Just as navigating a physical environment, navigating through the landscapes of spontaneous brain states may also require an internal cognitive map. Contemporary computation theories propose modeling a cognitive map from a reinforcement learning perspective and argue that the map would be predictive in nature, representing each state as its upcoming states. Here, we used resting-state fMRI to test the hypothesis that the spaces of spontaneously reoccurring brain states are cognitive map-like, and may exhibit future-oriented predictivity. We identified two discrete brain states of the navigation-related brain networks during rest. By combining pattern similarity and dimensional reduction analysis, we embedded the occurrences of each brain state in a two-dimensional space. Successor representation modeling analysis recognized that these brain state occurrences exhibit place cell-like representations, akin to those observed in a physical space. Moreover, we observed predictive transitions of reoccurring brain states, which strongly covaried with individual cognitive and emotional assessments. Our findings offer a novel perspective on the cognitive significance of spontaneous brain activity and support the theory of cognitive map as a unifying framework for mental navigation.

就像在物理环境中导航一样，在自发大脑状态的景观中导航也可能需要内部认知地图。当代计算理论提出从强化学习的角度对认知地图进行建模，并认为该地图本质上是可预测的，将每个状态表示为其即将到来的状态。在这里，我们使用静息态功能磁共振成像来测试这样的假设：自发重现的大脑状态的空间类似于认知地图，并且可能表现出面向未来的预测性。我们确定了休息期间与导航相关的大脑网络的两种离散的大脑状态。通过结合模式相似性和降维分析，我们将每个大脑状态的发生嵌入到二维空间中。随后的表征模型分析认识到，这些大脑状态的发生表现出类似细胞的表征，类似于在物理空间中观察到的情况。此外，我们观察到重复出现的大脑状态的预测转变，这与个人认知和情绪评估强烈协变。我们的研究结果为自发大脑活动的认知意义提供了新的视角，并支持认知图谱作为心理导航统一框架的理论。