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Recording Spatially Restricted Oscillations in the Hippocampus of Behaving Mice
Published on: July 1, 2018
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Multi-scale parameterization of neural rhythmicity with lagged Hilbert autocoherence.
Siqi Zhang1,2,3, Maciej J Szul2,3,4, Sotirios Papadopoulos2,3,5
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.
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.
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