Alamia Andrea, Gordillo Dario, Chkonia Eka, Roinishvili Maya, Cappe Celine, Herzog Michael H
Cerco, Centre National de la Recherche Scientifique, Université de Toulouse, Toulouse, France; Artificial and Natural Intelligence Toulouse Institute, Toulouse, France.
Laboratory of Psychophysics, Brain Mind Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Biol Psychiatry. 2025 Jul 15;98(2):167-174. doi: 10.1016/j.biopsych.2024.11.014. Epub 2024 Nov 29.
The computational mechanisms underlying psychiatric disorders are hotly debated. One hypothesis, grounded in the Bayesian predictive coding framework, proposes that patients with schizophrenia have abnormalities in encoding prior beliefs about the environment, resulting in abnormal sensory inference, which can explain core aspects of the psychopathology, such as some of its symptoms.
Here, we tested this hypothesis by identifying oscillatory traveling waves as neural signatures of predictive coding. We analyzed an electroencephalography dataset comprising 146 patients with schizophrenia and 96 age-matched healthy control participants during resting states and a visual backward masking task.
We found that patients with schizophrenia had stronger top-down alpha-band traveling waves compared with healthy control participants during resting state, supposedly reflecting overly precise priors at higher levels of the predictive processing hierarchy. We also found stronger bottom-up alpha-band waves in patients with schizophrenia during a visual task, consistent with the notion of enhanced signaling of sensory precision errors.
Our results yield a novel spatial-based characterization of oscillatory dynamics in schizophrenia, considering brain rhythms as traveling waves and providing a unique framework to study the different components involved in a predictive coding scheme. All together, our findings significantly advance our understanding of the mechanisms involved in fundamental pathophysiological aspects of schizophrenia, promoting a more comprehensive and hypothesis-driven approach to psychiatric disorders.
精神疾病背后的计算机制存在激烈争论。一种基于贝叶斯预测编码框架的假说提出,精神分裂症患者在编码关于环境的先验信念方面存在异常,导致异常的感觉推理,这可以解释精神病理学的核心方面,比如其一些症状。
在此,我们通过将振荡行波识别为预测编码的神经特征来检验这一假说。我们分析了一个脑电图数据集,该数据集包含146名精神分裂症患者和96名年龄匹配的健康对照参与者在静息状态及一项视觉逆向掩蔽任务期间的数据。
我们发现,在静息状态下,与健康对照参与者相比,精神分裂症患者具有更强的自上而下的α波段行波,推测这反映了预测处理层次结构较高水平上过度精确的先验。我们还发现,在视觉任务期间,精神分裂症患者具有更强的自下而上的α波段波,这与感觉精度误差信号增强的概念一致。
我们的结果产生了一种基于空间的精神分裂症振荡动力学新特征,将脑节律视为行波,并提供了一个独特的框架来研究预测编码方案中涉及的不同成分。总之,我们的发现显著推进了我们对精神分裂症基本病理生理方面所涉及机制的理解,促进了一种更全面且基于假说的精神疾病研究方法。