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Resting state functional connectivity in the human spinal cord.

Barry RL, Smith SA, Dula AN, Gore JC - Elife (2014)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Vanderbilt University Institute of Imaging Science, Nashville, United States Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, United States robert.l.barry@vanderbilt.edu.

No MeSH data available.


Related in: MedlinePlus

Functional connectivity matrices resulting from preprocessing pipeline permutations.As before, functional data were band-pass filtered between 0.01 and 0.08 Hz (*p<0.05; **p<0.01; Bonferroni corrected). For clarity the labels are not shown for each column/row but are the same as in Figure 5. (A) Preprocessing was performed as described in the Methods except CSF and WM regressors (steps #11 and #12) were not applied. Each GM sub-region is highly correlated with all other GM sub-regions and similarly each WM sub-region is highly correlated with all other WM sub-regions. Interestingly, there are no significant group-level correlations between GM and WM sub-regions, suggesting that ‘global’ GM fluctuations tend to be constrained to GM and ‘global’ WM fluctuations tend to be constrained to WM. (B) Preprocessing was performed as described in ‘Materials and methods’ except a WM regressor (step #12) was not applied. The application of only CSF regressors reduced correlations within both GM and WM, but all inter-region correlations remained and were statistically significant. (C) Preprocessing was performed exactly as described in ‘Materials and methods’ (i.e., this panel is the same as Figure 5A except for a larger dynamic range for z-scores). Regression of the principal eigenvector of all time series in the WM mask significantly altered correlations within both GM and WM. This unintuitive result may be explained if WM masks contain signal contributions from adjacent GM voxels, which is certainly possible given the small size of the GM butterfly and unavoidable partial volume effects, and sub-millimeter functional-to-anatomical warping inaccuracies caused by magnetic field inhomogeneities. (D) To investigate the possibility that WM masks contain fractions of GM voxels, and the impact of different regression masks on correlations between sub-regions, preprocessing was performed as described in ‘Materials and methods’ except step #12 extracted the principal eigenvector of all time series within a combined WM and GM mask. Given the small size of each GM mask relative to its neighboring WM mask, the principal eigenvector from the combined mask (WM&GM) should be similar to only using WM. Indeed, regression with these modified eigenvectors produces similar group-level correlations between WM sub-regions and introduce weak negative correlations between GM sub-regions. In GM, positive correlations between ventral horns and between dorsal horns still persist after this more invasive regressor, providing further evidence that these strong temporally correlated fluctuations are unlikely to be caused by physiological motion or spatially-correlated noise. To further investigate the spatial extent of global fluctuations within GM, we repeated the preprocessing as described in ‘Materials and methods’ but eroded the WM mask slightly before extracting the principal eigenvector. The results of this tertiary analysis (not shown) were slightly different but statistically similar to Figure 5A, demonstrating that voxels on and near the GM-WM boundary in fact characterize physiological fluctuations from both tissue types due to unavoidable partial volume effects and sub-millimeter warping inaccuracies. Overall, these supplementary analyses suggest that the preprocessing performed in the main manuscript (regression of the principal eigenvector from WM masks without erosion) is an appropriate strategy for suppressing extraneous fluctuations within both WM and GM without introducing substantial negative correlations between GM sub-regions. Finally, power spectra for WM and GM sub-regions in (C) are presented in Figure 5—figure supplement 2.DOI:http://dx.doi.org/10.7554/eLife.02812.009
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fig5s1: Functional connectivity matrices resulting from preprocessing pipeline permutations.As before, functional data were band-pass filtered between 0.01 and 0.08 Hz (*p<0.05; **p<0.01; Bonferroni corrected). For clarity the labels are not shown for each column/row but are the same as in Figure 5. (A) Preprocessing was performed as described in the Methods except CSF and WM regressors (steps #11 and #12) were not applied. Each GM sub-region is highly correlated with all other GM sub-regions and similarly each WM sub-region is highly correlated with all other WM sub-regions. Interestingly, there are no significant group-level correlations between GM and WM sub-regions, suggesting that ‘global’ GM fluctuations tend to be constrained to GM and ‘global’ WM fluctuations tend to be constrained to WM. (B) Preprocessing was performed as described in ‘Materials and methods’ except a WM regressor (step #12) was not applied. The application of only CSF regressors reduced correlations within both GM and WM, but all inter-region correlations remained and were statistically significant. (C) Preprocessing was performed exactly as described in ‘Materials and methods’ (i.e., this panel is the same as Figure 5A except for a larger dynamic range for z-scores). Regression of the principal eigenvector of all time series in the WM mask significantly altered correlations within both GM and WM. This unintuitive result may be explained if WM masks contain signal contributions from adjacent GM voxels, which is certainly possible given the small size of the GM butterfly and unavoidable partial volume effects, and sub-millimeter functional-to-anatomical warping inaccuracies caused by magnetic field inhomogeneities. (D) To investigate the possibility that WM masks contain fractions of GM voxels, and the impact of different regression masks on correlations between sub-regions, preprocessing was performed as described in ‘Materials and methods’ except step #12 extracted the principal eigenvector of all time series within a combined WM and GM mask. Given the small size of each GM mask relative to its neighboring WM mask, the principal eigenvector from the combined mask (WM&GM) should be similar to only using WM. Indeed, regression with these modified eigenvectors produces similar group-level correlations between WM sub-regions and introduce weak negative correlations between GM sub-regions. In GM, positive correlations between ventral horns and between dorsal horns still persist after this more invasive regressor, providing further evidence that these strong temporally correlated fluctuations are unlikely to be caused by physiological motion or spatially-correlated noise. To further investigate the spatial extent of global fluctuations within GM, we repeated the preprocessing as described in ‘Materials and methods’ but eroded the WM mask slightly before extracting the principal eigenvector. The results of this tertiary analysis (not shown) were slightly different but statistically similar to Figure 5A, demonstrating that voxels on and near the GM-WM boundary in fact characterize physiological fluctuations from both tissue types due to unavoidable partial volume effects and sub-millimeter warping inaccuracies. Overall, these supplementary analyses suggest that the preprocessing performed in the main manuscript (regression of the principal eigenvector from WM masks without erosion) is an appropriate strategy for suppressing extraneous fluctuations within both WM and GM without introducing substantial negative correlations between GM sub-regions. Finally, power spectra for WM and GM sub-regions in (C) are presented in Figure 5—figure supplement 2.DOI:http://dx.doi.org/10.7554/eLife.02812.009

Mentions: A group-level analysis of functional connectivity between sub-regions of spinal cord gray matter and adjacent white matter confirmed that the most robust correlations are observed between left and right ventral (motor) horns (p < 0.01; corrected), as well as between left and right dorsal (sensory) horns (p < 0.01; corrected) (Figure 5A). No significant group-level correlations (p > 0.05) were observed between other gray matter sub-regions, nor between spinal cord gray and white matter. Weak positive correlations were observed between left and right dorsal column white matter (p < 0.05; corrected), and negative correlations were observed between left ventral white matter and right dorsal white matter (p < 0.01; corrected), and between right ventral white matter and both left (p < 0.01; corrected) and right (p < 0.01; corrected) dorsal white matter. The apparent existence of negative correlations in resting state spinal cord data is not unexpected because anticorrelations are commonly observed in resting state analyses of the brain (Chang and Glover, 2010) and have been a topic of intense discussion for over a decade. The ranges of values within these six statistically significant distributions are presented as box-and-whisker plots (Figure 5B). The lower quartile is above zero in both gray matter plots, demonstrating that positive gray matter connectivity is a robust and reproducible measurement. In comparison, temporal correlations between white matter sub-regions are more variable and exhibit both positive and negative median correlations. The raw data used to generate this figure is provided as Figure 5—source data 1. Additional analyses were performed (Figure 5—figure supplement 1, Figure 5—figure supplement 3, and Figure 5—figure supplement 5) to confirm that positive gray matter correlations are stable across various preprocessing procedures whereas white matter correlations are positive before but negative or non-significant after white matter regression (step #12). These supplementary analyses also showed that weaker positive correlations between sub-regions of left and right dorsal white matter remained positive and significant across various preprocessing configurations.10.7554/eLife.02812.007Figure 5.Group-level functional connectivity between sub-regions of spinal gray matter (GM) and surrounding white matter (WM) within slices.


Resting state functional connectivity in the human spinal cord.

Barry RL, Smith SA, Dula AN, Gore JC - Elife (2014)

Functional connectivity matrices resulting from preprocessing pipeline permutations.As before, functional data were band-pass filtered between 0.01 and 0.08 Hz (*p<0.05; **p<0.01; Bonferroni corrected). For clarity the labels are not shown for each column/row but are the same as in Figure 5. (A) Preprocessing was performed as described in the Methods except CSF and WM regressors (steps #11 and #12) were not applied. Each GM sub-region is highly correlated with all other GM sub-regions and similarly each WM sub-region is highly correlated with all other WM sub-regions. Interestingly, there are no significant group-level correlations between GM and WM sub-regions, suggesting that ‘global’ GM fluctuations tend to be constrained to GM and ‘global’ WM fluctuations tend to be constrained to WM. (B) Preprocessing was performed as described in ‘Materials and methods’ except a WM regressor (step #12) was not applied. The application of only CSF regressors reduced correlations within both GM and WM, but all inter-region correlations remained and were statistically significant. (C) Preprocessing was performed exactly as described in ‘Materials and methods’ (i.e., this panel is the same as Figure 5A except for a larger dynamic range for z-scores). Regression of the principal eigenvector of all time series in the WM mask significantly altered correlations within both GM and WM. This unintuitive result may be explained if WM masks contain signal contributions from adjacent GM voxels, which is certainly possible given the small size of the GM butterfly and unavoidable partial volume effects, and sub-millimeter functional-to-anatomical warping inaccuracies caused by magnetic field inhomogeneities. (D) To investigate the possibility that WM masks contain fractions of GM voxels, and the impact of different regression masks on correlations between sub-regions, preprocessing was performed as described in ‘Materials and methods’ except step #12 extracted the principal eigenvector of all time series within a combined WM and GM mask. Given the small size of each GM mask relative to its neighboring WM mask, the principal eigenvector from the combined mask (WM&GM) should be similar to only using WM. Indeed, regression with these modified eigenvectors produces similar group-level correlations between WM sub-regions and introduce weak negative correlations between GM sub-regions. In GM, positive correlations between ventral horns and between dorsal horns still persist after this more invasive regressor, providing further evidence that these strong temporally correlated fluctuations are unlikely to be caused by physiological motion or spatially-correlated noise. To further investigate the spatial extent of global fluctuations within GM, we repeated the preprocessing as described in ‘Materials and methods’ but eroded the WM mask slightly before extracting the principal eigenvector. The results of this tertiary analysis (not shown) were slightly different but statistically similar to Figure 5A, demonstrating that voxels on and near the GM-WM boundary in fact characterize physiological fluctuations from both tissue types due to unavoidable partial volume effects and sub-millimeter warping inaccuracies. Overall, these supplementary analyses suggest that the preprocessing performed in the main manuscript (regression of the principal eigenvector from WM masks without erosion) is an appropriate strategy for suppressing extraneous fluctuations within both WM and GM without introducing substantial negative correlations between GM sub-regions. Finally, power spectra for WM and GM sub-regions in (C) are presented in Figure 5—figure supplement 2.DOI:http://dx.doi.org/10.7554/eLife.02812.009
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fig5s1: Functional connectivity matrices resulting from preprocessing pipeline permutations.As before, functional data were band-pass filtered between 0.01 and 0.08 Hz (*p<0.05; **p<0.01; Bonferroni corrected). For clarity the labels are not shown for each column/row but are the same as in Figure 5. (A) Preprocessing was performed as described in the Methods except CSF and WM regressors (steps #11 and #12) were not applied. Each GM sub-region is highly correlated with all other GM sub-regions and similarly each WM sub-region is highly correlated with all other WM sub-regions. Interestingly, there are no significant group-level correlations between GM and WM sub-regions, suggesting that ‘global’ GM fluctuations tend to be constrained to GM and ‘global’ WM fluctuations tend to be constrained to WM. (B) Preprocessing was performed as described in ‘Materials and methods’ except a WM regressor (step #12) was not applied. The application of only CSF regressors reduced correlations within both GM and WM, but all inter-region correlations remained and were statistically significant. (C) Preprocessing was performed exactly as described in ‘Materials and methods’ (i.e., this panel is the same as Figure 5A except for a larger dynamic range for z-scores). Regression of the principal eigenvector of all time series in the WM mask significantly altered correlations within both GM and WM. This unintuitive result may be explained if WM masks contain signal contributions from adjacent GM voxels, which is certainly possible given the small size of the GM butterfly and unavoidable partial volume effects, and sub-millimeter functional-to-anatomical warping inaccuracies caused by magnetic field inhomogeneities. (D) To investigate the possibility that WM masks contain fractions of GM voxels, and the impact of different regression masks on correlations between sub-regions, preprocessing was performed as described in ‘Materials and methods’ except step #12 extracted the principal eigenvector of all time series within a combined WM and GM mask. Given the small size of each GM mask relative to its neighboring WM mask, the principal eigenvector from the combined mask (WM&GM) should be similar to only using WM. Indeed, regression with these modified eigenvectors produces similar group-level correlations between WM sub-regions and introduce weak negative correlations between GM sub-regions. In GM, positive correlations between ventral horns and between dorsal horns still persist after this more invasive regressor, providing further evidence that these strong temporally correlated fluctuations are unlikely to be caused by physiological motion or spatially-correlated noise. To further investigate the spatial extent of global fluctuations within GM, we repeated the preprocessing as described in ‘Materials and methods’ but eroded the WM mask slightly before extracting the principal eigenvector. The results of this tertiary analysis (not shown) were slightly different but statistically similar to Figure 5A, demonstrating that voxels on and near the GM-WM boundary in fact characterize physiological fluctuations from both tissue types due to unavoidable partial volume effects and sub-millimeter warping inaccuracies. Overall, these supplementary analyses suggest that the preprocessing performed in the main manuscript (regression of the principal eigenvector from WM masks without erosion) is an appropriate strategy for suppressing extraneous fluctuations within both WM and GM without introducing substantial negative correlations between GM sub-regions. Finally, power spectra for WM and GM sub-regions in (C) are presented in Figure 5—figure supplement 2.DOI:http://dx.doi.org/10.7554/eLife.02812.009
Mentions: A group-level analysis of functional connectivity between sub-regions of spinal cord gray matter and adjacent white matter confirmed that the most robust correlations are observed between left and right ventral (motor) horns (p < 0.01; corrected), as well as between left and right dorsal (sensory) horns (p < 0.01; corrected) (Figure 5A). No significant group-level correlations (p > 0.05) were observed between other gray matter sub-regions, nor between spinal cord gray and white matter. Weak positive correlations were observed between left and right dorsal column white matter (p < 0.05; corrected), and negative correlations were observed between left ventral white matter and right dorsal white matter (p < 0.01; corrected), and between right ventral white matter and both left (p < 0.01; corrected) and right (p < 0.01; corrected) dorsal white matter. The apparent existence of negative correlations in resting state spinal cord data is not unexpected because anticorrelations are commonly observed in resting state analyses of the brain (Chang and Glover, 2010) and have been a topic of intense discussion for over a decade. The ranges of values within these six statistically significant distributions are presented as box-and-whisker plots (Figure 5B). The lower quartile is above zero in both gray matter plots, demonstrating that positive gray matter connectivity is a robust and reproducible measurement. In comparison, temporal correlations between white matter sub-regions are more variable and exhibit both positive and negative median correlations. The raw data used to generate this figure is provided as Figure 5—source data 1. Additional analyses were performed (Figure 5—figure supplement 1, Figure 5—figure supplement 3, and Figure 5—figure supplement 5) to confirm that positive gray matter correlations are stable across various preprocessing procedures whereas white matter correlations are positive before but negative or non-significant after white matter regression (step #12). These supplementary analyses also showed that weaker positive correlations between sub-regions of left and right dorsal white matter remained positive and significant across various preprocessing configurations.10.7554/eLife.02812.007Figure 5.Group-level functional connectivity between sub-regions of spinal gray matter (GM) and surrounding white matter (WM) within slices.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Vanderbilt University Institute of Imaging Science, Nashville, United States Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, United States robert.l.barry@vanderbilt.edu.

No MeSH data available.


Related in: MedlinePlus