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Parkinson's Disease (PD) exhibits heterogeneous cognitive deficits that may represent different cognitive phenotypes. While previous studies have described them in a "macro" manner, only one study has applied Network Analysis (NA) in PD. NA represents a model to explore relationships between cognitive abilities, aiding in understanding cognitive phenotypes. This study aims to verify whether the cognitive system undergoes reorganization in PD with Mild Cognitive Impairment (PD-MCI) patients. To explore this, a Level II cognitive assessment was administered to 275 PD patients, who were classified into two diagnostic categories: PD-Cognitive Unimpaired (CU) (n = 171) and PD-MCI (n = 104). NA was applied to construct Gaussian Graphical Models for each diagnostic group, where nodes represent cognitive tests and demographic factors, and edges represent their interconnections. The NA revealed substantial differences between the cognitive networks of PD-CU and PD-MCI patients. Specifically, the network of PD-MCI patients appears less sparse, with some weakened relationships between nodes. Overall, the results support the presence of a cognitive reorganization in PD-MCI patients, potentially indicating a functional compensation mechanism. In conclusion, this study enhances the understanding of the cognitive mechanisms underlying cognitive decline in patients with PD., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Introduction: Gaucher's disease (GD) is caused by biallelic mutations in the GBA1 gene, leading to reduced glucocerebrosidase (GCase) activity and substrate (glucosylceramide and glucosylsphingosine, GlcSph) accumulation. GBA1 variant carriers are at risk of Parkinson's disease (PD), but only those with biallelic mutations cross the threshold of GCase reduction, leading to substrate accumulation and GD. The link between GBA1 mutations, GD and PD is not fully understood. Here we aimed at reporting the results of a large PD population screening with dried blood spot tests for GD., Methods: We measured GCase activity and GlcSph levels in 1344 PD patients with dried blood spot tests, and performed GBA1 genetic sequencing., Results: While the GCase activity was reduced in GBA1-PD carriers compared to wild type PD, GlcSph was increased in GBA1-PD compared to GBA1-controls, regardless of the underlying type of GBA1 variant. 13.6 % and 0.4 % of PD patients had mono- or biallelic GBA1 mutations respectively. GCase deficiency, lipid accumulation and clinical manifestations of GD was detected in five PD patients with biallelic GBA1 mutations, of whom four had a risk combined with a GD causing variant., Conclusions: GlcSph appearing higher in PD may represent a reliable biomarker of the disease and deserves to be further investigated. This study highlights the importance of screening PD patients for possible underlying GD, which is a treatable condition that should not be missed. We diagnosed GD cases carrying a "risk" variant in one allele, which is an unprecedented finding deserving further investigation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: The burden of Parkinson Disease (PD) represents a key public health issue and it is essential to develop innovative and cost-effective approaches to promote sustainable diagnostic and therapeutic interventions. In this perspective the adoption of a P3 (predictive, preventive and personalized) medicine approach seems to be pivotal. The NeuroArtP3 (NET-2018-12366666) is a four-year multi-site project co-funded by the Italian Ministry of Health, bringing together clinical and computational centers operating in the field of neurology, including PD., Objective: The core objectives of the project are: i) to harmonize the collection of data across the participating centers, ii) to structure standardized disease-specific datasets and iii) to advance knowledge on disease's trajectories through machine learning analysis., Methods: The 4-years study combines two consecutive research components: i) a multi-center retrospective observational phase; ii) a multi-center prospective observational phase. The retrospective phase aims at collecting data of the patients admitted at the participating clinical centers. Whereas the prospective phase aims at collecting the same variables of the retrospective study in newly diagnosed patients who will be enrolled at the same centers., Results: The participating clinical centers are the Provincial Health Services (APSS) of Trento (Italy) as the center responsible for the PD study and the IRCCS San Martino Hospital of Genoa (Italy) as the promoter center of the NeuroartP3 project. The computational centers responsible for data analysis are the Bruno Kessler Foundation of Trento (Italy) with TrentinoSalute4.0 -Competence Center for Digital Health of the Province of Trento (Italy) and the LISCOMPlab University of Genoa (Italy)., Conclusions: The work behind this observational study protocol shows how it is possible and viable to systematize data collection procedures in order to feed research and to advance the implementation of a P3 approach into the clinical practice through the use of AI models., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Malaguti et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Some wearable solutions exploiting on-body acceleration sensors have been proposed to recognize Freezing of Gait (FoG) in people affected by Parkinson Disease (PD). Once a FoG event is detected, these systems generate a sequence of rhythmic stimuli to allow the patient restarting the gait. While these solutions are effective in detecting FoG events, they are unable to predict FoG to prevent its occurrence. This paper fills in the gap by presenting a machine learning-based approach that classifies accelerometer data from PD patients, recognizing a pre-FOG phase to further anticipate FoG occurrence in advance. Gait was monitored by three tri-axial accelerometer sensors worn on the back, hip and ankle. Gait features were then extracted from the accelerometer's raw data through data windowing and non-linear dimensionality reduction. A k-nearest neighbor algorithm (k-NN) was used to classify gait in three classes of events: pre-FoG, no-FoG and FoG. The accuracy of the proposed solution was compared to state-of-the-art approaches. Our study showed that: (i) we achieved performances overcoming the state-of-the-art approaches in terms of FoG detection, (ii) we were able, for the very first time in the literature, to predict FoG by identifying the pre-FoG events with an average sensitivity and specificity of, respectively, 94.1% and 97.1%, and (iii) our algorithm can be executed on resource-constrained devices. Future applications include the implementation on a mobile device, and the administration of rhythmic stimuli by a wearable device to help the patient overcome the FoG.

Background and Purpose: Although female gender, depressive symptoms and medical conditions predisposing to pain are more common in patients with Parkinson's disease (PD) with pain, no study has yet explored the relationship between pain and other non-motor symptoms (NMS)., Methods: A total of 321 consecutive patients with PD [190 men/131 women aged 68.3 (SD 9.2) years] attending four Italian movement disorder clinics were studied. Demographic/clinical data were obtained by a standardized interview and the NMS scale. The association of pain with motor and NMS was assessed by multivariable logistic regression models., Results: At the time of the study, 180 patients with PD (56%) reported chronic pain that, in most cases, was described as being muscular or arthralgic pain. Pain preceded the onset of motor signs in 36/180 patients. In the main-effect model, factors independently associated with pain were female sex [odds ratio (OR), 2.1; P = 0.01], medical conditions predisposing to pain (OR, 2.9; P < 0.001), Hoehn-Yahr staging (OR, 1.9; P = 0.04), motor complications (OR, 4.7; P = 0.04) and NMS belonging to the sleep/fatigue (OR, 1.6; P = 0.04) and mood/cognition (OR, 1.6; P = 0.03) domains. Most explanatory variables in the multivariable analysis were similarly distributed in patients in whom pain may have been related to PD or to a cause other than PD., Conclusions: We confirm that pain in PD is more frequent in women and in subjects with medical conditions predisposing to painful symptoms. Moreover, this strengthens the association between pain and motor severity measures and NMS domains, particularly sleep and mood disturbances., (© 2017 EAN.)

Background: Several studies documented abnormal nociceptive processing in PD patients. Pain central pathways are accessible by laser-evoked potentials (LEPs). LEPs recording show a N2/P2 complex mostly generated by the anterior cingulate cortex, preceded by an earlier negative component (N1), originating from the opercular cortex. Previous work demonstrated N2/P2 amplitude reduction in PD patients and suggested a centrally-acting pathomechanism for the genesis of pain. However, since a peripheral deafferentation has been recently demonstrated in PD, it is not clear if such LEP abnormalities reflect a mechanism acting centrally or not., Objective: To assess whether abnormalities of nociceptive inputs occur at central and/or peripheral level in pain-free PD patients with hemiparkinson using Nd:YAP LEPs., Methods: We recorded scalp Nd:YAP-LEPs to hand stimulation in 13 pain-free patients with unilateral PD and in 13 healthy subjects. Additionally, we collected laser pain-rating in both groups., Results: PD patients and normal subjects showed comparable N1, N2 and P2 latencies. The N2/P2 amplitude was significantly lower in PD patients than in controls, regardless of the clinically affected side, whereas the N1/P1 amplitude was not different. PD patients had higher pain-rating, indicative of hyperalgesia., Conclusions: These findings demonstrate that in the PD patients the abnormal processing of pain stimuli occurs at central rather than peripheral level. The co-existence of hyperalgesia and reduced amplitude of the N2/P2 complex, in spite of a normal N1/P1 component, suggests an imbalance between the medial and lateral pain systems. Such a dissociation might explain the genesis of central pain in PD., (Copyright © 2016. Published by Elsevier Ltd.)