fMRI has become an important investigation and research tool in trying to locate neural correlates of a function X,Y,Z in the brain. However notwithstanding the allure of seductive neuroscan images, fMRI studies at times leaves us as clueless about the brain and its organization as we were before the studies were conducted.
However , just like plain vanilla structural MRI coupled with BOLD signal analysis had led to fMRI, which was a step forward; the plain vanilla FMRI coupled with resting-state BOLD signal spontaneous fluctuations has led to resting state connectivity fMRI, also called rsc-fMRI, which is another step forward and does not juts enable us to pinpoint a function to a brain, but rather reveals the intrinsic organization of brain by revealing tightly coupled functional neural networks in the brain.
Let us take a step back to look at rsc-fMRI in detail. Basically it has been shown that the brain is never at rest, and at rest too, there are spontaneous fluctuations in the brain (of BOLD signal say in the case of fMRI). It is theorized that those brain areas that how correlated spontaneous fluctuations at rest are part of a functional network and this has been shown to be true by looking at the functional maps so revealed and looking at actual anatomical connectivity, and the circuit involvement in related tasks that the circuit is supposed to be involved in.
While to many people resting state fMRI brings to mind the Default Mode Network ,about which I have blogged before, at rest other brain functional circuits also show correlated spontaneous activity (one theory is that they show spontaneous activity so that important synaptic connections in the network can continue to remain in absence of external input/processing) and looking at such correlate activity one can discern that the regions involved form a functional network.
What is more rsc-fMRI is easy to administer, especially to populations like infants, demented people etc, who may not be able to participate in task-based fMRI studies because of their inability to execute a given task. rs-cfMRI on the other hand requires nothing much excpet lying down quietly in the scanner. The BOLD spontaneous fluctuations, from multiple subjects, are then analyzed using Independent Component Analysis (something like PCA or factor analysis that psychologists use in say personality traits studies) and the number as well as the regions involved in different function neural networks are thus revealed. the function networks thus revealed are called ICNs or Intrinsic Connectivity Networks.
Basic ICNs in humans range form 5 in infants to upto 16 in adults and though that seems like a vast range there seem to be good test-retest reliability and replicablity of basic ICNs found across the studies. Some of the variation seems to be an artifact of developmental maturation of ICNs over time.
I believe these ICNs can be arranged as per the eight stage model and the ACD model and also they have great significances for many psychiatric and neurodegenerative disorders; but for that you have to wait for the subsequent posts.
Zuo, X., Kelly, C., Adelstein, J., Klein, D., Castellanos, F., & Milham, M. (2010). Reliable intrinsic connectivity networks: Test–retest evaluation using ICA and dual regression approach NeuroImage, 49 (3), 2163-2177 DOI: 10.1016/j.neuroimage.2009.10.080
Zhang, H., Duan, L., Zhang, Y., Lu, C., Liu, H., & Zhu, C. (2011). Test–retest assessment of independent component analysis-derived resting-state functional connectivity based on functional near-infrared spectroscopy NeuroImage, 55 (2), 607-615 DOI: 10.1016/j.neuroimage.2010.12.007
AUER, D. (2008). Spontaneous low-frequency blood oxygenation level-dependent fluctuations and functional connectivity analysis of the ‘resting’ brain Magnetic Resonance Imaging, 26 (7), 1055-1064 DOI: 10.1016/j.mri.2008.05.008
Damoiseaux, J., Rombouts, S., Barkhof, F., Scheltens, P., Stam, C., Smith, S., & Beckmann, C. (2006). Consistent resting-state networks across healthy subjects Proceedings of the National Academy of Sciences, 103 (37), 13848-13853 DOI: 10.1073/pnas.0601417103