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Neural Network Abnormality and Its Clinical Implications in Patients with...
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Neural Network Abnormality and Its Clinical Implications in Patients with Schizophrenia and Psychotic Disorder

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Mental Health".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 12428

Special Issue Editor

Prof. Seung-Hwan Lee

E-Mail Website
Guest Editor
Department of Psychiatry, Inje University College of Medicine, Ilsan Paik Hospital, 170 Juhwa-ro, Ilsanseo-gu, Goyang 10380, Korea
Interests: schizophrenia; psychotic disorder; neural and functional network; MRI; EEG; MEG; fNIRS; brain stimulation; neurocognition; social cognition

Special Issue Information

Dear Colleagues,

Neural network abnormality is an important underpinning of pathology in patients with schizophrenia. This heated topic of research area serves as an experimental testbed for researchers engaged in finding neural abnormalities in patients with schizophrenia and related psychotic disorders. In this Special Issue, I would like to invite influential papers around the world that revolve around the topic of network abnormality and its clinical implications in patients with schizophrenia in areas such as MRI, EEG, MEG, fNIRS, brain stimulation, neurocognition, and social cognition. If you are seeking a suitable journal to publish your work in the aforementioned area, I highly encourage you to send your manuscripts to JCM, which is not only known for providing a quick response but also for carrying out a legitimate peer-review process which is both thorough and rapid, leading to the publication of your work in a short period.

Our main wish is to recruit valuable original articles. However, I also welcome a limited number of review articles.

The deadline for submission is 28 February 2021.

I look forward to seeing esteemed research from across the globe.

Best regards,

Prof. Seung-Hwan Lee
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • schizophrenia
  • psychotic disorder
  • neural and functional network
  • MRI
  • EEG
  • MEG
  • fNIRS
  • brain stimulation
  • neurocognition
  • social cognition

Published Papers (4 papers)

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Research

12 pages, 1537 KiB  
Article
Anhedonia Relates to the Altered Global and Local Grey Matter Network Properties in Schizophrenia
by Byung-Hoon Kim, Hesun Erin Kim, Jung Suk Lee and Jae-Jin Kim
J. Clin. Med. 2021, 10(7), 1395; https://doi.org/10.3390/jcm10071395 - 31 Mar 2021
Cited by 4 | Viewed by 2129
Abstract
Anhedonia is one of the major negative symptoms in schizophrenia and defined as the loss of hedonic experience to various stimuli in real life. Although structural magnetic resonance imaging has provided a deeper understanding of anhedonia-related abnormalities in schizophrenia, network analysis of the [...] Read more.
Anhedonia is one of the major negative symptoms in schizophrenia and defined as the loss of hedonic experience to various stimuli in real life. Although structural magnetic resonance imaging has provided a deeper understanding of anhedonia-related abnormalities in schizophrenia, network analysis of the grey matter focusing on this symptom is lacking. In this study, single-subject grey matter networks were constructed in 123 patients with schizophrenia and 160 healthy controls. The small-world property of the grey matter network and its correlations with the level of physical and social anhedonia were evaluated using graph theory analysis. In the global scale whole-brain analysis, the patients showed reduced small-world property of the grey matter network. The local-scale analysis further revealed reduced small-world property in the default mode network, salience/ventral attention network, and visual network. The regional-level analysis showed an altered relationship between the small-world properties and the social anhedonia scale scores in the cerebellar lobule in patients with schizophrenia. These results indicate that anhedonia in schizophrenia may be related to abnormalities in the grey matter network at both the global whole-brain scale and local–regional scale. Full article
(This article belongs to the Special Issue Neural Network Abnormality and Its Clinical Implications in Patients with Schizophrenia and Psychotic Disorder)
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13 pages, 3934 KiB  
Article
Attenuated Resting-State Functional Anticorrelation between Attention and Executive Control Networks in Schizotypal Personality Disorder
by Ji-Won Hur, Taekwan Kim, Kang Ik K. Cho and Jun Soo Kwon
J. Clin. Med. 2021, 10(2), 312; https://doi.org/10.3390/jcm10020312 - 15 Jan 2021
Cited by 3 | Viewed by 3769
Abstract
Exploring the disruptions to intrinsic resting-state networks (RSNs) in schizophrenia-spectrum disorders yields a better understanding of the disease-specific pathophysiology. However, our knowledge of the neurobiological underpinnings of schizotypal personality disorders mostly relies on research on schizotypy or schizophrenia. This study aimed to investigate [...] Read more.
Exploring the disruptions to intrinsic resting-state networks (RSNs) in schizophrenia-spectrum disorders yields a better understanding of the disease-specific pathophysiology. However, our knowledge of the neurobiological underpinnings of schizotypal personality disorders mostly relies on research on schizotypy or schizophrenia. This study aimed to investigate the RSN abnormalities of schizotypal personality disorder (SPD) and their clinical implications. Using resting-state data, the intra- and inter-network of the higher-order functional networks (default mode network, DMN; frontoparietal network, FPN; dorsal attention network, DAN; salience network, SN) were explored in 22 medication-free, community-dwelling, non-help seeking individuals diagnosed with SPD and 30 control individuals. Consequently, while there were no group differences in intra-network functional connectivity across DMN, FPN, DAN, and SN, the SPD participants exhibited attenuated anticorrelation between the right frontal eye field region of the DAN and the right posterior parietal cortex region of the FPN. The decreases in anticorrelation were correlated with increased cognitive–perceptual deficits and disorganization factors of the schizotypal personality questionnaire, as well as reduced independence–performance of the social functioning scale for all participants together. This study, which links SPD pathology and social functioning deficits, is the first evidence of impaired large-scale intrinsic brain networks in SPD. Full article
(This article belongs to the Special Issue Neural Network Abnormality and Its Clinical Implications in Patients with Schizophrenia and Psychotic Disorder)
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13 pages, 1792 KiB  
Article
EEG Source Network for the Diagnosis of Schizophrenia and the Identification of Subtypes Based on Symptom Severity—A Machine Learning Approach
by Jeong-Youn Kim, Hyun Seo Lee and Seung-Hwan Lee
J. Clin. Med. 2020, 9(12), 3934; https://doi.org/10.3390/jcm9123934 - 4 Dec 2020
Cited by 17 | Viewed by 2709
Abstract
A precise diagnosis and a comprehensive assessment of symptom severity are important clinical issues in patients with schizophrenia (SZ). We investigated whether electroencephalography (EEG) features obtained from EEG source network analyses could be effectively applied to classify the SZ subtypes based on symptom [...] Read more.
A precise diagnosis and a comprehensive assessment of symptom severity are important clinical issues in patients with schizophrenia (SZ). We investigated whether electroencephalography (EEG) features obtained from EEG source network analyses could be effectively applied to classify the SZ subtypes based on symptom severity. Sixty-four electrode EEG signals were recorded from 119 patients with SZ (53 males and 66 females) and 119 normal controls (NC, 51 males and 68 females) during resting-state with closed eyes. Brain network features (global and local clustering coefficient and global path length) were calculated from EEG source activities. According to positive, negative, and cognitive/disorganization symptoms, the SZ patients were divided into two groups (high and low) by positive and negative syndrome scale (PANSS). To select features for classification, we used the sequential forward selection (SFS) method. The classification accuracy was evaluated using 10 by 10-fold cross-validation with the linear discriminant analysis (LDA) classifier. The best classification accuracy was 80.66% for estimating SZ patients from the NC group. The best classification accuracy between low and high groups in positive, negative, and cognitive/disorganization symptoms were 88.10%, 75.25%, and 77.78%, respectively. The selected features well-represented the pathological brain regions of SZ. Our study suggested that resting-state EEG network features could successfully classify between SZ patients and the NC, and between low and high SZ groups in positive, negative, and cognitive/disorganization symptoms. Full article
(This article belongs to the Special Issue Neural Network Abnormality and Its Clinical Implications in Patients with Schizophrenia and Psychotic Disorder)
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16 pages, 1138 KiB  
Article
Altered Cortical Thickness-Based Individualized Structural Covariance Networks in Patients with Schizophrenia and Bipolar Disorder
by Sungkean Kim, Yong-Wook Kim, Hyeonjin Jeon, Chang-Hwan Im and Seung-Hwan Lee
J. Clin. Med. 2020, 9(6), 1846; https://doi.org/10.3390/jcm9061846 - 13 Jun 2020
Cited by 21 | Viewed by 3193
Abstract
Structural covariance is described as coordinated variation in brain morphological features, such as cortical thickness and volume, among brain structures functionally or anatomically interconnected to one another. Structural covariance networks, based on graph theory, have been studied in mental disorders. This analysis can [...] Read more.
Structural covariance is described as coordinated variation in brain morphological features, such as cortical thickness and volume, among brain structures functionally or anatomically interconnected to one another. Structural covariance networks, based on graph theory, have been studied in mental disorders. This analysis can help in understanding the brain mechanisms of schizophrenia and bipolar disorder. We investigated cortical thickness-based individualized structural covariance networks in patients with schizophrenia and bipolar disorder. T1-weighted magnetic resonance images were obtained from 39 patients with schizophrenia, 37 patients with bipolar disorder type I, and 32 healthy controls, and cortical thickness was analyzed via a surface-based morphometry analysis. The structural covariance of cortical thickness was calculated at the individual level, and covariance networks were analyzed based on graph theoretical indices: strength, clustering coefficient (CC), path length (PL) and efficiency. At the global level, both patient groups showed decreased strength, CC and efficiency, and increased PL, compared to healthy controls. In bipolar disorder, we found intermediate network measures among the groups. At the nodal level, schizophrenia patients showed decreased CCs in the left suborbital sulcus and the right superior frontal sulcus, compared to bipolar disorder patients. In addition, patient groups showed decreased CCs in the right insular cortex and the left superior occipital gyrus. Global-level network indices, including strength, CCs and efficiency, positively correlated, while PL negatively correlated, with the positive symptoms of the Positive and Negative Syndrome Scale for patients with schizophrenia. The nodal-level CC of the right insular cortex positively correlated with the positive symptoms of schizophrenia, while that of the left superior occipital gyrus positively correlated with the Young Mania Rating Scale scores for bipolar disorder. Altered cortical structural networks were revealed in patients, and particularly, the prefrontal regions were more altered in schizophrenia. Furthermore, altered cortical structural networks in both patient groups correlated with core pathological symptoms, indicating that the insular cortex is more vulnerable in schizophrenia, and the superior occipital gyrus is more vulnerable in bipolar disorder. Our individualized structural covariance network indices might be promising biomarkers for the evaluation of patients with schizophrenia and bipolar disorder. Full article
(This article belongs to the Special Issue Neural Network Abnormality and Its Clinical Implications in Patients with Schizophrenia and Psychotic Disorder)
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