Brain activity is characterized by brainwide spatiotemporal patterns that emerge from synapse-mediated interactions between individual neurons. Calcium imaging provides access to recordings of whole-brain activity at single-neuron resolution and, therefore, allows the study of how large-scale brain dynamics emerge from local activity. In this study, we use a statistical mechanics approach—the pairwise maximum entropy model—to infer microscopic network features from collective patterns of activity in the larval zebrafish brain and relate these features to the emergence of observed whole-brain dynamics. Our findings indicate that the pairwise interactions between neural populations and their intrinsic activity states are sufficient to explain observed whole-brain dynamics. In fact, the pairwise relationships between neuronal populations estimated with the maximum entropy model strongly correspond to observed structural connectivity patterns. Model simulations also demonstrated how tuning pairwise neuronal interactions drives transitions between observed physiological regimes and pathologically hyperexcitable whole-brain regimes. Finally, we use virtual resection to identify the brain structures that are important for maintaining the brain in a physiological dynamic regime. Together, our results indicate that whole-brain activity emerges from a complex dynamical system that transitions between basins of attraction whose strength and topology depend on the connectivity between brain areas. Published by the American Physical Society 2024

中文翻译：

---

自发的大脑活动来自斑马鱼幼鱼大脑中的成对相互作用


大脑活动的特征是全脑时空模式，这些模式来自单个神经元之间突触介导的相互作用。钙成像提供了对单神经元分辨率下全脑活动的记录的访问，因此，允许研究大规模大脑动力学如何从局部活动中出现。在这项研究中，我们使用统计力学方法——成对最大熵模型——从斑马鱼幼鱼大脑中的集体活动模式中推断出微观网络特征，并将这些特征与观察到的全脑动力学的出现联系起来。我们的研究结果表明，神经种群与其内在活动状态之间的成对相互作用足以解释观察到的全脑动力学。事实上，使用最大熵模型估计的神经元群之间的成对关系与观察到的结构连接模式密切相关。模型模拟还证明了调整成对神经元相互作用如何驱动观察到的生理状态和病理上过度兴奋的全脑状态之间的转换。最后，我们使用虚拟切除来识别对维持大脑处于生理动态状态很重要的大脑结构。总之，我们的结果表明，全脑活动来自一个复杂的动力学系统，该系统在吸引力盆之间转换，其强度和拓扑结构取决于大脑区域之间的连接。 美国物理学会 2024 年出版