Effective connectivity during animacy perception - Dynamic causal modelling of Human Connectome Project data

Scientific Reports

Volumn 4, Issue , 2014, Pages

  Hillebrandt, Hauke ^a,b   Friston, Karl J ^c   Blakemore, Sarah Jayne ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b HARVARD UNIVERSITY   (United States)

^c UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; ATTENTION; CONNECTOME; FEMALE; HUMAN; MALE; MOVEMENT PERCEPTION; NUCLEAR MAGNETIC RESONANCE IMAGING; PHOTOSTIMULATION; PHYSIOLOGY; PROCEDURES; SENSORY FEEDBACK; TEMPORAL LOBE;

ADULT; ATTENTION; CONNECTOME; FEEDBACK, SENSORY; FEMALE; HUMANS; MAGNETIC RESONANCE IMAGING; MALE; MOTION PERCEPTION; PHOTIC STIMULATION; TEMPORAL LOBE;

EID: 84906871635     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep06240     Document Type: Article

References (66)

1. Barrett, J. L. Exploring the natural foundations of religion. Trends Cogn. Sci. 4, 29-34 (2000). (Pubitemid 30066505)
2. Heider, F. & Simmel, M. An Experimental Study of Apparent Behavior. Am. J. Psychol. 57, 243-259 (1944).
3. Tremoulet, P. D. & Feldman, J. Perception of animacy from the motion of a single object. Perception 29, 943-951 (2000).
4. Pratt, J., Radulescu, P. V., Guo, R. M. & Abrams, R. A. It's Alive! Animate Motion Captures Visual Attention. Psychol. Sci. 21, 1724-1730 (2010).
5. Clark. Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. Sec. 3, 1-86 (2012).
6. Grill-Spector, K., Henson, R. & Martin, A. Repetition and the brain: neural models of stimulus-specific effects. Trends Cogn. Sci. 10, 14-23 (2006). (Pubitemid 43049768)
7. Cartmill, M. Rethinking Primate Origins. Science 184, 436-443 (1974).
8. Maye, A., Hsieh, C., Sugihara, G. & Brembs, B. Order in Spontaneous Behavior. PLoS ONE 2, e443 (2007).
9. Van Essen, D. et al. The WU-Minn Human Connectome Project: An overview. NeuroImage 80, 62-79 (2013).
10. Friston, K. & Penny, W. Post hoc Bayesian model selection. NeuroImage 56, 2089-2099 (2011).
11. Rosa, M. J., Friston, K. & Penny, W. Post-hoc selection of dynamic causal models. J. Neurosci. Methods 208, 66-78 (2012).
12. Born, R. T. & Bradley, D. C. Structure and Function of Visual Area Mt. Annu. Rev. Neurosci. 28, 157-189 (2005). (Pubitemid 41098903)
13. Castelli, F., Happé, F., Frith, U. & Frith, C. Movement and mind: A functional imaging study of perception and interpretation of complex intentionalmovement patterns. NeuroImage 12, 314-325 (2000).
14. Friston, K. Dynamic causal modeling and Granger causality Comments on: The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution. Neuroimage 58, 303-305 (2011).
15. Feldman, H. & Friston, K. Attention, uncertainty, and free-energy. Front. Hum. Neurosci. 4, 215 (2010).
16. Van Essen, D. et al. The Human Connectome Project: A data acquisition perspective. NeuroImage 62, 2222-2231 (2012).
17. Uǧurbil, K. et al. Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project. NeuroImage 80, 80-104 (2013).
18. Barch, D. M. et al. Function in the human connectome: task-fMRI and individual differences in behavior. NeuroImage 80, 169-189 (2013).
19. Feinberg, D. A. et al. Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging. PLoS ONE 5, e15710 (2010).
20. Moeller, S. et al. Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal wholebrain fMRI. Magn. Reson. Med. Off. J. Soc. Magn. Reson. Med. Soc. Magn. Reson. Med. 63, 1144-1153 (2010).
21. Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage 80, 105-124 (2013).
22. Kiebel, S. J., Klöppel, S., Weiskopf, N. & Friston, K. Dynamic causal modeling: a generative model of slice timing in fMRI. NeuroImage 34, 1487-1496 (2007). (Pubitemid 46187072)
23. Büchel, C., Holmes, A. P., Rees, G. & Friston, K. Characterizing Stimulus- Response Functions Using Nonlinear Regressors in Parametric fMRI Experiments. NeuroImage 8, 140-148 (1998). (Pubitemid 28471359)
24. Friston, K., Harrison, L. & Penny, W. Dynamic causal modelling. NeuroImage 19, 1273-1302 (2003). (Pubitemid 37025831)
25. Daunizeau, J., Stephan, K. E. & Friston, K. Stochastic dynamic causal modelling of fMRI data: Should we care about neural noise? NeuroImage 62, 464-481 (2012).
26. Stephan, K. E. et al. Nonlinear dynamic causal models for fMRI. NeuroImage 42, 649-662 (2008).
27. Marreiros, A. C., Kiebel, S. J. & Friston, K. Dynamic causal modelling for fMRI: a two-state model. NeuroImage 39, 269-278 (2008). (Pubitemid 350102199)
28. Goulden, N. et al. Sample size estimation for comparing parameters using dynamic causal modeling. Brain Connect. 2, 80-90 (2012).
29. Bland, J. M. & Altman, D. G. Statistics Notes: Transforming data. BMJ 312, 770-770 (1996).
30. Bland, J. M. & Altman, D. G. Statistics notes: Transformations, means, and confidence intervals. BMJ 312, 1079-1079 (1996).
31. Friston, K., Mattout, J., Trujillo-Barreto, N., Ashburner, J. & Penny, W. Variational free energy and the Laplace approximation. NeuroImage 34, 220-234 (2007). (Pubitemid 44765243)
32. Friston, K., Li, B., Daunizeau, J. & Stephan, K. E. Network discovery with DCM. NeuroImage 56, 1202-1221 (2011).
33. Kasess, C. H. et al. Multi-subject analyses with dynamic causal modeling. NeuroImage 49, 3065-3074 (2010).
34. Stephan, K. E. et al. Ten simple rules for dynamic causal modeling. NeuroImage 49, 3099-3109 (2010)
35. Penny, W., Stephan, K. E., Mechelli, A. & Friston, K. Comparing dynamic causal models. NeuroImage 22, 1157-1172 (2004). (Pubitemid 38829205)
36. Penny, W. et al. Comparing Families of Dynamic Causal Models. PLoS Comput. Biol. 6, e1000709 (2010).
37. Hillebrandt, H., Dumontheil, I., Blakemore, S. -J. & Roiser, J. P. Dynamic causal modelling of effective connectivity during perspective taking in a communicative task. NeuroImage 76, 116-124 (2013).
38. Eickhoff, S. B. et al. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. NeuroImage 25, 1325-1335 (2005). (Pubitemid 40591672)
39. Price, C. J. & Friston, K. Cognitive Conjunction: A New Approach to Brain Activation Experiments. NeuroImage 5, 261-270 (1997). (Pubitemid 27481510)
40. Friston, K., Penny, W. & Glaser, D. Conjunction revisited. NeuroImage 25, 661-667 (2005). (Pubitemid 40461705)
41. Lewis, J. W. & Van Essen, D. C. Corticocortical connections of visual, sensorimotor, and multimodal processing areas in the parietal lobe of the macaque monkey. J. Comp. Neurol. 428, 112-137 (2000).
42. Summerfield, C. et al. Predictive Codes for Forthcoming Perception in the Frontal Cortex. Science 314, 1311-1314 (2006). (Pubitemid 44848245)
43. Kass, R. E. & Raftery, A. E. Bayes Factors. J. Am. Stat. Assoc. 90, 773-795 (1995).
44. Zimmerman, D. W. A Note on the Influence of Outliers on Parametric and Nonparametric Tests. J. Gen. Psychol. 121, 391-401 (1994).
45. Pavlova, M. A. Biological Motion Processing as a Hallmark of Social Cognition. Cereb. Cortex 22, 981-995 (2012).
46. Herrington, J. D., Nymberg, C. & Schultz, R. T. Biological motion task performance predicts superior temporal sulcus activity. Brain Cogn. 77, 372-381 (2011).
47. Gilaie-Dotan, S., Kanai, R., Bahrami, B., Rees, G. & Saygin, A. P. Neuroanatomical correlates of biological motion detection. Neuropsychologia 51, 457-463 (2013).
48. Grossman, E. D., Battelli, L. & Pascual-Leone, A. Repetitive TMS over posterior STS disrupts perception of biological motion. Vision Res. 45, 2847-2853 (2005). (Pubitemid 41208293)
49. Gao, T., Scholl, B. J. & McCarthy, G. Dissociating the detection of intentionality from animacy in the right posterior superior temporal sulcus. J. Neurosci. Off. J. Soc. Neurosci. 32, 14276-14280 (2012).
50. Lee, S. M., Gao, T. & McCarthy, G. Attributing intentions to random motion engages the posterior superior temporal sulcus. Soc. Cogn. Affect. Neurosci. 9, 81-87 (2014).
51. Brown, E. C. & Brüne, M. The role of prediction in social neuroscience. Front. Hum. Neurosci. 6, 147 (2012).
52. Koster-Hale, J. & Saxe, R. Theory of Mind: A Neural Prediction Problem. Neuron 79, 836-848 (2013).
53. Friston, K. The free-energy principle: a unified brain theory? Nat. Rev. Neurosci. 11, 127-138 (2010).
54. Rao, R. P. & Ballard, D. H. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2, 79-87 (1999). (Pubitemid 30488944)
55. Klein, A. M., Zwickel, J., Prinz, W. & Frith, U. Animated triangles: an eye tracking investigation. Q. J. Exp. Psychol. 2006 62, 1189-1197 (2009).
56. Ramsey, R. & Hamilton, A. F. de C. Triangles have goals too: Understanding action representation in left aIPS. Neuropsychologia 48, 2773-2776 (2010).
57. Button, K. S. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365-376 (2013).
58. Friston, K. Ten ironic rules for non-statistical reviewers. NeuroImage 61, 1300-1310 (2012).
59. David, S. P. et al. Potential Reporting Bias in fMRI Studies of the Brain. PLoS ONE 8, e70104 (2013).
60. Kandel, E. R., Markram, H., Matthews, P. M., Yuste, R. & Koch, C. Neuroscience thinks big (and collaboratively). Nat. Rev. Neurosci. 14, 659-664 (2013).
61. Poldrack, R. A. et al. Toward open sharing of task-based fMRI data: the OpenfMRI project. Front. Neuroinformatics 7, 12 (2013).
62. Carp, J. Better living through transparency: improving the reproducibility of fMRI results through comprehensive methods reporting. Cogn. Affect. Behav. Neurosci. 13, 660-666 (2013).
63. Stephan, K. E., Tittgemeyer, M., Knösche, T. R., Moran, R. J. & Friston, K. Tractography-based priors for dynamic causal models. NeuroImage 47, 1628-1638 (2009).
64. Nguyen, V. T., Breakspear, M. & Cunnington, R. Fusing concurrent EEG-fMRI with dynamic causal modeling: Application to effective connectivity during face perception. NeuroImage (2013) doi:10. 1016/j. neuroimage. 2013. 06. 083.
65. Daunizeau, J., David, O. & Stephan, K. E. Dynamic causal modelling: a critical review of the biophysical and statistical foundations. NeuroImage 58, 312-322 (2011).
66. Logothetis, N. K. What we can do and what we cannot do with fMRI. Nature 453, 869-878 (2008). (Pubitemid 351832564)