{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T11:13:56Z","timestamp":1792840436820,"version":"4.5.0"},"reference-count":88,"publisher":"MIT Press","license":[{"start":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T00:00:00Z","timestamp":1745452800000},"content-version":"vor","delay-in-days":113,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-20-1-2002"],"award-info":[{"award-number":["N00014-20-1-2002"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-15-1-2861"],"award-info":[{"award-number":["N00014-15-1-2861"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["60744755-114407-UCB"],"award-info":[{"award-number":["60744755-114407-UCB"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000006","name":"Office of Naval Research","doi-asserted-by":"publisher","award":["N00014-22-1-2217"],"award-info":[{"award-number":["N00014-22-1-2217"]}],"id":[{"id":"10.13039\/100000006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"crossref","award":["RF1-AG029577"],"award-info":[{"award-number":["RF1-AG029577"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000053","name":"National Eye Institute","doi-asserted-by":"crossref","award":["EY019684"],"award-info":[{"award-number":["EY019684"]}],"id":[{"id":"10.13039\/100000053","id-type":"DOI","asserted-by":"crossref"}]},{"name":"NSF Science and Technology Center","award":["CCF-0939370"],"award-info":[{"award-number":["CCF-0939370"]}]}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,5,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The Voxelwise Encoding Model framework (VEM) is a powerful approach for functional brain mapping. In the VEM framework, features are extracted from the stimulus (or task) and used in an encoding model to predict brain activity. If the encoding model is able to predict brain activity in some part of the brain, then one may conclude that some information represented in the features is also encoded in the brain. In VEM, a separate encoding model is fitted on each spatial sample (i.e., each voxel). VEM has many benefits compared to other methods for analyzing and modeling neuroimaging data. Most importantly, VEM can use large numbers of features simultaneously, which enables the analysis of complex naturalistic stimuli and tasks. Therefore, VEM can produce high-dimensional functional maps that reflect the selectivity of each voxel to large numbers of features. Moreover, because model performance is estimated on a separate test dataset not used during fitting, VEM minimizes overfitting and inflated Type I error confounds that plague other approaches, and the results of VEM generalize to new subjects and new stimuli. Despite these benefits, VEM is still not widely used in neuroimaging, partly because no tutorials on this method are available currently. To demystify the VEM framework and ease its dissemination, this paper presents a series of hands-on tutorials accessible to novice practitioners. The VEM tutorials are based on free open-source tools and public datasets, and reproduce the analysis presented in previously published work.<\/jats:p>","DOI":"10.1162\/imag_a_00575","type":"journal-article","created":{"date-parts":[[2025,4,25]],"date-time":"2025-04-25T15:32:18Z","timestamp":1745595138000},"update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":24,"title":["The Voxelwise Encoding Model framework: A tutorial introduction to fitting encoding models to fMRI data"],"prefix":"10.1162","volume":"3","author":[{"given":"Tom","family":"Dupr\u00e9 la Tour","sequence":"first","affiliation":[{"name":"Helen Wills Neuroscience Institute, University of California, Berkeley, CA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matteo","family":"Visconti di Oleggio Castello","sequence":"additional","affiliation":[{"name":"Helen Wills Neuroscience Institute, University of California, Berkeley, CA, United States"},{"name":"Department of Neuroscience, University of California, Berkeley, CA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7273-1054","authenticated-orcid":false,"given":"Jack L.","family":"Gallant","sequence":"additional","affiliation":[{"name":"Helen Wills Neuroscience Institute, University of California, Berkeley, CA, United States"},{"name":"Department of Neuroscience, University of California, Berkeley, CA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"281","published-online":{"date-parts":[[2025,5,9]]},"reference":[{"key":"2025050915554808100_imag_a_00575-b1","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3389\/fninf.2014.00014","article-title":"Machine learning for neuroimaging with scikit-learn","volume":"8","author":"Abraham","year":"2014","journal-title":"Frontiers in Neuroinformatics"},{"key":"2025050915554808100_imag_a_00575-b2","unstructured":"Agrawal, P., Stansbury, D., Malik, J., & Gallant, J. L. (2014). Pixels to Voxels: Modeling visual representation in the human brain. In arXiv [q-bio.NC]. arXiv. http:\/\/arxiv.org\/abs\/1407.5104"},{"key":"2025050915554808100_imag_a_00575-b3","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/11557.001.0001","volume-title":"Statistical analysis of fMRI data, second edition","author":"Ashby","year":"2019"},{"key":"2025050915554808100_imag_a_00575-b4","doi-asserted-by":"crossref","first-page":"S125","DOI":"10.1016\/S1053-8119(09)71202-9","article-title":"Neural correlates of interspecies perspective taking in the post-mortem Atlantic Salmon: An argument for multiple comparisons correction","volume":"47","author":"Bennett","year":"2009","journal-title":"NeuroImage"},{"key":"2025050915554808100_imag_a_00575-b5","first-page":"214","article-title":"NLTK: The natural language toolkit","author":"Bird","year":"2004","journal-title":"Annual Meeting of the Association for Computational Linguistics"},{"issue":"5","key":"2025050915554808100_imag_a_00575-b6","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1038\/nrn3475","article-title":"Power failure: Why small sample size undermines the reliability of neuroscience","volume":"14","author":"Button","year":"2013","journal-title":"Nature Reviews. Neuroscience"},{"issue":"1","key":"2025050915554808100_imag_a_00575-b7","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1038\/s42003-022-03036-1","article-title":"Brains and algorithms partially converge in natural language processing","volume":"5","author":"Caucheteux","year":"2022","journal-title":"Communications Biology"},{"key":"2025050915554808100_imag_a_00575-b8","unstructured":"Chang, L., Huckins, J., Cheong, J. H., Brietzke, S., Lindquist, M. A., & Wager, T. D. (2020). ljchang\/dartbrains: An online open access resource for learning functional neuroimaging analysis methods in Python. Zenodo. https:\/\/doi.org\/10.5281\/ZENODO.3909718"},{"key":"2025050915554808100_imag_a_00575-b9","unstructured":"Chang, L., Manning, J., Baldassano, C., de la Vega, A., Fleetwood, G., Geerligs, L., Haxby, J., Lahnakoski, J., Parkinson, C., Shappell, H., Shim, W. M., Wager, T., Yarkoni, T., Yeshurun, Y., & Finn, E. (2020). Naturalistic-data-analysis\/naturalistic_data_analysis: Version 1.0. Zenodo. https:\/\/doi.org\/10.5281\/ZENODO.3937849"},{"issue":"11","key":"2025050915554808100_imag_a_00575-b10","doi-asserted-by":"crossref","first-page":"e0184923","DOI":"10.1371\/journal.pone.0184923","article-title":"The relation between statistical power and inference in fMRI","volume":"12","author":"Cremers","year":"2017","journal-title":"PLoS One"},{"issue":"6","key":"2025050915554808100_imag_a_00575-b11","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1038\/nn.3381","article-title":"Attention during natural vision warps semantic representation across the human brain","volume":"16","author":"\u00c7ukur","year":"2013","journal-title":"Nature Neuroscience"},{"issue":"2\u20133","key":"2025050915554808100_imag_a_00575-b12","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1002\/(SICI)1097-0193(1999)8:2\/3<109::AID-HBM7>3.0.CO;2-W","article-title":"Optimal experimental design for event-related fMRI","volume":"8","author":"Dale","year":"1999","journal-title":"Human Brain Mapping"},{"issue":"27","key":"2025050915554808100_imag_a_00575-b13","doi-asserted-by":"crossref","first-page":"6539","DOI":"10.1523\/JNEUROSCI.3267-16.2017","article-title":"The hierarchical cortical organization of human speech processing","volume":"37","author":"de Heer","year":"2017","journal-title":"The Journal of Neuroscience: The Official Journal of the Society for Neuroscience"},{"issue":"39","key":"2025050915554808100_imag_a_00575-b14","doi-asserted-by":"crossref","first-page":"7722","DOI":"10.1523\/JNEUROSCI.0675-19.2019","article-title":"The representation of semantic information across human cerebral cortex during listening versus reading is invariant to stimulus modality","volume":"39","author":"Deniz","year":"2019","journal-title":"The Journal of Neuroscience: The Official Journal of the Society for Neuroscience"},{"issue":"17","key":"2025050915554808100_imag_a_00575-b15","doi-asserted-by":"crossref","first-page":"3144","DOI":"10.1523\/JNEUROSCI.2459-21.2023","article-title":"Semantic representations during language comprehension are affected by context","volume":"43","author":"Deniz","year":"2023","journal-title":"The Journal of Neuroscience: The Official Journal of the Society for Neuroscience"},{"issue":"6","key":"2025050915554808100_imag_a_00575-b16","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.tics.2016.03.014","article-title":"Building a science of individual differences from fMRI","volume":"20","author":"Dubois","year":"2016","journal-title":"Trends in Cognitive Sciences"},{"issue":"2","key":"2025050915554808100_imag_a_00575-b17","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/j.neuroimage.2007.09.034","article-title":"Population receptive field estimates in human visual cortex","volume":"39","author":"Dumoulin","year":"2008","journal-title":"NeuroImage"},{"key":"2025050915554808100_imag_a_00575-b18","doi-asserted-by":"crossref","first-page":"119728","DOI":"10.1016\/j.neuroimage.2022.119728","article-title":"Feature-space selection with banded ridge regression","volume":"264","author":"Dupr\u00e9 la Tour","year":"2022","journal-title":"NeuroImage"},{"key":"2025050915554808100_imag_a_00575-b19","unstructured":"Dupr\u00e9 la Tour, T., Lu, M., Eickenberg, M., & Gallant, J. L. (2021, October 12). A finer mapping of convolutional neural network layers to the visual cortex. SVRHM 2021 Workshop @ NeurIPS. https:\/\/openreview.net\/pdf?id=EcoKpq43Ul8"},{"key":"2025050915554808100_imag_a_00575-b20","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.neuroimage.2016.10.001","article-title":"Seeing it all: Convolutional network layers map the function of the human visual system","volume":"152","author":"Eickenberg","year":"2017","journal-title":"NeuroImage"},{"issue":"1","key":"2025050915554808100_imag_a_00575-b21","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1038\/s41592-018-0235-4","article-title":"fMRIPrep: A robust preprocessing pipeline for functional MRI","volume":"16","author":"Esteban","year":"2019","journal-title":"Nature Methods"},{"key":"2025050915554808100_imag_a_00575-b22","unstructured":"Esteban, O., Markiewicz, C. J., Burns, C., Goncalves, M., Jarecka, D., Ziegler, E., Berleant, S., Ellis, D. G., Pinsard, B., Madison, C., Waskom, M., Notter, M. P., Clark, D., Manh\u00e3es-Savio, A., Clark, D., Jordan, K., Dayan, M., Halchenko, Y. O., Loney, F.,\u2026 Ghosh, S. (2020). nipy\/nipype: 1.5.0. Zenodo. https:\/\/doi.org\/10.5281\/ZENODO.596855"},{"key":"2025050915554808100_imag_a_00575-b23","unstructured":"Executable Books Community. (2020). Jupyter book. Zenodo. https:\/\/doi.org\/10.5281\/ZENODO.4539666"},{"issue":"4","key":"2025050915554808100_imag_a_00575-b24","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1002\/hbm.460020402","article-title":"Statistical parametric maps in functional imaging: A general linear approach","volume":"2","author":"Friston","year":"1994","journal-title":"Human Brain Mapping"},{"key":"2025050915554808100_imag_a_00575-b25","doi-asserted-by":"crossref","first-page":"23","DOI":"10.3389\/fninf.2015.00023","article-title":"Pycortex: An interactive surface visualizer for fMRI","volume":"9","author":"Gao","year":"2015","journal-title":"Frontiers in Neuroinformatics"},{"issue":"4","key":"2025050915554808100_imag_a_00575-b26","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1016\/j.neuron.2017.07.011","article-title":"Precision functional mapping of individual human brains","volume":"95","author":"Gordon","year":"2017","journal-title":"Neuron"},{"key":"2025050915554808100_imag_a_00575-b27","doi-asserted-by":"crossref","first-page":"13","DOI":"10.3389\/fninf.2011.00013","article-title":"Nipype: A flexible, lightweight and extensible neuroimaging data processing framework in python","volume":"5","author":"Gorgolewski","year":"2011","journal-title":"Frontiers in Neuroinformatics"},{"issue":"12","key":"2025050915554808100_imag_a_00575-b28","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1109\/42.897811","article-title":"Modeling the haemodynamic response in fMRI using smooth FIR filters","volume":"19","author":"Goutte","year":"2000","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"27","key":"2025050915554808100_imag_a_00575-b29","doi-asserted-by":"crossref","first-page":"10005","DOI":"10.1523\/JNEUROSCI.5023-14.2015","article-title":"Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream","volume":"35","author":"G\u00fc\u00e7l\u00fc","year":"2015","journal-title":"The Journal of Neuroscience: The Official Journal of the Society for Neuroscience"},{"key":"2025050915554808100_imag_a_00575-b30","doi-asserted-by":"crossref","DOI":"10.25080\/TCWV9851","volume-title":"Exploring network structure, dynamics, and function using NetworkX","author":"Hagberg","year":"2008"},{"key":"2025050915554808100_imag_a_00575-b31","doi-asserted-by":"crossref","unstructured":"Halchenko, Y. O., Meyer, K., Poldrack, B., Solanky, D. S., Wagner, A. S., Gors, J., MacFarlane, D., Pustina, D., Sochat, V., Ghosh, S. S., M\u00f6nch, C., Markiewicz, C. J., Waite, L., Shlyakhter, I., de la Vega, A., Hayashi, S., H\u00e4usler, C. O., Poline, J.-B., Kadelka, T.,\u2026 Hanke, M. (2021). DataLad: Distributed system for joint management of code, data, and their relationship. Journal of Open Source Software, 6(63), 3262. https:\/\/doi.org\/10.21105\/joss.03262","DOI":"10.21105\/joss.03262"},{"issue":"5","key":"2025050915554808100_imag_a_00575-b32","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1080\/23273798.2018.1499946","article-title":"The revolution will not be controlled: Natural stimuli in speech neuroscience","volume":"35","author":"Hamilton","year":"2020","journal-title":"Language, Cognition and Neuroscience"},{"issue":"1","key":"2025050915554808100_imag_a_00575-b33","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s12021-008-9041-y","article-title":"PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data","volume":"7","author":"Hanke","year":"2009","journal-title":"Neuroinformatics"},{"issue":"1","key":"2025050915554808100_imag_a_00575-b34","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.neuroimage.2004.05.012","article-title":"Parametric reverse correlation reveals spatial linearity of retinotopic human V1 BOLD response","volume":"23","author":"Hansen","year":"2004","journal-title":"NeuroImage"},{"issue":"7825","key":"2025050915554808100_imag_a_00575-b35","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array programming with NumPy","volume":"585","author":"Harris","year":"2020","journal-title":"Nature"},{"issue":"1","key":"2025050915554808100_imag_a_00575-b36","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.tics.2009.10.011","article-title":"Reliability of cortical activity during natural stimulation","volume":"14","author":"Hasson","year":"2010","journal-title":"Trends in Cognitive Sciences"},{"issue":"1","key":"2025050915554808100_imag_a_00575-b37","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1146\/annurev-neuro-062012-170325","article-title":"Decoding neural representational spaces using multivariate pattern analysis","volume":"37","author":"Haxby","year":"2014","journal-title":"Annual Review of Neuroscience"},{"issue":"1","key":"2025050915554808100_imag_a_00575-b38","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","article-title":"Ridge Regression: Biased estimation for nonorthogonal problems","volume":"12","author":"Hoerl","year":"1970","journal-title":"Technometrics: A Journal of Statistics for the Physical, Chemical, and Engineering Sciences"},{"issue":"2","key":"2025050915554808100_imag_a_00575-b39","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1088\/0954-898X_15_2_002","article-title":"Quantifying variability in neural responses and its application for the validation of model predictions","volume":"15","author":"Hsu","year":"2004","journal-title":"Network"},{"issue":"3","key":"2025050915554808100_imag_a_00575-b40","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","article-title":"Matplotlib: A 2D graphics environment","volume":"9","author":"Hunter","year":"2007","journal-title":"Computing in Science & Engineering"},{"issue":"7600","key":"2025050915554808100_imag_a_00575-b41","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1038\/nature17637","article-title":"Natural speech reveals the semantic maps that tile human cerebral cortex","volume":"532","author":"Huth","year":"2016","journal-title":"Nature"},{"key":"2025050915554808100_imag_a_00575-b42","unstructured":"Huth, A. G., Nishimoto, S., Vu, A. T., Dupr\u00e9 la Tour, T., & Gallant, J. L. (2022). Gallant lab natural short clips 3T fMRI data [Data set]. G-Node. https:\/\/doi.org\/10.12751\/G-NODE.VY1ZJD"},{"issue":"6","key":"2025050915554808100_imag_a_00575-b43","doi-asserted-by":"crossref","first-page":"1210","DOI":"10.1016\/j.neuron.2012.10.014","article-title":"A continuous semantic space describes the representation of thousands of object and action categories across the human brain","volume":"76","author":"Huth","year":"2012","journal-title":"Neuron"},{"key":"2025050915554808100_imag_a_00575-b44","doi-asserted-by":"crossref","unstructured":"Jain, S., & Huth, A. (2018). Incorporating context into language encoding models for fMRI. Advances in Neural Information Processing Systems, 327601. https:\/\/doi.org\/10.1101\/327601","DOI":"10.1101\/327601"},{"key":"2025050915554808100_imag_a_00575-b45","first-page":"13738","article-title":"Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech","volume":"33","author":"Jain","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"2","key":"2025050915554808100_imag_a_00575-b46","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1002\/hbm.20379","article-title":"Modeling low-frequency fluctuation and hemodynamic response timecourse in event-related fMRI","volume":"29","author":"Kay","year":"2008","journal-title":"Human Brain Mapping"},{"issue":"7185","key":"2025050915554808100_imag_a_00575-b47","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1038\/nature06713","article-title":"Identifying natural images from human brain activity","volume":"452","author":"Kay","year":"2008","journal-title":"Nature"},{"issue":"3","key":"2025050915554808100_imag_a_00575-b48","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1016\/j.neuron.2018.03.044","article-title":"A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy","volume":"98","author":"Kell","year":"2018","journal-title":"Neuron"},{"issue":"22","key":"2025050915554808100_imag_a_00575-b49","doi-asserted-by":"crossref","first-page":"eabe7547","DOI":"10.1126\/sciadv.abe7547","article-title":"Cortical response to naturalistic stimuli is largely predictable with deep neural networks","volume":"7","author":"Khosla","year":"2021","journal-title":"Science Advances"},{"key":"2025050915554808100_imag_a_00575-b50","first-page":"87","article-title":"Jupyter notebooks\u2014A publishing format for reproducible computational workflows","volume-title":"Positioning and power in academic publishing: Players, agents and agendas","author":"Kluyver","year":"2016"},{"issue":"1","key":"2025050915554808100_imag_a_00575-b51","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1038\/s41467-022-28091-4","article-title":"A self-supervised domain-general learning framework for human ventral stream representation","volume":"13","author":"Konkle","year":"2022","journal-title":"Nature Communications"},{"key":"2025050915554808100_imag_a_00575-b52","first-page":"4","article-title":"Representational similarity analysis\u2014Connecting the branches of systems neuroscience","volume":"2","author":"Kriegeskorte","year":"2008","journal-title":"Frontiers in Systems Neuroscience"},{"issue":"4","key":"2025050915554808100_imag_a_00575-b53","doi-asserted-by":"crossref","DOI":"10.52294\/31bb5b68-2184-411b-8c00-a1dacb61e1da","article-title":"BrainIAK: The brain imaging analysis kit","volume":"1","author":"Kumar","year":"2021","journal-title":"Aperture Neuro"},{"issue":"1","key":"2025050915554808100_imag_a_00575-b54","doi-asserted-by":"crossref","first-page":"e1007549","DOI":"10.1371\/journal.pcbi.1007549","article-title":"BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis","volume":"16","author":"Kumar","year":"2020","journal-title":"PLoS Computational Biology"},{"issue":"50","key":"2025050915554808100_imag_a_00575-b55","doi-asserted-by":"crossref","first-page":"10341","DOI":"10.1523\/JNEUROSCI.0118-21.2021","article-title":"Voxelwise encoding models show that cerebellar language representations are highly conceptual","volume":"41","author":"LeBel","year":"2021","journal-title":"The Journal of Neuroscience: The Official Journal of the Society for Neuroscience"},{"issue":"1","key":"2025050915554808100_imag_a_00575-b56","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.neuron.2018.11.004","article-title":"Human scene-selective areas represent 3D configurations of surfaces","volume":"101","author":"Lescroart","year":"2019","journal-title":"Neuron"},{"key":"2025050915554808100_imag_a_00575-b57","doi-asserted-by":"crossref","first-page":"135","DOI":"10.3389\/fncom.2015.00135","article-title":"Fourier power, subjective distance, and object categories all provide plausible models of BOLD responses in scene-selective visual areas","volume":"9","author":"Lescroart","year":"2015","journal-title":"Frontiers in Computational Neuroscience"},{"issue":"3","key":"2025050915554808100_imag_a_00575-b58","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1162\/jocn_e_01276","article-title":"Are we ready for real-world neuroscience?","volume":"31","author":"Matusz","year":"2019","journal-title":"Journal of Cognitive Neuroscience"},{"issue":"11","key":"2025050915554808100_imag_a_00575-b59","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1145\/219717.219748","article-title":"WordNet: A lexical database for English","volume":"38","author":"Miller","year":"1995","journal-title":"Communications of the ACM"},{"issue":"5880","key":"2025050915554808100_imag_a_00575-b60","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1126\/science.1152876","article-title":"Predicting human brain activity associated with the meanings of nouns","volume":"320","author":"Mitchell","year":"2008","journal-title":"Science"},{"issue":"2","key":"2025050915554808100_imag_a_00575-b61","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.neuroimage.2010.07.073","article-title":"Encoding and decoding in fMRI","volume":"56","author":"Naselaris","year":"2011","journal-title":"NeuroImage"},{"issue":"6","key":"2025050915554808100_imag_a_00575-b62","doi-asserted-by":"crossref","first-page":"902","DOI":"10.1016\/j.neuron.2009.09.006","article-title":"Bayesian reconstruction of natural images from human brain activity","volume":"63","author":"Naselaris","year":"2009","journal-title":"Neuron"},{"issue":"19","key":"2025050915554808100_imag_a_00575-b63","doi-asserted-by":"crossref","first-page":"1641","DOI":"10.1016\/j.cub.2011.08.031","article-title":"Reconstructing visual experiences from brain activity evoked by natural movies","volume":"21","author":"Nishimoto","year":"2011","journal-title":"Current Biology: CB"},{"key":"2025050915554808100_imag_a_00575-b64","author":"Nunez-Elizalde","year":"2024","journal-title":"Pymoten: Scientific python package for computing motion energy features from video"},{"key":"2025050915554808100_imag_a_00575-b65","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1016\/j.neuroimage.2019.04.012","article-title":"Voxelwise encoding models with non-spherical multivariate normal priors","volume":"197","author":"Nunez-Elizalde","year":"2019","journal-title":"NeuroImage"},{"key":"2025050915554808100_imag_a_00575-b66","unstructured":"Okuta, R., Unno, Y., Nishino, D., Hido, S., & Crissman. (2017). CuPy: A NumPy-compatible library for NVIDIA GPU calculations. https:\/\/learningsys.org\/nips17\/assets\/papers\/paper_16.pdf"},{"issue":"6251","key":"2025050915554808100_imag_a_00575-b67","doi-asserted-by":"crossref","first-page":"aac4716","DOI":"10.1126\/science.aac4716","article-title":"Estimating the reproducibility of psychological science","volume":"349","author":"Open Science Collaboration","year":"2015","journal-title":"Science"},{"key":"2025050915554808100_imag_a_00575-b68","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"arXiv [cs.LG]","author":"Paszke","year":"2019"},{"key":"2025050915554808100_imag_a_00575-b69","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Louppe, G., Prettenhofer, P., Weiss, R., Weiss, R. J., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825\u20132830. https:\/\/dl.acm.org\/doi\/10.5555\/1953048.2078195"},{"key":"2025050915554808100_imag_a_00575-b70","volume-title":"Statistical parametric mapping: The analysis of functional brain images","author":"Penny","year":"2011"},{"issue":"2","key":"2025050915554808100_imag_a_00575-b71","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1016\/j.neuroimage.2012.01.133","article-title":"The general linear model and fMRI: Does love last forever?","volume":"62","author":"Poline","year":"2012","journal-title":"NeuroImage"},{"issue":"11","key":"2025050915554808100_imag_a_00575-b72","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1038\/s41593-021-00921-6","article-title":"Visual and linguistic semantic representations are aligned at the border of human visual cortex","volume":"24","author":"Popham","year":"2021","journal-title":"Nature Neuroscience"},{"key":"2025050915554808100_imag_a_00575-b73","first-page":"109","article-title":"How linear are auditory cortical responses?","volume":"15","author":"Sahani","year":"2002","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"34","key":"2025050915554808100_imag_a_00575-b74","doi-asserted-by":"crossref","first-page":"14611","DOI":"10.1073\/pnas.0907682106","article-title":"Spectro-temporal modulation transfer function of single voxels in the human auditory cortex measured with high-resolution fMRI","volume":"106","author":"Sch\u00f6nwiesner","year":"2009","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"key":"2025050915554808100_imag_a_00575-b75","doi-asserted-by":"crossref","first-page":"10","DOI":"10.3389\/fncom.2016.00010","article-title":"Measuring the performance of neural models","volume":"10","author":"Schoppe","year":"2016","journal-title":"Frontiers in Computational Neuroscience"},{"issue":"5","key":"2025050915554808100_imag_a_00575-b76","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1016\/j.neuron.2013.06.034","article-title":"Natural scene statistics account for the representation of scene categories in human visual cortex","volume":"79","author":"Stansbury","year":"2013","journal-title":"Neuron"},{"issue":"Pt A","key":"2025050915554808100_imag_a_00575-b77","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.neuroimage.2017.06.035","article-title":"The feature-weighted receptive field: An interpretable encoding model for complex feature spaces","volume":"180","author":"St-Yves","year":"2018","journal-title":"NeuroImage"},{"issue":"4","key":"2025050915554808100_imag_a_00575-b78","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1016\/j.neuroimage.2006.06.062","article-title":"Inverse retinotopy: Inferring the visual content of images from brain activation patterns","volume":"33","author":"Thirion","year":"2006","journal-title":"NeuroImage"},{"issue":"3","key":"2025050915554808100_imag_a_00575-b79","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1002\/hbm.21037","article-title":"Structural and functional bases for individual differences in motor learning","volume":"32","author":"Tomassini","year":"2011","journal-title":"Human Brain Mapping"},{"key":"2025050915554808100_imag_a_00575-b80","article-title":"Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)","volume":"32","author":"Toneva","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"2025050915554808100_imag_a_00575-b81","doi-asserted-by":"crossref","first-page":"5611","DOI":"10.1038\/s41598-018-23696-6","article-title":"Identification of individual subjects on the basis of their brain anatomical features","volume":"8","author":"Valizadeh","year":"2018","journal-title":"Scientific Reports"},{"issue":"3","key":"2025050915554808100_imag_a_00575-b82","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental algorithms for scientific computing in Python","volume":"17","author":"Virtanen","year":"2020","journal-title":"Nature Methods"},{"key":"2025050915554808100_imag_a_00575-b83","unstructured":"Visconti di Oleggio Castello, M., Deniz, F., Dupr\u00e9 la Tour, T., & Gallant, J. L. (n.d.). Encoding models in functional magnetic resonance imaging: The Voxelwise Encoding Model framework."},{"issue":"11","key":"2025050915554808100_imag_a_00575-b84","doi-asserted-by":"crossref","first-page":"e112575","DOI":"10.1371\/journal.pone.0112575","article-title":"Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses","volume":"9","author":"Wehbe","year":"2014","journal-title":"PLoS One"},{"issue":"12","key":"2025050915554808100_imag_a_00575-b85","doi-asserted-by":"crossref","first-page":"4136","DOI":"10.1093\/cercor\/bhx268","article-title":"Neural encoding and decoding with deep learning for dynamic natural vision","volume":"28","author":"Wen","year":"2018","journal-title":"Cerebral Cortex (New York, N.Y.: 1991)"},{"key":"2025050915554808100_imag_a_00575-b86","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1146\/annurev.neuro.29.051605.113024","article-title":"Complete functional characterization of sensory neurons by system identification","volume":"29","author":"Wu","year":"2006","journal-title":"Annual Review of Neuroscience"},{"issue":"e1","key":"2025050915554808100_imag_a_00575-b87","first-page":"e1","article-title":"The generalizability crisis","volume":"45","author":"Yarkoni","year":"2020","journal-title":"The Behavioral and Brain Sciences"},{"issue":"6","key":"2025050915554808100_imag_a_00575-b88","doi-asserted-by":"crossref","first-page":"1100","DOI":"10.1177\/1745691617693393","article-title":"Choosing prediction over explanation in psychology: Lessons from machine learning","volume":"12","author":"Yarkoni","year":"2017","journal-title":"Perspectives on Psychological Science: A Journal of the Association for Psychological Science"}],"container-title":["Imaging Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/direct.mit.edu\/imag\/article-pdf\/doi\/10.1162\/imag_a_00575\/2514561\/imag_a_00575.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/direct.mit.edu\/imag\/article-pdf\/doi\/10.1162\/imag_a_00575\/2514561\/imag_a_00575.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T15:56:10Z","timestamp":1746806170000},"score":1,"resource":{"primary":{"URL":"https:\/\/direct.mit.edu\/imag\/article\/doi\/10.1162\/imag_a_00575\/128936\/The-Voxelwise-Encoding-Model-framework-A-tutorial"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":88,"URL":"https:\/\/doi.org\/10.1162\/imag_a_00575","relation":{},"ISSN":["2837-6056"],"issn-type":[{"value":"2837-6056","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025]]},"published":{"date-parts":[[2025]]},"article-number":"imag_a_00575"}}