nips nips2013 nips2013-183 nips2013-183-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Yannick Schwartz, Bertrand Thirion, Gael Varoquaux
Abstract: Imaging neuroscience links brain activation maps to behavior and cognition via correlational studies. Due to the nature of the individual experiments, based on eliciting neural response from a small number of stimuli, this link is incomplete, and unidirectional from the causal point of view. To come to conclusions on the function implied by the activation of brain regions, it is necessary to combine a wide exploration of the various brain functions and some inversion of the statistical inference. Here we introduce a methodology for accumulating knowledge towards a bidirectional link between observed brain activity and the corresponding function. We rely on a large corpus of imaging studies and a predictive engine. Technically, the challenges are to find commonality between the studies without denaturing the richness of the corpus. The key elements that we contribute are labeling the tasks performed with a cognitive ontology, and modeling the long tail of rare paradigms in the corpus. To our knowledge, our approach is the first demonstration of predicting the cognitive content of completely new brain images. To that end, we propose a method that predicts the experimental paradigms across different studies. 1
[1] N. Kanwisher, J. McDermott, and M. M. Chun, “The fusiform face area: a module in human extrastriate cortex specialized for face perception.,” J Neurosci, vol. 17, p. 4302, 1997.
[2] R. Poldrack, “Can cognitive processes be inferred from neuroimaging data?,” Trends in cognitive sciences, vol. 10, p. 59, 2006.
[3] A. Laird, J. Lancaster, and P. Fox, “Brainmap,” Neuroinformatics, vol. 3, p. 65, 2005.
[4] T. Yarkoni, R. Poldrack, T. Nichols, D. V. Essen, and T. Wager, “Large-scale automated synthesis of human functional neuroimaging data,” Nature Methods, vol. 8, p. 665, 2011.
[5] R. Poldrack, Y. Halchenko, and S. Hanson, “Decoding the large-scale structure of brain function by classifying mental states across individuals,” Psychological Science, vol. 20, p. 1364, 2009. 8
[6] S. Hanson and Y. Halchenko, “Brain reading using full brain support vector machines for object recognition: there is no face identification area,” Neural Computation, vol. 20, p. 486, 2008.
[7] G. Salimi-Khorshidi, S. M. Smith, J. R. Keltner, T. D. Wager, et al., “Meta-analysis of neuroimaging data: a comparison of image-based and coordinate-based pooling of studies,” Neuroimage, vol. 45, p. 810, 2009.
[8] C. Pallier, A. Devauchelle, and S. Dehaene, “Cortical representation of the constituent structure of sentences,” Proc Natl Acad Sci, vol. 108, p. 2522, 2011.
[9] J. Turner and A. Laird, “The cognitive paradigm ontology: design and application,” Neuroinformatics, vol. 10, p. 57, 2012.
[10] V. Michel, A. Gramfort, G. Varoquaux, E. Eger, C. Keribin, and B. Thirion, “A supervised clustering approach for fMRI-based inference of brain states,” Pattern Recognition, vol. 45, p. 2041, 2012.
[11] H. Shimodaira, “Improving predictive inference under covariate shift by weighting the log-likelihood function,” Journal of statistical planning and inference, vol. 90, p. 227, 2000.
[12] R. Poldrack, D. Barch, J. Mitchell, T. Wager, A. Wagner, J. Devlin, C. Cumba, and M. Milham, “Towards open sharing of task-based fMRI data: The openfMRI project (in press),” Frontiers in Neuroinformatics.
[13] T. Schonberg, C. Fox, J. Mumford, C. Congdon, C. Trepel, and R. Poldrack, “Decreasing ventromedial prefrontal cortex activity during sequential risk-taking: an fMRI investigation of the balloon analog risk task,” Frontiers in Neuroscience, vol. 6, 2012.
[14] S. Tom, C. Fox, C. Trepel, and R. Poldrack, “The neural basis of loss aversion in decision-making under risk,” Science, vol. 315, p. 515, 2007.
[15] A. Aron, M. Gluck, and R. Poldrack, “Long-term test–retest reliability of functional MRI in a classification learning task,” Neuroimage, vol. 29, p. 1000, 2006.
[16] K. Foerde, B. Knowlton, and R. Poldrack, “Modulation of competing memory systems by distraction,” Proc Natl Acad Sci, vol. 103, p. 11778, 2006.
[17] R. Poldrack, J. Clark, E. Pare-Blagoev, D. Shohamy, J. Creso Moyano, C. Myers, and M. Gluck, “Interactive memory systems in the human brain,” Nature, vol. 414, p. 546, 2001.
[18] G. Xue and R. Poldrack, “The neural substrates of visual perceptual learning of words: implications for the visual word form area hypothesis,” J Cognitive Neurosci, vol. 19, p. 1643, 2007.
[19] L. Vagharchakian, G. Dehaene-Lambertz, C. Pallier, and S. Dehaene, “A temporal bottleneck in the language comprehension network,” J Neurosci, vol. 32, p. 9089, 2012.
[20] G. Xue, A. Aron, and R. Poldrack, “Common neural substrates for inhibition of spoken and manual responses,” Cerebral Cortex, vol. 18, p. 1923, 2008.
[21] A. Kelly, L. Q. Uddin, B. B. Biswal, F. Castellanos, and M. Milham, “Competition between functional brain networks mediates behavioral variability,” Neuroimage, vol. 39, p. 527, 2008.
[22] J. Haxby, I. Gobbini, M. Furey, A. Ishai, J. Schouten, and P. Pietrini, “Distributed and overlapping representations of faces and objects in ventral temporal cortex,” Science, vol. 293, p. 2425, 2001.
[23] K. Duncan, C. Pattamadilok, I. Knierim, and J. Devlin, “Consistency and variability in functional localisers,” Neuroimage, vol. 46, p. 1018, 2009.
[24] P. Pinel, B. Thirion, S. Meriaux, A. Jobert, J. Serres, D. L. Bihan, J. B. Poline, and S. Dehaene, “Fast reproducible identification and large-scale databasing of individual functional cognitive networks,” BMC neuroscience, vol. 8, p. 91, 2007.
[25] P. Pinel and S. Dehaene, “Genetic and environmental contributions to brain activation during calculation,” NeuroImage, vol. in press, 2013.
[26] A. Knops, B. Thirion, E. M. Hubbard, V. Michel, and S. Dehaene, “Recruitment of an area involved in eye movements during mental arithmetic,” Science, vol. 324, p. 1583, 2009.
[27] J. Deng, A. Berg, K. Li, and L. Fei-Fei, “What does classifying more than 10,000 image categories tell us?,” in Computer Vision–ECCV 2010, p. 71, 2010.
[28] W. W. Seeley, V. Menon, A. F. Schatzberg, J. Keller, G. H. Glover, H. Kenna, A. L. Reiss, and M. D. Greicius, “Dissociable intrinsic connectivity networks for salience processing and executive control,” J neurosci, vol. 27, p. 2349, 2007. 9