Comparing EEG Nonlinearity in Deficit and Nondeficit Schizophrenia Patients: Preliminary Data

Cerquera, Alexander; Gjini, Klevest; Bowyer, Susan M.; Boutros, Nash N.

doi:10.1177/1550059417715388

Cited by 15 publications

(11 citation statements)

References 42 publications

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“…The data presented here are part of a larger study to probe the electrophysiological deviations associated with the DS syndrome. Evidence of decreased dimensionality in DS patients (signal complexity) was reported by Cerquera et al 21 and evidence for evoked potential deviations in the same group were also reported under a separate cover. 22 Schizophrenia patients were recruited from the outpatient clinics of Wayne State University, Department of Psychiatry and Behavioral Neurosciences.…”

Section: Introductionmentioning

confidence: 62%

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Deficit Versus Nondeficit Schizophrenia: An MEG-EEG Investigation of Resting State and Source Coherence—Preliminary Data

et al. 2019

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This study investigated the magneto- and electroencephalography (MEG and EEG, respectively) resting state to identify the deviations closely associated with the deficit syndrome (DS) in schizophrenia patients. Ten subjects in each group (control, DS, and nondeficit schizophrenia [NDS]) were included. Subjects underwent MEG-EEG recordings during a resting state condition. MEG coherence source imaging (CSI) in source space and spectral analysis in sensor space were performed. Significant differences were found between the 2 patient groups: (1) MEG and EEG spectral analysis showed significantly higher power at low frequencies (delta band) at sensor space in DS compared with NDS patients; (2) source analysis revealed larger power in the DS compared with NDS group at low frequencies in the frontal region; (3) NDS patients showed significantly higher MEG signal relative power in beta bands in sensor space compared with DS patients; (4) both DS and NDS patients showed higher EEG absolute power at higher beta band compared to controls; and (5) patients with DS were found to have a significantly higher MEG CSI than controls in the beta frequency band. These data support the observation of increased power in the low-frequency EEG/MEG rhythms associated with the DS. Increased power in the beta rhythms was more associated with the NDS.

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Section: Introductionmentioning

confidence: 62%

“…6. Fast Fourier transform on artifact-free epochs (total length of about 5-7 minutes) to obtain spectral power (absolute and relative) at different bands (delta[1][2][3][4], theta[4][5][6][7][8], alpha[8][9][10][11][12], beta1[12][13][14][15][16][17][18][19][20], beta2[20][21][22][23][24][25][26][27][28][29][30], and gamma).…”

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confidence: 99%

Deficit Versus Nondeficit Schizophrenia: An MEG-EEG Investigation of Resting State and Source Coherence—Preliminary Data

et al. 2019

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“…In the present study, we further found that FuzzyEn values in the frontal ROI (FZ and AF3) showed positive correlations with PANSSP and PANSSN. Higher FuzzyEn values and higher PANSSP or PANSSN scores indicated a more serious disease (Kay et al, 1987; Leucht et al, 2005; Cerquera et al, 2017). These findings indicated that the higher complexity of EEG signals was associated with the more serious clinical symptoms.…”

Section: Discussionmentioning

confidence: 99%

Abnormal Entropy Modulation of the EEG Signal in Patients With Schizophrenia During the Auditory Paired-Stimulus Paradigm

Xiang

Niu

Yan

et al. 2019

Front. Neuroinform.

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The complexity change in brain activity in schizophrenia is an interesting topic clinically. Schizophrenia patients exhibit abnormal task-related modulation of complexity, following entropy of electroencephalogram (EEG) analysis. However, complexity modulation in schizophrenia patients during the sensory gating (SG) task, remains unknown. In this study, the classical auditory paired-stimulus paradigm was introduced to investigate SG, and EEG data were recorded from 55 normal controls and 61 schizophrenia patients. Fuzzy entropy (FuzzyEn) was used to explore the complexity of brain activity under the conditions of baseline (BL) and the auditory paired-stimulus paradigm (S1 and S2). Generally, schizophrenia patients showed significantly higher FuzzyEn values in the frontal and occipital regions of interest (ROIs). Relative to the BL condition, the normalized values of FuzzyEn of normal controls were decreased greatly in condition S1 and showed less variance in condition S2. Schizophrenia patients showed a smaller decrease in the normalized values in condition S1. Moreover, schizophrenia patients showed significant diminution in the suppression ratios of FuzzyEn, attributed to the higher FuzzyEn values in condition S1. These results suggested that entropy modulation during the process of sensory information and SG was obvious in normal controls and significantly deficient in schizophrenia patients. Additionally, the FuzzyEn values measured in the frontal ROI were positively correlated with positive scores of Positive and Negative Syndrome Scale (PANSS), indicating that frontal entropy was a potential indicator in evaluating the clinical symptoms. However, negative associations were found between the FuzzyEn values of occipital ROIs and general and total scores of PANSS, likely reflecting the compensation effect in visual processing. Thus, our findings provided a deeper understanding of the deficits in sensory information processing and SG, which contribute to cognitive deficits and symptoms in patients with schizophrenia.

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“…In many studies on the diagnosis of depression and schizophrenia [88][89][90][91][92][93][94][95][96][97], the nonlinear EEG analysis have been reported, but no nonlinear EEG analysis with accuracy has been reported for comorbid psychotic disorders and depression in patients with epilepsy. Psychiatric comorbidities are common in patients with epilepsy [3], and associations for psychosis with the age at onset, duration of epilepsy, and seizure frequency have been reported.…”

Section: Comorbid Psychiatric Disordersmentioning

confidence: 99%

Is EEG a Useful Examination Tool for Diagnosis of Epilepsy and Comorbid Psychiatric Disorders?

Azuma¹

2021

Epilepsy - Update on Classification, Etiologies, Instrumental Diagnosis and Treatment

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Diagnosis of epilepsy usually involves interviewing the patients and the individuals who witnessed the seizure. An electroencephalogram (EEG) adds useful information for the diagnosis of epilepsy when epileptic abnormalities emerge. EEG exhibits nonlinearity and weak stationarity. Thus, nonlinear EEG analysis may be useful for clinical application. We examined only about English language studies of nonlinear EEG analysis that compared normal EEG and interictal EEG and reported the accuracy. We identified 60 studies from the public data of Andrzejak 2001 and two studies that did not use the data of Andrzejak 2001. Comorbid psychiatric disorders in patients with epilepsy were not reported in nonlinear EEG analysis except for one case series of comorbid psychotic disorders. Using a variety of feature extraction methods and classifier methods, we concluded that the studies that used the data of Andrzejak 2001 played a valuable role in EEG diagnosis of epilepsy. In the future, according to the evolution of artificial intelligence, deep learning, new nonlinear analysis methods, and the EEG association with the rating scale of the quality of life and psychiatric symptoms, we anticipate that EEG diagnosis of epilepsy, seizures, and comorbid psychiatric disorders in patients with epilepsy will be possible.

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Comparing EEG Nonlinearity in Deficit and Nondeficit Schizophrenia Patients: Preliminary Data

Cited by 15 publications

References 42 publications

Deficit Versus Nondeficit Schizophrenia: An MEG-EEG Investigation of Resting State and Source Coherence—Preliminary Data

Deficit Versus Nondeficit Schizophrenia: An MEG-EEG Investigation of Resting State and Source Coherence—Preliminary Data

Abnormal Entropy Modulation of the EEG Signal in Patients With Schizophrenia During the Auditory Paired-Stimulus Paradigm

Is EEG a Useful Examination Tool for Diagnosis of Epilepsy and Comorbid Psychiatric Disorders?

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