Research Highlights - Resting State Networks fMRI

The ability to acquire whole brain images rapidly (i.e. with short TR) using methods developed in this BTRC has proven to be critically important for detection of resting state networks (RSNs) from resting state functional imaging (R-fMRI) data [1]. Regions of the brain within an RSN display spontaneous fluctuations that are coherent within a network and distinct across different networks [2-6]. RSNs are thought to represent functional connectivity; as such, improved detection of RSNs are a primary target in the Human Connectome Project (http://humanconnectome.org/).

“Resting state” in resting state fMRI does not refer to the brain being at rest, which is never the case; rather it refers to the subject being at “rest” in the scanner, not performing a directed task or exposed to an external stimulus as in normal fMRI.

RSNs have been shown to be related to age and gender [7-9], and particularly the default mode network (DMN) has opened the possibility of using RSNs in clinical applications [10-14].

Example of five RSNs are shown in the figure above; three visual area networks (pink/blue/green), the default mode network (red) and a sensori-motor network. The data were acquired using methods we developed recently to increase the speed of whole brain imaging [1]. The data demonstrate that detection of the networks improve with increased speed of acquisition (i.e. decreasing TR). When all possible networks detected in a 100 component ICA analysis were considered, in each case there was improved statistical significance for the detection of these networks for the faster (i.e. short TR ) acquisition.

REFERENCES

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