利用功能连通性和大脑网络拓扑的侧向化识别偏头痛右向左分流的三分类模型:静息态 fMRI 研究。

IF 3.2 3区 医学 Q2 NEUROSCIENCES
Frontiers in Neuroscience Pub Date : 2024-11-12 eCollection Date: 2024-01-01 DOI:10.3389/fnins.2024.1488193
Weifang Nie, Weiming Zeng, Jiajun Yang, Lei Wang, Yuhu Shi
{"title":"利用功能连通性和大脑网络拓扑的侧向化识别偏头痛右向左分流的三分类模型:静息态 fMRI 研究。","authors":"Weifang Nie, Weiming Zeng, Jiajun Yang, Lei Wang, Yuhu Shi","doi":"10.3389/fnins.2024.1488193","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Right-to-left shunting has been significantly associated with migraine, although the neural mechanisms remain complex and not fully elucidated. The aim of this study was to investigate the variability of brain asymmetry in individuals with migraine with right-to-left shunting, migraine without right-to-left shunting and normal controls using resting-state fMRI technology and to construct a three-classification model.</p><p><strong>Methods: </strong>Firstly, asymmetries in functional connectivity and brain network topology were quantified to laterality indices. Secondly, the laterality indices were employed to construct a three-classification model using decision tree and random forest algorithms. Ultimately, through a feature score analysis, the key brain regions that contributed significantly to the classification were extracted, and the associations between these brain regions and clinical features were investigated.</p><p><strong>Results: </strong>Our experimental results showed that the initial classification accuracy reached 0.8961. Subsequently, validation using an independent sample set resulted in a classification accuracy of 0.8874. Further, after expanding the samples by the segmentation strategy, the classification accuracies were improved to 0.9103 and 0.9099. Additionally, the third sample set yielded a classification accuracy of 0.8745. Finally, 9 pivotal brain regions were identified and distributed in the default network, the control network, the visual network, the limbic network, the somatomotor network and the salience/ventral attention network.</p><p><strong>Discussion: </strong>The results revealed distinct lateralization features in the brains of the three groups, which were closely linked to migraine and right-to-left shunting symptoms and could serve as potential imaging biomarkers for clinical diagnosis. Our findings enhanced our understanding of migraine and right-to-left shunting mechanisms and offered insights into assisting clinical diagnosis.</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"18 ","pages":"1488193"},"PeriodicalIF":3.2000,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11588730/pdf/","citationCount":"0","resultStr":"{\"title\":\"A three-classification model for identifying migraine with right-to-left shunt using lateralization of functional connectivity and brain network topology: a resting-state fMRI study.\",\"authors\":\"Weifang Nie, Weiming Zeng, Jiajun Yang, Lei Wang, Yuhu Shi\",\"doi\":\"10.3389/fnins.2024.1488193\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Right-to-left shunting has been significantly associated with migraine, although the neural mechanisms remain complex and not fully elucidated. The aim of this study was to investigate the variability of brain asymmetry in individuals with migraine with right-to-left shunting, migraine without right-to-left shunting and normal controls using resting-state fMRI technology and to construct a three-classification model.</p><p><strong>Methods: </strong>Firstly, asymmetries in functional connectivity and brain network topology were quantified to laterality indices. Secondly, the laterality indices were employed to construct a three-classification model using decision tree and random forest algorithms. Ultimately, through a feature score analysis, the key brain regions that contributed significantly to the classification were extracted, and the associations between these brain regions and clinical features were investigated.</p><p><strong>Results: </strong>Our experimental results showed that the initial classification accuracy reached 0.8961. Subsequently, validation using an independent sample set resulted in a classification accuracy of 0.8874. Further, after expanding the samples by the segmentation strategy, the classification accuracies were improved to 0.9103 and 0.9099. Additionally, the third sample set yielded a classification accuracy of 0.8745. Finally, 9 pivotal brain regions were identified and distributed in the default network, the control network, the visual network, the limbic network, the somatomotor network and the salience/ventral attention network.</p><p><strong>Discussion: </strong>The results revealed distinct lateralization features in the brains of the three groups, which were closely linked to migraine and right-to-left shunting symptoms and could serve as potential imaging biomarkers for clinical diagnosis. Our findings enhanced our understanding of migraine and right-to-left shunting mechanisms and offered insights into assisting clinical diagnosis.</p>\",\"PeriodicalId\":12639,\"journal\":{\"name\":\"Frontiers in Neuroscience\",\"volume\":\"18 \",\"pages\":\"1488193\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11588730/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Neuroscience\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3389/fnins.2024.1488193\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fnins.2024.1488193","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
引用次数: 0

摘要

导言:右向左分流与偏头痛密切相关,但其神经机制仍然复杂,尚未完全阐明。本研究的目的是利用静息态 fMRI 技术研究伴有右向左分流的偏头痛患者、不伴有右向左分流的偏头痛患者和正常对照组患者大脑不对称的变异性,并构建一个三分类模型:方法:首先,将功能连接和大脑网络拓扑的不对称性量化为侧向性指数。方法:首先,将功能连接性和脑网络拓扑的不对称性量化为侧向性指数;其次,利用侧向性指数,采用决策树和随机森林算法构建三分类模型。最后,通过特征得分分析,提取出对分类有显著贡献的关键脑区,并研究了这些脑区与临床特征之间的关联:实验结果表明,最初的分类准确率达到了 0.8961。随后,使用独立样本集进行验证,分类准确率达到 0.8874。此外,通过分割策略扩大样本后,分类准确率提高到了 0.9103 和 0.9099。此外,第三个样本集的分类准确率为 0.8745。最后,确定了 9 个关键脑区,它们分布在默认网络、控制网络、视觉网络、边缘网络、躯体运动网络和显著性/内侧注意网络中:讨论:研究结果表明,三组偏头痛患者大脑侧化特征明显,这与偏头痛和右向左分流症状密切相关,可作为临床诊断的潜在影像生物标志物。我们的研究结果加深了我们对偏头痛和右向左分流机制的理解,并为辅助临床诊断提供了启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A three-classification model for identifying migraine with right-to-left shunt using lateralization of functional connectivity and brain network topology: a resting-state fMRI study.

Introduction: Right-to-left shunting has been significantly associated with migraine, although the neural mechanisms remain complex and not fully elucidated. The aim of this study was to investigate the variability of brain asymmetry in individuals with migraine with right-to-left shunting, migraine without right-to-left shunting and normal controls using resting-state fMRI technology and to construct a three-classification model.

Methods: Firstly, asymmetries in functional connectivity and brain network topology were quantified to laterality indices. Secondly, the laterality indices were employed to construct a three-classification model using decision tree and random forest algorithms. Ultimately, through a feature score analysis, the key brain regions that contributed significantly to the classification were extracted, and the associations between these brain regions and clinical features were investigated.

Results: Our experimental results showed that the initial classification accuracy reached 0.8961. Subsequently, validation using an independent sample set resulted in a classification accuracy of 0.8874. Further, after expanding the samples by the segmentation strategy, the classification accuracies were improved to 0.9103 and 0.9099. Additionally, the third sample set yielded a classification accuracy of 0.8745. Finally, 9 pivotal brain regions were identified and distributed in the default network, the control network, the visual network, the limbic network, the somatomotor network and the salience/ventral attention network.

Discussion: The results revealed distinct lateralization features in the brains of the three groups, which were closely linked to migraine and right-to-left shunting symptoms and could serve as potential imaging biomarkers for clinical diagnosis. Our findings enhanced our understanding of migraine and right-to-left shunting mechanisms and offered insights into assisting clinical diagnosis.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Frontiers in Neuroscience
Frontiers in Neuroscience NEUROSCIENCES-
CiteScore
6.20
自引率
4.70%
发文量
2070
审稿时长
14 weeks
期刊介绍: Neural Technology is devoted to the convergence between neurobiology and quantum-, nano- and micro-sciences. In our vision, this interdisciplinary approach should go beyond the technological development of sophisticated methods and should contribute in generating a genuine change in our discipline.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信