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Register a new userIdentification of EEG Dynamics During Freezing of Gait and Voluntary Stopping in Patients with Parkinsons Disease
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Mobility is severely impacted in patients with Parkinsons disease (PD), especially when they experience involuntary stopping from the freezing of gait (FOG). Understanding the neurophysiological difference between voluntary stopping and involuntary stopping caused by FOG is vital for the detection and potential intervention of FOG in the daily lives of patients. This study characterised the electroencephalographic (EEG) signature associated with FOG in contrast to voluntary stopping. The protocol consisted of a timed up-and-go (TUG) task and an additional TUG task with a voluntary stopping component, where participants reacted to verbal stop and walk instructions by voluntarily stopping or walking. Event-related spectral perturbation (ERSP) analysis was used to study the dynamics of the EEG spectra induced by different walking phases, which included normal walking, voluntary stopping and episodes of involuntary stopping (FOG), as well as the transition windows between normal walking and voluntary stopping or FOG. These results demonstrate for the first time that the EEG signal during the transition from walking to voluntary stopping is distinguishable from that of the transition to involuntary stopping caused by FOG. The EEG signature of voluntary stopping exhibits a significantly decreased power spectrum compared to that of FOG episodes, with distinctly different patterns in the delta and low-beta power in the central area. These findings suggest the possibility of a practical EEG-based treatment strategy that can accurately predict FOG episodes, excluding the potential confound of voluntary stopping.
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Over the years motor deficit in Parkinsons Disease (PD) patients was largely studied, however, no consistent pattern of relations between quantitative electroencephalography (qEEG) and motor scales emerged. There is a general lack of information on the relation between EEG changes and scales related to specific motor deficits. Therefore, the study aimed to investigate the relation between brain oscillatory activity alterations (EEG power bands) and most used PD-related motor deficit scales. A positive correlation was found between the freezing of the gait questionnaire (FOGQ) and delta spectral power band (rho=0.67; p=0.008), while a negative correlation with the same scale was observed in the alpha spectral power band (rho=-0.59, p=0.027). Additionally, motor scores measure by motor part of Unified Parkinsons Disease Rating Scale (UPDRS) correlated directly with theta (rho=0.55, p=0.040) and inversely with beta EEG power band (rho=-0.77, p=0.001). No significant correlation was found between spectral powers and Hoehn and Yahr (H&Y), BERG (Berg K. et. al. 1995), Modified Parkinson Activity Scale (MPAS), Six-Minute Walk Test (6MWT) and Timed Up and Go Test (TUG). In conclusion, our study supports the earlier findings suggesting a link between EEG slowing and motor decline, providing more insight into the relation between EEG alteration and deficits in different motor domains. These findings indicate that EEG assessment may be a useful biomarker for objective monitoring of progression and neurophysiological effect of rehabilitation approaches in PDs.
Alzheimers disease (AD) and Parkinsons disease (PD) are the two most common neurodegenerative disorders in humans. Because a significant percentage of patients have clinical and pathological features of both diseases, it has been hypothesized that the patho-cascades of the two diseases overlap. Despite this evidence, these two diseases are rarely studied in a joint manner. In this paper, we utilize clinical, imaging, genetic, and biospecimen features to cluster AD and PD patients into the same feature space. By training a machine learning classifier on the combined feature space, we predict the disease stage of patients two years after their baseline visits. We observed a considerable improvement in the prediction accuracy of Parkinsons dementia patients due to combined training on Alzheimers and Parkinsons patients, thereby affirming the claim that these two diseases can be jointly studied.
About 90 percent of people with Parkinsons disease (PD) experience decreased functional communication due to the presence of voice and speech disorders associated with dysarthria that can be characterized by monotony of pitch (or fundamental frequency), reduced loudness, irregular rate of speech, imprecise consonants, and changes in voice quality. Speech-language pathologists (SLPs) work with patients with PD to improve speech intelligibility using various intensive in-clinic speech treatments. SLPs also prescribe home exercises to enhance generalization of speech strategies outside of the treatment room. Even though speech therapies are found to be highly effective in improving vocal loudness and speech quality, patients with PD find it difficult to follow the prescribed exercise regimes outside the clinic and to continue exercises once the treatment is completed. SLPs need techniques to monitor compliance and accuracy of their patients exercises at home and in ecologically valid communication situations. We have designed EchoWear, a smartwatch-based system, to remotely monitor speech and voice exercises as prescribed by SLPs. We conducted a study of 6 individuals; three with PD and three healthy controls. To assess the performance of EchoWear technology compared with high quality audio equipment obtained in a speech laboratory. Our preliminary analysis shows promising outcomes for using EchoWear in speech therapies for people with PD. Keywords: Dysarthria; knowledge-based speech processing; Parkinsons disease; smartwatch; speech therapy; wearable system.
While the emerging evidence indicates that the pathogenesis of Parkinsons disease (PD) is strongly correlated to the accumulation of alpha-synuclein ({alpha}-syn) aggregates, there has been no clinical success in anti-aggregation agents for the disease to date. Here we show that graphene quantum dots (GQDs) exhibit anti-amyloid activity via direct interaction with {alpha}-syn. Employing biophysical, biochemical, and cell-based assays as well as molecular dynamics (MD) simulation, we find that GQDs have notable potency in not only inhibiting fibrillization of {alpha}-syn but also disaggregating mature fibrils in a time-dependent manner. Remarkably, GQDs rescue neuronal death and synaptic loss, reduce Lewy body (LB)/Lewy neurite (LN) formation, ameliorate mitochondrial dysfunctions, and prevent neuron-to-neuron transmission of {alpha}-syn pathology induced by {alpha}-syn preformed fibrils (PFFs) in neurons. In addition, in vivo administration of GQDs protects against {alpha}-syn PFFs-induced loss of dopamine neurons, LB/LN pathology, and behavioural deficits through the penetration of the blood-brain barrier (BBB). The finding that GQDs function as an anti-aggregation agent provides a promising novel therapeutic target for the treatment of PD and related {alpha}-synucleinopathies.
The loss of melanized neurons in the substantia nigra pars compacta is a primary feature in Parkinsons disease (PD). Iron deposition occurs in conjunction with this loss. Loss of nigral neurons should remove barriers for diffusion and increase diffusivity of water molecules in regions undergoing this loss. In metrics from single-compartment diffusion tensor imaging models, these changes should manifest as increases in mean diffusivity and the free water compartment as well as and reductions in fractional anisotropy. However, studies examining nigral diffusivity changes from PD with single-compartment models have yielded inconclusive results and emerging evidence in control subjects indicates that iron corrupts diffusivity metrics derived from single-compartment models. Iron-sensitive data and diffusion data were analyzed in two cohorts. The effect of iron on diffusion measures from single- and bi-compartment models was assessed in both cohorts. Measures sensitive to the free water compartment and iron content were found to increase in substantia nigra of the PD group in both cohorts. However, diffusion markers derived from the single-compartment model were not replicated across cohorts. Correlations were seen between single-compartment diffusion measures and iron markers in the discovery cohort and validation cohort but no correlation was observed between a measure from the bi-compartment model related to the free water compartment and iron markers in either cohort. The variability of single-compartment nigral diffusion metrics in PD may be attributed to competing influences of increased iron content, which drives diffusivity down, and increases in the free water compartment, which drives diffusivity up. In contrast to diffusion metrics derived from the single-compartment model, no relationship was seen between iron and the free water compartment in substantia nigra.
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