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ABSTRACT 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 ofhigh efficacyperturbation 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 toin vivopreparations, offer a controllablein vitroplatform that enables precise stimulation and full access to network dynamics. We first reconstruct theperturbome- 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 inferencein vitro, 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. Significance StatementNeuronal cultures are increasingly used as controllable platforms to study neuronal network dynamics, neuromodulation, and brain-inspired therapies. To fully exploit their potential, we need robust methods to probe and interpret causal interactions. Here, we develop a framework to reconstruct the perturbome—the network-wide map of responses to localized electrical stimulation—and show that it can be predicted from spontaneous activity alone. Using simple, model-free metrics of Effective Connectivity, we reveal that ongoing activity encodes causal structure and provides reliable proxies for stimulation outcomes. This validates EC as a practical measure of causal influence in vitro. Our methodology refines the use of neuronal cultures for brain-on-a-chip approaches, and paves the way for data-driven neuromodulation strategies in human stem cell–derived neurons and brain organoids.
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Scale-free behavioral dynamics directly linked with scale-free cortical dynamics
Naturally occurring body movements and collective neural activity both exhibit complex dynamics, often with scale-free, fractal spatiotemporal structure. Scale-free dynamics of both brain and behavior are important because each is associated with functional benefits to the organism. Despite their similarities, scale-free brain activity and scale-free behavior have been studied separately, without a unified explanation. Here, we show that scale-free dynamics of mouse behavior and neurons in the visual cortex are strongly related. Surprisingly, the scale-free neural activity is limited to specific subsets of neurons, and these scale-free subsets exhibit stochastic winner-take-all competition with other neural subsets. This observation is inconsistent with prevailing theories of scale-free dynamics in neural systems, which stem from the criticality hypothesis. We develop a computational model which incorporates known cell-type-specific circuit structure, explaining our findings with a new type of critical dynamics. Our results establish neural underpinnings of scale-free behavior and clear behavioral relevance of scale-free neural activity.
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- Award ID(s):
- 1912352
- PAR ID:
- 10490571
- Publisher / Repository:
- eLife
- Date Published:
- Journal Name:
- eLife
- Volume:
- 12
- ISSN:
- 2050-084X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.7554/eLife.79950
