scholarly journals The Use of Event-Related Potentials and Machine Learning to Improve Diagnostic Testing and Prediction of Disease Progression in Parkinson’s Disease

2021 ◽  
Author(s):  
Robin Vlieger ◽  
Elena Daskalaki ◽  
Deborah Apthorp ◽  
Christian J Lueck ◽  
Hanna Suominen
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Progression ◽  
Objective Assessment ◽  
Diagnostic Testing ◽  
Event Related Potentials ◽  
Disease Status ◽  
Related Potentials ◽  
Machine Learning Models

Current tests of disease status in Parkinson’s disease suffer from high variability, limiting their ability to determine disease severity and prognosis. Event-related potentials, in conjunction with machine learning, may provide a more objective assessment. In this study, we will use event-related potentials to develop machine learning models, aiming to provide an objective way to assess disease status and predict disease progression in Parkinson’s disease.

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

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0261947
Author(s):  
Sharon Hassin-Baer ◽  
Oren S. Cohen ◽  
Simon Israeli-Korn ◽  
Gilad Yahalom ◽  
Sandra Benizri ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Stage ◽  
Event Related Potentials ◽  
Brain Network ◽  
Cognitive Tasks ◽  
Network Analytics ◽  
Related Potentials ◽  
Advanced Analysis

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.

Machine learning models for the differential diagnosis of vascular parkinsonism and Parkinson’s disease using [123I]FP-CIT SPECT

2014 ◽  
Vol 42 (1) ◽  
pp. 112-119 ◽  
Author(s):  
I. Huertas-Fernández ◽  
F. J. García-Gómez ◽  
D. García-Solís ◽  
S. Benítez-Rivero ◽  
V. A. Marín-Oyaga ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Differential Diagnosis ◽  
Learning Models ◽  
Vascular Parkinsonism ◽  
Machine Learning Models

Habituation of event-related potentials in patients with Parkinson's disease

2000 ◽  
Vol 68 (5) ◽  
pp. 741-747 ◽  
Author(s):  
Chunhui Jiang ◽  
Yumiko Kaseda ◽  
Rumi Kumagai ◽  
Yoko Nakano ◽  
Shigenobu Nakamura
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Event Related Potentials ◽  
Related Potentials

scholarly journals A Neurophysiological Study of Semantic Processing in Parkinson’s Disease

2016 ◽  
Vol 23 (1) ◽  
pp. 78-89 ◽  
Author(s):  
Anthony J. Angwin ◽  
Nadeeka N.W. Dissanayaka ◽  
Alison Moorcroft ◽  
Katie L. McMahon ◽  
Peter A. Silburn ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Semantic Processing ◽  
Time Course ◽  
Event Related Potentials ◽  
Mean Amplitude ◽  
Repetition Effects ◽  
Related Potentials ◽  
The Mean ◽  
Written Word

AbstractObjectives: Cognitive-linguistic impairments in Parkinson’s disease (PD) have been well documented; however, few studies have explored the neurophysiological underpinnings of semantic deficits in PD. This study investigated semantic function in PD using event-related potentials. Methods: Eighteen people with PD and 18 healthy controls performed a semantic judgement task on written word pairs that were either congruent or incongruent. Results: The mean amplitude of the N400 for new incongruent word pairs was similar for both groups, however the onset latency was delayed in the PD group. Further analysis of the data revealed that both groups demonstrated attenuation of the N400 for repeated incongruent trials, as well as attenuation of the P600 component for repeated congruent trials. Conclusions: The presence of N400 congruity and N400 repetition effects in the PD group suggests that semantic processing is generally intact, but with a slower time course as evidenced by the delayed N400. Additional research will be required to determine whether N400 and P600 repetition effects are sensitive to further cognitive decline in PD. (JINS, 2017, 23, 78–89)

scholarly journals Topographic Mapping of P300 and Frontal Cognitive Function in Parkinson’s Disease

2000 ◽  
Vol 12 (3) ◽  
pp. 143-148 ◽  
Author(s):  
Mutsumi Iijima ◽  
Mikio Osawa ◽  
Makoto Iwata ◽  
Akiko Miyazaki ◽  
Hideaki Tei
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Frontal Lobe ◽  
Event Related Potentials ◽  
Wisconsin Card Sorting Test ◽  
P300 Amplitude ◽  
Oddball Paradigm ◽  
P300 Latency ◽  
Card Sorting ◽  
Related Potentials

The purpose of this study was to evaluate the relationship between P300 that is one of the event-related potentials and frontal cognitive functions in Parkinson’s disease (PD) without clinically apparent dementia.Subjects were 20 PD cases 48 to 79 years of age, all of whom were within normal limits on the Mini-Mental State examination, and 55 age-matched healthy adults.P300 was elicited with an auditory oddball paradigm and recorded at 15 sites on the scalp. Cognitive functioning of the frontal lobe was evaluated using the New Modified Wisconsin Card Sorting Test (WCST) and the Letter Pick-Out Test (LPOT) which reflects selective attention and semantic categorization.P300 latency was delayed in 30.0% of P300 demonstrated abnormal distribution in 20.0%. the WCST and the LPOT were abnormal in 15.0%, P300 latency significantly correlated with number of subcategories achieved on the WCST. P300 amplitude correlated with scores on the LPOT. These results suggest that cognitive dysfunction which linked partly to the frontal lobe might begin in PD even without clinically apparent dementia.

Event-related potentials reveal memory deficits in Parkinson's disease

1999 ◽  
Vol 8 (2) ◽  
pp. 165-172 ◽  
Author(s):  
Hisao Tachibana ◽  
Yasushi Miyata ◽  
Masanaka Takeda ◽  
Minoru Sugita ◽  
Tsunetaka Okita
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Event Related Potentials ◽  
Memory Deficits ◽  
Related Potentials

scholarly journals Longitudinal clustering analysis and prediction of Parkinson’s disease progression using radiomics and hybrid machine learning

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Mohammad R. Salmanpour ◽  
Mojtaba Shamsaei ◽  
Ghasem Hajianfar ◽  
Hamid Soltanian-Zadeh ◽  
Arman Rahmim
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Progression ◽  
Clustering Analysis ◽  
Hybrid Machine
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