Reduction of physiological noise with independent component analysis improves the detection of nociceptive responses with fMRI of the human spinal cord

Abstract The evaluation of spinal cord neuronal activity in humans with functional magnetic resonance imaging (fMRI) is technically challenging. Major difficulties arise from cardiac and respiratory movement artifacts that constitute significant sources of noise. In this paper we assessed the Correction of Structured noise using spatial Independent Component Analysis (CORSICA). FMRI data of the cervical spinal cord were acquired in 14 healthy subjects using gradient-echo EPI. Nociceptive electrical stimuli were applied to the thumb. Additional data with short TR (250 ms, to prevent aliasing) were acquired to generate a spatial map of physiological noise derived from Independent Component Analysis (ICA). Physiological noise was subsequently removed from the long-TR data after selecting independent components based on the generated noise map. Stimulus-evoked responses were analyzed using the general linear model, with and without CORSICA and with a regressor generated from the cerebrospinal fluid region. Results showed higher sensitivity to detect stimulus-related activation in the targeted dorsal segment of the cord after CORSICA. Furthermore, fewer voxels showed stimulus-related signal changes in the CSF and outside the spinal region, suggesting an increase in specificity. ICA can be used to effectively reduce physiological noise in spinal cord fMRI time series.