Vai ai contenuti. | Spostati sulla navigazione | Spostati sulla ricerca | Vai al menu | Contatti | Accessibilità

| Crea un account

Di Bono, Maria Grazia (2009) Beyond mind reading: advanced machine learning techniques for FMRI data analysis. [Tesi di dottorato]

Full text disponibile come:

[img]
Anteprima
Documento PDF
1579Kb

Abstract (inglese)

The advent of functional Magnetic Resonance Imaging (fMRI) has significantly improved the knowledge about the neural correlates of perceptual and cognitive processes. The aim of this thesis is to discuss the characteristics of different approaches for fMRI data analysis, from the conventional mass univariate analysis (General Linear Model - GLM), to the multivariate analysis (i.e., data-driven and pattern based methods), and propose a novel, advanced method (Functional ANOVA Models of Gaussian Kernels - FAM-GK) for the analysis of fMRI data acquired in the context of fast event-related experiments. FAM-GK is an embedded method for voxel selection and is able to capture the nonlinear spatio-temporal dynamics of the BOLD signals by performing nonlinear estimation of the experimental conditions. The impact of crucial aspects concerning the use of pattern recognition methods on the fMRI data analysis, such as voxel selection, the choice of classifier and tuning parameters, the cross-validation techniques, are investigated and discussed by analysing the results obtained in four neuroimaging case studies.
In a first study, we explore the robustness of nonlinear Support Vector regression (SVR), combined with a filter approach for voxel selection, in the case of an extremely complex regression problem, in which we had to predict the subjective experience of participants immersed in a virtual reality environment.
In a second study, we face the problem of voxel selection combined with the choice of the best classifier, and we propose a methodology based on genetic algorithms and nonlinear support vector machine (GA-SVM) efficiently combined in a wrapper approach.
In a third study we compare three pattern recognition techniques (i.e., linear SVM, nonlinear SVM, and FAM-GK) for investigating the neural correlates of the representation of numerical and non-numerical ordered sequences (i.e., numbers and letters) in the horizontal segment of the Intraparietal Sulcus (hIPS). The FAM-GK method significantly outperformed the other two classifiers. The results show a partial overlapping of the two representation systems suggesting the existence of neural substrates in hIPS codifying the cardinal and the ordinal dimensions of numbers and letters in a partially independent way.
Finally, in the last preliminary study, we tested the same three pattern recognition methods on fMRI data acquired in the context of a fast event-related experiment. The FAM-GK method shows a very high performance, whereas the other classifiers fail to achieve an acceptable classification performance.

Abstract (italiano)

L’avvento della tecnica di Risonanza Magnetica funzionale (fMRI) ha notevolmente migliorato le conoscenze sui correlati neurali sottostanti i processi cognitivi. Obiettivo di questa tesi è stato quello di illustrare e discutere criticamente le caratteristiche dei diversi approcci per l’analisi dei dati fMRI, dai metodi convenzionali di analisi univariata (General Linear Model - GLM) ai metodi di analisi multivariata (metodi data-driven e di pattern recognition), proponendo una nuova tecnica avanzata (Functional ANOVA Models of Gaussian Kernels - FAM-GK) per l’analisi di dati fMRI acquisiti con paradigmi sperimentali fast event-related. FAM-GK è un metodo embedded per la selezione dei voxels, che è in grado di catturare le dinamiche non lineari spazio-temporali del segnale BOLD, effettuando stime non lineari delle condizioni sperimentali. L’impatto degli aspetti critici riguardanti l’uso di tecniche di pattern recognition sull’analisi di dati fMRI, tra cui la selezione dei voxels, la scelta del classificatore e dei suoi parametri di apprendimento, le tecniche di cross-validation, sono valutati e discussi analizzando i risultati ottenuti in quattro casi di studio.
In un primo studio, abbiamo indagato la robustezza di Support Vector regression (SVR) non lineare, integrato con un approccio di tipo filter per la selezione dei voxels, in un caso di un problema di regressione estremamente complesso, in cui dovevamo predire l’esperienza soggettiva di alcuni partecipanti immersi in un ambiente di realtà virtuale.
In un secondo studio, abbiamo affrontato il problema della selezione dei voxels integrato con la scelta del miglior classificatore, proponendo un metodo basato sugli algoritmi genetici e SVM non lineare (GA-SVM) in un approccio di tipo wrapper.
In un terzo studio, abbiamo confrontato tre metodi di pattern recognition (SVM lineare, SVM non lineare e FAM-GK) per indagare i correlati neurali della rappresentazione di sequenze ordinate numeriche e non-numeriche (numeri e lettere) a livello del segmento orizzontale del solco intraparitale (hIPS). Le prestazioni di classificazione di FAM-GK sono risultate essere significativamente superiori rispetto a quelle degli alti due classificatori. I risultati hanno mostrato una parziale sovrapposizione dei due sistemi di rappresentazione, suggerendo l’esistenza di substrati neurali nelle regioni hIPS che codificano le dimensioni cardinale e ordinale dei numeri e delle lettere in modo parzialmente indipendente.
Infine, nel quarto studio preliminare, abbiamo testato e confrontato gli stessi tre classificatori su dati fMRI acquisiti durante un esperimento fast event-related. FAM-GK ha mostrato delle prestazioni di classificazione piuttosto elevate, mentre le prestazioni degli altri due classificatori sono risultate essere di poco superiori al caso.

Statistiche Download - Aggiungi a RefWorks
Tipo di EPrint:Tesi di dottorato
Relatore:Zorzi, Marco
Dottorato (corsi e scuole):Ciclo 21 > Scuole per il 21simo ciclo > SCIENZE PSICOLOGICHE > SCIENZE COGNITIVE
Data di deposito della tesi:02 Febbraio 2009
Anno di Pubblicazione:2009
Parole chiave (italiano / inglese):fMRI, multi-voxel pattern analysis, genetic algorithms, multivariate regression, feature selection, number processing
Settori scientifico-disciplinari MIUR:Area 11 - Scienze storiche, filosofiche, pedagogiche e psicologiche > M-PSI/01 Psicologia generale
Struttura di riferimento:Dipartimenti > Dipartimento di Psicologia dello Sviluppo e della Socializzazione
Codice ID:1812
Depositato il:02 Feb 2009
Simple Metadata
Full Metadata
EndNote Format

Bibliografia

I riferimenti della bibliografia possono essere cercati con Cerca la citazione di AIRE, copiando il titolo dell'articolo (o del libro) e la rivista (se presente) nei campi appositi di "Cerca la Citazione di AIRE".
Le url contenute in alcuni riferimenti sono raggiungibili cliccando sul link alla fine della citazione (Vai!) e tramite Google (Ricerca con Google). Il risultato dipende dalla formattazione della citazione.

1. Amaro, E.,Jr, & Barker, G. J. (2006). Study design in fMRI: Basic principles. Brain and Cognition, 60(3), 220-232. doi:10.1016/j.bandc.2005.11.009 Cerca con Google

2. Bach, F. R., Lanckriet, G. R. G., & Jordan, M. I. (2004). Multiple kernel learning, conic duality, and the SMO algorithm. ACM International Conference Proceeding Series. Cerca con Google

3. Bartfeld, E., & Grinvald, A. (1992). Relationships between orientation-preference pinwheels, cytochrome oxidase blobs, and ocular-dominance columns in primate striate cortex. Proceedings of the National Academy of Sciences of the United States of America, 89(24), 11905-11909. Cerca con Google

4. Berlinet, A., & Thomas-Agnan, C. (2004). Reproducing kernel hilbert spaces in probability and statistics. Kluwer Academic Publishers. Cerca con Google

5. Bhatt, S., Mbwana, J., Adeyemo, A., Sawyer, A., Hailu, A., & Vanmeter, J. (2008). Lying about facial recognition: An fMRI study. Brain and Cognition, doi:10.1016/j.bandc.2008.08.033 Cerca con Google

6. Binkofski, F., Fink, G. R., Geyer, S., Buccino, G., Gruber, O., Shah, N. J., et al. (2002). Neural activity in human primary motor cortex areas 4a and 4p is modulated differentially by attention to action. Journal of Neurophysiology, 88(1), 514-519. Cerca con Google

7. Birbaumer, N., Murguialday, A. R., & Cohen, L. (2008). Brain-computer interface in paralysis. Current Opinion in Neurology, 21(6), 634-638. doi:10.1097/WCO.0b013e328315ee2d Cerca con Google

8. Boyd, S. P., & Vandenberghe, L. (2004). Convex optimization. Cambridge University Press. Cerca con Google

9. Boynton, G. M., & Finney, E. M. (2003). Orientation-specific adaptation in human visual cortex. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 23(25), 8781-8787. Cerca con Google

10. Bradley, P. S., & Mangasarian, O. L. (1998). Feature selection via concave minimization and support vector machine. Paper presented at the 15th International Conference on Machine Learning, San Francisco. 82-90. Cerca con Google

11. Carlson, T. A., Schrater, P., & He, S. (2003). Patterns of activity in the categorical representations of objects. Journal of Cognitive Neuroscience, 15(5), 704-717. doi:10.1162/089892903322307429 Cerca con Google

12. Casarotti, M., Michielin, M., Zorzi, M., & Umilta, C. (2007). Temporal order judgment reveals how number magnitude affects visuospatial attention. Cognition, 102(1), 101-117. doi:10.1016/j.cognition.2006.09.001 Cerca con Google

13. Cox, D. D., & Savoy, R. L. (2003). Functional magnetic resonance imaging (fMRI) "brain reading": Detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage, 19(2 Pt 1), 261-270. Cerca con Google

14. Daly, J. J., & Wolpaw, J. R. (2008). Brain-computer interfaces in neurological rehabilitation. Lancet Neurology, 7(11), 1032-1043. doi:10.1016/S1474-4422(08)70223-0 Cerca con Google

15. Davatzikos, C., Ruparel, K., Fan, Y., Shen, D. G., Acharyya, M., Loughead, J. W., et al. (2005). Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection. NeuroImage, 28(3), 663-668. doi:10.1016/j.neuroimage.2005.08.009 Cerca con Google

16. De Martino, F., Gentile, F., Esposito, F., Balsi, M., Di Salle, F., Goebel, R., et al. (2007). Classification of fMRI independent components using IC-fingerprints and support vector machine classifiers. NeuroImage, 34(1), 177-194. doi:10.1016/j.neuroimage.2006.08.041 Cerca con Google

17. De Martino, F., Valente, G., Staeren, N., Ashburner, J., Goebel, R., & Formisano, E. (2008). Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. NeuroImage, 43(1), 44-58. doi:10.1016/j.neuroimage.2008.06.037 Cerca con Google

18. Dehaene, S., Bossini, S., & Giraux, P. (1993). The mental representation of parity and number magnitude. Journal of Experimental Psychology: General, 122, 371–396. Cerca con Google

19. Dehaene, S., Piazza, M., Pinel, P., & Cohen, L. (2003). Three parietal circuits for number processing. Cognitive Neuropsychology, 20, 487–506. Cerca con Google

20. Dehaene, S. (2003). The neural basis of the weber-fechner law: A logarithmic mental number line. Trends in Cognitive Sciences, 7(4), 145-147. Cerca con Google

21. Dehaene, S., Dupoux, E., & Mehler, J. (1990). Is numerical comparison digital? analogical and symbolic effects in two-digit number comparison. Journal of Experimental Psychology.Human Perception and Performance, 16(3), 626-641. Cerca con Google

22. Di Bono, M. G., & Zorzi, M. (2008). Decoding cognitive states from fMRI data using support vector regression. PsychNology Journal, 6(2), 189-201. Cerca con Google

23. Efron, B., & Tibshirani, R. (1993). An introduction to the bootstrap CHAPMAN & HALL/CRC, Boca Raton. Cerca con Google

24. Eger, E., Sterzer, P., Russ, M. O., Giraud, A. L., & Kleinschmidt, A. (2003). A supramodal number representation in human intraparietal cortex. Neuron, 37(4), 719-725. Cerca con Google

25. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27, 861-874. Cerca con Google

26. Fias, W., Lammertyn, J., Caessens, B., & Orban, G. A. (2007). Processing of abstract ordinal knowledge in the horizontal segment of the intraparietal sulcus. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 27(33), 8952-8956. doi:10.1523/JNEUROSCI.2076-07.2007 Cerca con Google

27. Fischer, M. H., Castel, A. D., Dodd, M. D., & Pratt, J. (2003). Perceiving numbers causes spatial shifts of attention. Nature Neuroscience, 6(6), 555-556. doi:10.1038/nn1066 Cerca con Google

28. Fischer, M. H., Warlop, N., Hill, R. L., & Fias, W. (2004). Oculomotor bias induced by number perception. Experimental Psychology, 51(2), 91-97. Cerca con Google

29. Forman, S. D., Cohen, J. D., Fitzgerald, M., Eddy, W. F., Mintun, M. A., & Noll, D. C. (1995). Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): Use of a cluster-size threshold. Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 33(5), 636-647. Cerca con Google

30. Friedman, J. H. (1991). Multivariate adaptive regression splines (with discussion). Annals of Statistics, 19, 1-141. Cerca con Google

31. Friston, K. J., Josephs, O., Rees, G., & Turner, R. (1998). Nonlinear event-related responses in fMRI. Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 39(1), 41-52. Cerca con Google

32. Genovese, C. R., Lazar, N. A., & Nichols, T. (2002). Thresholding of statistical maps in functional neuroimaging using the false discovery rate. NeuroImage, 15(4), 870-878. doi:10.1006/nimg.2001.1037 Cerca con Google

33. Geschwind, N. (1965a). Disconnexion syndromes in animals and man. I. Brain : A Journal of Neurology, 88(2), 237-294. Cerca con Google

34. Geschwind, N. (1965b). Disconnexion syndromes in animals and man. II. Brain : A Journal of Neurology, 88(3), 585-644. Cerca con Google

35. Gevers, W., Reynvoet, B., & Fias, W. (2003). The mental representation of ordinal sequences is spatially organized. Cognition, 87(3), B87-95. Cerca con Google

36. Gevers, W., Verguts, T., Reynvoet, B., Caessens, B., & Fias, W. (2006). Numbers and space: A computational model of the SNARC effect. Journal of Experimental Psychology.Human Perception and Performance, 32(1), 32-44. doi:10.1037/0096-1523.32.1.32 Cerca con Google

37. Glover, G. H. (1999). Deconvolution of impulse response in event-related BOLD fMRI. NeuroImage, 9(4), 416-429. Cerca con Google

38. Gobel, S. M., Calabria, M., Farne, A., & Rossetti, Y. (2006). Parietal rTMS distorts the mental number line: Simulating 'spatial' neglect in healthy subjects. Neuropsychologia, 44(6), 860-868. doi:10.1016/j.neuropsychologia.2005.09.007 Cerca con Google

39. Grill-Spector, K., Knouf, N., & Kanwisher, N. (2004). The fusiform face area subserves face perception, not generic within-category identification. Nature Neuroscience, 7(5), 555-562. doi:10.1038/nn1224 Cerca con Google

40. Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3(7), 1157-1182. Cerca con Google

41. Hakun, J. G., Ruparel, K., Seelig, D., Busch, E., Loughead, J. W., Gur, R. C., et al. (2008). Towards clinical trials of lie detection with fMRI. Social Neuroscience, , 1-10. doi:10.1080/17470910802188370 Cerca con Google

42. Hanson, S. J., Matsuka, T., & Haxby, J. V. (2004). Combinatorial codes in ventral temporal lobe for object recognition: Haxby (2001) revisited: Is there a "face" area? NeuroImage, 23(1), 156-166. doi:10.1016/j.neuroimage.2004.05.020 Cerca con Google

43. Hastie, ,Trevor J., & Tibshirani, ,Robert. (1990). Generalized additive models. London etc.: Chapman and Hall. Cerca con Google

44. Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001). Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science (New York, N.Y.), 293(5539), 2425-2430. doi:10.1126/science.1063736 Cerca con Google

45. Haynes, J. D. (2008). Detecting deception from neuroimaging signals--a data-driven perspective. Trends in Cognitive Sciences, 12(4), 126-7; author reply 127-8. doi:10.1016/j.tics.2008.01.003 Cerca con Google

46. Haynes, J. D., & Rees, G. (2005a). Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nature Neuroscience, 8(5), 686-691. doi:10.1038/nn1445 Cerca con Google

47. Haynes, J. D., & Rees, G. (2005b). Predicting the stream of consciousness from activity in human visual cortex. Current Biology : CB, 15(14), 1301-1307. doi:10.1016/j.cub.2005.06.026 Cerca con Google

48. Haynes, J. D., & Rees, G. (2006). Decoding mental states from brain activity in humans. Nature Reviews.Neuroscience, 7(7), 523-534. doi:10.1038/nrn1931 Cerca con Google

49. Haynes, J. D., Sakai, K., Rees, G., Gilbert, S., Frith, C., & Passingham, R. E. (2007). Reading hidden intentions in the human brain. Current Biology : CB, 17(4), 323-328. doi:10.1016/j.cub.2006.11.072 Cerca con Google

50. Henson, R. (2006). Forward inference using functional neuroimaging: Dissociations versus associations. Trends in Cognitive Sciences, 10(2), 64-69. doi:10.1016/j.tics.2005.12.005 Cerca con Google

51. Huettel, ,Scott A., Song, ,Allen W., & McCarthy, ,Gregory. (2004). Functional magnetic resonance imaging. Sunderland: Sinauer Associates. Cerca con Google

52. Huettel, S. A., & McCarthy, G. (2000). Evidence for a refractory period in the hemodynamic response to visual stimuli as measured by MRI. NeuroImage, 11(5 Pt 1), 547-553. doi:10.1006/nimg.2000.0553 Cerca con Google

53. Huettel, S. A., & McCarthy, G. (2001). The effects of single-trial averaging upon the spatial extent of fMRI activation. Neuroreport, 12(11), 2411-2416. Cerca con Google

54. Jacob, S. N., & Nieder, A. (2008). The ABC of cardinal and ordinal number representations. Trends in Cognitive Sciences, 12(2), 41-43. doi:10.1016/j.tics.2007.11.006 Cerca con Google

55. Jewell, G., & McCourt, M. E. (2000). Pseudoneglect: A review and meta-analysis of performance factors in line bisection tasks. Neuropsychologia, 38(1), 93-110. Cerca con Google

56. Kamitani, Y., & Tong, F. (2005). Decoding the visual and subjective contents of the human brain. Nature Neuroscience, 8(5), 679-685. doi:10.1038/nn1444 Cerca con Google

57. Kamitani, Y., & Tong, F. (2006). Decoding seen and attended motion directions from activity in the human visual cortex. Current Biology : CB, 16(11), 1096-1102. doi:10.1016/j.cub.2006.04.003 Cerca con Google

58. Kanwisher, N., McDermott, J., & Chun, M. M. (1997). The fusiform face area: A module in human extrastriate cortex specialized for face perception. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 17(11), 4302-4311. Cerca con Google

59. Kanwisher, N., Stanley, D., & Harris, A. (1999). The fusiform face area is selective for faces not animals. Neuroreport, 10(1), 183-187. Cerca con Google

60. Kanwisher, N., & Yovel, G. (2006). The fusiform face area: A cortical region specialized for the perception of faces. Philosophical Transactions of the Royal Society of London.Series B, Biological Sciences, 361(1476), 2109-2128. doi:10.1098/rstb.2006.1934 Cerca con Google

61. Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial Intelligence, 97(1-2), 273-324. Cerca con Google

62. Kozel, F. A., Revell, L. J., Lorberbaum, J. P., Shastri, A., Elhai, J. D., Horner, M. D., et al. (2004). A pilot study of functional magnetic resonance imaging brain correlates of deception in healthy young men. The Journal of Neuropsychiatry and Clinical Neurosciences, 16(3), 295-305. doi:10.1176/appi.neuropsych.16.3.295 Cerca con Google

63. Kriegeskorte, N., Goebel, R., & Bandettini, P. (2006). Information-based functional brain mapping. Proceedings of the National Academy of Sciences of the United States of America, 103(10), 3863-3868. doi:10.1073/pnas.0600244103 Cerca con Google

64. Kubler, A., & Birbaumer, N. (2008). Brain-computer interfaces and communication in paralysis: Extinction of goal directed thinking in completely paralysed patients? Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology, 119(11), 2658-2666. doi:10.1016/j.clinph.2008.06.019 Cerca con Google

65. Lanckriet, G., Cristianini, N., Bartlett, P., El Ghaoui, L., & Jordan, M. (2004). Learning the kernel matrix with semidefinite programming. The Journal of Machine Learning Research, 5, 27-72. Cerca con Google

66. Langleben, D. D., Loughead, J. W., Bilker, W. B., Ruparel, K., Childress, A. R., Busch, S. I., et al. (2005). Telling truth from lie in individual subjects with fast event-related fMRI. Human Brain Mapping, 26(4), 262-272. doi:10.1002/hbm.20191 Cerca con Google

67. Le Clec'H, G., Dehaene, S., Cohen, L., Mehler, J., Dupoux, E., Poline, J. B., et al. (2000). Distinct cortical areas for names of numbers and body parts independent of language and input modality. NeuroImage, 12(4), 381-391. doi:10.1006/nimg.2000.0627 Cerca con Google

68. Lee, J. H., Ryu, J., Jolesz, F. A., Cho, Z. H., & Yoo, S. S. (2008). Brain-machine interface via real-time fMRI: Preliminary study on thought-controlled robotic arm. Neuroscience Letters, doi:10.1016/j.neulet.2008.11.024 Cerca con Google

69. Lee, T. M., Liu, H. L., Tan, L. H., Chan, C. C., Mahankali, S., Feng, C. M., et al. (2002). Lie detection by functional magnetic resonance imaging. Human Brain Mapping, 15(3), 157-164. Cerca con Google

70. Lin, Y., & Zhang, H. H. (2006). Component selection and smoothing in multivariate nonparametric regression. The Annals of Statistics, 34(5), 2272-2297. Cerca con Google

71. Loetscher, T., & Brugger, P. (2007). Exploring number space by random digit generation. Experimental Brain Research.Experimentelle Hirnforschung.Experimentation Cerebrale, 180(4), 655-665. doi:10.1007/s00221-007-0889-0 Cerca con Google

72. Loetscher, T., Schwarz, U., Schubiger, M., & Brugger, P. (2008). Head turns bias the brain's internal random generator. Current Biology : CB, 18(2), R60-2. doi:10.1016/j.cub.2007.11.015 Cerca con Google

73. Loftus, A. M., Nicholls, M. E., Mattingley, J. B., & Bradshaw, J. L. (2008). Left to right: Representational biases for numbers and the effect of visuomotor adaptation. Cognition, 107(3), 1048-1058. doi:10.1016/j.cognition.2007.09.007 Cerca con Google

74. Longo, M. R., & Lourenco, S. F. (2007). Spatial attention and the mental number line: Evidence for characteristic biases and compression. Neuropsychologia, 45(7), 1400-1407. doi:10.1016/j.neuropsychologia.2006.11.002 Cerca con Google

75. Mapelli, D., Rusconi, E., & Umilta, C. (2003). The SNARC effect: An instance of the simon effect? Cognition, 88(3), B1-10. Cerca con Google

76. Menon, R. S., Ogawa, S., Hu, X., Strupp, J. P., Anderson, P., & Ugurbil, K. (1995). BOLD based functional MRI at 4 tesla includes a capillary bed contribution: Echo-planar imaging correlates with previous optical imaging using intrinsic signals. Magnetic Resonance in Medicine : Official Journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, 33(3), 453-459. Cerca con Google

77. Micchelli, C. A., & Pontil, M. (2005). Learning the kernel function via regularization. The Journal of Machine Learning Research, 6, 1099–1125. Cerca con Google

78. Micchelli, C. A., & Pontil, M. (2007). Feature space perspectives for learning the kernel. Machine Learning, 66(2), 297–319. Cerca con Google

79. Mitchell, T. M., Hutchinson, R., Niculescu, R. S., Pereira, F., Wang, X., Just, M., et al. (2004). Learning to decode cognitive states from brain images. Machine Learnig, 57, 145-175. Cerca con Google

80. Mitchell, T. M., Hutchinson, R., Just, M. A., Niculescu, R. S., Pereira, F., & Wang, X. (2003). Classifying instantaneous cognitive states from FMRI data. AMIA ...Annual Symposium Proceedings / AMIA Symposium.AMIA Symposium, , 465-469. Cerca con Google

81. Mourao-Miranda, J., Friston, K. J., & Brammer, M. (2007). Dynamic discrimination analysis: A spatial-temporal SVM. NeuroImage, 36(1), 88-99. doi:10.1016/j.neuroimage.2007.02.020 Cerca con Google

82. Nieder, A. (2005). Counting on neurons: The neurobiology of numerical competence. Nature Reviews.Neuroscience, 6(3), 177-190. doi:10.1038/nrn1626 Cerca con Google

83. Nieder, A., & Miller, E. K. (2004). A parieto-frontal network for visual numerical information in the monkey. Proceedings of the National Academy of Sciences of the United States of America, 101(19), 7457-7462. doi:10.1073/pnas.0402239101 Cerca con Google

84. Nijboer, F., Sellers, E. W., Mellinger, J., Jordan, M. A., Matuz, T., Furdea, A., et al. (2008). A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology, 119(8), 1909-1916. doi:10.1016/j.clinph.2008.03.034 Cerca con Google

85. Noirhomme, Q., Kitney, R. I., & Macq, B. (2008). Single-trial EEG source reconstruction for brain-computer interface. IEEE Transactions on Bio-Medical Engineering, 55(5), 1592-1601. doi:10.1109/TBME.2007.913986 Cerca con Google

86. Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: Multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10(9), 424-430. doi:10.1016/j.tics.2006.07.005 Cerca con Google

87. O'Toole, A. J., Jiang, F., Abdi, H., & Haxby, J. V. (2005). Partially distributed representations of objects and faces in ventral temporal cortex. Journal of Cognitive Neuroscience, 17(4), 580-590. doi:10.1162/0898929053467550 Cerca con Google

88. O'Toole, A. J., Jiang, F., Abdi, H., Penard, N., Dunlop, J. P., & Parent, M. A. (2007). Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data. Journal of Cognitive Neuroscience, 19(11), 1735-1752. doi:10.1162/jocn.2007.19.11.1735 Cerca con Google

89. Pauling, L., & Coryell, C. D. (1936). The magnetic properties and structure of hemoglobin, oxyhemoglobin and carbonmonoxyhemoglobin. Proceedings of the National Academy of Sciences of the United States of America, 22(4), 210-216. Cerca con Google

90. Piazza, M., & Dehaene, S. (2004). From number neurons to mental arithmetic: The cognitive neuroscience of number sense. The cognitive neurosciences (3rd ed., pp. 865–875). Cambridge, MA:MIT: Gazzaniga, M.S. Cerca con Google

91. Piazza, M., Izard, V., Pinel, P., Le Bihan, D., & Dehaene, S. (2004). Tuning curves for approximate numerosity in the human intraparietal sulcus. Neuron, 44(3), 547-555. doi:10.1016/j.neuron.2004.10.014 Cerca con Google

92. Piazza, M., Pinel, P., Le Bihan, D., & Dehaene, S. (2007). A magnitude code common to numerosities and number symbols in human intraparietal cortex. Neuron, 53(2), 293-305. doi:10.1016/j.neuron.2006.11.022 Cerca con Google

93. Piccione, F., Priftis, K., Tonin, P., Vidale, D., Furlan, R., Cabinato, M., et al. (2008). Task and stimulation paradigm effects in a P300 brain computer interface exploitable in a virtual environment: A pilot study.6(1), 99-108. Cerca con Google

94. Piccione, F., Giorgi, F., Tonin, P., Priftis, K., Giove, S., Silvoni, S., et al. (2006). P300-based brain computer interface: Reliability and performance in healthy and paralysed participants. Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology, 117(3), 531-537. doi:10.1016/j.clinph.2005.07.024 Cerca con Google

95. Pinel, P., Dehaene, S., Riviere, D., & LeBihan, D. (2001). Modulation of parietal activation by semantic distance in a number comparison task. NeuroImage, 14(5), 1013-1026. doi:10.1006/nimg.2001.0913 Cerca con Google

96. Poldrack, R. A. (2006). Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Sciences, 10(2), 59-63. doi:10.1016/j.tics.2005.12.004 Cerca con Google

97. Poldrack, R. A. (2007). Region of interest analysis for fMRI. Social Cognitive and Affective Neuroscience, 2(1), 67-70. doi:10.1093/scan/nsm006 Cerca con Google

98. Poldrack, R. A. (2008). The role of fMRI in cognitive neuroscience: Where do we stand? Current Opinion in Neurobiology, 18(2), 223-227. doi:10.1016/j.conb.2008.07.006 Cerca con Google

99. Polyn, S. M., Natu, V. S., Cohen, J. D., & Norman, K. A. (2005). Category-specific cortical activity precedes retrieval during memory search. Science (New York, N.Y.), 310(5756), 1963-1966. doi:10.1126/science.1117645 Cerca con Google

100. Posner, ,Michael I., & Raichle, ,Marcus E. (1994). Images of mind. New York: Scientific american library. Cerca con Google

101. Priftis, K., Zorzi, M., Meneghello, F., Marenzi, R., & Umilta, C. (2006). Explicit versus implicit processing of representational space in neglect: Dissociations in accessing the mental number line. Journal of Cognitive Neuroscience, 18(4), 680-688. doi:10.1162/jocn.2006.18.4.680 Cerca con Google

102. Proctor, R. W., & Cho, Y. S. (2006). Polarity correspondence: A general principle for performance of speeded binary classification tasks. Psychological Bulletin, 132(3), 416-442. doi:10.1037/0033-2909.132.3.416 Cerca con Google

103. Robson, M. D., Dorosz, J. L., & Gore, J. C. (1998). Measurements of the temporal fMRI response of the human auditory cortex to trains of tones. NeuroImage, 7(3), 185-198. doi:10.1006/nimg.1998.0322 Cerca con Google

104. Rossetti, Y., Jacquin-Courtois, S., Rode, G., Ota, H., Michel, C., & Boisson, D. (2004). Does action make the link between number and space representation? visuo-manual adaptation improves number bisection in unilateral neglect. Psychological Science : A Journal of the American Psychological Society / APS, 15(6), 426-430. doi:10.1111/j.0956-7976.2004.00696.x Cerca con Google

105. Roth, V. (2004). The generalized LASSO. IEEE Transactions on Neural Networks / a Publication of the IEEE Neural Networks Council, 15(1), 16-28. doi:10.1109/TNN.2003.809398 Cerca con Google

106. Santens, S., & Gevers, W. (2008). The SNARC effect does not imply a mental number line. Cognition, 108(1), 263-270. doi:10.1016/j.cognition.2008.01.002 Cerca con Google

107. Sato, J. R., Mourao-Miranda, J., Morais Martin Mda, G., Amaro, E.,Jr, Morettin, P. A., & Brammer, M. J. (2008). The impact of functional connectivity changes on support vector machines mapping of fMRI data. Journal of Neuroscience Methods, 172(1), 94-104. doi:10.1016/j.jneumeth.2008.04.008 Cerca con Google

108. Schwarz, W., & Keus, I. M. (2004). Moving the eyes along the mental number line: Comparing SNARC effects with saccadic and manual responses. Perception & Psychophysics, 66(4), 651-664. Cerca con Google

109. Schwarz, W., & Muller, D. (2006). Spatial associations in number-related tasks: A comparison of manual and pedal responses. Experimental Psychology, 53(1), 4-15. Cerca con Google

110. Signoretto, M., Pelckmans, K., & Suykens, J. A. K. (2008a). Functional ANOVA models: Convex-concave approach and concurvity analysis (Internal Report No. 08-203). ESAT-SISTA, K.U.Leuven (Leuven, Belgium): Retrieved from ftp://ftp.esat.kuleuven.ac.be/pub/SISTA//signoretto/Signoretto08203.pdf Vai! Cerca con Google

111. Signoretto, M., Pelckmans, K., & Suykens, J. A. K. (2008b). Quadratically constrained quadratic programming for subspace selection in kernel regression estimation. Paper presented at the 18th International Conference on Artificial Neural Networks (ICANN), Prague, Czech Republic. Cerca con Google

112. Spence, S. A., Kaylor-Hughes, C., Farrow, T. F., & Wilkinson, I. D. (2008). Speaking of secrets and lies: The contribution of ventrolateral prefrontal cortex to vocal deception. NeuroImage, 40(3), 1411-1418. doi:10.1016/j.neuroimage.2008.01.035 Cerca con Google

113. Spiridon, M., & Kanwisher, N. (2002). How distributed is visual category information in human occipito-temporal cortex? an fMRI study. Neuron, 35(6), 1157-1165. Cerca con Google

114. Steinwart, I., Hush, D., & Scovel, C. (2006). An explicit description of the reproducing kernel hilbert spaces of gaussian rbf kernels. IEEE Trans. Inform. Theory, 52, 4635–4643. Cerca con Google

115. Stoianov, I., Kramer, P., Umilta, C., & Zorzi, M. (2008). Visuospatial priming of the mental number line. Cognition, 106(2), 770-779. doi:10.1016/j.cognition.2007.04.013 Cerca con Google

116. Strother, S. C., Anderson, J., Hansen, L. K., Kjems, U., Kustra, R., Sidtis, J., et al. (2002). The quantitative evaluation of functional neuroimaging experiments: The NPAIRS data analysis framework. NeuroImage, 15(4), 747-771. doi:10.1006/nimg.2001.1034 Cerca con Google

117. Suykens, J. A., Vandewalle, J., & De Moor, B. (2001). Optimal control by least squares support vector machines. Neural Networks : The Official Journal of the International Neural Network Society, 14(1), 23-35. Cerca con Google

118. TEUBER, H. L. (1955). Physiological psychology. Annual Review of Psychology, 6, 267-296. doi:10.1146/annurev.ps.06.020155.001411 Cerca con Google

119. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B(Methodological), 58(1), 267–288. Cerca con Google

120. Umilta, C., Priftis, K., & Zorzi, M. (2008). The spatial representation of numbers: Evidence from neglect and pseudoneglect. Experimental Brain Research.Experimentelle Hirnforschung.Experimentation Cerebrale, doi:10.1007/s00221-008-1623-2 Cerca con Google

121. Vanduffel, W., Tootell, R. B., Schoups, A. A., & Orban, G. A. (2002). The organization of orientation selectivity throughout macaque visual cortex. Cerebral Cortex (New York, N.Y.: 1991), 12(6), 647-662. Cerca con Google

122. Vapnik, ,Vladimir N. (1998). Statistical learning theory. New York etc.: Wiley. Cerca con Google

123. Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks / a Publication of the IEEE Neural Networks Council, 10(5), 988-999. doi:10.1109/72.788640 Cerca con Google

124. Vazquez, A. L., & Noll, D. C. (1998). Nonlinear aspects of the BOLD response in functional MRI. NeuroImage, 7(2), 108-118. doi:10.1006/nimg.1997.0316 Cerca con Google

125. Worsley, K. J., & Friston, K. J. (1995). Analysis of fMRI time-series revisited--again. NeuroImage, 2(3), 173-181. doi:10.1006/nimg.1995.1023 Cerca con Google

126. Xiong, J., Gao, J., Lancaster, J. L., & Fox, P. T. (1995). Clustered pixels analysis of functional MRI activation studies of the human brain. Human Brain Mapping, 3, 287-301. Cerca con Google

127. Yang, L., Li, J., Yao, Y., & Wu, X. (2008). A P300 detection algorithm based on F-score feature selection and support vector machines. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi, 25(1), 23-6, 52. Cerca con Google

128. Zamarian, L., Egger, C., & Delazer, M. (2007). The mental representation of ordered sequences in visual neglect. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior, 43(4), 542-550. Cerca con Google

129. Zorzi, M., Mapelli, D., Rusconi, E., & Umilta, C. (2003). Automatic spatial coding of perceived gaze direction is revealed by the simon effect. Psychonomic Bulletin & Review, 10(2), 423-429. Cerca con Google

130. Zorzi, M., Priftis, K., Meneghello, F., Marenzi, R., & Umilta, C. (2006). The spatial representation of numerical and non-numerical sequences: Evidence from neglect. Neuropsychologia, 44(7), 1061-1067. doi:10.1016/j.neuropsychologia.2005.10.025 Cerca con Google

131. Zorzi, M., Priftis, K., & Umilta, C. (2002). Brain damage: Neglect disrupts the mental number line. Nature, 417(6885), 138-139. doi:10.1038/417138a Cerca con Google

Download statistics

Solo per lo Staff dell Archivio: Modifica questo record