Neurophysiological Predictors of Response to Medication in Parkinson's Disease

Abstract

Background: Although dopaminergic medication has been the foundation of Parkinson's disease (PD) therapy for decades, sensitive and specific therapeutic response biomarkers that allow for better treatment optimization are lacking. Objective: We tested whether the features of Transcranial Magnetic Stimulation-based neurophysiological measures taken off-medication are associated with dopaminergic medication-induced clinical effects. Method: Motor cortex excitability [short-latency intracortical inhibition (SICI), intracortical facilitation (ICF), short-latency afferent inhibition (SAI), and input-output (IO) curve], and plasticity [paired associative stimulation (PAS) protocol] neurophysiological measures were examined in 23 PD patients off-medication. Clinical features were quantified by the motor section of the Unified Parkinson's Disease Scale (total score and lateralized total, bradykinesia, and rigidity sub-scores), and the differences between measures off-medication and on-medication (following the usual morning dose), were determined. Total daily dopaminergic medication dose (expressed as levodopa equivalent daily dose-LEDD), was also determined. Results: SICI significantly correlated with changes in lateralized UPDRS motor and bradykinesia sub-scores, suggesting that patients with stronger basal intracortical inhibition benefit more from dopaminergic treatment than patients with weaker intracortical inhibition. Also, ICF significantly negatively correlated with LEDD, suggesting that patients with stronger intracortical facilitation require less dopaminergic medication to achieve optimal therapeutic benefit. Both associations were independent of disease severity and duration. Conclusions: The results suggest variability of (patho) physiological phenotypes related to intracortical inhibitory and facilitatory mechanisms determining clinical response to dopaminergic medication in PD. Measures of intracortical excitability may help predict patients' response to dopaminergic therapy, thus potentially providing a background for developing personalized therapy in PD.