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  11. Aperiodic 1/f Noise Drives Ripple Activity In Humans

Aperiodic 1/f noise drives ripple activity in humans

Frank J van Schalkwijk1, Randolph F Helfrich2,3,4

  • 1Hertie-Institute for Clinical Brain Research, Center for Neurology, University Hospital Tübingen, Tübingen, Germany. frankvanschalkwijk@gmail.com.

Nature Communications
|January 17, 2026

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View abstract on PubMed

Summary
This summary is machine-generated.

Sharp-wave ripples (SWRs) are often misidentified as noise. Most detected awake ripples in the human brain are false positives, highlighting the need for improved detection methods.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Sharp-wave ripples (SWRs) are crucial for memory consolidation and are observed in rodent and human brains during sleep and wakefulness.
  • Detecting SWRs across different brain states and regions, particularly in humans, presents significant challenges.
  • Existing ripple detection methods may be susceptible to artifacts from background neural activity.

Purpose of the Study:

  • To investigate the reliability of common sharp-wave ripple detection algorithms.
  • To assess the contribution of background cortical activity (1/f^χ noise) to putative ripple events.
  • To develop a method for estimating false positive rates in ripple detection.

Main Methods:

  • Analysis of intracranial EEG data from three studies involving human participants during sleep and cognitive tasks.

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  • Evaluation of five common ripple detection algorithms for their sensitivity to noise.
  • Simulation-based approach to quantify false positive rates and identify noise-resilient detection scenarios.

    • Main Results:

    • An average of 77% of detected awake ripples in the medial temporal lobe, including the hippocampus, were identified as false positives.
    • Putative ripples often represent noise modulated by region, brain state, and cognitive demand.
    • Task-related modulations of 1/f^χ background activity can lead to spurious ripple detections.

    Conclusions:

    • Current ripple detection methods may overestimate ripple occurrence due to high false positive rates.
    • 1/f^χ noise significantly impacts ripple detection, especially during cognitive engagement and across different brain states.
    • A simulation-based approach is valuable for assessing detection algorithm performance and understanding cortical processing.