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    Area of Science:

    • Neuroscience
    • Biophysics
    • Signal Processing

    Background:

    • fMRI scanning introduces severe artifacts in neural recordings.
    • Previous methods using PCA for EEG denoising during fMRI are limited in frequency range.
    • Extracellular neural recordings capture a broad frequency spectrum (1-6000 Hz).

    Purpose of the Study:

    • To estimate and remove fMRI artifacts from extracellular neural recordings at ultrahigh magnetic fields.
    • To adapt PCA-based denoising for the wide frequency range of extracellular field potentials (EFPs).
    • To enable simultaneous fMRI and complete neural recording.

    Main Methods:

    • Adaptation of PCA for EFP denoising, covering 1-6000 Hz.
    • Comparison of Singular Value Decomposition (SVD)-PCA Singular Value Shrinkage (SVS) with two shrinkage rules and sliding template subtraction.
    • Development of a novel technique using temporal first difference for estimating singular value upper bounds.

    Main Results:

    • SVS methods proved advantageous over sliding template subtraction, particularly for high-frequency extracellular action potentials (EAPs).
    • The developed techniques successfully uncovered EAPs during fMRI gradient interferences in artificial datasets.
    • Analysis of natural datasets from rat cortex confirmed the efficacy of SVS for both local field potentials (LFPs) and EAPs.

    Conclusions:

    • The proposed SVS methods effectively denoise extracellular neural recordings corrupted by fMRI artifacts.
    • This work facilitates simultaneous fMRI and high-fidelity neural recording (1-6000 Hz).
    • Enables further investigation of brain function and neurovascular coupling at ultrahigh fields.