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在患有左前额质瘤的患者中,大脑动态性质的共享和恶性瘤特异性功能可塑性

Siqi Cai^1,2, Yuchao Liang³, Yinyan Wang³

¹Paul. C. Lauterbur Research Centers for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China.

Cerebral cortex (New York, N.Y. : 1991)

|November 27, 2023

概括

动态大脑网络分析揭示了脑质瘤的独特模式,有助于疾病的特征. 这些网络动态可以准确地区分质瘤等级,为诊断提供潜在的新生物标志物.

科学领域:

神经科学是一个神经科学.
医疗成像医学成像
计算生物学计算生物学

背景情况:

脑质瘤的进展与时间变化的大脑活动相关.
了解大脑网络动态对于表征质瘤病理和功能变化至关重要.

研究的目的:

用滑窗方法研究左额头质瘤患者的动态功能网络特性.
描述结质瘤中的通用和恶性瘤特异性功能重塑.
评估动态网络特征对质瘤分级的预测价值.

主要方法:

移动窗口分析以评估动态功能连接性和低频波动的幅度.
对体感官网络,背部注意网络和皮层下核的表征.
将网络动态活动集群成不同的状态.
支持矢量机 (SVM) 模型利用动态特征进行质瘤分级.

主要成果:

质瘤在体感官网络中的动态幅度降低,在注意力和皮层下网络中的功能连接性改变.
低级质瘤在动态幅度和连接性上显示出混乱的修改.
高度质瘤显示皮层-亚皮层连接性减弱,尾状幅降低.
一个独特的网络状态,以弱连接为特征,在患者中较少发生.
一个SVM模型在区分低级质瘤和高级质瘤方面取得了87.9%的准确性.

结论:

动态功能网络特性有效预测质瘤恶性等级.
这些动态特征有望成为用于质瘤表征和分级的新生物标志物.
大脑网络动态提供了关于质瘤诱导的功能重塑的见解.

关键词:

动态网络活动活动动态网络活动功能连接性的功能连接性在左前额质瘤.网络配置网络配置.支持矢量机器的支持矢量机器.

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