Characterization of cardiac-related noise in fMRI of the cervical spinal cord

Magnetic resonance imaging (MRI) has recently been applied to study spinal cord function in humans. However, spinal functional MRI (fMRI) encounters major technical challenges with cardiac noise being considered a major source of noise. The present study relied on echo-planar imaging of the cervical cord at short TR (TR=250 ms; TE=40 ms; flip=45 degrees), combined with plethysmographic recordings to characterize the spatiotemporal properties of cardiac-induced signal changes in spinal fMRI. Frequency-based analyses examining signal change at the cardiac frequency confirmed mean fluctuations of about 10% (relative to the mean signal) in the spinal cord and surrounding cerebrospinal fluid (CSF), with maximal responses reaching up to 66% in some voxels. A spatial independent component analysis (sICA) confirmed that cardiac noise is an important source of variance in spinal fMRI with several components showing a response coherent with the cardiac frequency spectrum. The time course of the main cardiac components approximated a sinusoidal function tightly coupled to the cardiac systole with at least one component showing a comparable temporal profile across runs and subjects. Spatially, both the frequency-domain analysis and the sICA demonstrated cardiac noise distributed irregularly along the full rostrocaudal extent of the segments scanned with peaks concentrated in the ventral part of the lateral slices in all scans and subjects, consistent with the major channels of CSF flow. These results confirm that cardiac-induced changes are a significant source of noise likely to affect the detection of spinal Blood Oxygen Level Dependent (BOLD) responses. Most importantly, the complex spatiotemporal structure of cardiac noise is unlikely to be accounted for adequately by ad hoc linear methods, especially in data acquired using long TR (i.e. aliasing the cardiac frequency). However, the reliable spatiotemporal distribution of cardiac noise across scanning runs and within subjects may provide a valid means to identify and extract cardiac noise based on sICA methods.