Background: The subthalamic nucleus is the preferred target for deep brain stimulation in patients with advanced Parkinson’s disease. The site of permanent stimulation is the subject of ongoing debate, as stimulation both within and adjacent to the subthalamic nucleus may be effective. Objective: To assess the position of active electrode contacts in relation to the dorsal margin of the subthalamic nucleus as determined by intraoperative microrecordings and magnetic resonance imaging (MRI). Methods: In 25 patients suffering from severe levodopa sensitive parkinsonism, deep brain stimulating electrodes (n = 49) were implanted following mapping of the subthalamic nucleus by microrecording and microstimulation along five parallel tracks. Postoperative stereotactic radiography and fusion of pre- and postoperative MRI studies were used to determine the stereotactic position relative to the midcommissural point of the most effective electrode contacts selected for permanent stimulation (n = 49). Intraoperative microrecordings were analysed retrospectively to define the dorsal margin of the subthalamic nucleus. In cases where the dorsal margin could be defined in at least three microrecording tracks (n = 37) it was correlated with the position of the active contact using an algorithm developed for direct three dimensional comparisons. Results: Stimulation of the subthalamic nucleus resulted in marked improvement in levodopa sensitive parkinsonian symptoms and levodopa induced dyskinesias, with significant improvement in UPDRS III scores. In several instances, projection of the electrode artefacts onto the T2 weighted MRI visualised subthalamic nucleus of individual patients suggested that the electrodes had passed through the subthalamic nucleus. When the actual position of active electrode contacts (n = 35) was correlated with the dorsal margin of the subthalamic nucleus as defined neurophysiologically, most contacts were located either in proximity (± 1.0 mm) to the dorsal border of the subthalamic nucleus (32.4%) or further dorsal within the subthalamic region (37.8%). The other active contacts (29.7%) were detected within the dorsal (sensorimotor) subthalamic nucleus. The average position of all active contacts (n = 49) was 12.8 mm (± 1.0) lateral, 1.9 mm (± 1.4) posterior, and 1.6 mm (± 2.1) ventral to the midcommissural point. Conclusions: Subthalamic nucleus stimulation appears to be most effective in the border area between the upper subthalamic nucleus (sensorimotor part) and the subthalamic area containing the zona incerta, fields of Forel, and subthalamic nucleus projections.

Patients with advanced Parkinson’s disease regularly experience unstable motor states. Objective and reliable monitoring of these fluctuations is an unmet need. We used deep learning to classify motion data from a single wrist-worn IMU sensor recording in unscripted environments. For validation purposes, patients were accompanied by a movement disorder expert, and their motor state was passively evaluated every minute. We acquired a dataset of 8,661 minutes of IMU data from 30 patients, with annotations about the motor state (OFF,ON, DYSKINETIC) based on MDS-UPDRS global bradykinesia item and the AIMS upper limb dyskinesia item. Using a 1-minute window size as an input for a convolutional neural network trained on data from a subset of patients, we achieved a three-class balanced accuracy of 0.654 on data from previously unseen subjects. This corresponds to detecting the OFF, ON, or DYSKINETIC motor state at a sensitivity/specificity of 0.64/0.89, 0.67/0.67 and 0.64/0.89, respectively. On average, the model outputs were highly correlated with the annotation on a per subject scale (r = 0.83/0.84; p < 0.0001), and sustained so for the highly resolved time windows of 1 minute (r = 0.64/0.70; p < 0.0001). Thus, we demonstrate the feasibility of long-term motor-state detection in a free-living setting with deep learning using motion data from a single IMU.

Maturation of the corticospinal (CS) tract and hand motor function provide paradigms for central nervous system development. In this study, involving 112 participants (aged from 0.2 to 30 years), we evaluated central motor conduction times (CMCT) obtained with transcranial magnetic stimulation (TMS) during preinnervation conditions of facilitation and relaxation. Auditory reaction time, velocity of a ballistic movement of the arm, finger tapping, diadochokinesis, and fine motor visuomanual tracking were also examined. The maturation profiles for every parameter were calculated. CMCTs for the different preinnervation conditions reached adult values at different times and this could be explained by maturation of excitability at the cortical and spinal level. A stable phase for CMCTs and reaction time was reached during childhood. Parameters which measured motor speed and skill indicated that the development of these continued into adulthood. The maturation of the fast CS tract seems to be completed before the acquisition of the related motor performance has been accomplished. In conclusion, we could demonstrate that data from several neurophysiological methods can be combined and used to study the maturation of the function of the nervous system. This approach could allow appraisal of pathological conditions that show parallels with omissions or lack of developmental progress.