Complexity analysis of brain activity in attention-deficit/hyperactivity disorder: A multiscale entropy analysis

Li, Chenxi; Chen, Yanni; Li, Youjun; Wang, Jue; Liu, Tian

doi:10.1016/j.brainresbull.2016.03.007

Cited by 47 publications

(26 citation statements)

References 39 publications

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“…(Color figure online) Cogn Neurodyn (2017) 11:147-160 155 three studies are in accordance with our predictions obtained by PE or OI measures, which demonstrate that the resting EEG/MEG activities in PD patients present an overall loss of complexity compared to controls. Such predictions also provide further support for the proposal that a loss of complexity appears when the nervous system becomes functionally impaired (Stam et al 1994(Stam et al , 1995Akar et al 2015Akar et al , 2016Chenxi et al 2016;Gao et al 2011;Darbin et al 2013;Müller et al 2001;Keller et al, 2014). In addition to above literatures, there have also been studies on the complexity of EEG rhythms in different subbands.…”

Section: Discussionsupporting

confidence: 68%

“…Further, nonlinear analysis of brain activity has also revealed reliable information about the underlying dynamics related to cognitive process and brain pathology (Akar et al 2015(Akar et al , 2016Chenxi et al 2016;Bhattacharya and Lee 2016;Gao et al 2011). One common method was to characterize the complexity of the recorded neurosignals, since it carried important information about the cortical or subcortical dynamics (Akar et al 2015(Akar et al , 2016Chenxi et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…One common method was to characterize the complexity of the recorded neurosignals, since it carried important information about the cortical or subcortical dynamics (Akar et al 2015(Akar et al , 2016Chenxi et al 2016). Such nonlinear methods were different from spectral analysis, since they belonged to information theory or dynamical system theory (such as, chaos or fractal).…”

Section: Introductionmentioning

confidence: 99%

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Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease

Wang

Deng

et al. 2016

Cogn Neurodyn

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To investigate the abnormal brain activities in the early stage of Parkinson's disease (PD), the electroencephalogram (EEG) signals were recorded with 20 channels from non-dementia PD patients (18 patients, 8 females) and age matched healthy controls (18 subjects, 8 females) during the resting state. Two methods based on the ordinal patterns of the recorded series, i.e., permutation entropy (PE) and order index (OI), were introduced to characterize the complexity of the cortical activities for two groups. It was observed that the resting-state EEG of PD patients showed lower PE and higher OI than healthy controls, which indicated that the early-stage PD caused the reduced complexity of EEG. We further applied two methods to determine the complexity of EEG rhythms in five sub-bands. The results showed that the gamma, beta and alpha rhythms of PD patients were characterized by lower PE and higher OI, i.e., reduced complexity, than healthy subjects. No significant differences were observed in theta or delta rhythms between two groups. The findings suggested that PE and OI were promising methods to detect the abnormal changes in the dynamics of EEG signals associated with early-stage PD. Further, such changes in EEG complexity may be the early markers of the cortical or subcortical dysfunction caused by PD.

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Section: Discussionsupporting

confidence: 68%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease

Wang

Deng

et al. 2016

Cogn Neurodyn

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“…The alteration in the development of normal brain connectivity may lead to a variety of developmental/neuropsychiatric disorders, such as ASD and attention deficient/hyperactivity disorders (ADHD) [40]. Studies comparing MSE between normally-developing children and children with neuropsychiatric disorders have revealed information with potential diagnostic, therapeutic, or prognostic value [9][10][11]18,19].…”

Section: Developmental and Neuropsychiatric Disordersmentioning

confidence: 99%

“…EEG complexity had also been used to investigate the functional deficits in children with ADHD [18]. In an adult study of EEG complexity during visual tasks, the SamEn and MSE were increased in temporo-occipital regions at a higher attention level, especially at small scale factors, and showed better classification accuracy compared with the θ/β ratio [17].…”

Section: Attention-deficit/hyperactivity Disordermentioning

confidence: 99%

The Potential Application of Multiscale Entropy Analysis of Electroencephalography in Children with Neurological and Neuropsychiatric Disorders

Chu

Chang

Shieh

et al. 2017

Entropy

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Electroencephalography (EEG) is frequently used in functional neurological assessment of children with neurological and neuropsychiatric disorders. Multiscale entropy (MSE) can reveal complexity in both short and long time scales and is more feasible in the analysis of EEG. Entropy-based estimation of EEG complexity is a powerful tool in investigating the underlying disturbances of neural networks of the brain. Most neurological and neuropsychiatric disorders in childhood affect the early stage of brain development. The analysis of EEG complexity may show the influences of different neurological and neuropsychiatric disorders on different regions of the brain during development. This article aims to give a brief summary of current concepts of MSE analysis in pediatric neurological and neuropsychiatric disorders. Studies utilizing MSE or its modifications for investigating neurological and neuropsychiatric disorders in children were reviewed. Abnormal EEG complexity was shown in a variety of childhood neurological and neuropsychiatric diseases, including autism, attention deficit/hyperactivity disorder, Tourette syndrome, and epilepsy in infancy and childhood. MSE has been shown to be a powerful method for analyzing the non-linear anomaly of EEG in childhood neurological diseases. Further studies are needed to show its clinical implications on diagnosis, treatment, and outcome prediction.

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Resting‐state brain entropy in right temporal lobe epilepsy and its relationship with alertness

et al. 2019

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BackgroundTo date, no functional MRI (fMRI) studies have focused on brain entropy in right temporal lobe epilepsy (rTLE) patients. Here, we characterized brain entropy (BEN) alterations in patients with rTLE using resting‐state functional MRI(rs‐fMRI) and explored the relationship between BEN and alertness.MethodThirty‐one rTLE patients and 33 controls underwent MRI scanning to investigate differences in BEN and resting‐state functional connectivity (rs‐FC) in regions of interest (ROIs) between patients and controls. Correlation analyses were performed to examine relationships between the BEN of each ROI and alertness reaction times (RTs) in rTLE patients.ResultsCompared with controls, the BEN of rTLE patients was significantly increased in the right middle temporal gyrus, inferior temporal gyrus, and other regions of the left hemisphere and significantly decreased in the right middle frontal gyrus and left supplementary motor area (p < .05). The rs‐FCs between the ROIs (at p < .01, with the left superior parietal lobule and right precentral gyrus defined as ROI1 and ROI2, respectively) and the whole brain showed an increasing trend in rTLE patients. In addition, the BEN of ROI2 was associated with the intrinsic alertness and phasic alertness RTs of patients with rTLE.ConclusionsOur findings suggest that BEN is altered in patients with rTLE and that decreased BEN in the right precentral gyrus is positively related to intrinsic and phasic alertness; the abnormal FC in the brain regions with altered entropy suggests a reconstruction of brain functional connectivity. These findings suggest that BEN mapping may provide a useful tool for probing brain mechanisms related to TLE.

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Complexity analysis of brain activity in attention-deficit/hyperactivity disorder: A multiscale entropy analysis

Cited by 47 publications

References 39 publications

Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease

Complexity of resting-state EEG activity in the patients with early-stage Parkinson’s disease

The Potential Application of Multiscale Entropy Analysis of Electroencephalography in Children with Neurological and Neuropsychiatric Disorders

Resting‐state brain entropy in right temporal lobe epilepsy and its relationship with alertness

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