Decoding brain states from fMRI connectivity graphs

NeuroImage - Tập 56 - Trang 616-626 - 2011
Jonas Richiardi1,2, Hamdi Eryilmaz3, Sophie Schwartz3, Patrik Vuilleumier3, Dimitri Van De Ville1,2
1Medical Image Processing Lab, Ecole Polytechnique Fédérale de Lausanne, Switzerland
2Medical Image Processing Lab, University of Geneva, Switzerland
3Laboratory of Neurology and Imaging of Cognition, University of Geneva, Switzerland

Tài liệu tham khảo

Achard, 2006, A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs, The Journal of Neuroscience, 26, 63, 10.1523/JNEUROSCI.3874-05.2006 Alemán-Gómez, 2006, IBASPM: toolbox for automatic parcellation of brain structures Anderson, 2010, Classification of spatially unaligned fMRI scans, Neuroimage, 49, 2509, 10.1016/j.neuroimage.2009.08.036 Battle, 1987, A block spin construction of ondelettes. Part I: Lemarié functions, Communications in Mathematical Physics, 110, 601, 10.1007/BF01205550 Beckmann, 2004, Probabilistic independent component analysis for functional magnetic resonance imaging, IEEE Transactions on Medical Imaging, 23, 137, 10.1109/TMI.2003.822821 Benjamini Y., Hochberg Y., 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological) 57 (1), 289–300. URL http://www.jstor.org/stable/2346101. Biswal, 1995, Functional connectivity in the motor cortex of resting human brain using echo-planar MRI, Magnetic Resonance in Medicine, 34, 537, 10.1002/mrm.1910340409 Bluhm R.L., Miller J., Lanius R.A., Osuch E.A., Boksman, K., Neufeld, R.W.J., Théberge, J., Schaefer, B., Williamson, P., Jul 2007. Spontaneous low-frequency fluctuations in the BOLD signal in schizophrenic patients: anomalies in the default network. Schizophr Bull 33 (4), 1004–1012. URL http://dx.doi.org/10.1093/schbul/sbm052. Boland, 1989, Majority system and the Condorcet jury theorem, Statistician, 38, 181, 10.2307/2348873 Bray S., Chang C., Hoeft F., 2009. Applications of multivariate pattern classification analyses in developmental neuroimaging of healthy and clinical populations. Front Hum Neurosci 3, 32.URL http://dx.doi.org/10.3389/neuro.09.032.2009. Breiman L., Aug. 1996. Bagging predictors. Machine Learning 24 (2), 123–140. URL http://dx.doi.org/10.1023/A:1018054314350. Breiman, 1984 Buckner, R., Andrews-Hanna, J., Schacter, D., 2008. The brain's default network: anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences 1124, 1–38.URL http://www.scopus.com/inward/record.url?eid=2-s2.0-4194 9121294 partnerID=40. Bunke H., Shearer K., Mar. 1998. A graph distance metric based on the maximal common subgraph. Pattern Recognition Letters 19 (3–4), 255–259. URL http://www.sciencedirect.com/science/article/B6V15-3TX4 XGG-H/2/e8a61e2fc10e974b4d963e403ac9d74e. Calhoun, 2002, Independent component analysis of fMRI data in the complex domain, Magnetic Resonance in Medicine, 48, 180, 10.1002/mrm.10202 Conte, 2004, Thirty years of graph matching in pattern recognition, International Journal of Pattern Recognition and Artificial Intelligence, 18, 265, 10.1142/S0218001404003228 Cox, 2003, Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex, Neuroimage, 19, 261, 10.1016/S1053-8119(03)00049-1 Craddock R.C., Holtzheimer P.E., Hu, X.P., Mayberg H.S., Dec 2009. Disease state prediction from resting state functional connectivity. Magn Reson Med 62 (6), 1619–1628. URL http://dx.doi.org/10.1002/mrm.22159. Dale A.M., Fischl B., Sereno M.I., Feb 1999. Cortical surface-based analysis. I. segmentation and surface reconstruction. Neuroimage 9 (2), 179–194.URL http://dx.doi.org/10.1006/nimg.1998.0395. Damoiseaux, 2006, Consistent resting-state networks across healthy subjects, Proceedings of the National Academy of Science, 103, 13848, 10.1073/pnas.0601417103 De Martino, 2008, Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns, Neuroimage, 43, 44, 10.1016/j.neuroimage.2008.06.037 Duda, 2001 Ethofer, 2009, Decoding of emotional information in voice-sensitive cortices, Current Biology, 19, 1028, 10.1016/j.cub.2009.04.054 Fair D.A., Schlaggar B.L., Cohen A.L., Miezin F.M., Dosenbach N.U., Wenger K.K., Fox M.D., Snyder A.Z., Raichle M.E., Petersen S.E., Mar. 2007. A method for using blocked and event-related fMRI data to study “resting state” functional connectivity. NeuroImage 35 (1), 396–405. URL http://www.sciencedirect.com/science/article/B6WNP-4MVD VJP-6/2/2ab915a22f7120d0fac34075e98b7555. Fischl B., Sereno M.I., Dale A.M., Feb 1999. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9 (2), 195–207. URL http://dx.doi.org/10.1006/nimg.1998.0396. Frackowiak, 1997 Friedman J., Hastie T., Tibshirani R., 2000. Additive logistic regression: a statistical view of boosting. Annals of Statistics 28 (2), 337–407. URL http://www.scopus.com/inward/record.url?eid=2-s2.0-0034 164230 partnerID=40. Friston K., Frith C., Liddle P., Frackowiak R., 1993. Functional connectivity: the principal-component analysis of large (PET) data sets. Journal of Cerebral Blood Flow and Metabolism 13 (1), 5–14. URL http://www.scopus.com/inward/record.url?eid=2-s2.0-0027 441566 partnerID=40. Friston, 2003, Dynamic causal modeling, Neuroimage, 19, 1273, 10.1016/S1053-8119(03)00202-7 Gama J., Jun. 2004. Functional trees. Machine Learning 55 (3), 219–250. URL http://dx.doi.org/10.1023/B:MACH.0000027782.67192.13. Garrity A.G., Pearlson G.D., McKiernan K., Lloyd D., Kiehl K.A., Calhoun V.D., Mar 2007. Aberrant “default mode” functional connectivity in schizophrenia. The American Journal of Psychiatry 164 (3), 450–457. URL http://dx.doi.org/10.1176/appi.ajp.164.3.450. Geman S., Bienenstock E., Doursat R., Jan. 1992. Neural networks and the bias/variance dilemma. Neural Computation 4 (1), 1–58. URL http://dx.doi.org/10.1162/neco.1992.4.1.1. Golland Y., Bentin S., Gelbard H., Benjamini Y., Heller R., Nir Y., Hasson U., Malach R., 2007. Extrinsic and Intrinsic Systems in the Posterior Cortex of the Human Brain Revealed during Natural Sensory Stimulation. Cereb. Cortex 17 (4), 766–777. URL http://cercor.oxfordjournals.org/cgi/content/abstract/1 7/4/766. Greicius M.D., Krasnow B., Reiss A.L., Menon V., Jan. 2003. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences of the United States of America 100 (1), 253–258. URL http://www.pnas.org/content/100/1/253.abstract. Grill-Spector K., Malach R., 2004. The human visual cortex. Annual Review of Neuroscience 27, 649–677. URL http://dx.doi.org/10.1146/annurev.neuro.27.070203.14422 0. Guyon, 2007 Guyon, 2005, Result analysis of the NIPS 2003 feature selection challenge, Advances in Neural Information Processing Systems, 17, 545 Harrison B.J., Soriano-Mas C., Pujol J., Ortiz H., López-Solà M., Hernández-Ribas R., Deus J., Alonso P., Yücel M., Pantelis C., Menchon J.M., Cardoner N., Nov 2009. Altered corticostriatal functional connectivity in obsessive–compulsive disorder. Arch Gen Psychiatry 66 (11), 1189–1200. URL http://dx.doi.org/10.1001/archgenpsychiatry.2009.152. Hasson U., Nir Y., Levy I., Fuhrmann G., Malach R., Mar 2004. Intersubject synchronization of cortical activity during natural vision. Science 303 (5664), 1634–1640. URL http://dx.doi.org/10.1126/science.1089506. Haxby, 2001, Distributed and overlapping representations of faces and objects in ventral temporal cortex, Science, 293, 2425, 10.1126/science.1063736 Haynes, 2005, Predicting the orientation of invisible stimuli from activity in human primary visual cortex, Nature Neuroscience, 8, 686, 10.1038/nn1445 Haynes J.-D., Rees G., Jul 2006. Decoding mental states from brain activity in humans. Nature Reviews Neuroscience 7 (7), 523–534. URL http://dx.doi.org/10.1038/nrn1931. He B.J., Snyder A.Z., Vincent J.L., Epstein A., Shulman G.L., Corbetta M., Mar. 2007. Breakdown of functional connectivity in frontoparietal networks underlies behavioral deficits in spatial neglect. Neuron 53 (6), 905–918. URL http://www.sciencedirect.com/science/article/B6WSS-4N85 VSH-H/2/d252eb3b6814bea5d05b80d39a09f1f8. Kamitani, 2005, Decoding the visual and subjective contents of the human brain, Nature Neuroscience, 8, 679, 10.1038/nn1444 Kay, 2008, Identifying natural images from human brain activity, Nature, 452, 352, 10.1038/nature06713 Kennedy D.P., Courchesne E., Feb 2008. The intrinsic functional organization of the brain is altered in autism. Neuroimage 39 (4), 1877–1885. URL http://dx.doi.org/10.1016/j.neuroimage.2007.10.052. Kohavi, 1996, Bias plus variance for zero-one loss functions Kriegeskorte, 2006, Information-based functional brain mapping, PNAS, 103, 3863, 10.1073/pnas.0600244103 Landwehr N., Hall M., Frank E., May 2005. Logistic model trees. Machine Learning 59 (1), 161–205. URL http://dx.doi.org/10.1007/s10994-005-0466-3. Lowe M.J., Mock B.J., Sorenson J.A., Feb. 1998. Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations. NeuroImage 7 (2), 119–132. URL http://www.sciencedirect.com/science/article/B6WNP-45M2 Y15-1G/2/88964e40762eb2eadd8376e575d6e198. Mantini D., Perrucci M. G., Gratta C. D., Romani G. L., Corbetta M., Aug 2007. Electrophysiological signatures of resting state networks in the human brain. Proc National Academy of Sciences of the USA 104 (32), 13170–13175. URL http://dx.doi.org/10.1073/pnas.0700668104. Matan, 1996, On voting ensembles of classifiers (extended abstract) McKeown, 1998, Analysis of fMRI data by blind separation into independent spatial components, Human Brain Mapping, 6, 160, 10.1002/(SICI)1097-0193(1998)6:3<160::AID-HBM5>3.0.CO;2-1 Meynet J., Thiran J.-P., 2007. Information theoretic combination of classifiers with application to AdaBoost. In: Proc. 7th Int. Workshop on Multiple Classifier Systems. pp. 171–179. URL http://dx.doi.org/10.1007/978-3-540-72523-7_18. Mitchell, 2004, Learning to decode cognitive states from brain images, Machine Learning, 57, 145, 10.1023/B:MACH.0000035475.85309.1b Miyawaki, 2008, Visual image reconstruction from human brain activity using a combination of multiscale local image decoders, Neuron, 60, 915, 10.1016/j.neuron.2008.11.004 Mourao-Miranda, J., Bokde, A. L., Born, C., Hampel, H., Stetter, M., Dec. 2005. Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data. NeuroImage 28 (4), 980–995. URL http://www.sciencedirect.com/science/article/B6WNP-4HGM 79R-1/2/fcb89095569ad4445086c71d6eb5bfff. Mourao-Miranda, J., Friston, K. J., Brammer, M., May 2007. Dynamic discrimination analysis: a spatial-temporal SVM. NeuroImage 36 (1), 88–99. URL http://www.sciencedirect.com/science/article/B6WNP-4N43 RSS-1/2/e6ea9e5e840f8b1ff576cf3eec41147b. Nir Y., Hasson U., Levy I., Yeshurun Y., Malach R., 2006. Widespread functional connectivity and fMRI fluctuations in human visual cortex in the absence of visual stimulation. NeuroImage 30 (4), 1313–1324. URL http://www.sciencedirect.com/science/article/B6WNP-4J2M 1T1-6/2/9bf744bb378b01205deeffbf24e5391c. Norman, 2006, Beyond mind-reading: multivoxel pattern analysis of fMRI data, Trends in Cognitive Sciences, 10, 424, 10.1016/j.tics.2006.07.005 Raichle M.E., MacLeod A.M., Snyder A.Z., Powers W.J., Gusnard D.A., Shulman G.L., Jan 2001. A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America 98 (2), 676–682. URL http://dx.doi.org/10.1073/pnas.98.2.676. Richiardi, 2010, Vector space embedding of undirected graphs with fixed-cardinality vertex sequences for classification Sabuncu M.R., Singer B.D., Conroy B., Bryan R.E., Ramadge P.J., Haxby J.V., Jan 2010. Function-based intersubject alignment of human cortical anatomy. Cerebral Cortex 20 (1), 130–140. URL http://dx.doi.org/10.1093/cercor/bhp085. Salvador, 2005, Undirected graphs of frequency-dependent functional connectivity in whole brain networks, Philosophy Transactions of the Royal Society of London.Series B: Biological Sciences, 360, 937, 10.1098/rstb.2005.1645 Sato J.R., Fujita A., Thomaz, C.E., Martin M.D.G.M., Mourao-Miranda, J., Brammer M.J., Junior E.A., May 2009. Evaluating SVM and MLDA in the extraction of discriminant regions for mental state prediction. NeuroImage 46 (1), 105–114. URL http://www.sciencedirect.com/science/article/B6WNP-4VH4 DFS-2/2/a20c7c5a62098cdf796c78f0f36282f7. Sporns, 2000, Connectivity and complexity: the relationship between neuroanatomy and brain dynamics, Neural Networks, 13, 909, 10.1016/S0893-6080(00)00053-8 Teipel S.J., Bokde A.L., Meindl T., Amaro Jr., E., Soldner J., Reiser M.F., Herpertz S.C., Müller H.-J., Hampel H., 2010. White matter microstructure underlying default mode network connectivity in the human brain. NeuroImage 49 (3), 2021–2032. URL http://www.sciencedirect.com/science/article/B6WNP-4XJP 3YK-5/2/a6bddf6c37c06412bd12748b5672e21b. Thirion, 2006, Inverse retinotopy: inferring the visual content of images from brain activation patterns, Neuroimage, 33, 1104, 10.1016/j.neuroimage.2006.06.062 Tzourio-Mazoyer, 2002, Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain, Neuroimage, 15, 273, 10.1006/nimg.2001.0978 Umeyama, 1988, An eigendecomposition approach to weighted graph matching problems, IEEE Transactions on Pattern Analysis and Machine Intelligence, 10, 695, 10.1109/34.6778 Wang K., Liang M., Wang L., Tian L., Zhang X., Li K., Jiang T., 2007. Altered functional connectivity in early Alzheimer's disease: a resting-state fMRI study. Human Brain Mapping 28 (10), 967–978. URL http://dx.doi.org/10.1002/hbm.20324. Witten, 2005 Zalesky, 2010, Whole-brain anatomical networks: does the choice of nodes matter?, Neuroimage, 50, 970, 10.1016/j.neuroimage.2009.12.027