Abstract
Resting state functional connectivity MRI (fcMRI) has become a particularly useful tool for studying regional relationships in typical and atypical populations. Because many investigators have already obtained large data sets of task-related fMRI, the ability to use this existing task data for resting state fcMRI is of considerable interest. Two classes of data sets could potentially be modified to emulate resting state data. These data sets include: (1) "interleaved" resting blocks from blocked or mixed blocked/event-related sets, and (2) residual timecourses from event-related sets that lack rest blocks. Using correlation analysis, we compared the functional connectivity of resting epochs taken from a mixed blocked/event-related design fMRI data set and the residuals derived from event-related data with standard continuous resting state data to determine which class of data can best emulate resting state data. We show that, despite some differences, the functional connectivity for the interleaved resting periods taken from blocked designs is both qualitatively and quantitatively very similar to that of "continuous" resting state data. In contrast, despite being qualitatively similar to "continuous" resting state data, residuals derived from event-related design data had several distinct quantitative differences. These results suggest that the interleaved resting state data such as those taken from blocked or mixed blocked/event-related fMRI designs are well-suited for resting state functional connectivity analyses. Although using event-related data residuals for resting state functional connectivity may still be useful, results should be interpreted with care.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 396-405 |
| Number of pages | 10 |
| Journal | NeuroImage |
| Volume | 35 |
| Issue number | 1 |
| DOIs | |
| State | Published - Mar 2007 |
| Externally published | Yes |
Bibliographical note
Funding Information:The authors thank the participants in this study, as well as Jessica Church for logistical aid, Mark McAvoy for neuroimaging application development, and David Van Essen and his colleagues for the use of CARET for figures. This work was supported in part by the Washington University Chancellor’s Fellowship and UNCF* Merck Graduate Science Research Dissertation Fellowship to Damien Fair and by NIH NSADA (B.L.S.), NS32979 (S.E.P.), NS41255 (S.E.P.), and NS46424 (S.E.P.), The McDonnell Center for Higher Brain function (S.E.P., B.L.S.), and The Charles A. Dana Foundation (B.L.S.).