Objective assessment of impulse control disorder in patients with Parkinson’s disease using a low-cost LEGO-like EEG headset: a feasibility study

Abstract

Background Patients with Parkinson’s disease (PD) can develop impulse control disorders (ICDs) while undergoing a pharmacological treatment for motor control dysfunctions with a dopamine agonist (DA). Conventional clinical interviews or questionnaires can be biased and may not accurately diagnose at the early stage. A wearable electroencephalogram (EEG)-sensing headset paired with an examination procedure can be a potential user-friendly method to explore ICD-related signatures that can detect its early signs and progression by reflecting brain activity. Methods A stereotypical Go/NoGo test that targets impulse inhibition was performed on 59 individuals, including healthy controls, patients with PD, and patients with PD diagnosed by ICDs. We conducted two Go/NoGo sessions before and after the DA-pharmacological treatment for the PD and ICD groups. A low-cost LEGO-like EEG headset was used to record concurrent EEG signals. Then, we used the event-related potential (ERP) analytical framework to explore ICD-related EEG abnormalities after DA treatment. Results After the DA treatment, only the ICD-diagnosed PD patients made more behavioral errors and tended to exhibit the deterioration for the NoGo N2 and P3 peak amplitudes at fronto-central electrodes in contrast to the HC and PD groups. Particularly, the extent of the diminished NoGo-N2 amplitude was prone to be modulated by the ICD scores at Fz with marginal statistical significance (r = − 0.34, p = 0.07). Conclusions The low-cost LEGO-like EEG headset successfully captured ERP waveforms and objectively assessed ICD in patients with PD undergoing DA treatment. This objective neuro-evidence could provide complementary information to conventional clinical scales used to diagnose ICD adverse effects.

Parkinson’s Disease Management via Wearable Sensors: A Systematic Review

“…The use of EEG sensors suggests the possibility of objectively studying the cognitive decline in a neurodegenerative disease like Parkinson's. Based on this idea, a customized low-cost LEGO-like EEG sensing headset coupled with an assessment procedure has been proposed in [85] that can detect early symptoms and advancement in stages of PD by assessing brain activity and comparing responses with standard ICD indicating markers. Reference [36] is a comprehensive, fully integrated proposal of a system involving data capture via multiple sensors and processing based on multiple sensing technologies.…”

Electroencephalogram-Driven Machine-Learning Scenario for Assessing Impulse Control Disorder Comorbidity in Parkinson’s Disease Using a Low-Cost, Custom LEGO-Like Headset

“…This study is a follow-up to a previous study [13] that preliminarily assessed temporal event-related potential (ERP) signatures of ICD severity in patients with PD using a simple univariate linear regression analysis. We followed the same experimental protocol and data-collection setup to obtain data from a few more subjects and then demonstrated the applicability of a multivariate machine-learning approach to assess PD and ICD comorbidities.…”

“…Human brain imaging and mapping techniques that analyze brain signals with or without task engagement of interest, such as functional magnetic resonance imaging (fMRI) [7,12] and electroencephalography (EEG) [5,8,13], may shed light on qualitatively and quantitatively probing the underlying neural mechanisms of active ICDs in patients with PD. Despite the unique merits of different imaging modalities in terms of spatial and temporal details, their reports share common evidence that alternation or abnormality of brain activation in the frontal lobe is associated with impairment of inhibitory control (i.e., cognitive and motor impulsivity) in patients with PD and ICD symptoms compared with patients with typical PD.…”

“…Inspired by encouraging ICD-relevant EEG endeavors [5,8,13] and remarkable success in machine-learning-assisted diagnosis in PD (particularly for cognitive impairments) [21][22][23], this study attempted to construct an EEG-driven machinelearning scenario to identify ICD symptoms in patients with PD and further predict ICD severity (i.e., an ICD-aware CAD system) in compliance with a generalizable subject-wise CV procedure. Unlike most of the previous machine learning-based studies focusing on different aspects of PD (e.g., diagnose, staging, MCI comorbidity), this study is the first one, to the best of our knowledge, that assesses DA-associated ICD comorbidities in patients with PD using a machine-learning scenario.…”

Effects of 6-Month Combined Physical Exercise and Cognitive Training on Neuropsychological and Neurophysiological Function in Older Adults with Subjective Cognitive Decline: A Randomized Controlled Trial

“…On the other hand, response inhibition requires topdown modulation of cognitive control to withhold motor responses to non-target stimuli. The N2 and P3 components were two obvious electrophysiological markers induced by successful inhibition to Nogo stimuli in a Go-Nogo task [25][26][27]. The N2 component peaks around 200 to 300 ms over the frontocentral region, and its amplitude is found to be larger in the responses to Nogo than Go stimuli; the P3 component, occurring around 300 to 600 ms after the stimulus onset, is observed in the parietal region in responses to Go stimuli and in the frontocentral region in responses to Nogo stimuli [28,29].…”