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Background: Impulse control disorders related to alterations in the mesocorticolimbic dopamine network occur in Parkinson's disease (PD). Our objective was to investigate the functional neural substrates of reward processing and inhibitory control in these patients.Methods: Eighteen PD patients with impulse control disorders, 17 without this complication, and 18 healthy controls performed a version of the Iowa Gambling Task during functional magnetic resonance scanning under 3 conditions: positive, negative, and mixed feedback. Whole-brain contrasts, regions of interest, time courses, functional connectivity analyses, and brain-behavior associations were examined.Results: PD patients with impulse control disorders exhibited hyperactivation in subcortical and cortical regions typically associated with reward processing and inhibitory control compared with their PD and healthy control counterparts. Time-course analyses revealed that only PD patients with impulse control disorders exhibited stronger signal intensity during the initial versus final periods of the negative-feedback condition in bilateral insula, and right ventral striatum. Interestingly, hyperactivation of all the examined right-lateralized frontostriatal areas during negative feedback was positively associated with impulse control disorder severity. Importantly, positive associations between impulse control disorder severity and regional activations in the right insula and right inferior frontal gyrus, but not the right subthalamic nucleus, were mediated by functional connectivity with the right ventral striatum.Conclusions: During a reward-based task, PD patients with impulse control disorders showed hyperactivation in a right-lateralized network of regions including the subthalamic nucleus that was strongly associated with impulse control disorder severity. In these patients, the right ventral striatum in particular played a critical role in modulating the functional dynamics of right-lateralized inhibitory-control frontal regions when facing penalties. © 2019 International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Background: Recognizing clinical manifestations heralding the development of Alzheimer's disease (AD)-related cognitive impairment could improve the identification of individuals at higher risk of AD who may benefit from potential prevention strategies targeting preclinical population. We aim to characterize the association of body weight change with cognitive changes and AD biomarkers in cognitively unimpaired middle-aged adults., Methods: This prospective cohort study included data from cognitively unimpaired adults from the ALFA study (n = 2743), a research platform focused on preclinical AD. Cognitive and anthropometric data were collected at baseline between April 2013 and November 2014. Between October 2016 and February 2020, 450 participants were visited in the context of the nested ALFA+ study and underwent cerebrospinal fluid (CSF) extraction and acquisition of positron emission tomography images with [ 18 F]flutemetamol (FTM-PET). From these, 408 (90.1%) were included in the present study. We used data from two visits (average interval 4.1 years) to compute rates of change in weight and cognitive performance. We tested associations between these variables and between weight change and categorical and continuous measures of CSF and neuroimaging AD biomarkers obtained at follow-up. We classified participants with CSF data according to the AT (amyloid, tau) system and assessed between-group differences in weight change., Results: Weight loss predicted a higher likelihood of positive FTM-PET visual read (OR 1.27, 95% CI 1.00-1.61, p = 0.049), abnormal CSF p-tau levels (OR 1.50, 95% CI 1.19-1.89, p = 0.001), and an A+T+ profile (OR 1.64, 95% CI 1.25-2.20, p = 0.001) and was greater among participants with an A+T+ profile (p < 0.01) at follow-up. Weight change was positively associated with CSF Aβ42/40 ratio (β = 0.099, p = 0.032) and negatively associated with CSF p-tau (β = - 0.141, p = 0.005), t-tau (β = - 0.147 p = 0.004) and neurogranin levels (β = - 0.158, p = 0.002). In stratified analyses, weight loss was significantly associated with higher t-tau, p-tau, neurofilament light, and neurogranin, as well as faster cognitive decline in A+ participants only., Conclusions: Weight loss predicts AD CSF and PET biomarker results and may occur downstream to amyloid-β accumulation in preclinical AD, paralleling cognitive decline. Accordingly, it should be considered as an indicator of increased risk of AD-related cognitive impairment., Trial Registration: NCT01835717 , NCT02485730 , NCT02685969 .

Background: Dynamic functional network analysis may add relevant information about the temporal nature of the neurocognitive alterations in PD patients with impulse control disorders (PD-ICD). Our aim was to investigate changes in dynamic functional network connectivity (dFNC) in PD-ICD patients, and topological properties of such networks., Methods: Resting state fMRI was performed on 16 PD PD-ICD patients, 20 PD patients without ICD and 17 healthy controls, whose demographic, clinical and behavioral scores were assessed. We conducted a group spatial independent component analysis, sliding window and graph-theory analyses., Results: PD-ICD patients, in contrast to PD-noICD and HC subjects, were engaged across time in a brain configuration pattern characterized by a lack of between-network connections at the expense of strong within-network connections (State III) in temporal, frontoinsular and cingulate cortices, all key nodes of the salience network. Moreover, this increased maintenance of State III in PD-ICD patients was positively correlated with the severity of impulsivity and novelty seeking as measured by specific scales. While in State III, these patients also exhibited increased local efficiency in all the aforementioned areas., Conclusions: Our findings show for the first time that PD-ICD patients have a dynamic functional engagement of local connectivity involving the limbic circuit, leading to the inefficient modulation in emotional processing and reward-related decision-making. These results provide new insights into the pathophysiology of ICD in PD patients and indicate that the dFC study of fMRI could be a useful biomarker to identify patients at risk to develop ICD., Competing Interests: Declaration of competing interest Dr Rodriguez-Oroz received honoraria for lectures, travel and accommodation to attend scientific meetings from Abbvie, Zambon, Bial and Boston Scientific, and she received financial support for her research from national and local government funding agencies in Spain (Institute of Health Carlos III, Basque Country Local Government, and CIBERNED). None of the bodies influenced the content of the manuscript or the decision to publish in any way., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Background: Parkinson's Disease (PD) is a chronic neurodegenerative disease associated with motor problems such as gait impairment. Different systems based on 3D cameras, accelerometers or gyroscopes have been used in related works in order to study gait disturbances in PD. Kinect Ⓡ has also been used to build these kinds of systems, but contradictory results have been reported: some works conclude that Kinect does not provide an accurate method of measuring gait kinematics variables, but others, on the contrary, report good accuracy results., Methods: In this work, we have built a Kinect-based system that can distinguish between different PD stages, and have performed a clinical study with 30 patients suffering from PD belonging to three groups: early PD patients without axial impairment, more evolved PD patients with higher gait impairment but without Freezing of Gait (FoG), and patients with advanced PD and FoG. Those patients were recorded by two Kinect devices when they were walking in a hospital corridor. The datasets obtained from the Kinect were preprocessed, 115 features identified, some methods were applied to select the relevant features (correlation based feature selection, information gain, and consistency subset evaluation), and different classification methods (decision trees, Bayesian networks, neural networks and K-nearest neighbours classifiers) were evaluated with the goal of finding the most accurate method for PD stage classification., Results: The classifier that provided the best results is a particular case of a Bayesian Network classifier (similar to a Naïve Bayesian classifier) built from a set of 7 relevant features selected by the correlation-based on feature selection method. The accuracy obtained for that classifier using 10-fold cross validation is 93.40%. The relevant features are related to left shin angles, left humerus angles, frontal and lateral bents, left forearm angles and the number of steps during spin., Conclusions: In this paper, it is shown that using Kinect is adequate to build a inexpensive and comfortable system that classifies PD into three different stages related to FoG. Compared to the results of previous works, the obtained accuracy (93.40%) can be considered high. The relevant features for the classifier are: a) movement and position of the left arm, b) trunk position for slightly displaced walking sequences, and c) left shin angle, for straight walking sequences. However, we have obtained a better accuracy (96.23%) for a classifier that only uses features extracted from slightly displaced walking steps and spin walking steps. Finally, the obtained set of relevant features may lead to new rehabilitation therapies for PD patients with gait problems.

Cognitive decline is one of the most frequent and disabling nonmotor features of Parkinson's disease. Around 30% of patients with Parkinson's disease experience mild cognitive impairment, a well-established risk factor for the development of dementia. However, mild cognitive impairment in patients with Parkinson's disease is a heterogeneous entity that involves different types and extents of cognitive deficits. Because it is not currently known which type of mild cognitive impairment confers a higher risk of progression to dementia, it would be useful to define biomarkers that could identify these patients to better study disease progression and possible interventions. In this sense, the identification among patients with Parkinson's disease and mild cognitive impairment of biomarkers associated with dementia would allow the early detection of this process. This review summarizes studies from the past 25 years that have assessed the potential biomarkers of dementia and mild cognitive impairment in Parkinson's disease patients. Despite the potential importance, no biomarker has as yet been validated. However, features such as low levels of epidermal and insulin-like growth factors or uric acid in plasma/serum and of Aß in CSF, reduction of cerebral cholinergic innervation and metabolism measured by PET mainly in posterior areas, and hippocampal atrophy in MRI might be indicative of distinct deficits with a distinct risk of dementia in subgroups of patients. Longitudinal studies combining the existing techniques and new approaches are needed to identify patients at higher risk of dementia. © 2016 International Parkinson and Movement Disorder Society., (© 2016 International Parkinson and Movement Disorder Society.)

Dissection of the internal carotid artery is a rare cause of stroke overall, but causes 22% of strokes in younger patients. A common clinical presentation is as Claude Bernard Horner syndrome. We report a craniotomy with 30 degrees rotation of the neck (standard position) in a patient with no major risk factors for carotid dissection, who showed a Pourfour du Petit syndrome due to a dissection of the internal carotid artery. To the best of our knowledge, this is the first reported case in which a common surgical position causes an internal carotid artery dissection in a patient without relevant risk factors. The presentation with Pourfour du Petit syndrome is extremely unusual.