Personalized virtual brains of Alzheimer's Disease link dynamical biomarkers of fMRI with increased local excitability

Alzheimers disease (AD) is a neurodegenerative disorder characterized by the accumulation of abnormal beta-amyloid (A{beta}) and hyperphosphorylated Tau (pTau). These proteinopathies disrupt neuronal activity, causing, among others, an excessive and hypersynchronous neuronal firing that promotes hyperexcitability and leads to brain network dysfunction and cognitive deficits. In this study, we used computational network modeling to build a causal inference framework to explain AD-related abnormal brain activity. We constructed personalized brain network models with a set of working points to enable maximum dynamical complexity for each brain. Structural brain topographies were combined, either with excitotoxicity, or postsynaptic depression, as two leading mechanisms of the A{beta} and pTau on neuronal activity. By applying various levels of these putative mechanisms to the limbic regions that typically present, with the earliest and largest protein burden, we found that the excitotoxicity is sufficient and necessary to reproduce empirical biomarkers two biometrics associated with AD pathology: homotopic dysconnectivity and a decrease in limbic network dynamical fluidity. This observation was shown not only in the clinical groups (aMCI and AD), but also in healthy subjects that were virtually-diseased with excitotoxicity as these abnormal proteins can accumulate before the appearance of any cognitive changes. The same findings were independently confirmed by a mechanistic deep learning inference framework. Taken together, our results show the crucial role of protein burden-induced hyperexcitability in altering macroscopic brain network dynamics, and offer a mechanistic link between structural and functional biomarkers of cognitive dysfunction due to AD.