Hengen Keith B, Shew Woodrow L
Department of Biology, Washington University in Saint Louis, Saint Louis, MO 63130, USA.
Department of Physics, University of Arkansas, Fayetteville, AR 72701, USA.
Neuron. 2025 Aug 20;113(16):2582-2598.e2. doi: 10.1016/j.neuron.2025.05.020. Epub 2025 Jun 23.
Brains face selective pressure to optimize computation, broadly defined. This is achieved by mechanisms including development, plasticity, and homeostasis. Is there a universal optimum around which the healthy brain tunes itself, across time and individuals? The criticality hypothesis posits such a setpoint. Criticality is a state imbued with internally generated, multiscale, marginally stable dynamics that maximize the features of information processing. Experimental support emerged two decades ago and has accumulated at an accelerating pace despite disagreement. Here, we lay out the logic of criticality as a general computational endpoint and review experimental evidence. We perform a meta-analysis of 140 datasets published between 2003 and 2024. We find that a long-standing controversy is the product of a methodological choice with no bearing on underlying dynamics. Our results suggest that a new generation of research can leverage criticality-as a unifying principle of brain function-to accelerate understanding of behavior, cognition, and disease.
大脑面临着优化计算的选择性压力,这里的计算是广义定义的。这是通过包括发育、可塑性和内稳态等机制来实现的。健康的大脑在不同时间和个体间,是否围绕着一个普遍的最优状态来调整自身呢?临界性假说提出了这样一个设定点。临界性是一种具有内在生成的、多尺度的、边缘稳定动力学的状态,这种状态能使信息处理的特征最大化。二十年前就出现了实验支持,尽管存在分歧,但支持证据仍在加速积累。在这里,我们阐述临界性作为一般计算终点的逻辑,并回顾实验证据。我们对2003年至2024年间发表的140个数据集进行了荟萃分析。我们发现,一个长期存在的争议是方法选择的产物,与潜在动力学无关。我们的结果表明,新一代研究可以利用临界性——作为大脑功能的统一原则——来加速对行为、认知和疾病的理解。