Spinal Cord fMRI: How PCA Helps Clean Up the Noise

Researchers have tested a method that uses principal component analysis (PCA) to filter unwanted signals from spinal cord fMRI scans. The technique, called SpinalCompCor, picks out noise by looking at a region outside the spinal cord and cerebrospinal fluid. It then keeps only the most important components, usually about nine, to remove physiological interference like blood flow changes. In four different studies – two with motor tasks, one with breathing exercises, and two resting‑state scans – the PCA approach could explain a lot of the background noise. However, when scientists compared brain activity maps from groups of participants, adding these PCA regressors did not noticeably improve the results.One possible reason is that some of the PCA components overlap with signals related to the actual tasks, which can make the cleaning less effective. This overlap could be more pronounced when the imaging settings are different or when the data quality varies. Because of these uncertainties, experts advise using SpinalCompCor only when actual physiological recordings (like heart or breathing traces) are missing. The PCA method may not consistently match the precision of recordings across different experiments or equipment setups. Overall, while PCA can help reduce noise in spinal cord imaging, its benefits are limited and must be weighed against potential complications.