Glutamate and GABA concentration changes in the globus pallidus internus of Parkinson's patients during performance of implicit and declarative memory tasks: a report of two subjects.

The basal ganglia, typically associated with motor function, are involved in human cognitive processes, as demonstrated in behavioral, lesion, and noninvasive functional neuroimaging studies. Here we report task-contingent changes in concentrations of the neurotransmitters glutamate (Glu) and gamma-aminobutyric acid (GABA) in the globus pallidus internus (GPi) of two patients with Parkinson's disease undergoing deep brain stimulation surgery by utilizing in-vivo microdialysis measurements during performance of implicit and declarative memory tasks. Performance of an implicit memory task (weather prediction task-WPT) was associated with increased levels of glutamate and GABA in the GPi compared to their concentrations at baseline. On the other hand, performance of a declarative memory task (verbal learning task-VLT) was associated with decreased levels of glutamate and GABA in GPi compared to baseline during the encoding and immediate recall phase with less conclusive results during the delayed recall phase. These results are in line with hypothesized changes in these neurotransmitter levels: an increase of excitatory (Glu) input from subthalamic nucleus (STN) to GPi during implicit memory task performance and a decrease of inhibitory inputs (GABA) from globus pallidus externus (GPe) and striatum to GPi during declarative memory performance. Consistent with our previous report on in-vivo neurotransmitter changes during tasks in STN, these data provide corroborative evidence for the direct involvement of basal ganglia in cognitive functions and complements our model of the functional circuitry of basal ganglia in the healthy and Parkinson's disease affected brain.

Smartphone application for classification of motor impairment severity in Parkinson's disease.

Advanced hardware components embedded in modern smartphones have the potential to serve as widely available medical diagnostic devices, particularly when used in conjunction with custom software and tested algorithms. The goal of the present pilot study was to develop a smartphone application that could quantify the severity of Parkinson's disease (PD) motor symptoms, and in particular, bradykinesia. We developed an iPhone application that collected kinematic data from a small cohort of PD patients during guided movement tasks and extracted quantitative features using signal processing techniques. These features were used in a classification model trained to differentiate between overall motor impairment of greater and lesser severity using standard clinical scores provided by a trained neurologist. Using a support vector machine classifier, a classification accuracy of 0.945 was achieved under 6-fold cross validation, and several features were shown to be highly discriminatory between more severe and less severe motor impairment by area under the receiver operating characteristic curve (AUC > 0.85). Accurate classification for discriminating between more severe and less severe bradykinesia was not achieved with these methods. We discuss future directions of this work and suggest that this platform is a first step toward development of a smartphone application that has the potential to provide clinicians with a method for monitoring patients between clinical appointments.

Improving postural stability via computational modeling approach to deep brain stimulation programming.

Bilateral subthalamic (STN) deep brain stimulation (DBS) is generally effective in improving the cardinal motor signs of advanced Parkinson's disease (PD). However, in many cases postural instability is refractory to STN DBS. The goal of this project was to determine if postural instability could be improved with STN DBS by avoiding current spread to the non-motor territories of the STN. Stimulation parameters that maximized activation of a theoretically defined target region were determined via patient-specific computer models created in Cicerone. Postural stability was assessed under three conditions: Off DBS, Clinical DBS, and Model DBS. Clinical settings were the patients' DBS settings determined via traditional clinical practice and were considered optimized and stable for at least 6 months prior to study enrollment. Blinded and randomized evaluations were performed in five patients. Postural sway was significantly less during Model DBS compared to Clinical DBS. These results support the hypothesis that minimizing spread of current to non-motor territories of the STN can improve PD related instability with DBS.