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

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 (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 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.