McGrath J, Saha S, Chant D, Welham J: Schizophrenia: a concise overview of incidence, prevalence, and mortality. Epidemiol Rev. 2008, 30: 67-76. 10.1093/epirev/mxn001.

Article
PubMed
Google Scholar

Kordi-Tamandani DM, Vaziri S, Dahmardeh N, Torkamanzehi A: Evaluation of polymorphism, hypermethylation and expression pattern of CTLA4 gene in a sample of Iranian patients with schizophrenia. Mol Biol Rep. 2013, 40: 5123-5128. 10.1007/s11033-013-2614-3.

Article
CAS
PubMed
Google Scholar

Shayevitz C, Cohen OS, Faraone SV, Glatt SJ: A re-review of the association between the NOTCH4 locus and schizophrenia. Am J Med Genet B Neuropsychiatr Genet. 2012, 159B (5): 477-83. 10.1002/ajmg.b.32050.

Article
PubMed
Google Scholar

Chen Y, Tian L, Zhang F, Liu C, Lu T, Ruan Y, Wang L, Yan H, Yan J, Liu Q, Zhang H, Ma W, Yang J, Li K, Lv L, Zhang D, Yue W: Myosin Vb gene is associated with schizophrenia in Chinese Han population. Psychiatry Res. 2013, 207: 13-8. 10.1016/j.psychres.2013.02.026.

Article
CAS
PubMed
Google Scholar

Meda SA, Bhattarai M, Morris NA, Astur RS, Calhoun VD, Mathalon DH, Kiehl KA, Pearlson GD: An fMRI study of working memory in first-degree unaffected relatives of schizophrenia patients. Schizophr Res. 2008, 104: 85-95. 10.1016/j.schres.2008.06.013.

Article
PubMed Central
PubMed
Google Scholar

Szycik GR, Münte TF, Dillo W, Mohammadi B, Samii A, Emrich HM, Dietrich DE: Audiovisual integration of speech is disturbed in schizophrenia: an fMRI study. Schizophr Res. 2009, 110: 111-118. 10.1016/j.schres.2009.03.003.

Article
CAS
PubMed
Google Scholar

Chen J, Calhoun VD, Pearlson GD, Ehrlich S, Turner JA, Ho BC, Wassink TH, Michael AM, Liu J: Multifaceted genomic risk for brain function in schizophrenia. NeuroImage. 2012, 61: 866-875. 10.1016/j.neuroimage.2012.03.022.

Article
PubMed Central
PubMed
Google Scholar

Liu J, Ghassemi MM, Michael AM, Boutte D, Wells W, Perrone-Bizzozero N, Macciardi F, Mathalon DH, Ford JM, Potkin SG, Turner JA, Calhoun VD: An ICA with reference approach in identification of genetic variation and associated brain networks. Frontiers in Human Neuroscience. 2012, 6: 1-10.

Google Scholar

Yang H, Liu J, Sui J, Pearlson G, Calhoun VD: A Hybrid Machine Learning Method for Fusing fMRI and Genetic Data to Classify Schizophrenia. Frontiers in Human Neuroscience. 2010, 4: 1-9.

Article
Google Scholar

Meda SA, Jagannathan K, Gelernter J, Calhoun VD, Liu J, Stevens MC, Pearlson GD: A pilot multivariate parallel ICA study to investigate differential linkage between neural networks and genetic profiles in schizophrenia. NeuroImage. 2010, 53: 1007-1015. 10.1016/j.neuroimage.2009.11.052.

Article
PubMed Central
PubMed
Google Scholar

Liu J, Pearlson G, Windemuth A, Ruano G, Perrone-Bizzozero NI, Calhoun V: Combining fMRI and SNP data to investigate connections between brain function and genetics using parallel ICA. Hum Brain Mapp. 2009, 30: 241-255. 10.1002/hbm.20508.

Article
PubMed Central
PubMed
Google Scholar

Gribonval R, Nielsen M: Sparse decompositions in unions of bases. IEEE Trans Inf Theory. 2003, 49: 3320-3325. 10.1109/TIT.2003.820031.

Article
Google Scholar

Tropp JA, Gilbert AC, Muthukrishnan S, Strauss MJ: Improved sparse approximation over quasi-incoherent dictionaries. Proc 2003 IEEE Int Conf Image Process, Barcelona, Spain. 2003, 1: 137-140.

Google Scholar

Candes E, Romberg J, Tao T: Stable signal recovery from incomplete and inaccurate measurements. Comm On Pure and Applied Math. 2006, 59: 1207-1223. 10.1002/cpa.20124.

Article
Google Scholar

Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y: Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach. 2009, 31: 210-227.

Article
Google Scholar

Tang W, Cao H, Duan J, Wang YP: A compressed sensing based approach for subtyping of leukemia from gene expression data. J Bioinform Comput Biol. 2011, 9: 631-645. 10.1142/S0219720011005689.

Article
PubMed Central
CAS
PubMed
Google Scholar

Cao H, Duan J, Lin D, Wang YP: Sparse Representation Based Clustering for Integrated Analysis of Gene Copy Number Variation and Gene Expression Data. IJCA. 2012, 19: 131-138.

Google Scholar

Cao H, Deng HW, Li M, Wang YP: Classification of multicolor fluorescence in situ hybridization (M-FISH) images with sparse representation. IEEE Trans Nanobioscience. 2012, 11: 111-118.

Article
PubMed Central
PubMed
Google Scholar

Donoho DL, Elad M, Temlyakov VN: Stable recovery of sparse overcomplete representations in the presence of noise. IEEE Transactions on Information Theory. 2006, 52: 6-18.

Article
Google Scholar

Cai T, Wang L: Orthogonal Matching Pursuit for Sparse Signal Recovery. IEEE Trans on Inf Theory. 2011, 57: 1-26.

Article
Google Scholar

Li Y, Namburi P, Yu Z, Guan C, Feng J, Gu Z: Voxel selection in FMRI data analysis based on sparse representation. IEEE Trans Biomed Eng. 2009, 56: 2439-2451.

Article
PubMed
Google Scholar

Cao H, Duan J, Lin D, Calhoun V, Wang YP: Bio marker identification for diagnosis of schizophrenia with integrated analysis of fMRI and SNPs. Bioinformatics and Biomedicine (BIBM), 2012 IEEE International Conference on: 4-7 October 2012. 2012, 1-6. 10.1109/BIBM.2012.6392674.

Chapter
Google Scholar

Li YQ, Cichocki A, Amari S: Analysis of sparse representation and blind source separation. Neural Comput. 2004, 16: 1193-1234. 10.1162/089976604773717586.

Article
PubMed
Google Scholar

Davenport M, Duarte M, Hegde C, Baraniuk R: Introduction to compressive sensing. Connexions Web site. 2011, Apr 10, [http://cnx.org/content/m37172/1.7/]

Google Scholar

Donoho DL, Tsaig Y: Fast Solution of L1-Norm Minimization Problems When the Solution May Be Sparse. IEEE Trans on Information Theory. 2008, 54: 4789-4812.

Article
Google Scholar

Davis G, Mallat S, Avellaneda M: Greedy adaptive approximation. J Constr Approx. 1997, 13: 57-98. 10.1007/BF02678430.

Article
Google Scholar

Tropp JA: Greed is good: Algorithmic results for sparse approximation. IEEE Trans Inf Theory. 2004, 50: 2231-2242. 10.1109/TIT.2004.834793.

Article
Google Scholar

Tropp JA: Just relax: Convex programming methods for identifying sparse signals. IEEE Trans Inf Theory. 2006, 51: 1030-1051.

Article
Google Scholar

Barron A, Cohen A, Dahmen W, DeVore R: Approximation and learning by greedy algorithms. Ann Statist. 2008, 36: 64-94. 10.1214/009053607000000631.

Article
Google Scholar

Duan J, Soussen C, Brie D, Idier J, Wang YP: On LARS/homotopy equivalence conditions for over-determined LASSO. IEEE Signal Processing Letters. 2012, 19: 894-897.

Article
Google Scholar

Fisher RA, Yates F: Statistical tables for biological, agricultural and medical research. 1948, OCLC 14222135London: Oliver & Boyd, 26-27. 3

Google Scholar

Lee H, Lee DS, Kang H, Kim BN, Chung MK: Sparse brain network recovery under compressed sensing. IEEE TMI. 2011, 30: 1154-1165.

Google Scholar

Pascual-Leone A, Manoach DS, Birnbaum R: Goff DC Motor cortical excitability in schizophrenia. Biol Psychiatry. 2002, 52: 24-31. 10.1016/S0006-3223(02)01317-3.

Article
PubMed
Google Scholar

Kumari V, Gray JA, Honey GD, Soni W, Bullmore ET, Williams SC, Ng VW, Vythelingum GN, Simmons A, Suckling J, Corr PJ, Sharma T: Procedural learning in schizophrenia: a functional magnetic resonance imaging investigation. Schizophrenia Research. 2002, 57: 97-107. 10.1016/S0920-9964(01)00270-5.

Article
PubMed
Google Scholar

Onitsuka T, Shenton ME, Salisbury DF, Dickey CC, Kasai K, Toner SK, Frumin M, Kikinis R, Jolesz FA, McCarley RW: Middle and inferior temporal gyrus gray matter volume abnormalities in chronic schizophrenia: an MRI study. Am J Psychiatry. 2004, 161: 1603-11. 10.1176/appi.ajp.161.9.1603.

Article
PubMed Central
PubMed
Google Scholar