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Related Experiment Video

Updated: Apr 29, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Highcor: a novel data-driven regressor identification method for BOLD fMRI.

A T Curtis¹, R S Menon¹

¹Dept. of Medical Biophysics, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Ontario Canada; Robarts Research Institute, The University of Western Ontario, 100 Perth Drive, London, Ontario N6A 5K8, Canada.

|May 17, 2014

Summary

This summary is machine-generated.

A new method, highcor, uses BOLD fMRI signal phase to reduce physiological noise. This data-driven technique improves signal-to-noise ratio and task detection by identifying and filtering noise sources.

Keywords:

BOLD Filter Functional Human Magnetic resonance imaging Neuroimaging Noise Phase Regressor fMRI

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Area of Science:

Neuroimaging
Biomedical Engineering
Signal Processing

Background:

Physiological noise significantly impacts Blood-Oxygen-Level-Dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) data quality.
Existing data-driven techniques like CompCor (Component-based Noise Correction) are widely used but can be further optimized.

Purpose of the Study:

To introduce and evaluate a novel data-driven regressor selection technique, highcor, for reducing physiological noise in BOLD fMRI.
To compare the performance of highcor against CompCor and assess the benefits of a combined approach.

Main Methods:

Highcor identifies suspect voxels by correlating the temporal magnitude and phase components of the BOLD signal.
Temporal regressors are generated using Principal Component Analysis (PCA) on the selected voxel set.
Filtering performance was evaluated on high temporal resolution datasets and benchmarked against CompCor using 36 BOLD fMRI datasets during an anti-saccade task.

Main Results:

Highcor identified a unique set of voxels related to physiological noise, demonstrating robustness even at slow sampling rates.
Filtering with CompCor and highcor reduced cortical temporal standard deviation by 16.1%±3.1% and 18.1%±3.8%, respectively.
A combined highcor and CompCor approach further reduced temporal standard deviation by 31.4%±3.8%, improving mean temporal SNR and task detection.

Conclusions:

Highcor is an effective data-driven method for physiological noise reduction in BOLD fMRI, complementing existing techniques.
Combining highcor with CompCor offers superior noise reduction and enhances signal quality, leading to improved BOLD fMRI analysis.
The findings suggest that incorporating signal phase information is valuable for robust noise characterization and removal in fMRI studies.