Optimizing experimental design for comparing models of brain function

PLoS Computational Biology

Volumn 7, Issue 11, 2011, Pages

  Daunizeau, Jean ^a,b   Preuschoff, Kerstin ^b   Friston, Karl ^a   Stephan, Klaas ^a,b  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY OF ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION THEORY; INTELLIGENT SYSTEMS; NEUROIMAGING; RISK ASSESSMENT; RISK PERCEPTION; STATISTICS;

BAYESIAN DECISION THEORY; BRAIN FUNCTIONS; CANDIDATE MODELS; DESIGN OPTIMALITY; DYNAMIC CAUSAL MODELING; MODEL SELECTION; NETWORK DYNAMICS; OPTIMALITY CRITERIA; OPTIMISATIONS; SELECTION ACCURACY;

MONTE CARLO METHODS;

ACCURACY; ANALYTIC METHOD; ARTICLE; BAYES THEOREM; BOLD SIGNAL; BRAIN FUNCTION; CONTROLLED STUDY; DYNAMIC CASUAL MODELLING; EXPERIMENTAL DESIGN; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HEMODYNAMICS; HUMAN; INTERMETHOD COMPARISON; MONTE CARLO METHOD; MOTOR CORTEX; MOTOR PERFORMANCE; NONHUMAN; PREDICTION; SIMULATION; TASK PERFORMANCE; BIOLOGICAL MODEL; BIOLOGY; BRAIN; BRAIN MAPPING; COMPUTER SIMULATION; FEEDBACK SYSTEM; HISTOLOGY; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PSYCHOMOTOR PERFORMANCE; REPRODUCIBILITY; STANDARD;

BAYES THEOREM; BRAIN; BRAIN MAPPING; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; FEEDBACK, PHYSIOLOGICAL; HUMANS; MAGNETIC RESONANCE IMAGING; MODELS, NEUROLOGICAL; MONTE CARLO METHOD; PSYCHOMOTOR PERFORMANCE; REPRODUCIBILITY OF RESULTS; RESEARCH DESIGN;

EID: 81355153795     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1002280     Document Type: Article

References (49)

1. Friston KJ, (2011) Functional and Effective Connectivity: A Review. Brain Connectivity 1: 13-36.
2. Stephan KE, (2004) On the role of general system theory for functional neuroimaging. J Anat 205: 443-470.
3. McIntosh AR, Gonzalez-Lima F, (1994) Structural equation modeling and its application to network analysis in functional brain imaging. Hum Brain Mapp 2: 2-22.
4. Friston KJ, Harrison L, Penny WD, (2003) Dynamic Causal Modelling. Neuroimage 19: 1273-1302.
5. Penny W, Stephan KE, Mechelli A, Friston KJ, (2004) Comparing Dynamic Causal Models. Neuroimage 22: 1157-1172.
6. Josephs O, Henson RN, (1999) Event-related functional magnetic resonance imaging: modelling, inference and optimization. Philos Trans R Soc Lond B Biol Sci 354: 1215-1228.
7. Liu TT, Frank LR, Wong EC, Buxton RB, (2001) Detection power, estimation efficiency, and predictability in event-related fMRI. Neuroimage 13: 759-773.
8. Zahran AR, (2002) On the efficiency of designs for linear models in non-regular regions and the use of standard designs for generalized linear models. PhD thesis, Virginia Polytechnic Institute and State University, USA.
9. Mechelli A, Price CJ, Henson RN, (2003) The effect of high-pass filtering on the efficiency of response estimation: a comparison between blocked and randomised designs. Neuroimage 18: 798-805.
10. Henson R, (2007) Efficient experimental design for fMRI. In: Friston KJ, Ashburner JT, Kiebel SJ, Nichols TE, Penny WD, editors. Statistical Parametric Mapping Academic Press.
11. Friston K, (2002) Beyond phrenology: what can neuroimaging tell us about distributed circuitry? Annu Rev Neurosci 25: 221-250.
12. Wager TD, Nichols TE, (2003) Optimization of experimental design in fMRI: a general framework using a genetic algorithm. Neuroimage 18: 293-309.
13. McIntosh AR, (2000) Towards a network theory of cognition. Neural Netw 13: 861-870.
14. Daunizeau J, David O, Stephan KE, (2010) Dynamic Causal Modelling: a critical review of the biophysical and statistical foundations. Neuroimage 58: 312-322.
15. Smith SM, Miller KL, Salimi-Khorshidi G, Webster M, et al. (2011) Network modelling methods for FMRI. Neuro Image 54: 875-891.
16. Myung JI, Pitt MA, (2009) Optimal experimental design for model discrimination. Psychol Rev 116: 499-518.
17. Chaloner K, Verdini I, (1995) Bayesian experimental design: a review. Statist Sci 10: 273-304.
18. Lindley DV, (1972) Bayesian statistics - a review Philadelphia SIAM.
19. Robert C, (1992) L'analyse statistique Bayesienne Paris Economica.
20. Neyman J, Pearson E, (1933) On the problem of the most efficient tests of statistical hypotheses. Philos Trans Roy Soc Lond A 231: 289-337.
21. Lin J, (1991) Divergence measures based on the Shannon entropy. IEEE Trans Inform Theory 37: 151.
22. Topsoe F, (2000) Some inequalities for information divergence and related measures of discrimination. IEEE Trans Inform Theory 46: 1602-1609.
23. Penny W, Joao M, Flandin G, Daunizeau J, et al. (2010) Comparing Families of Dynamic Causal Models. PLoS Comp Biol 6: e1000709.
24. Stephan KE, Kasper L, Harrison L, Daunizeau J, et al. (2008) Nonlinear dynamic causal models for fMRI. Neuroimage 42: 649-662.
25. Friston KJ, Mattout J, Trujillo-Barreto N, Ashburner J, et al. (2007) Variational free energy and the Laplace approximation. Neuroimage 34: 220-234.
26. Friston KJ, Büchel C, Fink GR, Morris J, et al. (1997) Psychophysiological and modulatory interactions in neuroimaging. Neuro Image 6: 218-229.
27. Gitelman DR, Penny WD, Ashburner J, Friston KJ, (2003) Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution. Neuroimage 19: 200-207.
28. Friston KJ, Mechelli R, Turner R, Price CJ, (2002) Nonlinear response in fMRI: the balloon model, Volterra kernels and other hemodynamics. Neuroimage 12: 466-477.
29. Daunizeau J, Den Ouden HEM, Pessiglione M, Stephan KE, et al. (2010) Observing the observer (I): meta-Bayesian models of learning and decision making. PLoS ONE 5: e15554.
30. Busetto AG, Ong CS, Buhmann JM, (2009) Optimized expected information gain for nonlinear dynamical systems. pp. 97-104 In: Association for Computing Machinery (ACM) Int. Conf. Proc. Series 382 Proc. of the 26th Int. Conf. on Machine Learning (ICML 09); 14-18 June 2009; Montreal, Quebec, Canada.
31. Busetto AG, Buhmann JM, (2009) Structure Identification by Optimized Interventions. pp. 57-64 J. Machine Learn. Res. (JMLR) Proc. of the 12th Int. Conf. on Artific. Intell. and Stat. (AISTATS 09);16-18 April 2009; Florida, United States.
32. Stephan KE, Penny WD, Daunizeau J, Moran RJ, et al. (2009a) Bayesian model selection for group studies. Neuroimage 46: 1004-1017.
33. Moerbeek M, Van Breukelen GJP, Berger MPF, (2008) Optimal designs for multilevel studies. In: de Leeuw J, Meijer E, editors. Handbook of Multilevel Analysis New York Springer pp. 177-206.
34. Maus B, Van Breukelen GJP, Goebel R, Berger MPF, (2011a) Optimal design of multi-subject blocked fMRI experiments. Neuroimage 56: 1338-1352.
35. Maus B, Van Breukelen GJP, Goebel R, Berger MPF, (2011b) Optimal design for nonlinear estimation of the hemodynamic response function. Hum Brain Mapp in press. doi: 10.1002/hbm.21289.
36. Grabowski TJ, Bauer MD, Foreman D, Mehta S, et al. (2006) Adaptive pacing of visual stimulation for fMRI studies involving overt speech. Neuro Image 29: 1023-1030.
37. Xie J, Clare S, Gallichan D, Gunn RN, et al. (2010) Real-time adaptive sequential design for optimal acquisition of arterial spin labeling MRI data. Magn Reson Med 64: 203-10.
38. Lewi J, Butera R, Paninski L, (2009) Sequential optimal design of neurophysiology experiments. Neural Comp 21: 619-687.
39. Cavagnaro DR, Myung JL, Pitt MA, (2010) Adaptive design optimization: a mutual information-based approach to model discrimination in cognitive sciences. Neural Comp 22: 887-905.
40. Miezin FM, Maccotta L, Ollinger JM, (2000) Characterizing the hemodynamic response: effects of presentation rate, sampling procedure, and the possibility of ordering brain activity based on relative timing. Neuroimage 11: 735-759.
41. Hampson M, Hoffman RE, (2010) Transcranial magnetic stimulation and connectivity mapping: tools for studying the neural bases of brain disorders. Front Syst Neurosci 4: 40 doi:10.3389/fnsys.2010.00040.
42. Marreiros AC, Kiebel SJ, Friston KJ, (2008) Dynamic causal modelling for fMRI: A two-state model. Neuro Image 39: 269-278.
43. Husain FT, Nandipati G, Braun AR, Cohen LG, et al. (2002) Simulating transcranial magnetic stimulation during PET with a large-scale neural network model of the prefrontal cortex and the visual system. Neuroimage 15: 58-73.
44. Sarfeld AS, Diekhoff S, Wang LE, Liuzzi G, et al. (2011) Convergence of human brain mapping tools: Neuronavigated TMS Parameters and fMRI activity in the hand motor area. Hum Brain Mapp in press. doi: 10.1002/hbm.21272.
45. Daunizeau J, Kiebel S, Lemieux L, Friston KJ, et al. (2010) Stochastic nonlinear DCM for fMRI: neural noise and network dynamics. Proc. of the Hum. Brain Mapp. Conf. (OHBM 2010).
46. Li B, Daunizeau J, Stephan KE, Penny W, et al. (2011) Stochastic DCM and generalized filtering. Neuro Image 58: 442-457.
47. Daunizeau J, Friston KJ, Kiebel SJ, (2009) Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models. Physica D 238: 2089-2118.
48. Stephan KE, Friston KJ, Frith CD, (2009b) Dysconnection in schizophrenia: From abnormal synaptic plasticity to failures of self-monitoring. Schizophr Bull 35: 509-527.
49. Moran RJ, Symmonds M, Stephan KE, Friston KJ, et al. (2011) An in vivo assay of synaptic function mediating human cognition. Curr Biol 21: 1320-1325.