Nguyen Alex A, Maia Pedro D, Gao Xiao, F Damasceno Pablo, Raj Ashish
Department of Radiology and Biomedical Imaging, UC San Francisco, San Francisco, CA 94107, USA.
Bakar Computational Health Sciences Institute, UC San Francisco, San Francisco, CA 94158, USA.
Brain Sci. 2020 Jan 30;10(2):73. doi: 10.3390/brainsci10020073.
The release of a broad, longitudinal anatomical dataset by the Parkinson's Progression Markers Initiative promoted a surge of machine-learning studies aimed at predicting disease onset and progression. However, the excessive number of features used in these models often conceals their relationship to the Parkinsonian symptomatology.
The aim of this study is two-fold: (i) to predict future motor and cognitive impairments up to four years from brain features acquired at baseline; and (ii) to interpret the role of pivotal brain regions responsible for different symptoms from a neurological viewpoint.
We test several deep-learning neural network configurations, and report our best results obtained with an autoencoder deep-learning model, run on a 5-fold cross-validation set. Comparison with Existing Methods: Our approach improves upon results from standard regression and others. It also includes neuroimaging biomarkers as features.
The relative contributions of pivotal brain regions to each impairment change over time, suggesting a dynamical reordering of culprits as the disease progresses. Specifically, the Putamen is initially the most critical region accounting for the overall cognitive state, only being surpassed by the Substantia Nigra in later years. The Pallidum is the first region to influence motor scores, followed by the parahippocampal and ambient gyri, and the anterior orbital gyrus.
While the causal link between regional brain atrophy and Parkinson symptomatology is poorly understood, our methods demonstrate that the contributions of pivotal regions to cognitive and motor impairments are more dynamical than generally appreciated.
帕金森病进展标志物计划发布的一个广泛的纵向解剖学数据集推动了大量旨在预测疾病发作和进展的机器学习研究。然而，这些模型中使用的特征数量过多，常常掩盖了它们与帕金森症状学之间的关系。
本研究的目的有两个：（i）根据基线时获取的脑特征预测未来长达四年的运动和认知障碍；（ii）从神经学角度解释关键脑区对不同症状的作用。
我们测试了几种深度学习神经网络配置，并报告了在五折交叉验证集上运行自动编码器深度学习模型获得的最佳结果。与现有方法的比较：我们的方法改进了标准回归和其他方法的结果。它还包括神经影像学生物标志物作为特征。
关键脑区对每种损伤的相对贡献随时间变化，表明随着疾病进展，罪魁祸首在动态重新排序。具体而言，壳核最初是占总体认知状态的最关键区域，仅在后期被黑质超越。苍白球是影响运动评分的第一个区域，其次是海马旁回和环回，以及眶前回。
虽然区域脑萎缩与帕金森症状学之间的因果关系尚不清楚，但我们的方法表明，关键区域对认知和运动障碍的贡献比一般认为的更具动态性。
