AI 中文总结
该研究提出量子启发的张量网络框架，结合分类功能与可解释性，对脑结构MRI数据的两类脑疾病分类任务展开分析，识别出稳定的脑区特征并验证了方法的解释价值。
AI 中文摘要
识别脑疾病的稳健神经生物学特征需要兼具预测性能与特征交互可解释表示的机器学习方法。本文提出一种基于张量网络机器学习的量子启发框架，该框架学习以矩阵积态（一种最初为量子多体系统开发的变分近似）表示的灰质特征的分布式表示。训练后的模型不仅可作为分类器，还能提取特征间的量子连接相关性，这些相关性编码高阶特征交互，并被映射为加权图。该构造使我们能在单一表示中同时追踪：（i）网络的全局谱性质（捕捉集体学习动力学）；（ii）节点级中心性度量（提供单个脑区的可解释特征）。采用重复训练-测试采样方案，我们对结构磁共振成像数据上的两项复杂脑疾病分类任务进行分析：健康对照 vs 精神分裂症，健康对照 vs 双相情感障碍。节点级分析确定了一组稳定的灰质特征，最突出的是赫氏回、岛叶皮质和额叶区域，它们在多种中心性度量和多次重采样中均作为枢纽。这些中心性在重采样中的变异性低于SHAP值，支持该网络表示的解释价值。双相情感障碍的特征集是精神分裂症特征集的子集，与神经影像学研究中报道的分层神经解剖学改变一致，并为该层级提供基于网络的表征。
英文摘要
Identifying robust neurobiological signatures of brain disorders requires machine learning approaches that combine predictive performance with interpretable representations of feature interactions. Here we introduce a quantum-inspired framework based on tensor network machine learning that learns distributed representations of gray-matter features encoded in a Matrix Product State representation, a variational ansatz originally developed for quantum many-body systems. The trained model is then used not only as a classifier but to extract quantum connected correlations between features, which encode higher-order feature interactions and which we map onto a weighted graph. This construction allows us to track, within a single representation, both: (i) the global spectral properties of the network (capturing collective learning dynamics), and (ii) node-level centrality measures (providing interpretable signatures of individual brain regions). Using repeated train-test sampling schemes, we analyze two classification tasks on structural MRI data as examples of complex brain disorders: healthy controls versus schizophrenia and versus bipolar disorder. Node-level analysis identifies a stable set of gray-matter features, most prominently Heschl gyrus, insular cortex, and frontal regions, that act as hubs across multiple centrality measures and across resamplings. These centralities display lower variability across resamplings than Shapley values, supporting the interpretive value of the network representation. The bipolar feature set emerges as a subset of the schizophrenia one, consistent with the hierarchically organized neuroanatomical alterations reported in neuroimaging studies and offering a network-based characterization of this hierarchy.
