{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T09:30:04Z","timestamp":1777109404341,"version":"3.51.4"},"reference-count":66,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T00:00:00Z","timestamp":1585699200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T00:00:00Z","timestamp":1585699200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Neuroscience Methods"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1016\/j.jneumeth.2019.108567","type":"journal-article","created":{"date-parts":[[2020,1,27]],"date-time":"2020-01-27T11:16:50Z","timestamp":1580123810000},"page":"108567","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":17,"special_numbering":"C","title":["Comparison of feature selection methods based on discrimination and reliability for fMRI decoding analysis"],"prefix":"10.1016","volume":"335","author":[{"given":"Wenyan","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zonglei","family":"Zhen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2377-6093","authenticated-orcid":false,"given":"Xia","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.jneumeth.2019.108567_bib0005","series-title":"Signal Processing and Communication Application Conference","first-page":"2249","article-title":"Effect of voxel selection on temporal mesh model for brain decoding","author":"Afrasiyabi","year":"2016"},{"issue":"4","key":"10.1016\/j.jneumeth.2019.108567_bib0010","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.neuroimage.2013.11.043","article-title":"Searchlight-based multi-voxel pattern analysis of fMRI by cross-validated MANOVA","volume":"89","author":"Allefeld","year":"2014","journal-title":"NeuroImage"},{"key":"10.1016\/j.jneumeth.2019.108567_bib0015","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1016\/j.neuroimage.2015.09.072","article-title":"Validation of non-REM sleep stage decoding from resting state fMRI using linear support vector machines","volume":"125","author":"Altmann","year":"2016","journal-title":"NeuroImage"},{"issue":"8","key":"10.1016\/j.jneumeth.2019.108567_bib0020","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":"Barch","year":"2013","journal-title":"NeuroImage"},{"issue":"2","key":"10.1016\/j.jneumeth.2019.108567_bib0025","doi-asserted-by":"crossref","first-page":"1465","DOI":"10.1016\/j.neuroimage.2010.09.048","article-title":"Mapping anterior temporal lobe language areas with fMRI: a multicenter normative study","volume":"54","author":"Binder","year":"2010","journal-title":"NeuroImage"},{"key":"10.1016\/j.jneumeth.2019.108567_bib0030","series-title":"International Joint Conference on Neural Networks","first-page":"4637","article-title":"Classification from generation: recognizing deep grammatical information during reading from rapid event-related fMRI","author":"Bitan","year":"2016"},{"issue":"3","key":"10.1016\/j.jneumeth.2019.108567_bib0035","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.1016\/j.neuroimage.2012.08.035","article-title":"Decoding the perception of pain from fMRI using multivariate pattern analysis","volume":"63","author":"Brodersen","year":"2012","journal-title":"NeuroImage"},{"key":"10.1016\/j.jneumeth.2019.108567_bib0040","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1002\/1097-0193(200012)11:4<249::AID-HBM20>3.0.CO;2-5","article-title":"Estimation and detection of event-related fMRI signals with temporally correlated noise: a statistically efficient and unbiased approach","volume":"11","author":"Burock","year":"2000","journal-title":"Hum. Brain Mapp."},{"issue":"3","key":"10.1016\/j.jneumeth.2019.108567_bib0045","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1016\/j.neuroimage.2008.12.035","article-title":"Measuring fMRI reliability with the intra-class correlation coefficient","volume":"45","author":"Caceres","year":"2008","journal-title":"NeuroImage"},{"issue":"3","key":"10.1016\/j.jneumeth.2019.108567_bib0050","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1006\/nimg.2000.0612","article-title":"Movement and mind: a functional imaging study of perception and interpretation of complex intentional movement patterns","volume":"12","author":"Castelli","year":"2000","journal-title":"NeuroImage"},{"issue":"3","key":"10.1016\/j.jneumeth.2019.108567_bib0055","first-page":"1","article-title":"A multiple kernel learning approach to perform classification of groups from complex-valued fMRI data analysis: application to schizophrenia","volume":"87","author":"Castro","year":"2004","journal-title":"NeuroImage"},{"key":"10.1016\/j.jneumeth.2019.108567_bib0060","series-title":"Multinomial Regression. Handbook of Regression Analysis","author":"Chatterjee","year":"2012"},{"issue":"3","key":"10.1016\/j.jneumeth.2019.108567_bib0065","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1038\/nn.4499","article-title":"Computational approaches to fMRI analysis","volume":"20","author":"Cohen","year":"2017","journal-title":"Nat. Neurosci."},{"issue":"6","key":"10.1016\/j.jneumeth.2019.108567_bib0070","doi-asserted-by":"crossref","first-page":"3072","DOI":"10.1152\/jn.2000.84.6.3072","article-title":"Tracking the hemodynamic responses to reward and punishment in the striatum","volume":"84","author":"Delgado","year":"2000","journal-title":"Neurophysiology"},{"issue":"3","key":"10.1016\/j.jneumeth.2019.108567_bib0075","doi-asserted-by":"crossref","first-page":"297","DOI":"10.2307\/1932409","article-title":"Measures of the amount of ecologic association between species","volume":"26","author":"Dice","year":"1945","journal-title":"Ecology"},{"issue":"2","key":"10.1016\/j.jneumeth.2019.108567_bib0080","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1016\/j.neuroimage.2005.12.016","article-title":"A rapid fMRI task battery for mapping of visual, motor, cognitive, and emotional function","volume":"31","author":"Drobyshevsky","year":"2005","journal-title":"NeuroImage"},{"issue":"3","key":"10.1016\/j.jneumeth.2019.108567_bib0085","first-page":"145","article-title":"High classification accuracy for schizophrenia with rest and task fMRI data","volume":"6","author":"Du","year":"2012","journal-title":"Front. Hum. Neurosci."},{"key":"10.1016\/j.jneumeth.2019.108567_bib0090","series-title":"Kendall\u2019s Coefficient of Concordance. Encyclopedia of Statistics in Behavioral Science","author":"Field","year":"2005"},{"issue":"10","key":"10.1016\/j.jneumeth.2019.108567_bib0095","doi-asserted-by":"crossref","first-page":"e00801","DOI":"10.1002\/brb3.801","article-title":"Using machine learning to classify temporal lobe epilepsy based on diffusion MRI","volume":"7","author":"Gaizo","year":"2017","journal-title":"Brain Behav."},{"issue":"6","key":"10.1016\/j.jneumeth.2019.108567_bib0100","doi-asserted-by":"crossref","first-page":"3314","DOI":"10.1073\/pnas.96.6.3314","article-title":"Spatial attention affects brain activity in human primary visual cortex","volume":"96","author":"Gandhi","year":"1999","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"issue":"2","key":"10.1016\/j.jneumeth.2019.108567_bib0105","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.cogbrainres.2004.02.012","article-title":"Brain areas underlying visual mental imagery and visual perception: an fMRI study","volume":"20","author":"Ganis","year":"2004","journal-title":"Brain Res. Cogn. Brain Res."},{"key":"10.1016\/j.jneumeth.2019.108567_bib0110","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":"Glasser","year":"2013","journal-title":"NeuroImage"},{"issue":"3","key":"10.1016\/j.jneumeth.2019.108567_bib0115","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1002\/hbm.1031","article-title":"Feature-space clustering for fMRI meta-analysis","volume":"13","author":"Goutte","year":"2001","journal-title":"Hum. Brain Mapp."},{"issue":"10","key":"10.1016\/j.jneumeth.2019.108567_bib0120","doi-asserted-by":"crossref","first-page":"2489","DOI":"10.1093\/cercor\/bhs246","article-title":"Impaired processing of 3D motion-defined faces in mild cognitive impairment and healthy aging: an fMRI study","volume":"23","author":"Graewe","year":"2013","journal-title":"Cereb. Cortex"},{"issue":"1","key":"10.1016\/j.jneumeth.2019.108567_bib0125","first-page":"S87","article-title":"Resting-state fMRI distinguishes Alzheimer\u2019s disease from healthy aging and frontotemporal lobar degeneration","volume":"1","author":"Greicius","year":"2005","journal-title":"Alzheimers Dementia"},{"key":"10.1016\/j.jneumeth.2019.108567_bib0130","series-title":"International Conference on Computing for Sustainable Global Development","article-title":"Clustering based feature selection methods from fMRI data for classification of cognitive states of the human brain","author":"Gupta","year":"2016"},{"key":"10.1016\/j.jneumeth.2019.108567_bib0135","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learn. Res."},{"issue":"51","key":"10.1016\/j.jneumeth.2019.108567_bib0140","doi-asserted-by":"crossref","first-page":"13213","DOI":"10.1523\/JNEUROSCI.3446-06.2006","article-title":"Preference for immediate over delayed rewards is associated with magnitude of ventral striatal activity","volume":"26","author":"Hariri","year":"2006","journal-title":"J. Neurosci."},{"issue":"38","key":"10.1016\/j.jneumeth.2019.108567_bib0145","doi-asserted-by":"crossref","first-page":"16494","DOI":"10.1073\/pnas.1008121107","article-title":"Phonological decisions require both the left and right supramarginal gyri","volume":"107","author":"Hartwigsen","year":"2010","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"issue":"Pt. B","key":"10.1016\/j.jneumeth.2019.108567_bib0150","first-page":"314","article-title":"Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: evaluation using sensorimotor tasks","volume":"145","author":"Jang","year":"2016","journal-title":"NeuroImage"},{"issue":"3","key":"10.1016\/j.jneumeth.2019.108567_bib0155","first-page":"69","article-title":"Multiscale mining of fMRI data with hierarchical structured sparsity","volume":"5","author":"Jenatton","year":"2011","journal-title":"Int. Workshop Pattern Recognit. Neuroimage"},{"key":"10.1016\/j.jneumeth.2019.108567_bib0160","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.cobme.2017.09.008","article-title":"Dynamics of large-scale fMRI networks: deconstruct brain activity to build better models of brain function","volume":"3","author":"Karahanoglu","year":"2017","journal-title":"Curr. Opin. Biomed. Eng."},{"key":"10.1016\/j.jneumeth.2019.108567_bib0165","doi-asserted-by":"crossref","first-page":"177","DOI":"10.3389\/fpsyt.2016.00177","article-title":"Promises, pitfalls, and basic guidelines for applying machine learning classifiers to psychiatric imaging data, with autism as an example","volume":"7","author":"Kassraian-Fard","year":"2016","journal-title":"Front. Psychiatry"},{"key":"10.1016\/j.jneumeth.2019.108567_bib0170","series-title":"Chapter 9. Normal One-Way ANOVA. Introduction to Winbugs for Ecologists","first-page":"115","author":"K\u00e9ry","year":"2010"},{"issue":"4","key":"10.1016\/j.jneumeth.2019.108567_bib0175","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1016\/j.neuroimage.2007.06.030","article-title":"Combining the tools: activation- and information-based fMRI analysis","volume":"38","author":"Kriegeskorte","year":"2007","journal-title":"NeuroImage"},{"key":"10.1016\/j.jneumeth.2019.108567_bib0180","series-title":"International Joint Conference on Neural Networks","first-page":"1463","article-title":"Spatial SVM for feature selection and fMRI activation detection","author":"Liang","year":"2006"},{"key":"10.1016\/j.jneumeth.2019.108567_bib0185","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.ins.2017.11.035","article-title":"Double regularization methods for robust feature selection and SVM classification via DC programming","volume":"429","author":"L\u00f3pez","year":"2018","journal-title":"Inf. Sci."},{"issue":"1","key":"10.1016\/j.jneumeth.2019.108567_bib0190","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neuroimage.2005.07.005","article-title":"Non-white noise in fMRI: does modelling have an impact?","volume":"29","author":"Lund","year":"2006","journal-title":"NeuroImage"},{"issue":"1","key":"10.1016\/j.jneumeth.2019.108567_bib0195","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.neuroimage.2008.06.037","article-title":"Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns","volume":"43","author":"Martino","year":"2008","journal-title":"NeuroImage"},{"key":"10.1016\/j.jneumeth.2019.108567_bib0200","series-title":"IEEE International Symposium on Biomedical Imaging: From Nano To Macro","first-page":"592","article-title":"Mutual information-based feature selection enhances fMRI brain activity classification","author":"Michel","year":"2008"},{"issue":"6","key":"10.1016\/j.jneumeth.2019.108567_bib0205","doi-asserted-by":"crossref","first-page":"2041","DOI":"10.1016\/j.patcog.2011.04.006","article-title":"A supervised clustering approach for fMRI-based inference of brain states","volume":"45","author":"Michel","year":"2012","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.jneumeth.2019.108567_bib0210","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.neucom.2017.02.057","article-title":"Variational relevant sample-feature machine: a fully Bayesian approach for embedded feature selection","volume":"241","author":"Mirzaei","year":"2017","journal-title":"Neurocomputing"},{"issue":"2","key":"10.1016\/j.jneumeth.2019.108567_bib0215","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/s12021-013-9204-3","article-title":"A review of feature reduction techniques in neuroimaging","volume":"12","author":"Mwangi","year":"2014","journal-title":"Neuroinformatics"},{"issue":"2","key":"10.1016\/j.jneumeth.2019.108567_bib0220","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":"30","key":"10.1016\/j.jneumeth.2019.108567_bib0225","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.jneumeth.2015.08.020","article-title":"Effects of imaging modalities, brain atlases and feature selection on prediction of Alzheimer\u2019s disease","volume":"256","author":"Ota","year":"2015","journal-title":"J. Neurosci. Methods"},{"key":"10.1016\/j.jneumeth.2019.108567_bib0230","first-page":"2202","article-title":"Sparse overlapping sets lasso for multitask learning and its application to fMRI analysis","volume":"2013","author":"Rao","year":"2013","journal-title":"Comput. Sci."},{"issue":"47","key":"10.1016\/j.jneumeth.2019.108567_bib0235","doi-asserted-by":"crossref","first-page":"S57","DOI":"10.1016\/S1053-8119(09)70217-4","article-title":"Feature selection and classification of fMRI data using logistic regression with L1 norm regularization","volume":"47","author":"Ryali","year":"2009","journal-title":"NeuroImage"},{"issue":"7","key":"10.1016\/j.jneumeth.2019.108567_bib0240","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1118\/1.599017","article-title":"Feature selection and classifier performance in computer-aided diagnosis: the effect of finite sample size","volume":"27","author":"Sahiner","year":"2000","journal-title":"Med. Phys."},{"issue":"Suppl. 1","key":"10.1016\/j.jneumeth.2019.108567_bib0245","doi-asserted-by":"crossref","first-page":"S208","DOI":"10.1016\/j.neuroimage.2004.07.051","article-title":"Advances in functional and structural MR image analysis and implementation as FSL","volume":"23","author":"Smith","year":"2004","journal-title":"NeuroImage"},{"issue":"4","key":"10.1016\/j.jneumeth.2019.108567_bib0250","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1016\/j.neuroimage.2007.04.032","article-title":"Localizing the rostrolateral prefrontal cortex at the individual level","volume":"36","author":"Smith","year":"2007","journal-title":"NeuroImage"},{"issue":"3","key":"10.1016\/j.jneumeth.2019.108567_bib0255","first-page":"692","article-title":"Toward literature-based feature selection for diagnostic classification: a meta-analysis of resting-state fMRI in depression","volume":"8","author":"Sundermann","year":"2014","journal-title":"Front. Hum. Neurosci."},{"issue":"3","key":"10.1016\/j.jneumeth.2019.108567_bib0260","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1006\/nimg.2002.1200","article-title":"Noise reduction in BOLD-based fMRI using component analysis","volume":"17","author":"Thomas","year":"2002","journal-title":"NeuroImage"},{"key":"10.1016\/j.jneumeth.2019.108567_bib0265","series-title":"IEEE International Conference on Image Processing","article-title":"Spatially local and temporally smooth PCA for fMRI","author":"Ulfarsson","year":"2007"},{"issue":"1","key":"10.1016\/j.jneumeth.2019.108567_bib0270","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/2047-217X-3-28","article-title":"How machine learning is shaping cognitive neuroimaging","volume":"3","author":"Varoquaux","year":"2014","journal-title":"Gigascience"},{"key":"10.1016\/j.jneumeth.2019.108567_bib0275","first-page":"63","article-title":"Robust feature selection in resting-state fMRI connectivity based on population studies","author":"Venkataraman","year":"2010","journal-title":"Conf. Comput. Vis. Pattern Recognit. Workshops"},{"key":"10.1016\/j.jneumeth.2019.108567_bib0280","first-page":"191","article-title":"A novel approach for stable selection of informative redundant features from high dimensional fMRI data","volume":"146","author":"Wang","year":"2015","journal-title":"Comput. Sci."},{"key":"10.1016\/j.jneumeth.2019.108567_bib0285","first-page":"1","article-title":"Recognizing brain states using deep sparse recurrent neural network","author":"Wang","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.jneumeth.2019.108567_bib0290","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.neuroimage.2014.03.067","article-title":"Disease prediction based on functional connectomes using a scalable and spatially-informed support vector machine","volume":"96","author":"Watanabe","year":"2014","journal-title":"NeuroImage"},{"issue":"2","key":"10.1016\/j.jneumeth.2019.108567_bib0295","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1118\/1.3284974","article-title":"Effect of finite sample size on feature selection and classification: a simulation study","volume":"37","author":"Way","year":"2010","journal-title":"Med. Phys."},{"key":"10.1016\/j.jneumeth.2019.108567_bib0300","article-title":"Power spectrum analysis of functionally-weighted MR data: what\u2019s in the noise?","volume":"7","author":"Weisskoff","year":"1993","journal-title":"Soc. Magn. Reson. Med."},{"issue":"3","key":"10.1016\/j.jneumeth.2019.108567_bib0305","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1038\/nn.4478","article-title":"Building better biomarkers: brain models in translational neuroimaging","volume":"20","author":"Woo","year":"2017","journal-title":"Nat. Neurosci."},{"issue":"66","key":"10.1016\/j.jneumeth.2019.108567_bib0310","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.patcog.2016.12.001","article-title":"Multi-feature kernel discriminant dictionary learning for face recognition","volume":"2017","author":"Wu","year":"2017","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.jneumeth.2019.108567_bib0315","first-page":"5036","article-title":"Classification of borderline personality disorder based on spectral power of resting-state fMRI","author":"Xu","year":"2014","journal-title":"Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference"},{"key":"10.1016\/j.jneumeth.2019.108567_bib0320","first-page":"1205","article-title":"Efficient feature selection via analysis of relevance and redundancy","volume":"5","author":"Yu","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.jneumeth.2019.108567_bib0325","series-title":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","article-title":"Deep neural support vector machines for speech recognition","author":"Zhang","year":"2015"},{"key":"10.1016\/j.jneumeth.2019.108567_bib0330","first-page":"1","article-title":"Aberrant brain connectivity in schizophrenia detected via a fast Gaussian Graphical Model","volume":"99","author":"Zhang","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."}],"container-title":["Journal of Neuroscience Methods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0165027019304248?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0165027019304248?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T00:52:28Z","timestamp":1759020748000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0165027019304248"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4]]},"references-count":66,"alternative-id":["S0165027019304248"],"URL":"https:\/\/doi.org\/10.1016\/j.jneumeth.2019.108567","relation":{},"ISSN":["0165-0270"],"issn-type":[{"value":"0165-0270","type":"print"}],"subject":[],"published":{"date-parts":[[2020,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Comparison of feature selection methods based on discrimination and reliability for fMRI decoding analysis","name":"articletitle","label":"Article Title"},{"value":"Journal of Neuroscience Methods","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jneumeth.2019.108567","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"108567"}}