Qela Brendon, Damiani Stefano, De Santis Samanta, Groppi Federica, Pichiecchio Anna, Asteggiano Carlo, Brondino Natascia, Monteleone Alessio Maria, Grassi Luigi, Politi Pierluigi, Fusar-Poli Paolo, Fusar-Poli Laura
Department of Brain and Behavioral Sciences, University of Pavia, Italy.
Department of Neuroscience, University of Parma, Italy.
Neurosci Biobehav Rev. 2025 Feb;169:106020. doi: 10.1016/j.neubiorev.2025.106020. Epub 2025 Jan 17.
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.
预测编码框架假定，人类大脑会持续对环境进行预测，基于先前的经验和信念，最大化成功并最小化失败。这项符合PRISMA标准的系统综述旨在全面且跨诊断地研究神经精神疾病患者与健康对照者在预测编码方面的差异。我们纳入了72篇文章，包括以预测编码为主要结果并报告行为、神经影像学或电生理结果的病例对照研究。33项研究调查了精神分裂症谱系中的预测编码，33项研究了神经发育障碍中的预测编码，5项研究了情绪障碍中的预测编码，4项研究了神经认知障碍中的预测编码，1项研究了创伤后应激障碍中的预测编码，1项研究了物质使用障碍中的预测编码。异常球和类似异常球的范式最常用于量化预测编码表现。证据表明，在各种神经精神疾病中，大脑的预测编码能力存在异质性损伤，尤其是在精神分裂症和自闭症中。精神分裂症谱系中的患者表现出一致的非社交预测编码受损模式。相反，自闭症谱系中的预测编码缺陷对社交线索更具选择性。预测编码损伤与临床症状严重程度相关。这些发现强调了预测编码作为理解神经精神疾病人群认知功能障碍框架的潜在效用，尽管在未充分探索的情况下还需要更多证据，同时也要考虑药物使用和性别等潜在混杂因素。预测编码作为治疗反应决定因素的潜在作用也可被考虑用于制定个性化干预措施。
