Diminished EEG habituation to novel events effectively classifies Parkinson’s patients

Cavanagh, James F.; Kumar, Praveen; Mueller, Andrea A.; Richardson, Sarah Pirio; Mueen, Abdullah

doi:10.1016/j.clinph.2017.11.023

Cited by 85 publications

(111 citation statements)

References 47 publications

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“…In order to overcome the challenges that the EROs cannot be extracted completely via the conventional TFA method because they are affected with each other or artifacts in the time, space, and frequency domains to some degree and the selection of the regions of the associated evoked EROs is subject to the experimenter regarding the time window and the frequency range. The matrix Z T ∈ R M ×N (i.e., time samples were variables in columns, and the other factors, such as channels, conditions, and subjects, were integrated into rows which can be labeled as observations) was first formed from the synthetic and real datasets separately to explore the component(s) of interest as the application in the previous investigations (Dien, 2012;Fogarty et al, 2019;Cavanagh et al, 2018;Barry et al, 2019). Next, t-PCA and Promax rotation were fulfilled to decompose this matrix into R components, and the components of interest were then selected to project to all of the scalp electrodes to correct their variances and polarities indeterminacy.…”

Section: Proposed Approach For Data Processingmentioning

confidence: 99%

Objective Extraction of Evoked Event-related Oscillations from Time-frequency Representation of Event-related Potentials

Zhang

Ristaniemi

Cong

2020

Preprint

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Evoked event-related oscillations (EROs) have been widely used to explore the mechanisms of brain activities for both normal people and neuropsychiatric disease patients. The selection of regions of evoked EROs tends to be subjectively based on the previous studies and the visual inspection of grand averaged time-frequency representations (TFRs) which causes some missing or redundant information. Meanwhile, the evoked EROs cannot be fully extracted via the conventional time-frequency analysis (TFA) method because they are sometimes overlapped with each other or with artifacts in time, frequency, and space domains to some extent. Hence, these shortcomings may pose some challenges to investigate the related neuronal processes. A data-driven approach was introduced to fill the gaps as below: extracting the temporal and spatial components of interest simultaneously by principal component analysis and Promax rotation and projecting them to the electrode field to correct their variance and polarity indeterminacy, calculating the TFRs of the back-projected components, and determining the regions of interest objectively using the edge detection algorithm. We performed this novel approach and the conventional TFA method in analyzing both a synthetic dataset and an actual ERP dataset in a two-factor simple gambling paradigm of waiting time (short/long) and feedback (loss/gain) separately. Synthetic dataset results indicated that N2-theta and P3-delta oscillations were detected using the proposed approach, but, by comparison, only one oscillation was obtained via the conventional TFA method. Furthermore, the actual ERP dataset results of P3-delta for our approach revealed that it was sensitive to the waiting time (which also was found in the previous reports) but not for that of the conventional TFA method. This study manifested that the proposed approach can objectively extract evoked EROs, which allows a better understanding of the modulations of the oscillatory responses.

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Section: Proposed Approach For Data Processingmentioning

confidence: 99%

Objective Extraction of Evoked Event-related Oscillations from Time-frequency Representation of Event-related Potentials

Zhang

Ristaniemi

Cong

2020

Preprint

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“…To conclude, given that also many previous studies have demonstrated high classification accuracy of PD based on EEG data 5,6,15,17 , the evidence suggests that EEG can be a valuable source for PD classification. The present results draw attention to the fact that measurement conditions can significantly affect classification performance.…”

Section: Discussionmentioning

confidence: 70%

“…PD "slows down" the EEG spectrum 4,21,26,28 . Studies which have classified PD patients and neurologically healthy age-matched controls based on linear features calculated from different frequency bands have yielded approximately 75-82% accuracy 5,6 . Building on the assumption that instead of reflecting linear stochastic processes, EEG is characterized by non-linear dynamics 18 , studies have also shown that non-linear methods provide valuable additional information when examining how EEG of PD patients and healthy controls differ 15,17,27 .…”

Section: Introductionmentioning

confidence: 99%

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Resting state EEG as a biomarker of Parkinson’s disease: Influence of measurement conditions

Railo

Suuronen

Kaasinen

et al. 2020

Preprint

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Resting state electroencephalographic (EEG) recording could provide cost-effective means to aid in the detection of neurological disorders such as Parkinson's disease (PD). We examined how many electrodes are needed for classification of PD based on EEG, which electrode locations provide most value for classification, and whether data recorded eyes open or closed yield comparable results. We used a nested cross-validated classifier which included a budgetbased search algorithm for selecting the optimal electrodes for classification. By iterating over variable budgets, we show that with eyes open recording, only 10 electrodes, localized over motor and occipital areas enable relatively accurate classification (AUC = .82) between PD patients (N=20) and age-matched healthy control participants (N=20). Classification accuracy only slightly increased when all 64 electrodes were included (AUC = .85). With the data recorded eyes closed, classification was not statistically significantly above chance even with full set of 64 electrodes (AUC = .55). These results show that classification based on small number of EEG electrodes is a promising tool for classifying PD, but measurement conditions and electrode locations can have a significant effect on classifier performance.

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“…Frontal midline theta can be elicited in simple tasks that are viable within a clinical environment. Aberrant auditory orienting responses have already been advanced as candidate biomarkers, like diminished mismatch negativity (MMN) in schizophrenia (Javitt, Lee, Kantrowitz, & Martinez, ; Light et al, ) or diminished novelty habituation in Parkinson’s disease (Cavanagh et al, ). The MMN is a theta‐dominant response with separable frontal and temporal processes (Fuentemilla, Marco‐Pallarés, Münte, & Grau, ; Ko et al, ) that may interact with theta‐band phase synchrony (Choi et al, ).…”

Section: Clinical Practicementioning

confidence: 99%

Electrophysiology as a theoretical and methodological hub for the neural sciences

Cavanagh

2018

Psychophysiology

Self Cite

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Electrophysiology is a direct measure of neuronal processes, and it is uniquely sensitive to canonical neural operations that underlie emergent psychological operations. These qualities make it well suited for discovery of aberrant neural mechanisms that underlie complicated disease states. This technique is routinely utilized in vitro, in vivo, and in outpatient neurological clinics, offering a translatable bridge between animal models and human patients. The bench-to-bedside potential of this approach is unparalleled, yet it also remains undeveloped due to the slow inertia of legacy techniques and interpretations. In this review, I discuss these strengths of the method, and I detail compelling reasons why future advancements can have a direct and tangible influence over clinical practice. I hope to motivate a blurring of traditional boundaries between preclinical, computational, imaging, and clinical fields by advancing electrophysiology as a common hub for methodological integration and theoretical advancement.

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Diminished EEG habituation to novel events effectively classifies Parkinson’s patients

Abstract: These findings suggest that EEG responses to novel events in this rapid, simple, and inexpensive test have tremendous promise for tracking individual trajectories of cognitive dysfunction in Parkinson's disease.

Cited by 85 publications

References 47 publications

Objective Extraction of Evoked Event-related Oscillations from Time-frequency Representation of Event-related Potentials

Objective Extraction of Evoked Event-related Oscillations from Time-frequency Representation of Event-related Potentials

Resting state EEG as a biomarker of Parkinson’s disease: Influence of measurement conditions

Electrophysiology as a theoretical and methodological hub for the neural sciences

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