Tremor is the most common movement disorder, strongly increasing its incidence and prevalence with ageing. Upper limb tremors hamper the independent life of 65% of those suffering from them, greatly impacting their quality of life. Tremor is commonly managed with drugs or surgery, but these methods have associated risks and limited applicability and efficacy. We aim to validate technically and clinically novel methods to understand, and give support to tremor diagnosis. First, we will develop and test a novel concept for a non-invasive closedloop platform for tremor suppression that uses spiking activity of pools of motoneurons (MNs, i.e., neurons connecting the nervous system with muscles) to neuromodulate oscillatory activity in the central nervous system (CNS). Novel techniques to decompose, noninvasively and in real-time, the activity of MNs will be used. These techniques allow tracking the spiking activity of large pools of MNs in a muscle, which makes it possible to infer ongoing rhythmic activity in relevant parts of the CNS associated with tremor generation and propagation. Closing the loop, inferred corticospinal rhythms in the tremor band will be used to drive non-invasive transcranial or transcutaneous stimulation of the CNS. In addition, the proposed system will provide detailed recordings of the peripheral nervous systems together with information about the function of the autonomic nervous system and kinematic measurements.
It will constitute the most advanced system to date for the neurophysiological investigation of tremors and will implement a novel machine tool to support the classification of different types of tremors. By carrying out the activities in this project, we will gain new knowledge about how different types of tremors propagate to groups of muscles in the arms, how they get modulated in the context of stimulation of the brain or spinal cord in closed-loop with tremorgenic activity observed in the muscles, and how measures of the autonomic nervous system can complement neurophysiological information to distinguish different classes of tremors.