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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.

Original publication




Journal article


Comput Struct Biotechnol J

Publication Date





335 - 345


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