaviour in both healthy and stroke populations. This study investigated whether spontaneous resting-state functional connectivity could predict the degree of motor adaptation of right (dominant) upper limb reaching in response to a robot-mediated force field. Spontaneous neural activity was measured using resting-state electroencephalography (EEG) in healthy adults before a single session of motor adaptation. The degree of beta frequency (β; 15-25 Hz) resting-state functional connectivity between contralateral electrodes overlying the left primary motor cortex (M1) and the anterior prefrontal cortex (aPFC) could predict the subsequent degree of motor adaptation. This result provides novel evidence for the functional significance of resting-state synchronization dynamics in predicting the degree of motor adaptation in a healthy sample. This study constitutes a promising first step towards the identification of patients who will likely gain most from using robot-mediated upper limb rehabilitation training based on simple measures of spontaneous neural activity.
Keywords: Electroencephalogram; Functional connectivity; Motor adaptation; Partial least square regression; Resting-state; Robot-mediated force field.
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