Traditionally, in neuroimaging, model-free analyses are used to find significant differences between brain states via signal detection theory. Depending on the a priori assumptions about the underlying data, different spatio-temporal features can be analysed. Alternatively, model-based techniques infer features from the data and compare significance from model parameters. However, to assess transitions from one brain state to another remains a challenge in current paradigms. Here, we introduce a "Dynamic Sensitivity Analysis" framework that quantifies transitions between brain states in terms of stimulation ability to rebalance spatio-temporal brain activity towards a target state such as healthy brain dynamics. In practice, it means building a whole-brain model fitted to the spatio-temporal description of brain dynamics, and applying systematic stimulations in-silico to assess the optimal strategy to drive brain dynamics towards a target state. Further, we show how Dynamic Sensitivity Analysis extends to various brain stimulation paradigms, ultimately contributing to improving the efficacy of personalised clinical interventions.
Keywords: Brain State; Brain stimulation; Deep Brain Stimulation, DBS; Magnetic Resonance Imaging, MRI; Non-Invasive Brain Stimulations, NIBS; Position Emission Tomography, PET; Probability Metastable Substates, PMS; Spatio-temporal dynamics; Transcranial Magnetic Stimulation, TMS; Transition Probability Matrix, TPM; Whole-brain models; diffusion Magnetic Resonance Imaging, dMRI; dynamic Functional Connectivity, dFC; functional Magnetic Resonance Imaging, fMRI; static Functional Connectivity, sFC; transcranial Alternating Current Stimulation, tACS; transcranial Direct Stimulation, tDCS; transcranial Electric Stimulation, tES; transcranial Random Noise Stimulation, tRNS.
© 2022 The Authors.