Technical NoteStatic and dynamic characteristics of cerebral blood flow during the resting state
Introduction
There has been growing interest in resting brain activity (Raichle et al., 2001, Fox and Raichle, 2007, Greicius, 2008). Positron emission tomography (PET) first demonstrated that a set of brain regions including posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC), thalamus and insula exhibit higher cerebral blood flow (CBF) than the whole brain average in the resting state (Raichle et al., 2001). CBF in the majority of these regions decreases from its baseline level during a wide range of goal-directed tasks (Shulman et al., 1997, Mazoyer et al., 2001). Together, these brain regions have been called the default mode network (DMN). In contrast to the observation of static CBF in PET studies, dynamic interactions between brain regions have been revealed using resting-state functional magnetic resonance imaging (fMRI) (Biswal et al., 1995). Most of these fMRI studies utilized the synchrony of spontaneous fluctuations in the blood oxygenation level dependent (BOLD) signal to identify coherent brain networks and to assess alterations in connectivity strength in various brain disorders (e.g., Greicius et al., 2004, Bluhm et al., 2007, Greicius, 2008, Hong et al., 2009). While the dynamic signals in the DMN are highly correlated (Greicius et al., 2003, Fransson, 2005, Fox et al., 2005, Beckmann et al., 2005), the static and dynamic characteristics of resting-state signals have not been systematically studied under a single modality within the same subjects.
Perfusion imaging based on arterial spin labeling (ASL) has been widely used to measure resting-state blood flow in the brain (Detre etal., 1992). ASL approaches can also be utilized to measure dynamic, spontaneous CBF changes in the resting state. An early study demonstrated that spontaneous low frequency (< 0.1 Hz) flow-weighted fluctuations are highly synchronized within the motor system (Biswal et al., 1997). De Luca et al. (2006) observed several brain networks using resting-state ASL data from a single subject. Recently, Chuang et al. (2008) developed a strategy to reduce BOLD contamination in the resting-state CBF fluctuations and reported connectivity within the sensorimotor network.
Using ASL, we investigated the spatial distribution of static and dynamic CBF in the resting state within the same subjects. A multi-slice, pulsed ASL (PASL) technique was utilized to collect CBF time courses from healthy subjects. Static CBF was measured by voxel-wise averaging the CBF values over the time domain. Dynamic characteristics of the brain were assessed using three methods. Classic cross-correlation analysis with a predefined “seed” was used to examine functional connectivity to the seed. We also assessed the characteristics of the CBF dynamic fluctuations from the aspect of local information, which might provide novel and complementary information to the correlation analysis. Amplitude of low-frequency fluctuations (ALFF) (Zang et al., 2007), which quantifies the strength of the fluctuations in each voxel, was adopted in the current study to depict the local intensity of CBF fluctuations. Regional homogeneity (ReHo) (Zang et al., 2004), which reflects local synchrony by calculating similarity of dynamic fluctuations of voxels within a given cluster, was also used in the analysis to reveal local synchrony of CBF fluctuations. Since CBF is a single physiological parameter (vs. BOLD which is a composite of several parameters) and is probably more closely related to cerebral metabolism than BOLD, ReHo and ALFF results from CBF data may be more physiologically relevant than those from BOLD.
Section snippets
Participants
Twelve healthy subjects (26.3 ± 6.4 years old, 9 females, and 3 males) participated in the study. All subjects were screened with a questionnaire to ensure no history of neurological illness, psychiatric disorders or past drug abuse. They were recruited under a protocol approved by the Institutional Review Board of the Intramural Research Program of the National Institute on Drug Abuse. Signed informed consent was obtained from all participants prior to study enrollment.
Data acquisition
Functional MRI data were
Static characteristics of CBF in the resting brain
Static CBF t map is shown in Fig. 2A. Consistent with a previous PET study (Raichle et al., 2001), static CBF in the PCC, MPFC, thalamus and insula/STG were significantly higher than whole brain mean CBF. Fig. 2B shows that the average static CBF within these four ROIs was significantly higher than the global average (which was normalized as 1).
Dynamic characteristics of CBF in the resting brain
Dynamic CBF fluctuations of the PCC were significantly correlated with brain regions in the DMN, such as MPFC, inferior parietal lobule and insula/STG (
Discussion
In the current study, we investigated the static and dynamic characteristics of resting-state CBF signals recorded using a PASL technique. Static CBF was significantly higher in the PCC, thalamus, insula/STG and MPFC than the whole brain average CBF value. The dynamic measurement of CBF fluctuations showed that the PCC was highly synchronized with brain regions primarily in the DMN (Raichle et al., 2001). The dynamic CBF fluctuations of these four regions also showed high local synchrony (ReHo)
Acknowledgments
This work was supported by the Intramural Research Program of the National Institute on Drug Abuse (NIDA), National Institute of Health (NIH). The authors thank Dr. Christian F. Beckmann for providing dual regression method for ICA denoising, Dr. Xinian Zuo for preparing the vessel mask and suggestions for removing noise using ICA, and Dr. Thomas J. Ross and Dr. Yong He for helpful discussions.
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