Personalized stimulation therapies for disorders of consciousness: a computational approach to inducing healthy-like brain activity based on neural field theory

Objective. Disorders of consciousness (DoC) remain a significant challenge in neurology, with traditional brain stimulation therapies showing limited and inconsistent efficacy across patients. This study presents a novel computational approach grounded in neural field theory for constructing personalized stimulus signals designed to induce healthy-like neural activity patterns in individuals with DoC. Approach. We employ a simplified brain model fitted to the electroencephalogram (EEG) power spectrum of a DoC patient, simulating the individual’s neural dynamics. Using model equations and fitted parameters, we mathematically derive stimuli time series that cause the model to generate power spectra typical of healthy individuals. These stimuli are tailored for brain regions typically targeted by neuromodulation therapies, such as deep brain stimulation and repetitive transcranial magnetic stimulation. Main results. In silico simulations demonstrate that our method successfully induces healthy-like EEG power spectra in models fitted to DoC patients. Furthermore, when the model parameters were near a stability boundary, stimulation led to a bifurcation and lasting changes in the model’s activity beyond the stimulation period. Significance. By inducing healthy-like neural activity, this approach may effectively activate plasticity mechanisms during long-term treatment, potentially leading to sustained improvements in a patient’s condition. While further clinical adjustments and validation are needed, this method holds promise for improving therapeutic outcomes in DoC. Moreover, it offers potential extensions to other neurological conditions that could benefit from personalized brain stimulation therapies.