Analysing linear multivariate pattern transformations in neuroimaging data
Autoři:
Alessio Basti aff001; Marieke Mur aff002; Nikolaus Kriegeskorte aff003; Vittorio Pizzella aff001; Laura Marzetti aff001; Olaf Hauk aff002
Působiště autorů:
Department of Neuroscience, Imaging and Clinical Sciences, University of Chieti-Pescara, Chieti, Italy
aff001; MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, England, United Kingdom
aff002; Department of Psychology, Department of Neuroscience, Department of Electrical Engineering, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, United States of America
aff003; Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy
aff004
Vyšlo v časopise:
PLoS ONE 14(10)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0223660
Souhrn
Most connectivity metrics in neuroimaging research reduce multivariate activity patterns in regions-of-interests (ROIs) to one dimension, which leads to a loss of information. Importantly, it prevents us from investigating the transformations between patterns in different ROIs. Here, we applied linear estimation theory in order to robustly estimate the linear transformations between multivariate fMRI patterns with a cross-validated ridge regression approach. We used three functional connectivity metrics that describe different features of these voxel-by-voxel mappings: goodness-of-fit, sparsity and pattern deformation. The goodness-of-fit describes the degree to which the patterns in an input region can be described as a linear transformation of patterns in an output region. The sparsity metric, which relies on a Monte Carlo procedure, was introduced in order to test whether the transformation mostly consists of one-to-one mappings between voxels in different regions. Furthermore, we defined a metric for pattern deformation, i.e. the degree to which the transformation rotates or rescales the input patterns. As a proof of concept, we applied these metrics to an event-related fMRI data set consisting of four subjects that has been used in previous studies. We focused on the transformations from early visual cortex (EVC) to inferior temporal cortex (ITC), fusiform face area (FFA) and parahippocampal place area (PPA). Our results suggest that the estimated linear mappings explain a significant amount of response variance in the three output ROIs. The transformation from EVC to ITC shows the highest goodness-of-fit, and those from EVC to FFA and PPA show the expected preference for faces and places as well as animate and inanimate objects, respectively. The pattern transformations are sparse, but sparsity is lower than would have been expected for one-to-one mappings, thus suggesting the presence of one-to-few voxel mappings. The mappings are also characterised by different levels of pattern deformations, thus indicating that the transformations differentially amplify or dampen certain dimensions of the input patterns. While our results are only based on a small number of subjects, they show that our pattern transformation metrics can describe novel aspects of multivariate functional connectivity in neuroimaging data.
Klíčová slova:
Topographic maps – Normal distribution – Functional magnetic resonance imaging – Neuroimaging – Curve fitting – Vision – Permutation – Deformation
Zdroje
1. Marzetti L., Della Penna S., Snyder A. Z., Pizzella V., Nolte G., de Pasquale F. et al. (2013). Frequency specific interactions of MEG resting state activity within and across brain networks as revealed by the multivariate interaction measure. Neuroimage, 79, 172–183. doi: 10.1016/j.neuroimage.2013.04.062 23631996
2. Geerligs L., Cam-CAN, & Henson R. N. (2016). Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation. Neuroimage, 135, 16–31. doi: 10.1016/j.neuroimage.2016.04.047 27114055
3. Anzellotti S., Caramazza A., & Saxe R. (2017). Multivariate pattern dependence. PLoS computational biology, 13(11), e1005799. doi: 10.1371/journal.pcbi.1005799 29155809
4. Anzellotti S., & Coutanche M. N. (2018). Beyond Functional Connectivity: Investigating Networks of Multivariate Representations. Trends in Cognitive Sciences, 22(3), 258–269. doi: 10.1016/j.tics.2017.12.002 29305206
5. Basti A., Pizzella V., Chella F., Romani G. L., Nolte G., & Marzetti L. (2018). Disclosing large-scale directed functional connections in MEG with the multivariate phase slope index. Neuroimage, 175, 161–175. doi: 10.1016/j.neuroimage.2018.03.004 29524622
6. Anzellotti, S., Fedorenko, E., Caramazza, A., & Saxe, R. (2016). Measuring and Modeling Transformations of Information Between Brain Regions with fMRI. bioRxiv, 074856.
7. Kriegeskorte N., Mur M., & Bandettini P. (2008). Representational similarity analysis—connecting the branches of systems neuroscience. Front Syst Neurosci, 2, 4. doi: 10.3389/neuro.06.004.2008 19104670
8. Coutanche M. N., & Thompson-Schill S. L. (2013). Informational connectivity: identifying synchronized discriminability of multi-voxel patterns across the brain. Frontiers in human neuroscience, 7, 15. doi: 10.3389/fnhum.2013.00015 23403700
9. Ito T., Kulkarni K. R., Schultz D. H., Mill R. D., Chen R. H., Solomyak L. I. et al. (2017). Cognitive task information is transferred between brain regions via resting-state network topology. Nature communications, 8(1), 1027. doi: 10.1038/s41467-017-01000-w 29044112
10. Naselaris T., Kay K. N., Nishimoto S., & Gallant J. L. (2011). Encoding and decoding in fMRI. Neuroimage, 56(2), 400–410. doi: 10.1016/j.neuroimage.2010.07.073 20691790
11. Khaligh-Razavi S. M., & Kriegeskorte N. (2014). Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS computational biology, 10(11), e1003915. doi: 10.1371/journal.pcbi.1003915 25375136
12. Yamins D. L., Hong H., Cadieu C. F., Solomon E. A., Seibert D., & DiCarlo J. J. (2014). Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the National Academy of Sciences, 111(23), 8619–8624.
13. Güçlü U., & van Gerven M. A. (2015). Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. Journal of Neuroscience, 35(27), 10005–10014. doi: 10.1523/JNEUROSCI.5023-14.2015 26157000
14. Patel G. H., Kaplan D. M., & Snyder L. H. (2014). Topographic organization in the brain: searching for general principles. Trends in cognitive sciences, 18(7), 351–363. doi: 10.1016/j.tics.2014.03.008 24862252
15. Thivierge J. P., & Marcus G. F. (2007). The topographic brain: from neural connectivity to cognition. Trends in neurosciences, 30(6), 251–259. doi: 10.1016/j.tins.2007.04.004 17462748
16. Jbabdi S., Sotiropoulos S. N., & Behrens T. E. (2013). The topographic connectome. Current opinion in neurobiology, 23(2), 207–215. doi: 10.1016/j.conb.2012.12.004 23298689
17. Kriegeskorte N., Mur M., Ruff D. A., Kiani R., Bodurka J., Esteky H. et al. (2008). Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron, 60(6), 1126–1141. doi: 10.1016/j.neuron.2008.10.043 19109916
18. Mur M., Ruff D. A., Bodurka J., De Weerd P., Bandettini P. A., & Kriegeskorte N. (2012). Categorical, yet graded—single-image activation profiles of human category-selective cortical regions. Journal of Neuroscience, 32 (25), 8649–8662. doi: 10.1523/JNEUROSCI.2334-11.2012 22723705
19. Mur M., Meys M., Bodurka J., Goebel R., Bandettini P. A., & Kriegeskorte N. (2013). Human object-similarity judgments reflect and transcend the primate-IT object representation. Frontiers in psychology, 4, 128. doi: 10.3389/fpsyg.2013.00128 23525516
20. Jozwik K. M., Kriegeskorte N., & Mur M. (2016). Visual features as stepping stones toward semantics: Explaining object similarity in IT and perception with non-negative least squares. Neuropsychologia, 83, 201–226. doi: 10.1016/j.neuropsychologia.2015.10.023 26493748
21. Malach R., Reppas J. B., Benson R. R., Kwong K. K., Jiang H., Kennedy W. A. et al. (1995). Object-related activity revealed by functional magnetic resonance imaging in human occipital cortex. Proceedings of the National Academy of Sciences USA, 92, 8135–8139.
22. Kanwisher N., McDermott J., & Chun M. M. (1997). The fusiform face area: a module in human extrastriate cortex specialized for face perception. Journal of neuroscience, 17(11), 4302–4311. 9151747
23. Epstein R., & Kanwisher N. (1998). A cortical representation of the local visual environment. Nature, 392(6676), 598. doi: 10.1038/33402 9560155
24. Hoerl A. E., & Kennard R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55–67.
25. Ward E. J., Isik L., & Chun M. M. (2018). General transformations of object representations in human visual cortex. Journal of Neuroscience, 38(40), 8526–8537. doi: 10.1523/JNEUROSCI.2800-17.2018 30126975
26. Misaki M., Kim Y., Bandettini P. A., & Kriegeskorte N. (2010). Comparison of multivariate classifiers and response normalizations for pattern-information fMRI. Neuroimage, 53(1), 103–118. doi: 10.1016/j.neuroimage.2010.05.051 20580933
27. Esterman M., Tamber-Rosenau B. J., Chiu Y. C., & Yantis S. (2010). Avoiding non-independence in fMRI data analysis: leave one subject out. Neuroimage, 50(2), 572–576. doi: 10.1016/j.neuroimage.2009.10.092 20006712
28. Golub G. H., Heath M., & Wahba G. (1979). Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics, 21(2), 215–223.
29. Stoer J., & Bulirsch R. (2002). Introduction to Numerical Analysis (3rd ed.). Berlin, New York: Springer-Verlag.
30. Tibshirani R. (1996). Regression Shrinkage and Selection via the lasso. Journal of the Royal Statistical Society. Series B (methodological). Wiley. 58 (1): 267–88.
31. Higham N. J. (1986). Computing the polar decomposition—with applications. SIAM Journal on Scientific and Statistical Computing, 7(4), 1160–1174.
32. Boynton G. M., Engel S. A., Glover G. H., & Heeger D. J. (1996). Linear systems analysis of functional magnetic resonance imaging in human V1. Journal of Neuroscience, 16(13), 4207–4221. 8753882
33. Henriksson L., Khaligh-Razavi S. M., Kay K., & Kriegeskorte N. (2015). Visual representations are dominated by intrinsic fluctuations correlated between areas. Neuroimage, 114, 275–286. doi: 10.1016/j.neuroimage.2015.04.026 25896934
34. Walther A., Nili H., Ejaz N., Alink A., Kriegeskorte N., & Diedrichsen J. (2016). Reliability of dissimilarity measures for multi-voxel pattern analysis. Neuroimage, 137, 188–200. doi: 10.1016/j.neuroimage.2015.12.012 26707889
35. Haxby J. V., Guntupalli J. S., Connolly A. C., Halchenko Y. O., Conroy B. R., Gobbini M. I. et al. (2011). A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron, 72(2), 404–416. doi: 10.1016/j.neuron.2011.08.026 22017997
36. Zou H., & Hastie T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society, Series B: 301–320.
37. Boyd S. (2010). Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. Foundations and Trends in Machine Learning. Vol. 3, No. 1, pp. 1–122.
38. Deleus F., & Van Hulle M. M. (2011). Functional connectivity analysis of fMRI data based on regularized multiset canonical correlation analysis. Journal of Neuroscience methods, 197(1), 143–157. doi: 10.1016/j.jneumeth.2010.11.029 21277327
39. Friston K. J., Buechel C., Fink G. R., Morris J., Rolls E., & Dolan R. J. (1997). Psychophysiological and modulatory interactions in neuroimaging. Neuroimage, 6(3), 218–229. doi: 10.1006/nimg.1997.0291 9344826
40. O’Brien T. A., Kashinath K., Cavanaugh N. R., Collins W. D., & O’Brien J. P. (2016). A fast and objective multidimensional kernel density estimation method: fastKDE. Computational Statistics & Data Analysis, 101, 148–160.
41. Seth A. K., Barrett A. B., & Barnett L. (2015). Granger causality analysis in neuroscience and neuroimaging. Journal of Neuroscience, 35(8), 3293–3297. doi: 10.1523/JNEUROSCI.4399-14.2015 25716830
42. Webb J. T., Ferguson M. A., Nielsen J. A., & Anderson J. S. (2013). BOLD Granger causality reflects vascular anatomy. PloS one, 8(12), e84279. doi: 10.1371/journal.pone.0084279 24349569
43. Friston K. J., Preller K. H., Mathys C., Cagnan H., Heinzle J., Razi A. et al. (2019). Dynamic causal modelling revisited. Neuroimage, 199, 730–744. doi: 10.1016/j.neuroimage.2017.02.045 28219774
44. King J. R., & Dehaene S. (2014). Characterizing the dynamics of mental representations: the temporal generalization method. Trends in cognitive sciences, 18(4), 203–210. doi: 10.1016/j.tics.2014.01.002 24593982
45. Stokes P. A., & Purdon P. L. (2017). A study of problems encountered in Granger causality analysis from a neuroscience perspective. Proceedings of the National Academy of Sciences, 114(34), E7063–E7072.
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