刺激性肠综合征的分类使用大脑功能连接强度和机器学习
Qi Zhang1,2, Yue Xu1, Dingbo Guo3
1Department of Anorectal Surgery, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China.
这项研究在刺激性肠综合征 (IBS) 患者中发现了明显的脑网络变化,通过功能连接强度 (FCS) 和机器学习识别了用于诊断的潜在生物标志物.
科学领域:
- 神经科学是一个神经科学.
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 刺激性肠综合征 (IBS) 是一种常见的疾病,与大脑与肠道的相互作用有关.
- 目前对IBS大脑机制的理解有限,缺乏客观的诊断工具.
- 这项研究解决了在IBS诊断中需要客观生物标志物的需求.
研究的目的:
- 用功能连接强度 (FCS) 来研究IBS中大脑网络的改变.
- 开发一种机器学习分类器,以区分IBS患者与健康对照.
- 为了确定潜在的fMRI基于IBS分类的生物标志物.
主要方法:
- 休息状态fMRI扫描对31名IBS患者和30名健康对照 (HC) 进行.
- 用功能连接强度 (FCS) 分析来评估全球大脑连接.
- 使用支持矢量机 (SVM) 模型,根据FCS变化对IBS患者和HC进行分类.
主要成果:
- 在IBS患者中,左侧中间轨道前皮层 (mOFC) 的FCS增加.
- 在IBS患者的双边环状皮质/前 (PCC/Pcu) 和中间环状皮质 (MCC) 中观察到FCS降低.
- 在将IBS患者与HC患者区分时,SVM分类器实现了91.9%的准确性.
结论:
- 参与疼痛和情绪调节的大脑区域的FCS模式改变是IBS的特征.
- 鉴定到的FCS变化显示出作为IBS分类的有效生物标志物的前景.
- 这项研究有助于了解IBS的神经机制,并改善临床诊断.
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