Disrupted brain dynamics across the Alzheimer’s disease spectrum is related to tau accumulation

Abstract

AbstractBackgroundRecent evidence has implicated brain networks in the pathophysiology of Alzheimer’s disease (AD). However, the phenotypic variability in AD, inter‐individual differences in tau PET uptake patterns, and dynamic nature of functional connectivity have been relatively neglected. In this talk, I will discuss our recent work linking measures of dynamic brain organization to spatially independent tau PET patterns defined in a phenotypically diverse cohort.MethodsWe identified all Mayo Clinic participants with concurrent amyloid PET (PiB), tau PET (AV1451) and task‐free fMRI (impaired participants were all Aβ+). We used independent component analysis (ICA) to identify spatially independent patterns of tau PET uptake. For the fMRI data, we used a Hidden‐Markov Model (HMM) design to identify distinct patterns of activity/connectivity (‘states’). The ICA and HMM parameters were used to project/segment an independent cohort (ADNI) and subsequent longitudinal timepoints for the Mayo cohort. Multilevel Bayesian regression models were fit in Stan to explore the association between phenotype, component load and brain state.ResultsThe optimal HMM model identified 10 brain states (Figure 1). The optimal ICA decomposition identified 42 tau PET components, of which 13 were potentially related to AD pathobiology (Figure 1). Component loads and brain state dwell time differed between diagnostic groups in both Mayo and ADNI cohorts. One state was much more likely to occur in AD in both cohorts (State 8). Cross‐sectionally, State 8 dwell time was associated with higher tau loads, while higher baseline dwell‐time was predictive of ∆tau in participants with at least 2 timepoints (Figure 2).ConclusionWe found links between dynamics of functional connectivity, tau deposition, and cognitive impairment across the AD spectrum using two different cohorts and longitudinal data. Specifically: 1) our method for characterizing tau PET uptake was able to capture the magnitude and spatial pattern of binding across diverse phenotypes; 2) Our data supports a role for dynamic brain connectivity in cognitive impairment due to Alzheimer’s disease; 3) Dynamic connectivity appears to be directly related to AD pathobiology as measured by tau PET. Incorporating the time‐varying nature of connectivity will be important in future studies of connectome‐based disease progression in AD.