Personalizing virtual brains with neurodegenerative disease: noninvasive stimulation approach

Tracking of individual progression trajectory of neurodegenerative brain disease such as Alzheimer's disease (AD) can enable targeted interventions to prolong active living with increased quality of life and substantially reduce the socioeconomic burden.

Approaches based on mechanistic modeling have the capacity to integrate heterogeneous data and capture the inter-individual variability, but their application in the case of AD is challenging due to non-idetifiability of relevant parameters from spontaneous brain activity.

This project will use the recently developed technology for noninvasive brain stimulation - the temporal interference - to address the key question: is the response to stimulation sufficiently informative to allow estimation of the model parameters reflecting the position of an individual along the AD progression trajectory.

The main objective of this project is to develop and validate a proof-of-concept of a brain health status estimation workflow informed by both the response to targeted non-invasive stimulation (TI) and the resting state dynamics (fMRI), while leveraging the personalized model-based inference.

The project will advance along following main axes: systematic analysis of parameter identifiability using both stimulation and resting state paradigms, development of a personalization workflow combining structural data, resting state fMRI and the TI, and evaluation of model inversion performance using response to different stimulation targets.

The results of the project have the potential to pioneer personalized mechanistic model inversion in the context of AD, and to pave the way for further development of the AD monitoring workflows which can be adapted in routine health screening practices.