A whole‐brain functional connectivity model of Alzheimer's disease pathology

Abstract INTRODUCTION Alzheimer's disease (AD) is characterized by the presence of two proteinopathies, amyloid and tau, which have a cascading effect on the functional and structural organization of the brain. METHODS In this study, we used a supervised machine learning technique to build a model of functional connections that predicts cerebrospinal fluid (CSF) p‐tau/Aβ 42 (the PATH‐fc model). Resting‐state functional magnetic resonance imaging (fMRI) data from 289 older adults in the Alzheimer's Disease Neuroimaging Initiative (ADNI) were utilized for this model. RESULTS We successfully derived the PATH‐fc model to predict the ratio of p‐tau/Aβ 42 as well as cognitive functioning in older adults across the spectrum of healthy and pathological aging. However, the in‐sample fit magnitude was low, indicating a need for further model development. DISCUSSION Our pathology‐based model of functional connectivity included representation from multiple canonical networks of the brain with intra‐network connectivity associated with low pathology and inter‐network connectivity associated with higher levels of pathology. Highlights Whole‐brain functional connectivity model (PATH‐fc) is linked to AD pathophysiology. The PATH‐fc model predicts performance in multiple domains of cognitive functioning. The PATH‐fc model is a distributed model including representation from all canonical networks.