A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data

Medical Image Analysis

Volumn 17, Issue 3, 2013, Pages 365-374

  Wu, Guo Rong ^a,b   Liao, Wei ^c   Stramaglia, Sebastiano ^d   Ding, Ju Rong ^b,e   Chen, Huafu ^b   Marinazzo, Daniele ^a  

^a GHENT UNIVERSITY   (Belgium)

^b UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

^c HANGZHOU NORMAL UNIVERSITY   (China)

^d UNIVERSITY OF BARI   (Italy)

^e INDIANA UNIVERSITY   (United States)

Author keywords

Bold signal; Deconvolution; Effective connectivity; Granger causality

Indexed keywords

BIOLOGICALLY INSPIRED MODELS; BOLD SIGNAL; EFFECTIVE CONNECTIVITIES; GRANGER CAUSALITY; HEMODYNAMIC RESPONSE FUNCTIONS; NEURAL POPULATION DYNAMICS; RELEVANT INFORMATIONS; STATISTICAL PREDICTION;

BRAIN; CONVOLUTION; DECONVOLUTION; DIGITAL STORAGE; INFORMATION THEORY; SIGNAL SYSTEMS;

DATA FLOW ANALYSIS;

ARTICLE; BOLD SIGNAL; BRAIN BLOOD FLOW; BRAIN REGION; CEREBROSPINAL FLUID; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HEMODYNAMICS; HUMAN; IMAGE ANALYSIS; IMAGE QUALITY; NERVE CELL NETWORK; PRIORITY JOURNAL;

ALGORITHMS; BRAIN; BRAIN MAPPING; CONNECTOME; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; NERVE NET; NEURAL PATHWAYS; NEURONS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; REST; SENSITIVITY AND SPECIFICITY;

EID: 84875245879     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2013.01.003     Document Type: Article

References (58)

1. Achard S., Bullmore E. Efficiency and cost of economical brain functional networks. PLoS Computational Biology 2007, 3:e17.
2. Angelini L., De Tommaso M., Marinazzo D., Nitti L., Pellicoro M., Stramaglia S. Redundant variables and Granger causality. Physical Review E 2010, 81:037201.
3. Bakhtiari S., Hossein-Zadeh G. Subspace-based identification algorithm for characterizing causal networks in resting brain. NeuroImage 2012, 60:1236-1249.
4. Barnett L., Seth A.K. Behaviour of Granger causality under filtering: theoretical invariance and practical application. Journal of Neuroscience Methods 2011, 201:404-419.
5. Bassett D., Bullmore E. Small-world brain networks. The neuroscientist 2006, 12:512-523.
6. Birn R., Murphy K., Bandettini P. The effect of respiration variations on independent component analysis results of resting state functional connectivity. Human Brain Mapping 2008, 29:740-750.
7. Bressler S., Seth A. Wiener-Granger causality: a well established methodology. Neuroimage 2011, 58:323-329.
8. Büchel C., Friston K. Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. Cerebral Cortex 1997, 7:768-778.
9. Bullmore E., Bassett D. Brain graphs: graphical models of the human brain connectome. Annual Review of Clinical Psychology 2011, 7:113-140.
10. Bullmore E., Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience 2009, 10:186-198.
11. Buxton R., Wong E., Frank L. Dynamics of blood flow and oxygenation changes during brain activation: the balloon model. Magnetic Resonance in Medicine 1998, 39:855-864.
12. David O., Guillemain I., Saillet S., Reyt S., Deransart C., Segebarth C., Depaulis A. Identifying neural drivers with functional MRI: an electrophysiological validation. PLoS Biology 2008, 6:e315.
13. Deco G., Jirsa V. Ongoing cortical activity at rest: criticality, multistability, and ghost attractors. The Journal of Neuroscience 2012, 32:3366-3375.
14. Deshpande G., Sathian K., Hu X. Effect of hemodynamic variability on Granger causality analysis of fMRI. Neuroimage 2010, 52:884-896.
15. Eguiluz V., Chialvo D., Cecchi G., Baliki M., Apkarian A. Scale-free brain functional networks. Physical Review Letters 2005, 94:18102.
16. Feinberg D., Moeller S., Smith S., Auerbach E., Ramanna S., Glasser M., Miller K., Ugurbil K., Yacoub E. Multiplexed echo planar imaging for sub-second whole brain fMRI and fast diffusion imaging. PLoS One 2010, 5:e15710.
17. Fornito A., Zalesky A., Bullmore E. Network scaling effects in graph analytic studies of human resting-state fMRI data. Frontiers in Systems Neuroscience 2010, 4.
18. Friston K. Causal modelling and brain connectivity in functional magnetic resonance imaging. PLoS Biology 2009, 7:e1000033.
19. Friston K., Mechelli A., Turner R., Price C. Nonlinear responses in fMRI: the balloon model, volterra kernels, and other hemodynamics. NeuroImage 2000, 12:466-477.
20. Friston K., Harrison L., Penny W. Dynamic causal modelling. Neuroimage 2003, 19:1273-1302.
21. Friston K., Trujillo-Barreto N., Daunizeau J. Dem: a variational treatment of dynamic systems. Neuroimage 2008, 41:849-885.
22. Glover G. Deconvolution of impulse response in event-related bold fMRI. NeuroImage 1999, 9:416-429.
23. Hagmann P., Cammoun L., Gigandet X., Meuli R., Honey C., Wedeen V., Sporns O. Mapping the structural core of human cerebral cortex. PLoS Biology 2008, 6:e159.
24. Hamilton J., Chen G., Thomason M., Schwartz M., Gotlib I. Investigating neural primacy in major depressive disorder: multivariate Granger causality analysis of resting-state fMRI time-series data. Molecular Psychiatry 2010, 16:763-772.
25. Handwerker D., Ollinger J., D'Esposito M. Variation of bold hemodynamic responses across subjects and brain regions and their effects on statistical analyses. Neuroimage 2004, 21:1639-1651.
26. Handwerker D., Gonzalez-Castillo J., D'Esposito M., Bandettini P. The continuing challenge of understanding and modeling hemodynamic variation in fMRI. NeuroImage 2012, 62:1017-1023.
27. Havlicek M., Jan J., Brazdil M., Calhoun V. Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data. NeuroImage 2010, 53:65-77.
28. Havlicek M., Friston K., Jan J., Brazdil M., Calhoun V. Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering. NeuroImage 2011, 56:2109-2128.
29. He Y., Dagher A., Chen Z., Charil A., Zijdenbos A., Worsley K., Evans A. Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load. Brain 2009, 132:3366-3379.
30. Huettel S., Song A., McCarthy G. Functional Magnetic Resonance Imaging 2004, vol. 1. Sinauer Associates Sunderland, MA.
31. Kim S., Hendrich K., Hu X., Merkle H., Ugurbil K. Potential pitfalls of functional MRI using conventional gradient-recalled echo techniques. NMR in Biomedicine 1994, 7:69-74.
32. Knösche T., Tittgemeyer M. The role of long-range connectivity for the characterization of the functional-anatomical organization of the cortex. Frontiers in Systems Neuroscience 2011, 5.
33. Laufs H., Daunizeau J., Carmichael D., Kleinschmidt A. Recent advances in recording electrophysiological data simultaneously with magnetic resonance imaging. Neuroimage 2008, 40:515-528.
34. Lee A., Glover G., Meyer C. Discrimination of large venous vessels in time-course spiral blood-oxygen-level-dependent magnetic-resonance functional neuroimaging. Magnetic Resonance in Medicine 1995, 33:745-754.
35. Liao W., Ding J., Marinazzo D., Xu Q., Wang Z., Yuan C., Zhang Z., Lu G., Chen H. Small-world directed networks in the human brain: multivariate Granger causality analysis of resting-state fMRI. Neuroimage 2011, 54:2683-2694.
36. Logothetis N. What we can do and what we cannot do with fMRI. Nature 2008, 453:869-878.
37. Marinazzo D., Liao W., Pellicoro M., Stramaglia S. Grouping time series by pairwise measures of redundancy. Physics Letters A 2010, 374:4040-4044.
38. Marinazzo D., Pellicoro M., Stramaglia S. Causal information approach to partial conditioning in multivariate data sets. Computational and Mathematical Methods in Medicine 2012, 2012:8.
39. Marrelec G., Benali H., Ciuciu P., Pélégrini-Issac M., Poline J. Robust bayesian estimation of the hemodynamic response function in event-related bold fMRI using basic physiological information. Human Brain Mapping 2003, 19:1-17.
40. Petridou, N., Gaudes, C., Dryden, I., Francis, S., Gowland, P., in press. Periods of rest in fMRI contain individual spontaneous events which are related to slowly fluctuating spontaneous activity. Human Brain Mapping. doi:10.1002/hbm.21513.
41. Riera J., Watanabe J., Kazuki I., Naoki M., Aubert E., Ozaki T., Kawashima R. A state-space model of the hemodynamic approach: nonlinear filtering of bold signals. NeuroImage 2004, 21:547-567.
42. Roebroeck A., Formisano E., Goebel R. Mapping directed influence over the brain using Granger causality and fMRI. Neuroimage 2005, 25:230-242.
43. Roebroeck A., Formisano E., Goebel R. The identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution. Neuroimage 2011, 58:296-302.
44. Rubinov M., Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 2010, 52:1059-1069.
45. Ryali S., Supekar K., Chen T., Menon V. Multivariate dynamical systems models for estimating causal interactions in fMRI. Neuroimage 2011, 54:807-823.
46. Salvador R., Suckling J., Coleman M., Pickard J., Menon D., Bullmore E. Neurophysiological architecture of functional magnetic resonance images of human brain. Cerebral Cortex 2005, 15:1332-1342.
47. Smith S., Miller K., Salimi-Khorshidi G., Webster M., Beckmann C., Nichols T., Ramsey J., Woolrich M. Network modelling methods for fMRI. Neuroimage 2011, 54:875-891.
48. Stephan K.E., Roebroeck A. A short history of causal modeling of fMRI data. NeuroImage 2012, 62:856-863.
49. Supekar K., Menon V. Developmental maturation of dynamic causal control signals in higher-order cognition: a neurocognitive network model. PLoS Computational Biology 2012, 8:e1002374.
50. Tagliazucchi E., Balenzuela P., Fraiman D., Montoya P., Chialvo D. Spontaneous bold event triggered averages for estimating functional connectivity at resting state. Neuroscience Letters 2011, 488:158-163.
51. Tagliazucchi E., Balenzuela P., Fraiman D., Chialvo D. Criticality in large-scale brain fMRI dynamics unveiled by a novel point process analysis. Frontiers in Physiology 2012, 3.
52. Valdes-Sosa P., Roebroeck A., Daunizeau J., Friston K. Effective connectivity: influence, causality and biophysical modeling. Neuroimage 2011, 58:339-361.
53. Wang J., Wang L., Zang Y., Yang H., Tang H., Gong Q., Chen Z., Zhu C., He Y. Parcellation-dependent small-world brain functional networks: a resting-state fMRI study. Human Brain Mapping 2009, 30:1511-1523.
54. Wen W., Zhu W., He Y., Kochan N., Reppermund S., Slavin M., Brodaty H., Crawford J., Xia A., Sachdev P. Discrete neuroanatomical networks are associated with specific cognitive abilities in old age. The Journal of Neuroscience 2011, 31:1204-1212.
55. Wig G., Schlaggar B., Petersen S. Concepts and principles in the analysis of brain networks. Annals of the New York Academy of Sciences 2011, 1224:126-146.
56. Wu G., Stramaglia S., Marinazzo D. Decomposition of the transfer entropy: partial conditioning and informative clustering. Lecture Notes in Computer Science 2012, vol. 7663:226-233. Springer.
57. Zalesky A., Fornito A., Harding I., Cocchi L., Yücel M., Pantelis C., Bullmore E. Whole-brain anatomical networks: does the choice of nodes matter?. Neuroimage 2010, 50:970-983.
58. Zhang Z., Liao W., Chen H., Mantini D., Ding J., Xu Q., Wang Z., Yuan C., Chen G., Jiao Q., et al. Altered functional-structural coupling of large-scale brain networks in idiopathic generalized epilepsy. Brain 2011, 134:2912-2928.