To understand the biological characteristics of neurological disorders with functional connectivity (FC), recent studies have widely utilized deep learning-based models to identify the disease and conducted post-hoc analyses via explainable models to discover disease-related biomarkers. Most existing frameworks consist of three stages, namely, feature selection, feature extraction for classification, and analysis, where each stage is implemented separately. However, if the results at each stage lack reliability, it can cause misdiagnosis and incorrect analysis in afterward stages. In this study, we propose a novel unified framework that systemically integrates diagnoses (i.e., feature selection and feature extraction) and explanations. Notably, we devised an adaptive attention network as a feature selection approach to identify individual-specific disease-related connections. We also propose a functional network relational encoder that summarizes the global topological properties of FC by learning the inter-network relations without pre-defined edges between functional networks. Last but not least, our framework provides a novel explanatory power for neuroscientific interpretation, also termed counter-condition analysis. We simulated the FC that reverses the diagnostic information (i.e., counter-condition FC): converting a normal brain to be abnormal and vice versa. We validated the effectiveness of our framework by using two large resting-state functional magnetic resonance imaging (fMRI) datasets, Autism Brain Imaging Data Exchange (ABIDE) and REST-meta-MDD, and demonstrated that our framework outperforms other competing methods for disease identification. Furthermore, we analyzed the disease-related neurological patterns based on counter-condition analysis.

翻译：为理解神经疾病的功能连接（FC）生物特征，近期研究广泛采用基于深度学习的模型进行疾病识别，并通过可解释模型开展事后分析以发现疾病相关生物标志物。现有框架大多包含三个独立实施的阶段：特征选择、用于分类的特征提取以及分析。然而，若各阶段结果缺乏可靠性，将导致后续阶段的误诊和错误分析。本研究提出一种新型统一框架，系统性整合诊断（即特征选择与特征提取）与解释。值得关注的是，我们设计了自适应注意力网络作为特征选择方法，用于识别个体特异性疾病相关连接。同时提出功能网络关系编码器，通过无预定义功能网络间边的情况下学习网络间关系，总结FC的全局拓扑特性。最重要的是，本框架为神经科学解释提供了新型解释能力——反条件分析。我们模拟了反转诊断信息的FC（即反条件FC）：将正常脑部转为异常状态，反之亦然。通过使用两个大型静息态功能磁共振成像（fMRI）数据集——自闭症脑成像数据交换库（ABIDE）和REST-meta-MDD，验证了框架有效性，并证明本框架在疾病识别中优于其他对比方法。此外，我们基于反条件分析研究了疾病相关神经模式。