Spatial and Spectral Components of the BOLD Global Signal in Rat Resting-State Functional MRI
Nmachi Anumba
Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
Search for more papers by this authorEric Maltbie
Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
Search for more papers by this authorWen-Ju Pan
Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
Search for more papers by this authorTheodore J. LaGrow
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
Search for more papers by this authorNan Xu
Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
Search for more papers by this authorCorresponding Author
Shella Keilholz
Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
Correspondence
Shella Keilholz, Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, 1760 Haygood Dr NE, Suite W230, Atlanta, GA, USA.
Email: [email protected]
Search for more papers by this authorNmachi Anumba
Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
Search for more papers by this authorEric Maltbie
Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
Search for more papers by this authorWen-Ju Pan
Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
Search for more papers by this authorTheodore J. LaGrow
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
Search for more papers by this authorNan Xu
Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
Search for more papers by this authorCorresponding Author
Shella Keilholz
Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
Correspondence
Shella Keilholz, Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, 1760 Haygood Dr NE, Suite W230, Atlanta, GA, USA.
Email: [email protected]
Search for more papers by this authorAbstract
Purpose
In resting-state fMRI (rs-fMRI), the global signal average captures widespread fluctuations related to unwanted sources of variance such as motion and respiration, as well as widespread neural activity; however, relative contributions of neural and non-neural sources to the global signal remain poorly understood. This study sought to tackle this problem through the comparison of the BOLD global signal to an adjacent non-brain tissue signal, where neural activity was absent, from the same rs-fMRI scan obtained from anesthetized rats. In this dataset, motion was minimal and ventilation was phase-locked to image acquisition to minimize respiratory fluctuations. Data were acquired using three different anesthetics: isoflurane, dexmedetomidine, and a combination of dexmedetomidine and light isoflurane.
Methods
A power spectral density estimate, a voxel-wise spatial correlation via Pearson's correlation, and a co-activation pattern analysis were performed using the global signal and the non-brain tissue signal. Functional connectivity was calculated using Pearson's linear correlation on default mode network (DMN) regions.
Results
We report differences in the spectral composition of the two signals and show spatial selectivity within DMN structures that show an increased correlation to the global signal and decreased intra-network connectivity after global signal regression. All of the observed differences between the global signal and the non-brain tissue signal were maintained across anesthetics.
Conclusion
These results show that the global signal is distinct from the noise contained in the tissue signal, as support for a neural contribution. This study provides a unique perspective to the contents of the global signal and their origins.
Supporting Information
Filename | Description |
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mrm29824-sup-0001-supinfo.docxWord 2007 document , 5.5 MB | Table S1. Timeline of anesthetic application for each subject. Figure S1. Analysis of the last 30 min of dexmedetomidine scans. To account for any lingering effects of isoflurane on the first 30 min of dexmedetomidine scans, we also looked at the PSD estimate and spatial correlation of the global and tissue signals in the last 30 min of these scans. For both analyses, we saw similar results to those presented in the main analysis. (A) The tissue signal consisted of a higher contribution from lower frequencies than the brain global signal, with the tissue signal having a fALFF of 0.5396 and the global signal having a fALFF of 0.5035. (B) A voxel-wise correlation to the global signal shows higher correlation to the global signal from medial brain structures, as was seen in the first 30 min. (C) The same voxel-wise analysis to the tissue signal shows lower overall correlation, as was seen in the first 30 min. Figure S2. Correlation analysis in subject 1. This figure shows the individual voxel-wise correlation analysis to the global and tissue signals in subject 1. Figure S3. Correlation analysis in subject 2. This figure shows the individual voxel-wise correlation analysis to the global and tissue signals in subject 2. Figure S4. Correlation analysis in subject 3. This figure shows the individual voxel-wise correlation analysis to the global and tissue signals in subject 3. Figure S5. Correlation analysis in subject 4. This figure shows the individual voxel-wise correlation analysis to the global and tissue signals in subject 4. Figure S6. Correlation analysis in subject 5. This figure shows the individual voxel-wise correlation analysis to the global and tissue signals in subject 5. Figure S7. Correlation analysis in subject 6. This figure shows the individual voxel-wise correlation analysis to the global and tissue signals in subject 6. Figure S8. Correlation analysis in subject 7. This figure shows the individual voxel-wise correlation analysis to the global and tissue signals in subject 7. Figure S9. Correlation analysis in subject 8. This figure shows the individual voxel-wise correlation analysis to the global and tissue signals in subject 8. Figure S10. Spatial distribution of Power under Isoflurane. This figure shows the spatial distribution of power in the isoflurane scans over the original frequency range (0.01–0.1 Hz) and the frequency range used for dexmedetomidine and isodex (0.01–0.25 Hz). When the frequency range is expanded, the power levels throughout the brain increase by way of including more power values to the sum for each pixel. However, the distribution of power spatially does not change with the increased frequency range. In fact, the addition of more frequencies appears to increase the power in all regions proportionally so that the original spatial distribution of power is maintained. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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