Sparse regularization techniques provide novel insights into outcome integration processes

NeuroImage

Volumn 104, Issue , 2015, Pages 163-176

  Mohr, Holger ^a   Wolfensteller, Uta ^a   Frimmel, Steffi ^a   Ruge, Hannes ^a  

^a DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

Graph Net; Instruction based learning; MVPA; Outcome integration; Regularization; Structured sparsity

Indexed keywords

ACCURACY; ADULT; ALGORITHM; ANALYTIC METHOD; ARTICLE; BRAIN MAPPING; CONTROLLED STUDY; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; HUMAN; IMAGE ANALYSIS; INFORMATION PROCESSING; INSTRUCTION BASED LEARING; LEARNING STYLE; MALE; MATHEMATICAL COMPUTING; NEUROIMAGING; OUTCOME INTEGRATION PROCESS; SPARSE REGULARIZATION TECHNIQUE; SUPPORT VECTOR MACHINE; TASK PERFORMANCE; VISUAL STIMULATION; VOXEL BASED MORPHOMETRY; BRAIN; COMPUTER ASSISTED DIAGNOSIS; IMAGE PROCESSING; LEARNING; MULTIVARIATE ANALYSIS; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; PROCEDURES; REPRODUCIBILITY; STATISTICAL MODEL; YOUNG ADULT;

ADULT; ALGORITHMS; BRAIN; BRAIN MAPPING; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGE PROCESSING, COMPUTER-ASSISTED; LEARNING; LINEAR MODELS; MAGNETIC RESONANCE IMAGING; MALE; MULTIVARIATE ANALYSIS; NEUROIMAGING; REPRODUCIBILITY OF RESULTS; SUPPORT VECTOR MACHINE; YOUNG ADULT;

EID: 84908406060     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2014.10.025     Document Type: Article

References (72)

1. Abraham A., Dohmatob E., Thirion B., Samaras D., Varoquaux G. Extracting Brain Regions from Rest fMRI With Total-Variation Constrained Dictionary Learning 2013, MICCAI.
2. Baldassarre L., Mourao-Miranda J., Pontil M. Structured Sparsity Models for Brain Decoding from fMRI Data 2012, 5-8. IEEE.
3. Bradley, Mangasarian Feature selection via concave minimization and support vector machines. Proc. Fifteenth Int. Conf. Mach. Learn 1998, 82-90.
4. Braga R.M., Wilson L.R., Sharp D.J., Wise R.J.S., Leech R. Separable networks for top-down attention to auditory non-spatial and visuospatial modalities. NeuroImage 2013, 74:77-86.
5. Bunea F., She Y., Ombao H., Gongvatana A., Devlin K., Cohen R. Penalized least squares regression methods and applications to neuroimaging. NeuroImage 2011, 55:1519-1527.
6. Carroll M., Cecchi G., Rish I., Garg R., Rao A. Prediction and interpretation of distributed neural activity with sparse models. NeuroImage 2009, 44:112-122.
7. Cole M.W., Laurent P., Stocco A. Rapid instructed task learning: a new window into the human brain's unique capacity for flexible cognitive control. Cogn. Affect. Behav. Neurosci. 2012, 13:1-22.
8. Cools R., Clark L., Owen A.M., Robbins T.W. Defining the neural mechanisms of probabilistic reversal learning using event-related functional magnetic resonance imaging. J. Neurosci. 2002, 22:4563-4567.
9. Cortes C., Vapnik V. Support-vector networks. Mach. Learn. 1995, 20:273-297.
10. Daw N.D., Gershman S.J., Seymour B., Dayan P., Dolan R.J. Model-based influences on humans' choices and striatal prediction errors. Neuron 2011, 69:1204-1215.
11. Dohmatob E., Gramfort A., Thirion B., Varoquaux G. Benchmarking solvers for TV-l1 least-squares and logistic regression in brain imaging. PRNI 2014, 10.1109/PRNI.2014.6858516.
12. Dolan R.J., Dayan P. Goals and habits in the brain. Neuron 2013, 80:312-325.
13. Efron B., Tibshirani R., Johnstone I., Hastie T. Least angle regression. Ann. Stat. 2004, 32:407-499.
14. Etzel J.A., Zacks J.M., Braver T.S. Searchlight analysis: promise, pitfalls, and potential. NeuroImage 2013, 78:261-269.
15. Fan, Chang, Hsieh, Wang, Lin LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 2008, 9:1871-1874.
16. Friedman J.H., Hastie T., Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 2010, 33:1-22.
17. Friedrich A.M., Zentall T.R. A differential-outcome effect in pigeons using spatial hedonically nondifferential outcomes. Learn. Behav. 2011, 39:68-78.
18. Ganesh G., Burdet E., Haruno M., Kawato M. Sparse linear regression for reconstructing muscle activity from human cortical fMRI. NeuroImage 2008, 42:1463-1472.
19. Gramfort A., Thirion B., Varoquaux G. Identifying Predictive Regions from fMRI With TV-L1 Prior 2013, 17-20. IEEE.
20. Grosenick L., Greer S., Knutson B. Interpretable classifiers for fMRI improve prediction of purchases. IEEE Trans. Neural Syst. Rehabil. Eng. 2008, 16:539-548.
21. Grosenick L., Klingenberg B., Katovich K., Knutson B., Taylor J.E. Interpretable whole-brain prediction analysis with GraphNet. NeuroImage 2013, 72:304-321.
22. Hartstra E., Waszak F., Brass M. The implementation of verbal instructions: dissociating motor preparation from the formation of stimulus-response associations. NeuroImage 2012, 63:1143-1153.
23. Haxby J.V. Multivariate pattern analysis of fMRI: the early beginnings. NeuroImage 2012, 62:852-855.
24. Haxby J.V., Gobbini, Furey Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 2001, 293:2425-2430.
25. Haynes J.-D., Rees G. Decoding mental states from brain activity in humans. Nat. Rev. Neurosci. 2006, 7:523-534.
26. Hoerl Application of ridge analysis to regression problems. Chem. Eng. Prog. 1962, 58:54-59.
27. Hsu M., Bhatt M., Adolphs R., Tranel D., Camerer C.F. Neural systems responding to degrees of uncertainty in human decision-making. Science 2005, 310:1680-1683.
28. Huettel S.A. Behavioral, but not reward, risk modulates activation of prefrontal, parietal, and insular cortices. Cogn. Affect. Behav. Neurosci. 2006, 6:141-151.
29. Huettel S.A., Mack P.B., McCarthy G. Perceiving patterns in random series: dynamic processing of sequence in prefrontal cortex. Nat. Neurosci. 2002, 5:485-490.
30. Jenatton R., Gramfort A., Michel V., Obozinski G., Eger E., Bach F., Thirion B. Multiscale mining of fMRI data with hierarchical structured sparsity. SIAM J. Imaging Sci. 2012, 5:835-856.
31. Kanwisher N. Functional specificity in the human brain: a window into the functional architecture of the mind. Proc. Natl. Acad. Sci. 2010, 107:11163-11170.
32. Kaplan J.T., Meyer K. Multivariate pattern analysis reveals common neural patterns across individuals during touch observation. NeuroImage 2012, 60:204-212.
33. Kay K.N., Naselaris T., Prenger R.J., Gallant J.L. Identifying natural images from human brain activity. Nature 2008, 452:352-355.
34. Knops A., Thirion B., Hubbard E.M., Michel V., Dehaene S. Recruitment of an area involved in eye movements during mental arithmetic. Science 2009, 324:1583-1585.
35. Kriegeskorte N., Goebel R., Bandettini P. Information-based functional brain mapping. Proc. Natl. Acad. Sci. 2006, 103:3863-3868.
36. Kriegeskorte N., Simmons W.K., Bellgowan P.S.F., Baker C.I. Circular analysis in systems neuroscience: the dangers of double dipping. Nat. Neurosci. 2009, 12:535-540.
37. Michel V., Gramfort A., Varoquaux G., Eger E., Thirion B. Total variation regularization for fMRI-based prediction of behavior. IEEE Trans. Med. Imaging 2011, 30:1328-1340.
38. Mitchell T.M., Shinkareva S.V., Carlson A., Chang K.-M., Malave V.L., Mason R.A., Just M.A. Predicting human brain activity associated with the meanings of nouns. Science 2008, 320:1191-1195.
39. 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.
40. Ng B., Siless V., Varoquaux G., Poline J.-B., Thirion B., Abugharbieh R. Connectivity-Informed Sparse Classifiers for fMRI Brain Decoding 2012, 101-104. IEEE.
41. Nieuwenhuis S., Slagter H.A., von Geusau N.J.A., Heslenfeld D.J., Holroyd C.B. Knowing good from bad: differential activation of human cortical areas by positive and negative outcomes. Eur. J. Neurosci. 2005, 21:3161-3168.
42. O'Reilly J.X., Jbabdi S., Rushworth M.F.S., Behrens T.E.J. Brain systems for probabilistic and dynamic prediction: computational specificity and integration. PLoS Biol. 2013, 11:e1001662.
43. Pereira F., Botvinick M. Information mapping with pattern classifiers: a comparative study. NeuroImage 2011, 56:476-496.
44. Pereira F., Mitchell T., Botvinick M. Machine learning classifiers and fMRI: a tutorial overview. NeuroImage 2009, 45:S199-S209.
45. Preuschoff K., Bossaerts P., Quartz S.R. Neural differentiation of expected reward and risk in human subcortical structures. Neuron 2006, 51:381-390.
46. Preuschoff K., Quartz S.R., Bossaerts P. Human insula activation reflects risk prediction errors as well as risk. J. Neurosci. 2008, 28:2745-2752.
47. Rasmussen P.M., Hansen L.K., Madsen K.H., Churchill N.W., Strother S.C. Model sparsity and brain pattern interpretation of classification models in neuroimaging. Pattern Recognit. 2012, 45:2085-2100.
48. Reverberi C., Bonatti L.L., Frackowiak R.S.J., Paulesu E., Cherubini P., Macaluso E. Large scale brain activations predict reasoning profiles. NeuroImage 2012, 59:1752-1764.
49. Ruge H., Wolfensteller U. Rapid formation of pragmatic rule representations in the human brain during instruction-based learning. Cereb. Cortex 2010, 20:1656-1667.
50. Ruge H., Wolfensteller U. Functional integration processes underlying the instruction-based learning of novel goal-directed behaviors. NeuroImage 2013, 68:162-172.
51. Ryali S., Supekar K., Abrams D.A., Menon V. Sparse logistic regression for whole-brain classification of fMRI data. NeuroImage 2010, 51:752-764.
52. Schaffer C. A Conservation Law for Generalization Performance 1994, 259-265. ICML.
53. Schoenmakers S., Barth M., Heskes T., van Gerven M. Linear reconstruction of perceived images from human brain activity. NeuroImage 2013, 83:951-961.
54. Shultz S., Lee S.M., Pelphrey K., McCarthy G. The posterior superior temporal sulcus is sensitive to the outcome of human and non-human goal-directed actions. Soc. Cogn. Affect. Neurosci. 2010, 6:602-611.
55. Slawski M., Zu Castell W., Tutz G. Feature selection guided by structural information. Ann. Appl. Stat. 2010, 4:1056-1080.
56. Stelzer J., Chen Y., Turner R. Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): random permutations and cluster size control. NeuroImage 2013, 65:69-82.
57. Thirion B., Pinel P., Mériaux S., Roche A., Dehaene S., Poline J.-B. Analysis of a large fMRI cohort: statistical and methodological issues for group analyses. NeuroImage 2007, 35:105-120.
58. Thorndike E.L. Animal Intelligence; Experimental Studies, by Edward L. Thorndike 1911, The Macmillan company, New York.
59. Tibshirani Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B Methodol. 1996, 58:267-288.
60. Tikhonov Solution of incorrectly formulated problems and the regularization method. Sov. Math. Dokl. 1963, 4:1035-1038.
61. Trapold M.A. Are expectancies based upon different positive reinforcing events discriminably different?. Learn. Motiv. 1970, 1:129-140.
62. Tzourio-Mazoyer N., Landeau B., Papathanassiou D., Crivello F., Etard O., Delcroix N., Mazoyer B., Joliot M. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 2002, 15:273-289.
63. Urcuioli P.J. Behavioral and associative effects of differential outcomes in discrimination learning. Anim. Learn. Behav. 2005, 33:1-21.
64. Wager T.D., Atlas L.Y., Leotti L.A., Rilling J.K. Predicting individual differences in placebo analgesia: contributions of brain activity during anticipation and pain experience. J. Neurosci. 2011, 31:439-452.
65. Watanabe T., Kessler D., Scott C., Angstadt M., Sripada C. Disease prediction based on functional connectomes using a scalable and spatially-informed support vector machine. NeuroImage 2014, 96:183-202.
66. Wolfensteller U., Ruge H. On the timescale of stimulus-based action-effect learning. Q. J. Exp. Psychol. 2011, 64:1273-1289.
67. Wolfensteller U., Ruge H. Frontostriatal mechanisms in instruction-based learning as a hallmark of flexible goal-directed behavior. Front. Psychol. 2012, 3.
68. Wolpert D.H. The lack of a priori distinctions between learning algorithms. Neural Comput. 1996, 8:1341-1390.
69. Xue G., Ghahremani D.G., Poldrack R.A. Neural substrates for reversing stimulus-outcome and stimulus-response associations. J. Neurosci. 2008, 28:11196-11204.
70. Yamashita O., Sato M., Yoshioka T., Tong F., Kamitani Y. Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns. NeuroImage 2008, 42:1414-1429.
71. Zanto T.P., Rubens M.T., Thangavel A., Gazzaley A. Causal role of the prefrontal cortex in top-down modulation of visual processing and working memory. Nat. Neurosci. 2011, 14:656-661.
72. Zou H., Hastie T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat Methodol. 2005, 67:301-320.