Accurate autocorrelation modeling substantially improves fMRI reliability

Abstract

Given the recent controversies in some neuroimaging statistical methods, we compare the most frequently used functional Magnetic Resonance Imaging (fMRI) analysis packages: AFNI, FSL and SPM, with regard to temporal autocorrelation modeling. This process, sometimes known as pre-whitening, is conducted in virtually all task fMRI studies. Here, we employ eleven datasets containing 980 scans corresponding to different fMRI protocols and subject populations. We found that autocorrelation modeling in AFNI, although imperfect, performed much better than the autocorrelation modeling of FSL and SPM. The presence of residual autocorrelated noise in FSL and SPM leads to heavily confounded first level results, particularly for low-frequency experimental designs. SPM’s alternative pre-whitening method, FAST, performed better than SPM’s default. The reliability of task fMRI studies could be improved with more accurate autocorrelation modeling. We recommend that fMRI analysis packages provide diagnostic plots to make users aware of any pre-whitening problems.

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References 43

1. 1.

   Bullmore E, et al. Statistical methods of estimation and inference for functional MR image analysis. Magn. Reson. Med. 1996;35:261-277.

   Not in our library10.1002/mrm.1910350219

2. 2.

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

   Not in our library10.1016/j.neuroimage.2005.07.005

3. 3.

   Purdon PL, Weisskoff RM. Effect of temporal autocorrelation due to physiological noise and stimulus paradigm on voxel-level false-positive rates in fMRI. Hum. Brain Mapp. 1998;6:239-249.

   Not in our library10.1002/(sici)1097-0193(1998)6:4<239::aid-hbm4>3.0.co;2-4

4. 4.

   Worsley KJ, et al. A general statistical analysis for fMRI data. Neuroimage. 2002;15:1-15.

   Not in our library10.1006/nimg.2001.0933

5. 5.

   Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 1996;29:162-173.

   Not in our library10.1006/cbmr.1996.0014

6. 6.

   Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. Neuroimage. 2012;62:782-790.

   Not in our library10.1016/j.neuroimage.2011.09.015

7. 7.

   Penny, W. D., Friston, K. J., Ashburner, J. T., Kiebel, S. J. & Nichols, T. E. Statistical Parametric Mapping: the Analysis of Functional Brain Images (Academic Press, Cambridge 2011).

   Not in our library

8. 8.

   Chen G, Saad ZS, Nath AR, Beauchamp MS, Cox RW. FMRI group analysis combining effect estimates and their variances. Neuroimage. 2012;60:747-765.

   Not in our library10.1016/j.neuroimage.2011.12.060

9. 9.

   Woolrich MW, Ripley BD, Brady M, Smith SM. Temporal autocorrelation in univariate linear modeling of FMRI data. Neuroimage. 2001;14:1370-1386.

   Not in our library10.1006/nimg.2001.0931

10. 10.

    Friston KJ, et al. Classical and Bayesian inference in neuroimaging: applications. Neuroimage. 2002;16:484-512.

    Not in our library10.1006/nimg.2002.1091

11. 11.

    Todd N, et al. Evaluation of 2D multiband EPI imaging for high-resolution, whole-brain, task-based fMRI studies at 3T: Sensitivity and slice leakage artifacts. Neuroimage. 2016;124:32-42.

    Not in our library10.1016/j.neuroimage.2015.08.056

12. 12.

    Bollmann S, Puckett AM, Cunnington R, Barth M. Serial correlations in single-subject fMRI with sub-second TR. Neuroimage. 2018;166:152-166.

    Not in our library10.1016/j.neuroimage.2017.10.043

13. 13.

    Corbin, N., Todd, N., Friston, K. J. & Callaghan, M. F. Accurate modeling of temporal correlations in rapidly sampled fMRI time series. Hum. Brain Mapp.39, 3884–3897 (2018).

    Not in our library10.1002/hbm.24218

14. 14.

    Lenoski B, Baxter LC, Karam LJ, Maisog J, Debbins J. On the performance of autocorrelation estimation algorithms for fMRI analysis. IEEE J. Sel. Top. Signal Process. 2008;2:828-838.

    Not in our library10.1109/jstsp.2008.2007819

15. 15.

    Eklund A, Andersson M, Josephson C, Johannesson M, Knutsson H. Does parametric fMRI analysis with SPM yield valid results?—An empirical study of 1484 rest datasets. Neuroimage. 2012;61:565-578.

    Not in our library10.1016/j.neuroimage.2012.03.093

16. 16.

    Walter M, Stadler J, Tempelmann C, Speck O, Northoff G. High resolution fMRI of subcortical regions during visual erotic stimulation at 7 T. Magn. Reson. Mater. Phys., Biol. Med. 2008;21:103-111.

    Not in our library10.1007/s10334-007-0103-1

17. 17.

    Polimeni, J. R., Renvall, V., Zaretskaya, N. & Fischl, B. Analysis strategies for high-resolution UHF-fMRI data. Neuroimage168, 296–320 (2017).

    Not in our library10.1016/j.neuroimage.2017.04.053

18. 18.

    Buračas, G. T. & Boynton, G. M. Efficient design of event-related fMRI experiments using M-sequences. Neuroimage16, 801–813 (2002).

    Not in our library10.1006/nimg.2002.1116

19. 19.

    Sladky R, et al. Slice-timing effects and their correction in functional MRI. Neuroimage. 2011;58:588-594.

    Not in our library10.1016/j.neuroimage.2011.06.078

20. 20.

    Eklund, A., Nichols, T. E. & Knutsson, H. Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proc. Natl Acad. Sci. USA 113, 7900–7905 (2016).

    Not in our library10.1073/pnas.1602413113

21. 21.

    Eklund, A., Nichols, T., Andersson, M. & Knutsson, H. Empirically investigating the statistical validity of SPM, FSL and AFNI for single subject fMRI analysis. In Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on, 1376–1380 (IEEE, 2015).

    Not in our library

22. 22.

    Larkman DJ, et al. Use of multicoil arrays for separation of signal from multiple slices simultaneously excited. J. Magn. Reson. Imaging. 2001;13:313-317.

    Not in our library10.1002/1522-2586(200102)13:2<313::aid-jmri1045>3.0.co;2-w

23. 23.

    Geissler A, et al. Influence of fMRI smoothing procedures on replicability of fine scale motor localization. Neuroimage. 2005;24:323-331.

    Not in our library10.1016/j.neuroimage.2004.08.042

24. 24.

    Weibull A, Gustavsson H, Mattsson S, Svensson J. Investigation of spatial resolution, partial volume effects and smoothing in functional MRI using artificial 3D time series. Neuroimage. 2008;41:346-353.

    Not in our library10.1016/j.neuroimage.2008.02.015

25. 25.

    Mueller K, Lepsien J, Möller HE, Lohmann G. Commentary: cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Front. Hum. Neurosci. 2017;11:345.

    Not in our library10.3389/fnhum.2017.00345

26. 26.

    Eklund, A., Knutsson, H. & Nichols, T. E. Cluster failure revisited: impact of first level design and physiological noise on cluster false positive rates. Hum. Brain Mapp. 1–16 (2018).

    Not in our library10.1002/hbm.24350

27. 27.

    Friston K, et al. To smooth or not to smooth?: bias and efficiency in fMRI time-series analysis. Neuroimage. 2000;12:196-208.

    Not in our library10.1006/nimg.2000.0609

28. 28.

    Friston KJ, Stephan KE, Lund TE, Morcom A, Kiebel S. Mixed-effects and fMRI studies. Neuroimage. 2005;24:244-252.

    Not in our library10.1016/j.neuroimage.2004.08.055

29. 29.

    Woolrich MW, Behrens TE, Beckmann CF, Jenkinson M, Smith SM. Multilevel linear modelling for FMRI group analysis using Bayesian inference. Neuroimage. 2004;21:1732-1747.

    Not in our library10.1016/j.neuroimage.2003.12.023

30. 30.

    Bianciardi M, Cerasa A, Patria F, Hagberg G. Evaluation of mixed effects in event-related fMRI studies: impact of first-level design and filtering. Neuroimage. 2004;22:1351-1370.

    Not in our library10.1016/j.neuroimage.2004.02.039

31. 31.

    Mumford JA, Nichols T. Simple group fMRI modeling and inference. Neuroimage. 2009;47:1469-1475.

    Not in our library10.1016/j.neuroimage.2009.05.034

32. 32.
    A default mode of brain function · 2001

    Not checked
33. 33.

    Stark CE, Squire LR. When zero is not zero: the problem of ambiguous baseline conditions in fMRI. Proc. Natl. Acad. Sci. USA. 2001;98:12760-12766.

    Not in our library10.1073/pnas.221462998

34. 34.

    Welvaert M, Rosseel Y. A review of fMRI simulation studies. PLoS ONE. 2014;9:e101953.

    Not in our library10.1371/journal.pone.0101953

35. 35.

    Luo WL, Nichols TE. Diagnosis and exploration of massively univariate neuroimaging models. Neuroimage. 2003;19:1014-1032.

    Not in our library10.1016/s1053-8119(03)00149-6

36. 36.

    Biswal BB, et al. Toward discovery science of human brain function. Proc. Natl. Acad. Sci. USA. 2010;107:4734-4739.

    Not in our library10.1073/pnas.0911855107

37. 37.
    The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry · 2012

    No notice
38. 38.
    The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing · 2014

    No notice
39. 39.

    Hamid, A. I. A., Speck, O. & Hoffmann, M. B. Quantitative assessment of visual cortex function with fMRI at 7 Tesla-test-retest variability. Front. Hum. Neurosci.9, 1–11 (2015).

    Not in our library10.3389/fnhum.2015.00477

40. 40.

    Welvaert M, Durnez J, Moerkerke B, Verdoolaege G, Rosseel Y. neuRosim: an R package for generating fMRI data. J. Stat. Softw. 2011;44:1-18.

    Not in our library10.18637/jss.v044.i10

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