Frequency-based approach to the study of semantic brain networks connectivity

Journal of Neuroscience Methods

Volumn 212, Issue 2, 2013, Pages 181-189

  Bianchi, A M ^a   Marchetta, E ^b   Tana, M G ^a   Tettamanti, M ^c   Rizzo, G ^b  

^a POLITECNICO DI MILANO   (Italy)

^b INSTITUTE OF MOLECULAR BIOIMAGING AND PHYSIOLOGY   (Italy)

^c SAN RAFFAELE HOSPITAL   (Italy)

Author keywords

Effective connectivity; FMRI; Granger causality; MVAR models; Semantic networks; Spectral analysis

Indexed keywords

ARTICLE; BRAIN; BRAIN REGION; CLINICAL ARTICLE; CONNECTOME; FREQUENCY ANALYSIS; FUNCTIONAL MAGNETIC RESONANCE IMAGING; FUSIFORM GYRUS; HUMAN; INFERIOR FRONTAL GYRUS; MALE; METHODOLOGY; PARS TRIANGULARIS; PRIORITY JOURNAL; SEMANTICS;

ALGORITHMS; BRAIN; BRAIN MAPPING; COMPUTER SIMULATION; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MODELS, NEUROLOGICAL; NERVE NET; SEMANTICS;

EID: 84875112127     PISSN: 01650270     EISSN: 1872678X     Source Type: Journal    
DOI: 10.1016/j.jneumeth.2012.10.005     Document Type: Article

References (44)

1. Akaike H. A new look at the statistical model identification. IEEE Trans Autom Control 1974, 19(6):716-723.
2. Aguirre G.K., Zarahn E., D'Esposito M. The variability of human, BOLD hemodynamic responses. Neuroimage 1998, 8:360-369.
3. Baccala L.A., Sameshima K. Partial directed coherence: a new concept in neural structure determination. Biol Cybern 2001, 84:463-474.
4. Baria A.T., Baliki M.N., Parrish T., Apkarian A.V. Anatomical and functional assemblies of brain BOLD oscillations. J Neurosci 2011, 31:7910-7919.
5. Bentler P.M. Causal modeling via structural equation systems. Handbook of multivariate experimental psychology 1988, 317-335. Plenum Press, New York, NY, US. 2nd ed. J.R. Nesselroade, R.B. Cattell (Eds.).
6. Blinowska K. Review of the methods of determination of directed connectivity from multichannel data. Med Biol Eng Comput 2011, 49:521-529.
7. Bressler S.L., Menon V. Large-scale brain networks in cognition: emerging methods and principles. Trends Cogn Sci 2010, 14:277-290.
8. Bressler S.L., Seth A.K. Wiener-Granger causality: a well established methodology. Neuroimage 2011, 58:323-329.
9. Buchel C., Friston K.J. Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. Cereb Cortex 1997, 7:768-778.
10. David O. fMRI connectivity, meaning and empiricism Comments on: Roebroeck et al. The identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution. Neuroimage 2011, 58:306-309. author reply 10-1.
11. Demonet J.F., Thierry G., Cardebat D. Renewal of the neurophysiology of language: functional neuroimaging. Physiol Rev 2005, 85(05):49-95.
12. Deshpande G., Hu X., Stilla R., Sathian K. Effective connectivity during haptic perception: a study using Granger causality analysis of functional magnetic resonance imaging data. Neuroimage 2008, 40:1807-1814.
13. Deshpande G., LaConte S., James G.A., Peltier S., Hu X. Multivariate Granger causality analysis of fMRI data. Hum Brain Mapp 2009, 30:1361-1373.
14. Deshpande G., Santhanam P., Hu X. Instantaneous and causal connectivity in resting state brain networks derived from functional MRI data. Neuroimage 2011, 54:1043-1052.
15. Deshpande G., Sathian K., Hu X. Effect of hemodynamic variability on Granger causality analysis of fMRI. Neuroimage 2010, 52:884-896.
16. Ding M., Bressler S.L., Yang W., Liang H. Short-window spectral analysis of cortical event-related potentials by adaptive multivariate autoregressive modeling: data preprocessing, model validation, and variability assessment. Biol Cybern 2000, 83:35-45.
17. Franaszczuk P.J., Blinowska K.J., Kowalczyk M. The application of parametric multichannel spectral estimates in the study of electrical brain activity. Biol Cybern 1985, 51:239-247.
18. Friston K.J., Li B., Daunizeau J., Stephan K.E. Network discovery with DCM. Neuroimage 2011, 56:1202-1221.
19. Ghio M., Tettamanti M. Semantic domain-specific functional integration for action-related vs abstract concepts. Brain Lang 2010, 112:223-232.
20. Gideon S. Estimating the dimension of a model. Ann Stat 1978, 6:461-464.
21. Goebel R., Roebroeck A., Kim D.S., Formisano E. Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. Magn Reson Imaging 2003, 21:1251-1261.
22. Granger C.W.J. Investigating causal relations by econometric models and cross spectral methods. Econometrica 1969, 37:424-438.
23. Havlicek M., Jan J., Brazdil M., Calhoun V.D. Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data. Neuroimage 2010, 53:65-77.
24. Hemmelmann D., Ungureanu M., Hesse W., Wustenberg T., Reichenbach J.R., Witte O.W., et al. Modelling and analysis of time-variant directed interrelations between brain regions based on BOLD-signals. Neuroimage 2009, 45:722-737.
25. Kaminski M.J., Blinowska K.J. A new method of the description of the information flow in the brain structures. Biol Cybern 1991, 65:203-210.
26. Kaminski M., Ding M., Truccolo W.A., Bressler S.L. Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance. Biol Cybern 2001, 85:145-157.
27. Korzeniewska A., Manczak M., Kaminski M., Blinowska K.J., Kasicki S. Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method. J Neurosci Methods 2003, 125:195-207.
28. Logothetis N.K., Pauls J., Augath M., Trinath T., Oeltermann A. Neurophysiological investigation of the basis of the fMRI signal. Nature 2001, 412:150-157.
29. Lohmann G., Erfurth K., Muller K., Turner R. Critical comments on dynamic causal modelling. Neuroimage 2012, 59:2322-2329.
30. Martin A., Chao L.L. Semantic memory and the brain: structure and processes. Curr Opin Neurobiol 2001, 11:194-201.
31. McIntosh A.R., Gonzalez-Lima F. Structural equation modeling and its application to network analysis of functional brain imaging. Hum Brain Mapp 1994, 2:2-22.
32. Pappas S.S., Leros A.K., Katsikas S.K. Joint order and parameter estimation of multivariate autoregressive models using multi-model partitioning theory. Digit Signal Process 2006, 16:782-795.
33. Penny W.D., Stephan K.E., Mechelli A., Friston K.J. Modelling functional integration: a comparison of structural equation and dynamic causal models. Neuroimage 2004, 23(Suppl. 1):S264-S274.
34. Porcaro C., Zappasodi F., Rossini P.M., Tecchio F. Choice of multivariate autoregressive model order affecting real network functional connectivity estimate. Clin Neurophysiol 2009, 120:436-448.
35. Richiardi J., Eryilmaz H., Schwartz S., Vuilleumier P., Van De Ville D. Decoding brain states from fMRI connectivity graphs. Neuroimage 2011, 56:616-626.
36. Rizzo G., Vitali P., Baselli G., Tettamanti M., Scifo P., Cerutti S., et al. Exploring semantic memory areas by functional MRI. Handbook of neural engineering 2007, 211-224. John Wiley Sons, Inc., USA. M. Akay (Ed.).
37. Roebroeck A., Formisano E., Goebel R. Mapping directed influence over the brain using Granger causality and fMRI. Neuroimage 2005, 25:230-242.
38. Sato J.R., Takahashi D.Y., Arcuri S.M., Sameshima K., Morettin P.A., Baccala L.A. Frequency domain connectivity identification: an application of partial directed coherence in fMRI. Hum Brain Mapp 2009, 30:452-461.
39. Shelter B., Timmer J., Eichler M. Assessing the strength of directed influences among neural signals using renormalized partial directed coherence. J Neurosci Methods 2009, 179:121-130.
40. Smith S.M., Miller K.L., Salimi-Khorshidi G., Webster M., Beckmann C.F., Nichols T.E., et al. Network modeling methods for fMRI. Neuroimage 2011, 54:875-891.
41. Skidmore F., Korenkevych D., Liu Y., He G., Bullmore E., Pardalos P.M. Connectivity brain networks based on wavelet correlation analysis in Parkinson fMRI data. Neurosci Lett 2011, 499:47-51.
42. Theiler J., Eubank S., Longtin A., Galdrikian B., Farmer J.D. Testing for nonlinearity in time-series-the method of surrogate data. Physica D 1992, 58:77-94.
43. van Mierlo P., Carrette E., Hallez H., Vonck K., Van Roost D., Boon P., et al. Accurate epileptogenic focus localization through time-variant functional connectivity analysis of intracranial electroencephalographic signals. Neuroimage 2011, 56:1122-1133.
44. Vitali P., Abutalebi J., Tettamanti M., Rowe J., Scifo P., Fazio F., et al. Generating animal and tool names: an fMRI study of effective connectivity. Brain Lang 2005, 93:32-45.