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A rigorous approach for testing the constructionist hypotheses of brain function

Published online by Cambridge University Press:  23 May 2012

Gopikrishna Deshpande
Affiliation:
Auburn University MRI Research Center, Department of Electrical and Computer Engineering, and Department of Psychology, Auburn University, Auburn, AL 36849. [email protected]://www.eng.auburn.edu/users/gzd0005/
K. Sathian
Affiliation:
Departments of Neurology, Rehabilitation Medicine, and Psychology, Emory University, and Atlanta VAMC Rehabilitation R&D Center of Excellence, Atlanta, GA 30322. [email protected]://neurology.emory.edu/Faculty/Sathian.htm
Xiaoping Hu
Affiliation:
Coulter Department of Biomedical Engineering at Georgia Institute of Technology, and Center for Systems Imaging, Emory University, Atlanta, GA 30322. [email protected]://www.bme.emory.edu/~xhu/
Joseph A. Buckhalt
Affiliation:
Department of Special Education, Rehabilitation and Counseling, College of Education, Auburn University, Auburn, AL 36849-5222. [email protected]://www.auburn.edu/~buckhja/

Abstract

Although the target article provides strong evidence against the locationist view, evidence for the constructionist view is inconclusive, because co-activation of brain regions does not necessarily imply connectivity between them. We propose a rigorous approach wherein connectivity between co-activated regions is first modeled using exploratory Granger causality, and then confirmed using dynamic causal modeling or Bayesian modeling.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2012

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References

Deshpande, G., Hu, X., Stilla, R. & Sathian, K. (2008) Effective connectivity during haptic perception: A study using Granger causality analysis of functional magnetic resonance imaging data. NeuroImage 40(4):1807–14.CrossRefGoogle ScholarPubMed
Deshpande, G., LaConte, S., James, G., Peltier, S. & Hu, X. (2009) Multivariate Granger causality analysis of brain networks. Human Brain Mapping 30(4):1361–73.CrossRefGoogle Scholar
Deshpande, G., Santhanam, P. & Hu, X. (2011) Instantaneous and causal connectivity in resting state brain networks derived from functional MRI data. NeuroImage 54(2):1043–52.CrossRefGoogle ScholarPubMed
Deshpande, G., Sathian, K. & Hu, X. (2010) Assessing and compensating for zero-lag correlation effects in time-lagged Granger causality analysis of fMRI. IEEE Transactions on Biomedical Engineering 57(6):1446–56.CrossRefGoogle ScholarPubMed
Havlicek, M., Friston, K., Jan, J., Brazdil, M. & Calhoun, V. (2011) Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering. NeuroImage 56(4):2109–28.CrossRefGoogle ScholarPubMed
Marinazzo, D., Pellicoro, M. & Stramaglia, S. (2008) Kernel–Granger causality and the analysis of dynamical networks. Physical Review E 77:056215. (Online publication.) CrossRefGoogle ScholarPubMed
Ryali, S., Supekar, K., Chen, T. & Menon, V. (2011) Multivariate dynamical systems models for estimating causal interactions in fMRI. NeuroImage 54(2):807–23.CrossRefGoogle ScholarPubMed
Sathian, K., Lacey, S., Stilla, R., Gibson, G., Deshpande, G., Hu, X., LaConte, S. & Glielmi, C. (2011) Dual pathways for haptic and visual perception of spatial and texture information. NeuroImage 57(2):462–75.CrossRefGoogle ScholarPubMed
Sato, J., Junior, E., Takahashi, D., Felix, M., Brammer, M. & Morettin, P. (2006) A method to produce evolving functional connectivity maps during the course of an fMRI experiment using wavelet-based time-varying Granger causality. NeuroImage 31(1):187–96.CrossRefGoogle ScholarPubMed