Is Granger Causality a Viable Technique for Analyzing fMRI Data?

PLoS ONE

Volumn 8, Issue 7, 2013, Pages

  Wen, Xiaotong ^a   Rangarajan, Govindan ^b   Ding, Mingzhou ^a  

^a J Crayton Pruitt Family Department of Biomedical Engineering   (United States)

^b INDIAN INSTITUTE OF SCIENCE   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; DEPENDENT VARIABLE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; GRANGER CAUSALITY; HEMODYNAMICS; NERVE CELL NETWORK; NEUROSCIENCE; NOISE; RELIABILITY; STATISTICAL ANALYSIS;

ALGORITHMS; BRAIN; BRAIN MAPPING; COMPUTER SIMULATION; HEMODYNAMICS; HUMANS; MAGNETIC RESONANCE IMAGING; MODELS, NEUROLOGICAL; NERVE NET; NEURAL PATHWAYS; NEURONS; SIGNAL-TO-NOISE RATIO; TIME FACTORS;

EID: 84879828573     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0067428     Document Type: Article

References (76)

1. Granger C, (1969) Investigating causal relations by econometric models and crossspectral methods. Econometrica 37(3): 424-438.
2. Ding M, Chen Y, Bressler S (2006) Granger causality: Basic theory and application to neuroscience. In: Schelter S, Winterhalder N, Timmer J, editors. Handbook of Time Series Analysis. Berlin Wiley-VCH. p. 437-460.
3. Zhou Z, Ding M, Chen Y, Wright P, Lu Z, et al. (2009) Detecting directional influence in fMRI connectivity analysis using PCA based Granger causality. Brain Res. 1289: 22-29.
4. Bollimunta A, Mo J, Schroeder CE, Ding M, (2011) Neuronal mechanisms and attentional modulation of corticothalamic α oscillations. J Neurosci. 31(13): 4935-4943.
5. Bressler SL, Seth AK, (2011) Wiener-Granger causality: a well established methodology. Neuroimage. 58(2): 323-329.
6. Brovelli A, Ding M, Ledberg A, Chen Y, Nakamura R, et al. (2004) Beta oscillations in a large-scale sensorimotor cortical network: Directional influences revealed by Granger causality. Proc Natl Acad Sci. 101: 9849-9854.
7. Bollimunta A, Chen Y, Schroeder CE, Ding M, (2008) Neuronal mechanisms of cortical alpha oscillations in awake-behaving macaques. J Neurosci. 28(40): 9976-9988.
8. Curtis CE, Sun FT, Miller LM, D'Esposito M, (2005) Coherence between fMRI time-series distinguishes two spatial working memory networks. Neuroimage. 26(1): 177-183.
9. Gelfand I, Yaglom A, (1959) Calculation of the amount of information about a random function contained in another such function. Amer Math Soc Transl Ser. 2(12): 99.
10. Wen X, Mo J, Ding M, (2012) Exploring resting-state functional connectivity with total interdependence. Neuroimage. 60(2): 1587-1595.
11. Roebroeck A, Formisano E, Goebel R, (2005) Mapping directed influence over the brain using Granger causality and fMRI. Neuroimage 25(1): 230-242.
12. Roebroeck A, Formisano E, Goebel R, (2011) The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution. Neuroimage 58(2): 296-302.
13. David O, Guillemain I, Saillet S, Reyt S, Deransart C, et al. (2008) Identifying neural drivers with functional MRI: an electrophysiological validation. PLoS Biol 6(12): 2683-2697.
14. Witt ST, Meyerand ME, (2009) The Effects of Computational Method, Data Modeling, and TR on Effective Connectivity Results. Brain Imaging Behav 3(2): 220-231.
15. Deshpande G, Sathian K, Hu X, (2010) Effect of hemodynamic variability on Granger causality analysis of fMRI. Neuroimage 52(3): 884-896.
16. Florin E, Gross J, Pfeifer J, Fink GR, Timmermann L, (2010) The effect of filtering on Granger causality based multivariate causality measures. Neuroimage 50(2): 577-588.
17. Friston K (2011) 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(2): 303-5; author reply 10-11.
18. Smith SM, Miller KL, Salimi-Korshidi G, Webster M, Beckmann CF, et al. (2011) Network modelling methods for FMRI. Neuroimage 54(2): 875-891.
19. David O (2011) fMRI connectivity, meaning and empiricism Comments on: Roebroecket al. The identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution. Neuroimage 58(2): 306-9; author reply 10-11.
20. Friston K, Moran R, Seth AK, (2013) Analysing connectivity with Granger causality and dynamic causal modelling. Curr Opin Neurobiol 23(2): 172-178.
21. Aguirre GK, Zarahn E, D'esposito M, (1998) The variability of human, BOLD hemodynamic responses. Neuroimage 8(4): 360-369.
22. Smith SM, Bandettini PA, Miller KL, Behrens TEJ, Friston KJ, et al. (2012) The danger of systematic bias in group-level FMRI-lag-based causality estimation. Neuroimage 59(2): 1228-1229.
23. Nalatore H, Ding M, Rangarajan G, (2007) Mitigating the effects of measurement noise on Granger causality. Phys Rev E Stat Nonlin Soft Matter Phys 75(3 Pt 1): 031123.
24. Nalatore H, Ding M, Rangarajan G, (2009) Denoising neural data with state-space smoothing: method and application. J Neurosci Methods 179(1): 131-141.
25. Schippers MB, Renken R, Keysers C, (2011) The effect of intra- and inter-subject variability of hemodynamic responses on group level Granger causality analyses. Neuroimage 57(1): 22-36.
26. Rypma B, Berger JS, Prabhakaran V, Bly BM, Kimberg DY, et al. (2006) Neural correlates of cognitive efficiency. Neuroimage 33(3): 969-979.
27. Miao X, Wu X, Li R, Chen K, Yao L, (2011) Altered connectivity pattern of hubs in default-mode network with Alzheimer's disease: an Granger causality modeling approach. PLoS One 6(10): e25546.
28. Boynton GM, Engel SA, Glover GH, Heeger DJ, (1996) Linear systems analysis of functional magnetic resonance imaging in human V1. J Neurosci 16(13): 4207-4221.
29. Friston KJ, Fletcher P, Josephs O, Holmes A, Rugg MD, et al. (1998) Event-related fMRI: characterizing differential responses. Neuroimage 7(1): 30-40.
30. Geweke J, (1982) Measurement of linear dependence and feedback between multiple time series. J Am Stat Assoc 77(378): 304-313.
31. Goebel R, Roebroeck A, Kim DS, Formisano E, (2003) Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. Magn Reson Imaging 21(10): 1251-1261.
32. Zang ZX, Yan CG, Dong ZY, Huang J, Zang YF, (2012) Granger causality analysis implementation on MATLAB: a graphic user interface toolkit for fMRI data processing. J Neurosci Methods 203(2): 418-426.
33. Hu S, Dai G, Worrell GA, Dai Q, Liang H, (2011) Causality analysis of neural connectivity: critical examination of existing methods and advances of new methods. IEEE Trans Neural Netw 22(6): 829-844.
34. Hu S, Liang H, (2012) Causality analysis of neural connectivity: New tool and limitations of spectralGranger causality. Neurocomputing 76: 44-47.
35. Schwarz G, (1978) Estimating the dimension of a model. Annals of Statistics 6(2): 461-464.
36. Bressler SL, Tang W, Sylvester CM, Shulman GL, Corbetta M, (2008) Top-down control of human visual cortex by frontal and parietal cortex in anticipatory visual spatial attention. J Neurosci 28(40): 10056-61.
37. Hamilton JP, Chen G, Thomason ME, Schwartz ME, Gotlib IH, (2011) Investigating neural primacy in Major Depressive Disorder: multivariate Granger causality analysis of resting-state fMRI time-series data. Mol Psychiatry 16(7): 763-772.
38. Chang C, Thomason ME, Glover GH, (2008) Mapping and correction of vascular hemodynamic latency in the BOLD signal. Neuroimage 43(1): 90-102.
39. Dale AM, Buckner RL, (1997) Selective averaging of rapidly presented individual trials using fMRI. Hum Brain Mapp 5(5): 329-340.
40. Steffener J, Tabert M, Reuben A, Stern Y, (2010) Investigating hemodynamic response variability at the group level using basis functions. Neuroimage 49(3): 2113-2122.
41. Katwal SB, Gore JC, Gatenby JC, Rogers BP, (2012) Measuring relative timings of brain activities using fMRI. Neuroimage 66C: 436-448.
42. Menon RS, Luknowsky DC, Gati JS, (1998) Mental chronometry using latency-resolved functional MRI. Proc Natl Acad Sci U S A 95(18): 10902-10907.
43. Wen X, Yao L, Liu Y, Ding M, (2012) Causal interactions in attention networks predict behavioral performance. J Neurosci 32(4): 1284-1292.
44. Wen X, Liu Y, Yao L, Ding M, (2013) Top-down regulation of default mode activity in spatial visual attention. J Neurosci 33(15): 6444-6453.
45. Schmolesky MT, Wang Y, Pu M, Leventhal AG, (2000) Degradation of stimulus selectivity of visual cortical cells in senescent rhesus monkeys. Nat Neurosci 3(4): 384-390.
46. Rolls E (1992) Neurophysiology and functions of the primate amygdala. In: Aggleton J, editor. The amygdaleThe amygdale. New York: Wiley; p. 143-165.
47. Rolls ET, (2000) Memory systems in the brain. Annu Rev Psychol 51: 599-630.
48. Rolls ET, (2000) Functions of the primate temporal lobe cortical visual areas in invariant visual object and face recognition. Neuron 27(2): 205-218.
49. Rolls ET, (2000) Hippocampo-cortical and cortico-cortical backprojections. Hippocampus 10(4): 380-388.
50. Nakamura K, Matsumoto K, Mikami A, Kubota K, (1994) Visual response properties of single neurons in the temporal pole of behaving monkeys. J Neurophysiol 71(3): 1206-1221.
51. Lamme VA, Roelfsema PR, (2000) The distinct modes of vision offered by feedforward and recurrent processing. Trends Neurosci 23(11): 571-579.
52. Nishitani N, Hari R, (2002) Viewing lip forms: cortical dynamics. Neuron 36(6): 1211-1220.
53. Evdokimidis I, Smyrnis N, Constantinidis TS, Gourtzelidis P, Papageorgiou C, (2001) Frontal-parietal activation differences observed before the execution of remembered saccades: an event-related potentials study. Brain Res Cogn Brain Res 12(1): 89-99.
54. McDowell JE, Kissler JM, Berg P, Dyckman KA, Gao Y, et al. (2005) Electroencephalography/magnetoencephalography study of cortical activities preceding prosaccades and antisaccades. Neuroreport 16(7): 663-668.
55. Sestieri C, Pizzella V, Cianflone F, Luca Romani G, Corbetta M, (2007) Sequential activation of human oculomotor centers during planning of visually-guided eye movements: a combined fMRI-MEG study. Front Hum Neurosci 1: 1.
56. Seth AK, Chorley P, Barnett LC, (2013) Granger causality analysis of fMRI BOLD signals is invariant to hemodynamic convolution but not downsampling. Neuroimage 65: 540-555.
57. Feinberg DA, Moeller S, Smith SM, Auerbach E, Ramanna S, et al. (2010) Multiplexed echo planar imaging for sub-second whole brain FMRI and fast diffusion imaging. PLoS One 5(12): e15710.
58. Rogers BP, Katwal SB, Morgan VL, Asplund CL, Gore JC, (2010) Functional MRI and multivariate autoregressive models. Magn Reson Imaging 28(8): 1058-1065.
59. Handwerker DA, Ollinger JM, D'Esposito M, (2004) Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses. Neuroimage 21(4): 1639-1651.
60. Wagner AD, Schacter DL, Rotte M, Koutstaal W, Maril A, et al. (1998) Building memories: remembering and forgetting of verbal experiences as predicted by brain activity. Science 281(5380): 1188-1191.
61. Marrelec G, Benali H, Ciuciu P, Pélégrini-Issac M, Poline JB, (2003) Robust Bayesian estimation of the hemodynamic response function in event-related BOLD fMRI using basic physiological information. Hum Brain Mapp 19(1): 1-17.
62. Wager TD, Keller MC, Lacey SC, Jonides J, (2005) Increased sensitivity in neuroimaging analyses using robust regression. Neuroimage 26(1): 99-113.
63. Casanova R, Ryali S, Serences J, Yang L, Kraft R, et al. (2008) The impact of temporal regularization on estimates of the BOLD hemodynamic response function: a comparative analysis. Neuroimage 40(4): 1606-1618.
64. Kay KN, David SV, Prenger RJ, Hansen KA, Gallant JL, (2008) Modeling low-frequency fluctuation and hemodynamic response timecourse in event-related fMRI. Hum Brain Mapp 29(2): 142-156.
65. Luchtmann M, Jachau K, Tempelmann C, Bernarding J, (2010) Alcohol induced region-dependent alterations of hemodynamic response: implications for the statistical interpretation of pharmacological fMRI studies. Exp Brain Res 204(1): 1-10.
66. Magon S, Basso G, Farace P, Ricciardi GK, Beltramello A, et al. (2009) Reproducibility of BOLD signal change induced by breath holding. Neuroimage 45(3): 702-712.
67. Bandettini PA, Wong EC, (1997) A hypercapnia-based normalization method for improved spatial localization of human brain activation with fMRI. NMR Biomed 10(4-5): 197-203.
68. Davis TL, Kwong KK, Weisskoff RM, Rosen BR, (1998) Calibrated functional MRI: mapping the dynamics of oxidative metabolism. Proc Natl Acad Sci U S A 95(4): 1834-1839.
69. Cohen ER, Rostrup E, Sidaros K, Lund TE, Paulson OB, et al. (2004) Hypercapnic normalization of BOLD fMRI: comparison across field strengths and pulse sequences. Neuroimage 23(2): 613-624.
70. Chen Y, Bressler SL, Ding M, (2006) Frequency decomposition of conditional Granger causality and application to multivariate neural field potential data. J Neurosci Methods 150(2): 228-37.
71. Shokri-Kojori E, Motes MA, Rypma B, Krawczyk DC, (2012) The network architecture of cortical processing in visuo-spatial reasoning. Sci Rep 2: 411.
72. Biswal BB, Eldreth DA, Motes MA, Rypma B, (2010) Task-dependent individual differences in prefrontal connectivity. Cereb Cortex 20(9): 2188-2197.
73. Jiao Q, Lu G, Zhang Z, Zhong Y, Wang Z, et al. (2011) Granger causal influence predicts BOLD activity levels in the default mode network. Hum Brain Mapp 32(1): 154-161.
74. Corbetta M, Shulman GL, (2002) Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci 3(3): 201-215.
75. Corbetta M, Patel G, Shulman GL, (2008) The reorienting system of the human brain: from environment to theory of mind. Neuron 58(3): 306-324.
76. Dosenbach NU, Visscher KM, Palmer ED, Miezin FM, Wenger KK, et al. (2006) A core system for the implementation of task sets. Neuron 50(5): 799-812.