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

GRSNC, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada.
NeuroImage (Impact Factor: 6.36). 07/2012; 63(1):245-52. DOI: 10.1016/j.neuroimage.2012.06.057
Source: PubMed


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.

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Available from: Mathieu Piché, Jan 13, 2014
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    • "In each subject, temporal signal-to-noise ratio (TSNR) was measured in spinal gray matter upon completion of the functional-to-anatomical affine registration (step #9) as well as after the application of CSF and white matter 'regressors of no interest' (steps #11 and #12). Across all 22 subjects, we observed a 30% increase in median TSNR (from 29.3 to 38.1) after the application of these few regressors, demonstrating the importance of characterizing and removing structured noise sources (Xie et al., 2012). After band-pass filtering to isolate the frequency range of interest "
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    ABSTRACT: Functional magnetic resonance imaging using blood oxygenation level dependent (BOLD) contrast is well established as one of the most powerful methods for mapping human brain function. Numerous studies have measured how low-frequency BOLD signal fluctuations from the brain are correlated between voxels in a resting state, and have exploited these signals to infer functional connectivity within specific neural circuits. However, to date there have been no previous substantiated reports of resting state correlations in the spinal cord. In a cohort of healthy volunteers, we observed robust functional connectivity between left and right ventral (motor) horns, and between left and right dorsal (sensory) horns. Our results demonstrate that low-frequency BOLD fluctuations are inherent in the spinal cord as well as the brain, and by analogy to cortical circuits, we hypothesize that these correlations may offer insight into the execution and maintenance of sensory and motor functions both locally and within the cerebrum.
    eLife Sciences 08/2014; 3(3):e02812. DOI:10.7554/eLife.02812 · 9.32 Impact Factor
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    • "ICA approaches offer the possibility to automatically identify sources on physiological noise, however, their usefulness must be judged against the possibility of true signal being identified as a noise source and removed from the data. One approach to mitigate this outcome is to acquire resting and task fMRI data, and use the resting data to define physiological noise components, for subsequent removal from the task data (e.g., Xie et al., 2012). Concerning the model-based techniques, such as RETROICOR (Hu et al., 1995; Josephs et al., 1997; Glover et al., 2000) and PNM (Brooks et al., 2008; Harvey et al., 2008) these will always be subject to the limitations of the basis functions used to describe the physiological processes they are attempting to model. "
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    ABSTRACT: The brainstem is directly involved in controlling blood pressure, respiration, sleep/wake cycles, pain modulation, motor, and cardiac output. As such it is of significant basic science and clinical interest. However, the brainstem's location close to major arteries and adjacent pulsatile cerebrospinal fluid filled spaces, means that it is difficult to reliably record functional magnetic resonance imaging (fMRI) data from. These physiological sources of noise generate time varying signals in fMRI data, which if left uncorrected can obscure signals of interest. In this Methods Article we will provide a practical introduction to the techniques used to correct for the presence of physiological noise in time series fMRI data. Techniques based on independent measurement of the cardiac and respiratory cycles, such as retrospective image correction (RETROICOR, Glover et al., 2000), will be described and their application and limitations discussed. The impact of a physiological noise model, implemented in the framework of the general linear model, on resting fMRI data acquired at 3 and 7 T is presented. Data driven approaches based such as independent component analysis (ICA) are described. MR acquisition strategies that attempt to either minimize the influence of physiological fluctuations on recorded fMRI data, or provide additional information to correct for their presence, will be mentioned. General advice on modeling noise sources, and its effect on statistical inference via loss of degrees of freedom, and non-orthogonality of regressors, is given. Lastly, different strategies for assessing the benefit of different approaches to physiological noise modeling are presented.
    Frontiers in Human Neuroscience 10/2013; 7:623. DOI:10.3389/fnhum.2013.00623 · 3.63 Impact Factor
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    • "These signals were undersampled in our study which means their fundamental frequency was aliased onto the unknown spectrum, leaving a chance of coinciding with one of the frequencies of interest, especially a higher harmonic. Such fluctuations have been shown to be mostly confined to locations within and near large blood vessels and/or CSF, or the outline of the brain [82]–[84]. Therefore, the activity related to physiological fluctuations would most probably be classified into one of non-GM subgroups, either stimulus-related non-GM group if its frequency matched that of SPT, or just noise. "
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    ABSTRACT: Although auditory processing has been widely studied with conventional parametric methods, there have been a limited number of independent component analysis (ICA) applications in this area. The purpose of this study was to examine spatiotemporal behavior of brain networks in response to passive auditory stimulation using ICA. Continuous broadband noise was presented binaurally to 19 subjects with normal hearing. ICA was performed to segregate spatial networks, which were subsequently classified according to their temporal relation to the stimulus using power spectrum analysis. Classification of separated networks resulted in 3 stimulus-activated, 9 stimulus-deactivated, 2 stimulus-neutral (stimulus-dependent but not correlated with the stimulation timing), and 2 stimulus-unrelated (fluctuations that did not follow the stimulus cycles) components. As a result of such classification, spatiotemporal subdivisions were observed in a number of cortical structures, namely auditory, cingulate, and sensorimotor cortices, where parts of the same cortical network responded to the stimulus with different temporal patterns. The majority of the classified networks seemed to comprise subparts of the known resting-state networks (RSNs); however, they displayed different temporal behavior in response to the auditory stimulus, indicating stimulus-dependent temporal segregation of RSNs. Only one of nine deactivated networks coincided with the "classic" default-mode network, suggesting the existence of a stimulus-dependent default-mode network, different from that commonly accepted.
    PLoS ONE 06/2013; 8(6):e66424. DOI:10.1371/journal.pone.0066424 · 3.23 Impact Factor
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