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Identification of progressive mild cognitive impairment patients using incomplete longitudinal MRI scans

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Abstract

Distinguishing progressive mild cognitive impairment (pMCI) from stable mild cognitive impairment (sMCI) is critical for identification of patients who are at risk for Alzheimer’s disease (AD), so that early treatment can be administered. In this paper, we propose a pMCI/sMCI classification framework that harnesses information available in longitudinal magnetic resonance imaging (MRI) data, which could be incomplete, to improve diagnostic accuracy. Volumetric features were first extracted from the baseline MRI scan and subsequent scans acquired after 6, 12, and 18 months. Dynamic features were then obtained using the 18th month scan as the reference and computing the ratios of feature differences for the earlier scans. Features that are linearly or non-linearly correlated with diagnostic labels are then selected using two elastic net sparse learning algorithms. Missing feature values due to the incomplete longitudinal data are imputed using a low-rank matrix completion method. Finally, based on the completed feature matrix, we build a multi-kernel support vector machine (mkSVM) to predict the diagnostic label of samples with unknown diagnostic statuses. Our evaluation indicates that a diagnosis accuracy as high as 78.2 % can be achieved when information from the longitudinal scans is used—6.6 % higher than the case using only the reference time point image. In other words, information provided by the longitudinal history of the disease improves diagnosis accuracy.

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Notes

  1. http://www.adni-info.org/.

  2. http://mipav.cit.nih.gov.

  3. http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/.

References

  • Adeli-Mosabbeb E, Thung KH, An L, Shi F, Shen D (2015) Robust feature-sample linear discriminant analysis for brain disorders diagnosis. Neural Information Processing Systems (NIPS)

  • Ashburner J, Friston KJ (2000) Voxel-based morphometry—the methods. Neuroimage 11:805–821

    Article  CAS  PubMed  Google Scholar 

  • Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Patt Recogn 30:1145–1159

    Article  Google Scholar 

  • Burgess N, Maguire EA, O’Keefe J (2002) The human hippocampus and spatial and episodic memory. Neuron 35:625–641

    Article  CAS  PubMed  Google Scholar 

  • Candès EJ, Recht B (2009) Exact matrix completion via convex optimization. Found Comp Math 9:717–772

    Article  Google Scholar 

  • Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2:27

    Google Scholar 

  • Davatzikos C, Bhatt P, Shaw LM, Batmanghelich KN, Trojanowski JQ (2011) Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiology of aging 32:2322–e19

  • Di Paola M, Di Iulio F, Cherubini A, Blundo C, Casini A, Sancesario G, Passafiume D, Caltagirone C, Spalletta G (2010) When, where, and how the corpus callosum changes in MCI and AD- a multimodal MRI study. Neurology 74:1136–1142

    Article  PubMed  Google Scholar 

  • Doraiswamy P, Bieber F, Kaiser L, Krishnan K, Reuning-Scherer J, Gulanski B (1997) The Alzheimer’s disease assessment scale patterns and predictors of baseline cognitive performance in multicenter Alzheimer’s disease trials. Neurology 48:1511–1517

    Article  CAS  PubMed  Google Scholar 

  • Duda RO, Hart PE, Stork DG (2012) Pattern classification. Wiley

  • Friedman J, Hastie T, Tibshirani R (2010) A note on the group lasso and a sparse group lasso. arXiv preprint arXiv:1001.0736

  • Gauthier S, Reisberg B, Zaudig M, Petersen RC, Ritchie K, Broich K, Belleville S, Brodaty H, Bennett D, Chertkow H et al (2006) Mild cognitive impairment. Lancet 367:1262–1270

    Article  PubMed  Google Scholar 

  • Goldberg A, Zhu X, Recht B, Xu J, Nowak R (2010) Transduction with matrix completion: three birds with one stone. Adv Neural Inform Process Syst 23:757–765

    Google Scholar 

  • Gönen M, Alpaydın E (2011) Multiple kernel learning algorithms. J Mach Learn Res 12:2211–2268

    Google Scholar 

  • Haacke EM, Brown RW, Thompson MR, Venkatesan R (1999) Magnetic resonance imaging. Wiley,Liss New York

  • Hinrichs C, Singh V, Xu G, Johnson S (2009) MKL for robust multi-modality AD classification. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2009. Springer, pp 786–794

  • Hinrichs C, Singh V, Xu G, Johnson SC (2011) Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. Neuroimage 55:574–589

    Article  PubMed  Google Scholar 

  • Hua X, Leow AD, Parikshak N, Lee S, Chiang MC, Toga AW, Jack CR Jr, Weiner MW, Thompson PM (2008) Tensor-based morphometry as a neuroimaging biomarker for Alzheimer’s disease: an MRI study of 676 AD, MCI, and normal subjects. Neuroimage 43:458–469

    Article  PubMed  PubMed Central  Google Scholar 

  • Huang L, Gao Y, Jin Y, Thung KH, Shen D (2015) Soft-split sparse regression based random forest for predicting future clinical scores of Alzheimers disease. In: Machine Learning in Medical Imaging. Springer, pp 246–254

  • Huang S, Li J, Ye J, Wu T, Chen K, Fleisher A, Reiman E (2011) Identifying Alzheimer’s disease-related brain regions from multi-modality neuroimaging data using sparse composite linear discrimination analysis. In: Advances in Neural Information Processing Systems, pp 1431–1439

  • Jack C, Dickson D, Parisi J, Xu Y, Cha R, Obrien P, Edland S, Smith G, Boeve B, Tangalos E et al (2002) Antemortem MRI findings correlate with hippocampal neuropathology in typical aging and dementia. Neurology 58:750–757

    Article  PubMed  PubMed Central  Google Scholar 

  • Jack C, Petersen R, Xu Y, OBrien P, Smith G, Ivnik R, Boeve B, Waring S, Tangalos E, Kokmen E (1999) Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology 52:1397–1397

  • Jack CR, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, Borowski B, Britson PJ, Whitwell L, Ward JC et al (2008) The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imag 27:685–691

  • Kabani NJ (1998) A 3D atlas of the human brain. Neuroimage 7:S717

    Google Scholar 

  • Kohannim O, Hua X, Hibar DP, Lee S, Chou YY, Toga AW, Jack CR, Weiner MW, Thompson PM (2010) Boosting power for clinical trials using classifiers based on multiple biomarkers. Neurobiol Aging 31:1429–1442

    Article  PubMed  PubMed Central  Google Scholar 

  • Lee SS (2000) Noisy replication in skewed binary classification. Comp Stat Data Anal 34:165–191

    Article  Google Scholar 

  • Li F, Tran L, Thung KH, Ji S, Shen D, Li J (2015) A robust deep model for improved classification of AD/MCI patients. Biomed Health Inform

  • Liu F, Wee CY, Chen H, Shen D (2014) Inter-modality relationship constrained multi-modality multi-task feature selection for alzheimer’s disease and mild cognitive impairment identification. NeuroImage 84:466–475

    Article  PubMed  Google Scholar 

  • Liu J, Ji S, Ye J (2009) SLEP: Sparse Learning with Efficient Projections. organizationArizona State University

  • Liu M, Suk HI, Shen D (2013) Multi-task sparse classifier for diagnosis of MCI conversion to AD with longitudinal MR images, In: Wu G, Zhang D, Shen D, Yan P, Suzuki K, Wang F (eds) Machine Learning in Medical Imaging. Springer International Publishing. vol 8184 if series Lecture Notes in Computer Science, pp 243–250

  • Ma S, Goldfarb D, Chen L (2011) Fixed point and Bregman iterative methods for matrix rank minimization. Math Program 128:321–353

    Article  Google Scholar 

  • MacCallum RC, Browne MW, Sugawara HM (1996) Power analysis and determination of sample size for covariance structure modeling. Psychol Methods 1:130

    Article  Google Scholar 

  • Poulin SP, Dautoff R, Morris JC, Barrett LF, Dickerson BC (2011) Amygdala atrophy is prominent in early Alzheimer’s disease and relates to symptom severity. Psychiatry Res Neuroimag 194:7–13

    Article  Google Scholar 

  • Querbes O, Aubry F, Pariente J, Lotterie JA, Démonet JF, Duret V, Puel M, Berry I, Fort JC, Celsis P et al (2009) Early diagnosis of Alzheimer’s disease using cortical thickness: impact of cognitive reserve. Brain 132:2036–2047

    Article  PubMed  PubMed Central  Google Scholar 

  • Rakotomamonjy A, Bach FR, Canu S, Grandvalet Y (2008) SimpleMKL. Journal of Machine Learning Research 9

  • Ranganath C (2006) Working memory for visual objects: complementary roles of inferior temporal, medial temporal, and prefrontal cortex. Neuroscience 139:277–289

    Article  CAS  PubMed  Google Scholar 

  • Sanroma, G., Wu, G., Thung, K., Guo, Y., Shen, D., 2014. Novel multi-atlas segmentation by matrix completion, in: Machine Learning in Medical Imaging. Springer, pp. 207–214

  • Schneider T (2001) Analysis of incomplete climate data: Estimation of mean values and covariance matrices and imputation of missing values. J Clim 14:853–871

    Article  Google Scholar 

  • Shen D, Davatzikos C (2002) HAMMER: hierarchical attribute matching mechanism for elastic registration. IEEE Trans Medical Imag 21:1421–1439

    Article  Google Scholar 

  • Simon N, Friedman J, Hastie T, Tibshirani R (2013) A sparse-group lasso. J Comp Graph Stat 22:231–245

    Article  Google Scholar 

  • Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Medical Imag 17:87–97

    Article  CAS  Google Scholar 

  • Smith EE, Kosslyn SM (2006) Cognitive psychology: Mind and brain. Pearson Prentice Hall

  • Speed T (2003) Statistical analysis of gene expression microarray data. CRC Press

  • Stanislav K, Alexander V, Maria P, Evgenia N, Boris V (2013) Anatomical characteristics of cingulate cortex and neuropsychological memory tests performance. Procedia-Social Behav Sci 86:128–133

    Article  Google Scholar 

  • Stefan J, Pruessner T, Faltraco JC, Born F, Rocha-Unold C, Evans M, Möller A, Hampel HJ (2006) Comprehensive dissection of the medial temporal lobe in ad: measurement of hippocampus, amygdala, entorhinal, perirhinal and parahippocampal cortices using MRI. J Neurol 253:794–800

  • Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293

    Article  CAS  PubMed  Google Scholar 

  • Thung KH, Wee CY, Yap PT, Shen D (2013) Identification of Alzheimers Disease using incomplete multimodal dataset via matrix shrinkage and completion, in: Machine Learning in Medical Imaging. Springer, pp. 163–170

  • Thung KH, Wee CY, Yap PT, Shen D (2014) Neurodegenerative disease diagnosis using incomplete multi-modality data via matrix shrinkage and completion. Neuroimage 91:386–400

    Article  PubMed  PubMed Central  Google Scholar 

  • Thung KH, Yap PT, Adeli ME, Shen D (2015) Joint diagnosis and conversion time prediction of progressive mild cognitive impairment (pmci) using low-rank subspace clustering and matrix completion, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015. Springer, pp 527–534

  • Tibshirani, R., 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological) , 267–288

  • Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, Botstein D, Altman RB (2001) Missing value estimation methods for DNA microarrays. Bioinformatics 17:520–525

    Article  CAS  PubMed  Google Scholar 

  • van der Heijden GJ, Donders T, AR, Stijnen T, Moons KG (2006) Imputation of missing values is superior to complete case analysis and the missing-indicator method in multivariable diagnostic research: a clinical example. J Clin Epidemiol 59:1102–1109

  • Wang H, Nie F, Huang H, Risacher SL, Saykin AJ, Shen L et al (2012) Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning. Bioinformatics 28:i127–i136

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang PJ, Saykin AJ, Flashman LA, Wishart HA, Rabin LA, Santulli RB, McHugh TL, MacDonald JW, Mamourian AC (2006) Regionally specific atrophy of the corpus callosum in AD, MCI and cognitive complaints. Neurobiol Aging 27:1613–1617

    Article  PubMed  Google Scholar 

  • Wang Y, Nie J, Yap PT, Li G, Shi F, Geng X, Guo L, Shen D, Initiative ADN et al (2014) Knowledge-guided robust mri brain extraction for diverse large-scale neuroimaging studies on humans and non-human primates. PloS ONE 9:e77810

    Article  PubMed  PubMed Central  Google Scholar 

  • Wang Y, Nie J, Yap PT, Shi F, Guo L, Shen D (2011) Robust deformable-surface-based skull-stripping for large-scale studies, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2011. Springer, pp 635–642

  • Wechsler D (1945) A standardized memory scale for clinical use. J Psychol 19:87–95

    Article  Google Scholar 

  • Wee CY, Yap PT, Denny K, Browndyke JN, Potter GG, Welsh-Bohmer KA, Wang L, Shen D (2012a) Resting-state multi-spectrum functional connectivity networks for identification of mci patients. PLOS ONE 7:e37828

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wee CY, Yap PT, Li W, Denny K, Browndyke JN, Potter GG, Welsh-Bohmer KA, Wang L, Shen D (2011) Enriched white matter connectivity networks for accurate identification of MCI patients. NeuroImage 54:1812–1822

    Article  PubMed  Google Scholar 

  • Wee CY, Yap PT, Shen D (2013) Prediction of alzheimer’s disease and mild cognitive impairment using cortical morphological change patterns. Human Brain Mapping 34:3411–3425

    Article  PubMed  Google Scholar 

  • Wee CY, Yap PT, Wang L, Shen D (2014) Group-constrained sparse fmri connectivity modeling for mild cognitive impairment identification. Brain Struct Func 219:641–656

    Article  Google Scholar 

  • Wee CY, Yap PT, Zhang D, Denny K, Browndyke JN, Potter GG, Welsh-Bohmer KA, Wang L, Shen D (2012b) Identification of MCI individuals using structural and functional connectivity networks. NeuroImage 59:2045–2056

    Article  PubMed  Google Scholar 

  • Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Liu E et al (2013) The Alzheimer’s disease neuroimaging initiative: A review of papers published since its inception. Alzheimer’s Dementia 9:e111–e194

    Article  PubMed  PubMed Central  Google Scholar 

  • Whitwell JL, Shiung MM, Przybelski S, Weigand SD, Knopman DS, Boeve BF, Petersen RC, Jack CR (2008) MRI patterns of atrophy associated with progression to AD in amnestic mild cognitive impairment. Neurology 70:512–520

    Article  CAS  PubMed  Google Scholar 

  • Yonelinas A, Hopfinger J, Buonocore M, Kroll N, Baynes K (2001) Hippocampal, parahippocampal and occipital-temporal contributions to associative and item recognition memory: an fMRI study. Neuroreport 12:359–363

    Article  CAS  PubMed  Google Scholar 

  • Zhang D, Liu J, Shen D (2012) Temporally-constrained group sparse learning for longitudinal data analysis, in: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2012. Springer, pp 264–271

  • Zhang D, Shen D (2012a) Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. Neuroimage 59:895–907

    Article  PubMed  Google Scholar 

  • Zhang D, Shen D (2012b) Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers. PloS ONE 7:e33182

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhang Y, Brady M, Smith S (2001) Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Medical Imag 20:45–57

    Article  CAS  Google Scholar 

  • Zhou J, Liu J, Narayan VA, Ye J (2012) Modeling disease progression via fused sparse group lasso, in: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, organizationACM. pp 1095–1103

  • Zhou L, Wang Y, Li Y, Yap PT, Shen D et al (2011) Hierarchical anatomical brain networks for MCI prediction: revisiting volumetric measures. PloS ONE 6:e21935

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhu X, Suk HI, Zhu Y, Thung KH, Wu G, Shen D (2015) Multi-view classification for identification of Alzheimers disease, in: Machine Learning in Medical Imaging. Springer, pp 255–262

  • Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J Royal Stat Soc B (Stat Methodol) 67:301–320

    Article  Google Scholar 

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Acknowledgments

This work was supported in part by NIH Grants AG041721, AG042599, EB006733, EB008374, and EB009634. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) National Institutes of Health Grant U01 AG024904. ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Amorfix, Bayer Schering Pharma AG, Bioclinica Inc., Biogen Idec, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, Innogenetics, IXICO, Janssen Alzheimer Immunotherapy, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Meso Scale Diagnostic, & LLC, Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Servier, Synarc, Inc., and Takeda Pharmaceuticals, as well as non-profit partners the Alzheimer’s Association and Alzheimer’s Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH Grants P30 AG010129, K01 AG030514, and the Dana Foundation.

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Authors and Affiliations

  1. Department of Radiology and BRIC, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Kim-Han Thung, Chong-Yaw Wee, Pew-Thian Yap & Dinggang Shen

  2. Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea

    Dinggang Shen

  3. Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore, Singapore

    Chong-Yaw Wee

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  1. Kim-Han Thung
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Correspondence to Dinggang Shen.

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For the Alzheimer’s Disease Neuroimaging Initiative.

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

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Thung, KH., Wee, CY., Yap, PT. et al. Identification of progressive mild cognitive impairment patients using incomplete longitudinal MRI scans. Brain Struct Funct 221, 3979–3995 (2016). https://doi.org/10.1007/s00429-015-1140-6

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