Whole-brain functional connectivity predicts regional tau PET in preclinical Alzheimer’s disease

Preclinical Alzheimer's disease, characterized by the abnormal accumulation of amyloid-β prior to cognitive symptoms, presents a critical opportunity for early intervention. Past work has described functional connectivity (FC) changes in preclinical Alzheimer's disease, yet the predictive <i>nature</i> between the functional connectome and Alzheimer's disease pathology during this window remains unexplored. We applied connectome-based predictive modelling to investigate the ability of resting-state whole-brain FC to predict tau (18F-flortaucipir) and amyloid-β (18F-florbetapir) PET binding in preclinical Alzheimer's disease (A4, <i>n</i> = 342 amyloid-β-positive, age 65-85). Separate models were developed to predict amyloid PET signal in the posterior cingulate, precuneus, and cortical composite regions, and to predict tau PET signal in each of 14 cortical regions that demonstrated meaningful tau elevation as identified through a Gaussian mixture model approach. 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 regional tau. We found that whole-brain FC predicts regional tau PET, outperforming FC-amyloid-β 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 banks of the superior temporal sulcus), while models for regions of earlier tau pathology (entorhinal, parahippocampal, fusiform, and amygdala) performed poorly. Importantly, FC-based models predicted tau PET signal in the Alzheimer's Disease Neuroimaging Intitative-3 dataset (amyloid-β-positive, <i>n</i> = 211, age 55-90) in tau-elevated but not tau-negative individuals. For the posterior cingulate tau model, the most accurate model in A4, 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 with tau were from nodes of heteromodal association areas, particularly within the prefrontal and parietal cortices. These findings reveal that whole-brain FC meaningfully predicts tau PET in preclinical Alzheimer's disease, particularly in regions affected in advanced disease, and are relevant across the Alzheimer's disease clinical spectrum in individuals with elevated tau PET burden. This suggests that functional connectivity, likely in conjunction with other factors, may play a key role in early processes that facilitate later-stage tau spread. These models highlight the potential of the functional connectome for the early detection and monitoring of Alzheimer's disease pathology, especially in later-stage target regions.