Decoding brain states using backward edge elimination and graph kernels in fMRI connectivity networks

Journal of Neuroscience Methods

Volumn 212, Issue 2, 2013, Pages 259-268

  Mokhtari, Fatemeh ^a   Hossein Zadeh, Gholam Ali ^a,b  

^a UNIVERSITY OF TEHRAN   (Iran)

^b INSTITUTE FOR STUDIES IN THEORETICAL PHYSICS AND MATHEMATICS IPM   (Iran)

Author keywords

Backward edge elimination; Connectivity graphs; Decoding brain states; FMRI; Graph kernels

Indexed keywords

ACCURACY; ADULT; ARTICLE; ASSOCIATION; BACKWARD EDGE ELIMINATION; BRAIN STATE; CLASSIFICATION; ELIMINATION REACTION; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; HUMAN EXPERIMENT; KERNEL METHOD; MALE; NERVOUS SYSTEM PARAMETERS; NEUROIMAGING; NORMAL HUMAN; PARIETAL LOBE; PERCEPTION; POSTERIOR CINGULATE; PRIORITY JOURNAL; STIMULUS RESPONSE; SUPPORT VECTOR MACHINE; TASK PERFORMANCE;

ADULT; BRAIN; BRAIN MAPPING; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; SUPPORT VECTOR MACHINES; YOUNG ADULT;

EID: 84875087325     PISSN: 01650270     EISSN: 1872678X     Source Type: Journal    
DOI: 10.1016/j.jneumeth.2012.10.012     Document Type: Article

References (52)

1. Achard S., Salvador R., Whitcher B., Suckling J., Bullmore E. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci 2006, 26:63-72.
2. Baumgartner R., Ryner L., Richter W., Summers R., Jarmasz M., Somorjai R. Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis. Magn Reson Imaging 2000, 18:89-94.
3. Bishop C.M. SpringerLink. Pattern recognition and machine learning 2006, Springer, New York.
4. Borgwardt K.M. Graph kernels 2007, Ludwig Maximilian University of Munich.
5. Borgwardt K.M., Kriegel H.P. Shortest-path kernels on graphs. Proc IEEE Int Conf on Data Mining 2005, 74-81.
6. Boser B.E., Guyon I.M., Vapnik V.N. A training algorithm for optimal margin classifiers. Proc ACM Workshop on Computational Learning Theory 1992, 144-152.
7. Buckner R.L., Carroll D.C. Self-projection and the brain. Trends Cogn Sci 2007, 11:49-57.
8. Bunke H., Allermann G. Inexact graph matching for structural pattern recognition. Pattern Recogn Lett 1983, 1:245-253.
9. Cao J., Worsley K. The geometry of correlation fields with an application to functional connectivity of the brain. Ann Appl Probab 1999, 9:1021-1057.
10. Chapelle O., Keerthi S. Multi-class feature selection with support vector machines. Proc Am Stat Assoc 2008.
11. Chen X.J., Kovacevic N., Lobaugh N.J., Sled J.G., Henkelman R.M., Henderson J.T. Neuroanatomical differences between mouse strains as shown by high-resolution 3D MRI. Neuroimage 2006, 29:99-105.
12. Chung F.R.K. Spectral graph theory. Am Math Soc 1997.
13. Cortes C., Vapnik V. Support-vector networks. Mach Learn 1995, 20:273-297.
14. Ethofer T., Van De Ville D., Scherer K., Vuilleumier P. Decoding of emotional information in voice-sensitive cortices. Curr Biol 2009, 19:1028-1033.
15. Fair D.A., Schlaggar B.L., Cohen A.L., Miezin F.M., Dosenbach N.U.F., Wenger K.K., et al. A method for using blocked and event-related fMRI data to study "resting state" functional connectivity. Neuroimage 2007, 35:396-405.
16. Freeman J., Donner T.H., Heeger D.J. Inter-area correlations in the ventral visual pathway reflect feature integration. J Vis 2011, 11.
17. Fries P. A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn Sci 2005, 9:474-480.
18. Gärtner T., Flach P., Wrobel S. On graph kernels: Hardness results and efficient alternatives. Lect Notes Artif Int 2003, 129-143.
19. Gaüzère B., Brun L., Villemin D. Two new graphs kernels in chemoinformatics. Pattern Recogn Lett 2012, 33:2038-2047.
20. Gold C., Holub A., Sollich P. Bayesian approach to feature selection and parameter tuning for support vector machine classifiers. Neural Netw 2005, 18:693-701.
21. Grady C.L., Protzner A.B., Kovacevic N., Strother S.C., Afshin-Pour B., Wojtowicz M., et al. A multivariate analysis of age-related differences in default mode and task-positive networks across multiple cognitive domains. Cereb Cortex 2010, 20:1432-1447.
22. Guyon I., Weston J., Barnhill S., Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn 2002, 46:389-422.
23. Hayasaka S., Laurienti P.J. Comparison of characteristics between region-and voxel-based network analyses in resting-state fMRI data. Neuroimage 2010, 50:499-508.
24. Horváth T., Gärtner T., Wrobel S. Cyclic pattern kernels for predictive graph mining. Proc ACM Int Conf on Knowledge Discovery and Data Mining 2004, 158-167.
25. Hyvärinen A., Oja E. Independent component analysis: algorithms and applications. Neural Netw 2000, 13:411-430.
26. Joseph R. Neuropsychiatry, neuropsychology, and clinical neuroscience: Emotion, evolution, cognition, language, memory, brain damage, and abnormal behavior 1996, Williams & Wilkins Co., Baltimore.
27. Kashima H., Tsuda K., Inokuchi A Marginalized kernels between labeled graphs. Proc Int Conf on Machine Learning 2003, 321-328.
28. Kelly A., Uddin L.Q., Biswal B.B., Castellanos F.X., Milham M.P. Competition between functional brain networks mediates behavioral variability. Neuroimage 2008, 39:527-537.
29. Kovačević N., Henderson J., Chan E., Lifshitz N., Bishop J., Evans A., et al. A three-dimensional MRI atlas of the mouse brain with estimates of the average and variability. Cereb Cortex 2005, 15:639-645.
30. Kühn S., Bodammer N.C., Brass M. Dissociating mental states related to doing nothing by means of fMRI pattern classification. Neuroimage 2010, 53:1294-1300.
31. LaConte S.M., Peltier S.J., Hu X.P. Real-time fMRI using brain-state classification. Hum Brain Mapp 2007, 28:1033-1044.
32. Levine B., Kovacevic N., Nica E., Cheung G., Gao F., Schwartz M., et al. The Toronto traumatic brain injury study Injury severity and quantified MRI. Neurology 2008, 70:771-778.
33. McIntosh A., Bookstein F., Haxby J.V., Grady C. Spatial pattern analysis of functional brain images using partial least squares. Neuroimage 1996, 3:143-157.
34. Protzner A.B., McIntosh A.R. The interplay of stimulus modality and response latency in neural network organization for simple working memory tasks. J Neurosci 2007, 27:3187-3197.
35. Rakotomamonjy A. Variable selection using SVM based criteria. J Mach Learn Res 2003, 3:1357-1370.
36. Ramon J, Gärtner T. Expressivity versus efficiency of graph kernels. Technical Report Int Workshop on Mining Graphs. Trees and Sequences 2003; 65-74.
37. Richiardi J., Eryilmaz H., Schwartz S., Vuilleumier P., Van De Ville D. Decoding brain states from fMRI connectivity graphs. Neuroimage 2011, 56:616-626.
38. Richiardi J., Van De Ville D., Eryilmaz H. Low-dimensional embedding of functional connectivity graphs for brain state decoding. Proc IEEE Workshop on Brain Decoding 2010, 21-24.
39. Robnik-Šikonja M., Kononenko I. Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn 2003, 53:23-69.
40. Saeys Y., Inza I., Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics 2007, 23:2507-2517.
41. Salvador R., Suckling J., Coleman M.R., Pickard J.D., Menon D., Bullmore E. Neurophysiological architecture of functional magnetic resonance images of human brain. Cereb Cortex 2005, 15:1332-1342.
42. Shervashidze N., Borgwardt K.M. Fast subtree kernels on graphs. Adv Neural Inf Proc Syst 2009, 22:1660-1668.
43. Shervashidze N., Vishwanathan S., Petri T., Mehlhorn K., Borgwardt K. Efficient graphlet kernels for large graph comparison. Proc Int Conf on Artificial Intelligence and Statistics 2009.
44. Shirer W., Ryali S., Rykhlevskaia E., Menon V., Greicius M. Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb Cortex 2012, 22:158-165.
45. Sitaram R., Lee S., Ruiz S., Rana M., Veit R., Birbaumer N. Real-time support vector classification and feedback of multiple emotional brain states. Neuroimage 2011, 56:753-765.
46. Spreng R.N., Mar R.A., Kim A.S.N. The common neural basis of autobiographical memory, prospection, navigation, theory of mind, and the default mode: a quantitative meta-analysis. J Cogn Neurosci 2009, 21:489-510.
47. Todeschini R., Consonni V. Handbook of molecular descriptors 2008, Wiley-VCH, Weinheim, Germany.
48. Van den Heuvel M., Stam C., Boersma M., Hulshoff Pol H. Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain. Neuroimage 2008, 43:528-539.
49. Vapnik V. Pattern recognition using generalized portrait method. Automat Rem Contr 1963, 24:774-780.
50. Weston J., Mukherjee S., Chapelle O., Pontil M., Poggio T., Vapnik V. Feature selection for SVMs. Adv Neural Inf Proc Syst 2001, 668-674.
51. Wiener H. Structural determination of paraffin boiling points. J Am Chem Soc 1947, 69:17-20.
52. Worsley K.J., Chen J.I., Lerch J., Evans A.C. Comparing functional connectivity via thresholding correlations and singular value decomposition. Philos Trans R Soc Lond B Biol Sci 2005, 360:913-920.