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Long-range white-matter pathways enable efficient spontaneous neural activity propagation in the human brain

Efficient brain-wide communication requires neural activity to traverse long anatomical distances rapidly. Here we examine how propagation timing is jointly associated with spatial geometry, functional network organization, and long-range white-matter pathways and their microstructural properties. And we ask whether the same rules govern epileptiform and physiological activity. Using stereo-EEG and diffusion spectrum imaging from 47 epilepsy patients (26 males and 21 females), we quantified inter-regional propagation with two complementary delay estimators: event-based interictal epileptiform discharge (IED) traveling waves and continuous lagged-correlation delays during IED-free periods. We found that IED propagation traversing gray and white matter formed reproducible spatiotemporal motifs that deviated from randomized null models, indicating structured routing rather than random spread. Epileptiform and physiological propagation delays increased over short ranges but saturated at longer distances, indicating that geometry alone cannot account for long-range fast propagation. Beyond geometry, stronger structural connectivity and higher functional connectivity were associated with shorter delays, and intrinsic functional modules facilitated efficient communication: within-network propagation was faster than between-network propagation. Crucially, diffusion-derived quantitative anisotropy (QA) revealed a microstructural mechanism for long-range fast propagation: long-range white-matter tracts showed higher QA, and QA was positively associated with apparent propagation velocity. Together, these results identify convergent, architecture-dependent constraints on propagation timing that generalize across epileptiform and normal activity, providing a principled bridge between macroscale connectome organization and fast intracranial spatiotemporal dynamics.

Significance statement Efficient communication across long anatomical distances is fundamental for the human brain. By integrating stereo-EEG with diffusion spectrum imaging, this study shows that brain-wide information propagation is not determined by distance alone, but is critically supported by long-range white-matter pathways, their microstructural properties, and intrinsic functional network organization. We also find that both pathological epileptiform discharges and physiological spontaneous activity follow shared propagation rules, exhibiting distance saturation, structural facilitation, and preferential within-network transmission. These findings provide a microstructure-grounded account of how the human brain achieves fast, efficient large-scale communication, bridging macroscale connectome architecture with millisecond-scale neural dynamics.

The Brain Health Accelerator Seeks to Revolutionize Neuroscience Research

For decades, researchers across institutions have peered into microscopes and dived into data to try to understand how diseases like Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis (ALS) affect the brain. While scientists have made many important insights into these conditions, breakthrough therapies to cure or even treat them remain out of reach.

To expedite understanding of and treatments for neurodegenerative diseases, the Allen Institute launched the Brain Health accelerator. The project, announced today, is a global initiative that will leverage cutting-edge technology with the goal of improving modeling, therapeutic development, and the understanding of disease mechanisms. With funding support from the Allen Institute, the Bezos family, Amazon Web Services, the National Institutes of Health, EverythingALS, and other partners, the project financial contribution is $400 million.

One of the challenges in studying diseases in the human brain and identifying treatment strategies has been the scale and complexity of the organ. The brain consists of many distinct parts, and studying disease mechanisms requires samples from large numbers of individuals. Additionally, while technological advancements in transcriptomics, proteomics, neuroimaging, and AI have helped researchers study the brain in finer detail, researchers have not always integrated many of these approaches into the same project.

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