予測符号化と神経計算精神医学：精神疾患を理解するためのメカニズム的フレームワーク

Predictive coding offers a powerful computational framework for understanding brain function and psychiatric disorders at a mechanistic level. This perspective synthesizes advances in computational psychiatry, proposing that mental disorders can be conceptualized as specific alterations in the brain's predictive inference machinery. We first outline the theoretical foundations of predictive coding, including Bayesian inference, free-energy minimization, and neural population dynamics, illustrating how these abstract computational principles map onto specific neural circuits and biophysical mechanisms. We then argue that diverse psychiatric conditions can be understood within this unified framework. Taken together, these links between theory, generative models and empirical data suggest a route by which predictive coding might be rendered a testable, modifiable, falsifiable construct within biological psychiatry. Beyond offering conceptual clarity, this framework has significant clinical implications, including the development of mechanistic biomarkers, personalized treatment approaches based on computational phenotypes, and novel therapeutic interventions targeting specific inferential abnormalities. By grounding psychiatric symptoms in aberrant predictive processes implemented in neural circuitry, this approach promises a more mechanistic understanding of mental disorders and a path toward more targeted, effective interventions.