Suppr超能文献

基于低频振幅分数的机器学习分析:识别血液透析伴失眠患者的神经影像学鉴别标志物。

Identification of discriminative neuroimaging markers for patients on hemodialysis with insomnia: a fractional amplitude of low frequency fluctuation-based machine learning analysis.

机构信息

The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, 510120, China.

Department of Radiology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, 450000, China.

出版信息

BMC Psychiatry. 2023 Jan 4;23(1):9. doi: 10.1186/s12888-022-04490-1.

Abstract

BACKGROUND AND OBJECTIVE

Insomnia is one of the common problems encountered in the hemodialysis (HD) population, but the mechanisms remain unclear. we aimed to (1) detect the spontaneous brain activity pattern in HD patients with insomnia (HDWI) by using fractional fractional amplitude of low frequency fluctuation (fALFF) method and (2) further identify brain regions showing altered fALFF as neural markers to discriminate HDWI patients from those on hemodialysis but without insomnia (HDWoI) and healthy controls (HCs).

METHOD

We compared fALFF differences among HDWI subjects (28), HDWoI subjects (28) and HCs (28), and extracted altered fALFF features for the subsequent discriminative analysis. Then, we constructed a support vector machine (SVM) classifier to identify distinct neuroimaging markers for HDWI.

RESULTS

Compared with HCs, both HDWI and HDWoI patients exhibited significantly decreased fALFF in the bilateral calcarine (CAL), right middle occipital gyrus (MOG), left precentral gyrus (PreCG), bilateral postcentral gyrus (PoCG) and bilateral temporal middle gyrus (TMG), whereas increased fALFF in the bilateral cerebellum and right insula. Conversely, increased fALFF in the bilateral CAL/right MOG and decreased fALFF in the right cerebellum was observed in HDWI patients when compared with HDWoI patients. Moreover, the SVM classification achieved a good performance [accuracy = 82.14%, area under the curve (AUC) = 0.8202], and the consensus brain regions with the highest contributions to classification were located in the right MOG and right cerebellum.

CONCLUSION

Our result highlights that HDWI patients had abnormal neural activities in the right MOG and right cerebellum, which might be potential neural markers for distinguishing HDWI patients from non-insomniacs, providing further support for the pathological mechanism of HDWI.

摘要

背景与目的

失眠是血液透析(HD)人群中常见的问题之一,但机制尚不清楚。我们旨在(1)使用分数低频振幅(fALFF)方法检测伴有失眠的 HD 患者(HDWI)的自发脑活动模式,(2)进一步识别显示改变的 fALFF 的脑区作为区分 HDWI 患者与无失眠的 HD 患者(HDWoI)和健康对照(HCs)的神经标记物。

方法

我们比较了 HDWI 受试者(28 例)、HDWoI 受试者(28 例)和 HCs(28 例)之间的 fALFF 差异,并提取了改变的 fALFF 特征用于后续的判别分析。然后,我们构建了一个支持向量机(SVM)分类器来识别用于 HDWI 的独特神经影像学标记物。

结果

与 HCs 相比,HDWI 和 HDWoI 患者双侧距状回(CAL)、右侧中枕叶回(MOG)、左侧中央前回(PreCG)、双侧中央后回(PoCG)和双侧颞中回(TMG)的 fALFF 显著降低,而双侧小脑和右侧岛叶的 fALFF 增加。相反,与 HDWoI 患者相比,HDWI 患者双侧 CAL/右侧 MOG 的 fALFF 增加,右侧小脑的 fALFF 减少。此外,SVM 分类取得了较好的性能[准确率=82.14%,曲线下面积(AUC)=0.8202],对分类贡献最大的共识脑区位于右侧 MOG 和右侧小脑。

结论

我们的结果强调 HDWI 患者右侧 MOG 和右侧小脑的神经活动异常,这可能是区分 HDWI 患者与非失眠患者的潜在神经标记物,为 HDWI 的病理机制提供了进一步的支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fa4/9811801/76ec710d5c54/12888_2022_4490_Fig1_HTML.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验