Op-brai130049 1..12

Freezing of gait is a devastating symptom of advanced Parkinson’s disease yet the neural correlates of this phenomenon remain poorly understood. In this study, severity of freezing of gait was assessed in 18 patients with Parkinson’s disease on a series of timed ‘up and go’ tasks, in which all patients suffered from episodes of clinical freezing of gait. The same patients also underwent functional magnetic resonance imaging with a virtual reality gait paradigm, performance on which has recently been shown to correlate with actual episodes of freezing of gait. Statistical parametric maps were created that compared the blood oxygen leveldependent response associated with paroxysmal motor arrests (freezing) to periods of normal motor output. The results of a random effects analysis revealed that these events were associated with a decreased blood oxygen level-dependent response in sensorimotor regions and an increased response within frontoparietal cortical regions. These signal changes were inversely correlated with the severity of clinical freezing of gait. Motor arrests were also associated with decreased blood oxygen level-dependent signal bilaterally in the head of caudate nucleus, the thalamus and the globus pallidus internus. Utilizing a mixed event-related/block design, we found that the decreased blood oxygen level-dependent response in the globus pallidus and the subthalamic nucleus persisted even after controlling for the effects of cognitive load, a finding which supports the notion that paroxysmal increases in basal ganglia outflow are associated with the freezing phenomenon. This method also revealed a decrease in the blood oxygen level-dependent response within the mesencephalic locomotor region during motor arrests, the magnitude of which was positively correlated with the severity of clinical freezing of gait. These results provide novel insights into the pathophysiology underlying freezing of gait and lend support to models of freezing of gait that implicate dysfunction across coordinated neural networks.