Safai Apoorva, Vakharia Nirvi, Prasad Shweta, Saini Jitender, Shah Apurva, Lenka Abhishek, Pal Pramod Kumar, Ingalhalikar Madhura
Symbiosis Center for Medical Image Analysis, Symbiosis Institute of Technology, Symbiosis International University, Pune, India.
Department of Neurology, National Institute of Mental Health and Neurosciences, Bengaluru, India.
Front Neurosci. 2022 Feb 23;15:741489. doi: 10.3389/fnins.2021.741489. eCollection 2021.
A multimodal connectomic analysis using diffusion and functional MRI can provide complementary information on the structure-function network dynamics involved in complex neurodegenerative network disorders such as Parkinson's disease (PD). Deep learning-based graph neural network models generate higher-level embeddings that could capture intricate structural and functional regional interactions related to PD.
This study aimed at investigating the role of structure-function connections in predicting PD, by employing an end-to-end graph attention network (GAT) on multimodal brain connectomes along with an interpretability framework.
The proposed GAT model was implemented to generate node embeddings from the structural connectivity matrix and multimodal feature set containing morphological features and structural and functional network features of PD patients and healthy controls. Graph classification was performed by extracting topmost node embeddings, and the interpretability framework was implemented using saliency analysis and attention maps. Moreover, we also compared our model with unimodal models as well as other state-of-the-art models.
Our proposed GAT model with a multimodal feature set demonstrated superior classification performance over a unimodal feature set. Our model demonstrated superior classification performance over other comparative models, with 10-fold CV accuracy and an F1 score of 86% and a moderate test accuracy of 73%. The interpretability framework highlighted the structural and functional topological influence of motor network and cortico-subcortical brain regions, among which structural features were correlated with onset of PD. The attention maps showed dependency between large-scale brain regions based on their structural and functional characteristics.
Multimodal brain connectomic markers and GAT architecture can facilitate robust prediction of PD pathology and provide an attention mechanism-based interpretability framework that can highlight the pathology-specific relation between brain regions.
使用扩散张量成像和功能磁共振成像进行的多模态连接组学分析，可以为帕金森病（PD）等复杂神经退行性网络疾病中涉及的结构-功能网络动力学提供补充信息。基于深度学习的图神经网络模型可生成更高级别的嵌入，从而捕捉与PD相关的复杂结构和功能区域间相互作用。
本研究旨在通过在多模态脑连接组上采用端到端图注意力网络（GAT）以及一个可解释性框架，来探究结构-功能连接在预测PD中的作用。
所提出的GAT模型用于从结构连接矩阵以及包含PD患者和健康对照的形态学特征、结构和功能网络特征的多模态特征集中生成节点嵌入。通过提取最顶层的节点嵌入进行图分类，并使用显著性分析和注意力图来实现可解释性框架。此外，我们还将我们的模型与单模态模型以及其他先进模型进行了比较。
我们提出的具有多模态特征集的GAT模型在分类性能上优于单模态特征集。我们的数据表明，该模型在分类性能上优于其他对比模型，10倍交叉验证准确率和F1分数为86%，中等测试准确率为73%。可解释性框架突出了运动网络和皮质-基底节脑区的结构和功能拓扑影响，其中结构特征与PD的发病相关。注意力图显示了基于其结构和功能特征的大规模脑区之间的依赖性。
多模态脑连接组学标记和GAT架构有助于对PD病理进行可靠预测，并提供一个基于注意力机制的可解释性框架，该框架可以突出脑区之间的病理特异性关系。
