{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T12:10:23Z","timestamp":1761394223337,"version":"build-2065373602"},"reference-count":68,"publisher":"Elsevier BV","issue":"10","license":[{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,10,1]],"date-time":"2021-10-01T00:00:00Z","timestamp":1633046400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,8,26]],"date-time":"2022-08-26T00:00:00Z","timestamp":1661472000000},"content-version":"am","delay-in-days":329,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Clinical Neurophysiology"],"published-print":{"date-parts":[[2021,10]]},"DOI":"10.1016\/j.clinph.2021.06.036","type":"journal-article","created":{"date-parts":[[2021,8,16]],"date-time":"2021-08-16T11:37:04Z","timestamp":1629113824000},"page":"2540-2550","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":6,"title":["Random walks on B distributed resting-state functional connectivity to identify Alzheimer's disease and Mild Cognitive Impairment"],"prefix":"10.1016","volume":"132","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0421-8651","authenticated-orcid":false,"given":"Mohammadmahdi","family":"Rahimiasl","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nasrollah","family":"Moghadam Charkari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Foad","family":"Ghaderi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.clinph.2021.06.036_b0005","doi-asserted-by":"crossref","DOI":"10.3389\/fninf.2014.00014","article-title":"Machine learning for neuroimaging with scikit-learn","volume":"8","author":"Abraham","year":"2014","journal-title":"Front Neuroinform"},{"key":"10.1016\/j.clinph.2021.06.036_b0010","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1186\/s12918-018-0662-y","article-title":"Integrating node embeddings and biological annotations for genes to predict disease-gene associations","volume":"12","author":"Ata","year":"2018","journal-title":"BMC Syst Biol"},{"issue":"1","key":"10.1016\/j.clinph.2021.06.036_b0015","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.media.2007.06.004","article-title":"Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain","volume":"12","author":"AVANTS","year":"2008","journal-title":"Med Image Anal"},{"issue":"1","key":"10.1016\/j.clinph.2021.06.036_b0020","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.neuroimage.2007.04.042","article-title":"A component based noise correction method (CompCor) for BOLD and perfusion based fMRI","volume":"37","author":"Behzadi","year":"2007","journal-title":"Neuroimage"},{"issue":"3","key":"10.1016\/j.clinph.2021.06.036_b0025","doi-asserted-by":"crossref","first-page":"e0194479","DOI":"10.1371\/journal.pone.0194479","article-title":"Random support vector machine cluster analysis of resting-state fMRI in Alzheimer\u2019s disease","volume":"13","author":"Bi","year":"2018","journal-title":"PLoS One"},{"key":"10.1016\/j.clinph.2021.06.036_b0030","doi-asserted-by":"crossref","first-page":"340","DOI":"10.3389\/fpsyt.2018.00340","article-title":"Weighted random support vector machine clusters analysis of resting-state fMRI in mild cognitive impairment","volume":"9","author":"Bi","year":"2018","journal-title":"Front Psychiatry"},{"issue":"3","key":"10.1016\/j.clinph.2021.06.036_b0035","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.jalz.2007.04.381","article-title":"Forecasting the global burden of Alzheimer\u2019s disease","volume":"3","author":"Brookmeyer","year":"2007","journal-title":"Alzheimer\u2019s Dement"},{"issue":"5","key":"10.1016\/j.clinph.2021.06.036_b0040","doi-asserted-by":"crossref","first-page":"2322","DOI":"10.1152\/jn.00339.2011","article-title":"The organization of the human cerebellum estimated by intrinsic functional connectivity","volume":"106","author":"Buckner","year":"2011","journal-title":"J Neurophysiol"},{"issue":"3","key":"10.1016\/j.clinph.2021.06.036_b0045","doi-asserted-by":"crossref","first-page":"1","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"},{"issue":"9","key":"10.1016\/j.clinph.2021.06.036_b0050","doi-asserted-by":"crossref","first-page":"3282","DOI":"10.1002\/hbm.23240","article-title":"High-order resting-state functional connectivity network for MCI classification","volume":"37","author":"Chen","year":"2016","journal-title":"Hum Brain Mapp"},{"issue":"5","key":"10.1016\/j.clinph.2021.06.036_b0055","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1016\/j.neurobiolaging.2008.06.015","article-title":"Posterior cingulate cortex atrophy and regional cingulum disruption in mild cognitive impairment and Alzheimer\u2019s disease","volume":"31","author":"Choo","year":"2010","journal-title":"Neurobiol Aging"},{"key":"10.1016\/j.clinph.2021.06.036_b0060","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1037\/h0026256","article-title":"Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit","volume":"70","author":"Cohen","year":"1968","journal-title":"Psychol Bull"},{"issue":"2","key":"10.1016\/j.clinph.2021.06.036_b0065","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1006\/nimg.1998.0395","article-title":"Cortical surface-based analysis: I. Segmentation and surface reconstruction","volume":"9","author":"Dale","year":"1999","journal-title":"Neuroimage"},{"issue":"3","key":"10.1016\/j.clinph.2021.06.036_b0070","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.jalz.2012.01.006","article-title":"Amyloid positron emission tomography with 18F-flutemetamol and structural magnetic resonance imaging in the classification of mild cognitive impairment and Alzheimer\u2019s disease","volume":"9","author":"Duara","year":"2013","journal-title":"Alzheimer\u2019s Dement"},{"issue":"3","key":"10.1016\/j.clinph.2021.06.036_b0075","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.jalz.2016.02.002","article-title":"Preclinical Alzheimer\u2019s disease: definition, natural history, and diagnostic criteria","volume":"12","author":"Dubois","year":"2016","journal-title":"Alzheimer\u2019s Dement"},{"key":"10.1016\/j.clinph.2021.06.036_b0080","doi-asserted-by":"crossref","DOI":"10.3389\/fnhum.2016.00238","article-title":"Slowing of Hippocampal Activity Correlates with Cognitive Decline in Early Onset Alzheimer\u2019s Disease. An MEG Study with Virtual Electrodes","volume":"10","author":"Engels","year":"2016","journal-title":"Front Hum Neurosci"},{"issue":"1","key":"10.1016\/j.clinph.2021.06.036_b0085","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s11682-017-9686-y","article-title":"Modifications in resting state functional anticorrelation between default mode network and dorsal attention network: comparison among young adults, healthy elders and mild cognitive impairment patients","volume":"12","author":"Esposito","year":"2018","journal-title":"Brain Imaging Behav"},{"issue":"1","key":"10.1016\/j.clinph.2021.06.036_b0090","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1038\/s41592-018-0235-4","article-title":"fMRIPrep: a robust preprocessing pipeline for functional MRI","volume":"16","author":"Esteban","year":"2019","journal-title":"Nat Methods"},{"key":"10.1016\/j.clinph.2021.06.036_b0095","first-page":"111","article-title":"FMRIPrep: A robust preprocessing pipeline for functional MRI","volume":"16","author":"Esteban","year":"2018","journal-title":"BioRxiv"},{"unstructured":"Esteban O, Markiewicz CJ, Burns C, Goncalves M, Jarecka D, Ziegler E, et al. nipy\/nipype: 1.5.1 2020. DOI: 10.5281\/ZENODO.4035081.","key":"10.1016\/j.clinph.2021.06.036_b0100"},{"key":"10.1016\/j.clinph.2021.06.036_b0105","first-page":"1871","article-title":"LIBLINEAR: A library for large linear classification","volume":"9","author":"Fan","year":"2008","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.clinph.2021.06.036_b0110","doi-asserted-by":"crossref","first-page":"S102","DOI":"10.1016\/S1053-8119(09)70884-5","article-title":"Unbiased nonlinear average age-appropriate brain templates from birth to adulthood","volume":"47","author":"Fonov","year":"2009","journal-title":"Neuroimage"},{"issue":"4","key":"10.1016\/j.clinph.2021.06.036_b0115","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1002\/hbm.460020402","article-title":"Statistical parametric maps in functional imaging: A general linear approach","volume":"2","author":"Friston","year":"1994","journal-title":"Hum Brain Mapp"},{"issue":"7615","key":"10.1016\/j.clinph.2021.06.036_b0120","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1038\/nature18933","article-title":"A multi-modal parcellation of human cerebral cortex","volume":"536","author":"Glasser","year":"2016","journal-title":"Nature"},{"key":"10.1016\/j.clinph.2021.06.036_b0125","doi-asserted-by":"crossref","first-page":"13","DOI":"10.3389\/fninf.2011.00013","article-title":"Nipype: A flexible, lightweight and extensible neuroimaging data processing framework in Python","volume":"5","author":"Gorgolewski","year":"2011","journal-title":"Front Neuroinform"},{"issue":"1","key":"10.1016\/j.clinph.2021.06.036_b0130","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.neuroimage.2009.06.060","article-title":"Accurate and robust brain image alignment using boundary-based registration","volume":"48","author":"Greve","year":"2009","journal-title":"Neuroimage"},{"doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J. Node2vec: Scalable feature learning for networks. Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., vol. 13-17- Augu, 2016, p. 855\u201364. DOI: 10.1145\/2939672.2939754.","key":"10.1016\/j.clinph.2021.06.036_b0135","DOI":"10.1145\/2939672.2939754"},{"year":"2014","author":"Huntenburg","series-title":"Evaluating nonlinear coregistration of BOLD EPI and T1 images","key":"10.1016\/j.clinph.2021.06.036_b0140"},{"issue":"4","key":"10.1016\/j.clinph.2021.06.036_b0145","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.jalz.2018.02.018","article-title":"NIA-AA research framework: toward a biological definition of Alzheimer\u2019s disease","volume":"14","author":"Jack","year":"2018","journal-title":"Alzheimer\u2019s Dement"},{"key":"10.1016\/j.clinph.2021.06.036_b0150","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1006\/nimg.2002.1132","article-title":"Improved optimization for the robust and accurate linear registration and motion correction of brain images","volume":"17","author":"Jenkinson","year":"2002","journal-title":"Neuroimage"},{"key":"10.1016\/j.clinph.2021.06.036_b0155","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.media.2018.03.013","article-title":"Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease","volume":"47","author":"Jie","year":"2018","journal-title":"Med Image Anal"},{"key":"10.1016\/j.clinph.2021.06.036_b0160","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1109\/TBME.2013.2284195","article-title":"Integration of network topological and connectivity properties for neuroimaging classification","volume":"61","author":"Jie","year":"2014","journal-title":"IEEE Trans Biomed Eng"},{"issue":"3","key":"10.1016\/j.clinph.2021.06.036_b0165","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1007\/s11682-015-9448-7","article-title":"Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer\u2019s disease","volume":"10","author":"Khazaee","year":"2016","journal-title":"Brain Imaging Behav"},{"issue":"11","key":"10.1016\/j.clinph.2021.06.036_b0170","doi-asserted-by":"crossref","first-page":"2132","DOI":"10.1016\/j.clinph.2015.02.060","article-title":"Identifying patients with Alzheimer\u2019s disease using resting-state fMRI and graph theory","volume":"126","author":"Khazaee","year":"2015","journal-title":"Clin Neurophysiol"},{"issue":"2","key":"10.1016\/j.clinph.2021.06.036_b0175","doi-asserted-by":"crossref","first-page":"e1005350","DOI":"10.1371\/journal.pcbi.1005350","article-title":"Mindboggling morphometry of human brains","volume":"13","author":"Klein","year":"2017","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"10.1016\/j.clinph.2021.06.036_b0180","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1137\/0701007","article-title":"Evaluation of Noisy Data","volume":"1","author":"Lanczos","year":"1964","journal-title":"J Soc Ind Appl Math Ser B Numer Anal"},{"issue":"2","key":"10.1016\/j.clinph.2021.06.036_b0185","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.jneumeth.2006.10.023","article-title":"Application and comparison of classification algorithms for recognition of Alzheimer\u2019s disease in electrical brain activity (EEG)","volume":"161","author":"Lehmann","year":"2007","journal-title":"J Neurosci Methods"},{"key":"10.1016\/j.clinph.2021.06.036_b0190","first-page":"2358","article-title":"Modular preprocessing pipelines can reintroduce artifacts into fMRI data","volume":"40","author":"Lindquist","year":"2018","journal-title":"BioRxiv"},{"issue":"2-3","key":"10.1016\/j.clinph.2021.06.036_b0195","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.schres.2005.11.020","article-title":"Decreased volume of left and total anterior insular lobule in schizophrenia","volume":"83","author":"Makris","year":"2006","journal-title":"Schizophr Res"},{"key":"10.1016\/j.clinph.2021.06.036_b0200","first-page":"3111","article-title":"Distributed Representations of Words and Phrases and their Compositionality","author":"Mikolov","year":"2013","journal-title":"NIPS"},{"issue":"4","key":"10.1016\/j.clinph.2021.06.036_b0205","doi-asserted-by":"crossref","first-page":"1415","DOI":"10.1016\/j.neuroimage.2008.10.031","article-title":"Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI","volume":"44","author":"Misra","year":"2009","journal-title":"Neuroimage"},{"key":"10.1016\/j.clinph.2021.06.036_b0210","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.neuroimage.2014.03.028","article-title":"Reduction of motion-related artifacts in resting state fMRI using aCompCor","volume":"96","author":"Muschelli","year":"2014","journal-title":"Neuroimage"},{"issue":"5","key":"10.1016\/j.clinph.2021.06.036_b0215","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.acra.2012.02.003","article-title":"Battle against Alzheimer\u2019s Disease. The Scope and Potential Value of Magnetic Resonance Imaging Biomarkers","volume":"19","author":"Paquerault","year":"2012","journal-title":"Acad Radiol"},{"key":"10.1016\/j.clinph.2021.06.036_b0220","doi-asserted-by":"crossref","DOI":"10.3389\/fgene.2019.00226","article-title":"Predicting Parkinson\u2019s disease genes based on node2vec and autoencoder","volume":"10","author":"Peng","year":"2019","journal-title":"Front Genet"},{"key":"10.1016\/j.clinph.2021.06.036_b0225","series-title":"Proc. ACM SIGKDD Int Conf. Knowl. Discov. Data Min.","first-page":"701","article-title":"Online learning of social representations","author":"Perozzi","year":"2014"},{"issue":"4","key":"10.1016\/j.clinph.2021.06.036_b0230","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1016\/j.neuron.2011.09.006","article-title":"Functional Network Organization of the Human Brain","volume":"72","author":"Power","year":"2011","journal-title":"Neuron"},{"key":"10.1016\/j.clinph.2021.06.036_b0235","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.neuroimage.2013.08.048","article-title":"Methods to detect, characterize, and remove motion artifact in resting state fMRI","volume":"84","author":"Power","year":"2014","journal-title":"Neuroimage"},{"issue":"3","key":"10.1016\/j.clinph.2021.06.036_b0240","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1016\/j.neuroimage.2009.10.003","article-title":"Complex network measures of brain connectivity: Uses and interpretations","volume":"52","author":"Rubinov","year":"2010","journal-title":"Neuroimage"},{"key":"10.1016\/j.clinph.2021.06.036_b0245","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.neuroimage.2012.08.052","article-title":"An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data","volume":"64","author":"Satterthwaite","year":"2013","journal-title":"Neuroimage"},{"doi-asserted-by":"crossref","unstructured":"Sharaev M., Artemov A., Kondrateva E., Ivanov S., Sushchinskaya S., Bernstein A., et al. Learning connectivity patterns via graph Kernels for fMRI-based depression diagnostics. IEEE Int. Conf. Data Min. Work. ICDMW, vol. 2018- Novem, 2019, p. 308\u201314. doi: 10.1109\/icdmw.2018.00051.","key":"10.1016\/j.clinph.2021.06.036_b0250","DOI":"10.1109\/ICDMW.2018.00051"},{"key":"10.1016\/j.clinph.2021.06.036_b0255","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-62378-0","article-title":"Alzheimer\u2019s disease, mild cognitive impairment, and normal aging distinguished by multi-modal parcellation and machine learning","volume":"10","author":"Sheng","year":"2020","journal-title":"Sci Rep"},{"key":"10.1016\/j.clinph.2021.06.036_b0260","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.bbr.2019.03.004","article-title":"A novel joint HCPMMP method for automatically classifying Alzheimer\u2019s and different stage MCI patients","volume":"365","author":"Sheng","year":"2019","journal-title":"Behav Brain Res"},{"issue":"3","key":"10.1016\/j.clinph.2021.06.036_b0265","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\u2019s disease patients","volume":"12","author":"Son","year":"2017","journal-title":"PLoS One"},{"key":"10.1016\/j.clinph.2021.06.036_b0270","series-title":"Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics)","first-page":"8","article-title":"Classifying Stages of Mild Cognitive Impairment via Augmented Graph Embedding","volume":"11846","author":"Tang","year":"2019"},{"key":"10.1016\/j.clinph.2021.06.036_b0275","series-title":"Proc. 24th Int Conf. World Wide Web 2015","first-page":"1067","article-title":"LINE: Large-scale information network embedding","author":"Tang","year":"2015"},{"issue":"3","key":"10.1016\/j.clinph.2021.06.036_b0280","doi-asserted-by":"crossref","first-page":"e0152472","DOI":"10.1371\/journal.pone.0152472","article-title":"Characterization and correction of geometric distortions in 814 Diffusion Weighted Images","volume":"11","author":"Treiber","year":"2016","journal-title":"PLoS One"},{"issue":"6","key":"10.1016\/j.clinph.2021.06.036_b0285","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","article-title":"N4ITK: Improved N3 bias correction","volume":"29","author":"Tustison","year":"2010","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"10.1016\/j.clinph.2021.06.036_b0290","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1006\/nimg.2001.0978","article-title":"Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain","volume":"15","author":"Tzourio-Mazoyer","year":"2002","journal-title":"Neuroimage"},{"key":"10.1016\/j.clinph.2021.06.036_b0295","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.neuroimage.2017.11.025","article-title":"A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer\u2019s disease","volume":"167","author":"de Vos","year":"2018","journal-title":"Neuroimage"},{"key":"10.1016\/j.clinph.2021.06.036_b0300","doi-asserted-by":"crossref","first-page":"17","DOI":"10.3389\/fninf.2017.00017","article-title":"Evaluation of field map and nonlinear registration methods for correction of susceptibility artifacts in diffusion MRI","volume":"11","author":"Wang","year":"2017","journal-title":"Front Neuroinform"},{"key":"10.1016\/j.clinph.2021.06.036_b0305","first-page":"1","article-title":"Classification of Alzheimer\u2019s disease, mild cognitive impairment and normal control subjects using resting-state fmri based network connectivity analysis","volume":"6","author":"Wang","year":"2018","journal-title":"IEEE J Transl Eng Heal Med"},{"key":"10.1016\/j.clinph.2021.06.036_b0310","doi-asserted-by":"crossref","DOI":"10.3389\/fnagi.2019.00326","article-title":"Altered Directed Functional Connectivity of the Hippocampus in Mild Cognitive Impairment and Alzheimer\u2019s Disease: A Resting-State fMRI Study","volume":"11","author":"Xue","year":"2019","journal-title":"Front Aging Neurosci"},{"issue":"3","key":"10.1016\/j.clinph.2021.06.036_bib336","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1152\/jn.00338.2011","article-title":"The organization of the human cerebral cortex estimated by intrinsic functional connectivity","volume":"106","author":"Yeo","year":"2011","journal-title":"J Neurophysiol"},{"key":"10.1016\/j.clinph.2021.06.036_b0315","doi-asserted-by":"crossref","first-page":"572","DOI":"10.3389\/fpsyt.2019.00572","article-title":"Classification of early and late mild cognitive impairment using functional brain network of resting-state fMRI","volume":"10","author":"Zhang","year":"2019","journal-title":"Front Psychiatry"},{"issue":"2","key":"10.1016\/j.clinph.2021.06.036_b0320","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1109\/TNB.2015.2403274","article-title":"Resting-state whole-brain functional connectivity networks for MCI classification using L2-regularized logistic regression","volume":"14","author":"Zhang","year":"2015","journal-title":"IEEE Trans Nanobioscience"},{"key":"10.1016\/j.clinph.2021.06.036_b0325","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/42.906424","article-title":"Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm","volume":"20","author":"Zhang","year":"2001","journal-title":"IEEE Trans Med Imaging"},{"key":"10.1016\/j.clinph.2021.06.036_b0330","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1186\/s12859-019-3124-7","article-title":"Identifying Alzheimer\u2019s disease-related proteins by LRRGD","volume":"20","author":"Zhao","year":"2019","journal-title":"BMC Bioinformatics"},{"issue":"4","key":"10.1016\/j.clinph.2021.06.036_b0335","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1145\/279232.279236","article-title":"Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization","volume":"23","author":"Zhu","year":"1997","journal-title":"ACM Trans Math Softw"}],"container-title":["Clinical Neurophysiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1388245721006945?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1388245721006945?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T12:05:40Z","timestamp":1761393940000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1388245721006945"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10]]},"references-count":68,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["S1388245721006945"],"URL":"https:\/\/doi.org\/10.1016\/j.clinph.2021.06.036","relation":{},"ISSN":["1388-2457"],"issn-type":[{"type":"print","value":"1388-2457"}],"subject":[],"published":{"date-parts":[[2021,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Random walks on B distributed resting-state functional connectivity to identify Alzheimer's disease and Mild Cognitive Impairment","name":"articletitle","label":"Article Title"},{"value":"Clinical Neurophysiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.clinph.2021.06.036","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}