背景技术已经显示出功能连通性随时间波动。本研究旨在从静息状态fMRI数据中识别具有动态功能连接(dFC)的重度抑郁症(MDD)。这将有助于产生早期抑郁症诊断工具,增强我们对抑郁症病因的认识。方法收集178例受试者的静息态功能磁共振成像资料,包括89例MDD和89例健康对照。我们提出了一个用于dFC分析的时空学习和解释框架。开发了一种有效的时空模型,用于使用dFC对健康对照的MDD进行分类。该模型是一个堆叠神经网络模型,它通过基于多层感知器的空间编码器学习网络结构信息,并通过基于Transformer的时间编码器学习时变模式。我们建议用重要性特征提取和无序相关模式探索的两阶段解释方法来解释时空模型。引入逐层相关传播(LRP)方法提取模型中最相关的输入特征,利用LRP的注意机制提取dFC的重要时间步长。与疾病相关的功能联系,大脑区域,并对模型中的大脑状态进行了进一步的探索和识别。结果我们在使用dFC数据从健康对照中识别MDD方面取得了最好的分类性能。最重要的功能连接,大脑区域,并且已经确定了与MDD密切相关的动态状态。限制数据预处理可能会影响模型的分类性能,这项研究需要在更大的患者人群中进一步验证.结论实验结果表明,所提出的时空模型能够有效地对MDD进行分类,并揭示抑郁症中dFC的结构和时间模式。
BACKGROUND: Functional connectivity has been shown to fluctuate over time. The present study aimed to identifying major depressive disorders (MDD) with dynamic functional connectivity (dFC) from resting-state fMRI data, which would be helpful to produce tools of early depression diagnosis and enhance our understanding of depressive etiology.
METHODS: The resting-state fMRI data of 178 subjects were collected, including 89 MDD and 89 healthy controls. We propose a spatio-temporal learning and explaining framework for dFC analysis. A yet effective spatio-temporal model is developed to classifying MDD from healthy controls with dFCs. The model is a stacking neural network model, which learns network structure information by a multi-layer perceptron based spatial encoder, and learns time-varying patterns by a Transformer based temporal encoder. We propose to explain the spatio-temporal model with a two-stage explanation method of importance feature extracting and disorder-relevant pattern exploring. The layer-wise relevance propagation (LRP) method is introduced to extract the most relevant input features in the model, and the attention mechanism with LRP is applied to extract the important time steps of dFCs. The disorder-relevant functional connections, brain regions, and brain states in the model are further explored and identified.
RESULTS: We achieved the best classification performance in identifying MDD from healthy controls with dFC data. The top important functional connectivity, brain regions, and dynamic states closely related to MDD have been identified.
CONCLUSIONS: The data preprocessing may affect the classification performance of the model, and this study needs further validation in a larger patient population.
CONCLUSIONS: The experimental results demonstrate that the proposed spatio-temporal model could effectively classify MDD, and uncover structural and temporal patterns of dFCs in depression.