Endogenous oscillations and networks in functional magnetic resonance imaging

Since functional magnetic resonance imaging (fMRI) first emerged as a new tool for mapping human brain function in the early 1990s, we have seen an extraordinary growth in the number of papers and research groups using this technique. Because of its relatively high resolution, sensitivity, and ease of use, fMRI has largely eclipsed positron emission tomography (PET) as a tool for locating changes in brain hemodynamics associated with performance of an experimentally controlled task. Indeed, the dominant paradigm for fMRI research to date has been strongly informed by conceptual or statistical tools—such as principles of cognitive subtraction for experimental design and linear modeling for data analysis—that had first proven their value in relation to PET and could be rapidly translated to fMRI, as the newer technology became available. The array of available activation strategies for fMRI has continued to grow rapidly, progressing beyond blocked and event-related designs to those that involve more natural behavior in a scanner, and further informed by more precise, temporally registered, and continuous behavioral and physiologic measures. In the last few years, the field has also witnessed the explosive emergence of interest in what can broadly be described as endogenous aspects of fMRI data. Alongside the prior preoccupation with identifying regions of task-related activation (BOLD amplitude increases driven by an exogenous, experimental task), there is increasing awareness that regional deactivation (BOLD amplitude decreases, often in the same or similar regions regardless of task details) might point to the existence of spatiotemporally coordinated systems of physiological activity, sometimes described as default mode or resting state networks, that are characteristic of the brain’s endogenous state [Greicius et al., 2008]. There is also convergent evidence for complex organization of resting state or “no task” fMRI signals following from the seminal observation by Biswal et al. [1995] that BOLD time series measured in the motor cortex at rest are highly correlated with contralateral motor cortex and other spatially distributed components of the motor system. It has subsequently been shown by many groups, using diverse methods of data analysis, that the very low frequency (<0.1 Hz) oscillations captured by echo planar imaging are typically correlated between laterally symmetric and/or functionally related brain regions and that the overall topological organization of these functional connections between regions has the attributes of a complex, small-world network [Achard et al., 2006]. Moreover, investigators have begun to address integrative questions, relating endogenous fMRI dynamics to variability of task-related activation or cognitive performance [Fox et al., 2006], and exploring the potential utility of endogenous fMRI markers for characterizing neurophysiological changes associated with disease or drug treatments. In general, one could summarize the current position by saying that fMRI was originally assimilated by the field as a faster, safer, easier, and anatomically more precise form of PET, but recent work has tended to refocus attention on its capacity—like EEG or MEG—to inform us also about the brain’s ability to sustain spatially coherent systems of endogenous oscillations. Another way of framing this distinction, following Raichle and Snyder [2007], is in terms of the long-standing historical tension between reflexive and intrinsic models of brain function: the recent shift of emphasis to consideration of endogenous fMRI properties marks a step away from the field’s nearly unanimous prior assumption of reflexive brain models and toward formulation of more intrinsically informed models. While the field has seen a rapid increase in the number of papers related to endogenous contrast, many open issues remain. These include how to best analyze, and more generally, how to best interpret these data. Lastly, the question of how to best apply these fascinating findings is perhaps the most exciting and wide open. So this is a strategically important issue for the functional MRI community, which raises a host of interesting new questions, and we thought it would be timely to draw together in a special issue of Human Brain Mapping a selection of original contributions from some of the leading groups working in this area. The papers represent many aspects of the field, including some contributions primarily using electrophysiological data or computational analysis to inform interpretation of fMRI data, and can be subdivided into two approximately equal sets of papers on Modeling and Applications.