Concurrent neurodegenerative and vascular pathologies pose a diagnostic challenge in the clinical setting, with histopathology remaining the definitive modality for dementia-type diagnosis. To address this clinical challenge, we introduce a neuropathology-based, data-driven, multi-label deep learning framework to identify and quantify in-vivo biomarkers for Alzheimer's disease (AD), vascular dementia (VD), and Lewy body dementia (LBD) using antemortem T1-weighted MRI scans of 423 demented and 361 control participants from NACC and ADNI datasets. Based on the best-performing deep learning model, explainable heatmaps are extracted to visualize disease patterns, and the novel Deep Signature of Pathology Atrophy REcognition (DeepSPARE) indices are developed, where a higher DeepSPARE score indicates more brain alterations associated with that specific pathology. A substantial discrepancy in clinical and neuropathology diagnosis was observed in the demented patients: 71% of them had more than one pathology, but 67% of them were clinically diagnosed as AD only. Based on these neuropathology diagnoses and leveraging cross-validation principles, the deep learning model achieved the best performance with a balanced accuracy of 0.844, 0.839, and 0.623 for AD, VD, and LBD, respectively, and was used to generate the explainable deep-learning heatmaps and DeepSPARE indices. The explainable deep-learning heatmaps revealed distinct neuroimaging brain alteration patterns for each pathology: the AD heatmap highlighted bilateral hippocampal regions, the VD heatmap emphasized white matter regions, and the LBD heatmap exposed occipital alterations. The DeepSPARE indices were validated by examining their associations with cognitive testing, neuropathological, and neuroimaging measures using linear mixed-effects models. The DeepSPARE-AD index was associated with MMSE, Trail B, memory, PFDR-adjustedhippocampal volume, Braak stages, CERAD scores, and Thal phases (PFDR-adjusted < 0.05). The DeepSPARE-VD index was associated with white matter hyperintensity volume and cerebral amyloid angiopathy (PFDR-adjusted < 0.001). The DeepSPARE-LBD index was associated with Lewy body stages (PFDR-adjusted < 0.05). The findings were replicated in an out-of-sample ADNI dataset by testing associations with cognitive, imaging, plasma, and CSF measures. CSF and plasma pTau181 were significantly associated with DeepSPARE-AD in the AD/MCIΑβ+ group (PFDR-adjusted < 0.001), and CSF α-synuclein was associated solely with DeepSPARE-LBD (PFDR-adjusted = 0.036). Overall, these findings demonstrate the advantages of our innovative deep-learning framework in detecting antemortem neuroimaging signatures linked to different pathologies. The newly deep learning-derived DeepSPARE indices are precise, pathology-sensitive, and single-valued noninvasive neuroimaging metrics, bridging the traditional widely available in-vivo T1 imaging with histopathology.

中文翻译：

---

深度学习揭示了阿尔茨海默病、血管和路易体痴呆症的病理学证实的神经影像学特征


并发的神经退行性和血管病变在临床环境中构成了诊断挑战，组织病理学仍然是痴呆类型诊断的确定性模式。为了应对这一临床挑战，我们引入了一个基于神经病理学、数据驱动的多标签深度学习框架，以识别和量化阿尔茨海默病 （AD）、血管性痴呆 （VD） 和路易体痴呆 （LBD） 的体内生物标志物，使用来自 NACC 和 ADNI 数据集的 423 名痴呆和 361 名对照参与者的生前 T1 加权 MRI 扫描。基于性能最佳的深度学习模型，提取可解释的热图以可视化疾病模式，并开发了新的病理萎缩认知深度特征 （DeepSPARE） 指数，其中 DeepSPARE 分数越高表示与该特定病理相关的大脑改变越多。在痴呆症患者中观察到临床和神经病理学诊断存在很大差异：其中 71% 的患者患有不止一种病理，但其中 67% 的患者在临床上仅被诊断为 AD。基于这些神经病理学诊断并利用交叉验证原则，深度学习模型实现了最佳性能，AD、VD 和 LBD 的平衡准确率分别为 0.844、0.839 和 0.623，并用于生成可解释的深度学习热图和 DeepSPARE 指数。可解释的深度学习热图揭示了每种病理的不同神经影像学大脑改变模式：AD 热图突出显示了双侧海马区域，VD 热图强调了白质区域，LBD 热图暴露了枕骨改变。 通过使用线性混合效应模型检查它们与认知测试、神经病理学和神经影像学测量的关联来验证 DeepSPARE 指数。DeepSPARE-AD 指数与 MMSE 、 Trail B 、 记忆、 PFDR 调整后的海马体积、 Braak 分期、 CERAD 评分和 Thal 期 （PFDR 调整后的 < 0.05） 相关。DeepSPARE-VD 指数与白质高信号体积和脑淀粉样血管病 （PFDR 校正 < 0.001） 相关。DeepSPARE-LBD 指数与 Lewy 体期相关 （PFDR 校正 < 0.05）。通过测试与认知、成像、血浆和 CSF 测量的关联，这些发现在样本外 ADNI 数据集中复制。AD/MCIΑβ + 组 CSF 和血浆 pTau181 与 DeepSPARE-AD 显著相关 （PFDR 校正 < 0.001），CSF α-突触核蛋白仅与 DeepSPARE-LBD 相关 （PFDR 校正 = 0.036）。总体而言，这些发现证明了我们创新的深度学习框架在检测与不同病理相关的生前神经影像学特征方面的优势。新开发的深度学习衍生的 DeepSPARE 指数是精确的、对病理学敏感的单值无创神经影像学指标，将传统广泛使用的体内 T1 成像与组织病理学联系起来。