Nonlinear Oscillatory Connectivity in Patients with Dystonia : an Eeg Study

Writer’s cramp is a neurological movement disorder that abnormal contractions of muscles result in the twisting of fixed postures or muscle spasm during specific tasks. Due to the rareness and nonspecific symptoms ( in some circumstances, it is associated with parkinsonian disorders), there is no test to confirm diagnosis of writer's cramp, except the comprehensive observations by experts. It was estimated that the average time between the appearance of the first symptoms and a clear diagnosis of focal dystonia was about 1 year. Diagnostic delay in dystonia may result in the delay of a proper treatment and it was proposed that one of the future research priorities in task-specific dystonias is the development of diagnostic criteria for limb dystonia. Evidences from previous rTMS studies as well as neuroimaging data have accumulated that focal hand dystonia has the neurological origin. Specifically, the hyper activities of theta oscillations in globus pallidus and beta rhythms in motor cortex were seen in patients with focal dystonia when performing motor tasks. In addition to the alternation of the subcortical-cortical loop, dystonia can be considered a cortical network disorder as the remote healthy nodes of the brain, such as supplementary motor area / anterior cingulate cortex (SMA/ACC), lateral premotor cortex (PMC), and primary motor cortex (M1) may react and rearrange themselves in response to the primary abnormality of globus pallidus by means of network reorganization, possibly in theta-beta interactions. However, because of the nonlinearity nature of cross frequency theta-beta coupling, there has no study that investigates this cross frequency coupling in the neuronal network. In this proposal, we aim to test whether there are abnormal theta-beta couplings between cortical areas in patients with dystonia when they perform nonsymptomic motor tasks by using EEG and dynamic causal modelling. Importantly, we will apply artificial intelligence (AI) methods to extract the neuromarkers that can significantly differentiate the motor network of dystonia patients from that of normal subjects. Having established the neuromarkers for dystonia, in the second year, we will use data from patients with Parkinson's disease performing the identical task to test whether these neuromarkers are specific to patients with dystonia. In addition, we will also acquire some more data from dystonia patients to perform the construct validity of these neuromarkers. Finally, we will discuss the translational potential of these neuromarkers .Finally, we will discuss the translational potential of these neuromarkers.