A number of recent studies have shown that micro-motion can have substantial effects on connectivity analyses. This is a difficult topic and, right now (2013) a moving target. However, there are a couple things that seem to be clear (e.g., the order of your preprocessing steps matters — for instance you should run Band pass filtering after the nuisance regressors).
Here is a recent presentation of the topic (which has benefitted from lots of input and slides from Jesse Rissman): Presentation on Motion & fMRI Connectivity.
Here are some important readings on the topic. While I suggest reading all of them, if you are low on toner ink and the world is on fire, I marked with ‘*’ those you might want to print (this is, however, a subjective choice).
1. What is the problem?
- *Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59(3):2142-54.
- Van Dijk KR, Sabuncu MR, Buckner RL. (2012) The influence of head motion on intrinsic functional connectivity MRI. Neuroimage. 59(1):431-8.
- *Satterthwaite TD, Wolf DH, Loughead J, Ruparel K, Elliott MA, Hakonarson H, Gur RC, Gur RE. (2012) Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth. Neuroimage. 2012 Mar;60(1):623-32
2. What can we do about it? (Note, in particular the preferred pipelines in Satterthwaite and Jo)
- *Satterthwaite TD, Elliott MA, Gerraty RT, Ruparel K, Loughead J, Calkins ME, Eickhoff SB, Hakonarson H, Gur RC, Gur RE, Wolf DH. (2013) An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage. 64:240-56.
- *Hang Joon Jo, Stephen J. Gotts, Richard C. Reynolds, et al., (2013) Effective Preprocessing Procedures Virtually Eliminate Distance-Dependent Motion Artifacts in Resting State FMRI. Journal of Applied Mathematics, 2013.
- Hallquist MN, Hwang K, Luna B (2013) The nuisance of nuisance regression: Spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. Neuroimage. 82:208-25.
- Yan CG, Cheung B, Kelly C, Colcombe S, Craddock RC, Di Martino A, Li Q, Zuo XN, Castellanos FX, Milham MP. (2013) A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage. 76:183-201.
Note: this is work-in-progress and a moving target, as new papers come out the field might converge – The two paper above, however, will give you a good understanding of the kind of pipeline you should use.
3. For task based analyses
- Christodoulou AG, Bauer TE, Kiehl KA, Feldstein Ewing SW, Bryan AD, Calhoun VD. (2013) A quality control method for detecting and suppressing uncorrected residual motion in fMRI studies. Magn Reson Imaging. 31(5):707-17.