Resting State Functional Connectivity Signature Differentiates Cognitively Normal from Individuals Who Convert to Symptomatic Alzheimer's Disease

Abstract

Background: Changes in resting state functional connectivity (rs-fc) occur in Alzheimer's disease (AD), but few longitudinal rs-fc studies have been performed. Most studies focus on single networks and not a global measure of rs-fc. Although the amyloid tau neurodegeneration (AT(N)) framework is increasingly utilized by the AD community, few studies investigated when global rs-fc signature changes occur within this model.

Objective: 1) Identify a global rs-fc signature that differentiates cognitively normal (CN) individuals from symptomatic AD. 2) Assess when longitudinal changes in rs-fc occur relative to conversion to symptomatic AD. 3) Compare rs-fc with amyloid, tau, and neurodegeneration biomarkers.

Methods: A global rs-fc signature composed of intra-network connections was longitudinally evaluated in a cohort of cognitively normal participants at baseline (n = 335). Biomarkers, including cerebrospinal fluid (Aβ42 and tau), structural magnetic resonance imaging, and positron emission tomography were obtained.

Results: Global rs-fc signature distinguished CN individuals from individuals who developed symptomatic AD. Changes occurred nearly four years before conversion to symptomatic AD. The global rs-fc signature most strongly correlated with markers of neurodegeneration.

Conclusion: The global rs-fc signature changes near symptomatic onset and is likely a neurodegenerative biomarker. Rs-fc changes could serve as a biomarker for evaluating potential therapies for symptomatic conversion to AD.

Keywords: Alzheimer’s disease; biomarkers; neuroimaging; observational studies.

Figure 1.

A series of post-processing steps were performed on rs-fc data in order to reduce dimensionality and generate a single summary value. 298 seed-based functional regions-of-interest (ROI) were identified and Pearson correlations between each were calculated and Z transformed for normality. These ROIs were organized into thirteen resting state networks (RSNs), with ROIs of unknown function classified as “unassigned”. Correlations within each of the thirteen RSNs were averaged to obtain a 13 × 13 matrix for each individual. We then performed PCA on the intra-network connections for each individual, that is, the diagonal of each individual’s rs-fc matrix. We calculated the dot product of the eigenvector corresponding to the first principal component and the intra-network connections on an individual-by-individual basis to obtain a single summary value referred to as “AD global rs-fc signature”. Certain networks have been previously shown to be affected by AD. Post hoc analyses using the DMN x DMN intra-network composite and the MEM x MEM intra-network composite were therefore performed. These intra-network composites are labeled for clarity here.

Figure 2.

Changes in intra-network rs-fc signature over time for cognitively normal individuals (clinical dementia rating (CDR) 0) who either converted to symptomatic AD (CDR >0) (converters) or who remained cognitively normal (CDR 0) (non-converter). Significant differences between the two groups were seen 3.9 years before symptomatic conversion.

Figure 3.

Correlations between intra-network rs-fc signature and biomarkers of Alzheimer disease (AD) In particular, intra-network rs-fc was correlated with one of the amyloid (A) measures (cerebrospinal fluid (CSF) AΒ42, but not PET-PIB) and the volumetric measures of neurodegeneration normalized hippocampal volume, and AD cortical signature), but not CSF measures of tau (T) or neurodegeneration (N). The intra-network rs-fc correlated strongest with volumetric markers of neurodegeneration.