Technical Report

You have free access to this content

Discrete dynamic Bayesian network analysis of fMRI data

  1. John Burge1,2,
  2. Terran Lane1,
  3. Hamilton Link1,
  4. Shibin Qiu1,
  5. Vincent P. Clark2,3,4,*

Article first published online: 7 NOV 2007

DOI: 10.1002/hbm.20490

Issue

Human Brain Mapping

Human Brain Mapping

Volume 30, Issue 1, pages 122–137, January 2009

Additional Information

How to Cite

Burge, J., Lane, T., Link, H., Qiu, S. and Clark, V. P. (2009), Discrete dynamic Bayesian network analysis of fMRI data. Hum. Brain Mapp., 30: 122–137. doi: 10.1002/hbm.20490

Author Information

  1. 1

    Department of Computer Science, University of New Mexico, Albuquerque

  2. 2

    The MIND Institute, Albuquerque, New Mexico

  3. 3

    Department of Psychology, University of New Mexico, Albuquerque

  4. 4

    Department of Neuroscience, University of New Mexico, Albuquerque

*Department of Psychology, 118 Logan Hall, MSC03 2220, 1 University of New Mexico, Albuquerque, NM 87131-1161

Publication History

  1. Issue published online: 22 DEC 2008
  2. Article first published online: 7 NOV 2007
  3. Manuscript Accepted: 27 AUG 2007
  4. Manuscript Revised: 14 AUG 2007
  5. Manuscript Received: 27 MAY 2006

Funded by

  • National Institute of Drug Abuse, NIH. Grant Number: 1R01DA12852
  • National Institute of Mental Health, NSF/NIH. Grant Number: 1R01MH076282
  • The MIND Institute. Grant Number: DE-FG02-99ER62764

SEARCH

SEARCH BY CITATION

ARTICLE TOOLS

View Full Article (HTML) Get PDF (1066K)

REFERENCES

  • Akaike H ( 1973): Information theory and an extension of the maximum likelihood principle. In: PetrovBN,CsakiF, editors. Second International Symposium on Information Theory. Budapest: Academiai Kiado. pp 267–281.
  • Amaral DG,Bauman MD,Schumann CM ( 2003): The amygdala and autism: Implications from non-human primate studies. Genes Brain Behavior 2: 295–302.
    Direct Link:
  • Azari NP,Pettigrew KD,Schapiro MB,Haxby JV,Grady CL,Pietrini P,Salerno JA,Heston LL,Rapoport SI,Horwitz B ( 1993). Early detection of Alzheimer's disease: A statistical approach using positron emission tomographic data. J Cerebral Blood Flow Metab 13: 438–447.
  • Baccala LA,Sameshima K ( 2001): Partial directed coherence: A new concept in neural structure determination. Biol Cybern 84: 463–474.
  • Bauman MD,Lavenex P,Mason WA,Capitanio JP,Amaral DG ( 2004): The development of social behavior following neonatal amygdala lesions in rhesus monkeys. J Cogn Neurosci 16: 1388–1411.
  • Bhattacharya S,Ho MR,Purkayastha S ( 2006): A Bayesian approach to modeling dynamic effective connectivity with fMRI data. Neuroimage 30: 794–812.
  • Braak H,Braak E ( 1995): Staging of Alzheimers-disease-related neurofibrillary changes. Neurobiol Aging 6: 271–278.
  • Breakspear M,Brammer MJ,Bullmore ET,Das P,Williams LM ( 2004): Spatiotemporal wavelet resampling for functional neuroimaging data. Hum Brain Mapp 23: 1–25.
    Direct Link:
  • Büchel C,Friston KJ ( 1997): Modulation of connectivity in visual pathways by attention: Cortical interactions evaluated with structural equation modeling and fMRI. Cerebral Cortex 7: 768–778.
  • Buckner RL,Snyder A,Sanders A,Marcus R,Morris J ( 2000): Functional brain imaging of young, nondemented, and demented older adults. J Cogn Neurosci 12: 24–34.
  • Burge J,Lane T ( 2005a): Learning class-discriminative dynamic Bayesian networks. Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany.
  • Burge J,Lane T ( 2005b): Comprehensibility of generative versus class discriminative dynamic Bayesian multinets. Human Comprehensible Machine Learning Workshop. AAAI, Pittsburg, PA.
  • Burge J,Lane T ( 2006): Bayesian network structure search on hierarchically related random variables. Proceedings of the 17th European Conference on Machine Learning, Berlin, Germany.
  • Burges C ( 1998): A Tutorial on Support Vector Machines for Pattern Recognition. Boston: Kluwer Academic Publishers.
  • Charniak E ( 1991): Bayesian networks without tears. AI Mag 12: 4.
  • Chickering D,Geiger D,Heckerman D ( 1994): Learning Bayesian networks is NP-Hard. Technical Report MSR-TR-94-17. Microsoft.
  • Cooper G,Herskovits E ( 1992): A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9: 309–347.
  • Cox RW ( 1996): AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29: 162–173.
  • Duda RO,Hart PE,Stork DG ( 2001): Pattern Classification, 2nd ed. New York: Wiley Interscience. ISBN 0-471-05669-3.
  • fMRIDC ( 2004): The fMRI Data Center. Dartmouth. Available at http://lx50.fmridc.org/f/fmridc.
  • Friston KJ ( 2005): Models of brain function in neuroimaging. Ann Rev Psychol 56: 57–87.
  • Friston KJ,Harrison L,Penny W ( 2003): Dynamic causal modeling. NeuroImage 19: 1273–1302.
  • Fung G,Mangasarian OL ( 2001): Proximal support vector machine classifiers. Proceedings of the Seventh ACM SIGKDD. San Francisco. pp 77–86.
  • Gold S,Christian B,Arndt S,Zeien G,Cizadlo T,Johnson DL,Flaum M,Andreasen NC ( 1998): Functional MRI statistical software packages: A comparative analysis. Hum Brain Mapp 6: 73–84.
    Direct Link:
  • Gomezisla T,Price JL,McKeel DW,Morris JC,Growdon JH,Hyman BT ( 1996): Profound loss of layer II entorhinal cortex neurons occurs in very mild Alzheimer's disease. J Neurosci 16: 4491–4500.
  • Grady CL,Furey ML,Pietrini P,Horwitz B,Rapoport SL ( 2001): Altered brain functional connectivity and impaired short-term memory in Alzheimer's disease. Brain 124: 739–756.
  • Grossman D,Domingos P ( 2004): Learning Bayesian network classifiers by maximizing conditional likelihood. International Conference on Machine Learning, Alberta, Canada. pp 361–368.
  • Heckerman D,Geiger D,Chickering DM ( 1995), Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning 20: 197–243.
  • Hojen-Sorensen P,Hansen L,Rasmussen C ( 2000): Bayesian modeling of fMRI time series. In: SollaSA,LeenTK, MullerKR. editors. Advances in Neural Information Processing Systems. Cambridge: MIT Press.
  • Jensen FV ( 2001): Bayesian Networks and Decision Graphs. New York: Springer-Verlag.
  • Lahaye P,Poline J,Flandin G,Dodel S,Garnero L ( 2003): Functional connectivity: Studying nonlinear, delayed interactions between BOLD signals. Neuroimage 20: 962–974.
  • Lancaster JL,Woldorff MG,Parsons LM,Liotti M,Freitas CS,Rainey L,Kochunov PV,Nickerson D,Mikiten SA,Fox PT ( 2000): Automated Talairach Atlas labels for functional brain mapping. Hum Brain Mapp 10: 120–131.
    Direct Link:
  • Mitchell T,Hutchinson R,Just M,Niculescu RS,Pereira F,Wang X ( 2003): Classifying Instantaneous Cognitive States from fMRI Data. American Medical Informatics Association Annual Symposium. October 2003.
  • Moore A,Wong W ( 2003): Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Search Learning. Proceedings of the Twentieth International Conference on Machine Learning. Arlington, VA: AUAI Press. pp 552–559.
  • Morris JC ( 1993): Clinical dementia rating. Neurology 43: 2412–2414.
  • Murphy K ( 2002): Dynamic Bayesian Networks: Representation, Inference and Learning. PhD dissertation, Berkeley, University of California, Computer Science Division.
  • Papoulis A ( 1991): Probability, Random Variables, and Stochastic Processes, 3rd ed. New York: McGraw-Hill.
  • Patel R,Bowman F,Rilling J ( 2006): A Bayesian approach to determining connectivity of the human brain. Hum Brain Mapp 27: 267–276.
    Direct Link:
  • Pearl J ( 1986): Fusion, propagation and structuring in belief networks. Artif Intell 29: 241–288.
  • Pearl J ( 1998): Graphs, causality, and structural equation models. Sociologic Methods Res 27: 226–284.
  • Penny WD,Stephan KE,Mechelli A,Friston KJ ( 2004), Modeling functional integration: A comparison of structural equation and dynamic causal models. Neuroimage 23 ( Suppl 1): S264–S274.
  • Prichard D,Theiler J ( 1994), Generating surrogate data for time series with several simultaneously measured variables. Phys Rev Lett 14: 951–954.
  • Rosenbaum RS,Furey ML,Horwitz B,Grady CL ( 2004): Altered communication between emotion-related brain regions supports short-term memory in Alzheimer's disease. Soc Neurosci Abstr 203: 8.
  • Sander D,Grafman J,Zalla T ( 2003): The human amygdala: An evolved system for relevance detection. Rev Neurosci 14: 303–316.
  • Schwarz G ( 1978): Estimating the dimension of a model. Ann Stat 6: 461–464.
  • Smith VA,Yu J,Smulders TV,Hartemink AJ,Jarvis ED ( 2006): Computational inference of neural information flow networks. PLoS Comput Biol 6: 1436–1449.
  • Smith SM,Jenkinson M,Woolrich MW,Beckmann CF,Behrens TEJ,Johansen-Berg H,Bannister PR,De Luca M,Drobnjak I,Flitney DE,Niazy R,Saunders J,Vickers J,Zhang Y,De Stefano N,Brady JM,Matthews PM ( 2004): Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23 ( Suppl 1): 208–219.
  • Suzuki WA,Amaral DG ( 1994): Topographic organization of the reciprocal connections between the monkey entorhinal cortex and the perirhinal and parahippocampal cortices. J Neurosci 14: 1856–1877.
  • Veropoulos K,Campbell C,Cristianini N ( 1999) Controlling the sensitivity of support vector machines. Proceedings of the International Joint Conference on Artificial Intelligence, Workshop ML3, Stockholm, Sweden, pp 55–60.
  • Woolrich MW,Ripley BD,Brady M,Smith SM ( 2001): Temporal autocorrelation in univariate linear modeling of FMRI Data. Neuroimage 14: 1370–1386.
View Full Article (HTML) Get PDF (1066K)