Electroencephalography-based machine learning for cognitive profiling in Parkinson's disease:Preliminary results

Background Cognitive symptoms are common in patients with Parkinson's disease. Characterization of a patient's cognitive profile is an essential step toward the identification of predictors of cognitive worsening. Objective The aim of this study was to investigate the use of the combination of resting-state EEG and data-mining techniques to build characterization models. Methods Dense EEG data from 118 patients with Parkinson's disease, classified into 5 different groups according to the severity of their cognitive impairments, were considered. Spectral power analysis within 7 frequency bands was performed on the EEG signals. The obtained quantitative EEG features of 100 patients were mined using 2 machine-learning algorithms to build and train characterization models, namely, support vector machines and k-nearest neighbors models. The models were then blindly tested on data from 18 patients. Results The overall classification accuracies were 84% and 88% for the support vector machines and k-nearest algorithms, respectively. The worst classifications were observed for patients from groups with small sample sizes, corresponding to patients with the severe cognitive deficits. Whereas for the remaining groups for whom an accurate diagnosis was required to plan the future healthcare, the classification was very accurate. Conclusion These results suggest that EEG features computed from a daily clinical practice exploration modality in-that it is nonexpensive, available anywhere, and requires minimal cooperation from the patient-can be used as a screening method to identify the severity of cognitive impairment in patients with Parkinson's disease. (c) 2018 International Parkinson and Movement Disorder Society</p

Brain metabolic abnormalities during gait with freezing in Parkinson’s disease

International audienc

Anticipating Tomorrow: Tailoring Parkinson's Symptomatic Therapy Using Predictors of Outcome

Background: Although research into Parkinson's disease (PD) subtypes and outcome predictions has continued to advance, recommendations for using outcome prediction to guide current treatment decisions remain sparse. Objectives: To provide expert opinion‐based recommendations for individually tailored PD symptomatic treatment based on knowledge of risk prediction and subtypes. Methods: Using a modified Delphi approach, members of the Movement Disorders Society (MDS) Task Force on PD subtypes generated a series of general recommendations around the question: “Using what you know about genetic/biological/clinical subtypes (or any individual‐level predictors of outcome), what advice would you give for selecting symptomatic treatments for an individual patient now, based on what their subtype or individual characteristics predict about their future disease course?” After four iterations and revisions, those recommendations with over 75% endorsement were adopted. Results: A total of 19 recommendations were endorsed by a group of 13 panelists. The recommendations primarily centered around two themes: (1) incorporating future risk of cognitive impairment into current treatment plans; and (2) identifying future symptom clusters that might be forestalled with a single medication. Conclusions: These recommendations provide clinicians with a framework for integrating future outcomes into patient‐specific treatment choices. They are not prescriptive guidelines, but adaptable suggestions, which should be tailored to each individual. They are to be considered as a first step of a process that will continue to evolve as additional stakeholders provide new insights and as new information becomes available. As individualized risk prediction advances, the path to better tailored treatment regimens will become clearer

Comparative Gene Expression Analysis throughout the Life Cycle of Leishmania braziliensis: Diversity of Expression Profiles among Clinical Isolates

Leishmania is a group of parasites (Protozoa, Trypanosomatidae) responsible for a wide spectrum of clinical forms. Among the factors explaining this phenotypic polymorphism, parasite features are important contributors. One approach to identify them consists in characterizing the gene expression profiles throughout the life cycle. In a recent study, the transcriptome of 3 Leishmania species was compared and this revealed species-specific differences, albeit in a low number. A key issue, however, is to ensure that the observed differences are indeed species-specific and not specific of the strains selected for representing the species. In order to illustrate the relevance of this concern, we analyzed here the gene expression profiles of 5 clinical isolates of L. braziliensis at seven time points of the life cycle. Our results clearly illustrate the unique character of each isolate in terms of gene expression dynamics: one Leishmania strain is not necessarily representative of a given species

Brief Communication External globus pallidus stimulation modulates brain connectivity in Huntington&apos;s disease

Positron emission tomography with O-15-labeled water was used to study at rest the neurophysiological effects of bilateral external globus pallidus (GPe) deep brain stimulation in patients with Huntington&apos;s disease (HD). Five patients were compared with a control group in the on and off states of the stimulator. External globus pallidus stimulation decreased neuronal activity and modulated cerebral connectivity within the basal ganglia-thalamocortical circuitry, the sensorimotor, and the default-mode networks. These data indicate that GPe stimulation modulates functional integration in HD patients in accordance with the basal ganglia-thalamocortical circuit model

Role of Basal Ganglia Circuits in Resisting Interference by Distracters: A swLORETA Study

BACKGROUND: The selection of task-relevant information requires both the focalization of attention on the task and resistance to interference from irrelevant stimuli. Both mechanisms rely on a dorsal frontoparietal network, while focalization additionally involves a ventral frontoparietal network. The role of subcortical structures in attention is less clear, despite the fact that the striatum interacts significantly with the frontal cortex via frontostriatal loops. One means of investigating the basal ganglia's contributions to attention is to examine the features of P300 components (i.e. amplitude, latency, and generators) in patients with basal ganglia damage (such as in Parkinson's disease (PD), in which attention is often impaired). Three-stimulus oddball paradigms can be used to study distracter-elicited and target-elicited P300 subcomponents. METHODOLOGY/PRINCIPAL FINDINGS: In order to compare distracter- and target-elicited P300 components, high-density (128-channel) electroencephalograms were recorded during a three-stimulus visual oddball paradigm in 15 patients with early PD and 15 matched healthy controls. For each subject, the P300 sources were localized using standardized weighted low-resolution electromagnetic tomography (swLORETA). Comparative analyses (one-sample and two-sample t-tests) were performed using SPM5® software. The swLORETA analyses showed that PD patients displayed fewer dorsolateral prefrontal (DLPF) distracter-P300 generators but no significant differences in target-elicited P300 sources; this suggests dysfunction of the DLPF cortex when the executive frontostriatal loop is disrupted by basal ganglia damage. CONCLUSIONS/SIGNIFICANCE: Our results suggest that the cortical attention frontoparietal networks (mainly the dorsal one) are modulated by the basal ganglia. Disruption of this network in PD impairs resistance to distracters, which results in attention disorders

Identification of genetic variants associated with Huntington's disease progression: a genome-wide association study

Background Huntington's disease is caused by a CAG repeat expansion in the huntingtin gene, HTT. Age at onset has been used as a quantitative phenotype in genetic analysis looking for Huntington's disease modifiers, but is hard to define and not always available. Therefore, we aimed to generate a novel measure of disease progression and to identify genetic markers associated with this progression measure. Methods We generated a progression score on the basis of principal component analysis of prospectively acquired longitudinal changes in motor, cognitive, and imaging measures in the 218 indivduals in the TRACK-HD cohort of Huntington's disease gene mutation carriers (data collected 2008–11). We generated a parallel progression score using data from 1773 previously genotyped participants from the European Huntington's Disease Network REGISTRY study of Huntington's disease mutation carriers (data collected 2003–13). We did a genome-wide association analyses in terms of progression for 216 TRACK-HD participants and 1773 REGISTRY participants, then a meta-analysis of these results was undertaken. Findings Longitudinal motor, cognitive, and imaging scores were correlated with each other in TRACK-HD participants, justifying use of a single, cross-domain measure of disease progression in both studies. The TRACK-HD and REGISTRY progression measures were correlated with each other (r=0·674), and with age at onset (TRACK-HD, r=0·315; REGISTRY, r=0·234). The meta-analysis of progression in TRACK-HD and REGISTRY gave a genome-wide significant signal (p=1·12 × 10−10) on chromosome 5 spanning three genes: MSH3, DHFR, and MTRNR2L2. The genes in this locus were associated with progression in TRACK-HD (MSH3 p=2·94 × 10−8 DHFR p=8·37 × 10−7 MTRNR2L2 p=2·15 × 10−9) and to a lesser extent in REGISTRY (MSH3 p=9·36 × 10−4 DHFR p=8·45 × 10−4 MTRNR2L2 p=1·20 × 10−3). The lead single nucleotide polymorphism (SNP) in TRACK-HD (rs557874766) was genome-wide significant in the meta-analysis (p=1·58 × 10−8), and encodes an aminoacid change (Pro67Ala) in MSH3. In TRACK-HD, each copy of the minor allele at this SNP was associated with a 0·4 units per year (95% CI 0·16–0·66) reduction in the rate of change of the Unified Huntington's Disease Rating Scale (UHDRS) Total Motor Score, and a reduction of 0·12 units per year (95% CI 0·06–0·18) in the rate of change of UHDRS Total Functional Capacity score. These associations remained significant after adjusting for age of onset. Interpretation The multidomain progression measure in TRACK-HD was associated with a functional variant that was genome-wide significant in our meta-analysis. The association in only 216 participants implies that the progression measure is a sensitive reflection of disease burden, that the effect size at this locus is large, or both. Knockout of Msh3 reduces somatic expansion in Huntington's disease mouse models, suggesting this mechanism as an area for future therapeutic investigation