Rethinking scale in network neuroscience: Contributions and opportunities at the nanoscale

Abstract

Network science has been applied widely to study brain network organization, especially at the meso-scale, where nodes represent brain areas and edges reflect interareal connectivity inferred from imaging or tract-tracing data. While this approach has yielded important insights into large-scale brain network architecture, its foundational assumptions often misalign with the biological realities of neural systems. In this review, we argue that network science finds its most direct and mechanistically grounded application in nanoscale connectomics-wiring diagrams reconstructed at the level of individual neurons and synapses, often from high-resolution electron microscopy volumes. At this finer scale, core network concepts such as paths, motifs, communities, and centrality acquire concrete biological interpretations. Unlike meso-scale models, nanoscale connectomes are typically derived from individual animals, preserve synaptic resolution, and are richly annotated with cell types, neurotransmitter identities, and morphological detail. These properties enable biologically grounded, mechanistically interpretable analyses of circuit structure and function. We review how nanoscale data support new forms of network modeling, from realistic dynamical simulations to topology-informed circuit inference, and outline emerging directions in multimodal integration, cross-species comparisons, and generative modeling. We also emphasize the continued importance of meso- and macro-scale connectomics, especially in human neuroscience, and discuss how nanoscale insights can inform interpretation at coarser scales. Together, these efforts point toward a multi-scale future for network neuroscience, grounded in the strengths of each resolution.