مقاله انگلیسی رایگان در مورد آنالیز شبکه مغزی هدایت شده در افسردگی اضطرابی و غیر اضطرابی – الزویر 2023

 

مشخصات مقاله
ترجمه عنوان مقاله تجزیه و تحلیل شبکه مغز ی هدایت شده در افسردگی اضطرابی و غیر اضطرابی بر اساس بازسازی منبع EEG و نظریه گراف
عنوان انگلیسی مقاله Directed brain network analysis in anxious and non-anxious depression based on EEG source reconstruction and graph theory
نشریه الزویر
انتشار مقاله سال 2023
تعداد صفحات مقاله انگلیسی 13 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) Scopus – Master Journals List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
6.185 در سال 2022
شاخص H_index 94 در سال 2023
شاخص SJR 1.071 در سال 2022
شناسه ISSN 1746-8108
شاخص Quartile (چارک) Q1 در سال 2022
فرضیه ندارد
مدل مفهومی ندارد
پرسشنامه ندارد
متغیر ندارد
رفرنس دارد
رشته های مرتبط روانشناسی – پزشکی
گرایش های مرتبط روانشناسی بالینی – روانپزشکی – مغز و اعصاب
نوع ارائه مقاله
ژورنال
مجله  پردازش و کنترل سیگنال زیست پزشکی – Biomedical Signal Processing and Control
دانشگاه Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
کلمات کلیدی شبکه مغزی هدایت شده – نظریه گراف – محلی سازی منبع EEG – اختلال افسردگی اساسی (MDD) – اتصال موثر
کلمات کلیدی انگلیسی Directed brain network – Graph theory – EEG source localization – Major depressive disorder (MDD) – Effective connectivity
شناسه دیجیتال – doi
https://doi.org/10.1016/j.bspc.2023.104666
لینک سایت مرجع https://www.sciencedirect.com/science/article/abs/pii/S174680942300099X
کد محصول e17442
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Abstract
1 Introduction
2 Materials and methods
3 Results
4 Discussion
5 Conclusion
Declaration of Competing Interest
Acknowledgement
Data availability
References

بخشی از متن مقاله:

Abstract

     Patients with anxious depression have more severe symptoms, more side effects, and higher resistance to treatment than patients with non-anxious depression; therefore, it is crucial to clarify the differences between these two types of patients. In this study, a 5-minute resting EEG was recorded in 15 patients with anxious depression and 9 patients with non-anxious depression under eyes open and closed conditions. Sixty-eight subcortical regions were extracted using exact low resolution brain electromagnetic tomography (eLORETA). The directed transfer function was then used to construct brain networks. Specific features based on graph theory including the strength of connectivity and betweenness centrality (BC) were calculated from the networks. Finally, significant features were selected using the Mann-Whitney U test, and patients were classified into anxious and non-anxious depressive groups using the Support Vector Machine (SVM). Results showed that features of outward connectivity strength led to the highest accuracy, F-score, and specificity with 91.66%, 87. 5%, and 100% in the eyes-closed state, respectively. Moreover, we found that the strength of connectivity in both directions increased for the anxious depressive group during the eyes-open state. In particular, higher outward connectivity was observed in the right hemisphere for the anxious depressive group. Further findings also revealed that features with the most significant difference were mainly associated with the beta band. In addition, significant increased inward and outward connectivity and decreased nodal centrality were observed in the posterior regions of the default mode network. These preliminary findings might provide new insights into the recognition of anxious depressed patients.

Introduction

     Major depressive disorder (MDD) affects 6% of the global adult population annually and is one of the most prevalent psychiatric disorders [1]. About half of MDD patients also have anxiety, such as social anxiety disorder (SAD), generalized anxiety disorder (GAD), or panic disorder (PD) [2]. When MDD co-occurs with anxiety, the risk of suicide becomes greater, and patients may require long-term treatment [3,4]. Currently, however, there is a lack of accepted treatment for patients with anxious depression. They usually receive the same treatment strategies that are given for depression or anxiety. Consequently, the rate of treatment resistance is higher for them than for patients with pure depression or anxiety [5]. Moreover, the rate of medical utilization increased rapidly for patients with comorbid depression and anxiety [4]. Therefore, it is necessary to distinguish anxious depressed patients from non-anxious depressed patients in order to improve diagnosis and treatment methods.

Conclusion

     To the best of our knowledge, this is the first study that has investigated differences in the directed brain network in anxious and nonanxious depressed patients using an effective connectivity measure and the EEG source connectivity method. Network metrics comprising directed node strength and BC were analyzed by a statistical test and the machine learning approach. Classification results demonstrate outward connectivity strength had the highest performance in separating the two groups of patients, while all features performed better in the eyes-closed state than in the eyes-open state. Our main findings related to the strength of connectivity consist of increased node strength during the eyes-open condition, especially a higher out-strength in the right hemisphere for the anxious depressive group. In addition, we found that beta oscillations reflect the most altered brain network in terms of node strength and BC. Further analysis revealed that connectivity and centrality in most regions of posterior DMN were changed significantly. To conclude, the obtained results could potentially lead to the understanding of the underlying brain network of patients with comorbid depression-anxiety disorder, however, more research still is needed regarding the anxious depressive disorder.

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