Differentiating BOLD and non-BOLD signals in fMRI time series using cross-cortical depth delay patterns

Area of Science:

• Neuroimaging
• Biophysics

Background:

• Functional Magnetic Resonance Imaging (fMRI) resolution has dramatically improved.
• High-resolution fMRI enables novel analytical strategies for enhanced sensitivity and neuronal specificity.
• Voxel size allows sampling across the vascular hierarchy, resolving hemodynamic changes from parenchymal to pial vessels.

Purpose of the Study:

• To investigate the feasibility of using cross-cortical depth temporal delay patterns to differentiate blood-oxygen-level-dependent (BOLD) signals from non-BOLD noise.
• To develop an Independent Component Analysis (ICA)-based framework for fMRI de-noising that utilizes across-depth signal progression.

Main Methods:

• An ICA-based de-noising framework was developed, focusing on across-depth temporal dependence instead of across-echo dependence.
• The framework was tested on visual task fMRI data acquired at varying spatiotemporal resolutions (1.1-2.0 mm voxels).

Main Results:

• The proposed framework successfully categorized BOLD and non-BOLD signal components using cross-cortical depth temporal delay patterns.
• The method demonstrated efficacy across different spatial and temporal resolutions.

Conclusions:

• Cross-cortical depth temporal delay patterns are a viable method for distinguishing BOLD from non-BOLD signals in high-resolution fMRI.
• This ICA-based framework offers an alternative to multi-echo ICA for de-noising fMRI data when multi-echo acquisitions are unavailable.