Predictive coding in neuropsychiatric disorders: A systematic transdiagnostic review

Abstract

The predictive coding framework postulates that the human brain continuously generates predictions about the environment, maximizing successes and minimizing failures based on prior experiences and beliefs. This PRISMA-compliant systematic review aims to comprehensively and transdiagnostically examine the differences in predictive coding between individuals with neuropsychiatric disorders and healthy controls. We included 72 articles including case-control studies investigating predictive coding as the primary outcome and reporting behavioral, neuroimaging, or electrophysiological findings. Thirty-three studies investigated predictive coding in the schizophrenia spectrum, 33 in neurodevelopmental disorders, 5 in mood disorders, 4 in neurocognitive disorders, 1 in post-traumatic stress disorder, and 1 in substance use disorders. Oddball and oddball-like paradigms were most frequently used to quantify predictive coding performance. Evidence showed heterogeneous impairments in the predictive coding abilities of the brain across neuropsychiatric disorders, particularly in schizophrenia and autism. Patients within the schizophrenia spectrum showed a consistent pattern of impaired non-social predictive coding. Conversely, predictive coding deficits were more selective for social cues in the autism spectrum. Predictive coding impairments were correlated with clinical symptom severity. These findings underscore the potential utility of predictive coding as a framework for understanding cognitive dysfunctions in the neuropsychiatric population, even though more evidence is needed on underexplored conditions, also considering potential confounders such as medication use and sex/gender. The potential role of predictive coding as a determinant of treatment response may also be considered to tailor personalized interventions.