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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 after band-pass filtering between 0.01 and 0.07 Hz.Functional connectivity matrices resulting from preprocessing pipeline permutations after band-pass filtering between 0.01 and 0.07 Hz to suppress power from likely physiological noise at ∼0.75 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, and step #13 used a different frequency bandwidth. (B) Preprocessing was performed as described in ‘Materials and methods’ except a WM regressor (step #12) was not applied and step #13 used a different frequency bandwidth. (C) Preprocessing was performed as described in ‘Materials and methods’ except step #13 used a different frequency bandwidth. (D) 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 and step #13 used a different frequency bandwidth. Only a couple of minor changes are apparent between these matrices and those presented in Figure 5—figure supplement 1, suggesting that a slight decrease in the upper filter range from 0.08 Hz to 0.07 Hz to further suppress physiological noise does not appear to have a significant impact on the group-level connectivity analyses. Power spectra for WM and GM sub-regions in (C) are presented in Figure 5—figure supplement 4.DOI:http://dx.doi.org/10.7554/eLife.02812.011
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fig5s3: Functional connectivity matrices resulting from preprocessing pipeline permutations after band-pass filtering between 0.01 and 0.07 Hz.Functional connectivity matrices resulting from preprocessing pipeline permutations after band-pass filtering between 0.01 and 0.07 Hz to suppress power from likely physiological noise at ∼0.75 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, and step #13 used a different frequency bandwidth. (B) Preprocessing was performed as described in ‘Materials and methods’ except a WM regressor (step #12) was not applied and step #13 used a different frequency bandwidth. (C) Preprocessing was performed as described in ‘Materials and methods’ except step #13 used a different frequency bandwidth. (D) 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 and step #13 used a different frequency bandwidth. Only a couple of minor changes are apparent between these matrices and those presented in Figure 5—figure supplement 1, suggesting that a slight decrease in the upper filter range from 0.08 Hz to 0.07 Hz to further suppress physiological noise does not appear to have a significant impact on the group-level connectivity analyses. Power spectra for WM and GM sub-regions in (C) are presented in Figure 5—figure supplement 4.DOI:http://dx.doi.org/10.7554/eLife.02812.011

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 after band-pass filtering between 0.01 and 0.07 Hz.Functional connectivity matrices resulting from preprocessing pipeline permutations after band-pass filtering between 0.01 and 0.07 Hz to suppress power from likely physiological noise at ∼0.75 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, and step #13 used a different frequency bandwidth. (B) Preprocessing was performed as described in ‘Materials and methods’ except a WM regressor (step #12) was not applied and step #13 used a different frequency bandwidth. (C) Preprocessing was performed as described in ‘Materials and methods’ except step #13 used a different frequency bandwidth. (D) 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 and step #13 used a different frequency bandwidth. Only a couple of minor changes are apparent between these matrices and those presented in Figure 5—figure supplement 1, suggesting that a slight decrease in the upper filter range from 0.08 Hz to 0.07 Hz to further suppress physiological noise does not appear to have a significant impact on the group-level connectivity analyses. Power spectra for WM and GM sub-regions in (C) are presented in Figure 5—figure supplement 4.DOI:http://dx.doi.org/10.7554/eLife.02812.011
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fig5s3: Functional connectivity matrices resulting from preprocessing pipeline permutations after band-pass filtering between 0.01 and 0.07 Hz.Functional connectivity matrices resulting from preprocessing pipeline permutations after band-pass filtering between 0.01 and 0.07 Hz to suppress power from likely physiological noise at ∼0.75 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, and step #13 used a different frequency bandwidth. (B) Preprocessing was performed as described in ‘Materials and methods’ except a WM regressor (step #12) was not applied and step #13 used a different frequency bandwidth. (C) Preprocessing was performed as described in ‘Materials and methods’ except step #13 used a different frequency bandwidth. (D) 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 and step #13 used a different frequency bandwidth. Only a couple of minor changes are apparent between these matrices and those presented in Figure 5—figure supplement 1, suggesting that a slight decrease in the upper filter range from 0.08 Hz to 0.07 Hz to further suppress physiological noise does not appear to have a significant impact on the group-level connectivity analyses. Power spectra for WM and GM sub-regions in (C) are presented in Figure 5—figure supplement 4.DOI:http://dx.doi.org/10.7554/eLife.02812.011
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