{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T08:32:17Z","timestamp":1723019537902},"reference-count":17,"publisher":"Hindawi Limited","license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61502091","N161604001","N150408001","71325002","61225012"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2018]]},"abstract":"Alzheimer\u2019s disease (AD) has been not only the substantial financial burden to the health care system but also the emotional burden to patients and their families. Predicting cognitive performance of subjects from their magnetic resonance imaging (MRI) measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer\u2019s disease. Recently, the multitask learning (MTL) methods with sparsity-inducing norm (e.g., l<\/mml:mi><\/mml:mrow>2,1<\/mml:mn><\/mml:mrow><\/mml:msub><\/mml:mrow><\/mml:math>-norm) have been widely studied to select the discriminative feature subset from MRI features by incorporating inherent correlations among multiple clinical cognitive measures. However, these previous works formulate the prediction tasks as a linear regression problem. The major limitation is that they assumed a linear relationship between the MRI features and the cognitive outcomes. Some multikernel-based MTL methods have been proposed and shown better generalization ability due to the nonlinear advantage. We quantify the power of existing linear and nonlinear MTL methods by evaluating their performance on cognitive score prediction of Alzheimer\u2019s disease. Moreover, we extend the traditional l<\/mml:mi><\/mml:mrow>2,1<\/mml:mn><\/mml:mrow><\/mml:msub><\/mml:mrow><\/mml:math>-norm to a more general l<\/mml:mi><\/mml:mrow>q<\/mml:mi><\/mml:mrow><\/mml:msub>l<\/mml:mi><\/mml:mrow>1<\/mml:mn><\/mml:mrow><\/mml:msub><\/mml:math>-norm (q<\/mml:mi>\u2265<\/mml:mo>1<\/mml:mn><\/mml:math>). Experiments on the Alzheimer\u2019s Disease Neuroimaging Initiative database showed that the nonlinear l<\/mml:mi><\/mml:mrow>2,1<\/mml:mn><\/mml:mrow><\/mml:msub>l<\/mml:mi><\/mml:mrow>q<\/mml:mi><\/mml:mrow><\/mml:msub><\/mml:math>-MKMTL method not only achieved better prediction performance than the state-of-the-art competitive methods but also effectively fused the multimodality data.<\/jats:p>","DOI":"10.1155\/2018\/7429782","type":"journal-article","created":{"date-parts":[[2018,1,24]],"date-time":"2018-01-24T23:30:49Z","timestamp":1516836649000},"page":"1-13","source":"Crossref","is-referenced-by-count":5,"title":["Linearized and Kernelized Sparse Multitask Learning for Predicting Cognitive Outcomes in Alzheimer\u2019s Disease"],"prefix":"10.1155","volume":"2018","author":[{"given":"Xiaoli","family":"Liu","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, Northeastern University, Shenyang, China"},{"name":"Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7859-2769","authenticated-orcid":true,"given":"Peng","family":"Cao","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Northeastern University, Shenyang, China"}]},{"given":"Jinzhu","family":"Yang","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Northeastern University, Shenyang, China"},{"name":"Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China"}]},{"given":"Dazhe","family":"Zhao","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Northeastern University, Shenyang, China"},{"name":"Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China"}]}],"member":"98","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1001\/archneur.1985.04060100083029"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1097\/00002093-200304000-00002"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2314712"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2010.03.051"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2377-12-46"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-007-5040-8"},{"key":"9","volume-title":"High-Order Multi-Task Feature Learning to Identify Longitudinal Phenotypic Markers for Alzheimers Disease Progression Prediction","year":"2012"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2016.2527796"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2014.2342533"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2012.09.002"},{"key":"14","first-page":"41","volume-title":"Multi-task feature learning","year":"2007"},{"key":"24","first-page":"702","year":"2011","journal-title":"Advances in Neural Information Processing Systems"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.03.073"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2006.01.021"},{"key":"18","first-page":"49","year":"2016","journal-title":"5th Workshop on Data Mining for Medicine and Healthcare"},{"key":"20","year":"1981"},{"key":"22","first-page":"2491","volume":"9","year":"2008","journal-title":"Journal of Machine Learning Research"}],"container-title":["Computational and Mathematical Methods in Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2018\/7429782.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2018\/7429782.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2018\/7429782.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,18]],"date-time":"2020-05-18T22:25:47Z","timestamp":1589840747000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cmmm\/2018\/7429782\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":17,"alternative-id":["7429782","7429782"],"URL":"https:\/\/doi.org\/10.1155\/2018\/7429782","relation":{},"ISSN":["1748-670X","1748-6718"],"issn-type":[{"value":"1748-670X","type":"print"},{"value":"1748-6718","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]}}}