Multivariate pattern analysis of fMRI data for imaginary and real colours in grapheme-colour synaesthesia.
colour perception
human vision
mental imagery
synesthesia
Journal
The European journal of neuroscience
ISSN: 1460-9568
Titre abrégé: Eur J Neurosci
Pays: France
ID NLM: 8918110
Informations de publication
Date de publication:
09 2020
09 2020
Historique:
received:
09
01
2020
revised:
22
04
2020
accepted:
03
05
2020
pubmed:
10
5
2020
medline:
22
6
2021
entrez:
9
5
2020
Statut:
ppublish
Résumé
Grapheme-colour synaesthesia is a subjective phenomenon related to perception and imagination, in which some people involuntarily but systematically associate specific, idiosyncratic colours to achromatic letters or digits. Its investigation is relevant to unravel the neural correlates of colour perception in isolation from low-level neural processing of spectral components, as well as the neural correlates of imagination by being able to reliably trigger imaginary colour experiences. However, functional MRI studies using univariate analyses failed to provide univocal evidence of the activation of the "colour network" by synaesthesia. Applying multivariate (multivoxel) pattern analysis (MVPA) on 20 synaesthetes and 20 control participants, we tested whether the neural processing of real colours (concentric rings) and synaesthetic colours (black graphemes) shared patterns of activations. Region of interest analyses in retinotopically and anatomically defined visual areas revealed neither evidence of shared circuits for real and synaesthetic colour processing, nor processing difference between synaesthetes and controls. We also found no correlation with individual experiences, characterised by measuring the strength of synaesthetic associations. The whole brain searchlight analysis led to similar results. We conclude that revealing the neural coding of the synaesthetic experience of colours is a hard task which requires the improvement of our current methodology: for example involving more individuals and achieving higher MR signal to noise ratio and spatial resolution. So far, we have not found any evidence of the involvement of the cortical colour network in the subjective experience of synaesthetic colours.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3434-3456Subventions
Organisme : Agence Nationale de la Recherche
ID : ANR-11-BSH2-010
Organisme : Agence Nationale de la Recherche
ID : ANR-11-INBS-0006
Informations de copyright
© 2020 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.
Références
Bannert, M. M., & Bartels, A. (2013). Decoding the yellow of a gray banana. Current Biology, 23(22), 2268-2272. https://doi.org/10.1016/j.cub.2013.09.016
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 48. https://doi.org/10.18637/jss.v067.i01
Bordier, C., Hupé, J. M., & Dojat, M. (2015). Quantitative evaluation of fMRI retinotopic maps, from V1 to V4, for cognitive experiments. Frontiers in Human Neuroscience, 9, 277. https://doi.org/10.3389/fnhum.2015.00277
Brouwer, G. J., & Heeger, D. J. (2009). Decoding and reconstructing color from responses in human visual cortex. Journal of Neuroscience, 29(44), 13992-14003. https://doi.org/10.1523/JNEUROSCI.3577-09.2009
Caspers, J., Zilles, K., Eickhoff, S. B., Schleicher, A., Mohlberg, H., & Amunts, K. (2013). Cytoarchitectonical analysis and probabilistic mapping of two extrastriate areas of the human posterior fusiform gyrus. Brain Structure and Function, 218(2), 511-526. https://doi.org/10.1007/s00429-012-0411-8
Caspers, S., Eickhoff, S. B., Geyer, S., Scheperjans, F., Mohlberg, H., Zilles, K., & Amunts, K. (2008). The human inferior parietal lobule in stereotaxic space. Brain Structure and Function, 212(6), 481-495. https://doi.org/10.1007/s00429-008-0195-z
Caspers, S., Geyer, S., Schleicher, A., Mohlberg, H., Amunts, K., & Zilles, K. (2006). The human inferior parietal cortex: Cytoarchitectonic parcellation and interindividual variability. NeuroImage, 33(2), 430-448. https://doi.org/10.1016/j.neuroimage.2006.06.054
Chiou, R., & Rich, A. N. (2014). The role of conceptual knowledge in understanding synaesthesia: Evaluating contemporary findings from a ‘hub-and-spoke’ perspective. Frontiers in Psychology, 5(105), 1-18. https://doi.org/10.3389/fpsyg.2014.00105
Chiou, R., Rich, A. N., Rogers, S., & Pearson, J. (2018). Exploring the functional nature of synaesthetic colour: Dissociations from colour perception and imagery. Cognition, 177, 107-121. https://doi.org/10.1016/j.cognition.2018.03.022
Choi, H. J., Zilles, K., Mohlberg, H., Schleicher, A., Fink, G. R., Armstrong, E., & Amunts, K. (2006). Cytoarchitectonic identification and probabilistic mapping of two distinct areas within the anterior ventral bank of the human intraparietal sulcus. The Journal of Comparative Neurology, 495(1), 53-69. https://doi.org/10.1002/cne.20849
Chun, C. A., & Hupé, J. M. (2013). Mirror-touch and ticker tape experiences in synesthesia. Frontiers in Psychology, 4, 776. https://doi.org/10.3389/fpsyg.2013.00776
Chun, C. A., & Hupé, J. M. (2016). Are synesthetes exceptional beyond their synesthetic associations? A systematic comparison of creativity, personality, cognition, and mental imagery in synesthetes and controls. British Journal of Psychology, 107, 397-418. https://doi.org/10.1111/bjop.12146
Cichy, R. M., Heinzle, J., & Haynes, J. D. (2012). Imagery and perception share cortical representations of content and location. Cerebral Cortex, 22(2), 372-380. https://doi.org/10.1093/cercor/bhr106
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. https://doi.org/10.1016/S1053-8119(03)00049-1
Cumming, G. (2012). Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis. New York, NY: Routledge.
de Valpine, P., Turek, D., Paciorek, C. J., Anderson-Bergman, C., Lang, D. T., & Bodik, R. (2017). Programming with models: Writing statistical algorithms for general model structures with NIMBLE. Journal of Computational and Graphical Statistics, 26(2), 403-413. https://doi.org/10.1080/10618600.2016.1172487
Dehaene, S., & Cohen, L. (2011). The unique role of the visual word form area in reading. Trends in Cognitive Sciences, 15(6), 254-262. https://doi.org/10.1016/j.tics.2011.04.003
Dojat, M., Pizzagalli, F., & Hupé, J. M. (2018). Magnetic resonance imaging does not reveal structural alterations in the brain of grapheme-color synesthetes. PLoS ONE, 13(4), e0194422. https://doi.org/10.1371/journal.pone.0194422
Eagleman, D. M., Kagan, A. D., Nelson, S. S., Sagaram, D., & Sarma, A. K. (2007). A standardized test battery for the study of synesthesia. Journal of Neuroscience Methods, 159(1), 139-145. https://doi.org/10.1016/j.jneumeth.2006.07.012
Edquist, J., Rich, A. N., Brinkman, C., & Mattingley, J. B. (2006). Do synaesthetic colours act as unique features in visual search? Cortex, 42(2), 222-231. https://doi.org/10.1016/S0010-9452(08)70347-2
Eickhoff, S. B., Paus, T., Caspers, S., Grosbras, M. H., Evans, A. C., Zilles, K., & Amunts, K. (2007). Assignment of functional activations to probabilistic cytoarchitectonic areas revisited. NeuroImage, 36(3), 511-521. https://doi.org/10.1016/j.neuroimage.2007.03.060
Eklund, A., Nichols, T. E., & Knutsson, H. (2016). Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. Proceedings of the National Academy of Sciences of the United States of America, 113(28), 7900-7905. https://doi.org/10.1073/pnas.1602413113
Flournoy, T. (1893). Des Phénomènes de Synopsie (Audition Colorée): Photismes, Schèmes Visuels, Personnifications. Paris, France: Alcan.
Formisano, E., & Kriegeskorte, N. (2012). Seeing patterns through the hemodynamic veil-the future of pattern-information fMRI. NeuroImage, 62(2), 1249-1256. https://doi.org/10.1016/j.neuroimage.2012.02.078
Friston, K. J., Zarahn, E., Josephs, O., Henson, R. N., & Dale, A. M. (1999). Stochastic designs in event-related fMRI. NeuroImage, 10, 607-619. https://doi.org/10.1006/nimg.1999.0498
Galton, F. (1880). Statistics of mental imagery. Mind, 5, 301-318.
Gebuis, T., Nijboer, T. C., & Van der Smagt, M. J. (2009). Multiple dimensions in bi-directional synesthesia. European Journal of Neuroscience, 29(8), 1703-1710. https://doi.org/10.1111/j.1460-9568.2009.06699.x
Grill-Spector, K., Kourtzi, Z., & Kanwisher, N. (2001). The lateral occipital complex and its role in object recognition. Vision Research, 41(10-11), 1409-1422. https://doi.org/10.1016/S0042-6989(01)00073-6
Hebart, M. N., & Baker, C. I. (2017). Deconstructing multivariate decoding for the study of brain function. NeuroImage, 180, 4-18. https://doi.org/10.1016/j.neuroimage.2017.08.005
Hupé, J. M. (2015). Statistical inferences under the Null hypothesis: Common mistakes and pitfalls in neuroimaging studies. Frontiers in Neuroscience, 9, 18. https://doi.org/10.3389/fnins.2015.00018
Hupé, J. M., Bordier, C., & Dojat, M. (2012a). A BOLD signature of eyeblinks in the visual cortex. NeuroImage, 61, 149-161. https://doi.org/10.1016/j.neuroimage.2012.03.001
Hupé, J. M., Bordier, C., & Dojat, M. (2012b). The neural bases of grapheme-color synesthesia are not localized in real color sensitive areas. Cerebral Cortex, 22, 1622-1633. https://doi.org/10.1093/cercor/bhr236
Hupé, J. M., & Dojat, M. (2015). A critical review of the neuroimaging literature on synesthesia. Frontiers in Human Neuroscience, 9, 103. https://doi.org/10.3389/fnhum.2015.00103
Janik McErlean, A. B., & Banissy, M. J. (2017). Color processing in synesthesia: What synesthesia can and cannot tell us about mechanisms of color processing. Topics in Cognitive Science, 9(1), 215-227. https://doi.org/10.1111/tops.12237
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. https://doi.org/10.1073/pnas.0600244103
Kruschke, J. K. (2015). Doing Bayesian data analysis, Second Edition: A tutorial with R, JAGS, and Stan. Burlington, MA: Academic Press/Elsevier.
Kruschke, J. K., & Liddell, T. M. (2018). The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychonomic Bulletin & Review, 25, 178-206. https://doi.org/10.3758/s13423-016-1221-4
Lorenz, S., Weiner, K. S., Caspers, J., Mohlberg, H., Schleicher, A., Bludau, S., … Amunts, K. (2017). Two new cytoarchitectonic areas on the human mid-fusiform gyrus. Cerebral Cortex, 27(1), 373-385. https://doi.org/10.1093/cercor/bhv225
Misaki, M., Kim, Y., Bandettini, P. A., & Kriegeskorte, N. (2010). Comparison of multivariate classifiers and response normalizations for pattern-information fMRI. NeuroImage, 53, 103-118. https://doi.org/10.1016/j.neuroimage.2010.05.051
Mumford, J. A., Turner, B. O., Ashby, F. G., & Poldrack, R. A. (2012). Deconvolving BOLD activation in event-related designs for multivoxel pattern classification analyses. NeuroImage, 59(3), 2636-2643. https://doi.org/10.1016/j.neuroimage.2011.08.076
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. https://doi.org/10.1016/j.tics.2006.07.005
Parkes, L. M., Marsman, J. B., Oxley, D. C., Goulermas, J. Y., & Wuerger, S. M. (2009). Multivoxel fMRI analysis of color tuning in human primary visual cortex. Journal of Vision, 9(1), 1-13. https://doi.org/10.1167/9.1.1
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825-2830.
Poldrack, R. A. (2007). Region of interest analysis for fMRI. Social Cognitive and Affective Neuroscience, 2(1), 67-70. https://doi.org/10.1093/scan/nsm006
Reddy, L., Tsuchiya, N., & Serre, T. (2010). Reading the mind's eye: Decoding category information during mental imagery. NeuroImage, 50(2), 818-825. https://doi.org/10.1016/j.neuroimage.2009.11.084
Reeder, R. R. (2016). Individual differences shape the content of visual representations. Vision Research, 141, 266-281. https://doi.org/10.1016/j.visres.2016.08.008
Rouw, R., & Scholte, H. S. (2010). Neural basis of individual differences in synesthetic experiences. Journal of Neuroscience, 30(18), 6205-6213. https://doi.org/10.1523/JNEUROSCI.3444-09.2010
Rouw, R., & Scholte, H. S. (2016). Personality and cognitive profiles of a general synesthetic trait. Neuropsychologia, 88, 35-48. https://doi.org/10.1016/j.neuropsychologia.2016.01.006
Ruiz, M. J. (2014). Codage Cortical de la Synesthésie Graphème-Couleur. Doctoral dissertation, Université de Grenoble. Retrieved from https://hal-univ-tlse3.archives-ouvertes.fr/tel-01091142
Ruiz, M. J., & Hupé, J. M. (2015). Assessment of the hemispheric lateralization of grapheme-color synesthesia with Stroop-type tests. PLoS ONE, 10(3), e0119377. https://doi.org/10.1371/journal.pone.0119377
Ruiz, M. J., Hupé, J. M., & Dojat, M. (2012). Use of pattern-information analysis in vision science: a pragmatic examination. In F. Wang, D. Shen, P. Yan, & K. Suzuki (Eds.), Machine learning in medical imaging. MLMI 2012. Lecture notes in computer science (Vol. 7588, pp. 103-110). Berlin and Heidelberg, Germany: Springer. https://doi.org/10.1007/978-3-642-35428-1_13
Scheperjans, F., Hermann, K., Eickhoff, S. B., Amunts, K., Schleicher, A., & Zilles, K. (2008). Observer-independent cytoarchitectonic mapping of the human superior parietal cortex. Cerebral Cortex, 18(4), 846-867. https://doi.org/10.1093/cercor/bhm116
Shifferman, E. (2015). More than meets the fMRI: The unethical apotheosis of neuroimages. Journal of Cognitive Neuroethology, 3, 57-116.
Simner, J., & Carmichael, D. A. (2015). Is synaesthesia a dominantly female trait? Cognitive Neuroscience, 6(2-3), 68-76. https://doi.org/10.1080/17588928.2015.1019441
Simner, J., Mulvenna, C., Sagiv, N., Tsakanikos, E., Witherby, S. A., Fraser, C., … Ward, J. (2006). Synaesthesia: The prevalence of atypical cross-modal experiences. Perception, 35(8), 1024-1033. https://doi.org/10.1068/p5469
Stelzer, J., Lohmann, G., Mueller, K., Buschmann, T., & Turner, R. (2014). Deficient approaches to human neuroimaging. Frontiers in Human Neuroscience, 8(462), 1-16. https://doi.org/10.3389/fnhum.2014.00462
Stokes, M., Thompson, R., Cusack, R., & Duncan, J. (2009). Top-down activation of shape-specific population codes in visual cortex during mental imagery. Journal of Neuroscience, 29, 1565-1572. https://doi.org/10.1523/JNEUROSCI.4657-08.2009
Stoughton, C. M., & Conway, B. R. (2008). Neural basis for unique hues. Current Biology, 18, R698-R699. https://doi.org/10.1016/j.cub.2008.06.018
Thirion, B., Duchesnay, E., Hubbard, E., Dubois, J., Poline, J. B., Lebihan, D., & Dehaene, S. (2006). Inverse retinotopy: Inferring the visual content of images from brain activation patterns. NeuroImage, 33(4), 1104-1116. https://doi.org/10.1016/j.neuroimage.2006.06.062
Tootell, R. B. H., & Nasr, S. (2017). Columnar segregation of magnocellular and parvocellular streams in human extrastriate cortex. Journal of Neuroscience, 37(33), 8014-8032. https://doi.org/10.1523/jneurosci.0690-17.2017
Turner, R. (2016). Uses, misuses, new uses and fundamental limitations of magnetic resonance imaging in cognitive science. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 371(1705), 1-11. https://doi.org/10.1098/rstb.2015.0349
Vasseur, F., Delon-Martin, C., Bordier, C., Warnking, J., Lamalle, L., Segebarth, C., & Dojat, M. (2010). fMRI retinotopic mapping at 3 T: Benefits gained from correcting the spatial distortions due to static field inhomogeneity. Journal of Vision, 10(12), 30. https://doi.org/10.1167/10.12.30
Ward, J. (2013). Synesthesia. Annual Reviews in Psychology, 64, 49-75. https://doi.org/10.1146/annurev-psych-113011-143840
Watson, M. R., Chromy, J., Crawford, L., Eagleman, D. M., Enns, J. T., & Akins, K. A. (2017). The prevalence of synaesthesia depends on early language learning. Consciousness and Cognition, 48, 212-231. https://doi.org/10.1016/j.concog.2016.12.004
Witthoft, N., & Winawer, J. (2013). Learning, memory, and synesthesia. Psychological Science, 24(3), 258-265. https://doi.org/10.1177/0956797612452573
Yarkoni, T., Poldrack, R. A., Van Essen, D. C., & Wager, T. D. (2010). Cognitive neuroscience 2.0: Building a cumulative science of human brain function. Trends Cogn. Sci., 14(11), 489-496. https://doi.org/10.1016/j.tics.2010.08.004