Consistent resting-state networks across healthy subjects

Abstract

Typical functional MRI (fMRI) research focuses on the change in blood oxygenation level-dependent (BOLD) signal caused by the neural response to an externally controlled stimulus/task. The fMRI signal during “on” periods is contrasted with recordings during a baseline or control condition, resulting in the relative signal change because of the specific process being studied. Recently, increased attention has been directed at investigating the features of the baseline state of the brain. Of particular interest are low-frequency fluctuations (≈0.01–0.1 Hz) observed in the BOLD signal, which have been found to display spatial structure comparable to task-related activation (1–3). There is an ongoing discussion as to whether these fluctuations in the BOLD signal predominantly reflect changes of the underlying brain physiology independent of neuronal function (4–6), or instead reflect the neuronal baseline activity of the brain when goal-directed neuronal action and external input are absent (7, 8). The view that coherencies in resting fluctuations represent functional resting-state networks linked to underlying neuronal modulations is consistent with the appearance of these coherencies within cortical gray matter areas of known functional relevance. For example, one of the first studies of resting fluctuations identified the motor network (9). More recent studies have identified associated fluctuations in brain regions involved in visual, motor, language, and auditory processing (10–17). Brain regions that show greater BOLD signal during rest than during any one of a broad range of experimental tasks have also received attention. The default-mode network has been hypothesized to be active during rest and suspended/deactivated when specific goal-directed behavior is needed, as demonstrated in a task-related positron-emission tomography study (18). Connectivity has been reported between regions in the brain that form the default-mode network during resting states as well as inverse correlations among prefrontal regions (which show increased activity during a cognitive task) and the posterior cingulate cortex (an area within the default-mode network) (16, 17). However, although the observation that these resting fluctuations are located in gray matter is consistent with the notion of their representing neuronal modulations, a recent study shows a correlation between changes in respiration and BOLD signal also located in gray matter areas (6).

In this article, we focus on the following questions: (i) How many coherent spatiotemporal patterns can we distinguish? (ii) How strong are these fluctuations? and (iii) How consistent are these fluctuations across subjects and sessions? To infer these signal coherencies, most studies apply a region-of-interest cross-correlation analysis approach (9–13), where the spatial characteristics of these resting fluctuations are estimated by using correlation analysis against a reference time course derived from secondary recordings (19) or the resting data itself (seed-voxel-based correlation analysis) (9). More recently, some studies employed a model-free analysis by using independent component analysis (ICA) (14, 15, 20) instead of time-course regression. Such decompositions are of particular importance because they allow for a simultaneous separation into individual maps. These decompositions can simultaneously extract a variety of different coherent resting networks and separate such effects from other signal modulations such as those induced by head motion or physiological confounds, such as the cardiac pulsation or the respiratory cycle (13, 15).

ICA-based studies have identified components that resemble several functionally relevant cortical networks such as visual and auditory cortical areas as well as the default-mode network. Different studies have identified qualitatively similar areas of functional coherence across subjects, but the extent to which these fluctuations are consistent within a population has not previously been quantified. To characterize this level of consistency between and within subjects, it is necessary to employ techniques designed for the analysis of multisession/multisubject fMRI data. A promising method for the investigation of coherent signals at a group level is the recently described tensor probabilistic ICA (PICA) (21). This analysis simultaneously decomposes group fMRI data into modes describing variations across space, time, and subjects. It has been demonstrated that the tensor-PICA approach can provide useful representations of group fMRI data in task-related fMRI experiments and that it also seems capable of analyzing resting-state studies (21). In this study, we apply tensor-PICA to resting-state fMRI data with the aim of identifying resting coherencies that are consistent across subjects and sessions. To quantify the consistency of such patterns, we used bootstrapping. Based on 20 resting data sets, we generated 100 surrogate multisubject data sets from which we estimated these fluctuations. Using these separate estimates, we quantified common networks in terms of their expected percentage BOLD signal change, which provides a measure of the dynamics of these fluctuations. In addition, we calculated the amount of typical percentage variation around the expected percentage BOLD signal change at each voxel's location, which encodes the uncertainty of estimating any given voxel as part of an associated coherent network.

Acknowledgments

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