Synopsis

An overview of current analysis methods for assessing functional connectivity using resting state fMRI data. A brief review of important preprocessing steps necessary for quality resting state data as well as various complex network analysis methods, including structural equation modeling, clustering methods and graph theoretic methods. Dynamic functional connectivity methods are briefly discussed.

Functional Connectivity with Resting State fMRI

• Brief review(1,2)

• Effect limited to (n < 0.1Hz) (4)

• Brief review of suggested pre-processing pipeline Quality of results depends on high quality data preprocessing. Preprocessing steps can critically impact results(5) ·

• Data: rapid (sms) or conventional Issues related to acquisition choice ·

• Removing physiologic noise (6,7) RETROICOR, PESTICA RVT, CVT ·

• Global signal ·

• Vascular response ·

• Example: HCP pipeline ·

Structural equation modeling(8) ·

Independent Components Analysis(9) ·

Clustering methods(10)

Graph Theoretic methods(11)

• Parcellation methods(12) Graph theoretic methods require reduction of voxel level data into groups of nodes that must be homogeneous

• Global network analysis(13) Functional integration of networks

• Intermediate network analysis(14) Networks partitioned into modules and nodes (Rich club)

• Local networks(15) Analyses of individual nodes

Dynamic Functional Connectivity(16)

Acknowledgements

References

1. Biswal, B, Yetkin, FZ, Haughton, VM, Hyde, JS: Functional connectivity in the motor cortex of resting human brain. Magnetic Resonance in Medicine, 34, 537-41, (1995).

2. Lowe, MJ, Mock, BJ, Sorenson, JA: Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations. Neuroimage, 7, 119-32, (1998).

3. Lowe, M. J., Sakaie, K. E., Beall, E. B., Calhoun, V. D., Bridwell, D. A., Rubinov, M., & Rao, S. M. (2016). Modern Methods for Interrogating the Human Connectome. J Int Neuropsychol Soc, 22, 105-119.

4. Cordes, D, Haughton, VM, Arfanakis, K, Carew, JD, Turski, PA, Moritz, CH, Quigley, MA, Meyerand, ME: Frequencies contributing to functional connectivity in the cerebral cortex in "resting-state" data. AJNR Am J Neuroradiol, 22, 1326-33, (2001).

5. Gargouri, F., Kallel, F., Delphine, S., Ben Hamida, A., Lehericy, S., & Valabregue, R. (2018). The Influence of Preprocessing Steps on Graph Theory Measures Derived from Resting State fMRI. Front Comput Neurosci, 12, 8.

6. Lund, TE, Madsen, KH, Sidaros, K, Luo, WL, Nichols, TE: Non-white noise in fMRI: does modelling have an impact? Neuroimage, 29, 54-66, (2006).

7. Beall, E. B. and M. J. Lowe (2007). "Isolating physiologic noise sources with independently determined spatial measures." Neuroimage 37(4): 1286-300.

8. Guardia-Olmos, J., Pero-Cebollero, M., & Gudayol-Ferre, E. (2018). Meta-Analysis of the Structural Equation Models' Parameters for the Estimation of Brain Connectivity with fMRI. Front Behav Neurosci, 12, 19.

9. Beckmann, CF, DeLuca, M, Devlin, JT, Smith, SM: Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci, 360, 1001-13, (2005).

10. Shams, S. M., Afshin-Pour, B., Soltanian-Zadeh, H., Hossein-Zadeh, G. A., & Strother, S. C. (2015). Automated iterative reclustering framework for determining hierarchical functional networks in resting state fMRI. Hum Brain Mapp, 36, 3303-3322.

11. Medaglia, J. D. (2017). Graph Theoretic Analysis of Resting State Functional MR Imaging. Neuroimaging Clin N Am, 27, 593-607.

12. Craddock, R. C., Jbabdi, S., Yan, C. G., Vogelstein, J. T., Castellanos, F. X., Di Martino, A., Kelly, C., Heberlein, K., Colcombe, S., & Milham, M. P. (2013). Imaging human connectomes at the macroscale. Nat Methods, 10, 524-539.

13. Bassett, D. S., & Bullmore, E. (2006). Small-world brain networks. Neuroscientist, 12, 512-523.

14. van den Heuvel, M. P., & Sporns, O. (2011). Rich-club organization of the human connectome. J Neurosci, 31, 15775-15786.

15. van den Heuvel, M. P., & Sporns, O. (2013). Network hubs in the human brain. Trends Cogn Sci, 17, 683-696.

16. Chen, J. E., Rubinov, M., & Chang, C. (2017). Methods and Considerations for Dynamic Analysis of Functional MR Imaging Data. Neuroimaging Clin N Am, 27, 547-560.