ArticleCommunications biology2025
Automated inference of disease mechanisms in patient-hiPSC-derived neuronal networks.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Article
- Quantifying spike train synchrony and directionality: Measures and Applications.Biological cybernetics · 2026Review
- Neural timescales from a computational perspective.Nature neuroscience · 2026Review
- Hierarchical Afferent Connectivity Drives Population-Wide Bursting Dynamics in a Computational Model of Human-Derived Excitatory Neuronal Networks.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- WhenAPL bioengineering · 2025Review
- CACNA1A haploinsufficiency leads to reduced synaptic function and increased intrinsic excitability.Brain : a journal of neurology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Human induced pluripotent stem cells (hiPSCs)-derived neuronal networks on multi-electrode arrays (MEAs) are a powerful tool for studying neurological disorders. The electric activity patterns of these networks differ between healthy and patient-derived neurons, reflecting underlying pathology. However, elucidating these underlying molecular mechanisms requires strenuous additional experiments. Computational models can link observable network activity to underlying mechanisms by estimating biophysical model parameters that simulate the experimental observations, but this is challenging. Here, we address this challenge using simulation-based inference (SBI), a machine-learning approach, to automatically estimate all model parameters that can explain network activity. We show how SBI can accurately estimate parameters that replicate the activity of healthy hiPSC-derived neuronal networks, pinpoint molecular mechanisms affected by pharmacological agents, and identify key disease mechanisms in patient-derived neuronal networks. This demonstrates SBI's potential to automate and enhance the discovery of in vitro disease mechanisms from MEA measurements, advancing research with hiPSC-derived neuronal networks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.