孔源性视网膜脱离中白质连接体的功能拓扑结构被破坏:来自图论和机器学习的见解。

IF 1.6 4区 医学 Q4 NEUROSCIENCES
Yu Ji, Zhuo-Er Dong, Yi-Chong Duan, Li-Li Yao, Xiao-Rong Wu
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引用次数: 0

摘要

背景:已知孔源性视网膜脱离(RRD)可诱导与视力相关的灰质区域的功能改变。然而,RRD对白质(WM)连接体的影响在很大程度上仍未被探索。方法:应用图论方法评价RRD患者WM连接体的功能网络拓扑结构。然后使用支持向量机(SVM)分类器结合SHapley加性解释(SHAP)来区分RRD患者和健康对照(hc),并识别驱动模型预测的关键大脑区域。结果:与hc相比,RRD患者在整体和节点网络拓扑结构上都表现出明显的破坏。基于网络的统计数据确定了23个连通性发生改变的子网。值得注意的是,SVM和SHAP分析的整合表明,中间中心性(Bc)是最具判别性的拓扑特征,曲线下面积为0.9211。结论:这些发现表明,RRD破坏了中央视觉和高阶认知网络中的关键枢纽,导致了特征性的网络重组。此外,Bc有望作为RRD的早期神经成像生物标志物。总的来说,我们的研究结果促进了对RRD神经适应性变化的理解,并支持网络拓扑指标在早期诊断中的临床应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Disrupted functional topology of the white matter connectome in rhegmatogenous retinal detachment: insights from graph theory and machine learning.

Background: Rhegmatogenous retinal detachment (RRD) is known to induce functional alterations in the gray matter regions associated with vision. However, the impact of RRD on the white matter (WM) connectome remains largely unexplored.

Methods: We applied graph theory to evaluate the functional network topology of the WM connectome in RRD patients. A support vector machine (SVM) classifier, combined with SHapley Additive exPlanations (SHAP), was then employed to distinguish RRD patients from healthy controls (HCs) and to identify key brain regions driving model predictions.

Results: Compared to HCs, RRD patients exhibited significant disruptions in both global and nodal network topology. Network-based statistics identified 23 subnetworks with altered connectivity. Notably, the integration of SVM and SHAP analyses revealed that betweenness centrality (Bc) was the most discriminative topological feature, achieving an area under the curve of 0.9211.

Conclusion: These findings suggest that RRD disrupts critical hubs within the central visual and higher-order cognitive networks, leading to characteristic network reorganization. Moreover, Bc shows promise as an early neuroimaging biomarker for RRD. Overall, our results advance the understanding of neuroadaptive changes in RRD and support the clinical application of network topological metrics in early diagnosis.

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来源期刊
Neuroreport
Neuroreport 医学-神经科学
CiteScore
3.20
自引率
0.00%
发文量
150
审稿时长
1 months
期刊介绍: NeuroReport is a channel for rapid communication of new findings in neuroscience. It is a forum for the publication of short but complete reports of important studies that require very fast publication. Papers are accepted on the basis of the novelty of their finding, on their significance for neuroscience and on a clear need for rapid publication. Preliminary communications are not suitable for the Journal. Submitted articles undergo a preliminary review by the editor. Some articles may be returned to authors without further consideration. Those being considered for publication will undergo further assessment and peer-review by the editors and those invited to do so from a reviewer pool. The core interest of the Journal is on studies that cast light on how the brain (and the whole of the nervous system) works. We aim to give authors a decision on their submission within 2-5 weeks, and all accepted articles appear in the next issue to press.
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