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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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Network science characteristics of brain-derived neuronal cultures deciphered from quantitative phase imaging data
Abstract Understanding the mechanisms by which neurons create or suppress connections to enable communication in brain-derived neuronal cultures can inform how learning, cognition and creative behavior emerge. While prior studies have shown that neuronal cultures possess self-organizing criticality properties, we further demonstrate that in vitro brain-derived neuronal cultures exhibit a self-optimization phenomenon. More precisely, we analyze the multiscale neural growth data obtained from label-free quantitative microscopic imaging experiments and reconstruct the in vitro neuronal culture networks (microscale) and neuronal culture cluster networks (mesoscale). We investigate the structure and evolution of neuronal culture networks and neuronal culture cluster networks by estimating the importance of each network node and their information flow. By analyzing the degree-, closeness-, and betweenness-centrality, the node-to-node degree distribution (informing on neuronal interconnection phenomena), the clustering coefficient/transitivity (assessing the “small-world” properties), and the multifractal spectrum, we demonstrate that murine neurons exhibit self-optimizing behavior over time with topological characteristics distinct from existing complex network models. The time-evolving interconnection among murine neurons optimizes the network information flow, network robustness, and self-organization degree. These findings have complex implications for modeling neuronal cultures and potentially on how to design biological inspired artificial intelligence.
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- Award ID(s):
- 1735252
- PAR ID:
- 10289849
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 10
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41598-020-72013-7
