Background Primary progressive aphasia (PPA) defines a group of neurodegenerative disorders characterised by language decline. Three PPA variants correlate with distinct underlying pathologies: semantic variant PPA (svPPA) with transactive response DNA-binding protein of 43 kD (TDP-43) proteinopathy, agrammatic variant PPA (agPPA) with tau deposition and logopenic variant PPA (lvPPA) with Alzheimer’s disease (AD). Our objectives were to differentiate PPA variants using clinical and neuroimaging features, assess progression and evaluate structural MRI and a novel 18-F fluorodeoxyglucose positron emission tomography (FDG-PET) image decomposition machine learning algorithm for neuropathology prediction. Methods We analysed 82 autopsied patients diagnosed with PPA from 1998 to 2022. Clinical histories, language characteristics, neuropsychological results and brain imaging were reviewed. A machine learning framework using a k -nearest neighbours classifier assessed FDG-PET scans from 45 patients compared with a large reference database. Results PPA variant distribution: 35 lvPPA (80% AD), 28 agPPA (89% tauopathy) and 18 svPPA (72% frontotemporal lobar degeneration-TAR DNA-binding protein (FTLD-TDP)). Apraxia of speech was associated with 4R-tauopathy in agPPA, while pure agrammatic PPA without apraxia was linked to 3R-tauopathy. Longitudinal data revealed language dysfunction remained the predominant deficit for patients with lvPPA, agPPA evolved to corticobasal or progressive supranuclear palsy syndrome (64%) and svPPA progressed to behavioural variant frontotemporal dementia (44%). agPPA-4R-tauopathy exhibited limited pre-supplementary motor area atrophy, lvPPA-AD displayed temporal atrophy extending to the superior temporal sulcus and svPPA-FTLD-TDP had severe temporal pole atrophy. The FDG-PET-based machine learning algorithm accurately predicted clinical diagnoses and underlying pathologies. Conclusions Distinguishing 3R-taupathy and 4R-tauopathy in agPPA may rely on apraxia of speech presence. Additional linguistic and clinical features can aid neuropathology prediction. Our data-driven brain metabolism decomposition approach effectively predicts underlying neuropathology. Data are available upon reasonable request. For some reason this section cannot be edited.

中文翻译：

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原发性进行性失语症的临床放射学和神经病理学评估

背景 原发性进行性失语症（PPA）定义了一组以语言衰退为特征的神经退行性疾病。三种 PPA 变体与不同的潜在病理相关：语义变体 PPA (svPPA) 与 43 kD 的交互反应 DNA 结合蛋白 (TDP-43) 蛋白病、语法错误变体 PPA (agPPA) 与 tau 沉积以及与阿尔茨海默病相关的逻辑减少变体 PPA (lvPPA)疾病（AD）。我们的目标是利用临床和神经影像特征区分 PPA 变异，评估进展并评估结构 MRI 和用于神经病理学预测的新型 18-F 氟脱氧葡萄糖正电子发射断层扫描 (FDG-PET) 图像分解机器学习算法。方法 我们分析了 1998 年至 2022 年 82 名尸检诊断为 PPA 的患者。回顾了临床病史、语言特征、神经心理学结果和脑成像。使用 ak 最近邻分类器的机器学习框架评估了 45 名患者的 FDG-PET 扫描，并与大型参考数据库进行了比较。结果 PPA 变异分布：35 lvPPA（80% AD）、28 agPPA（89% tau 蛋白病）和 18 svPPA（72% 额颞叶变性-TAR DNA 结合蛋白（FTLD-TDP））。言语失用与 agPPA 中的 4R-tau 病相关，而无失用的纯语法性 PPA 与 3R-tau 病相关。纵向数据显示，语言功能障碍仍然是 lvPPA 患者的主要缺陷，agPPA 发展为皮质基底节或进行性核上性麻痹综合征 (64%)，svPPA 发展为行为变异型额颞叶痴呆 (44%)。 agPPA-4R-tau蛋白病表现出有限的补充前运动区萎缩，lvPPA-AD表现出延伸至颞上沟的颞部萎缩，svPPA-FTLD-TDP表现出严重的颞极萎缩。基于 FDG-PET 的机器学习算法可以准确预测临床诊断和潜在病理。结论 agPPA 中区分 3R-tau 蛋白病和 4R-tau 蛋白病可能依赖于言语失用。其他语言和临床特征可以帮助神经病理学预测。我们的数据驱动的大脑代谢分解方法可以有效地预测潜在的神经病理学。数据可根据合理要求提供。由于某种原因，无法编辑此部分。