Causal interpretation rules for encoding and decoding models in neuroimaging

NeuroImage

Volumn 110, Issue , 2015, Pages 48-59

  Weichwald, Sebastian ^a   Meyer, Timm ^a   Özdenizci, Ozan ^b   Schölkopf, Bernhard ^a   Ball, Tonio ^c   Grosse Wentrup, Moritz ^a  

^a MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS   (Germany)

^b SABANCI UNIVERSITY   (Turkey)

^c UNIVERSITY OF FREIBURG   (Germany)

Author keywords

Causal inference; Decoding models; Encoding models; Interpretation; Pattern recognition

Indexed keywords

ADULT; ARTICLE; CAUSAL MODELING; CONTROLLED STUDY; DECODING MODEL; ELECTROENCEPHALOGRAM; ENCODING MODEL; HUMAN; HUMAN EXPERIMENT; INFORMATION PROCESSING; LEARNING; MALE; NEUROIMAGING; NORMAL HUMAN; PRIORITY JOURNAL; STATISTICAL ANALYSIS; STIMULUS RESPONSE; TASK PERFORMANCE; THEORETICAL STUDY; VISUOMOTOR COORDINATION; ALGORITHM; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BRAIN MAPPING; ELECTROENCEPHALOGRAPHY; EPIDEMIOLOGY; IMAGE PROCESSING; PHYSIOLOGY; PROCEDURES; PSYCHOMOTOR PERFORMANCE; SENSORY FEEDBACK; STATISTICS AND NUMERICAL DATA; YOUNG ADULT;

ADULT; ALGORITHMS; BRAIN MAPPING; CAUSALITY; ELECTROENCEPHALOGRAPHY; FEEDBACK, SENSORY; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; LEARNING; MALE; MODELS, NEUROLOGICAL; NEURAL NETWORKS (COMPUTER); NEUROIMAGING; PATTERN RECOGNITION, AUTOMATED; PSYCHOMOTOR PERFORMANCE; YOUNG ADULT;

EID: 84924787038     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2015.01.036     Document Type: Article

References (45)

1. Atlas L.Y., Bolger N., Lindquist M.A., Wager T.D. Brain mediators of predictive cue effects on perceived pain. J. Neurosci. 2010, 30(39):12964-12977.
2. Belouchrani A., Abed-Meraim K., Cardoso J.-F., Moulines E. A blind source separation technique using second-order statistics. IEEE Trans. Signal Process. 1997, 45(2):434-444.
3. Breiman Leo Random forests. Mach. Learn. 2001, 45(1):5-32.
4. Breiman Leo Manual on Setting up, Using, and Understanding Random Forests V3.1 (accessed 2013-03-22). http://oz.berkeley.edu/users/breiman/Using_random_forests_V3.1.pdf.
5. Bressler S.L., Menon V. Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn. Sci. 2010, 14(6):277-290.
6. David Olivier, Kiebel Stefan J., Harrison Lee M., Mattout Jérémie, Kilner James M., Friston Karl J. Dynamic causal modeling of evoked responses in EEG and MEG. NeuroImage 2006, 30(4):1255-1272.
7. Davis Tyler, LaRocque Karen F., Mumford Jeanette A., Norman Kenneth A., Wagner Anthony D., Poldrack Russell A. What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis. NeuroImage 2014, 97:271-283.
8. Delorme A., Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 2004, 134(1):9-21.
9. Delorme A., Palmer J., Onton J., Oostenveld R., Makeig S. Independent EEG sources are dipolar. PLoS ONE 2012, 7(2):e30135.
10. Friston Karl J., Holmes Andrew P., Worsley Keith J., Poline J.-P., Frith Chris D., Frackowiak Richard S.J. Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 1994, 2(4):189-210.
11. Friston K.J., Harrison L., Penny W. Dynamic causal modelling. NeuroImage 2003, 19(4):1273-1302.
12. Gomez-Rodriguez M., Peters J., Hill J., Schökopf B., Gharabaghi A., Grosse-Wentrup M. Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery. J. Neural Eng. 2011, 8(3):036005.
13. Granger C.W.J. Investigating causal relations by econometric models and cross-spectral methods. Econometrica J. Econ. Soc. 1969, 37(3):424-438.
14. Gretton Arthur, Fukumizu Kenji, Teo Choon H., Song Le, Schölkopf Bernhard, Smola Alex J. A kernel statistical test of independence. Advances in Neural Information Processing Systems 2008, 585-592.
15. Grosse-Wentrup Moritz, Schölkopf Bernhard, Hill Jeremy Causal influence of gamma oscillations on the sensorimotor rhythm. NeuroImage 2011, 56(2):837-842.
16. Hamann Stephan Cognitive and neural mechanisms of emotional memory. Trends Cogn. Sci. 2001, 5(9):394-400.
17. Haufe Stefan, Meinecke Frank, Görgen Kai, Dähne Sven, Haynes John-Dylan, Blankertz Benjamin, Bießmann Felix On the interpretation of weight vectors of linear models in multivariate neuroimaging. NeuroImage 2014, 87:96-110.
18. Holland Paul W. Statistics and causal inference. J. Am. Stat. Assoc. 1986, 81(396):945-960.
19. Hoyer Patrik O., Hyvarinen Aapo, Scheines Richard, Spirtes Peter, Ramsey Joseph, Lacerda Gustavo, Shimizu Shohei Causal discovery of linear acyclic models with arbitrary distributions. Proceedings of 24th Conf. on Uncertainty in Artificial Intelligence (UAI 2008) 2008, 282-289.
20. Jordan A. On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes. Adv. Neural Inf. Process. Syst. 2002, 14:841.
21. Kriegeskorte Nikolaus, Goebel Rainer, Bandettini Peter Information-based functional brain mapping. Proc. Natl. Acad. Sci. U. S. A. 2006, 103(10):3863-3868.
22. Lio Guillaume, Boulinguez Philippe Greater robustness of second order statistics than higher order statistics algorithms to distortions of the mixing matrix in blind source separation of human EEG: implications for single-subject and group analyses. NeuroImage 2013, 67:137-152.
23. Lizier Joseph T., Prokopenko Mikhail Differentiating information transfer and causal effect. Eur. Phys. J. BCondens. Matter Complex Syst. 2010, 73(4):605-615.
24. Meek Christopher Strong completeness and faithfulness in Bayesian networks. Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95) 1995, 411-418. Morgan Kaufmann, San Francisco, CA.
25. Meyer Timm, Peters Jan, Zander Thorsten O., Schölkopf Bernhard, Grosse-Wentrup Moritz Predicting motor learning performance from electroencephalographic data. J. Neuroeng. Rehabil. 2014, 11(1):24.
26. Mitchell Tom M., Hutchinson Rebecca, Niculescu Radu S., Pereira Francisco, Wang Xuerui, Just Marcel, Newman Sharlene Learning to decode cognitive states from brain images. Mach. Learn. 2004, 57(1-2):145-175.
27. Mumford Jeanette A., Ramsey Joseph D. Bayesian networks for fMRI: a primer. NeuroImage 2014, 86:573-582.
28. Myers Nicholas E., Stokes Mark G., Walther Lena, Nobre Anna C. Oscillatory brain state predicts variability in working memory. J. Neurosci. 2014, 34(23):7735-7743.
29. Naselaris Thomas, Kay Kendrick N., Nishimoto Shinji, Gallant Jack L. Encoding and decoding in fMRI. NeuroImage 2011, 56(2):400-410.
30. Pearl Judea Causality: Models, Reasoning and Inference 2000, Cambridge University Press.
31. Pereira Francisco, Mitchell Tom, Botvinick Matthew Machine learning classifiers and fMRI: a tutorial overview. NeuroImage 2009, 45(1):S199-S209.
32. Ramsey J.D., Hanson S.J., Hanson C., Halchenko Y.O., Poldrack R.A., Glymour C. Six problems for causal inference from fMRI. NeuroImage 2010, 49(2):1545-1558.
33. Rees Geraint, Kreiman Gabriel, Koch Christof Neural correlates of consciousness in humans. Nat. Rev. Neurosci. 2002, 3(4):261-270.
34. Reichenbach H. The Direction of Time 1956, University of California Press, Berkeley.
35. Roebroeck Alard, Formisano Elia, Goebel Rainer Mapping directed influence over the brain using granger causality and fMRI. NeuroImage 2005, 25(1):230-242.
36. Schölkopf Bernhard, Janzing Dominik, Peters Jonas, Sgouritsa Eleni, Zhang Kun, Mooij Joris, Schölkopf Bernhard, Janzing Dominik, Peters Jonas, Sgouritsa Eleni, Zhang Kun, Mooij Joris On causal and anticausal learning. 29th International Conference on Machine Learning (ICML 2012) 2012, 1255-1262.
37. Shimizu Shohei, Hoyer Patrik O., Hyvärinen Aapo, Kerminen Antti A linear non-gaussian acyclic model for causal discovery. J. Mach. Learn. Res. 2006, 7:2003-2030.
38. Spirtes Peter, Glymour Clark N., Scheines Richard Causation, Prediction, and Search 2000, MIT Press.
39. Strobl Carolin, Boulesteix Anne-Laure, Kneib Thomas, Augustin Thomas, Zeileis Achim Conditional variable importance for random forests. BMC Bioinf. 2008, 9(1):307.
40. Todd Michael T., Nystrom Leigh E., Cohen Jonathan D. Confounds in multivariate pattern analysis: theory and rule representation case study. NeuroImage 2013, 77:157-165.
41. Waldorp Lourens, Christoffels Ingrid, van de Ven Vincent Effective connectivity of fMRI data using ancestral graph theory: dealing with missing regions. NeuroImage 2011, 54(4):2695-2705.
42. Ward Lawrence M. Synchronous neural oscillations and cognitive processes. Trends Cogn. Sci. 2003, 7(12):553-559.
43. Weichwald Sebastian, Scholkopf Bernhard, Ball Tonio, Grosse-Wentrup Moritz Causal and anti-causal learning in pattern recognition for neuroimaging. Pattern Recognition in Neuroimaging, 2014 International Workshop on 2014, 1-4.
44. Woolgar Alexandra, Golland Polina, Bode Stefan Coping with confounds in multivoxel pattern analysis: what should we do about reaction time differences? A comment on Todd, Nystrom & Cohen 2013. NeuroImage 2014, 98:506-512.
45. Zhang Kun, Peters Jonas, Janzing Dominik, Schölkopf Bernhard Kernel-based conditional independence test and application in causal discovery. CoRR 2012, abs/1202.3775.