{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T06:08:42Z","timestamp":1744870122905,"version":"3.28.0"},"reference-count":25,"publisher":"MIT Press","issue":"1","content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,12,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>A hypothesis in the study of the brain is that sparse coding is realized in information representation of external stimuli, which has been experimentally confirmed for visual stimulus recently. However, unlike the specific functional region in the brain, sparse coding in information processing in the whole brain has not been clarified sufficiently. In this study, we investigate the validity of sparse coding in the whole human brain by applying various matrix factorization methods to functional magnetic resonance imaging data of neural activities in the brain. The result suggests the sparse coding hypothesis in information representation in the whole human brain, because extracted features from the sparse matrix factorization (MF) method, sparse principal component analysis (SparsePCA), or method of optimal directions (MOD) under a high sparsity setting or an approximate sparse MF method, fast independent component analysis (FastICA), can classify external visual stimuli more accurately than the nonsparse MF method or sparse MF method under a low sparsity setting.<\/jats:p>","DOI":"10.1162\/neco_a_01628","type":"journal-article","created":{"date-parts":[[2023,12,5]],"date-time":"2023-12-05T22:29:42Z","timestamp":1701815382000},"page":"128-150","update-policy":"http:\/\/dx.doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":2,"title":["Performance Evaluation of Matrix Factorization for fMRI Data"],"prefix":"10.1162","volume":"36","author":[{"given":"Yusuke","family":"Endo","sequence":"first","affiliation":[{"name":"Department of Mechanical Systems Engineering, Graduate School of Science and Engineering, Ibaraki University, Ibaraki 316-8511, Japan 22nm417r@vc.ibaraki.ac.jp"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Koujin","family":"Takeda","sequence":"additional","affiliation":[{"name":"Department of Mechanical Systems Engineering, Graduate School of Science and Engineering, Ibaraki University, Ibaraki 316-8511, Japan koujin.takeda.kt@vc.ibaraki.ac.jp"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"281","published-online":{"date-parts":[[2023,12,12]]},"reference":[{"key":"2023121400172870500_bib1","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1098\/rstb.2005.1634","article-title":"Investigations into resting-state connectivity using independent component analysis","volume":"360","author":"Beckmann","year":"2005","journal-title":"Philosophical Transactions of the Royal Society B"},{"issue":"6","key":"2023121400172870500_bib2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1006908","article-title":"Neural correlates of sparse coding and dimensionality reduction","volume":"15","author":"Beyeler","year":"2019","journal-title":"PLOS Computational Biology"},{"key":"2023121400172870500_bib3","first-page":"785","article-title":"Xgboost: A scalable tree boosting system","volume-title":"In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Chen","year":"2016"},{"key":"2023121400172870500_bib4","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/0165-1684(94)90029-9","article-title":"Independent component analysis, a new concept?","volume":"36","author":"Comon","year":"1994","journal-title":"Signal Processing"},{"issue":"3","key":"2023121400172870500_bib5","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Machine Learning"},{"key":"2023121400172870500_bib6","doi-asserted-by":"publisher","first-page":"2183","DOI":"10.1117\/12.173207","article-title":"Adaptive time-frequency decompositions","volume":"33","author":"Davis","year":"1994","journal-title":"Optical Engineering"},{"key":"2023121400172870500_bib7","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1016\/j.mri.2012.10.003","article-title":"Performance evaluation of negative matrix factorization algorithms to estimate task-related neuronal activities from fMRI data","volume":"31","author":"Ding","year":"2013","journal-title":"Magnetic Resonance Imaging"},{"key":"2023121400172870500_bib8","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1214\/009053604000000067","article-title":"Least angle regression","volume":"32","author":"Efron","year":"2004","journal-title":"Annals of Statistics"},{"key":"2023121400172870500_bib9","first-page":"2443","article-title":"Method of optimal directions for frame design","volume":"5","author":"Engan","year":"1999","journal-title":"IEEE International Conference on Acoustics, Speech, and Signal Processing"},{"key":"2023121400172870500_bib10","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1146\/annurev-neuro-062111-150410","article-title":"Compressed sensing, sparsity, and dimensionality in neuronal information processing and data analysis","volume":"35","author":"Ganguli","year":"2012","journal-title":"Annual Review of Neuroscience"},{"key":"2023121400172870500_bib11","doi-asserted-by":"publisher","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":"Han","year":"2019","journal-title":"NeuroImage"},{"key":"2023121400172870500_bib12","doi-asserted-by":"publisher","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":"Haxby","year":"2001","journal-title":"Science"},{"key":"2023121400172870500_bib13","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/S0893-6080(00)00026-5","article-title":"Independent component analysis: Algorithms and applications","volume":"13","author":"Hyv\u00e4rinen","year":"2000","journal-title":"Neural Networks"},{"key":"2023121400172870500_bib14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2016.12.003","article-title":"Task FMRI data analysis based on supervised stochastic coordinate coding","volume":"38","author":"Lv","year":"2017","journal-title":"Medical Image Analysis"},{"key":"2023121400172870500_bib15","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1038\/381607a0","article-title":"Emergence of simple-cell receptive field properties by learning a sparse code for natural images","volume":"381","author":"Olshausen","year":"1996","journal-title":"Nature"},{"key":"2023121400172870500_bib16","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1109\/ACSSC.1993.342465","article-title":"Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition","volume-title":"Proceedings of 27th Asilomar Conference on Signals, Systems and Computers","author":"Pati","year":"1993"},{"key":"2023121400172870500_bib17","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.neunet.2020.12.007","article-title":"Sparse deep dictionary learning identifies differences of time-varying functional connectivity in brain neuro-developmental study","volume":"135","author":"Qiao","year":"2021","journal-title":"Neural Networks"},{"key":"2023121400172870500_bib18","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"Journal of the Royal Statistical Society B"},{"key":"2023121400172870500_bib19","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2021.625737","article-title":"Stable meta-networks, noise, and artifacts in the human connectome: Low- to high-dimensional independent components analysis as a hierarchy of intrinsic connectivity networks","volume":"15","author":"Wylie","year":"2021","journal-title":"Frontiers in Neuroscience"},{"key":"2023121400172870500_bib20","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.jneumeth.2017.03.008","article-title":"Decoding the encoding of functional brain networks: An fMRI classification comparison of nonnegative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms","volume":"282","author":"Xie","year":"2017","journal-title":"Journal of Neuroscience Methods"},{"key":"2023121400172870500_bib21","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-020-14645-x","article-title":"Natural images are reliably represented by sparse and variable populations of neurons in visual cortex","volume":"11","author":"Yoshida","year":"2020","journal-title":"Nature Communications"},{"journal-title":"Delmar: Deep linear matrix approximately reconstruction to extract hierarchical functional connectivity in the human brain.","year":"2022","author":"Zhang","key":"2023121400172870500_bib22"},{"journal-title":"Demand: Deep matrix approximately nonlinear decomposition to identify meta, canonical, and sub-spatial pattern of functional magnetic resonance imaging in the human brain.","year":"2022","author":"Zhang","key":"2023121400172870500_bib23"},{"issue":"1","key":"2023121400172870500_bib24","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1109\/TBME.2018.2831186","article-title":"Experimental comparisons of sparse dictionary learning and independent component analysis for brain network inference from fMRI data","volume":"66","author":"Zhang","year":"2019","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"2","key":"2023121400172870500_bib25","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1198\/106186006X113430","article-title":"Sparse principal component analysis","volume":"15","author":"Zou","year":"2006","journal-title":"Journal of Computational and Graphical Statistics"}],"container-title":["Neural Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/direct.mit.edu\/neco\/article-pdf\/36\/1\/128\/2195572\/neco_a_01628.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/direct.mit.edu\/neco\/article-pdf\/36\/1\/128\/2195572\/neco_a_01628.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T04:49:21Z","timestamp":1730782161000},"score":1,"resource":{"primary":{"URL":"https:\/\/direct.mit.edu\/neco\/article\/36\/1\/128\/118268\/Performance-Evaluation-of-Matrix-Factorization-for"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,12]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12,12]]},"published-print":{"date-parts":[[2023,12,12]]}},"URL":"https:\/\/doi.org\/10.1162\/neco_a_01628","relation":{},"ISSN":["0899-7667","1530-888X"],"issn-type":[{"type":"print","value":"0899-7667"},{"type":"electronic","value":"1530-888X"}],"subject":[],"published-other":{"date-parts":[[2024,1]]},"published":{"date-parts":[[2023,12,12]]}}}