A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence.


Journal

Nature neuroscience
ISSN: 1546-1726
Titre abrégé: Nat Neurosci
Pays: United States
ID NLM: 9809671

Informations de publication

Date de publication:
01 2022
Historique:
received: 01 03 2021
accepted: 12 10 2021
pubmed: 18 12 2021
medline: 7 4 2022
entrez: 17 12 2021
Statut: ppublish

Résumé

Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. Here we present the Natural Scenes Dataset (NSD), in which high-resolution functional magnetic resonance imaging responses to tens of thousands of richly annotated natural scenes were measured while participants performed a continuous recognition task. To optimize data quality, we developed and applied novel estimation and denoising techniques. Simple visual inspections of the NSD data reveal clear representational transformations along the ventral visual pathway. Further exemplifying the inferential power of the dataset, we used NSD to build and train deep neural network models that predict brain activity more accurately than state-of-the-art models from computer vision. NSD also includes substantial resting-state and diffusion data, enabling network neuroscience perspectives to constrain and enhance models of perception and memory. Given its unprecedented scale, quality and breadth, NSD opens new avenues of inquiry in cognitive neuroscience and artificial intelligence.

Identifiants

pubmed: 34916659
doi: 10.1038/s41593-021-00962-x
pii: 10.1038/s41593-021-00962-x
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, N.I.H., Intramural Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

116-126

Subventions

Organisme : NIBIB NIH HHS
ID : P41 EB015894
Pays : United States
Organisme : NINDS NIH HHS
ID : P30 NS076408
Pays : United States
Organisme : NCRR NIH HHS
ID : S10 RR026783
Pays : United States
Organisme : NIH HHS
ID : S10 OD017974
Pays : United States
Organisme : Intramural NIH HHS
ID : ZIA MH002909
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB030896
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB029272
Pays : United States

Commentaires et corrections

Type : CommentIn

Informations de copyright

© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.

Références

de Vries, S. E. J. et al. A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex. Nat. Neurosci. 23, 138–151 (2020).
pubmed: 31844315 doi: 10.1038/s41593-019-0550-9
Siegle, J. H. et al. Survey of spiking in the mouse visual system reveals functional hierarchy. Nature 592, 86–92 (2021).
Stringer, C., Pachitariu, M., Steinmetz, N., Carandini, M. & Harris, K. D. High-dimensional geometry of population responses in visual cortex. Nature 571, 361–365 (2019).
pubmed: 31243367 pmcid: 6642054 doi: 10.1038/s41586-019-1346-5
Markram, H. et al. Reconstruction and simulation of neocortical microcircuitry. Cell 163, 456–492 (2015).
pubmed: 26451489 doi: 10.1016/j.cell.2015.09.029
Van Essen, D. C. et al. The WU-Minn human connectome project: an overview. Neuroimage 80, 62–79 (2013).
pubmed: 23684880 doi: 10.1016/j.neuroimage.2013.05.041
Zheng, Z. et al. A complete electron microscopy volume of the brain of adult Drosophila melanogaster. Cell 174, 730–743 (2018).
pubmed: 30033368 pmcid: 6063995 doi: 10.1016/j.cell.2018.06.019
Van Essen, D. C. et al. Mapping visual cortex in monkeys and humans using surface-based atlases. Vis. Res. 41, 1359–1378 (2001).
pubmed: 11322980 doi: 10.1016/S0042-6989(01)00045-1
Grill-Spector, K. & Malach, R. The human visual cortex. Annu. Rev. Neurosci. 27, 649–677 (2004).
pubmed: 15217346 doi: 10.1146/annurev.neuro.27.070203.144220
Wheeler, M. E., Petersen, S. E. & Buckner, R. L. Memory’s echo: vivid remembering reactivates sensory-specific cortex. Proc. Natl Acad. Sci. USA 97, 11125–11129 (2000).
pubmed: 11005879 pmcid: 27159 doi: 10.1073/pnas.97.20.11125
Breedlove, J. L., St-Yves, G., Olman, C. A. & Naselaris, T. Generative feedback explains distinct brain activity codes for seen and mental images. Curr. Biol. 30, 2211–2224 (2020).
pubmed: 32359428 doi: 10.1016/j.cub.2020.04.014
Kay, K. N., Weiner, K. S. & Grill-Spector, K. Attention reduces spatial uncertainty in human ventral temporal cortex. Curr. Biol. 25, 595–600 (2015).
pubmed: 25702580 pmcid: 4348205 doi: 10.1016/j.cub.2014.12.050
Huth, A. G., Nishimoto, S., Vu, A. T. & Gallant, J. L. A continuous semantic space describes the representation of thousands of object and action categories across the human brain. Neuron 76, 1210–1224 (2012).
pubmed: 23259955 pmcid: 3556488 doi: 10.1016/j.neuron.2012.10.014
Krizhevsky, A. Learning Multiple Layers of Features from Tiny Images. https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf (University of Toronto, 2009).
Lin, T.-Y. et al. Microsoft COCO: Common Objects in Context. European Conference on Computer Vision. https://link.springer.com/chapter/10.1007/978-3-319-10602-1_48 , 740–755 (Springer, 2014).
Güçlü, U. & van Gerven, M. A. J. Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. J. Neurosci. 35, 10005–10014 (2015).
pubmed: 26157000 pmcid: 6605414 doi: 10.1523/JNEUROSCI.5023-14.2015
Khaligh-Razavi, S.-M. & Kriegeskorte, N. Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS Comput. Biol. 10, e1003915 (2014).
pubmed: 25375136 pmcid: 4222664 doi: 10.1371/journal.pcbi.1003915
Seeliger, K. et al. End-to-end neural system identification with neural information flow. PLoS Comput. Biol. 17, e1008558 (2021).
pubmed: 33539366 pmcid: 7888598 doi: 10.1371/journal.pcbi.1008558
Stansbury, D. E., Naselaris, T. & Gallant, J. L. Natural scene statistics account for the representation of scene categories in human visual cortex. Neuron 79, 1025–1034 (2013).
pubmed: 23932491 pmcid: 5464350 doi: 10.1016/j.neuron.2013.06.034
St-Yves, G. & Naselaris, T. The feature-weighted receptive field: an interpretable encoding model for complex feature spaces. Neuroimage 180, 188–202 (2018).
Yamins, D. L. K. et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proc. Natl Acad. Sci. USA 111, 8619–8624 (2014).
pubmed: 24812127 pmcid: 4060707 doi: 10.1073/pnas.1403112111
Naselaris, T. et al. Cognitive computational neuroscience: a new conference for an emerging discipline. Trends Cogn. Sci. 22, 365–367 (2018).
pubmed: 29500078 pmcid: 5911192 doi: 10.1016/j.tics.2018.02.008
Chang, N. et al. BOLD5000, a public fMRI dataset while viewing 5000 visual images. Sci. Data 6, 49 (2019).
pubmed: 31061383 pmcid: 6502931 doi: 10.1038/s41597-019-0052-3
Horikawa, T. & Kamitani, Y. Generic decoding of seen and imagined objects using hierarchical visual features. Nat. Commun. 8, 15037 (2017).
pubmed: 28530228 pmcid: 5458127 doi: 10.1038/ncomms15037
Kay, K. N., Naselaris, T., Prenger, R. J. & Gallant, J. L. Identifying natural images from human brain activity. Nature 452, 352–355 (2008).
pubmed: 18322462 pmcid: 3556484 doi: 10.1038/nature06713
Triantafyllou, C. et al. Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters. Neuroimage 26, 243–250 (2005).
pubmed: 15862224 doi: 10.1016/j.neuroimage.2005.01.007
Brady, T. F., Konkle, T., Alvarez, G. A. & Oliva, A. Visual long-term memory has a massive storage capacity for object details. Proc. Natl Acad. Sci. USA 105, 14325–14329 (2008).
pubmed: 18787113 pmcid: 2533687 doi: 10.1073/pnas.0803390105
Haxby, J. V., Guntupalli, J. S., Nastase, S. A. & Feilong, M. Hyperalignment: modeling shared information encoded in idiosyncratic cortical topographies. eLife 9, e56601 (2020).
pubmed: 32484439 pmcid: 7266639 doi: 10.7554/eLife.56601
Power, J. D., Lynch, C. J., Adeyemo, B. & Petersen, S. E. A critical, event-related appraisal of denoising in resting-state fMRI studies. Cereb. Cortex 30, 5544–5559 (2020).
pubmed: 32494823 doi: 10.1093/cercor/bhaa139
Roth, Z. N., Ryoo, M. & Merriam, E. P. Task-related activity in human visual cortex. PLoS Biol. 18, e3000921 (2020).
pubmed: 33156829 pmcid: 7673548 doi: 10.1371/journal.pbio.3000921
Benson, N. C. et al. The human connectome project 7 Tesla retinotopy dataset: description and population receptive field analysis. J. Vis. 18, 23 (2018).
Stigliani, A., Weiner, K. S. & Grill-Spector, K. Temporal processing capacity in high-level visual cortex is domain specific. J. Neurosci. 35, 12412–12424 (2015).
pubmed: 26354910 pmcid: 4563034 doi: 10.1523/JNEUROSCI.4822-14.2015
Kay, K. et al. A critical assessment of data quality and venous effects in sub-millimeter fMRI. Neuroimage 189, 847–869 (2019).
pubmed: 30731246 doi: 10.1016/j.neuroimage.2019.02.006
Gordon, E. M. et al. Precision functional mapping of individual human brains. Neuron 95, 791–807 (2017).
pubmed: 28757305 pmcid: 5576360 doi: 10.1016/j.neuron.2017.07.011
Kang, X., Yund, E. W., Herron, T. J. & Woods, D. L. Improving the resolution of functional brain imaging: analyzing functional data in anatomical space. Magn. Reson. Imaging 25, 1070–1078 (2007).
pubmed: 17707169 doi: 10.1016/j.mri.2006.12.005
Kay, K. N., Rokem, A., Winawer, J., Dougherty, R. F. & Wandell, B. GLMdenoise: a fast, automated technique for denoising task-based fMRI data. Front. Neurosci. 7, 247 (2013).
pubmed: 24381539 pmcid: 3865440 doi: 10.3389/fnins.2013.00247
Rokem, A. & Kay, K. Fractional ridge regression: a fast, interpretable reparameterization of ridge regression. Gigascience 9, giaa133 (2020).
Albrecht, D. G. & Hamilton, D. B. Striate cortex of monkey and cat: contrast response function. J. Neurophysiol. 48, 217–237 (1982).
pubmed: 7119846 doi: 10.1152/jn.1982.48.1.217
Wagner, A. D., Shannon, B. J., Kahn, I. & Buckner, R. L. Parietal lobe contributions to episodic memory retrieval. Trends Cogn. Sci. 9, 445–453 (2005).
pubmed: 16054861 doi: 10.1016/j.tics.2005.07.001
Spaniol, J. et al. Event-related fMRI studies of episodic encoding and retrieval: meta-analyses using activation likelihood estimation. Neuropsychologia 47, 1765–1779 (2009).
pubmed: 19428409 doi: 10.1016/j.neuropsychologia.2009.02.028
Gonzalez-Castillo, J. et al. Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis. Proc. Natl Acad. Sci. USA 109, 5487–5492 (2012).
pubmed: 22431587 pmcid: 3325687 doi: 10.1073/pnas.1121049109
Maaten, Lvander & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
Connolly, A. C. et al. The representation of biological classes in the human brain. J. Neurosci. 32, 2608–2618 (2012).
pubmed: 22357845 pmcid: 3532035 doi: 10.1523/JNEUROSCI.5547-11.2012
Naselaris, T., Stansbury, D. E. & Gallant, J. L. Cortical representation of animate and inanimate objects in complex natural scenes. J. Physiol. Paris 106, 239–249 (2012).
pubmed: 22472178 pmcid: 3407302 doi: 10.1016/j.jphysparis.2012.02.001
Long, B., Yu, C.-P. & Konkle, T. Mid-level visual features underlie the high-level categorical organization of the ventral stream. Proc. Natl Acad. Sci. USA 115, E9015–E9024 (2018).
pubmed: 30171168 pmcid: 6156638 doi: 10.1073/pnas.1719616115
Henriksson, L., Khaligh-Razavi, S.-M., Kay, K. & Kriegeskorte, N. Visual representations are dominated by intrinsic fluctuations correlated between areas. Neuroimage 114, 275–286 (2015).
pubmed: 25896934 doi: 10.1016/j.neuroimage.2015.04.026
Naselaris, T., Kay, K. N., Nishimoto, S. & Gallant, J. L. Encoding and decoding in fMRI. Neuroimage 56, 400–410 (2011).
pubmed: 20691790 doi: 10.1016/j.neuroimage.2010.07.073
Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25 https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html , 1097–1105 (2012).
Cadena, S. A. et al. Deep convolutional models improve predictions of macaque V1 responses to natural images. PLoS Comput. Biol. 15, e1006897 (2019).
pubmed: 31013278 pmcid: 6499433 doi: 10.1371/journal.pcbi.1006897
Wang, A., Tarr, M. & Wehbe, L. Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity. In Advances in Neural Information Processing Systems 32 https://papers.nips.cc/paper/2019/hash/f490c742cd8318b8ee6dca10af2a163f-Abstract.html , 15475–15485 (2019).
Sinz, F. H., Pitkow, X., Reimer, J., Bethge, M. & Tolias, A. S. Engineering a less artificial intelligence. Neuron 103, 967–979 (2019).
pubmed: 31557461 doi: 10.1016/j.neuron.2019.08.034
Aliko, S., Huang, J., Gheorghiu, F., Meliss, S. & Skipper, J. I. A naturalistic neuroimaging database for understanding the brain using ecological stimuli. Sci. Data 7, 347 (2020).
pubmed: 33051448 pmcid: 7555491 doi: 10.1038/s41597-020-00680-2
Nastase, S. A., Liu, Y.-F., Hillman, H., Norman, K. A. & Hasson, U. Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space. Neuroimage 217, 116865 (2020).
pubmed: 32325212 doi: 10.1016/j.neuroimage.2020.116865
Taylor, J. R. et al. The cambridge centre for ageing and neuroscience (Cam-CAN) data repository: structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. Neuroimage 144, 262–269 (2017).
pubmed: 26375206 doi: 10.1016/j.neuroimage.2015.09.018
Bellec, P. & Boyle, J. A. Bridging the gap between perception and action: the case for neuroimaging, AI and video games. Preprint at https://psyarxiv.com/3epws (2019).
Pinho, A. L. et al. Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping. Sci. Data 5, 180105 (2018).
pubmed: 29893753 pmcid: 5996851 doi: 10.1038/sdata.2018.105
Poldrack, R. A. et al. Long-term neural and physiological phenotyping of a single human. Nat. Commun. 6, 8885 (2015).
pubmed: 26648521 doi: 10.1038/ncomms9885
Seeliger, K., Sommers, R. P., Güçlü, U., Bosch, S. E. & van Gerven, M. A. J. A large single-participant fMRI dataset for probing brain responses to naturalistic stimuli in space and time. Preprint at https://www.biorxiv.org/content/10.1101/687681v1 (2019).
Naselaris, T., Allen, E. & Kay, K. Extensive sampling for complete models of individual brains. Curr. Opin. Behav. Sci. 40, 45–51 (2021).
doi: 10.1016/j.cobeha.2020.12.008
Polimeni, J. R., Renvall, V., Zaretskaya, N. & Fischl, B. Analysis strategies for high-resolution UHF-fMRI data. Neuroimage 168, 296–320 (2018).
pubmed: 28461062 doi: 10.1016/j.neuroimage.2017.04.053
Harms, M. P. et al. Extending the Human Connectome Project across ages: imaging protocols for the Lifespan Development and Aging projects. Neuroimage 183, 972–984 (2018).
pubmed: 30261308 doi: 10.1016/j.neuroimage.2018.09.060
Power, J. D. et al. Customized head molds reduce motion during resting state fMRI scans. Neuroimage 189, 141–149 (2019).
pubmed: 30639840 doi: 10.1016/j.neuroimage.2019.01.016
Brainard, D. H. The Psychophysics Toolbox. Spat. Vis. 10, 433–436 (1997).
pubmed: 9176952 doi: 10.1163/156856897X00357
Pelli, D. G. The VideoToolbox software for visual psychophysics: transforming numbers into movies. Spat. Vis. 10, 437–442 (1997).
pubmed: 9176953 doi: 10.1163/156856897X00366
Caesar, H., Uijlings, J. & Ferrari, V. COCO-Stuff: Thing and Stuff classes in context. In IEEE/CVF Conf. Computer Vision and Pattern Recognition https://doi.ieeecomputersociety.org/10.1109/CVPR.2018.00132 1209–1218 (2018).
Schira, M. M., Tyler, C. W., Breakspear, M. & Spehar, B. The foveal confluence in human visual cortex. J. Neurosci. 29, 9050–9058 (2009).
pubmed: 19605642 pmcid: 6665445 doi: 10.1523/JNEUROSCI.1760-09.2009
Shahid, A., Wilkinson, K., Marcu, S. & Shapiro, C. M. Stanford Sleepiness Scale (SSS). In: STOP, THAT and One Hundred Other Sleep Scales (eds. Shahid, A., Wilkinson, K., Marcu, S. & Shapiro, C. M.) 369–370 (Springer, 2012).
Marks, D. F. Visual imagery differences in the recall of pictures. Br. J. Psychol. 64, 17–24 (1973).
pubmed: 4742442 doi: 10.1111/j.2044-8295.1973.tb01322.x
Torgesen, J. K., Wagner, R. & Rashotte, C. TOWRE-2: Test of Word Reading Efficiency (Pearson, 2012).
Duchaine, B. & Nakayama, K. The Cambridge Face Memory Test: results for neurologically intact individuals and an investigation of its validity using inverted face stimuli and prosopagnosic participants. Neuropsychologia 44, 576–585 (2006).
pubmed: 16169565 doi: 10.1016/j.neuropsychologia.2005.07.001
Tardif, J., Watson, M., Giaschi, D. & Gosselin, F. Measuring the contrast sensitivity function in just three clicks. J. Vis. 16, 966–966 (2016).
doi: 10.1167/16.12.966
Arora, S., Liang, Y. & Ma, T. A simple but tough-to-beat baseline for sentence embeddings. https://openreview.net/pdf?id=SyK00v5xx (2017).
Kriegeskorte, N. & Mur, M. Inverse MDS: inferring dissimilarity structure from multiple item arrangements. Front. Psychol. 3, 245 (2012).
pubmed: 22848204 pmcid: 3404552 doi: 10.3389/fpsyg.2012.00245
Kay, K., Jamison, K. W., Zhang, R.-Y. & Uğurbil, K. A temporal decomposition method for identifying venous effects in task-based fMRI. Nat. Methods 17, 1033–1039 (2020).
pubmed: 32895538 pmcid: 7721302 doi: 10.1038/s41592-020-0941-6
Avants, B. B. et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54, 2033–2044 (2011).
pubmed: 20851191 doi: 10.1016/j.neuroimage.2010.09.025
Yushkevich, P. A. et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31, 1116–1128 (2006).
pubmed: 16545965 doi: 10.1016/j.neuroimage.2006.01.015
Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods 16, 111–116 (2019).
pubmed: 30532080 doi: 10.1038/s41592-018-0235-4
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142–2154 (2012).
pubmed: 22019881 doi: 10.1016/j.neuroimage.2011.10.018
Handwerker, D. A., Gonzalez-Castillo, J., D’Esposito, M. & Bandettini, P. A. The continuing challenge of understanding and modeling hemodynamic variation in fMRI. Neuroimage 62, 1017–1023 (2012).
pubmed: 22366081 doi: 10.1016/j.neuroimage.2012.02.015
Hoerl, A. E. & Kennard, R. W. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12, 55–67 (1970).
doi: 10.1080/00401706.1970.10488634
Kay, K. N., Winawer, J., Mezer, A. & Wandell, B. Compressive spatial summation in human visual cortex. J. Neurophysiol. 110, 481–494 (2013).
pubmed: 23615546 pmcid: 3727075 doi: 10.1152/jn.00105.2013
Lage-Castellanos, A., Valente, G., Formisano, E. & De Martino, F. Methods for computing the maximum performance of computational models of fMRI responses. PLoS Comput. Biol. 15, e1006397 (2019).
pubmed: 30849071 pmcid: 6426260 doi: 10.1371/journal.pcbi.1006397
Biswal, B., Yetkin, F. Z., Haughton, V. M. & Hyde, J. S. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541 (1995).
pubmed: 8524021 doi: 10.1002/mrm.1910340409
Nili, H. et al. A toolbox for representational similarity analysis. PLoS Comput. Biol. 10, e1003553 (2014).
pubmed: 24743308 pmcid: 3990488 doi: 10.1371/journal.pcbi.1003553
Kriegeskorte, N., Mur, M. & Bandettini, P. Representational similarity analysis—connecting the branches of systems neuroscience. Front. Syst. Neurosci. 2, 4 (2008).
pubmed: 19104670 pmcid: 2605405
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Gorgolewski, K. J. et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci. Data 3, 1–9 (2016).
doi: 10.1038/sdata.2016.44
Cichy, R. M., Roig, G. & Oliva, A. The Algonauts Project. Nat. Mach. Intell. 1, 613 (2019).
doi: 10.1038/s42256-019-0127-z

Auteurs

Emily J Allen (EJ)

Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, MN, USA.
Department of Psychology, University of Minnesota, Minneapolis, MN, USA.

Ghislain St-Yves (G)

Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA.
Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA.

Yihan Wu (Y)

Graduate Program in Cognitive Science, University of Minnesota, Minneapolis, MN, USA.

Jesse L Breedlove (JL)

Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA.
Department of Psychology, University of Minnesota, Minneapolis, MN, USA.

Jacob S Prince (JS)

Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA.
Department of Psychology, Harvard University, Cambridge, MA, USA.

Logan T Dowdle (LT)

Department of Neuroscience, Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA.
Department of Neurosurgery, Center for Magnetic Resonance Research (CMRR), University of Minnesota, Minneapolis, MN, USA.

Matthias Nau (M)

National Institute of Mental Health (NIMH), Bethesda MD, USA.

Brad Caron (B)

Program in Neuroscience, Indiana University, Bloomington IN, USA.
Program in Vision Science, Indiana University, Bloomington IN, USA.

Franco Pestilli (F)

Department of Psychology, University of Texas at Austin, Austin, TX, USA.
Center for Perceptual Systems, University of Texas at Austin, Austin, TX, USA.
Institute for Neuroscience, University of Texas at Austin, Austin, TX, USA.

Ian Charest (I)

Center for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK.
cerebrUM, Département de Psychologie, Université de Montréal, Montréal QC, Canada.

J Benjamin Hutchinson (JB)

Department of Psychology, University of Oregon, Eugene, OR, USA.

Thomas Naselaris (T)

Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA.
Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA.

Kendrick Kay (K)

Center for Magnetic Resonance Research (CMRR), Department of Radiology, University of Minnesota, Minneapolis, MN, USA. kay@umn.edu.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH