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Institute
Gait Initiation in Parkinson’s Disease: Impact of Dopamine Depletion and Initial Stance Condition
(2020)
Postural instability, in particular at gait initiation (GI), and resulting falls are a major determinant of poor quality of life in subjects with Parkinson’s disease (PD). Still, the contribution of the basal ganglia and dopamine on the feedforward postural control associated with this motor task is poorly known. In addition, the influence of anthropometric measures (AM) and initial stance condition on GI has never been consistently assessed. The biomechanical resultants of anticipatory postural adjustments contributing to GI [imbalance (IMB), unloading (UNL), and stepping phase) were studied in 26 unmedicated subjects with idiopathic PD and in 27 healthy subjects. A subset of 13 patients was analyzed under standardized medication conditions and the striatal dopaminergic innervation was studied in 22 patients using FP-CIT and SPECT. People with PD showed a significant reduction in center of pressure (CoP) displacement and velocity during the IMB phase, reduced first step length and velocity, and decreased velocity and acceleration of the center of mass (CoM) at toe off of the stance foot. All these measurements correlated with the dopaminergic innervation of the putamen and substantially improved with levodopa. These results were not influenced by anthropometric parameters or by the initial stance condition. In contrast, most of the measurements of the UNL phase were influenced by the foot placement and did not correlate with putaminal dopaminergic innervation. Our results suggest a significant role of dopamine and the putamen particularly in the elaboration of the IMB phase of anticipatory postural adjustments and in the execution of the first step. The basal ganglia circuitry may contribute to defining the optimal referent body configuration for a proper initiation of gait and possibly gait adaptation to the environment.
Activation of the basal ganglia has been shown during the preparation and execution of movement. However, the functional interaction of cortical and subcortical brain areas during movement and the relative contribution of dopaminergic striatal innervation remains unclear. We recorded local field potential (LFP) activity from the subthalamic nucleus (STN) and high-density electroencephalography (EEG) signals in four patients with Parkinson’s disease (PD) off dopaminergic medication during a multi-joint motor task performed with their dominant and non-dominant hand. Recordings were performed by means of a fully-implantable deep brain stimulation (DBS) device at 4 months after surgery. Three patients also performed a single-photon computed tomography (SPECT) with [123I]N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane (FP-CIT) to assess striatal dopaminergic innervation. Unilateral movement execution led to event-related desynchronization (ERD) followed by a rebound after movement termination event-related synchronization (ERS) of oscillatory beta activity in the STN and primary sensorimotor cortex of both hemispheres. Dopamine deficiency directly influenced movement-related beta-modulation, with greater beta-suppression in the most dopamine-depleted hemisphere for both ipsi- and contralateral hand movements. Cortical-subcortical, but not interhemispheric subcortical coherencies were modulated by movement and influenced by striatal dopaminergic innervation, being stronger in the most dopamine-depleted hemisphere. The data are consistent with a role of dopamine in shielding subcortical structures from an excessive cortical entrapment and cross-hemispheric coupling, thus allowing fine-tuning of movement.
Dermal and cardiac autonomic fiber involvement in Parkinson's disease and multiple system atrophy
(2021)
Pathological aggregates of alpha-synuclein in peripheral dermal nerve fibers can be detected in patients with idiopathic Parkinson's disease and multiple system atrophy. This study combines skin biopsy staining for p-alpha-synuclein depositions and radionuclide imaging of the heart with [\(^{123}\)I]-metaiodobenzylguanidine to explore peripheral denervation in both diseases. To this purpose, 42 patients with a clinical diagnosis of Parkinson's disease or multiple system atrophy were enrolled. All patients underwent a standardized clinical workup including neurological evaluation, neurography, and blood samples. Skin biopsies were obtained from the distal and proximal leg, back, and neck for immunofluorescence double labeling with anti-p-alpha-synuclein and anti-PGP9.5. All patients underwent myocardial [\(^{123}\)I]-metaiodobenzylguanidine scintigraphy. Dermal p-alpha-synuclein was observed in 47.6% of Parkinson's disease patients and was mainly found in autonomic structures. 81.0% of multiple system atrophy patients had deposits with most of cases in somatosensory fibers. The [\(^{123}\)I]-metaiodobenzylguanidine heart-to-mediastinum ratio was lower in Parkinson's disease than in multiple system atrophy patients (1.94 +/- 0.63 vs. 2.91 +/- 0.96; p < 0.0001). Irrespective of the diagnosis, uptake was lower in patients with than without p-alpha-synuclein in autonomic structures (1.42 +/- 0.51 vs. 2.74 +/- 0.83; p < 0.0001). Rare cases of Parkinson's disease with p-alpha-synuclein in somatosensory fibers and multiple system atrophy patients with deposits in autonomic structures or both fiber types presented with clinically overlapping features. In conclusion, this study suggests that alpha-synuclein contributes to peripheral neurodegeneration and mediates the impairment of cardiac sympathetic neurons in patients with synucleinopathies. Furthermore, it indicates that Parkinson's disease and multiple system atrophy share pathophysiologic mechanisms of peripheral nervous system dysfunction with a clinical overlap.
Freezing of gait (FOG) is a sudden episodic inability to produce effective stepping despite the intention to walk. It typically occurs during gait initiation (GI) or modulation and may lead to falls. We studied the anticipatory postural adjustments (imbalance, unloading, and stepping phase) at GI in 23 patients with Parkinson’s disease (PD) and FOG (PDF), 20 patients with PD and no previous history of FOG (PDNF), and 23 healthy controls (HCs). Patients performed the task when off dopaminergic medications. The center of pressure (CoP) displacement and velocity during imbalance showed significant impairment in both PDNF and PDF, more prominent in the latter patients. Several measurements were specifically impaired in PDF patients, especially the CoP displacement along the anteroposterior axis during unloading. The pattern of segmental center of mass (SCoM) movements did not show differences between groups. The standing postural profile preceding GI did not correlate with outcome measurements. We have shown impaired motor programming at GI in Parkinsonian patients. The more prominent deterioration of unloading in PDF patients might suggest impaired processing and integration of somatosensory information subserving GI. The unaltered temporal movement sequencing of SCoM might indicate some compensatory cerebellar mechanisms triggering time-locked models of body mechanics in PD.
Gait disturbances are common manifestations of Parkinson’s disease (PD), with unmet therapeutic needs. Inertial measurement units (IMUs) are capable of monitoring gait, but they lack neurophysiological 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 using electromyographic (EMG) channels during overground walking in patients with PD. We recorded six parkinsonian patients while they walked 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 bilaterally (i.e., tibialis anterior, soleus, gastrocnemius medialis, gastrocnemius lateralis, and vastus lateralis). Gait events could be detected with high temporal precision (median displacement of <50 ms), low numbers of missed events (<2%), and next to no false-positive event detections (<0.1%). Swing and stance phases could thus be determined with high fidelity (median F1-score of ~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, which allows the simultaneous acquisition of an electromyographic signal to be performed. This gait analysis approach has the potential to make additional measurement devices such as IMUs and force plates less essential, thereby reducing financial and preparation overheads and discomfort factors in gait studies.
Low-frequency oscillatory patterns of pallidal local field potentials (LFPs) have been proposed as a physiomarker for dystonia and hold the promise for personalized adaptive deep brain stimulation. Head tremor, a low-frequency involuntary rhythmic movement typical of cervical dystonia, may cause movement artifacts in LFP signals, compromising the reliability of low-frequency oscillations as biomarkers for adaptive neurostimulation. We investigated chronic pallidal LFPs with the Percept\(^{TM}\) PC (Medtronic PLC) device in eight subjects with dystonia (five with head tremors). We applied a multiple regression approach to pallidal LFPs in patients with head tremors using kinematic information measured with an inertial measurement unit (IMU) and an electromyographic signal (EMG). With IMU regression, we found tremor contamination in all subjects, whereas EMG regression identified it in only three out of five. IMU regression was also superior to EMG regression in removing tremor-related artifacts and resulted in a significant power reduction, especially in the theta-alpha band. Pallido-muscular coherence was affected by a head tremor and disappeared after IMU regression. Our results show that the Percept PC can record low-frequency oscillations but also reveal spectral contamination due to movement artifacts. IMU regression can identify such artifact contamination and be a suitable tool for its removal.