心因性勃起功能障碍的脑活动改变和功能连通性:结合LOOCV-SVM-RFE和rs-fMRI的发现。

IF 2.9 3区 医学 Q2 NEUROSCIENCES
Xue Liu, Peining Niu, Jinchen He, Guowei Du, Yan Xu, Tao Liu, Zhaoxu Yang, Shaowei Liu, Yun Chen, Jianhuai Chen
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引用次数: 0

摘要

心因性勃起功能障碍(pED)常伴有脑活动异常。本研究旨在开发一种自动分类器,通过识别基于大脑的特征来区分pED和健康对照(hc)。从45例pED患者和43例hc患者获得静息状态功能磁共振成像数据。计算各组间的区域均匀性(ReHo)和功能连通性(FC)值并进行比较。此外,基于改变后的ReHo和FC值,结合递归特征消除(RFE)的支持向量机(SVM)分类器,采用留一交叉验证建立了SVM-RFE诊断模型。患者表现为左侧颞中回(与左侧内侧额上回和楔骨有降低的FC值)、额下回眶部(在同一区域有降低的FC值)、额下回三角形部分、扣带前回(与左侧颞下回、扣带前回、楔骨和右侧辅助运动区有降低的FC值)和额中回。右侧钙质裂隙的ReHo值升高。该诊断模型表现出优异的诊断性能,准确率达到90.80%。本研究发现pED患者大脑特定区域的区域活动和FC改变,这可能与pED的发展有关。机器学习的应用证实了大脑中这些功能变化的独特特征。该诊断模型的高准确性为开发客观的心理障碍诊断工具提供了一个有希望的方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Altered brain activity and functional connectivity in psychogenic erectile dysfunction: Combining findings from LOOCV-SVM-RFE and rs-fMRI.

Psychogenic erectile dysfunction (pED) is often accompanied by abnormal brain activities. This study aimed to develop an automaticclassifier to distinguish pED from healthy controls (HCs) by identified brain-basedcharacteristics. Resting-state functional magnetic resonance imaging data were acquired from 45 pED patients and 43 HCs. Regional homogeneity (ReHo) and functional connectivity (FC) values were calculated and compared between groups. Moreover, based on altered ReHo and FC values, support vector machine (SVM) classifier, incorporating recursive feature elimination (RFE), an SVM-RFE diagnostic model was established using leave-one-out cross-validation. Patients demonstrated reduced ReHo values in the left middle temporal gyrus (had decreased FC values with the left medial superior frontal gyrus and cuneus), orbital part of inferior frontal gyrus (had decreased FC values within the same region), triangular part of inferior frontal gyrus, anterior cingulate gyrus (had decreased FC values with the left inferior temporal gyrus, anterior cingulate gyrus, cuneus and right supplementary motor area) and middle frontal gyrus. The right calcarine fissure displayed increased ReHo values. The diagnostic model demonstrated excellent performance, achieving an accuracy rate of 90.80%. This study identified altered regional activity and FC in specific brain regions of pED patients, which might be related to the development of pED. The application of machine learning confirmed the distinctive characteristics of these functional changes in the brain. The high accuracy of our diagnostic model suggested a promising direction for developing objective diagnostic tools for psychological disorders.

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来源期刊
Neuroscience
Neuroscience 医学-神经科学
CiteScore
6.20
自引率
0.00%
发文量
394
审稿时长
52 days
期刊介绍: Neuroscience publishes papers describing the results of original research on any aspect of the scientific study of the nervous system. Any paper, however short, will be considered for publication provided that it reports significant, new and carefully confirmed findings with full experimental details.
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