fMRI resting state networks define distinct modes of long-distance interactions in the human brain
Introduction
The functioning of the human brain during rest can be investigated using different functional imaging techniques (Biswal et al., 1995, Shulman et al., 1997, Gusnard and Raichle, 2001). While the resting state is an ill-defined condition, consistent functional patterns across individuals should represent common “default” or “idling” state activity. Long-range coherences in these activities therefore could reflect strong functional connectivities.
fMRI images obtained using blood oxygen level dependent (BOLD) contrast show signal fluctuations at rest. These fluctuations occur at low frequencies (0.01–0.05 Hz) and have been shown to be coherent across widely separated (although functionally related) brain regions (e.g., bihemispheric sensorimotor cortices) (Biswal et al., 1995, Lowe et al., 1998, Cordes et al., 2000). Regions showing coherent fluctuations therefore constitute a “resting state network” (RSN). We and others have appreciated that there is more than one spatially distinct RSN in a resting brain image dataset, with each RSN having a distinct signal time-course (De Luca et al., 2002, Greicius et al., 2003).
Whether the fluctuations of resting fMRI signal in RSNs are a direct consequence of neuronal activity or whether they reflect phenomena such as cardio-respiratory motion or vascular modulation is uncertain. The normally low sampling rate of fMRI images (Jezzard et al., 2002) causes temporal aliasing of variations of the BOLD fMRI signal induced by cardiac and respiratory cycles into a low-frequency range, similar to that of the RSN signal fluctuations. Some low-frequency coherences in resting BOLD fMRI data are clearly a consequence of this physiological noise (Lowe et al., 1998, Xiong et al., 1999, Cordes et al., 2000). However, studies conducted in ways that avoid aliasing of the fMRI signal (using a fast image sampling rate) show that many low-frequency coherences are still present, suggesting that RSNs and (higher frequency) physiological noise are phenomenologically distinct processes (Biswal et al., 1995, Lowe et al., 1998). Additional patterns related directly to vascular processes independent of cortical neuronal function have been identified as low-frequency fluctuations in resting fMRI data (Kiviniemi et al., 2000, Wise et al., 2004). The most direct data relating some patterns of low-frequency coherence in fMRI data to neuronal activity come from evidence that the underlying fluctuations are correlated with modulations of cortical electrical activity detected by EEG (Goldman et al., 2002, Leopold et al., 2003, Moosmann et al., 2003, Laufs et al., 2003). The observation of changes in patterns with neurological disease (e.g., Alzheimer's disease; Greicius et al., 2004) is consistent with this.
An important concern in studying RSNs is whether the method used for their identification is appropriately sensitive, yet relatively unbiased. Methods based on direct correlations with time-courses of signal change identified from a “seed” voxel are limited to applications to regions for which there is an a priori expectation of a network pattern.
Here, we have applied probabilistic ICA (PICA) to the characterization of RSNs in resting brain BOLD contrast datasets. We have made a series of observations designed to test: i. the independence of PICA-defined RSNs from artifacts related to cardio-respiratory motion; ii. the localization of potential generators of RSNs; iii. the relation of BOLD RSNs to coherences defined with perfusion imaging; iv. the reproducibility of RSNs across subjects; and v. the specific patterns of coherent activity across the brain.
Section snippets
Methods
All fMRI data were acquired from healthy volunteers (age range 22–51 years). In all experiments, subjects were at rest; they were instructed to relax with their eyes closed, without falling asleep, as confirmed by the subjects after completion of the experiment. MRI data were acquired on a 3 T Varian/Siemens MRI system at the Oxford Centre for Functional Magnetic Imaging of the Brain, except the data of experiment 1, which were collected on a 1.5 T Philips Gyroscan MRI system at the NMR Centre
Characterization of spatiotemporally distinct patterns of coherent signals in BOLD and ASL images from the unstimulated brain: resting state networks
PICA applied to a time series of echo planar brain images acquired from a subject at rest using either typical (3 s) or short (0.12 s) TR generates a series of spatiotemporally distinct patterns of coherent signal changes defined by BOLD fMRI (Fig. 1). Coherent RSN patterns can be identified having most power at very low frequencies (0.01–0.05 Hz, Figs. 1A, C). These are spatially very similar at both long and typical TR (compare also with Fig. 3, Fig. 4, Fig. 5) and are also temporally very
Discussion
Several previous reports have described specific patterns of low-frequency coherent signal in time series of gradient echo MRI from unstimulated brain (the brain “at rest”). The most commonly recognized pattern includes particularly the sensorimotor cortex bilaterally (corresponding to RSN 3 defined here) (Biswal et al., 1995, Lowe et al., 1998, Xiong et al., 1999). Other work characterized a predominantly occipital network (corresponding to RSN1 here) (Goldman and Cohen, 2003, Moosmann et al.,
Acknowledgments
We are grateful to Dr. Giandomenico Iannetti and Dr. Joe Devlin for helpful discussion on RSN neuroscience meaning. We are grateful for financial support from the European PhD program, the UK Medical Research Council, the UK Engineering and Physical Sciences Research Council, and GlaxoSmithKline. We are grateful to S. Clare for the high spatial resolution data, M. Lowe for the low TR data, and to P. Chiarelli for the ASL data.
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