A Microfluidic Platform for Spatiotemporal Dissection of Neurodegeneration Across Hierarchical Human Neural Circuits

Abstract Understanding how neurodegenerative diseases initiate and propagate through neural circuits remains a fundamental challenge in neuroscience. The earliest stages occur years before symptoms emerge, making them inaccessible to study in patients. Microfluidic platforms, where neurons communicate across chambers through microchannels accessible only to their axons, have opened new experimental avenues. However, existing models lack the complexity and precision needed to track how individual circuit components respond to focal pathological changes over time. Here we present a 33-chamber cortical network-on-chip integrating human iPSC-derived excitatory neurons, inhibitory neurons, and astrocytes in a six-layer feedforward architecture recapitulating the laminar structure of the neocortex. Amyloid-β is applied globally, while progerin-induced accelerated ageing in a single chamber establishes a defined disease core. Continuous recordings using high-density microelectrode arrays reveal progressive, layer-dependent changes in firing dynamics and network topology. Machine-learning-based feature analysis identifies a multiparametric electrophysiological signature distinguishing healthy from disease-affected chambers, enabling studies of the earliest timepoint at which pathology becomes detectable. This establishes a scalable framework for mechanistic studies of neurodegeneration and identification of electrophysiological biomarkers of disease progression.