Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON, Canada.
Neurosciences and Mental Health, SickKids Research Institute, Toronto, ON, Canada.
J Neural Eng. 2020 Dec 16;17(6). doi: 10.1088/1741-2552/abc8d6.
. The present study explores the effectiveness of incorporating temporal information in predicting post-traumatic stress disorder (PTSD) severity using magnetoencephalography (MEG) imaging data. The main objective was to assess the relationship between longitudinal MEG functional connectome data, measured across a variety of neural oscillatory frequencies and collected at two timepoints (Phase I and II), against PTSD severity captured at the later time point.. We used an in-house developed informatics solution, featuring a two-step process featuring pre-learn feature selection (CV-SVR-rRF-FS, cross-validation with support vector regression (SVR) and recursive random forest feature selection) and deep learning (long-short term memory recurrent neural network, LSTM-RNN) techniques.. The pre-learn step selected a small number of functional connections (or edges) from Phase I MEG data associated with Phase II PTSD severity, indexed using the PTSD CheckList (PCL) score. This strategy identified the functional edges affected by traumatic exposure and indexed disease severity, either permanently or evolving dynamically over time, for optimal predictive performance. Using the selected functional edges, LSTM modelling was used to incorporate the Phase II MEG data into longitudinal regression models. Single timepoint (Phase I and Phase II MEG data) SVR models were generated for comparison. Assessed with holdout test data, alpha and high gamma bands showed enhanced predictive performance with the longitudinal models comparing to the Phase I single timepoint models. The best predictive performance was observed for lower frequency ranges compared to the higher frequencies (low gamma), for both model types.. This study identified the neural oscillatory signatures that benefited from additional temporal information when estimating the outcome of PTSD severity using MEG functional connectome data. Crucially, this approach can similarly be applied to any other mental health challenge, using this effective informatics foundation for longitudinal tracking of pathological brain states and predicting outcome with a MEG-based neurophysiology imaging system.
本研究探讨了在使用脑磁图（MEG）成像数据预测创伤后应激障碍（PTSD）严重程度时纳入时间信息的有效性。主要目的是评估在两个时间点（第一阶段和第二阶段）测量的跨多种神经振荡频率的纵向 MEG 功能连接组学数据与稍后时间点 PTSD 严重程度之间的关系。我们使用了内部开发的信息学解决方案，该解决方案具有两步流程，包括预学习特征选择（CV-SVR-rRF-FS，支持向量回归（SVR）和递归随机森林特征选择的交叉验证）和深度学习（长短时记忆递归神经网络，LSTM-RNN）技术。预学习步骤从第一阶段 MEG 数据中选择了一小部分与第二阶段 PTSD 严重程度相关的功能连接（或边缘），使用 PTSD 检查表（PCL）得分进行索引。该策略确定了受创伤暴露影响并索引疾病严重程度的功能边缘，无论是永久性的还是随时间动态演变的，以实现最佳预测性能。使用所选功能边缘，使用 LSTM 建模将第二阶段 MEG 数据纳入纵向回归模型。生成了单时间点（第一阶段和第二阶段 MEG 数据）SVR 模型进行比较。使用保留测试数据评估，与单时间点第一阶段模型相比，alpha 和高伽马波段的纵向模型显示出增强的预测性能。与较高频率（低伽马）相比，两种模型类型都观察到较低频率范围的最佳预测性能。本研究确定了在使用 MEG 功能连接组学数据估计 PTSD 严重程度的结果时，从额外时间信息中受益的神经振荡特征。至关重要的是，这种方法可以类似地应用于任何其他心理健康挑战，使用这种有效的信息学基础对病理脑状态进行纵向跟踪，并使用基于 MEG 的神经生理学成像系统预测结果。
