ABSTRACT
The significance of oscillations that characterize the subthalamic nucleus in Parkinson's disease is still under debate. Here, we analysed the spectral and spatial characteristics of 314 microelectrode trajectories from 128 patients undergoing subthalamic nucleus deep brain stimulation surgery for Parkinson's disease. We correlated the subthalamic nucleus pathophysiology with the outcome of surgery, as evaluated by the third section of the Unified Parkinson's Disease Rating Scale (motor score), which was subdivided into tremor, rigidity, limb-bradykinesia and axial-bradykinesia subscores. beta-oscillatory activity (13-30 Hz) comprised a continuous stretch within the subthalamic nucleus, and was limited to a distinctly-bounded dorsolateral oscillatory region. Although less consistent and more sporadic, low-frequency (3-7 Hz) power was also increased in the dorsolateral oscillatory region. In contrast, the more ventral subthalamic nucleus was characterized by consistently reduced beta and increased gamma (30-100 Hz) activity. Neuronal responses to passive arm movement (analysed by their alignment to goniometer tracing of the joints' angular displacement) were significantly more common in the dorsolateral oscillatory region than the ventral subthalamic nucleus region (62 versus 25% of sites tested respectively, P<0.01). The length of the dorsolateral oscillatory region recorded in the macroelectrode-implanted trajectory predicted a favourable response to subthalamic nucleus deep brain stimulation (R=0.67, P<0.0001). This correlation was also evident for improvement in the specific symptom subscores of rigidity, limb-bradykinesia and axial-bradykinesia (P<0.05). Similarly, increased subthalamic nucleus beta power was associated with postoperative improvement. In contrast, the preoperative response to levodopa did not correlate with dorsolateral oscillatory region length (P=0.33), however, it did tend to be associated with increased beta (and decreased low frequency) subthalamic nucleus power. Finally, the active macroelectrode contact, independently selected by optimal clinical outcome, coincided with the dorsolateral oscillatory region centre. On average, the location of the active contact was not significantly different from the dorsolateral oscillatory region centre (P=0.10), but was significantly different from the subthalamic nucleus centre (P<0.0001). We conclude that the spatial extent of the dorsolateral oscillatory region, which overlaps the motor territories of the subthalamic nucleus, predicts the outcome of subthalamic nucleus deep brain stimulation. Thus the frequency and spatial characteristics of the subthalamic nucleus trajectory may be used for deep brain stimulation outcome optimization.
Subject(s)
Beta Rhythm , Deep Brain Stimulation , Parkinson Disease/therapy , Subthalamic Nucleus/physiology , Beta Rhythm/methods , Deep Brain Stimulation/methods , Humans , Parkinson Disease/physiopathology , Predictive Value of Tests , Treatment OutcomeABSTRACT
Positive therapeutic response without adverse side effects to subthalamic nucleus deep brain stimulation (STN DBS) for Parkinson's disease (PD) depends to a large extent on electrode location within the STN. The sensorimotor region of the STN (seemingly the preferred location for STN DBS) lies dorsolaterally, in a region also marked by distinct beta (13-30 Hz) oscillations in the parkinsonian state. In this study, we present a real-time method to accurately demarcate subterritories of the STN during surgery, based on microelectrode recordings (MERs) and a Hidden Markov Model (HMM). Fifty-six MER trajectories were used, obtained from 21 PD patients who underwent bilateral STN DBS implantation surgery. Root mean square (RMS) and power spectral density (PSD) of the MERs were used to train and test an HMM in identifying the dorsolateral oscillatory region (DLOR) and nonoscillatory subterritories within the STN. The HMM demarcations were compared to the decisions of a human expert. The HMM identified STN-entry, the ventral boundary of the DLOR, and STN-exit with an error of -0.09 +/- 0.35, -0.27 +/- 0.58, and -0.20 +/- 0.33 mm, respectively (mean +/- standard deviation), and with detection reliability (error < 1 mm) of 95, 86, and 91%, respectively. The HMM was successful despite a very coarse clustering method and was robust to parameter variation. Thus, using an HMM in conjunction with RMS and PSD measures of intraoperative MER can provide improved refinement of STN entry and exit in comparison with previously reported automatic methods, and introduces a novel (intra-STN) detection of a distinct DLOR-ventral boundary.
Subject(s)
Deep Brain Stimulation/methods , Markov Chains , Parkinson Disease/therapy , Subthalamic Nucleus/physiology , Action Potentials/physiology , Aged , Algorithms , Beta Rhythm , Electrodes, Implanted , Female , Humans , Male , Microelectrodes , Middle Aged , Reproducibility of Results , Spectrum AnalysisABSTRACT
Twiddler's syndrome describes the intentional external manipulation or spontaneous rotation of implanted devices such as cardiac pacemakers. Here we report the same phenomenon occurring in a patient with an implanted deep brain stimulator generator. The clinical syndrome is described and potential technical nuances to prevent its occurrence are suggested.
