This article is about resting state networks and functional imaging. For information regarding the Default Mode Network specifically, see Default mode network. For other uses, see Rest (disambiguation).
Resting state fMRI
Movie of the in vivo BOLD signal from the cortical surface of a human subject from HCP, acquired using resting state fMRI, pre-processed to suppress the noise in data[1][2] and played back at a real-time rate. The BOLD signal intensities are visualized on a smoothed cortical surface. At each point on the cortex, white color represents the average BOLD signal, while blue and red colors represents lower and higher signal than average BOLD signal respectively.[2]
Purpose
Evaluate regional interactions that occur in resting state(brain mapping)
Resting state fMRI (rs-fMRI or R-fMRI) is a method of functional magnetic resonance imaging (fMRI) that is used in brain mapping to evaluate regional interactions that occur in a resting or task-negative state, when an explicit task is not being performed.[3][4] A number of resting-state brain networks have been identified, one of which is the default mode network.[5] These brain networks are observed through changes in blood flow in the brain which creates what is referred to as a blood-oxygen-level dependent (BOLD) signal that can be measured using fMRI.
Because brain activity is intrinsic, present even in the absence of an externally prompted task, any brain region will have spontaneous fluctuations in BOLD signal. The resting state approach is useful to explore the brain's functional organization and to examine if it is altered in neurological or mental disorders. Because of the resting state aspect of this imaging, data can be collected from a range of patient groups including people with intellectual disabilities, pediatric groups, and even those that are unconscious.[6][7] Resting-state functional connectivity research has revealed a number of networks which are consistently found in healthy subjects, different stages of consciousness and across species, and represent specific patterns of synchronous activity.[8][9][10]
^Smith SM, Beckmann CF, Andersson J, Auerbach EJ, Bijsterbosch J, Douaud G, et al. (October 2013). "Resting-state fMRI in the Human Connectome Project". NeuroImage. 80: 144–168. doi:10.1016/j.neuroimage.2013.05.039. PMC 3720828. PMID 23702415.
^Sharaev MG, Zavyalova VV, Ushakov VL, Kartashov SI, Velichkovsky BM (2016). "Effective Connectivity within the Default Mode Network: Dynamic Causal Modeling of Resting-State fMRI Data". Frontiers in Human Neuroscience. 10: 14. doi:10.3389/fnhum.2016.00014. PMC 4740785. PMID 26869900.
^Agcaoglu O, Wilson TW, Wang YP, Stephen J, Calhoun VD (June 2019). "Resting state connectivity differences in eyes open versus eyes closed conditions". Human Brain Mapping. 40 (8): 2488–2498. doi:10.1002/hbm.24539. PMC 6865559. PMID 30720907.
^Smitha KA, Akhil Raja K, Arun KM, Rajesh PG, Thomas B, Kapilamoorthy TR, Kesavadas C (August 2017). "Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks". The Neuroradiology Journal. 30 (4): 305–317. doi:10.1177/1971400917697342. PMC 5524274. PMID 28353416.
^Rosazza C, Minati L (October 2011). "Resting-state brain networks: literature review and clinical applications". Neurological Sciences. 32 (5): 773–785. doi:10.1007/s10072-011-0636-y. PMID 21667095. S2CID 17222.
^Cole DM, Smith SM, Beckmann CF (2010). "Advances and pitfalls in the analysis and interpretation of resting-state FMRI data". Frontiers in Systems Neuroscience. 4: 8. doi:10.3389/fnsys.2010.00008. PMC 2854531. PMID 20407579.
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