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Coupling between gamma oscillation and fMRI signal in the rat somatosensory cortex: its dependence on systemic physiological parameters.

Akira Sumiyoshi | Hideaki Suzuki | Takeshi Ogawa | Jorge J Riera | Hiroaki Shimokawa | Ryuta Kawashima
NeuroImage | 2012

The simultaneous recordings of neuronal and hemodynamic signals have revealed a significant involvement of high frequency bands (e.g., gamma range, 25-70 Hz) in neurovascular coupling. However, the dependence on a physiological parameter is unknown. In this study, we performed simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings in 12 Wistar rats using a conventional forepaw stimulation paradigm and concurrently monitored the systemic physiological parameters of the partial pressure of arterial oxygen, partial pressure of arterial carbon dioxide, pH, mean arterial blood pressure, and heart rate through the rat femoral artery. The high frequency bands in the artifact-free EEG signals, especially those in the gamma range, demonstrated a maximum correlation with fMRI signals in the rat somatosensory cortex. A multiple linear regression analysis demonstrated that the correlation coefficient between the gamma power and fMRI signal depended on the actual values of the physiological parameters (R(2)=0.20, p<0.05), whereas the gamma power and fMRI signal by itself were independent. Among the parameters, the heart rate had a statistically significant slope (95% CI: 0.00027-0.0016, p<0.01) in a multiple linear regression model. These results indicate that neurovascular coupling is mainly driven by gamma oscillations, as expected, but coupling or potential decoupling is strongly influenced by systemic physiological parameters, which dynamically reflect the baseline vital status of the subject.

Pubmed ID: 22245345

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MarsBaR region of interest toolbox for SPM (tool)

RRID:SCR_009605

A toolbox for SPM which provides routines for region of interest analysis. Features include region of interest definition, combination of regions of interest with simple algebra, extraction of data for regions with and without SPM preprocessing (scaling, filtering), and statistical analyses of ROI data using the SPM statistics machinery.

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