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Objective The purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson’s disease from healthy brain function using electroencephalography (EEG) event-related potentials (ERPs) in combination with Brain Network Analytics (BNA) technology and machine learning (ML) algorithms. Background Currently, diagnosis of PD depends mainly on motor signs and symptoms. However, there is need for biomarkers that detect PD at an earlier stage to allow intervention and monitoring of potential disease-modifying therapies. Cognitive impairment may appear before motor symptoms, and it tends to worsen with disease progression. While ERPs obtained during cognitive tasks performance represent processing stages of cognitive brain functions, they have not yet been established as sensitive or specific markers for early-stage PD. Methods Nineteen PD patients (disease duration of ≤2 years) and 30 healthy controls (HC) underwent EEG recording while performing visual Go/No-Go and auditory Oddball cognitive tasks. ERPs were analyzed by the BNA technology, and a ML algorithm identified a combination of features that distinguish early PD from HC. We used a logistic regression classifier with a 10-fold cross-validation. Results The ML algorithm identified a neuromarker comprising 15 BNA features that discriminated early PD patients from HC. The area-under-the-curve of the receiver-operating characteristic curve was 0.79. Sensitivity and specificity were 0.74 and 0.73, respectively. The five most important features could be classified into three cognitive functions: early sensory processing (P50 amplitude, N100 latency), filtering of information (P200 amplitude and topographic similarity), and response-locked activity (P-200 topographic similarity preceding the motor response in the visual Go/No-Go task). Conclusions This pilot study found that BNA can identify patients with early PD using an advanced analysis of ERPs. These results need to be validated in a larger PD patient sample and assessed for people with premotor phase of PD.

Deep sequencing was used to determine complete nucleotide sequences of echovirus 11 (EV11) strains isolated from a chronically infected patient with CVID as well as from cases of acute enterovirus infection. Phylogenetic analysis showed that EV11 strains that circulated in Israel in 1980-90s could be divided into four clades. EV11 strains isolated from a chronically infected individual belonged to one of the four clades and over a period of 4 years accumulated mutations at a relatively constant rate. Extrapolation of mutations accumulation curve into the past suggested that the individual was infected with circulating EV11 in the first half of 1990s. Genomic regions coding for individual viral proteins did not appear to be under strong selective pressure except for protease 3C that was remarkably conserved. This may suggest its important role in maintaining persistent infection.

OBJECTIVE: To evaluate how bilateral subthalamic nucleus deep brain stimulation (STN-DBS) affects visuo-motor coordination (VMC) in patients with Parkinson's disease (PD). BACKGROUND: VMC involves multi-sensory integration, motor planning, executive function and attention. VMC deficits are well-described in PD. STN-DBS conveys marked motor benefit in PD, but pyscho-cognitive complications are recognized and the effect on VMC is not known. METHODS: Thirteen PD patients with bilateral STN-DBS underwent neurological, cognitive, and mood assessment before VMC testing with optimal DBS stimulation parameters ('on-stimulation') and then, on the same day without any medication changes, after DBS silencing and establishing motor function deterioration ('off-stimulation'). Twelve age-matched healthy controls performed 2 successive VMC testing sessions, with a break of similar duration to that of the PD group. The computer cursor was controlled with a dome-shaped 'mouse' hidden from view that minimized tremor effects. Movement duration, hand velocity, tracking continuity, directional control variables, and feedback utilization variables were measured. MANOVA was performed on (1) clinically measured motor function, (2) VMC performance and (3) mood and attention, looking for main and interaction effects of: (1) group (controls/PD), (2) test-order (controls: first/second, PD: on-stimulation/off-stimulation), (3) path (sine/square/circle) and (4) hand (dominant/non-dominant). RESULTS: Unified PD Rating Scale (UPDRS) Part III worsened off-stimulation versus on-stimulation (mean: 42.3 versus 21.6, p = 0.02), as did finger tapping (p = 0.02), posture-gait (p = 0.01), upper limb function (p