Intersubject MVPD: Empirical comparison of fMRI denoising methods for connectivity analysis

Autoři: Yichen Li aff001;  Rebecca Saxe aff003;  Stefano Anzellotti aff004
Působiště autorů: Courant Institute of Mathematical Sciences, New York University, New York, NY, United States of America aff001;  Department of Computer Science, New York University, New York, NY, United States of America aff002;  Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States of America aff003;  Department of Psychology, Boston College, Chestnut Hill, MA, United States of America aff004
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222914


Noise is a major challenge for the analysis of fMRI data in general and for connectivity analyses in particular. As researchers develop increasingly sophisticated tools to model statistical dependence between the fMRI signal in different brain regions, there is a risk that these models may increasingly capture artifactual relationships between regions, that are the result of noise. Thus, choosing optimal denoising methods is a crucial step to maximize the accuracy and reproducibility of connectivity models. Most comparisons between denoising methods require knowledge of the ground truth: of what is the ‘real signal’. For this reason, they are usually based on simulated fMRI data. However, simulated data may not match the statistical properties of real data, limiting the generalizability of the conclusions. In this article, we propose an approach to evaluate denoising methods using real (non-simulated) fMRI data. First, we introduce an intersubject version of multivariate pattern dependence (iMVPD) that computes the statistical dependence between a brain region in one participant, and another brain region in a different participant. iMVPD has the following advantages: 1) it is multivariate, 2) it trains and tests models on independent partitions of the real fMRI data, and 3) it generates predictions that are both between subjects and between regions. Since whole-brain sources of noise are more strongly correlated within subject than between subjects, we can use the difference between standard MVPD and iMVPD as a ‘discrepancy metric’ to evaluate denoising techniques (where more effective techniques should yield smaller differences). As predicted, the difference is the greatest in the absence of denoising methods. Furthermore, a combination of removal of the global signal and CompCorr optimizes denoising (among the set of denoising options tested).

Klíčová slova:

Central nervous system – Functional magnetic resonance imaging – Noise reduction – Preprocessing – principal component analysis – Simulation and modeling – Statistical data


1. Ishai A. Let’s face it: it’sa cortical network. Neuroimage. 2008;40(2):415–419. doi: 10.1016/j.neuroimage.2007.10.040 18063389

2. Anzellotti S, Caramazza A. From parts to identity: invariance and sensitivity of face representations to different face halves. Cerebral Cortex. 2015;26(5):1900–1909. doi: 10.1093/cercor/bhu337 25628344

3. Assaf Y, Pasternak O. Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review. Journal of molecular neuroscience. 2008;34(1):51–61. doi: 10.1007/s12031-007-0029-0 18157658

4. Biswal B, Zerrin Yetkin F, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic resonance in medicine. 1995;34(4):537–541. doi: 10.1002/mrm.1910340409 8524021

5. Thomas Yeo B, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of neurophysiology. 2011;106(3):1125–1165. doi: 10.1152/jn.00338.2011

6. Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nature medicine. 2017;23(1):28. doi: 10.1038/nm.4246 27918562

7. Haxby JV, Gobbini MI, Furey ML, Ishai A, Schouten JL, Pietrini P. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science. 2001;293(5539):2425–2430. doi: 10.1126/science.1063736 11577229

8. Haynes JD, Rees G. Neuroimaging: decoding mental states from brain activity in humans. Nature Reviews Neuroscience. 2006;7(7):523. doi: 10.1038/nrn1931 16791142

9. Norman KA, Polyn SM, Detre GJ, Haxby JV. Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in cognitive sciences. 2006;10(9):424–430. doi: 10.1016/j.tics.2006.07.005 16899397

10. Anzellotti S, Caramazza A, Saxe R. Multivariate pattern dependence. PLoS computational biology. 2017;13(11):e1005799. doi: 10.1371/journal.pcbi.1005799 29155809

11. Power JD, Schlaggar BL, Petersen SE. Recent progress and outstanding issues in motion correction in resting state fMRI. Neuroimage. 2015;105:536–551. doi: 10.1016/j.neuroimage.2014.10.044 25462692

12. Hasson U, Nir Y, Levy I, Fuhrmann G, Malach R. Intersubject synchronization of cortical activity during natural vision. science. 2004;303(5664):1634–1640. doi: 10.1126/science.1089506 15016991

13. Hasson U, Furman O, Clark D, Dudai Y, Davachi L. Enhanced intersubject correlations during movie viewing correlate with successful episodic encoding. Neuron. 2008;57(3):452–462. doi: 10.1016/j.neuron.2007.12.009 18255037

14. Wilson SM, Molnar-Szakacs I, Iacoboni M. Beyond superior temporal cortex: intersubject correlations in narrative speech comprehension. Cerebral cortex. 2007;18(1):230–242. doi: 10.1093/cercor/bhm049 17504783

15. Hanke M, Adelhöfer N, Kottke D, Iacovella V, Sengupta A, Kaule FR, et al. A studyforrest extension, simultaneous fMRI and eye gaze recordings during prolonged natural stimulation. Scientific data. 2016;3:160092. doi: 10.1038/sdata.2016.92 27779621

16. Labs A, Reich T, Schulenburg H, Boennen M, Mareike G, Golz M, et al. Portrayed emotions in the movie “Forrest Gump”. F1000Research. 2015;4. doi: 10.12688/f1000research.6230.1 25977755

17. Sengupta A, Kaule FR, Guntupalli JS, Hoffmann MB, Häusler C, Stadler J, et al. A studyforrest extension, retinotopic mapping and localization of higher visual areas. Scientific data. 2016;3:160093. doi: 10.1038/sdata.2016.93 27779618

18. Joshi AA, Chong M, Li J, Choi S, Leahy RM. Are you thinking what I’m thinking? Synchronization of resting fMRI time-series across subjects. NeuroImage. 2018;172:740–752. doi: 10.1016/j.neuroimage.2018.01.058 29428580

19. Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data. 2016;3:160044. doi: 10.1038/sdata.2016.44 27326542

20. Esteban O, Markiewicz C, Blair RW, Moodie C, Isik AI, Aliaga AE, et al. FMRIPrep: a robust preprocessing pipeline for functional MRI. bioRxiv. 2018; p. 306951.

21. Friston K, Josephs O, Zarahn E, Holmes A, Rouquette S, Poline JB. To smooth or not to smooth?: Bias and efficiency in fmri time-series analysis. NeuroImage. 2000;12(2):196–208. doi: 10.1006/nimg.2000.0609 10913325

22. Smith AM, Lewis BK, Ruttimann UE, Frank QY, Sinnwell TM, Yang Y, et al. Investigation of low frequency drift in fMRI signal. Neuroimage. 1999;9(5):526–533. doi: 10.1006/nimg.1999.0435 10329292

23. Friston KJ, Williams S, Howard R, Frackowiak RS, Turner R. Movement-related effects in fMRI time-series. Magnetic resonance in medicine. 1996;35(3):346–355. doi: 10.1002/mrm.1910350312 8699946

24. Macey PM, Macey KE, Kumar R, Harper RM. A method for removal of global effects from fMRI time series. Neuroimage. 2004;22(1):360–366. doi: 10.1016/j.neuroimage.2003.12.042 15110027

25. Behzadi Y, Restom K, Liau J, Liu TT. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage. 2007;37(1):90–101. doi: 10.1016/j.neuroimage.2007.04.042 17560126

26. Murphy K, Bodurka J, Bandettini PA. How long to scan? The relationship between fMRI temporal signal to noise ratio and necessary scan duration. Neuroimage. 2007;34(2):565–574. doi: 10.1016/j.neuroimage.2006.09.032 17126038

27. Guntupalli JS, Haxby JV. Inter-subject hyperalignment of neural representational space. Chance. 2010;1:7.

28. Haxby JV, Guntupalli JS, Connolly AC, Halchenko YO, Conroy BR, Gobbini MI, et al. A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron. 2011;72(2):404–416. doi: 10.1016/j.neuron.2011.08.026 22017997

29. Coutanche MN, Thompson-Schill SL. Informational connectivity: identifying synchronized discriminability of multi-voxel patterns across the brain. Frontiers in human neuroscience. 2013;7:15. doi: 10.3389/fnhum.2013.00015 23403700

30. Crowe DA, Goodwin SJ, Blackman RK, Sakellaridi S, Sponheim SR, MacDonald AW III, et al. Prefrontal neurons transmit signals to parietal neurons that reflect executive control of cognition. Nature Neuroscience. 2013;16(10):1484. doi: 10.1038/nn.3509 23995071

31. Anzellotti S, Coutanche MN. Beyond Functional Connectivity: Investigating Networks of Multivariate Representations. Trends in cognitive sciences. 2018. doi: 10.1016/j.tics.2017.12.002 29305206

32. Kriegeskorte N. Intersubject information mapping: revealing canonical representations of complex natural stimuli. bioRxiv. 2015; p. 016436.

33. Bishop CM. Bayesian pca. In: Advances in neural information processing systems; 1999. p. 382–388.

34. Liu TT. Noise contributions to the fMRI signal: An overview. NeuroImage. 2016;143:141–151. doi: 10.1016/j.neuroimage.2016.09.008 27612646

35. Jo HJ, Saad ZS, Simmons WK, Milbury LA, Cox RW. Mapping sources of correlation in resting state FMRI, with artifact detection and removal. Neuroimage. 2010;52(2):571–582. doi: 10.1016/j.neuroimage.2010.04.246 20420926

36. Muschelli J, Nebel MB, Caffo BS, Barber AD, Pekar JJ, Mostofsky SH. Reduction of motion-related artifacts in resting state fMRI using aCompCor. Neuroimage. 2014;96:22–35. doi: 10.1016/j.neuroimage.2014.03.028 24657780

37. Power JD, Mitra A, Laumann TO, Snyder AZ, Schlaggar BL, Petersen SE. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage. 2014;84:320–341. doi: 10.1016/j.neuroimage.2013.08.048 23994314

Článek vyšel v časopise


2019 Číslo 9

Nejčtenější v tomto čísle

Tomuto tématu se dále věnují…


Zvyšte si kvalifikaci online z pohodlí domova

Ulcerative colitis_muž_břicho_střeva
Ulcerózní kolitida
nový kurz

Blokátory angiotenzinových receptorů (sartany)
Autoři: MUDr. Jiří Krupička, Ph.D.

Antiseptika a prevence ve stomatologii
Autoři: MUDr. Ladislav Korábek, CSc., MBA

Citikolin v neuroprotekci a neuroregeneraci: od výzkumu do klinické praxe nejen očních lékařů
Autoři: MUDr. Petr Výborný, CSc., FEBO

Zánětlivá bolest zad a axiální spondylartritida – Diagnostika a referenční strategie
Autoři: MUDr. Monika Gregová, Ph.D., MUDr. Kristýna Bubová

Všechny kurzy
Kurzy Doporučená témata