基于结构连接体的认知缺陷预测建模在治疗的质瘤患者的认知缺陷
Michel Friedrich1, Christian P Filss1, Philipp Lohmann1
1Institute of Neuroscience and Medicine (INM-1, INM-3, INM-4, INM-11), Forschungszentrum Juelich, Juelich, Germany.
Neuro-oncology advances
|January 10, 2024
概括
质瘤患者中的患者.
科学领域:
- 神经科学是一个神经科学.
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
背景情况:
- 质瘤及其治疗可以导致影响认知功能的脑病变.
- 维持大脑网络之间的结构连接被假设为质瘤患者的认知至关重要.
研究的目的:
- 为了研究结构性大脑网络连接和脑瘤患者的认知表现之间的关系.
- 为了确定这一群体认知缺陷的关键大脑区域和连接.
主要方法:
- 121名质瘤患者接受了认知测试和先进的混合PET/MRI.
- 整个大脑纤维连接矩阵是使用O-(2-[18F]乙烯) -L-氨酸PET/MRI和通道图生成的.
- 机器学习模型被用来识别预测性网络连接和关键的皮质区域.
主要成果:
- 与对照组相比,质瘤患者在大多数测试中表现出明显较低的认知分数.
- 机器学习模型使用很小比例的结构连接预测认知得分,其中84%的预测连接跨越不同的大脑网络.
- 关键的皮层区域包括视觉,体力运动,注意力和默认模式网络中的节点,特别是在左侧.
结论:
- 质瘤患者的认知表现与网络间的结构连接性密切相关.
- 已建立的网络枢纽的完整性,也与其他神经疾病有关,对于在质瘤治疗后保持认知是至关重要的.
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