Alzheimer’s disease (AD) is defined by synaptic and neuronal degeneration and loss accompanied by amyloid beta (Aβ) plaques and tau neurofibrillary tangles (NFTs)^1,2,3. In vivo animal experiments indicate that both Aβ and tau pathologies synergistically interact to impair neuronal circuits⁴. For example, the hypersynchronous epileptiform activity observed in over 60% of AD cases⁵ may be generated by surrounding Aβ and/or tau deposition yielding neuronal network hyperactivity^5,6. Cortical and hippocampal network hyperexcitability precedes memory impairment in AD models^7,8. In an apparent feedback loop, endogenous neuronal activity, in turn, regulates Aβ aggregation, in both animal models and computational simulations^9,10. Multiple other factors involved in AD pathogenesis-remarkably, neuroinflammatory dysregulations-also seemingly influence neuronal firing and act on hypo/hyperexcitation patterns^11,12,13. Thus, mounting evidence suggest that neuronal excitability changes are a key mechanistic event appearing early in AD and a tentative therapeutic target to reverse disease symptoms^3,4,7,14. However, the exact patterns of Aβ, tau and other disease factors’ neuronal activity alterations in AD’s neurodegenerative progression are unclear as in vivo and non-invasive measuring of neuronal excitability in human subjects remains impractical.

Brain imaging and electrophysiological monitoring constitute a reliable readout for brain network degeneration likely associating with AD’s neuro-functional alterations^{3,15,16,17,18}. Patients present distinct resting-state blood-oxygen-level-dependent (BOLD) signal content in the low frequency fluctuations range (0.01–0.08 Hz)^16,19. These differences increase with disease progression, from cognitively unimpaired (CU) controls to mild cognitive impairment (MCI) to AD, correlating with performance on cognitive tests¹⁶. Another characteristic functional change is the slowing of the electro-(magneto-) encephalogram (E/MEG), with the signal shifting towards low frequency bands^15,18. Electrophysiological spectral changes associate with brain atrophy and with losing connections to hub regions including the hippocampus, occipital and posterior areas of the default mode network²⁰. All these damages are known to occur in parallel with cognitive impairment²⁰. Disease processes also manifest differently given subject-specific genetic and environmental conditions^1,21. Models of multiple pathological markers and physiology represent a promising avenue for revealing the connection between individual AD fingerprints and cognitive deficits^3,18,22.

In effect, large-scale neuronal dynamical models of brain re-organization have been used to test disease-specific hypotheses by focusing on the corresponding causal mechanisms^23,24,25. By considering brain topology (the structural connectome¹⁸) and regional profiles of a pathological agent²⁴, it is possible to recreate how a disorder develops, providing supportive or conflicting evidence on the validity of a hypothesis²³. Generative models follow average activity in relatively large groups of excitatory and inhibitory neurons (neural masses), with large-scale interactions generating E/MEG signals and/or functional MRI observations²⁶. Through neural mass modeling, personalized virtual brains were built to describe Aβ pathology effects on AD-related EEG slowing²⁵ and several hypotheses for neuronal hyperactivation have been tested²⁷. Simulated resting-state functional MRI across the AD spectrum was used to estimate biophysical parameters associated with cognitive deterioration²⁸. In addition, different intervention strategies to counter neuronal hyperactivity in AD have been tested^10,22. Notably, comprehensive computational approaches combining pathophysiological patterns and functional network alterations allow the quantification of non-observable biological parameters²⁹ like neuronal excitability values in a subject-specific basis^{1,3,18,21,23,24}, facilitating the design of personalized treatments targeting the root cause(s) of functional alterations in AD.