Perspectives on How Human Simultaneous Multi-Modal Imaging Adds Directionality to Spread Models of Alzheimer's Disease.

Julia Neitzel,Rachel Nuttall,Christian Sorg

doi:10.3389/fneur.2018.00026

Abstract

Previous animal research suggests that the spread of pathological agents in Alzheimer’s disease (AD) follows the direction of signaling pathways. Specifically, tau pathology has been suggested to propagate in an infection-like mode along axons, from transentorhinal cortices to medial temporal lobe cortices and consequently to other cortical regions, while amyloid-beta (Aβ) pathology seems to spread in an activity-dependent manner among and from isocortical regions into limbic and then subcortical regions. These directed connectivity-based spread models, however, have not been tested directly in AD patients due to the lack of an in vivo method to identify directed connectivity in humans. Recently, a new method—metabolic connectivity mapping (MCM)—has been developed and validated in healthy participants that uses simultaneous FDG-PET and resting-state fMRI data acquisition to identify directed intrinsic effective connectivity (EC). To this end, postsynaptic energy consumption (FDG-PET) is used to identify regions with afferent input from other functionally connected brain regions (resting-state fMRI). Here, we discuss how this multi-modal imaging approach allows quantitative, whole-brain mapping of signaling direction in AD patients, thereby pointing out some of the advantages it offers compared to other EC methods (i.e., Granger causality, dynamic causal modeling, Bayesian networks). Most importantly, MCM provides the basis on which models of pathology spread, derived from animal studies, can be tested in AD patients. In particular, future work should investigate whether tau and Aβ in humans propagate along the trajectories of directed connectivity in order to advance our understanding of the neuropathological mechanisms causing disease progression.

Highlights

Alzheimer’s disease (AD) is characterized by the extracellular accumulation of misfolded amyloid-β peptides (Aβ), i.e., Aβ plaques, and intracellular aggregates of hyperphosphorylated tau proteins, i.e., neurofibrillary tangles (NFTs) [1]
We propose that metabolic connectivity mapping (MCM) is a promising new tool that, based on the benefits of multi-modal MR-positron emission tomography (PET) imaging, allows one to map intrinsic EC (iEC) changes in AD patients and to link such changes with pathology spread
We suggest that, for a pair of regions sharing intact unidrectional effective connectivity (EC) and a significant gradient of pathology, some variance in this pathology gradient across patients can be explained by variance in the strength of EC beyond underlying functional or structural connectivity

Summary

INTRODUCTION

Alzheimer’s disease (AD) is characterized by the extracellular accumulation of misfolded amyloid-β peptides (Aβ), i.e., Aβ plaques, and intracellular aggregates of hyperphosphorylated tau proteins, i.e., neurofibrillary tangles (NFTs) [1]. A novel approach to identify EC in humans integrates undirected FC with local energy consumption based on simultaneously acquired 18F-fludeoxyglucose (FDG) PET and resting-state functional magnetic resonance imaging [17] This method, called “metabolic connectivity mapping,” reveals ongoing or iEC (Figure 2). Other researchers have used statistical approaches to infer EC from undirected fMRI data, including Granger causality mapping (GCM) [20, 21], dynamic causal modeling (DCM) [22], and Bayesian network (BN) learning [23] These methods have been used to investigate changed network dynamics in AD patients, reporting disrupted EC in the DMN, though with certain caveats. Other multi-modal imaging techniques such as fMRI with MR spectroscopy or flumazenil-PET may offer interesting insight into AD pathology and FC [for reviews, see Ref [53,54,55]] but, unlike FDG-PET/fMRI, they do not yet offer the key aspect of directionality of functional pathways, along which animal models have shown amyloid-β and tau pathology to spread [7]

Other Neurodegenerative Conditions

Findings

CONCLUSION