Stimulus-Evoked Network Dynamics in Human Cortical Organoids: From a Graph-Computational Framework to Repeated-Stimulation Depression

2026-07-30

AI Summary

This study investigates whether human cortical organoids possess structured information processing capabilities beyond generating spontaneous synchronous activity. To address this, the authors developed a graph-based computational framework integrated with longitudinal recordings from high-density microelectrode arrays to analyze stimulus-evoked neural propagation dynamics. A developmentally matched unstimulated control group was introduced to disentangle effects of repeated stimulation from those of maturation. Methodologically, the work introduces a biologically inspired message-passing principle to constrain integration depth and employs stimulus-conditioned functional graphs, graph-constrained dynamical models (graph neural networks), and a propagation depth metric. Results reveal that organoids exhibit only rapid, near-synchronous bursts in response to stimuli without spatial propagation; moreover, repeated stimulation markedly suppresses responsiveness, with the proportion of activated electrodes declining from 93% during the first stimulus to 10% by the fifth, underscoring their limited capacity for information processing.

Abstract

Human cortical organoids provide an experimentally accessible model of early neural circuit formation, yet whether their activity reflects structured information processing rather than spontaneous synchronization is unclear. We developed a graph-computational framework to quantify stimulus-evoked propagation. This includes stimulus-conditioned functional graphs, a graph-constrained dynamical (graph-neural-network) model used as a system-identification tool, a biological message-passing principle bounding integration depth by observable propagation depth, and a suite of graph-level metrics. We carried this program out in full on longitudinal HD-MEA recordings from three organoids. Once the true acquisition sampling rate and stimulus timing were recovered, the evoked response proved to be a fast, near-synchronous network burst with no measurable outward propagation (peak-latency vs. distance slope = 0). The propagation/integration-depth metrics (Deff ,reachability index, dmax) therefore do not apply, and per-day connectivity graphs were not reliably estimable at the available trial count, a negative result with methodological consequences for applying such metrics to organoid data. Reframing around synchrony, response-population size and shared variability revealed a control-validated phenomenon, i.e., repeated daily stimulation progressively depressed and spatially contracted the evoked response. That repeated stimulation reshapes organoid networks is established, but longitudinal designs in which every preparation is stimulated cannot separate this from developmental maturation. We break that confound with a developmentally-matched, stimulation-naive control, where at day 7, an organoid receiving its first-ever stimulation engaged 93% of the array, whereas organoids with five prior sessions engaged 10%.

Problem

• cortical organoids
• stimulus-evoked activity
• network dynamics
• repeated-stimulation depression
• developmental maturation

Innovation

• graph-computational framework
• stimulus-evoked dynamics
• cortical organoids
• repeated-stimulation depression
• system identification