Verim B., Demirlek C., Demir M., Zorlu N., Yalınçetin B., Gürbüz M., ...Daha Fazla
NEUROSCIENCE APPLIED, cilt.3, sa.Supplement 2, ss.453, 2024 (Scopus)
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Yayın Türü:
Makale / Özet
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Cilt numarası:
3
Sayı:
Supplement 2
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Basım Tarihi:
2024
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Doi Numarası:
10.1016/j.nsa.2024.104971
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Dergi Adı:
NEUROSCIENCE APPLIED
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Derginin Tarandığı İndeksler:
Scopus
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Sayfa Sayıları:
ss.453
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Dokuz Eylül Üniversitesi Adresli:
Evet
Özet
Background: Schizophrenia and bipolar disorder are chronic psychiatric disorders associated with brain structural and functional abnormalities [1,2]. Recently, research which has focused on connectomics has highlighted critical aberrations in these psychiatric disorders. Alterations in the functional connectome have also been noted in the initial stages of psychosis [3] and bipolar disorder [4]. Despite these important findings, no study has yet encompassed both individuals at clinical high-risk for, and those diagnosed with, first-episode psychosis and bipolar disorder. Objective: The aim of this study was to examine how brain network organization changes in high-risk and early psychosis and bipolar disorder groups compared to each other and healthy controls. Methods: Thirty individuals with first-episode psychosis (FEP), 23 individuals with first-episode bipolar disorder (FEBD), 30 individuals at ultra-high risk for psychosis (UHR-P), 23 individuals at ultra-high risk for bipolar disorder (UHR-BD) and 19 healthy controls (HC) were included in this study. Each participant completed a clinical interview utilizing the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I) [5] and underwent 3 Tesla resting-state functional magnetic resonance imaging (rs-fMRI). The functional connectivity matrix was constructed in MATLAB [6]. We examined global network measures, including characteristic path length, cluster coefficient, small-worldness, modularity, global efficiency and rich club. All measures were calculated with the Brain Connectivity Toolbox [7] running on MATLAB. For graph theory analyses, only positive correlations were calculated and the network density was set at 30 percent. Multivariate analysis of covariance (MANCOVA) was used to compare three groups on graph theoretical measures, with stage and category included as covariates. Results: Compared to healthy controls, a significant decrease in the modularity (F=4.1, p=0.04) parameter was detected in the high-risk group for psychosis (UHR-P). No significant decrease was found in any graph theory parameter in any other group compared to healthy controls. MANCOVA results revealed that neither the stage (F=1.61, p=0.1) nor the category (F=1.45, p=0.21) had a significant effect. Additionally, the interaction between stage and category was not significant (F=1.35, p=0.21). A 2-way (stage and category) MANCOVA analysis was performed for the 30 k value for the rich club structure in the range of K=9-38. Similarly, the effects of stage (F=1.18, p=0.20) and category (F=1.13, p=0.32) were not found to be significant, and the interaction effect (F=0.96, p=0.57) was also not significant.
Discussion: In line with existing literature on chronic patients with
schizophrenia, there is a decrease in modularity observed in individuals at ultra-high risk for psychosis (UHR-P). This decrease signifies a disruption in the network's ability to process specialized information efficiently. Future studies would benefit from conducting multimodal longitudinal research with larger sample sizes to gain deeper understanding into the neurobiological mechanisms underlying the disruptions observed in the functional and structural connectome. Elucidating the heterogeneity of these disorders’ early stages may lead to more individualized and targeted therapeutic interventions.