Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Acta Neuropathol. 2024 Sep 3;148(1):37. doi: 10.1007/s00401-024-02789-9.
The medial temporal lobe (MTL) is a hotspot for neuropathology, and measurements of MTL atrophy are often used as a biomarker for cognitive decline associated with neurodegenerative disease. Due to the aggregation of multiple proteinopathies in this region, the specific relationship of MTL atrophy to distinct neuropathologies is not well understood. Here, we develop two quantitative algorithms using deep learning to measure phosphorylated tau (p-tau) and TDP-43 (pTDP-43) pathology, which are both known to accumulate in the MTL and are associated with MTL neurodegeneration. We focus on these pathologies in the context of Alzheimer's disease (AD) and limbic predominant age-related TDP-43 encephalopathy (LATE) and apply our deep learning algorithms to distinct histology sections, on which MTL subregions were digitally annotated. We demonstrate that both quantitative pathology measures show high agreement with expert visual ratings of pathology and discriminate well between pathology stages. In 140 cases with antemortem MR imaging, we compare the association of semi-quantitative and quantitative postmortem measures of these pathologies in the hippocampus with in vivo structural measures of the MTL and its subregions. We find widespread associations of p-tau pathology with MTL subregional structural measures, whereas pTDP-43 pathology had more limited associations with the hippocampus and entorhinal cortex. Quantitative measurements of p-tau pathology resulted in a significantly better model of antemortem structural measures than semi-quantitative ratings and showed strong associations with cortical thickness and volume. By providing a more granular measure of pathology, the quantitative p-tau measures also showed a significant negative association with structure in a severe AD subgroup where semi-quantitative ratings displayed a ceiling effect. Our findings demonstrate the advantages of using quantitative neuropathology to understand the relationship of pathology to structure, particularly for p-tau, and motivate the use of quantitative pathology measurements in future studies.
内侧颞叶（MTL）是神经病理学的热点，MTL 萎缩的测量通常被用作与神经退行性疾病相关的认知能力下降的生物标志物。由于该区域存在多种蛋白病的聚集，因此 MTL 萎缩与特定神经病理学之间的具体关系尚不清楚。在这里，我们使用深度学习开发了两种定量算法来测量磷酸化 tau（p-tau）和 TDP-43（pTDP-43）病理学，这两种病理学都已知在 MTL 中积累，并与 MTL 神经退行性变有关。我们专注于阿尔茨海默病（AD）和边缘优势型年龄相关性 TDP-43 脑病（LATE）中的这些病理学，并将我们的深度学习算法应用于不同的组织学切片，其中 MTL 亚区被数字化标记。我们证明，这两种定量病理学测量与病理专家的视觉评估高度一致，并且可以很好地区分病理阶段。在 140 例有生前 MRI 的病例中，我们比较了这些病理学的半定量和定量死后测量与 MTL 及其亚区的体内结构测量之间的关联。我们发现 p-tau 病理学与 MTL 亚区结构测量广泛相关，而 pTDP-43 病理学与海马体和内嗅皮层的相关性有限。p-tau 病理学的定量测量导致对生前结构测量的模型明显优于半定量评分，并且与皮质厚度和体积呈强烈关联。通过提供更细粒度的病理学测量，定量 p-tau 测量还显示出与严重 AD 亚组结构的显著负相关，而半定量评分则显示出上限效应。我们的研究结果表明，使用定量神经病理学来了解病理学与结构之间的关系具有优势，尤其是对于 p-tau，并且激励在未来的研究中使用定量病理学测量。
