Assessing brain state transitions via whole brain modelling for potentiating cognitive training intervention in Alzheimer’s Disease

Traditionally, in resting-state functional MRI, differences between brain states are detected by a characterisation of the brain's dynamical regime. Depending on the a-priori assumptions about the underlying data, fMRI features are extracted such as regional activations, functional connectivity or fractional occupancy of dynamic functional connectivity approaches. However, to assess how brain states transition between each other remains a challenge in current paradigms. One of the solutions has been proposed in seminal work by Deco and colleagues, where the authors analysed transitions from awake state to sleep state and back, by analysing the regional perturbations that drive the brain dynamics to the sleep state from awake state and vice versa (Deco et al., 2019 PNAS). Taking this approach further, we introduce “dynamic sensitivity analysis”, an approach that quantifies transitions between brain states in terms of regional ability to rebalance spatio-temporal brain activity. In practice, it means building a whole-brain model based on dynamic functional connectivity, and, by stimulating brain regions, assessing the impact on the brain dynamics (Vohryzek et al., 2023 Computational and Structural Biotechnology Journal). To demonstrate the rationale, we show how this approach can be valuable for enhancing cognitive intervention in Alzheimer’s disease in paradigms using non-invasive transcranial electrical stimulation (tES).

One of the potential and promising adjuvant therapies for Alzheimer’s disease is that of tES to potentiate cognitive training interventions. Conceptually, this is achieved by driving brain dynamics towards an optimal state for an effective facilitation of cognitive training interventions. We used whole-brain models to design non-invasive stimulation strategies for re-establishing healthy brain dynamics, and to facilitate effective cognitive interventions in Alzheimer’s disease. We quantified the empirical differences between the healthy (HC, N = 58), Mild Cognitive Impairment (MCI N = 19) and Alzheimer’s Disease (AD, N = 16) groups in terms of changes to their dynamic functional connectivity profiles, and fitted subject-specific whole-brain models to the altered brain states of MCI and AD. Using an in-silico stimulation protocol, we were able to rank brain regions according to their proclivity to drive an increase in patient’s brain dynamics detected in MCI and AD brain states towards that of healthy controls. These were mainly along the brain’s medial axis both in the anterior and posterior parts. The required intensity of stimulation for successful outcome was lower for the MCI brain state suggesting less extensive protocols compared to the AD group. Crucially, the proclivity of a region to rebalance the dynamics to healthier brain state was correlated with the structural nodal degree. In the future, the selected brain regions informed by the in-silico models, can be used for an electrode placement optimization of tES interventions in clinical context.