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A novel temporal filtering strategy for functional MRI using UNFOLD.

S Domsch¹, A Lemke, S Weingärtner

¹Department of Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Germany. sebastian.domsch@medma.uni-heidelberg.de

|April 10, 2012

Summary

A new temporal filtering method improves functional MRI (fMRI) by reducing artifacts and increasing activated voxels. This advance enhances fMRI

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Abstracts of Presentations at the International Conference on Basic and Clinical Multimodal Imaging (BaCI), a Joint Conference of the International Society for Neuroimaging in Psychiatry (ISNIP), the International Society for Functional Source Imaging (ISFSI), the International Society for Bioelectromagnetism (ISBEM), the International Society for Brain Electromagnetic Topography (ISBET), and the EEG and Clinical Neuroscience Society (ECNS), in Geneva, Switzerland, September 5-8, 2013.

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Area of Science:

Neuroimaging
Magnetic Resonance Imaging
Signal Processing

Background:

Functional magnetic resonance imaging (fMRI) faces challenges in temporal resolution at high spatial resolutions.
The UNFOLD method accelerates fMRI acquisition, enabling higher acceleration factors but introducing spatial aliasing artifacts.
Existing temporal filtering methods (low-pass, multi-band) have limitations, especially at high UNFOLD factors, impacting temporal resolution and statistical modeling.

Purpose of the Study:

To introduce a novel temporal filtering strategy for fMRI data acquired with the UNFOLD method.
To reduce temporal autocorrelations more effectively than existing multi-band filtering techniques.
To improve the accuracy and sensitivity of statistical mapping in fMRI time series analysis.

Main Methods:

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A new temporal filtering strategy was developed to address spatial aliasing artifacts from UNFOLD k-space sampling.
The proposed filter was compared against multi-band filtering using finger-tapping and resting-state fMRI datasets.
Varying filter configurations, including transition bands, were tested for both methods.

Main Results:

The novel filtering strategy significantly reduced temporal autocorrelations compared to multi-band filtering.
Linear regression analysis showed a decrease in false positives by up to 34% with the proposed filter.
The number of activated voxels was not significantly affected, leading to an effective increase in activated voxels per false positive.

Conclusions:

The proposed temporal filtering strategy offers a significant improvement for fMRI data processed with the UNFOLD method.
This method enhances the effective number of activated voxels by reducing false positives without compromising sensitivity.
The strategy effectively increases activated voxels by up to 41% at a 5% significance level, improving fMRI analysis.