Reduction in Alpha Peak Frequency and Coherence on Quantitative Electroencephalography in Patients with Schizophrenia

Yeum, Tae Sung; Kang, Ung Gu

doi:10.3346/jkms.2018.33.e179

Cited by 25 publications

(16 citation statements)

References 29 publications

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“…2012 ; Goldstein et al. 2015 ; Yeum and Kang 2018 ; Murphy and Öngür 2019 ) and may be related to symptoms ( Garakh et al. 2012 ) or duration of illness ( Goldstein et al.…”

Section: Discussionmentioning

confidence: 99%

Phase-Amplitude Markers of Synchrony and Noise: A Resting-State and TMS-EEG Study of Schizophrenia

Freche

Naim-Feil

Hess

et al. 2020

Cerebral Cortex Communications

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The electroencephalogram (EEG) of schizophrenia patients is known to exhibit a reduction of signal-to-noise ratio and of phase locking, as well as a facilitation of excitability, in response to a variety of external stimuli. Here we demonstrate these effects in transcranial magnetic stimulation (TMS)-evoked potentials and in the resting-state EEG. To ensure veracity we used three weekly sessions and analyzed both resting state and TMS-EEG data. For the TMS responses our analysis verifies known results. For the resting state we introduce the methodology of mean-normalized variation to the EEG analysis (quartile-based coefficient of variation), which allows for a comparison of narrow-band EEG amplitude fluctuations to narrow-band Gaussian noise. This reveals that amplitude fluctuations in the delta, alpha and beta bands of healthy controls are different from those in schizophrenia patients, on time scales of tens of seconds. We conclude that the EEG-measured cortical activity patterns of schizophrenia patients are more similar to noise, both in alpha and beta resting state and in TMS responses. Our results suggest that the ability of neuronal populations to form stable, locally and temporally correlated activity is reduced in schizophrenia, a conclusion that is in accord with previous experiments on TMS-EEG and on resting-state EEG.

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“…2012 ; Goldstein et al. 2015 ; Yeum and Kang 2018 ; Murphy and Öngür 2019 ) and may be related to symptoms ( Garakh et al. 2012 ) or duration of illness ( Goldstein et al.…”

Section: Discussionmentioning

confidence: 99%

Phase-Amplitude Markers of Synchrony and Noise: A Resting-State and TMS-EEG Study of Schizophrenia

Freche

Naim-Feil

Hess

et al. 2020

Cerebral Cortex Communications

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“…The inconsistent results are reported on delta and theta band in two studies (14, 15). Compared to delta and theta band, the decrease of alpha band activity in resting state (eyes closed) is the dominant result in SZ researches (13, 16). The alpha activity is negatively correlated to positive symptoms of SZ patients (17, 18).…”

Section: Introductionmentioning

confidence: 99%

“…It is highly likely that the topological configuration of functional brain networks can be used to evaluate treatment effects, including those related to cognitive function, and, in some cases, can predict the risk of psychosis (16, 39). To construct functional brain network, the use of resting-state EEG avoids the experimental errors caused by participants’ incompatibility and reduces the difficulty of detection (13).…”

Section: Introductionmentioning

confidence: 99%

Occipital Alpha Connectivity During Resting-State Electroencephalography in Patients With Ultra-High Risk for Psychosis and Schizophrenia

et al. 2019

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Schizophrenia patients always show cognitive impairment, which is proved to be related to hypo-connectivity or hyper-connectivity. Further, individuals with an ultra-high risk for psychosis also show abnormal functional connectivity-related cognitive impairment, especially in the alpha rhythm. Thus, the identification of functional networks is essential to our understanding of the disorder. We investigated the resting-state functional connectivity of the alpha rhythm measured by electroencephalography (EEG) to reveal the relation between functional network and clinical symptoms. The participants included 28 patients with first-episode schizophrenia (FES), 28 individuals with ultra-high risk for psychosis (UHR), and 28 healthy controls (HC). After the professional clinical symptoms evaluation, all the participants were instructed to keep eyes closed for 3-min resting-state EEG recording. The 3-min EEG data were segmented into artefact-free epochs (the length was 3 s), and the functional connectivity of the alpha phase was estimated using the phase lag index (PLI), which measures the phase differences of EEG signals. The FES and UHR groups displayed increased resting-state PLI connectivity compared with the HC group [F(2,74) = 10.804, p < 0.001]. Significant increases in the global efficiency, the local efficiency, and the path length were found in the FES and UHR groups compared with those of the HC group. FES and UHR showed an increased degree of connectivity compared with HC. The degree of the left occipital lobe area was higher in the UHR group than in the FES group. The hypothesis of disconnection is confirmed. Furthermore, differences between the UHR and FES group were found, which is valuable for producing clinical significance before the onset of schizophrenia.

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“…Indeed, the emergence of QEEG has enabled researchers to extract a multitude of variables that can be quantitatively measured. What made studies on EEG and psychiatric disorders more objective [11]. The power (absolute and relative), the coherence, the frequency of the peaks of the different waves (alpha, delta, beta, theta, gamma, etc.)…”

Section: Introductionmentioning

confidence: 99%

“…The study of brain connectivity is one of the major issues in neuroscience, with the aim of better understanding the organization and functioning of the brain, whether in healthy or pathological individuals. Brain Connectivity Analysis uses Phase Locking Value (PLV), Phase Lag Index (PLI) and Directed Transfer Function (DTF) among other measures [5], [11], [14]. This paper presents the first phase of the work.…”

Section: Introductionmentioning

confidence: 99%

Study of Metrics that Could be Considered as Inputs to an Intelligent System for Diagnosing Schizophrenia based on an Electroencephalogram (EEG)

Nanfack¹,

Morel²

2020

EJERS

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Intelligent systems are now part of human daily life. This justifies its application in various fields. However, the field of psychiatry still seems to be at a disadvantage. In this paper, we are interested in measures that can be used as input to an intelligent system for detecting brain diseases using an electroencephalogram (EEG). Indeed, spectral analysis and the study of brain connectivity are two methods of EEG analysis that can be used to characterize schizophrenia. The spectral analysis allows to calculate the power (absolute and relative), the frequency peak among others. Concerning the study of cerebral connectivity, the Phase Lag Index (PLI), which is an adjacency matrix ; which is an adjacency matrix used to assess brain connectivity. Once the PLI is obtained, units such as degree, density and strength on each channel are calculated. These units are evaluated on twenty (28) EEGs, fourteen (14) of people suffering from schizophrenia and fourteen (14) of healthy people. Once the PLI is obtained, units such as degree, density and strength on each channel are calculated. These units are evaluated on twenty (28) EEGs, fourteen (14) of people suffering from schizophrenia and fourteen (14) of healthy people. On the other hand, the value of strength is always lower in sick people than in healthy people. This is true regardless of the frequency band or channel used. This study shows that values such as degree, density, strength of a predefined adjacency matrix, then power, peak frequency band can be used as input values of an intelligent system for diagnosing psychiatric or brain diseases such as schizophrenia.

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Reduction in Alpha Peak Frequency and Coherence on Quantitative Electroencephalography in Patients with Schizophrenia

Cited by 25 publications

References 29 publications

Phase-Amplitude Markers of Synchrony and Noise: A Resting-State and TMS-EEG Study of Schizophrenia

Phase-Amplitude Markers of Synchrony and Noise: A Resting-State and TMS-EEG Study of Schizophrenia

Occipital Alpha Connectivity During Resting-State Electroencephalography in Patients With Ultra-High Risk for Psychosis and Schizophrenia

Study of Metrics that Could be Considered as Inputs to an Intelligent System for Diagnosing Schizophrenia based on an Electroencephalogram (EEG)

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