Technical NoteReduction of physiological noise with independent component analysis improves the detection of nociceptive responses with fMRI of the human spinal cord
Highlights
► Physiological noise correction using ICA in fMRI of the human spinal cord. ► Nociceptive responses detected in the cervical spinal cord of 14 healthy subjects. ► Increased sensitivity and specificity after ICA-based noise correction (CORSICA).
Introduction
Following the success of functional magnetic resonance imaging (fMRI) in the investigation of brain function, fMRI of the spinal cord was shown to be technically feasible in both humans (Backes et al., 2001, Bouwman et al., 2008, Brooks et al., 2008, Cohen-Adad et al., 2010, Eippert et al., 2009, Giulietti et al., 2008, Govers et al., 2007, Madi et al., 2001, Maieron et al., 2007, Majcher et al., 2007, Stracke et al., 2005, Stroman et al., 2004, Summers et al., 2010, Valsasina et al., 2008, Yoshizawa et al., 1996) and animals (Cohen-Adad et al., 2009, Endo et al., 2008, Lawrence et al., 2007, Lilja et al., 2006, Majcher et al., 2007, Zhao et al., 2008). However, results remain controversial due to the low reproducibility of spinal cord fMRI (Bouwman et al., 2008, Giove et al., 2004). Because of the relatively small cross-sectional size of the spinal cord grey matter, even very little motion can contaminate the signal. Spinal cord motion and in-flow effects from the surrounding cerebrospinal fluid (CSF) along with cardiac and respiratory cycles greatly degrade the quality of fMRI data by adding unwanted variance in the time series (Figley and Stroman, 2007, Giove et al., 2004, Kong et al., 2012, Stroman, 2006, Stroman et al., 2005). Non-invasive acquisition of functional images of the spinal cord is very challenging but remains much needed even though there is currently no methodological consensus on the adequate means to address this challenge.
Several methods exist to minimize physiological-related noise. Respiratory-gated acquisition and breath-hold have been employed to reduce the effect of respiratory activity (Stroman and Ryner, 2001, Stroman et al., 1999), although with moderate success (Stroman, 2005). Cardiac gating has also been used in the brain (Guimaraes et al., 1998) and spinal cord (Backes et al., 2001) but this requires substantially longer acquisition time. Moreover, variable TR can introduce additional signal variance due to T1-effects, which are difficult to correct in the spinal cord due to the necessity of acquiring a robust T1 map. Additionally, heart rate can correlate with the experimental paradigm — especially for painful stimuli, therefore spurious activations can appear in the statistical map as TR would also correlate with heart rate (Tousignant-Laflamme et al., 2005).
Post-hoc correction can be used to reduce physiological noise in fMRI time series, as shown in the brain (Behzadi et al., 2007, Deckers et al., 2006, Glover et al., 2000, Hu et al., 2005, Lund et al., 2006, Thomas et al., 2002, Tohka et al., 2008) and spinal cord (Brooks et al., 2008, Figley and Stroman, 2007, Kong et al., 2012, Stroman, 2006). One method based on the RETROspective Image CORrection (RETROICOR) algorithm (Glover et al., 2000) consists in estimating a set of regressors based on external physiological recordings (pulse oxymeter and respiration trace) to be included in the general linear model (GLM). Although it has shown great success in capturing the variance of physiological noise in several fMRI studies, this approach might inappropriately model the shape of physiological-related signal by a combination of sine and cosine functions. Alternatively, data-driven methods aim at extracting the physiological noise part of the fMRI data from the data itself. One such method performs CORrection of structured noise using Spatial Independent Component Analysis (CORSICA) (Perlbarg et al., 2007). The principle of CORSICA is to estimate a noise map via the spatial independent component analysis (ICA) and to remove the corresponding components from the functional data before testing for the effect of interest. The noise components can be identified using anatomical priors, as in (Perlbarg et al., 2007), or based on separate data acquisition using a short TR to assess the spatial distribution of noise at rest. Short-TR data are used to avoid aliasing, particularly for cardiac signal, whose main spectrum typically ranges from 0.8 to 1.4 Hz. Therefore a sampling frequency of at least 2 × 1.4 Hz (or TR < 350 ms) is required to satisfy the Nyquist condition. CORSICA proved to be useful in reducing physiological noise in brain fMRI time series (Schrouff et al., 2011, Vanhaudenhuyse et al., 2010). The main assumption of CORSICA is that physiological noise is spatially structured. Our recent investigation on the spatial distribution of physiological noise in spinal cord fMRI demonstrated that cardiac-related noise — one major source of signal variance in spinal cord fMRI — is spatially structured and stable within each individual (Piché et al., 2009). Brooks et al. performed ICA on spinal cord fMRI data acquired with short TR (200 ms) to allow exploration of the physiological noise spectra (Brooks et al., 2008). They showed that cardiac and respiratory effects are easily extracted by the ICA decomposition. The cardiac-related signal was mostly located in the CSF region and in the carotid and vertebral vessels, while the respiratory-related signal usually appeared at the interface between connective tissues (neck, muscles). The interaction between cardiac and respiratory signal (amplitude modulation) also appeared in the ICA. In addition, a low-frequency component (< 0.1 Hz) was robustly identified in multiple subjects. All these observations suggest that a map of physiological noise can be computed for each subject and subsequently used within the same subject in various experiments.
In this paper we assessed whether CORSICA can improve the sensitivity and specificity of BOLD responses to nociceptive stimuli in the cervical spinal cord.
Section snippets
Acquisitions
All experimental procedures conformed to the standards set by the latest revision of the Declaration of Helsinki and were approved by the Research Ethics Board of our institution (“Comité mixte d'éthique de la recherche du Regroupement Neuroimagerie Québec; CMER-RNQ). All participants gave written informed consent, acknowledging their right to withdraw from the experiment without prejudice, and received compensation for their travel expenses, time and commitment. FMRI acquisitions were carried
Results
Fig. 4 shows T-maps of responses to nociceptive stimuli in four representative subjects, without and with physiological noise correction using CORSICA. Overall, higher peak T-score (subjects #1, #3, #4) and higher voxel count (subjects #3, #4) of stimulus-related responses was detected in the cord with CORSICA. Fewer stimulus-related responses were observed outside the spinal cord with CORSICA, suggesting an increase in spatial specificity. Similar results were obtained in the 10 other
Discussion
This study assessed the efficiency of the CORSICA method to correct physiological noise fluctuation in spinal cord fMRI time series. This method utilizes ICA from short-TR data to derive subject-dependent spatial map of physiological noise and subsequently used to remove physiological-noise-related components from long-TR fMRI data based on the similarity criterion. CORSICA showed an increase in sensitivity and specificity for the detection of BOLD responses to noxious stimuli in the spinal
Conclusion
One of the most challenging aspects of spinal cord fMRI is the effect of physiological noise on the detection of the BOLD signal. As the fMRI community has moved to higher field strengths, physiological noise has become an increasingly important confound limiting the sensitivity and the specificity of fMRI studies (Liu et al., 2006). It is therefore even more important to limit this confound via post-processing techniques such as the one presented here. While several approaches are being
Acknowledgments
We thank C. Hurst and A. Cyr for assistance with MRI acquisitions. We thank the reviewers for their very insightful comments. This work was supported by the Canadian Institutes of Health Research and the Quebec Pain Research Network of the “Fonds de recherche en santé du Québec” (P.R.), the National MS Society [FG1892A1/1] (J.C.A), the Canada Research Chair on the Spinal Cord and the SensoriMotor Rehabilitation Research Team (SMRRT) of the Canadian Institute of Health Research (S.R.). G. Xie
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- 1
Current address: Department of Anesthesiology, University of Saskatchewan, Saskatoon, SK, Canada.
- 2
Current address: Department of Chiropractic, Université du Québec à Trois-Rivières, QC, Canada.