Resting state functional MRI (R-fMRI) is a relatively new and powerful method for evaluating regional interactions that occur when a subject is not performing an explicit task.
Low-frequency (<0.1 Hz) BOLD fluctuations often show strong correlations at rest even in distant gray matter regions. Fluctuations in spontaneous neural activity are presumed to underlie the BOLD fluctuations, though the exact mechanisms giving rise to the neural fluctuations remain unclear. The spatial patterns of R-fMRI correlations are stable, in that they are similar across multiple ‘resting’ states, such as eyes-open, eyes-closed, and fixation, and across individuals and sessions. Because of the lack of task demands, R-fMRI unburdens experimental design, subject compliance, and training demands, making it attractive for studies of development and clinical populations.
From experiments in the macaque monkey, R-fMRI correlations often overlap with known anatomical pathways, but they sometimes involve regions that are not directly connected. Hence, functional connectivity (R-fMRI) and anatomical connectivity (tractography) are complementary yet related measures that together provide a powerful approach to analyzing brain circuitry.
Numerous studies, including many by members of our consortium, demonstrate that these spatial patterns are closely related to neural subsystems revealed by task-activation fMRI (T-fMRI). Regions that co-activate with a seed region in different tasks tend to be positively correlated with the seed region at rest. A map constructed from a single seed shows a specific pattern of correlation across the brain. By implication, this suggests that even relatively nearby seeds can show quite different patterns of correlation. Thus, the spatial layout of correlations from different origins may aid in brain parcellation. The spatial patterns of correlation can also be used to create extensive systems/network level descriptions of functional interactions across brain regions that can be compared to anatomical connectivity descriptions, and task-evoked functional activations.
Several methods have been proposed to acquire and analyze R-fMRI data. A major goal of HCP is to find optimal combination(s) of methods to parcellate brain regions and understand relationships between them. This entails optimization of many aspects of data acquisition (scan duration, spatial resolution, spatial smoothing during pre-processing) and data analysis (seed-based approaches and independent component analysis approaches).
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