Characterization of EEG Data Revealing Relationships With Cognitive and Motor Symptoms in Parkinson's Disease: A Systematic Review

Wang, Qing; Meng, Lin; Pang, Jun; Zhu, Xiaodong; Ming, Dong

doi:10.3389/fnagi.2020.587396

Cited by 46 publications

(32 citation statements)

References 88 publications

(188 reference statements)

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“…Preceding neuroimaging studies using SPECT have associated RTs slowness to nigrostriatal degeneration, and decreased glucose metabolism in the prefrontal cortex (Frings et al, 2020). In PD, changes are observed in multiple pathways involving various brain regions, mainly basal ganglia, thalamus, and prefrontal cortex (Wang et al, 2020). In this regard, the present results suggest that the basic nature of alertness may be important enough to justify deficits in the performance of more complex tasks, and could be conditioned to connectivity problems between the frontal lobe and the basal ganglia (Dirnberger and Jahanshahi, 2013; Szewczyk‐Krolikowski et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Components determining the slowness of information processing in parkinson’s disease

Arroyo-Ferrer

Periáñez

Ríos-Lago³

et al. 2021

Brain and Behavior

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Section: Discussionmentioning

confidence: 99%

Components determining the slowness of information processing in parkinson’s disease

Arroyo-Ferrer

Periáñez

Ríos-Lago³

et al. 2021

Brain and Behavior

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“…Abnormal ERPs can be observed in individuals with neurological conditions in which brain circuitry is impaired, and consequently show cognitive dysfunction. In line with this, ERP measures have yielded various electrophysiological markers representing cognitive impairment in PD (reviewed in [36,37]).…”

Section: Introductionmentioning

confidence: 91%

“…Previous studies showed differences between PD patients and HC in the AOB task, mainly in the P300 ERP components, which include P3a and P3b. The amplitude of the P3a component elicited by the Novel stimuli, which is thought to be mediated by dopaminergic activity PLOS ONE [65] and to reflect attention allocation towards potentially important changes in the environment, is attenuated in PD (reviewed in [36,37]). The P3b component elicited by the Target stimuli, reflects updating of working memory representations in the prefrontal cortex [39,65].…”

Section: Plos Onementioning

confidence: 99%

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Identification of an early-stage Parkinson’s disease neuromarker using event-related potentials, brain network analytics and machine-learning

et al. 2022

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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.

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“…In the reference literature, EEG signal analysis was historically used in different applications to assess the activation pathways and to understand basic mechanisms underlying motor functions [7]. This led to more recent specific studies focused on neuromotor rehabilitation for investigating how activity patterns change depending on the location of the cortical lesions and on different rehabilitation treatments in different diseases such as stroke [8], Parkinson's disease [9], and others. The evolution of the EEG signal analysis during the last decades shifted the focus from the time domain to the frequency domain analysis [10] with the more recent use of functional and effective connectivity approaches [11] to better understand neural network changes occurring in physiological and pathological conditions.…”

Section: Introductionmentioning

confidence: 99%

Combined Use of EMG and EEG Techniques for Neuromotor Assessment in Rehabilitative Applications: A Systematic Review

Brambilla

Pirovano

Mira

et al. 2021

Sensors

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Electroencephalography (EEG) and electromyography (EMG) are widespread and well-known quantitative techniques used for gathering biological signals at cortical and muscular levels, respectively. Indeed, they provide relevant insights for increasing knowledge in different domains, such as physical and cognitive, and research fields, including neuromotor rehabilitation. So far, EEG and EMG techniques have been independently exploited to guide or assess the outcome of the rehabilitation, preferring one technique over the other according to the aim of the investigation. More recently, the combination of EEG and EMG started to be considered as a potential breakthrough approach to improve rehabilitation effectiveness. However, since it is a relatively recent research field, we observed that no comprehensive reviews available nor standard procedures and setups for simultaneous acquisitions and processing have been identified. Consequently, this paper presents a systematic review of EEG and EMG applications specifically aimed at evaluating and assessing neuromotor performance, focusing on cortico-muscular interactions in the rehabilitation field. A total of 213 articles were identified from scientific databases, and, following rigorous scrutiny, 55 were analyzed in detail in this review. Most of the applications are focused on the study of stroke patients, and the rehabilitation target is usually on the upper or lower limbs. Regarding the methodological approaches used to acquire and process data, our results show that a simultaneous EEG and EMG acquisition is quite common in the field, but it is mostly performed with EMG as a support technique for more specific EEG approaches. Non-specific processing methods such as EEG-EMG coherence are used to provide combined EEG/EMG signal analysis, but rarely both signals are analyzed using state-of-the-art techniques that are gold-standard in each of the two domains. Future directions may be oriented toward multi-domain approaches able to exploit the full potential of combined EEG and EMG, for example targeting a wider range of pathologies and implementing more structured clinical trials to confirm the results of the current pilot studies.

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Characterization of EEG Data Revealing Relationships With Cognitive and Motor Symptoms in Parkinson's Disease: A Systematic Review

Cited by 46 publications

References 88 publications

Components determining the slowness of information processing in parkinson’s disease

Components determining the slowness of information processing in parkinson’s disease

Identification of an early-stage Parkinson’s disease neuromarker using event-related potentials, brain network analytics and machine-learning

Combined Use of EMG and EEG Techniques for Neuromotor Assessment in Rehabilitative Applications: A Systematic Review

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