Detrended fluctuation analysis of resting EEG in depressed outpatients and healthy controls

Lee, Jung Hoon; Yang, Byung-Hwan; Lee, Jang-Han; Choi, J. W.; Choi, Ihn-Geun; Kim, Saebyul

doi:10.1016/j.clinph.2007.08.001

Cited by 82 publications

(74 citation statements)

References 42 publications

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“…And the global coherence of the MDD group is significantly higher than that of the healthy group in the theta band (4-8 Hz). In alpha (8-13 Hz) and beta bands (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), there is no diacritic difference in both groups.…”

Section: Global Coherencementioning

confidence: 99%

“…Some results based on EEG oscillations demonstrated that patients with MDD had more frontal theta, alpha, and beta oscillations [18][19][20]. Using EEG oscillations, Lee et al [21] suggested that the brain affected by a major depressive disorder showed a slower decay of the long-range temporal (auto)correlations (LRTC). Recently, an EEG oscillations study on MDD reported that depressive brain was manifested in the superposition of distributed multiple oscillations [22].…”

Section: Complexitymentioning

confidence: 99%

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A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

Jing

et al. 2017

Complexity

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A large number of studies demonstrated that major depressive disorder (MDD) is characterized by the alterations in brain functional connections which is also identifiable during the brain's "resting-state." But, in the present study, the approach of constructing functional connectivity is often biased by the choice of the threshold. Besides, more attention was paid to the number and length of links in brain networks, and the clustering partitioning of nodes was unclear. Therefore, minimum spanning tree (MST) analysis and the hierarchical clustering were first used for the depression disease in this study. Resting-state electroencephalogram (EEG) sources were assessed from 15 healthy and 23 major depressive subjects. Then the coherence, MST, and the hierarchical clustering were obtained. In the theta band, coherence analysis showed that the EEG coherence of the MDD patients was significantly higher than that of the healthy controls especially in the left temporal region. The MST results indicated the higher leaf fraction in the depressed group. Compared with the normal group, the major depressive patients lost clustering in frontal regions. Our findings suggested that there was a stronger brain interaction in the MDD group and a left-right functional imbalance in the frontal regions for MDD controls.

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Section: Global Coherencementioning

confidence: 99%

Section: Complexitymentioning

confidence: 99%

A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

Jing

et al. 2017

Complexity

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“…As sample entropy cannot capture all details of neurodynamics, detrended fluctuation analysis (DFA) was applied to the same data. DFA is a method for determining the statistical self-affinity of a time series, it is widely used to evaluate the degree of complexity of electrophysiological signals by detecting long range correlations embedded in a nonstationary time series (Lee et al 2004(Lee et al , 2007. DFA needs longer data length, but is good at analyze fluctuations from local trends, therefore it can avoid artifacts from apparent long range correlations (Cirugeda-Roldan et al 2014).…”

Section: Discussionmentioning

confidence: 99%

Combined nonlinear metrics to evaluate spontaneous EEG recordings from chronic spinal cord injury in a rat model: a pilot study

Wang

et al. 2016

Cogn Neurodyn

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Spinal cord injury (SCI) is a high-cost disability and may cause permanent loss of movement and sensation below the injury location. The chance of cure in human after SCI is extremely limited. Instead, neural regeneration could have been seen in animals after SCI, and such regeneration could be retarded by blocking neural plasticity pathways, showing the importance of neural plasticity in functional recovery. As an indicator of nonlinear dynamics in the brain, sample entropy was used here in combination with detrended fluctuation analysis (DFA) and Kolmogorov complexity to quantify functional plasticity changes in spontaneous EEG recordings of rats before and after SCI. The results showed that the sample entropy values were decreased at the first day following injury then gradually increased during recovery. DFA and Kolmogorov complexity results were in consistent with sample entropy, showing the complexity of the EEG time series was lost after injury and partially regained in 1 week. The tendency to regain complexity is in line with the observation of behavioral rehabilitation. A critical time point was found during the recovery process after SCI. Our preliminary results suggested that the combined use of these nonlinear dynamical metrics could provide a quantitative and predictive way to assess the change of neural plasticity in a spinal cord injury rat model.

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“…While the field is too broad to comprehensively review for the scope of this report, we will discuss one of the most frequently utilized methods for analyzing time series with scalefree dynamics, the detrended fluctuation analysis (DFA) [18], which has also been extensively utilized on human EEG signals [16,[19][20][21][22][23]. DFA is an efficient technique to assess monofractal power-law scaling in the presence of nonstationary trends in the data [24].…”

Section: Introductionmentioning

confidence: 99%

“…DFA is an efficient technique to assess monofractal power-law scaling in the presence of nonstationary trends in the data [24]. DFA (combined with frequency filtering) has been shown to be useful as a tool to characterize differences in brain states in depression [21], sleep stages [19], and in hypnosis [20]. However, the application of DFA to EEG has also been generally limited to frequency-filtered portions of the EEG signal, due to the presence of different scaling regimes in the unfiltered signals [8].…”

Section: Introductionmentioning

confidence: 99%

Multifractal detrended fluctuation analysis of human EEG: preliminary investigation and comparison with the wavelet transform modulus maxima technique

Zorick¹,

Mandelkern²

2013

PsycEXTRA Dataset

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Detrended fluctuation analysis of resting EEG in depressed outpatients and healthy controls

Cited by 82 publications

References 42 publications

A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

Combined nonlinear metrics to evaluate spontaneous EEG recordings from chronic spinal cord injury in a rat model: a pilot study

Multifractal detrended fluctuation analysis of human EEG: preliminary investigation and comparison with the wavelet transform modulus maxima technique

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