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Can quantum probability help analyze the behavior of functional brain networks?

Published online by Cambridge University Press:  14 May 2013

Arpan Banerjee  and
Barry Horwitz
Arpan Banerjee
Affiliation:
Brain Imaging and Modeling Section, Voice, Speech and Language Branch, National Institute on Deafness and other Communication Disorders, National Institutes of Health, Bethesda, MD 20892-1402. [email protected]@mail.nih.govhttp://www.nidcd.nih.gov/research/scientists/pages/horwitzb.aspx
Barry Horwitz
Affiliation:
Brain Imaging and Modeling Section, Voice, Speech and Language Branch, National Institute on Deafness and other Communication Disorders, National Institutes of Health, Bethesda, MD 20892-1402. [email protected]@mail.nih.govhttp://www.nidcd.nih.gov/research/scientists/pages/horwitzb.aspx
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Abstract

Pothos & Busemeyer (P&B) argue how key concepts of quantum probability, for example, order/context, interference, superposition, and entanglement, can be used in cognitive modeling. Here, we suggest that these concepts can be extended to analyze neurophysiological measurements of cognitive tasks in humans, especially in functional neuroimaging investigations of large-scale brain networks.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 36 , Issue 3 , June 2013 , pp. 278 - 279
Copyright
Copyright © Cambridge University Press 2013 

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References

Banerjee, A., Pillai, A. S., Sperling, J. R., Smith, J. F. & Horwitz, B. (2012a) Temporal microstructure of cortical networks (TMCN) underlying task-related differences. NeuroImage 62:1643–57.Google Scholar
Banerjee, A., Tognoli, E., Assisi, C. G., Kelso, J. A. & Jirsa, V. K. (2008) Mode level cognitive subtraction (MLCS) quantifies spatiotemporal reorganization in large-scale brain topographies. NeuroImage 42(2):663–74.Google Scholar
Banerjee, A., Tognoli, E., Kelso, J. A. & Jirsa, V. K. (2012b) Spatiotemporal re-organization of large-scale neural assemblies underlies bimanual coordination. Neuroimage 62(3):1582–92.Google Scholar
Beckmann, C. F., DeLuca, M., Devlin, J. T. & Smith, S. M. (2005) Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society of London B 360:1001–13.Google Scholar
Bullmore, E. & Sporns, O. (2009) Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Review Neuroscience 10(3):186–98.Google Scholar
Debaere, F., Swinnen, S. P., Beatse, E., Sunaert, S., Van Hecke, P. & Duysens, J. (2001) Brain areas involved in interlimb coordination: a distributed network. Neuroimage 14(5):947–58.CrossRefGoogle ScholarPubMed
Edelman, G. M. & Gally, J. A. (2001) Degeneracy and complexity in biological systems. Proceedings of the National Academy of Sciences of the United States America 98(24):13,763–68.CrossRefGoogle ScholarPubMed
Friston, K. J., Harrison, L. & Penny, W. (2003) Dynamic causal modelling. NeuroImage 19:1273–302.Google Scholar
Horwitz, B., Grady, C. L., Haxby, J. V., Ungerleider, L. G., Schapiro, M. B. & Mishkin, M. (1992) Functional associations among human posterior extrastriate brain regions during object and spatial vision. Journal of Cognitive Neuroscience 4:311–22.Google Scholar
Kim, J. & Horwitz, B. (2009) How well does Structural Equation Modeling reveal abnormal brain anatomical connections? An fMRI simulation study. Neuroimage 45:1190–98.Google Scholar
McIntosh, A. R. (2004) Contexts and catalysts: A resolution of the localization and integration of function in the brain. Neuroinformatics 2:175–82.CrossRefGoogle ScholarPubMed
McIntosh, A. R., Grady, C. L., Ungerleider, L. G., Haxby, J. V., Rapoport, S. I. & Horwitz, B. (1994) Network analysis of cortical visual pathways mapped with PET. Journal of Neurosciences, 14:655–66.CrossRefGoogle ScholarPubMed
Molholm, S., Ritter, W., Javitt, D. C. & Foxe, J. J. (2004) Multisensory visual-auditory object recognition in humans: a high-density electrical mapping study. Cerebral Cortex 14(4):452–65.CrossRefGoogle ScholarPubMed
Noppeney, U., Friston, K. J. & Price, C. J. (2004) Degenerate neuronal systems sustaining cognitive functions. Journal of Anatomy 205(6):433–42.Google Scholar
Price, C. J. & Friston, K. J. (2002) Degeneracy and cognitive anatomy. Trends in Cognitive Sciences 6(10):416–21.Google Scholar
Sternberg, S. (2011) Modular processes in mind and brain. Cognitive Neuropsychology 28(3–4):156–208.Google Scholar
Tononi, G., Sporns, O. & Edelman, G. M. (1999) Measures of degeneracy and redundancy in biological networks. Proceedings of the National Academy of Sciences of the United States America 96(6):3257–62.CrossRefGoogle ScholarPubMed