大脑动力学 复杂性作为帕金森病认知衰退的标志
Eleonora Fiorenzato1, Sadaf Moaveninejad2, Luca Weis1,3
1Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy.
概括
碎形维度 (FD) 比fALFF更好地区分帕金森病 (PD) 的认知状态,揭示了与痴呆症相关的大脑复杂性增加. 作为PD认知衰退的预后生物标志物,FD显示出前景.
科学领域:
- 神经科学是一个神经科学.
- 生物标志物发现发现
- 机器学习在医学中的应用
背景情况:
- 休息状态功能磁共振成像 (rs-fMRI) 分析大脑动态.
- 希古奇的碎形维度 (FD) 量化了大脑活动中的复杂性.
- 在帕金森病 (PD) 认知衰退中,FD可能检测到神经元相互作用的复杂性.
研究的目的:
- 将FD与低频波动的分数振幅 (fALFF) 进行比较,以区分PD认知状态.
- 使用机器学习 (ML) 来确定区分PD-NC,PD-MCI和PDD的最佳方法.
- 探索fALFF,FD和临床/认知PD特征之间的相关性.
主要方法:
- 分析了来自118名PD患者和35名对照组的rs-fMRI数据.
- 患者分为PD与正常认知 (PD-NC),轻度认知障碍 (PD-MCI) 和痴呆症 (PDD) 的PD.
- 计算并用于训练ML模型的FD和fALFF指标.
主要成果:
- 在区分PD-认知状态方面,FD的准确度达到了78%,超过了fALFF (62%).
- 在传感器,中央执行 (CEN) 和默认模式网络 (DMN) 中观察到复杂性增加 (FD).
- 在CEN和DMN中减少自发活动 (fALFF);与痴呆和认知衰退相关的复杂性增加.
结论:
- PD认知衰退涉及改变自发活动和增加CEN和DMN的时间复杂性.
- 这些网络变化可能表明隔离的增加.
- 提议将FD作为PD认知衰退的预后生物标志物.
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