在功能子网络规模上预测末期病患者的认知功能状态
Yu Lu1, Tongqiang Liu2, Quan Sheng1
1School of Microelectronics and Control Engineering, Changzhou University, Changzhou 213164, China.
Mathematical biosciences and engineering : MBE
|March 29, 2024
概括
这项研究引入了一个新的框架,通过分析大脑功能子网络来预测末期病 (ESRD) 患者的认知功能. 这种方法为更好的临床决策和干预策略提供了更精确的特征.
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:
- 功能磁共振成像 (fMRI) 揭示了对理解认知至关重要的大脑功能网络.
- 全球网络的拓参数提供了广泛的观点,但缺乏对区域相互作用的详细见解.
- 现有的方法很难捕捉大脑网络中的复杂关系和信息传输.
研究的目的:
- 开发一个框架,以功能子网络规模 (CFSFSS) 预测末期病 (ESRD) 患者的认知功能状态.
- 通过检查子网络而不是全球网络来提高认知功能预测的准确性.
- 为ESRD患者的认知障碍提供更精确的临床决策和干预工具.
主要方法:
- 通过将不同网络指标的节点结合起来,提出了功能子网络规模的认知功能框架 (CFSFSS).
- 提取功能子网络的拓属性参数作为特征,使用最小冗余最大相关性 (mRMR) 进行选择.
- 优化支持向量回归 (SVR) 参数,使用增强的鱼优化算法 (E-WOA) 进行全球优化.
主要成果:
- 与其他方法相比,CFSFSS表现出优越的预测性能.
- 实现了低误差指标:平均绝对误差 (MAE) 为0.5951,平均绝对百分比误差 (MAPE) 为0.0281,根平均平方误差 (RMSE) 为0.9994.
- 确定了与认知功能状态相关的活跃大脑区域,产生了更精确的预测特征.
结论:
- 功能子网络方法为预测ESRD患者认知功能状态提供了更精确的特征.
- CFSFSS提高了认知功能预测的准确性,有助于临床决策和认知障碍干预.
- 这种方法为与ESRD认知衰退相关的复杂大脑网络变化提供了有价值的见解.
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