Fast-ripples are emergent properties of neuronal networks

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Authors: Laurent Sheybani, Yichen Qiu, Prince Kumar Singh, Umesh Vivekananda, Neil Burgess, Beate Diehl, A W McEvoy, Anna Miserocchi, James A. Bisby, Tawfeeq Shekh‐Ahmad, Gabriele Lignani, Daniel Bush
+1 more Matthew C Walker
Cited by: 2
References: 82

Abstract

Fast-ripples (250-500 Hz) have been proposed as a promising biomarker in epilepsy, but their specificity remains unclear. In particular, it is uncertain whether they reflect chance coincident neural activity or distinctly generated pathological entities. We combined in silico simulations, neuronal cultures, the intra-hippocampal kainate rat model of hippocampal epilepsy, and human microwire recordings to investigate whether fast-ripples occur more frequently than expected by the chance aggregation of action potentials. Our simulations showed that chance aggregation can generate fast-ripples and that their incidence changes depending on brain state, an observation that we confirmed in our rodent data. The likelihood of exceeding chance depended on biological system complexity and vigilance state: fast-ripples in neuronal cultures did not surpass chance levels, whereas those in awake – but not sleeping – rodents did. Similarly, the incidence of fast-ripples in awake human recordings was ∼30% greater than expected by chance. As such, our findings suggest that most fast-ripples reflect stochastic network activity rather than distinctly generated pathological entities. This work does not rule out FRs as potential indicators of epileptogenic tissue, but it does challenge prevailing assumptions about their generation and specificity. Their higher prevalence in epileptogenic tissue is likely primarily due to increased excitation and/or neural synchronization, rather than peculiar abnormalities in network behavior.

Fields of Study & Subjects

Neural dynamics and brain function Neuroscience and Neuropharmacology Research Functional Brain Connectivity Studies

Publication details

Journal / source
eLife
Publisher
eLife Sciences Publications, Ltd
Publication date
23 April 2026
Document type
Preprint