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Register a new userNigral diffusivity, but not free water, correlates with iron content in Parkinsons disease
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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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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.
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.
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.
A large number of mathematical models have been proposed to describe the measured signal in diffusion-weighted (DW) magnetic resonance imaging (MRI) and infer properties about the white matter microstructure. However, a head-to-head comparison of DW-MRI models is critically missing in the field. To address this deficiency, we organized the White Matter Modeling Challenge during the International Symposium on Biomedical Imaging (ISBI) 2015 conference. This competition aimed at identifying the DW-MRI models that best predict unseen DW data. in vivo DW-MRI data was acquired on the Connectom scanner at the A.A.Martinos Center (Massachusetts General Hospital) using gradients strength of up to 300 mT/m and a broad set of diffusion times. We focused on assessing the DW signal prediction in two regions: the genu in the corpus callosum, where the fibres are relatively straight and parallel, and the fornix, where the configuration of fibres is more complex. The challenge participants had access to three-quarters of the whole dataset, and their models were ranked on their ability to predict the remaining unseen quarter of data. In this paper we provide both an overview and a more in-depth description of each evaluated model, report the challenge results, and infer trends about the model characteristics that were associated with high model ranking. This work provides a much needed benchmark for DW-MRI models. The acquired data and model details for signal prediction evaluation are provided online to encourage a larger scale assessment of diffusion models in the future.
Parkinsons disease (PD) is the second most common neurodegenerative disease worldwide and affects around 1% of the (60+ years old) elderly population in industrial nations. More than 80% of PD patients suffer from motor symptoms, which could be well addressed if a personalized medication schedule and dosage could be administered to them. However, such personalized medication schedule requires a continuous, objective and precise measurement of motor symptoms experienced by the patients during their regular daily activities. In this work, we propose the use of a wrist-worn smart-watch, which is equipped with 3D motion sensors, for estimating the motor fluctuation severity of PD patients in a free-living environment. We introduce a novel network architecture, a post-training scheme and a custom loss function that accounts for label noise to improve the results of our previous work in this domain and to establish a novel benchmark for nine-level PD motor state estimation.
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