Computational predictive processing models of consciousness: a systematic review of non-invasive brain signal analysis in disorders of consciousness

Introduction: The clinical assessment of patients with Disorders of Consciousness (DoC), ranging from the Vegetative State (VS/UWS) to the Minimally Conscious State (MCS), remains a significant challenge in neurology. Gold-standard behavioral tools are prone to high misdiagnosis rates because they depend on overt motor responses, which may be masked by physical impairments. Consequently, there is an urgent need for objective neurophysiological biomarkers to identify residual awareness. Predictive Processing (PP) is a leading theory that views the brain as a hierarchical inference engine. Under this framework, the brain minimizes "prediction errors" between internal generative models and sensory inputs. Neural signatures of these errors, such as the Mismatch Negativity (MMN), provide a window into the brain's automatic modeling of environmental regularities, serving as a proxy for conscious processing. Objective: This systematic review aims to identify and appraise peer-reviewed studies from the past 15 years that apply computational PP models to non-invasive brain signals in DoC patients. It synthesizes evidence for their diagnostic and prognostic utility and identifies methodological hurdles to clinical translation. Methods: ≈2045), paradigms, and computational methods, including multivariate pattern analysis and deep learning, were extracted and appraised. Results: The evidence reveals a transition from simple waveform averaging to high-dimensional decoding of hierarchical prediction errors. Global information-sharing markers effectively distinguish conscious states, while the temporal progression of prediction error signatures in the early stages of coma demonstrates high specificity for predicting awakening. Conclusion: Computational PP models offer a transformative path toward reducing misdiagnosis. Future research must prioritize 24-hour continuous monitoring and multimodal data fusion to translate these theoretical frameworks into viable bedside clinical tools.