Frequency Nesting Interactions in the Subthalamic Nucleus Correlate With the Step Phases for Parkinson’s Disease

Jin, Luyao; Shi, Wenbin; Chu-ting, Zhang; Yeh, Chien-Hung

doi:10.3389/fphys.2022.890753

Cited by 9 publications

(8 citation statements)

References 52 publications

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“…The emotion recognition accuracy of the proposed GC+ATD scheme can reach 84.91%, which is always better than DASM+RASM and DACU features with average improvements of 15.24% and 17.49%, respectively. Compared with the transfer entropy (TE) (Shi et al, 2018 ) and phase amplitude coupling (PAC) (Jin et al, 2022 ) features of EEG signal in the same-frequency bands and cross-frequency bands, the proposed ATD-GC feature can achieve an average improvement of 10.37 and 13.35%, respectively. In addition, compared with the traditional GC feature with the same-frequency band, the GC+ATD method has increased by 8.43%, this result further proves that the cross-frequency causal analysis of EEG signals can further improve the performance of emotion recognition.…”

Section: Resultsmentioning

confidence: 99%

EEG emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres

Zhang

Chen

et al. 2022

Front. Neurosci.

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EEG emotion recognition based on Granger causality (GC) brain networks mainly focus on the EEG signal from the same-frequency bands, however, there are still some causality relationships between EEG signals in the cross-frequency bands. Considering the functional asymmetric of the left and right hemispheres to emotional response, this paper proposes an EEG emotion recognition scheme based on cross-frequency GC feature extraction and fusion in the left and right hemispheres. Firstly, we calculate the GC relationship of EEG signals according to the frequencies and hemispheres, and mainly focus on the causality of the cross-frequency EEG signals in left and right hemispheres. Then, to remove the redundant connections of the GC brain network, an adaptive two-stage decorrelation feature extraction scheme is proposed under the condition of maintaining the best emotion recognition performance. Finally, a multi-GC feature fusion scheme is designed to balance the recognition accuracy and feature number of each GC feature, which comprehensively considers the influence of the recognition accuracy and computational complexity. Experimental results on the DEAP emotion dataset show that the proposed scheme can achieve an average accuracy of 84.91% for four classifications, which improved the classification accuracy by up to 8.43% compared with that of the traditional same-frequency band GC features.

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Section: Resultsmentioning

confidence: 99%

EEG emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres

Zhang

Chen

et al. 2022

Front. Neurosci.

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“…More specifically, MEMD involves applying a sinusoidal masking signal. Following this, a desired frequency for each band is selected to improve MEMD 41,42, thereby allowing each IMF to capture the dynamics of a distinct frequency band of interest. In this study, the frequency bands of interest were the alpha (α, 8-12 Hz), low beta (Lβ, 13-21 Hz), high beta (Hβ, 22-35 Hz), and gamma (γ ∼ 40Hz) bands.…”

Section: Methodsmentioning

confidence: 99%

Auditory cues modulate the short timescale dynamics of STN activity during stepping in Parkinson’s disease

Yeh,

Xu,

Shi

et al. 2023

Preprint

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BackgroundGait impairment has a major impact on motor performance and quality of life in patients with Parkinson’s disease (PD). The neurophysiological mechanisms of gait impairment remain poorly understood, meaning that treatment options are currently limited. It is believed that basal ganglia oscillatory activity at β frequencies (15-30 Hz) may be a contributor to gait impairment in PD, but the precise dynamics of this oscillatory activity during gait remain unclear. Auditory cues are known to lead to improvements in gait kinematics in PD. If the neurophysiological mechanisms of this cueing effect were better understood they could be leveraged to treat gait impairments using brain-computer interface (BCI) technologies.ObjectiveWe aimed to characterize the dynamics of subthalamic nucleus (STN) oscillatory activity during stepping movements in PD and to establish the neurophysiological mechanisms by which auditory cues modulate gait.MethodsWe used local field potentials (LFPs) to study STN oscillatory activity in eight PD patients while stepping in place with auditory cueing. Hidden Markov Models (HMMs) were used to discover dynamic brain states that occurred pre-sound, on-sound, and post-sound cues.ResultsThe occurrence of β bursts was suppressed during and after auditory cues. This manifested as a decrease in their fractional occupancy and state lifetimes. Interestingly, α transients showed the opposite effect, with fractional occupancy and state lifetimes increasing during and after auditory cues.ConclusionsWe found transient oscillatory states in the STN LFP during stepping and showed that α and β oscillations are differentially modulated by auditory cues during stepping in PD.

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“…Causality and connectivity measurements applied on the scalp EEG do not allow interpretation of the interacting brain sources, since the channel sites cannot be seen as approximations of the anatomical locations of sources, and then spurious connectivity can be detected between sensors on the scalp [ 60 , 61 , 62 , 63 , 64 ]. To overcome this issue, EEG source imaging has been widely applied over the past years to localize the anatomical sources (source space) of a given scalp measurement (sensor space) [ 65 , 66 , 67 , 68 , 69 , 70 , 71 ].…”

Section: Introductionmentioning

confidence: 99%

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

et al. 2023

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Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros–cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.

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Frequency Nesting Interactions in the Subthalamic Nucleus Correlate With the Step Phases for Parkinson’s Disease

Cited by 9 publications

References 52 publications

EEG emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres

EEG emotion recognition based on cross-frequency granger causality feature extraction and fusion in the left and right hemispheres

Auditory cues modulate the short timescale dynamics of STN activity during stepping in Parkinson’s disease

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

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