Differentiating BOLD and non-BOLD signals in fMRI time series using cross-cortical depth delay patterns
Year: 2025
Type: article
Abstract: Over the past two decades, rapid advancements in magnetic resonance technology have significantly enhanced the imaging resolution of functional Magnetic Resonance Imaging (fMRI), far surpassing its initial capabilities. Beyond mapping brain functional architecture at unprecedented scales, high-spatial-resolution acquisitions have also inspired and enabled several novel analytical strategies that can potentially improve the sensitivity and neuronal specificity of fMRI. With small voxels, one can sample from different levels of the vascular hierarchy within the cerebral cortex and resolve the temporal progression of hemodynamic changes from parenchymal to pial vessels. We propose that this characteristic pattern of temporal progression across cortical depths can aid in distinguishing neurogenic blood-oxygenation-level-dependent (BOLD) signals from typical nuisance factors arising from non-BOLD origins, such as head motion and pulsatility. In this study, we examine the feasibility of applying cross-cortical depth temporal lag (CortiLag) patterns to automatically categorize BOLD and non-BOLD signal components in modern-resolution BOLD-fMRI data. We construct an independent component analysis (ICA)-based framework for fMRI de-noising, analogous to previously proposed multi-echo (ME) ICA, except that here we explore the across-depth instead of across-echo dependence to distinguish BOLD and non-BOLD components. The efficacy of this framework is demonstrated using visual task data at three graded spatiotemporal resolutions (voxel sizes = 1.1, 1.5, and 2.0 mm isotropic at temporal intervals = 1700, 1120, and 928 ms). The proposed CortiLag-ICA framework leverages prior knowledge of the spatiotemporal properties of BOLD-fMRI and serves as an alternative to ME-ICA for cleaning moderate- and high-spatial-resolution fMRI data when multi-echo acquisitions are not available.
Source: Imaging Neuroscience
DOI: 10.1162/imag.a.910
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
Funders: Centre d'Imagerie BioMédicale, National Institutes of Health, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, National Institute on Aging, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering, National Center for Research Resources
Awards: G1006224846, G1307871421, Relating functional MRI to neuronal activity: accounting for effects of microarchitecture, G1960588227, Impact of sex differences in immune function on shared risk for cardiometabolic disorder & Alzheimer's disease, G2193096401, Resting state connectivity: Biophysical basis for and improved fMRI measurements, G2958284124, CRCNS: Computational Modeling of Microvascular Effects in Cortical Laminar fMRI, G393103480, G4480946400, G46444004, G4906623738, Relating functional MRI to neuronal activity: accounting for effects of microarchitecture, G5777629816, fMRI Technologies for Imaging at the Limit of Biological Spatiotemporal Resolution, G6987557050, Improving Human fMRI through Modeling and Imaging Microvascular Dynamics, G738160294, 7T Translational MRI System, Center for Functional Imaging Technologies, Probing the biophysical basis of large-scale brain dynamics using simultaneous PET/fMRI and EEG/PET/fMRI, G9000200654, Local Neuronal Drive and Neuromodulatory Control of Activity in the Pial Neurovascular Circuit, Comprehensive prospective correction of head motion in functional MRI, Multi-modal imaging of the metabolic and neurochemical mechanisms underlying task-evoked negative BOLD signals, Center for Mesoscale Mapping, Probing the Biophysical Basis of Large-Scale Brain Dynamics Using Simultaneous PET/fMRI and EEG/PET/fMRI, Silicon Graphics Prism Extreme 128P/1TB
OpenAlex ID: W4414221529