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Related Experiment Video

Updated: Jun 29, 2026

Recording Spatially Restricted Oscillations in the Hippocampus of Behaving Mice

Recording Spatially Restricted Oscillations in the Hippocampus of Behaving Mice

Published on: July 1, 2018

Multi-scale parameterization of neural rhythmicity with lagged Hilbert autocoherence.

Siqi Zhang^1,2,3, Maciej J Szul^2,3,4, Sotirios Papadopoulos^2,3,5

¹The Sixty-Third Research Institute, National University of Defense Technology, Nanjing, China.

Imaging Neuroscience (Cambridge, Mass.)

|November 13, 2025

Summary

We introduce lagged Hilbert autocoherence (LHaC), a new method to analyze neural oscillations. LHaC improves upon existing techniques by providing more accurate and resolved estimates of signal rhythmicity, especially for transient events.

Keywords:

bursts lagged autocoherence lagged coherence oscillations periodic activity rhythmicity spectral parameterization

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Last Updated: Jun 29, 2026

Recording Spatially Restricted Oscillations in the Hippocampus of Behaving Mice

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Area of Science:

Systems and cognitive neuroscience
Neurophysiology
Signal processing

Background:

Analysis of neural activity in different frequency bands is crucial in neuroscience.
Current methods for quantifying signal rhythmicity, like Fourier-based lagged autocoherence, have limitations including poor spectral accuracy and resolution.
These limitations are particularly problematic at higher frequencies and in low signal-to-noise ratio ranges.

Purpose of the Study:

To introduce a novel continuous estimator, lagged Hilbert autocoherence (LHaC), to overcome the limitations of existing methods for analyzing neural oscillations.
To compare the empirical behavior of LHaC with lagged Fourier autocoherence (LFaC) in simulations.
To demonstrate the practical utility of LHaC in identifying frequency-specific differences in rhythmicity and tracking learning-related changes in neural oscillations.

Main Methods:

Developed LHaC, a continuous estimator utilizing frequency-domain multiplication for precise bandpass filtering.
Computed instantaneous analytic signals via the Hilbert transform.
Employed thresholding using amplitude covariation of phase-shuffled surrogate data for robust analysis.
Compared LHaC with LFaC in simulations with controlled rhythmic structures.

Main Results:

LHaC provides more spectrally resolved estimates of rhythmicity compared to LFaC.
LHaC is more sensitive to the duration of transient, short-lived oscillatory events.
Demonstrated LHaC's utility in identifying frequency-specific rhythmicity differences between conditions and tracking learning-related neural oscillation changes.

Conclusions:

Lagged Hilbert autocoherence (LHaC) offers a refined and practical approach to characterizing neurophysiological rhythmicity.
LHaC addresses key limitations of previous methods, enhancing the accuracy and resolution of oscillatory signal analysis.
The method shows significant promise for advancing research in systems and cognitive neuroscience.