No abstract

Excessive beta activity has been shown in local field potential recordings from the cortico-basal ganglia loop of Parkinson's disease patients and in its various animal models. Recent evidence suggests that enhanced beta oscillations may play a central role in the pathophysiology of the disorder and that beta activity may be directly linked to the motor impairment. However, the temporal evolution of exaggerated beta oscillations during the ongoing dopaminergic neurodegeneration and its relation to the motor impairment and histological changes are still unknown. We investigated motor behavioral, in-vivo electrophysiological (subthalamic nucleus, motor cortex) and histological changes (striatum, substantia nigra compacta) 2, 5, 10 and 20-30 days after a 6-hydroxydopamine injection into the medial forebrain bundle in Wistar rats. We found strong correlations between subthalamic beta power and motor impairment. No correlation was found for beta power in the primary motor cortex. Only subthalamic but not cortical beta power was strongly correlated with the histological markers of the dopaminergic neurodegeneration. Significantly increased subthalamic beta oscillations could be detected before this increase was found in primary motor cortex. At the latest observation time point, a significantly higher percentage of long beta bursts was found. Our study is the first to show a strong relation between subthalamic beta power and the dopaminergic neurodegeneration. Thus, we provide additional evidence for an important pathophysiological role of subthalamic beta oscillations and prolonged beta bursts in Parkinson's disease.

Aggregating voxel-level statistical dependencies between multivariate time series is an important intermediate step when characterising functional connectivity (FC) between larger brain regions. However, there are numerous ways in which voxel-level data can be aggregated into inter-regional FC, and the advantages of each of these approaches are currently unclear. In this study we generate ground-truth data and compare the performances of various pipelines that estimate directed and undirected linear FC between regions. We test the ability of several existing and novel FC analysis pipelines to identify the true regions within which connectivity was simulated. We test various inverse modelling algorithms, strategies to aggregate time series within regions, and connectivity metrics. Furthermore, we investigate the influence of the number of interactions, the signal-to-noise ratio, the noise mix, the interaction time delay, and the number of active sources per region on the ability of detecting FC. The best-performing FC pipeline consists of the following steps: (1) Source projection using the linearly-constrained minimum variance (LCMV) beamformer. (2) Principal component analysis (PCA) using the same fixed number of components within every region. (3) Calculation of the multivariate interaction measure (MIM) for every region pair to assess undirected FC, or calculation of time-reversed Granger Causality (TRGC) to assess directed FC. Lowest performance is obtained with pipelines involving the absolute value of coherency. Interestingly, the combination of dynamic imaging of coherent sources (DICS) beamforming with directed FC metrics that aggregate information across multiple frequencies leads to unsatisfactory results. We formulate recommendations based on these results that may increase the validity of future experimental connectivity studies. We further introduce the free ROIconnect plugin for the EEGLAB toolbox that includes the recommended methods and pipelines that are presented here. We show an exemplary application of the best performing pipeline to the analysis EEG data recorded during motor imagery.

Gait disturbances are common manifestations of Parkinson's disease (PD), with unmet therapeutic needs. Inertial measurement units (IMU) are capable of monitoring gait, but they lack neu-rophysiological information that may be crucial for studying gait disturbances in these patients. Here, we present a machine-learning approach to approximate IMU angular velocity profiles, and subsequently gait events from electromyographic (EMG) channels. We recorded six parkinsonian patients while walking for at least three minutes. Patient-agnostic regression models were trained on temporally-embedded EMG time series of different combinations of up to five leg muscles bi-laterally (i.e., tibialis anterior, soleus, gastrocnemius medialis, gastrocnemius lateralis, and vastus lateralis). Gait events could be detected with high temporal precision (median displace-ment <50 msec), low numbers of missed events (<2%), and next to no false positive event detec-tions (<0.1%). Swing and stance phases could thus be determined with high fidelity (median F1 score ~0.9). Interestingly, the best performance was obtained using as few as two EMG probes placed on the left and right vastus lateralis. Our results demonstrate the practical utility of the proposed EMG-based system for gait event prediction while allowing the simultaneous acquisition of an electromyographic signal. This gait analysis approach has the potential to make additional measurement devices such as IMU and force plates less essential, and thereby to reduce financial and preparation overheads and discomfort factors in gait studies.