{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T23:50:13Z","timestamp":1792799413871,"version":"4.5.0"},"reference-count":86,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2019,4,1]],"date-time":"2019-04-01T00:00:00Z","timestamp":1554076800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2019,4,1]],"date-time":"2019-04-01T00:00:00Z","timestamp":1554076800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2020,3,14]],"date-time":"2020-03-14T00:00:00Z","timestamp":1584144000000},"content-version":"am","delay-in-days":348,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01 AG024904"],"award-info":[{"award-number":["U01 AG024904"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000005","name":"Department of Defense","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100000005","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering"},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000024","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":[[2019,4]]},"DOI":"10.1016\/j.jneumeth.2019.01.011","type":"journal-article","created":{"date-parts":[[2019,2,5]],"date-time":"2019-02-05T13:22:31Z","timestamp":1549372951000},"page":"84-99","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":70,"special_numbering":"C","title":["A Novel Texture Extraction Technique with T1 Weighted MRI for the Classification of Alzheimer\u2019s Disease"],"prefix":"10.1016","volume":"318","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5114-9866","authenticated-orcid":false,"given":"Krishnakumar","family":"Vaithinathan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Latha","family":"Parthiban","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jneumeth.2019.01.011_bib0005","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.neucom.2016.08.041","article-title":"Recognition of Alzheimer's disease and mild cognitive impairment with multimodal image-derived biomarkers and multiple kernel learning","volume":"220","author":"Ahmed","year":"2017","journal-title":"Neurocomputing"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0010","series-title":"Machine Learning: ECML 2004: 15th European Conference on Machine Learning","first-page":"39","article-title":"Applying support vector machines to imbalanced datasets","author":"Akbani","year":"2004"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0015","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.jalz.2018.02.001","article-title":"2018 Alzheimer's disease facts and figures","volume":"14","author":"Alzheimer's Association","year":"2018","journal-title":"Alzheimer's & Dementia"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0020","doi-asserted-by":"crossref","first-page":"269","DOI":"10.3233\/JAD-160594","article-title":"Prediction of incipient Alzheimer's disease dementia in patients with mild cognitive impairment","volume":"55","author":"Ardekani","year":"2017","journal-title":"Journal of Alzheimer's Disease"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0025","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1016\/j.neuroimage.2005.02.018","article-title":"Unified segmentation","volume":"26","author":"Ashburner","year":"2005","journal-title":"Neuroimage"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0030","series-title":"Statistics for High-Dimensional Data: Methods, Theory and Applications","author":"B\u00fchlmann","year":"2011"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0035","doi-asserted-by":"crossref","first-page":"S367","DOI":"10.1016\/j.euroneuro.2016.09.392","article-title":"M1 - cerebroviz: An r package for anatomical visualization of spatiotemporal brain data","volume":"27","author":"Bahl","year":"2017","journal-title":"European Neuropsychopharmacology"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0040","series-title":"Information Processing in Medical Imaging","first-page":"510","article-title":"Detecting and classifying linear structures in mammograms using random forests","author":"Berks","year":"2011"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0045","doi-asserted-by":"crossref","first-page":"2455","DOI":"10.1093\/brain\/awn151","article-title":"Neuroanatomical correlates of neuropsychiatric symptoms in Alzheimer's disease","volume":"131","author":"Bruen","year":"2008","journal-title":"Brain"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0050","first-page":"2313","article-title":"The Dantzig selector: statistical estimation when p is much larger than n","author":"Candes","year":"2007","journal-title":"Ann. Stat."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0055","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1016\/j.crad.2004.07.008","article-title":"Texture analysis of medical images","volume":"59","author":"Castellano","year":"2004","journal-title":"Clin. Radiol."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0060","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.pscychresns.2006.04.003","article-title":"Volumetric reduction of the corpus callosum in Alzheimer's disease in vivo as assessed with voxel-based morphometry","volume":"154","author":"Chaim","year":"2007","journal-title":"Psychiatry Res. Neuroimaging"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0065","doi-asserted-by":"crossref","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: a library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0070","first-page":"263","article-title":"Automated multi-modality image registration based on information theory","volume":"vol. 3","author":"Collignon","year":"1995"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0075","doi-asserted-by":"crossref","first-page":"766","DOI":"10.1016\/j.neuroimage.2010.06.013","article-title":"Automatic classification of patients with Alzheimer's disease from structural MRI: a comparison of ten methods using the ADNI database","volume":"56","author":"Cuingnet","year":"2011","journal-title":"NeuroImage"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0080","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.asoc.2012.07.029","article-title":"Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification","volume":"13","author":"Dai","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0085","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1023\/A:1008641220268","article-title":"Feature subset selection within a simulated annealing data mining algorithm","volume":"9","author":"Debuse","year":"1997","journal-title":"J. Intell. Inf. Syst."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0090","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.jneumeth.2017.12.010","volume":"302","author":"Dimitriadis","year":"2018","journal-title":"J. Neurosci. Methods"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0095","doi-asserted-by":"crossref","first-page":"S12","DOI":"10.1186\/1471-2105-7-S2-S12","article-title":"Improving the performance of SVM-RFE to select genes in microarray data","volume":"7","author":"Ding","year":"2006","journal-title":"BMC Bioinform."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0100","doi-asserted-by":"crossref","first-page":"1398","DOI":"10.1016\/j.neuroimage.2009.09.049","article-title":"An analysis of image texture, tumor location, and mgmt promoter methylation in glioblastoma using magnetic resonance imaging","volume":"49","author":"Drabycz","year":"2010","journal-title":"NeuroImage"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0105","series-title":"Pattern Classification","author":"Duda","year":"2001"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0110","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1109\/IACS.2018.8355455","article-title":"Statistical features and voxel-based morphometry for Alzheimer's disease classification","author":"Farouk","year":"2018","journal-title":"2018 9th International Conference on Information and Communication Systems (ICICS)"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0115","doi-asserted-by":"crossref","first-page":"1546","DOI":"10.1109\/ISBI.2018.8363868","article-title":"Alzheimer's disease diagnosis based on anatomically stratified texture analysis of the hippocampus in structural MRI","author":"Feng","year":"2018","journal-title":"2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0120","series-title":"Machine Learning Proceedings 1994: Proceedings of the Eighth International Conference","article-title":"Greedy attribute selection","year":"2014"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0125","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization paths for generalized linear models via coordinate descent","volume":"33","author":"Friedman","year":"2010","journal-title":"J. Stat. Softw."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0130","doi-asserted-by":"crossref","first-page":"11468","DOI":"10.1109\/ACCESS.2017.2714579","article-title":"Case-based statistical learning: a non-parametric implementation with a conditional-error rate SVM","volume":"5","author":"G\u00f3rriz","year":"2017","journal-title":"IEEE Access"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0135","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1136\/jnnp.2009.199935","article-title":"Regional brain atrophy and functional disconnection across Alzheimer's disease evolution","volume":"82","author":"Gili","year":"2011","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0140","doi-asserted-by":"crossref","first-page":"e00942","DOI":"10.1002\/brb3.942","article-title":"Characterization of brain anatomical patterns by comparing region intensity distributions: applications to the description of Alzheimer's disease","volume":"8","author":"Giraldo","year":"2018","journal-title":"Brain Behav."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0145","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1016\/j.neurobiolaging.2011.02.012","article-title":"Alzheimer's disease markers, hypertension, and gray matter damage in normal elderly","volume":"33","author":"Glodzik","year":"2012","journal-title":"Neurobiol. Aging"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0150","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.1016\/j.neuroimage.2010.05.036","article-title":"Alterations in multiple measures of white matter integrity in normal women at high risk for Alzheimer's disease","volume":"52","author":"Gold","year":"2010","journal-title":"NeuroImage"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0155","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.bbadis.2011.07.009","article-title":"White matter integrity and vulnerability to Alzheimer's disease: preliminary findings and future directions","volume":"1822","author":"Gold","year":"2012","journal-title":"Biochim. Biophys. Acta (BBA) \u2013 Mol. Basis Dis."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0160","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.neuroimage.2012.09.065","article-title":"Random forest-based similarity measures for multi-modal classification of Alzheimer's disease","volume":"65","author":"Gray","year":"2013","journal-title":"NeuroImage"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0165","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene selection for cancer classification using support vector machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Mach. Learn."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0170","series-title":"Clinical Applicability of MRI Texture Analysis (Ph.D. thesis)","author":"Harrison","year":"2011"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0175","series-title":"Laplacian Score for Feature Selection","year":"2005"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0180","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.mri.2003.07.007","article-title":"Influence of age and osteoporosis on calcaneus trabecular bone structure: a preliminary in vivo MRI study by quantitative texture analysis","volume":"22","author":"Herlidou","year":"2004","journal-title":"Magn. Reson. Imaging"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0185","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.compmedimag.2018.08.002","article-title":"Adaptive fusion of texture-based grading for Alzheimer's disease classification","volume":"70","author":"Hett","year":"2018","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0190","series-title":"Information Processing in Medical Imaging","first-page":"25","article-title":"Combining generative and discriminative models for semantic segmentation of CT scans via active learning","author":"Iglesias","year":"2011"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0195","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1002\/jmri.21049","article-title":"The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods","volume":"27","author":"Jack","year":"2008","journal-title":"J. Magn. Reson. Imaging"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0200","article-title":"The class imbalance problem: significance and strategies","author":"Japkowicz","year":"2000","journal-title":"Proc. of the Int\u2019l Conf. on Artificial Intelligence"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0205","doi-asserted-by":"crossref","first-page":"809","DOI":"10.3174\/ajnr.A2061","article-title":"Texture analysis: a review of neurologic MR imaging applications","volume":"31","author":"Kassner","year":"2010","journal-title":"Am. J. Neuroradiol."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0210","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.neucom.2014.09.072","article-title":"Early diagnosis of Alzheimer's disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images","volume":"151","author":"Khedher","year":"2015","journal-title":"Neurocomputing"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0215","doi-asserted-by":"crossref","DOI":"10.1155\/2014\/536217","article-title":"3D texture analysis in renal cell carcinoma tissue image grading","volume":"2014","author":"Kim","year":"2014","journal-title":"Comput. Math. Methods Med."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0220","series-title":"Proceedings of the Ninth International Workshop on Machine Learning","article-title":"A practical approach to feature selection","year":"1992"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0225","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","article-title":"Wrappers for feature subset selection","volume":"97","author":"Kohavi","year":"1997","journal-title":"Artif. Intell."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0230","doi-asserted-by":"crossref","first-page":"13","DOI":"10.3389\/fnagi.2017.00013","article-title":"MRI-based classification models in prediction of mild cognitive impairment and dementia in late-life depression","volume":"9","author":"Lebedeva","year":"2017","journal-title":"Front. Aging Neurosci."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0235","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/0304-3940(91)90918-J","article-title":"Cholinergic neuronal loss in the globus pallidus of Alzheimer disease patients","volume":"123","author":"Leh\u00e9ricy","year":"1991","journal-title":"Neurosci. Lett."},{"issue":"2","key":"10.1016\/j.jneumeth.2019.01.011_bib0240","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1109\/TCBB.2016.2635144","article-title":"Classification of Alzheimer's disease using whole brain hierarchical network","volume":"15","author":"Liu","year":"2018","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0245","first-page":"1","article-title":"Alzheimer's disease classification based on individual hierarchical networks constructed with 3D texture features","volume":"PP","author":"Liu","year":"2017","journal-title":"IEEE Trans. Nanobiosci."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0250","doi-asserted-by":"crossref","first-page":"4625","DOI":"10.1109\/TIP.2014.2351620","article-title":"Robust volumetric texture classification of magnetic resonance images of the brain using local frequency descriptor","volume":"23","author":"Maani","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0255","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0117759","article-title":"Voxel based texture analysis of the brain","volume":"10","author":"Maani","year":"2015","journal-title":"PLOS ONE"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0260","doi-asserted-by":"crossref","first-page":"2361","DOI":"10.1088\/1361-6560\/aa5dbe","article-title":"DTI measurements for Alzheimer's classification","volume":"62","author":"Maggipinto","year":"2017","journal-title":"Phys. Med. Biol."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0265","series-title":"Atlas of the Human Brain","author":"Mai","year":"2007"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0270","first-page":"266","article-title":"Texture features based detection of Parkinson's disease on DaTSCAN images","author":"Mart\u00ednez-Murcia","year":"2013","journal-title":"International Work-Conference on the Interplay Between Natural and Artificial Computation"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0275","first-page":"470","article-title":"Evaluating Alzheimer's disease diagnosis using texture analysis","author":"Mart\u00ednez-Murcia","year":"2017","journal-title":"Annual Conference on Medical Image Understanding and Analysis"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0280","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1212\/WNL.34.7.939","article-title":"Clinical diagnosis of Alzheimer's disease report of the NINCDS-ADRDA work group under the auspices of department of health and human services task force on Alzheimer's disease","volume":"34","author":"McKhann","year":"1984","journal-title":"Neurology"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0285","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1504\/IJBIC.2010.036158","article-title":"Correlation based feature selection method","volume":"2","author":"Michalak","year":"2010","journal-title":"Int. J. Bio-Inspired Comput."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0290","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"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0295","series-title":"Statistical Parametric Mapping: The Analysis of Functional Brain Images","author":"Penny","year":"2011"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0300","series-title":"Glmnet for Matlab","author":"Qian","year":"2013"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0305","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1177\/1533317507308779","article-title":"Distinct MRI atrophy patterns in autopsy-proven Alzheimer's disease and frontotemporal lobar degeneration","volume":"22","author":"Rabinovici","year":"2008","journal-title":"Am. J. Alzheimer's Dis. Other Dement."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0310","first-page":"506","article-title":"Automatic irregular texture detection in brain MRI without human supervision","author":"Rachmadi","year":"2018","journal-title":"International Conference on Medical Image Computing, Computer-Assisted Intervention"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0315","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.jneumeth.2017.12.005","article-title":"Ensemble of random forests one vs. rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares","volume":"302","author":"Ram\u00edrez","year":"2018","journal-title":"J. Neurosci. Methods"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0320","series-title":"Feature Selection Library (MATLAB Toolbox)","author":"Roffo","year":"2016"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0325","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1002\/jmri.21231","article-title":"Gray and white matter changes in Alzheimer's disease: a diffusion tensor imaging study","volume":"27","author":"Rose","year":"2008","journal-title":"J. Magn. Reson. Imaging"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0330","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/TNN.2003.809398","article-title":"The generalized lasso","volume":"15","author":"Roth","year":"2004","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0335","first-page":"1","article-title":"MRI brain pattern analysis for detection of Alzheimer's disease using random forest classifier","author":"Selvathi","year":"2016","journal-title":"Intell. Decis. Technol."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0340","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0345","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.neuroimage.2006.06.010","article-title":"Four subgroups of Alzheimer's disease based on patterns of atrophy using VBM and a unique pattern for early onset disease","volume":"33","author":"Shiino","year":"2006","journal-title":"Neuroimage"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0350","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1016\/j.neuroimage.2010.04.013","article-title":"Voxel-based analysis of Alzheimer's disease pet imaging using a triplet of radiotracers: PIB, FDDNP, and FDG","volume":"52","author":"Shin","year":"2010","journal-title":"Neuroimage"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0355","doi-asserted-by":"crossref","first-page":"e0173426","DOI":"10.1371\/journal.pone.0173426","article-title":"Structural and functional connectional fingerprints in mild cognitive impairment and Alzheimer's disease patients","volume":"12","author":"Son","year":"2017","journal-title":"PLOS ONE"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0360","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.procs.2017.09.125","article-title":"Feature selection techniques for prediction of neuro-degenerative disorders: a case-study with Alzheimer's and Parkinson's disease","volume":"115","author":"Tejeswinee","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0365","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1109\/CETIC4.2018.8530943","article-title":"Analysis of Alzheimer condition in T1-weighted MR images using texture features and k-NN classifier","author":"Telagarapu","year":"2018","journal-title":"International CET Conference on Control, Communication, and Computing (IC4)"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0370","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1159\/000090971","article-title":"Structural changes of the corpus callosum in mild cognitive impairment and Alzheimer's disease","volume":"21","author":"Thomann","year":"2006","journal-title":"Dement. Geriatr. Cogn. Disord."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0375","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","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0380","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s11222-008-9088-5","article-title":"Penalized regression with correlation-based penalty","volume":"19","author":"Tutz","year":"2009","journal-title":"Stat. Comput."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0385","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s00521-013-1368-0","article-title":"A review of feature selection methods based on mutual information","volume":"24","author":"Vergara","year":"2014","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0390","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.neubiorev.2017.01.002","article-title":"Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications","volume":"74","author":"Vieira","year":"2017","journal-title":"Neurosci. Biobehav. Rev."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0395","doi-asserted-by":"crossref","first-page":"359","DOI":"10.3233\/JAD-160102","article-title":"Multimodal classification of mild cognitive impairment based on partial least squares","volume":"54","author":"Wang","year":"2016","journal-title":"J. Alzheimer's Dis."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0400","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1016\/j.phpro.2012.03.160","article-title":"AdaBoost for feature selection, classification and its relation with SVM, a review","volume":"25","author":"Wang","year":"2012","journal-title":"Phys. Procedia"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0405","doi-asserted-by":"crossref","DOI":"10.1155\/2017\/1952373","article-title":"Brain MR image classification for Alzheimer's disease diagnosis based on multifeature fusion","volume":"2017","author":"Xiao","year":"2017","journal-title":"Comput. Math. Methods Med."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0410","volume":"3","year":"2003"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0415","article-title":"Markov blanket feature selection using representative sets","author":"Yu","year":"2016","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0420","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s11682-011-9142-3","article-title":"3D texture analysis on MRI images of Alzheimer's disease","volume":"6","author":"Zhang","year":"2012","journal-title":"Brain Imaging Behav."},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0425","first-page":"2","article-title":"MRI texture analysis in multiple sclerosis","volume":"2012","author":"Zhang","year":"2012","journal-title":"J. Biomed. Imaging"},{"key":"10.1016\/j.jneumeth.2019.01.011_bib0430","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"}],"container-title":["Journal of Neuroscience Methods"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0165027019300184?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0165027019300184?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,28]],"date-time":"2025-09-28T00:48:09Z","timestamp":1759020489000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0165027019300184"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4]]},"references-count":86,"alternative-id":["S0165027019300184"],"URL":"https:\/\/doi.org\/10.1016\/j.jneumeth.2019.01.011","relation":{},"ISSN":["0165-0270"],"issn-type":[{"value":"0165-0270","type":"print"}],"subject":[],"published":{"date-parts":[[2019,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A Novel Texture Extraction Technique with T1 Weighted MRI for the Classification of Alzheimer\u2019s Disease","name":"articletitle","label":"Article Title"},{"value":"Journal of Neuroscience Methods","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jneumeth.2019.01.011","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}