Analysis of complexity and dynamic functional connectivity based on resting-state EEG in early Parkinson’s disease patients with mild cognitive impairment

Yi, Guosheng; Wang, Liufang; Chu, Chunguang; Liu, Chen; Zhu, Xiaodong; Shen, Xiao; Li, Zhen; Wang, Fei; Yang, Manyi

doi:10.1007/s11571-021-09722-w

Cited by 17 publications

(5 citation statements)

References 55 publications

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“…Recent advancements in fractal analysis have significantly enriched the study of EEG signals, especially in the context of neurodegenerative diseases [ 12 , 13 , 14 ]. While the application of the fractal dimension and the Hurst exponent have been applied in the context of neurodegenerative diseases [ 15 , 16 , 17 , 18 , 19 ], these metrics have often been applied in isolation, overlooking their potential synergy. The fractal dimension was interpreted as a measure of structural complexity [ 20 ], which quantifies the irregularity, intricacy, and self-similarity of a nonlinear complex system.…”

Section: Introductionmentioning

confidence: 99%

Multi-Threshold Recurrence Rate Plot: A Novel Methodology for EEG Analysis in Alzheimer’s Disease and Frontotemporal Dementia

Zheng,

Xiong,

Zhang

2024

Brain Sciences

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This study introduces Multi-Threshold Recurrence Rate Plots (MTRRP), a novel methodology for analyzing dynamic patterns in complex systems, such as those influenced by neurodegenerative diseases in brain activity. MTRRP characterizes how recurrence rates evolve with increasing recurrence thresholds. A key innovation of our approach, Recurrence Complexity, captures structural complexity by integrating local randomness and global structural features through the product of Recurrence Rate Gradient and Recurrence Hurst, both derived from MTRRP. We applied this technique to resting-state EEG data from patients diagnosed with Alzheimer’s Disease (AD), Frontotemporal Dementia (FTD), and age-matched healthy controls. The results revealed significantly higher recurrence complexity in the occipital areas of AD and FTD patients, particularly pronounced in the Alpha and Beta frequency bands. Furthermore, EEG features derived from MTRRP were evaluated using a Support Vector Machine with leave-one-out cross-validation, achieving a classification accuracy of 87.7%. These findings not only underscore the utility of MTRRP in detecting distinct neurophysiological patterns associated with neurodegenerative diseases but also highlight its broader applicability in time series analysis, providing a substantial tool for advancing medical diagnostics and research.

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Section: Introductionmentioning

confidence: 99%

Multi-Threshold Recurrence Rate Plot: A Novel Methodology for EEG Analysis in Alzheimer’s Disease and Frontotemporal Dementia

Zheng,

Xiong,

Zhang

2024

Brain Sciences

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“…In this study, we aimed to explore the possible relationships between temporal stabilities of large-scale brain dFCs and suicidality in MDD using the dynamic network model and a validated metric, the temporal correlation coefficient [ 12 , 21 , 22 , 23 ]. We firstly investigated the alterations in temporal correlation coefficients in MDD patients at the levels of whole brain and nine well-established key networks including the sensorimotor, visual, auditory, default-mode, frontoparietal, cingulo-opercular, salience, subcortical, and attention networks [ 24 , 25 ], respectively.…”

Section: Introductionmentioning

confidence: 99%

Temporal Stability of Dynamic Default Mode Network Connectivity Negatively Correlates with Suicidality in Major Depressive Disorder

Ouyang

Long

et al. 2022

Brain Sciences

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Previous studies have demonstrated that the suicidality in patients with major depressive disorder (MDD) is related to abnormal brain functional connectivity (FC) patterns. However, little is known about its relationship with dynamic functional connectivity (dFC) based on the assumption that brain FCs fluctuate over time. Temporal stabilities of dFCs within the whole brain and nine key networks were compared between 52 MDD patients and 21 age, sex-matched healthy controls (HCs) using resting-state functional magnetic resonance imaging and temporal correlation coefficients. The alterations in MDD were further correlated with the scores of suicidality item in the Hamilton Rating Scale for Depression (HAMD). Compared with HCs, the MDD patients showed a decreased temporal stability of dFC as indicated by a significantly decreased temporal correlation coefficient at the global level, as well as within the default mode network (DMN) and subcortical network. In addition, temporal correlation coefficients of the DMN were found to be significantly negatively correlated with the HAMD suicidality item scores in MDD patients. These results suggest that MDD may be characterized by excessive temporal fluctuations of dFCs within the DMN and subcortical network, and that decreased stability of DMN connectivity may be particularly associated with the suicidality in MDD.

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“…In [ 29 ], constant sliding window analysis is used to analyze the differences between 30 patients with early Parkinson’s with mild cognitive impairment and 37 patients with early Parkinson’s without mild cognitive impairment. In [ 29 ], window lengths were set to 500 to 2000 data points (samples) with a step of 10 samples. The overlap between windows was also set to 0–250 data points.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Connectivity Analysis Using Adaptive Window Size

Šverko

Vrankić

Vlahinić

et al. 2022

Sensors

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In this paper, we propose a new method to study and evaluate the time-varying brain network dynamics. The proposed RICI-imCPCC method (relative intersection of confidence intervals for the imaginary component of the complex Pearson correlation coefficient) is based on an adaptive window size and the imaginary part of the complex Pearson correlation coefficient. It reduces the weaknesses of the existing method of constant sliding window analysis with narrow and wide windows. These are the low temporal precision and low reliability for short connectivity periods for wide windows, and high susceptibility to noise for narrow windows, all resulting in low estimation accuracy. The proposed method overcomes these shortcomings by dynamically adjusting the window width using the RICI rule, which is based on the statistical properties of the area around the observed sample. In this paper, we compare the RICI-imCPCC with the existing constant sliding window analysis method and describe its advantages. First, the mathematical principles are established. Then, the comparison between the existing and the proposed method using synthetic and real electroencephalography (EEG) data is presented. The results show that the proposed RICI-imCPCC method has improved temporal resolution and estimation accuracy compared to the existing method and is less affected by the noise. The estimation error energy calculated for the RICI-imCPCC method on synthetic signals was lower by a factor of 1.22 compared to the error of the constant sliding window analysis using narrow window size imCPCC, by a factor of 2.87 compared to using wide window size imCPCC, by a factor of 6.69 compared to using narrow window size wPLI, and by a factor of 4.72 compared to using wide window size wPLI. Analysis of the real signals shows the ability of the proposed method to detect a P300 response and to detect a decrease in dynamic connectivity due to desynchronization and blockage of mu-rhythms.

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Analysis of complexity and dynamic functional connectivity based on resting-state EEG in early Parkinson’s disease patients with mild cognitive impairment

Cited by 17 publications

References 55 publications

Multi-Threshold Recurrence Rate Plot: A Novel Methodology for EEG Analysis in Alzheimer’s Disease and Frontotemporal Dementia

Multi-Threshold Recurrence Rate Plot: A Novel Methodology for EEG Analysis in Alzheimer’s Disease and Frontotemporal Dementia

Temporal Stability of Dynamic Default Mode Network Connectivity Negatively Correlates with Suicidality in Major Depressive Disorder

Dynamic Connectivity Analysis Using Adaptive Window Size

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