Denoising spinal cord fMRI data: Approaches to acquisition and analysis

Abstract

General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. AbstractFunctional magnetic resonance imaging (fMRI) of the human spinal cord is a difficult endeavour due to the cord's small cross-sectional diameter, signal drop-out as well as image distortion due to magnetic field inhomogeneity, and the confounding influence of physiological noise from cardiac and respiratory sources. Nevertheless, there is great interest in spinal fMRI due to the spinal cord's role as the principal sensorimotor interface between the brain and the body and its involvement in a variety of sensory and motor pathologies. In this review, we give an overview of the various methods that have been used to address the technical challenges in spinal fMRI, with a focus on reducing the impact of physiological noise. We start out by describing acquisition methods that have been tailored to the special needs of spinal cord fMRI and aim to increase the signal-to-noise ratio and reduce distortion in obtained images. Following this, we concentrate on image processing and analysis approaches that address the detrimental effects of noise. While these include variations of standard pre-processing methods such as motion correction and spatial filtering, the main focus lies on denoising techniques that can be applied to taskbased as well as resting-state data sets. We review both model-based approaches that rely on externally acquired respiratory and cardiac signals as well as data-driven approaches that estimate and correct for noise using the data themselves. We conclude with an outlook on techniques that have been successfully applied for noise reduction in brain imaging and whose use might be beneficial for fMRI of the human spinal cord.3

Robust spinal cord resting‐state fMRI using independent component analysis‐based nuisance regression noise reduction

“…Recent studies have adopted adjustments to rsfMRI image acquisition to successfully overcome challenges such as the small size of the spinal cord and susceptibility artifacts . One key problem that remains is non‐ignorable physiological noise …”

“…Spinal physiological noise can be mainly divided into nonrigid tissue motion and cerebrospinal fluid (CSF) pulsation, Tissue motion noise is mainly caused by respiration, and respiration‐related susceptibility changes cause artifacts in rsfMRI images. These artifacts introduce intravoxel dephasing, which results in image shifting, intensity shading in the phase‐encoding direction, and signal variation .…”

Contributions of neuroimaging in singing voice studies: a systematic review

“…After reading the abstracts, two more articles were excluded, since one presented a unique approach regarding the spoken voice 6 and the other referred to the dopaminergic activation of bird song 7 . When the original version of the remaining articles were read in their entirety, the researchers independently excluded five more articles because of the following reasons: a focus on the auditory-motor mapping of pitch 8 control, an analysis of the performance of the cerebral cortex after transcranial stimulation while reading and performing musical pieces 9 , a focus on the use of melodic intonation therapy to improve severe cases of non-fluent aphasia 10 , as well as two other studies because these were systematic reviews 11,12 . During the stages of research, study evaluations, and data analyses, the researchers located opposing viewpoints in the respective analyses, compared the results, and settled disagreements by consensus (Figure 1).…”

“…One of the first studies employing neuroimaging was performed by Perry et al 3 , who used positron 02 -systematic reviews 11,12 Articles included in the qualitative synthesis To investigate the multifactorial vocal system using PET, to assess the listening and response system of non-professional singers, during repeated singing, harmonizing new melodies, or singing monotonically.…”

Neuroimaging mechanisms in pain: from discovery to translation

“…Riddled with complex hurdles to overcome from specialist hardware requirements, image acquisition limitations, horrendous physiological noise that contaminate real signal and really poor signal-to-noise opportunities when compared with the brain alongside image analysis difficulties, it is a miracle that any progress has been made-yet it has. Our group alongside others have doggedly worked through the challenges, 15,32,40,41,64,105,130 and we are now in a place where robust data can be collected verifying things we know should be true from a physiological 17,74 to cognitive perspective (eg, involved in cognitive distraction and placebo analgesia through the DPMS). 31,103 What is exciting now is that we are discovering new biology-for example, the fact that the human spinal cord has intrinsically organised resting state networks.…”