Spontaneous Dynamics Predict the Effects of Targeted Intervention in Hippocampal Neuronal Cultures.

Achieving targeted perturbations of neural activity is essential for dissecting the causal architecture of brain circuits. A crucial challenge in targeted manipulation experiments is the identification of perturbation sites whose stimulation exerts desired effects, currently done with costly trial-and-error procedures. Can one predict stimulation effects solely based on observations of the circuit activity, in the absence of perturbation? We answer this question in dissociated neuronal cultures on High-Density Microelectrode Arrays (HD-MEAs), which, compared to preparations, offer a controllable platform that enables precise stimulation and full access to network dynamics. We first reconstruct the - the full map of network responses to focal electrical stimulation - by sequentially activating individual single sites and quantifying their network-wide effects. The measured perturbome patterns cluster into functional modules, with limited spread across clusters. We then demonstrate that the perturbome can be predicted from spontaneous activity alone. Using short baseline recordings in the absence of perturbations, we estimate Effective Connectivity (EC) and show that it predicts the spatial organization of the perturbome, including spatial clusters and local connectivity. Our results demonstrate that spontaneous dynamics encode the latent causal structure of neural circuits and that EC metrics can serve as effective, model-free proxies for stimulation outcomes. This framework enables data-driven targeting and causal inference , with potential applications to more complex preparations such as human iPSC-derived neurons and brain organoids, with implications for both basic research and therapeutic strategies targeting neurological disorders.