Inception: Simulating Personalized Long-Term Recovery in Disorders of Consciousness using Whole-Brain Computational Perturbations

Abstract

Advancements in the treatment of Disorders of Consciousness have seen significant progress with per-turbative techniques and pharmacological therapies. Despite their potential, the underlying mechanisms of their variable efficacy remain poorly understood. To address this challenge, recent studies have utilised whole-brain modelling to simulate in silico perturbations. However, existing models focus exclusively on system behaviour during active stimulations, leaving unexplored how the brain dynamics evolve in post-acute and long-term stages, crucial in the recovery of consciousness. Here, we introduce Inception, a novel personalized approach to in silico perturbation modelling. We use a whole-brain models to simulate the perturbations and to unravel information about the long term effects of the proposed intervention. Applied to fMRI data from patients in a minimally conscious state and an unresponsive wakefulness state, our approach effectively simulates the transition to a healthy state, generating perturbed data closely resembling healthy brain activity. Moreover, we show that Inception enhances patient classification through machine learning, outperforming functional connectivity-based approaches. Finally, we investigate the correlation between perturbation responses and brain neuroreceptors, proposing that Inception might capture the long-term effects of pharmacological interventions in Disorders of Consciousness treatment.