Details of Research Outputs

TitleAssessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages
Author (Name in English or Pinyin)
Liu, Yunbi1,2,3,5; Yue, Ling4; Xiao, Shifu4; Yang, Wei3; Shen, Dinggang1,2; Liu, Mingxia1,2
Date Issued2022
Source PublicationMEDICAL IMAGE ANALYSIS
ISSN1361-8415
DOI10.1016/j.media.2021.102266
Indexed BySCIE
Education discipline科技类
Published range国外学术期刊
Volume Issue Pages卷: 75
References
[1] Abdulrab, K., Heun, R., Subjective memory impairment. A review of its definitions indicates the need for a comprehensive set of standardised and validated criteria. Eur. Psychiatry 23:5 (2008), 321–330.
[2] Amariglio, R.E., Becker, J.A., Carmasin, J., Wadsworth, L.P., Lorius, N., Sullivan, C., Maye, J.E., Gidicsin, C., Pepin, L.C., Sperling, R.A., et al. Subjective cognitive complaints and amyloid burden in cognitively normal older individuals. Neuropsychologia 50:12 (2012), 2880–2886.
[3] Ashburner, J., Friston, K.J., Unified segmentation. NeuroImage 26:3 (2005), 839–851.
[4] Association, A., et al. 2018 Alzheimer's disease facts and figures. Alzheimer's Dementia 14:3 (2018), 367–429.
[5] Barnes, L.L., Schneider, J.A., Boyle, P.A., Bienias, J.L., Bennett, D.A., Memory complaints are related to Alzheimer disease pathology in older persons. Neurology 67:9 (2006), 1581–1585.
[6] Buckley, R.F., Maruff, P., Ames, D., Bourgeat, P., Martins, R.N., Masters, C.L., Rainey-Smith, S., Lautenschlager, N., Rowe, C.C., Savage, G., et al. Subjective memory decline predicts greater rates of clinical progression in preclinical Alzheimer's disease. Alzheimer's Dementia 12:7 (2016), 796–804.
[7] Buckley, R.F., Villemagne, V.L., Masters, C.L., Ellis, K.A., Rowe, C.C., Johnson, K., Sperling, R., Amariglio, R., A conceptualization of the utility of subjective cognitive decline in clinical trials of preclinical Alzheimer's disease. J. Mol. Neurosci. 60:3 (2016), 354–361.
[8] Campos, S., Pizarro, L., Valle, C., Gray, K.R., Rueckert, D., Allende, H., Evaluating imputation techniques for missing data in ADNI: a patient classification study. Iberoamerican Congress on Pattern Recognition, 2015, Springer, 3–10.
[9] Caselli, R.J., Chen, K., Locke, D.E.C., Lee, W., Roontiva, A., Bandy, D., Fleisher, A.S., Reiman, E.M., Subjective cognitive decline: self and informant comparisons. Alzheimer's Dementia 10:1 (2014), 93–98.
[10] Cheng, B., Liu, M., Shen, D., Li, Z., Zhang, D., Multi-domain transfer learning for early diagnosis of Alzheimer's disease. Neuroinformatics 15:2 (2017), 115–132.
[11] Cheng, B., Liu, M., Suk, H.-I., Shen, D., Zhang, D., Multimodal manifold-regularized transfer learning for MCI conversion prediction. Brain Imaging Behav. 9:4 (2015), 913–926.
[12] Cheng, B., Liu, M., Zhang, D., Munsell, B.C., Shen, D., Domain transfer learning for MCI conversion prediction. IEEE Trans. Biomed. Eng. 62:7 (2015), 1805–1817.
[13] Da, X., Toledo, J.B., Zee, J., Wolk, D.A., Xie, S.X., Ou, Y., Shacklett, A., Parmpi, P., Shaw, L., Trojanowski, J.Q., et al. Integration and relative value of biomarkers for prediction of MCI to AD progression: spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers. NeuroImage Clinical 4 (2014), 164–173.
[14] Ellis, K.A., Bush, A.I., Darby, D., De Fazio, D., Foster, J., Hudson, P., Lautenschlager, N.T., Lenzo, N., Martins, R.N., Maruff, P., et al. The Australian imaging, biomarkers and lifestyle (AIBL) study of aging: Methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer's disease. Int. Psychogeriatrics 21:4 (2009), 672–687.
[15] Filipovych, R., Davatzikos, C., Semi-supervised pattern classification of medical images: application to mild cognitive impairment (MCI). NeuroImage 55:3 (2011), 1109–1119.
[16] Fischl, B., Freesurfer. NeuroImage 62:2 (2012), 774–781.
[17] van der Flier, W.M., Van Buchem, M.A., Weverling-Rijnsburger, A.W.E., Mutsaers, E.R., Bollen, E.L., Admiraal-Behloul, F., Westendorp, R.G.J., Middelkoop, H.A.M., Memory complaints in patients with normal cognition are associated with smaller hippocampal volumes. J. Neurol. 251:6 (2004), 671–675.
[18] Gray, K.R., Aljabar, P., Heckemann, R.A., Hammers, A., Rueckert, D., Random forest-based similarity measures for multi-modal classification of Alzheimer's disease. NeuroImage 65 (2013), 167–175.
[19] Hinrichs, C., Singh, V., Xu, G., Johnson, S.C., Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. NeuroImage 55:2 (2011), 574–589.
[20] Hor, S., Moradi, M., Learning in data-limited multimodal scenarios: scandent decision forests and tree-based features. Med. Image Anal. 34 (2016), 30–41.
[21] Huang, J., Ling, C.X., Using AUC and accuracy in evaluating learning algorithms. IEEE Trans. Knowl. Data Eng. 17:3 (2005), 299–310.
[22] Jack, C.R. Jr., Albert, M.S., Knopman, D.S., McKhann, G.M., Sperling, R.A., Carrillo, M.C., Thies, B., Phelps, C.H., Introduction to the recommendations from the national institute on aging-Alzheimer's association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimer's Dementia 7:3 (2011), 257–262.
[23] Jack, C.R. Jr., Bernstein, M.A., Fox, N.C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P.J., Jennifer, L.W., Ward, C., The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27:4 (2008), 685–691.
[24] Jack, C.R. Jr., Knopman, D.S., Jagust, W.J., Petersen, R.C., Weiner, M.W., Aisen, P.S., Shaw, L.M., Vemuri, P., Wiste, H.J., Weigand, S.D., et al. Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 12:2 (2013), 207–216.
[25] Jack Jr, C.R., Knopman, D.S., Jagust, W.J., Shaw, L.M., Aisen, P.S., Weiner, M.W., Petersen, R.C., Trojanowski, J.Q., Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol. 9:1 (2010), 119–128.
[26] Jessen, F., Amariglio, R.E., Van Boxtel, M., Breteler, M., Ceccaldi, M., Chételat, G., Dubois, B., Dufouil, C., Ellis, K.A., Van Der Flier, W.M., et al. A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer's disease. Alzheimer's Dementia 10:6 (2014), 844–852.
[27] Jessen, F., Wolfsgruber, S., Wiese, B., Bickel, H., Mösch, E., Kaduszkiewicz, H., Pentzek, M., Riedel-Heller, S.G., Luck, T., Fuchs, A., et al. AD dementia risk in late MCI, in early MCI, and in subjective memory impairment. Alzheimer's Dementia 10:1 (2014), 76–83.
[28] Kamnitsas, K., Baumgartner, C., Ledig, C., Newcombe, V., Simpson, J., Kane, A., Menon, D., Nori, A., Criminisi, A., Rueckert, D., et al. Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. International Conference on Information Processing in Medical Imaging, 2017, Springer, 597–609.
[29] Kawachi, T., Ishii, K., Sakamoto, S., Sasaki, M., Mori, T., Yamashita, F., Matsuda, H., Mori, E., Comparison of the diagnostic performance of FDG-PET and VBM-MRI in very mild Alzheimer's disease. Eur. J. Nucl. Med. Mol. Imaging 33:7 (2006), 801–809.
[30] Khan, N.M., Abraham, N., Hon, M., Transfer learning with intelligent training data selection for prediction of Alzheimer's disease. IEEE Access 7 (2019), 72726–72735.
[31] Kohannim, O., Hua, X., Hibar, D.P., Lee, S., Chou, Y.-Y., Toga, A.W., Jack Jr, C.R., Weiner, M.W., Thompson, P.M., Boosting power for clinical trials using classifiers based on multiple biomarkers. Neurobiol. Aging 31:8 (2010), 1429–1442.
[32] Kryscio, R.J., Abner, E.L., Cooper, G.E., Fardo, D.W., Jicha, G.A., Nelson, P.T., Smith, C.D., Van Eldik, L.J., Wan, L., Schmitt, F.A., Self-reported memory complaints: implications from a longitudinal cohort with autopsies. Neurology 83:15 (2014), 1359–1365.
[33] Lassila, T., Faria, H.M., Sarrami-Foroushani, A., Meneghello, F., Venneri, A., Frangi, A.F., Multi-modal synthesis of ASL-MRI features with KPLS regression on heterogeneous data. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2018, Springer, 473–481.
[34] Li, R., Zhang, W., Suk, H.-I., Wang, L., Li, J., Shen, D., Ji, S., Deep learning based imaging data completion for improved brain disease diagnosis. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2014, Springer, 305–312.
[35] Lian, C., Liu, M., Zhang, J., Shen, D., Hierarchical fully convolutional network for joint atrophy localization and Alzheimer's disease diagnosis using structural MRI. IEEE Trans. Pattern Anal. Mach. Intell. 42:4 (2020), 880–893.
[36] Liu, M., Zhang, D., Chen, S., Xue, H., Joint binary classifier learning for ECOC-based multi-class classification. IEEE Trans. Pattern Anal. Mach. Intell. 38:11 (2015), 2335–2341.
[37] Liu, M., Zhang, D., Shen, D., Hierarchical fusion of features and classifier decisions for Alzheimer's disease diagnosis. Hum. Brain Mapp. 35:4 (2014), 1305–1319.
[38] Liu, M., Zhang, J., Adeli, E., Shen, D., Landmark-based deep multi-instance learning for brain disease diagnosis. Med. Image Anal. 43 (2018), 157–168.
[39] Liu, M., Zhang, J., Yap, P.-T., Shen, D., View-aligned hypergraph learning for Alzheimer's disease diagnosis with incomplete multi-modality data. Med. Image Anal. 36 (2017), 123–134.
[40] Liu, S., Liu, S., Cai, W., Che, H., Pujol, S., Kikinis, R., Feng, D., Fulham, M.J., Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease. IEEE Trans. Biomed. Eng. 62:4 (2014), 1132–1140.
[41] Liu, Y., Pan, Y., Yang, W., Ning, Z., Yue, L., Liu, M., Shen, D., Joint neuroimage synthesis and representation learning for conversion prediction of subjective cognitive decline. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020, Springer International Publishing, Cham, 583–592.
[42] Mirza, M., Osindero, S., Conditional generative adversarial nets, 2014. arXiv preprint arXiv:1411.1784.
[43] Pan, Y., Liu, M., Lian, C., Xia, Y., Shen, D., Disease-image specific generative adversarial network for brain disease diagnosis with incomplete multi-modal neuroimages. MICCAI, 2019, Springer, 137–145.
[44] Pan, Y., Liu, M., Lian, C., Xia, Y., Shen, D., Spatially-constrained Fisher representation for brain disease identification with incomplete multi-modal neuroimages. IEEE Trans. Med Imaging 39:9 (2020), 2965–2975.
[45] Pan, Y., Liu, M., Xia, Y., Shen, D., Disease-image-specific learning for diagnosis-oriented neuroimage synthesis with incomplete multi-modality data. IEEE Trans. Pattern Anal. Mach. Intell., 2021.
[46] Perrin, R.J., Fagan, A.M., Holtzman, D.M., Multimodal techniques for diagnosis and prognosis of Alzheimer's disease. Nature 461:7266 (2009), 916–922.
[47] Rusinek, H., De Leon, M.J., George, A.E., Stylopoulos, L.A., Chandra, R., Smith, G., Rand, T., Mourino, M., Kowalski, H., Alzheimer disease: measuring loss of cerebral gray matter with MR imaging. Radiology 178:1 (1991), 109–114.
[48] Scheef, L., Spottke, A., Daerr, M., Joe, A., Striepens, N., Kölsch, H., Popp, J., Daamen, M., Gorris, D., Heneka, M.T., et al. Glucose metabolism, gray matter structure, and memory decline in subjective memory impairment. Neurology 79:13 (2012), 1332–1339.
[49] Sharma, A., Hamarneh, G., Missing MRI pulse sequence synthesis using multi-modal generative adversarial network. IEEE Trans. Med. Imaging 39:4 (2019), 1170–1183.
[50] Shi, J., Zheng, X., Li, Y., Zhang, Q., Ying, S., Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer's disease. IEEE J. Biomed. Health Inform. 22:1 (2017), 173–183.
[51] Sikka, A., Peri, S.V., Bathula, D.R., MRI to FDG-PET: cross-modal synthesis using 3D U-Net for multi-modal Alzheimer's classification. International Workshop on Simulation and Synthesis in Medical Imaging, 2018, Springer, 80–89.
[52] Stewart, R., Dufouil, C., Godin, O., Ritchie, K., Maillard, P., Delcroix, N., Crivello, F., Mazoyer, B., Tzourio, C., Neuroimaging correlates of subjective memory deficits in a community population. Neurology 70:18 (2008), 1601–1607.
[53] Striepens, N., Scheef, L., Wind, A., Popp, J., Spottke, A., Cooper-Mahkorn, D., Suliman, H., Wagner, M., Schild, H.H., Jessen, F., Volume loss of the medial temporal lobe structures in subjective memory impairment. Dement Geriatr Cogn. Disord. 29:1 (2010), 75–81.
[54] Suk, H.-I., Lee, S.-W., Shen, D., Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage 101 (2014), 569–582.
[55] Thung, K.-H., Yap, P.-T., Shen, D., Multi-stage diagnosis of Alzheimer's disease with incomplete multimodal data via multi-task deep learning. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, 2017, Springer, 160–168.
[56] Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M., Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15:1 (2002), 273–289.
[57] Van Tulder, G., de Bruijne, M., Why does synthesized data improve multi-sequence classification?. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015, Springer, 531–538.
[58] Xiao, S., Lewis, M., Mellor, D., McCabe, M., Byrne, L., Wang, T., Wang, J., Zhu, M., Cheng, Y., Yang, C., et al. The China longitudinal ageing study: overview of the demographic, psychosocial and cognitive data of the shanghai sample. J. Mental Health 25:2 (2016), 131–136.
[59] Yi, X., Walia, E., Babyn, P., Generative adversarial network in medical imaging: a review. Med. Image Anal., 2019, 101552.
[60] Young, J., Modat, M., Cardoso, M.J., Mendelson, A., Cash, D., Ourselin, S., Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment. NeuroImage Clinical 2 (2013), 735–745.
[61] Yue, L., Hu, D., Zhang, H., Wen, J., Wu, Y., Li, W., Sun, L., Li, X., Wang, J., Li, G., et al. Prediction of 7-year's conversion from subjective cognitive decline to mild cognitive impairment. Hum. Brain Mapp. 42:1 (2021), 192–203.
[62] Yue, L., Wang, T., Wang, J., Li, G., Wang, J., Li, X., Li, W., Hu, M., Xiao, S., Asymmetry of hippocampus and amygdala defect in subjective cognitive decline among the community dwelling chinese. Front. Psychiatry, 9, 2018, 226.
[63] Zhang, J., Liu, M., An, L., Gao, Y., Shen, D., Alzheimer's disease diagnosis using landmark-based features from longitudinal structural MR images. IEEE J. Biomed. Health Inform. 21:6 (2017), 1607–1616.
[64] Zhou, T., Thung, K.-H., Liu, M., Shen, D., Brain-wide genome-wide association study for Alzheimer's disease via joint projection learning and sparse regression model. IEEE Trans. Biomed. Eng. 66:1 (2018), 165–175.
[65] Zhou, T., Thung, K.-H., Liu, M., Shi, F., Zhang, C., Shen, D., Multi-modal latent space inducing ensemble SVM classifier for early dementia diagnosis with neuroimaging data. Med. Image Anal., 60, 2020, 101630.
[66] Zhu, J.-Y., Park, T., Isola, P., Efros, A.A., Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, 2017, 2223–2232.
[67] Zu, C., Jie, B., Liu, M., Chen, S., Shen, D., Zhang, D., Label-aligned multi-task feature learning for multimodal classification of Alzheimer's disease and mild cognitive impairment. Brain Imaging Behav. 10:4 (2016), 1148–1159.
Citation statistics
Cited Times:18[WOS]   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Identifierhttps://irepository.cuhk.edu.cn/handle/3EPUXD0A/2512
CollectionSchool of Science and Engineering
Corresponding AuthorYue, Ling; Liu, Mingxia
Affiliation
1.Univ N Carolina, Dept Radiol, Chapel Hill, NC 27599 USA
2.Univ N Carolina, BRIC, Chapel Hill, NC 27599 USA
3.Southern Med Univ, Sch Biomed Engn, Guangzhou 510515, Peoples R China
4.Shanghai Jiao Tong Univ, Sch Med, Shanghai Mental Hlth Ctr, Dept Geriatr Psychiat, Shanghai 200240, Peoples R China
5.Chinese Univ Hong Kong , Sch Sci & Engn, Shenzhen 518172, Guangdong, Peoples R China
Recommended Citation
GB/T 7714
Liu, Yunbi,Yue, Ling,Xiao, Shifuet al. Assessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages[J]. MEDICAL IMAGE ANALYSIS,2022.
APA Liu, Yunbi, Yue, Ling, Xiao, Shifu, Yang, Wei, Shen, Dinggang, & Liu, Mingxia. (2022). Assessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages. MEDICAL IMAGE ANALYSIS.
MLA Liu, Yunbi,et al."Assessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages".MEDICAL IMAGE ANALYSIS (2022).
Files in This Item:
There are no files associated with this item.
Related Services
Usage statistics
Google Scholar
Similar articles in Google Scholar
[Liu, Yunbi]'s Articles
[Yue, Ling]'s Articles
[Xiao, Shifu]'s Articles
Baidu academic
Similar articles in Baidu academic
[Liu, Yunbi]'s Articles
[Yue, Ling]'s Articles
[Xiao, Shifu]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Liu, Yunbi]'s Articles
[Yue, Ling]'s Articles
[Xiao, Shifu]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.