ArticleFrontiers in psychiatry2019
Occipital Alpha Connectivity During Resting-State Electroencephalography in Patients With Ultra-High Risk for Psychosis and Schizophrenia.
Article in Frontiers in psychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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8 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- EEG-Based Measures in At-Risk Mental State and Early Stages of Schizophrenia: A Systematic Review.Frontiers in psychiatry · 2021Pooled it
- Quantitative EEG of frontal and central cortices during calm memory retrieval in schizophrenia.Discover mental health · 2026Article
- The Role of Alpha Oscillations among the Main Neuropsychiatric Disorders in the Adult and Developing Human Brain: Evidence from the Last 10 Years of Research.Biomedicines · 2022Review
- A survey of brain network analysis by electroencephalographic signals.Cognitive neurodynamics · 2022Article
- Resting state alpha oscillatory activity is a valid and reliable marker of schizotypy.Scientific reports · 2021Article
- Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals.Physical and engineering sciences in medicine · 2020Article
- Neurophysiologic Characterization of Resting State Connectivity Abnormalities in Schizophrenia Patients.Frontiers in psychiatry · 2020Article
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10 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
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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