Aberrant dynamic functional and effective connectivity changes of the primary visual cortex in patients with retinal detachment via machine learning.

IF 1.6 4区 医学 Q4 NEUROSCIENCES
Neuroreport Pub Date : 2024-12-04 Epub Date: 2024-10-03 DOI:10.1097/WNR.0000000000002100
Yu Ji, Yuan-Yuan Wang, Qi Cheng, Wen-Wen Fu, Ben-Liang Shu, Bin Wei, Qin-Yi Huang, Xiao-Rong Wu
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引用次数: 0

Abstract

Objective: Previous neuroimaging studies have identified significant alterations in brain functional activity in retinal detachment (RD) patients, these investigations predominantly concentrated on local functional activity changes. The potential directional alterations in functional connectivity within the primary visual cortex (V1) in RD patients remain to be elucidated.

Methods: In this study, we employed seed-based functional connectivity analysis along with Granger causality analysis to examine the directional alterations in dynamic functional connectivity (dFC) within the V1 region of patients diagnosed with RD. Finally, a support vector machine algorithm was utilized to classify patients with RD and healthy controls (HCs).

Results: RD patients exhibited heightened dynamic functional connectivity (dFC) and dynamic effective connectivity (dEC) between the Visual Network (VN) and default mode network (DMN), as well as within the VN, compared to HCs. Conversely, dFC between VN and auditory network (AN) decreased, and dEC between VN and sensorimotor network (SMN) significantly reduced. In state 4, RD patients had higher frequency. Notably, variations in dFC originating from the left V1 region proved diagnostically effective, achieving an AUC of 0.786.

Conclusion: This study reveals significant alterations in the connectivity between the VN and the default mode network in patients with RD. These changes may disrupt visual information processing and higher cognitive integration in RD patients. Additionally, alterations in the left V1 region and whole-brain dFC show promising potential in aiding the diagnosis of RD. These findings offer valuable insights into the neural mechanisms underlying visual and cognitive impairments associated with RD.

通过机器学习研究视网膜脱离患者初级视觉皮层的动态功能和有效连接性异常变化。
目的:以往的神经影像学研究发现视网膜脱离(RD)患者的大脑功能活动发生了显著变化,这些研究主要集中于局部功能活动的变化。RD患者初级视觉皮层(V1)内功能连接的潜在方向性改变仍有待阐明:在这项研究中,我们采用了基于种子的功能连接分析和格兰杰因果关系分析来研究被诊断为 RD 患者的 V1 区域内动态功能连接(dFC)的方向性改变。最后,利用支持向量机算法对RD患者和健康对照组(HC)进行分类:结果:与健康对照组相比,RD 患者的视觉网络(VN)和默认模式网络(DMN)之间以及 VN 内部的动态功能连通性(dFC)和动态有效连通性(dEC)均有所提高。相反,VN与听觉网络(AN)之间的dFC降低,VN与感觉运动网络(SMN)之间的dEC显著降低。在状态 4 中,RD 患者的频率更高。值得注意的是,源自左侧 V1 区域的 dFC 变化证明诊断有效,AUC 达到 0.786:本研究揭示了 RD 患者的 VN 与默认模式网络之间的连接发生了显著变化。这些变化可能会破坏 RD 患者的视觉信息处理和高级认知整合。此外,左侧 V1 区和全脑 dFC 的改变显示出在帮助诊断 RD 方面的巨大潜力。这些发现为研究与 RD 相关的视觉和认知障碍的神经机制提供了有价值的见解。
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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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