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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

Power spectra across gray and white matter sub-regions for data filtered between 0.01 and 0.07 Hz.Power spectra for (A) ventral and (B) dorsal GM sub-regions in Figure 5—figure supplement 3C that exhibit significant positive correlations in the original analysis (Figure 5A; z > 1.65; one-tailed), and all WM sub-regions. For each frequency the plotted power represents median power across slices and subjects. Within a range from 0.015 Hz to 0.065 Hz, ventral GM similarly exhibits 31% more power than WM whereas dorsal GM exhibits 5% more power. The narrower bandwidth completely suppresses the noise peak at ∼0.75 Hz but the results from functional connectivity analyses are almost unchanged (Figure 5—figure supplement 1 vs Figure 5—figure supplement 3), confirming that this peak does not significantly affect the overall results. Our original decision to filter resting state spinal cord data between 0.01 Hz and 0.08 Hz (step #13) was motivated by the approach most commonly used for resting state analyses in the brain (filtering between 0.01 Hz and 0.08–0.1 Hz), although it is not yet clear if this frequency range is optimal for resting state spinal cord analyses. To investigate the possibility that frequencies above 0.08 Hz may contribute to inherent functional connectivity, the analyses in Figure 5—figure supplement 1 are once again repeated after data are filtered with a wider band-pass filter between 0.01 Hz and 0.13 Hz. These results are presented in Figure 5—figure supplement 5.DOI:http://dx.doi.org/10.7554/eLife.02812.012
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fig5s4: Power spectra across gray and white matter sub-regions for data filtered between 0.01 and 0.07 Hz.Power spectra for (A) ventral and (B) dorsal GM sub-regions in Figure 5—figure supplement 3C that exhibit significant positive correlations in the original analysis (Figure 5A; z > 1.65; one-tailed), and all WM sub-regions. For each frequency the plotted power represents median power across slices and subjects. Within a range from 0.015 Hz to 0.065 Hz, ventral GM similarly exhibits 31% more power than WM whereas dorsal GM exhibits 5% more power. The narrower bandwidth completely suppresses the noise peak at ∼0.75 Hz but the results from functional connectivity analyses are almost unchanged (Figure 5—figure supplement 1 vs Figure 5—figure supplement 3), confirming that this peak does not significantly affect the overall results. Our original decision to filter resting state spinal cord data between 0.01 Hz and 0.08 Hz (step #13) was motivated by the approach most commonly used for resting state analyses in the brain (filtering between 0.01 Hz and 0.08–0.1 Hz), although it is not yet clear if this frequency range is optimal for resting state spinal cord analyses. To investigate the possibility that frequencies above 0.08 Hz may contribute to inherent functional connectivity, the analyses in Figure 5—figure supplement 1 are once again repeated after data are filtered with a wider band-pass filter between 0.01 Hz and 0.13 Hz. These results are presented in Figure 5—figure supplement 5.DOI:http://dx.doi.org/10.7554/eLife.02812.012

Mentions: 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.


Resting state functional connectivity in the human spinal cord.

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

Power spectra across gray and white matter sub-regions for data filtered between 0.01 and 0.07 Hz.Power spectra for (A) ventral and (B) dorsal GM sub-regions in Figure 5—figure supplement 3C that exhibit significant positive correlations in the original analysis (Figure 5A; z > 1.65; one-tailed), and all WM sub-regions. For each frequency the plotted power represents median power across slices and subjects. Within a range from 0.015 Hz to 0.065 Hz, ventral GM similarly exhibits 31% more power than WM whereas dorsal GM exhibits 5% more power. The narrower bandwidth completely suppresses the noise peak at ∼0.75 Hz but the results from functional connectivity analyses are almost unchanged (Figure 5—figure supplement 1 vs Figure 5—figure supplement 3), confirming that this peak does not significantly affect the overall results. Our original decision to filter resting state spinal cord data between 0.01 Hz and 0.08 Hz (step #13) was motivated by the approach most commonly used for resting state analyses in the brain (filtering between 0.01 Hz and 0.08–0.1 Hz), although it is not yet clear if this frequency range is optimal for resting state spinal cord analyses. To investigate the possibility that frequencies above 0.08 Hz may contribute to inherent functional connectivity, the analyses in Figure 5—figure supplement 1 are once again repeated after data are filtered with a wider band-pass filter between 0.01 Hz and 0.13 Hz. These results are presented in Figure 5—figure supplement 5.DOI:http://dx.doi.org/10.7554/eLife.02812.012
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fig5s4: Power spectra across gray and white matter sub-regions for data filtered between 0.01 and 0.07 Hz.Power spectra for (A) ventral and (B) dorsal GM sub-regions in Figure 5—figure supplement 3C that exhibit significant positive correlations in the original analysis (Figure 5A; z > 1.65; one-tailed), and all WM sub-regions. For each frequency the plotted power represents median power across slices and subjects. Within a range from 0.015 Hz to 0.065 Hz, ventral GM similarly exhibits 31% more power than WM whereas dorsal GM exhibits 5% more power. The narrower bandwidth completely suppresses the noise peak at ∼0.75 Hz but the results from functional connectivity analyses are almost unchanged (Figure 5—figure supplement 1 vs Figure 5—figure supplement 3), confirming that this peak does not significantly affect the overall results. Our original decision to filter resting state spinal cord data between 0.01 Hz and 0.08 Hz (step #13) was motivated by the approach most commonly used for resting state analyses in the brain (filtering between 0.01 Hz and 0.08–0.1 Hz), although it is not yet clear if this frequency range is optimal for resting state spinal cord analyses. To investigate the possibility that frequencies above 0.08 Hz may contribute to inherent functional connectivity, the analyses in Figure 5—figure supplement 1 are once again repeated after data are filtered with a wider band-pass filter between 0.01 Hz and 0.13 Hz. These results are presented in Figure 5—figure supplement 5.DOI:http://dx.doi.org/10.7554/eLife.02812.012
Mentions: 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.

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