Engemann Denis A, Mellot Apolline, Höchenberger Richard, Banville Hubert, Sabbagh David, Gemein Lukas, Ball Tonio, Gramfort Alexandre
Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland; Université Paris-Saclay, Inria, CEA, Palaiseau, France; Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neurology, D-04103, Leipzig, Germany.
Université Paris-Saclay, Inria, CEA, Palaiseau, France.
Neuroimage. 2022 Nov 15;262:119521. doi: 10.1016/j.neuroimage.2022.119521. Epub 2022 Jul 26.
Population-level modeling can define quantitative measures of individual aging by applying machine learning to large volumes of brain images. These measures of brain age, obtained from the general population, helped characterize disease severity in neurological populations, improving estimates of diagnosis or prognosis. Magnetoencephalography (MEG) and Electroencephalography (EEG) have the potential to further generalize this approach towards prevention and public health by enabling assessments of brain health at large scales in socioeconomically diverse environments. However, more research is needed to define methods that can handle the complexity and diversity of M/EEG signals across diverse real-world contexts. To catalyse this effort, here we propose reusable benchmarks of competing machine learning approaches for brain age modeling. We benchmarked popular classical machine learning pipelines and deep learning architectures previously used for pathology decoding or brain age estimation in 4 international M/EEG cohorts from diverse countries and cultural contexts, including recordings from more than 2500 participants. Our benchmarks were built on top of the M/EEG adaptations of the BIDS standard, providing tools that can be applied with minimal modification on any M/EEG dataset provided in the BIDS format. Our results suggest that, regardless of whether classical machine learning or deep learning was used, the highest performance was reached by pipelines and architectures involving spatially aware representations of the M/EEG signals, leading to R scores between 0.60-0.74. Hand-crafted features paired with random forest regression provided robust benchmarks even in situations in which other approaches failed. Taken together, this set of benchmarks, accompanied by open-source software and high-level Python scripts, can serve as a starting point and quantitative reference for future efforts at developing M/EEG-based measures of brain aging. The generality of the approach renders this benchmark reusable for other related objectives such as modeling specific cognitive variables or clinical endpoints.
群体水平建模可以通过将机器学习应用于大量脑图像来定义个体衰老的定量指标。这些从普通人群中获得的脑年龄指标有助于刻画神经疾病人群的疾病严重程度，改善诊断或预后的评估。脑磁图（MEG）和脑电图（EEG）有潜力通过在社会经济多样化的环境中大规模评估脑健康，进一步将这种方法推广到预防和公共卫生领域。然而，需要更多研究来定义能够处理不同现实世界背景下M/EEG信号的复杂性和多样性的方法。为推动这项工作，我们在此提出用于脑年龄建模的竞争性机器学习方法的可重复使用基准。我们在来自不同国家和文化背景的4个国际M/EEG队列中，对先前用于病理学解码或脑年龄估计的流行经典机器学习管道和深度学习架构进行了基准测试，这些队列包括来自2500多名参与者的记录。我们的基准是基于BIDS标准的M/EEG改编版本构建的，提供了可以在以BIDS格式提供的任何M/EEG数据集上进行最小修改即可应用的工具。我们的结果表明，无论使用经典机器学习还是深度学习，涉及M/EEG信号空间感知表示的管道和架构都能达到最高性能，相关系数R在0.60 - 0.74之间。即使在其他方法失败的情况下，手工制作的特征与随机森林回归相结合也能提供稳健的基准。总之，这组基准，连同开源软件和高级Python脚本，可以作为未来开发基于M/EEG的脑衰老测量方法的起点和定量参考。该方法的通用性使这个基准可用于其他相关目标，如对特定认知变量或临床终点进行建模。
