{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T06:14:49Z","timestamp":1772172889255,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1008943","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,9,16]],"date-time":"2021-09-16T00:00:00Z","timestamp":1631750400000}}],"reference-count":74,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T00:00:00Z","timestamp":1630627200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"crossref","award":["MH071589"],"award-info":[{"award-number":["MH071589"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100000025","name":"National Institute of Mental Health","doi-asserted-by":"crossref","award":["MH112517"],"award-info":[{"award-number":["MH112517"]}],"id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100011586","name":"College of Health and Behavioral Sciences, University of Central Arkansas","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100011586","id-type":"DOI","asserted-by":"publisher"}]},{"name":"WU-Minn Consortium","award":["1U54MH091657"],"award-info":[{"award-number":["1U54MH091657"]}]},{"name":"McDonnell Center for Systems Neuroscience at Washington University"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Insights from functional Magnetic Resonance Imaging (fMRI), as well as recordings of large numbers of neurons, reveal that many cognitive, emotional, and motor functions depend on the multivariate interactions of brain signals. To\n                    <jats:italic>decode<\/jats:italic>\n                    brain dynamics, we propose an architecture based on recurrent neural networks to uncover distributed spatiotemporal signatures. We demonstrate the potential of the approach using human fMRI data during movie-watching data and a continuous experimental paradigm. The model was able to learn spatiotemporal patterns that supported 15-way movie-clip classification (\u223c90%) at the level of brain regions, and binary classification of experimental conditions (\u223c60%) at the level of voxels. The model was also able to learn individual differences in measures of fluid intelligence and verbal IQ at levels comparable to that of existing techniques. We propose a dimensionality reduction approach that uncovers low-dimensional trajectories and captures essential informational (i.e., classification related) properties of brain dynamics. Finally,\n                    <jats:italic>saliency<\/jats:italic>\n                    maps and lesion analysis were employed to characterize brain-region\/voxel importance, and uncovered how dynamic but consistent changes in fMRI activation influenced decoding performance. When applied at the level of voxels, our framework implements a dynamic version of multivariate pattern analysis. Our approach provides a framework for visualizing, analyzing, and discovering dynamic spatially distributed brain representations during naturalistic conditions.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1008943","type":"journal-article","created":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T13:45:38Z","timestamp":1630676738000},"page":"e1008943","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":8,"title":["Learning brain dynamics for decoding and predicting individual differences"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8306-4832","authenticated-orcid":true,"given":"Joyneel","family":"Misra","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0403-5812","authenticated-orcid":true,"given":"Srinivas Govinda","family":"Surampudi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manasij","family":"Venkatesh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chirag","family":"Limbachia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8620-5650","authenticated-orcid":true,"given":"Joseph","family":"Jaja","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4696-6903","authenticated-orcid":true,"given":"Luiz","family":"Pessoa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2021,9,3]]},"reference":[{"key":"pcbi.1008943.ref001","doi-asserted-by":"crossref","DOI":"10.1037\/a0027737","article-title":"Perceiving Event Dynamics and Parsing Hollywood Films","volume":"38","author":"J Cutting","year":"2012","journal-title":"Journal of experimental psychology Human perception and performance"},{"issue":"4","key":"pcbi.1008943.ref002","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1162\/jocn_a_01363","article-title":"Dynamic Threat Processing","volume":"31","author":"C Meyer","year":"2019","journal-title":"Journal of Cognitive Neuroscience"},{"issue":"1","key":"pcbi.1008943.ref003","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s42003-020-01537-5","article-title":"Controllability over stressor decreases responses in key threat-related brain areas","volume":"4","author":"C Limbachia","year":"2021","journal-title":"Communications Biology"},{"issue":"5539","key":"pcbi.1008943.ref004","doi-asserted-by":"crossref","first-page":"2425","DOI":"10.1126\/science.1063736","article-title":"Distributed and overlapping representations of faces and objects in ventral temporal cortex","volume":"293","author":"JV Haxby","year":"2001","journal-title":"Science"},{"issue":"5","key":"pcbi.1008943.ref005","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1038\/nn1445","article-title":"Predicting the orientation of invisible stimuli from activity in human primary visual cortex","volume":"8","author":"JD Haynes","year":"2005","journal-title":"Nature Neuroscience"},{"issue":"5","key":"pcbi.1008943.ref006","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1038\/nn1444","article-title":"Decoding the visual and subjective contents of the human brain","volume":"8","author":"Y Kamitani","year":"2005","journal-title":"Nature Neuroscience"},{"issue":"2","key":"pcbi.1008943.ref007","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":"T Naselaris","year":"2011","journal-title":"NeuroImage"},{"issue":"2","key":"pcbi.1008943.ref008","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1016\/j.neuroimage.2010.05.081","article-title":"Decoding brain states from fMRI connectivity graphs","volume":"56","author":"J Richiardi","year":"2011","journal-title":"NeuroImage"},{"issue":"10","key":"pcbi.1008943.ref009","doi-asserted-by":"crossref","first-page":"e1005649","DOI":"10.1371\/journal.pcbi.1005649","article-title":"Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition","volume":"13","author":"TN Rubin","year":"2017","journal-title":"PLOS Computational Biology"},{"key":"pcbi.1008943.ref010","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/B978-0-12-397025-1.00345-6","volume-title":"Brain Mapping","author":"C Allefeld","year":"2015"},{"issue":"1","key":"pcbi.1008943.ref011","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.neuroimage.2007.02.020","article-title":"Dynamic discrimination analysis: A spatial\u2013temporal SVM","volume":"36","author":"J Mour\u00e3o-Miranda","year":"2007","journal-title":"NeuroImage"},{"issue":"24","key":"pcbi.1008943.ref012","doi-asserted-by":"crossref","first-page":"9998","DOI":"10.1073\/pnas.1102433108","article-title":"Unraveling the distributed neural code of facial identity through spatiotemporal pattern analysis","volume":"108","author":"A Nestor","year":"2011","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"2","key":"pcbi.1008943.ref013","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.neuroimage.2011.03.047","article-title":"Spatio-temporal models of mental processes from fMRI","volume":"57","author":"F Janoos","year":"2011","journal-title":"NeuroImage"},{"key":"pcbi.1008943.ref014","doi-asserted-by":"crossref","unstructured":"Loula J, Baroni M, Lake BM. Rearranging the Familiar: Testing Compositional Generalization in Recurrent Networks. arXiv:180707545 [cs]. 2018.","DOI":"10.18653\/v1\/W18-5413"},{"issue":"1","key":"pcbi.1008943.ref015","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.neuroimage.2009.01.025","article-title":"Modeling fMRI data generated by overlapping cognitive processes with unknown onsets using Hidden Process Models","volume":"46","author":"RA Hutchinson","year":"2009","journal-title":"NeuroImage"},{"issue":"2","key":"pcbi.1008943.ref016","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1016\/j.neuroimage.2011.06.053","article-title":"Utilizing temporal information in fMRI decoding: Classifier using kernel regression methods","volume":"58","author":"C Chu","year":"2011","journal-title":"NeuroImage"},{"issue":"1","key":"pcbi.1008943.ref017","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1162\/netn_a_00116","article-title":"Questions and controversies in the study of time-varying functional connectivity in resting fMRI","volume":"4","author":"DJ Lurie","year":"2020","journal-title":"Network Neuroscience"},{"issue":"8","key":"pcbi.1008943.ref018","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1093\/scan\/nsaa114","article-title":"Tools of the trade: estimating time-varying connectivity patterns from fMRI data","volume":"16","author":"A Iraji","year":"2020","journal-title":"Social Cognitive and Affective Neuroscience"},{"issue":"2","key":"pcbi.1008943.ref019","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.neuron.2014.10.015","article-title":"The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery","volume":"84","author":"VD Calhoun","year":"2014","journal-title":"Neuron"},{"key":"pcbi.1008943.ref020","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.neuroimage.2016.12.061","article-title":"The dynamic functional connectome: State-of-the-art and perspectives","volume":"160","author":"MG Preti","year":"2017","journal-title":"NeuroImage"},{"key":"pcbi.1008943.ref021","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1016\/j.apenergy.2018.12.004","article-title":"Assessment of deep recurrent neural network-based strategies for short-term building energy predictions","volume":"236","author":"C Fan","year":"2019","journal-title":"Applied Energy"},{"key":"pcbi.1008943.ref022","unstructured":"Byron MY, Cunningham JP, Santhanam G, Ryu SI, Shenoy KV, Sahani M. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. In: Advances in neural information processing systems; 2009. p. 1881\u20131888."},{"issue":"2","key":"pcbi.1008943.ref023","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1038\/nrn2558","article-title":"State-dependent computations: spatiotemporal processing in cortical networks","volume":"10","author":"DV Buonomano","year":"2009","journal-title":"Nature Reviews Neuroscience"},{"key":"pcbi.1008943.ref024","first-page":"214262","article-title":"A theory of multineuronal dimensionality, dynamics and measurement","author":"P Gao","year":"2017","journal-title":"BioRxiv"},{"issue":"4","key":"pcbi.1008943.ref025","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1016\/j.neuron.2018.01.004","article-title":"Motor Cortex Embeds Muscle-like Commands in an Untangled Population Response","volume":"97","author":"AA Russo","year":"2018","journal-title":"Neuron"},{"issue":"5","key":"pcbi.1008943.ref026","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.neuron.2019.09.002","article-title":"The Low-Dimensional Neural Architecture of Cognitive Complexity Is Related to Activity in Medial Thalamic Nuclei","volume":"104","author":"JM Shine","year":"2019","journal-title":"Neuron"},{"key":"pcbi.1008943.ref027","article-title":"Non-linear manifold learning in fMRI uncovers a low-dimensional space of brain dynamics","author":"S Gao","year":"2020","journal-title":"bioRxiv"},{"key":"pcbi.1008943.ref028","article-title":"Representational similarity analysis\u2014connecting the branches of systems neuroscience","volume":"2","author":"N Kriegeskorte","year":"2008","journal-title":"Frontiers in Systems Neuroscience"},{"key":"pcbi.1008943.ref029","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.neuroimage.2013.05.041","article-title":"The WU-Minn Human Connectome Project: An overview","volume":"80","author":"DC Van Essen","year":"2013","journal-title":"NeuroImage"},{"key":"pcbi.1008943.ref030","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.neuroimage.2013.04.127","article-title":"The minimal preprocessing pipelines for the Human Connectome Project","volume":"80","author":"MF Glasser","year":"2013","journal-title":"Neuroimage"},{"key":"pcbi.1008943.ref031","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.neuroimage.2016.11.049","article-title":"Tradeoffs in pushing the spatial resolution of fMRI for the 7T Human Connectome Project","volume":"154","author":"AT Vu","year":"2017","journal-title":"NeuroImage"},{"issue":"9","key":"pcbi.1008943.ref032","doi-asserted-by":"crossref","first-page":"3095","DOI":"10.1093\/cercor\/bhx179","article-title":"Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI","volume":"28","author":"A Schaefer","year":"2018","journal-title":"Cerebral Cortex"},{"issue":"2","key":"pcbi.1008943.ref033","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Y Bengio","year":"1994","journal-title":"IEEE transactions on neural networks"},{"key":"pcbi.1008943.ref034","doi-asserted-by":"crossref","unstructured":"Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, et al. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. arXiv:14061078 [cs, stat]. 2014.","DOI":"10.3115\/v1\/D14-1179"},{"issue":"8","key":"pcbi.1008943.ref035","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"S Hochreiter","year":"1997","journal-title":"Neural Computation"},{"key":"pcbi.1008943.ref036","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv:14123555 [cs]. 2014."},{"issue":"10","key":"pcbi.1008943.ref037","doi-asserted-by":"crossref","first-page":"1550","DOI":"10.1109\/5.58337","article-title":"Backpropagation through time: what it does and how to do it","volume":"78","author":"PJ Werbos","year":"1990","journal-title":"Proceedings of the IEEE"},{"key":"pcbi.1008943.ref038","unstructured":"Kingma DP, Ba J. Adam: A Method for Stochastic Optimization; 2014. Available from: http:\/\/arxiv.org\/abs\/1412.6980."},{"key":"pcbi.1008943.ref039","unstructured":"Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, et al. TensorFlow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation. OSDI\u201916. USA: USENIX Association; 2016. p. 265\u2013283."},{"issue":"7","key":"pcbi.1008943.ref040","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1109\/TMI.2017.2715285","article-title":"Modeling Task fMRI Data Via Deep Convolutional Autoencoder","volume":"37","author":"H Huang","year":"2018","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"pcbi.1008943.ref041","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.neuroimage.2019.05.039","article-title":"Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex","volume":"198","author":"K Han","year":"2019","journal-title":"NeuroImage"},{"key":"pcbi.1008943.ref042","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.mri.2019.05.031","article-title":"Machine learning in resting-state fMRI analysis","volume":"64","author":"M Khosla","year":"2019","journal-title":"Magnetic Resonance Imaging"},{"key":"pcbi.1008943.ref043","unstructured":"Simonyan K, Vedaldi A, Zisserman A. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. arXiv:13126034 [cs]. 2014."},{"issue":"3","key":"pcbi.1008943.ref044","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/S0304-3800(02)00257-0","article-title":"Review and comparison of methods to study the contribution of variables in artificial neural network models","volume":"160","author":"M Gevrey","year":"2003","journal-title":"Ecological Modelling"},{"key":"pcbi.1008943.ref045","first-page":"1803","article-title":"How to Explain Individual Classification Decisions","volume":"11","author":"D Baehrens","year":"2010","journal-title":"The Journal of Machine Learning Research"},{"key":"pcbi.1008943.ref046","first-page":"254","volume-title":"Biocomputing 2017","author":"J Lanchantin","year":"2016"},{"issue":"6","key":"pcbi.1008943.ref047","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1038\/89044","article-title":"Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks","volume":"7","author":"J Khan","year":"2001","journal-title":"Nature Medicine"},{"key":"pcbi.1008943.ref048","doi-asserted-by":"crossref","unstructured":"Lea C, Vidal R, Reiter A, Hager GD. Temporal Convolutional Networks: A Unified Approach to Action Segmentation. In: Hua G, J\u00e9gou H, editors. Computer Vision\u2014ECCV 2016 Workshops. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2016. p. 47\u201354.","DOI":"10.1007\/978-3-319-49409-8_7"},{"key":"pcbi.1008943.ref049","unstructured":"Bai S, Kolter JZ, Koltun V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:180301271. 2018."},{"key":"pcbi.1008943.ref050","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.neuroimage.2013.05.033","article-title":"Function in the human connectome: task-fMRI and individual differences in behavior","volume":"80","author":"DM Barch","year":"2013","journal-title":"Neuroimage"},{"issue":"3","key":"pcbi.1008943.ref051","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1038\/nprot.2016.178","article-title":"Using connectome-based predictive modeling to predict individual behavior from brain connectivity","volume":"12","author":"X Shen","year":"2017","journal-title":"Nature Protocols"},{"issue":"1","key":"pcbi.1008943.ref052","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.jneumeth.2007.03.024","article-title":"Nonparametric statistical testing of EEG- and MEG-data","volume":"164","author":"E Maris","year":"2007","journal-title":"Journal of Neuroscience Methods"},{"issue":"1","key":"pcbi.1008943.ref053","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"N Srivastava","year":"2014","journal-title":"The journal of machine learning research"},{"key":"pcbi.1008943.ref054","doi-asserted-by":"crossref","unstructured":"Ojala M, Garriga GC. Permutation Tests for Studying Classifier Performance. In: 2009 Ninth IEEE International Conference on Data Mining. Miami Beach, FL, USA: IEEE; 2009. p. 908\u2013913. Available from: http:\/\/ieeexplore.ieee.org\/document\/5360332\/.","DOI":"10.1109\/ICDM.2009.108"},{"key":"pcbi.1008943.ref055","doi-asserted-by":"crossref","first-page":"S234","DOI":"10.1016\/j.neuroimage.2004.07.012","article-title":"Wavelets and functional magnetic resonance imaging of the human brain","volume":"23","author":"E Bullmore","year":"2004","journal-title":"Neuroimage"},{"issue":"5","key":"pcbi.1008943.ref056","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1073\/pnas.1713532115","article-title":"Robust prediction of individual creative ability from brain functional connectivity","volume":"115","author":"RE Beaty","year":"2018","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"1756","key":"pcbi.1008943.ref057","doi-asserted-by":"crossref","first-page":"20170284","DOI":"10.1098\/rstb.2017.0284","article-title":"A distributed brain network predicts general intelligence from resting-state human neuroimaging data","volume":"373","author":"J Dubois","year":"2018","journal-title":"Philosophical Transactions of the Royal Society B: Biological Sciences"},{"key":"pcbi.1008943.ref058","doi-asserted-by":"crossref","first-page":"e6","DOI":"10.1017\/pen.2018.8","article-title":"Resting-State Functional Brain Connectivity Best Predicts the Personality Dimension of Openness to Experience","volume":"1","author":"J Dubois","year":"2018","journal-title":"Personality Neuroscience"},{"issue":"2","key":"pcbi.1008943.ref059","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1093\/scan\/nsy002","article-title":"Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals","volume":"13","author":"WT Hsu","year":"2018","journal-title":"Social Cognitive and Affective Neuroscience"},{"key":"pcbi.1008943.ref060","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.neuroimage.2018.08.038","article-title":"Connectome-based individualized prediction of temperament trait scores","volume":"183","author":"R Jiang","year":"2018","journal-title":"NeuroImage"},{"issue":"1","key":"pcbi.1008943.ref061","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1146\/annurev-neuro-092619-094115","article-title":"Computation Through Neural Population Dynamics","volume":"43","author":"S Vyas","year":"2020","journal-title":"Annual Review of Neuroscience"},{"key":"pcbi.1008943.ref062","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.neuroimage.2017.08.005","article-title":"Deconstructing multivariate decoding for the study of brain function","volume":"180","author":"MN Hebart","year":"2018","journal-title":"NeuroImage"},{"issue":"2","key":"pcbi.1008943.ref063","doi-asserted-by":"crossref","first-page":"1","DOI":"10.51628\/001c.24619","article-title":"Strong and weak principles of neural dimension reduction","volume":"5","author":"MD Humphries","year":"2021","journal-title":"Neurons, Behavior, Data analysis, and Theory"},{"key":"pcbi.1008943.ref064","doi-asserted-by":"crossref","unstructured":"Jazayeri M, Ostojic S. Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity. arXiv:210704084 [q-bio]. 2021.","DOI":"10.1016\/j.conb.2021.08.002"},{"key":"pcbi.1008943.ref065","doi-asserted-by":"crossref","first-page":"e10989","DOI":"10.7554\/eLife.10989","article-title":"Demixed principal component analysis of neural population data","volume":"5","author":"D Kobak","year":"2016","journal-title":"eLife"},{"key":"pcbi.1008943.ref066","doi-asserted-by":"crossref","first-page":"109063","DOI":"10.1016\/j.jneumeth.2020.109063","article-title":"Narrowband multivariate source separation for semi-blind discovery of experiment contrasts","volume":"350","author":"MB Zuure","year":"2021","journal-title":"Journal of Neuroscience Methods"},{"issue":"1","key":"pcbi.1008943.ref067","doi-asserted-by":"crossref","first-page":"3564","DOI":"10.1038\/s41467-020-17404-0","article-title":"Unexpected complexity of everyday manual behaviors","volume":"11","author":"Y Yan","year":"2020","journal-title":"Nature Communications"},{"key":"pcbi.1008943.ref068","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1016\/j.neuroimage.2018.11.016","article-title":"Brain dynamics and temporal trajectories during task and naturalistic processing","volume":"186","author":"M Venkatesh","year":"2019","journal-title":"NeuroImage"},{"issue":"11","key":"pcbi.1008943.ref069","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1038\/nn.4135","article-title":"Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity","volume":"18","author":"ES Finn","year":"2015","journal-title":"Nat Neurosci"},{"issue":"2","key":"pcbi.1008943.ref070","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1038\/s41593-018-0312-0","article-title":"Human cognition involves the dynamic integration of neural activity and neuromodulatory systems","volume":"22","author":"JM Shine","year":"2019","journal-title":"Nat Neurosci"},{"key":"pcbi.1008943.ref071","doi-asserted-by":"crossref","first-page":"117963","DOI":"10.1016\/j.neuroimage.2021.117963","article-title":"Movie-watching outperforms rest for functional connectivity-based prediction of behavior","volume":"235","author":"ES Finn","year":"2021","journal-title":"NeuroImage"},{"issue":"1","key":"pcbi.1008943.ref072","doi-asserted-by":"crossref","first-page":"2043","DOI":"10.1038\/s41467-018-04387-2","article-title":"Trait paranoia shapes inter-subject synchrony in brain activity during an ambiguous social narrative","volume":"9","author":"ES Finn","year":"2018","journal-title":"Nat Commun"},{"key":"pcbi.1008943.ref073","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.3389\/fnins.2019.01321","article-title":"Analyzing Neuroimaging Data Through Recurrent Deep Learning Models","volume":"13","author":"AW Thomas","year":"2019","journal-title":"Frontiers in Neuroscience"},{"key":"pcbi.1008943.ref074","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.neuroimage.2016.10.038","article-title":"Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines","volume":"145","author":"G Varoquaux","year":"2017","journal-title":"NeuroImage"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1008943","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,9,16]],"date-time":"2021-09-16T00:00:00Z","timestamp":1631750400000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1008943","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,8]],"date-time":"2023-01-08T10:28:28Z","timestamp":1673173708000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1008943"}},"subtitle":[],"editor":[{"given":"Daniele","family":"Marinazzo","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,9,3]]},"references-count":74,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,9,3]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1008943","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.03.27.437315","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,3]]}}}