Investigating Focal Connectivity Deficits in Alzheimer's Disease Using Directional Brain Networks Derived from Resting-State fMRI

Abstract

Connectivity analysis of resting-state fMRI has been widely used to identify biomarkers of Alzheimer's disease (AD) based on brain network aberrations. However, it is not straightforward to interpret such connectivity results since our understanding of brain functioning relies on regional properties (activations and morphometric changes) more than connections. Further, from an interventional standpoint, it is easier to modulate the activity of regions (using brain stimulation, neurofeedback, etc.) rather than connections. Therefore, we employed a novel approach for identifying focal directed connectivity deficits in AD compared to healthy controls. In brief, we present a model of directed connectivity (using Granger causality) that characterizes the coupling among different regions in healthy controls and Alzheimer's disease. We then characterized group differences using a (between-subject) generative model of pathology, which generates latent connectivity variables that best explain the (within-subject) directed connectivity. Crucially, our generative model at the second (between-subject) level explains connectivity in terms of local or regionally specific abnormalities. This allows one to explain disconnections among multiple regions in terms of regionally specific pathology; thereby offering a target for therapeutic intervention. Two foci were identified, locus coeruleus in the brain stem and right orbitofrontal cortex. Corresponding disrupted connectivity network associated with the foci showed that the brainstem is the critical focus of disruption in AD. We further partitioned the aberrant connectomic network into four unique sub-networks, which likely leads to symptoms commonly observed in AD. Our findings suggest that fMRI studies of AD, which have been largely cortico-centric, could in future investigate the role of brain stem in AD.

Keywords: Alzheimer's disease; brain stem; disease foci; effective connectivity; functional MRI; orbitofrontal cortex.

Figure 1

General model of the Foci identification technique. Parameters in circles indicate random variables. Please refer to the text for a description of the variables.

Figure 2

A flow chart of the Foci identification technique. The foci-identification technique posits that the latent connectivities can be stochastically generated from a distribution mode, and that the observed connectivity data are a noisy measurement of the latent unmeasured connectivity. Latent variables of the model were randomly initialized, and the variational EM algorithm was used to obtain the posterior distribution Q (both the nine-state distribution of latent functional connectivity and distribution over binary vector R) and model parameters to minimize the variational free energy. Then the disrupted foci and corresponding dysfunctional connections can be identified.

Figure 3

Sagittal view (A) and axial view (B) of the disease foci and corresponding disrupted connections. Regions in red are the identified affected foci, located in Locus Coeruleus and Right orbitofrontal cortex. Regions in blue are the non-foci regions that were connected from/to the disease foci. A schematic of the identified network is also shown for better visualization of the network architecture (C). The expansions for the abbreviations are as follows: SFG, superior frontal gyrus; MFG, middle frontal gyrus; IFG, inferior frontal gyrus; MTG, middle temporal gyrus; PHG, parahippocampal gyrus; MOG, middle occipital gyrus; OFC, orbitofrontal cortex.

Figure 4

Disrupted networks associated with the diseased foci, showing the entire network partitioned into four unique subnetworks: (A) LC-PFC working memory system, (B) LC-PHG emotional memory system, (C) LC-visual sensory system, and (D) LC-MTG language system. SFG, superior frontal gyrus; MFG, middle frontal gyrus; IFG, inferior frontal gyrus; MTG, middle temporal gyrus; PHG, parahippocampal gyrus; MOG, middle occipital gyrus; OFC, orbitofrontal cortex.