时间叶中的语言和记忆网络变化:功能和结构连接性研究
Alireza Fallahi1, Mohammad-Reza Nazem-Zadeh1, Narges Hosseini-Tabatabaei1
1From the Department of Biomedical Engineering (Alireza.Fallahi.), Hamedan University of Technology, Hamedan, Iran; Department of Neurosciences, Addiction Studies & Neuroimaging (Mohammad-Reza Nazem-Zadeh), School of Advanced Technologies in Medicine, Medical School (Narges Hosseini-Tabatabaei), Tehran University of Medical Sciences, Tehran, Iran; Isfahan Neuroscience Research Center (Jafar Mehvari Habibabadi), Isfahan University of Medical Sciences, Isfahan, Iran; Pars Advanced Medical Research Center (Seyed-Sohrab Hashemi-Fesharaki), Pars Hospital, Tehran, Iran; CIPCE, School of Electrical and Computer Engineering (Hamid Soltanian-Zadeh), Faculty of Engineering, University of Tehran, Tehran, Iran; Department of Neurosciences (Mohammad-Reza Nazem-Zadeh), Monash University, Melbourne, VIC, Australia; Medical Image Analysis Lab., Departments of Radiology and Research Administration (Hamid Soltanian-Zadeh), Henry Ford Health System, Detroit, MI, USA.
这项研究揭示了叶 (TLE) 患者的大脑连接的改变,在手术前显示功能性超连接性,在手术后显示低连接性. 机器学习使用这些连接模式准确地识别了TLE横向化.
科学领域:
- 神经科学是一个神经科学.
- 医疗成像医学成像
- 计算神经科学是一种神经科学.
背景情况:
- 时间叶 (TLE) 涉及到大脑网络的复杂变化.
- 了解TLE中的语言记忆网络 (LMN) 对诊断和治疗至关重要.
- 在TLE中术前和术后网络变化需要详细调查.
研究的目的:
- 评估TLE中LMN功能和结构连接的术前和术后变化.
- 利用图形理论和机器学习来实现自动的TLE横向化.
- 探索TLE患者大脑连接和认知功能之间的关系.
主要方法:
- 休息状态fMRI和扩散张力成像 (DTI) 数据来自35名TLE患者和20名健康对照 (HCs).
- 在手术前和手术后的LMN内计算功能和结构连接.
- 应用图形理论,特征选择和机器学习算法 (SVM,天真贝叶斯,逻辑回归) 进行分类和横向化.
主要成果:
- 在外科手术前,TLE患者表现出功能性增加和结构性连接性降低,在手术后逆转.
- 机器学习模型,特别是带有遗传算法的SVM,在分类TLE亚型 (LTLE与RTLE) 和区分TLE与HC时实现了高精度 (高达97%).
- 关键的变化区域包括近海马 (LTLE) 和部区域 (RTLE);结构变化源于双边尾和左侧语言区域.
- 在连接特征和与记忆相关的认知得分之间发现了显著的相关性.
结论:
- TLE的特点是LMN内的动态功能和结构连接性改变,具有明显的手术前和后模式.
- 机器学习方法显示出对客观的TLE诊断和横向化的巨大潜力.
- 这项研究强调了先进的神经成像和计算方法在理解TLE病理生理学的有用性.
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