Predicting intrinsic brain activity

NeuroImage

Volumn 82, Issue , 2013, Pages 127-136

  Craddock, R Cameron ^a,b,c   Milham, Michael P ^b,c   LaConte, Stephen M ^a,d  

^a VIRGINIA TECH CARILION SCHOOL OF MEDICINE   (United States)

^b CHILD MIND INSTITUTE   (United States)

^c NATHAN S KLINE INSTITUTE FOR PSYCHIATRIC RESEARCH   (United States)

^d VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

Author keywords

Effective connectivity; FMRI; Functional connectivity; Functional magnetic resonance imaging; Multi voxel pattern analysis; Multivariate; MVPA; Regression; Resting state

Indexed keywords

ACCURACY; ADULT; ARTICLE; BRAIN FUNCTION; BRAIN REGION; CONNECTOME; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; HUMAN EXPERIMENT; IMAGE PROCESSING; MALE; MULTIVARIATE LOGISTIC REGRESSION ANALYSIS; NEUROANATOMY; NEUROIMAGING; NORMAL HUMAN; PREDICTION; PRIORITY JOURNAL; PROCESS OPTIMIZATION; REPRODUCIBILITY; RESTING STATE NETWORK; VOXEL BASED MORPHOMETRY;

EFFECTIVE CONNECTIVITY; FMRI; FUNCTIONAL CONNECTIVITY; FUNCTIONAL MAGNETIC RESONANCE IMAGING; MULTI-VOXEL PATTERN ANALYSIS; MULTIVARIATE; MVPA; REGRESSION; RESTING STATE;

ADULT; BRAIN; BRAIN MAPPING; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MIDDLE AGED; NEURAL PATHWAYS; SUPPORT VECTOR MACHINES; YOUNG ADULT;

EID: 84879725804     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2013.05.072     Document Type: Article

References (61)

1. Benjamini Y., Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 1995, 57(1):289-300.
2. Biswal B.B., Mennes M., Zuo X.-N.X., Gohel S., Kelly C., Smith S.M.S.M., Beckmann C.F.C.F., Adelstein J.S.J.S., Buckner R.L.R.L., Colcombe S., et al. Toward discovery science of human brain function. Proc. Natl. Acad. Sci. 2010, 107:4734.
3. Boser B.E., Guyon I.M., Vapnik V.N. A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory (ACM) 1992, 144-152.
4. Braun U., Plichta M.M., Esslinger C., Sauer C., Haddad L., Grimm O., Mier D., Mohnke S., Heinz A., Erk S., et al. Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures. NeuroImage 2012, 59:1404-1412.
5. Bullmore E., Fadili J., Maxim V., Sendur L., Whitcher B., Suckling J., Brammer M., Breakspear M. Wavelets and functional magnetic resonance imaging of the human brain. NeuroImage 2004, 23(Suppl. 1):S234-S249.
6. Cherkassky V., Ma Y. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw. 2004, 17(1):113-126.
7. Cherkassky V.S., Mulier F. Learning From Data: Concepts, Theory, and Methods 2007, IEEE Press: Wiley-Interscience, Hoboken, N.J. 2nd ed.
8. Chu C., Ni Y., Tan G., Saunders C.J., Ashburner J. Kernel regression for fMRI pattern prediction. NeuroImage 2011, 56:662-673.
9. Chu C., Handwerker D.A., Bandettini P.A., Ashburner J. Measuring the consistency of global functional connectivity using kernel regression methods. IEEE International Workshop on Pattern Recognition in NeuroImaging 2011, 41-44.
10. Cox R.W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 1996, 29:162-173.
11. Cox D.D., Savoy R.L. Functional magnetic resonance imaging (fMRI) "brain reading": detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage 2003, 19:261-270.
12. Craddock R.C., Holtzheimer P.E., Hu X.P., Mayberg H.S. Disease state prediction from resting state functional connectivity. Magn. Reson. Med. 2009, 62:1619-1628.
13. Craddock R., James G., Holtzheimer P., Hu X., Mayberg H. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum. Brain Mapp. 2012, 33:1914. 10.1002/hbm.21333.
14. Drucker H., Burges C., Kaufman L., Smola A.J., Vapnik V. Support vector regression machines. Advances in Neural Information Processing Systems 1997, 9:155-161. MIT Press.
15. Friston K.J. Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp. 1994, 2:56-78.
16. Friston K.J., Frith C. Time dependent changes in effective connectivity measured with PET. Hum. Brain Mapp. 1993, 1:69-79.
17. Friston K.J., Williams S., Howard R., Frackowiak R.S., Turner R. Movement-related effects in fMRI time-series. Magn. Reson. Med. 1996, 35:346-355.
18. Friston K.J., Harrison L., Penny W. Dynamic causal modelling. NeuroImage 2003, 19(4):1273-1302.
19. Haxby J.V., Gobbini M.I., Furey M.L., Ishai a, Schouten J.L., Pietrini P. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 2001, 293:2425-2430.
20. Haynes J.D., Rees G. Decoding mental states from brain activity in humans. Nat. Rev. Neurosci. 2006, 7:523-534.
21. Jenkinson M., Bannister P., Brady M., Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 2002, 17:825-841.
22. Joachims T. Making large-scale support vector machine learning practical. Advances in Kernel Methods - Support Vector Learning 1999, MIT Press. B. Schölkopf, C. Burges, A. Smola (Eds.).
23. Kamitani Y., Tong F. Decoding the visual and subjective contents of the human brain. Nat. Neurosci. 2005, 8:679-685.
24. Kjems U., Hansen L.K., Anderson J.S., Frutiger S., Muley S., Sidtis J., Rottenberg D., Strother S.C. The quantitative evaluation of functional neuroimaging experiments: mutual information learning curves. NeuroImage 2002, 15:772-786.
25. Kriegeskorte N., Goebel R., Bandettini P. Information-based functional brain mapping. Proc. Natl. Acad. Sci. U. S. A. 2006, 103:3863-3868.
26. LaConte S.M. Decoding fMRI brain states in real-time. NeuroImage 2011, 56:440-454.
27. LaConte S.M., Anderson J., Muley S., Ashe J., Frutiger S., Rehm K., Hansen L.K., Yacoub E., Hu X., Rottenberg D., et al. The evaluation of preprocessing choices in single-subject BOLD fMRI using NPAIRS performance metrics. NeuroImage 2003, 18:10-27.
28. LaConte S.M., Strother S.C., Cherkassky V., Anderson J., Hu X.P. Support vector machines for temporal classification of block design fMRI data. NeuroImage 2005, 26:317-329.
29. LaConte S.M., Peltier S.J., Hu X.P. Real-time fMRI using brain-state classification. Hum. Brain Mapp. 2007, 28:1033-1044.
30. Lange N., Strother S.C., Anderson J.R., Nielsen F.a, Holmes a.P., Kolenda T., Savoy R., Hansen L.K. Plurality and resemblance in fMRI data analysis. NeuroImage 1999, 10:282-303.
31. Lohmann G., Erfurth K., Müller K., Turner R. Critical comments on dynamic causal modelling. NeuroImage 2012, 59(3):2322-2329.
32. Lund T.E., Madsen K.H., Sidaros K., Luo W.-L., Nichols T.E. Non-white noise in fMRI: does modelling have an impact?. NeuroImage 2006, 29:54-66.
33. Marrelec G., Bellec P., Krainik A., Duffau H., Pélégrini-Issac M., Lehéricy S., Benali H., Doyon J. Regions, systems, and the brain: hierarchical measures of functional integration in fMRI. Med. Image Anal. 2008, 12:484-496.
34. McIntosh A.R., Gonzalez-Lima F. Structural equation modeling and its application to network analysis in functional brain imaging. Hum. Brain Mapp. 1994, 2(1-2):2-22.
35. Mesulam M.M. From sensation to cognition. Brain 1998, 121(Pt 6):1013-1052.
36. Mitchell T.M., Hutchinson R., Niculescu R.S., Pereira F., Wang X., Just M., Newman S. Learning to decode cognitive states from brain images. Mach. Learn. 2004, 57:145-175.
37. Mourão-Miranda J., Bokde A.L.W., Born C., Hampel H., Stetter M. Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data. NeuroImage 2005, 28:980-995.
38. Mugler J.P., Brookman J.R. Three-dimensional magnetization-prepared rapid gradient-echo imaging (3D MP RAGE). Magn. Reson. Med. 1990, 15:152-157.
39. Müller K., Smola A., Rätsch G., Schölkopf B., Kohlmorgen J., Vapnik V. Predicting time series with support vector machines. Proceedings of the 7th International Conference on Artificial Neural Networks 1997, 999-1004. Springer-Verlag.
40. Murphy K., Bodurka J., Bandettini P.a How long to scan? The relationship between fMRI temporal signal to noise ratio and necessary scan duration. NeuroImage 2007, 34:565-574.
41. Polyn S.M., Natu V.S., Cohen J.D., Norman K.a Category-specific cortical activity precedes retrieval during memory search. Science 2005, 310:1963-1966.
42. Power J.D., Barnes K.a, Snyder A.Z., Schlaggar B.L., Petersen S.E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 2012, 59:2142-2154.
43. Ryali S., Chen T., Supekar K., Menon V. Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty. NeuroImage 2011, 59:3852-3861.
44. Shaw M.E., Strother S.C., Gavrilescu M., Podzebenko K., Waites A., Watson J., Anderson J., Jackson G., Egan G. Evaluating subject specific preprocessing choices in multisubject fMRI data sets using data-driven performance metrics. NeuroImage 2003, 19:988-1001.
45. Shehzad Z., Kelly a M.C., Reiss P.T., Gee D.G., Gotimer K., Uddin L.Q., Lee S.H., Margulies D.S., Roy A.K., Biswal B.B., et al. The resting brain: unconstrained yet reliable. Cereb. Cortex 2009, 19:2209-2229.
46. Smith S.M., Jenkinson M., Woolrich M.W., Beckmann C.F., Behrens T.E.J., Johansen-Berg H., Bannister P.R., De Luca M., Drobnjak I., Flitney D.E., et al. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 2004, 23(Suppl. 1):S208-S219.
47. Smith S.M., Miller K.L., Moeller S., Xu J., Auerbach E.J., Woolrich M.W., Beckmann C.F., et al. Temporally-independent functional modes of spontaneous brain activity. Proc. Natl. Acad. Sci. U.S.A. 2012, 109(8):3131-3136. 10.1073/pnas.1121329109.
48. Smola A.J., Schölkopf B. A tutorial on support vector regression. Stat. Comput. 2004, 14:199-222.
49. Stone J.V., Kötter R. Making connections about brain connectivity. Trends Cogn. Sci. 2002, 6:327-328.
50. Strother S.C., Anderson J., Hansen L.K., Kjems U., Kustra R., Sidtis J., Frutiger S., Muley S., LaConte S.M., Rottenberg D. The quantitative evaluation of functional neuroimaging experiments: the NPAIRS data analysis framework. NeuroImage 2002, 15:747-771.
51. Tononi G. Complexity and coherency: integrating information in the brain. Trends Cogn. Sci. 1998, 2:474-484.
52. Van Dijk K.R.a., Sabuncu M.R., Buckner R.L. The influence of head motion on intrinsic functional connectivity MRI. NeuroImage 2011, 59:431-438.
53. Vapnik V., Golowich S., Smola A. Support vector method for function approximation, regression estimation and signal processing. Advances in Neural Information Processing Systems 1996, 9. MIT Press.
54. Varoquaux G., Gramfort A., Poline J.B. Brain covariance selection: better individual functional connectivity models using population prior. Advances in Neural Information Processing Systems 2010, MIT Press.
55. Wang J.-H., Zuo X.-N., Gohel S., Milham M.P., Biswal B.B., He Y. Graph theoretical analysis of functional brain networks: test-retest evaluation on short- and long-term resting-state functional MRI data. PLoS One 2011, 6:e21976.
56. Zhang Y., Brady M., Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 2001, 20:45-57.
57. Zou H., Hastie T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 2005, 67(2):301-320.
58. Zou Q.-H., Zhu C.-Z., Yang Y., Zuo X.-N., Long X.-Y., Cao Q.-J., Wang Y.-F., Zang Y.-F. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J. Neurosci. Methods 2008, 172:137-141.
59. Zuo X.-N., Kelly C., Adelstein J.S., Klein D.F., Castellanos F.X., Milham M.P. Reliable intrinsic connectivity networks: test-retest evaluation using ICA and dual regression approach. NeuroImage 2010, 49:2163-2177.
60. Zuo X.-N., Di Martino A., Kelly C., Shehzad Z.E., Gee D.G., Klein D.F., Castellanos F.X., Biswal B.B., Milham M.P. The oscillating brain: complex and reliable. NeuroImage 2010, 49:1432-1445.
61. Zuo X.-N., Ehmke R., Mennes M., Imperati D., Castellanos F.X., Sporns O., Milham M.P. Network centrality in the human functional connectome. Cereb. Cortex 2011, 22(8):1862-1875.