Abuwarda Hamid, Trainer Anne, Horien Corey, Shen Xilin, Ju Suyeon, Constable R Todd, Fredericks Carolyn
bioRxiv. 2025 Mar 25:2024.04.02.587791. doi: 10.1101/2024.04.02.587791.
Preclinical Alzheimer's disease (AD), characterized by the abnormal accumulation of amyloid prior to cognitive symptoms, presents a critical opportunity for early intervention. Past work has described functional connectivity changes in preclinical disease, yet the interplay between AD pathology and the functional connectome during this window remains unexplored. We applied connectome-based predictive modeling to investigate the ability of resting-state whole-brain functional connectivity to predict tau (18F-flortaucipir) and amyloid (18F-florbetapir) PET binding in a preclinical AD cohort (A4, =342, age 65-85). Separate predictive models were developed for each of 14 regions, and model performance was assessed using a Spearman's correlation between predicted and observed PET binding standard uptake value ratios. We assessed the validity of significant models by applying them to an external dataset, and visualized the underlying connectivity that was positively and negatively correlated to posterior cingulate tau binding, the most successful model. We found that whole brain functional connectivity predicts regional tau PET, outperforming amyloid PET models. The best performing tau models were for regions affected in Braak stage IV-V regions (posterior cingulate, precuneus, lateral occipital cortex, middle temporal, inferior temporal, and Bank STS), while models for regions of earlier tau pathology (entorhinal, parahippocampal, fusiform, and amygdala) performed poorly. Importantly, tau models generalized to a symptomatic AD cohort (ADNI; amyloid positive, = 211, age 55-90), in tau-elevated but not tau-negative individuals. For the posterior cingulate A4 tau model, the most successful model, the predictive edges positively correlated with posterior cingulate tau predominantly came from nodes within temporal, limbic, and cerebellar regions. The most predictive edges negatively associated to tau were from nodes of heteromodal association areas, particularly within the prefrontal and parietal cortices. These findings reveal that whole-brain functional connectivity predicts tau PET in preclinical AD and generalizes to a clinical dataset specifically in individuals with abnormal tau PET, highlighting the relevance of the functional connectome for the early detection and monitoring of AD pathology.
临床前阿尔茨海默病（AD）以认知症状出现之前淀粉样蛋白的异常积累为特征，为早期干预提供了关键机会。过去的研究描述了临床前疾病中的功能连接变化，但在此阶段AD病理学与功能连接组之间的相互作用仍未得到探索。我们应用基于连接组的预测模型来研究静息态全脑功能连接预测临床前AD队列（A4，n = 342，年龄65 - 85岁）中tau（18F-氟代tau蛋白显像剂）和淀粉样蛋白（18F-氟代贝他淀粉样蛋白显像剂）PET结合的能力。针对14个区域分别开发了预测模型，并使用预测的和观察到的PET结合标准摄取值比率之间的斯皮尔曼相关性评估模型性能。我们通过将显著模型应用于外部数据集来评估其有效性，并可视化与最成功的模型——后扣带回tau结合呈正相关和负相关的潜在连接。我们发现全脑功能连接可预测区域tau PET，优于淀粉样蛋白PET模型。表现最佳的tau模型针对的是Braak分期IV - V期受影响的区域（后扣带回、楔前叶、枕外侧皮质、颞中回、颞下回和颞上沟脑岛盖部），而针对早期tau病理学区域（内嗅区、海马旁回、梭状回和杏仁核）的模型表现不佳。重要的是，tau模型在tau升高但非tau阴性的个体中推广到了有症状的AD队列（ADNI；淀粉样蛋白阳性，n = 211，年龄55 - 90岁）。对于最成功的模型——后扣带回A4 tau模型，与后扣带回tau呈正相关的预测边缘主要来自颞叶、边缘叶和小脑区域内的节点。与tau呈负相关的最具预测性的边缘来自异模态联合区的节点，特别是前额叶和顶叶皮质内的节点。这些发现表明，全脑功能连接可预测临床前AD中的tau PET，并特别在tau PET异常的个体中推广到临床数据集，突出了功能连接组对于AD病理学早期检测和监测的相关性。
