Temporally constrained group sparse learning for longitudinal data analysis in Alzheimer's disease

IEEE Transactions on Biomedical Engineering

Volumn 64, Issue 1, 2017, Pages 238-249

  Jie, Biao ^a,b   Liu, Mingxia ^a   Liu, Jun ^c   Zhang, Daoqiang ^a   Shen, Dinggang ^d,e  

^a NANJING UNIVERSITY OF AERONAUTICS AND ASTRONAUTICS   (China)

^b ANHUI NORMAL UNIVERSITY   (China)

^c SAS INSTITUTE INC   (United States)

^d UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^e Korea University   (South Korea)

Author keywords

Alzheimer's Disease (AD); group sparsity; longitudinal data analysis; mild cognitive impairment (MCI); sparse learning; temporal smoothness

Indexed keywords

BRAIN; BRAIN MAPPING; DATA HANDLING; DIAGNOSIS; INFORMATION ANALYSIS; LINEAR REGRESSION; NEUROIMAGING; OPTIMIZATION; REGRESSION ANALYSIS;

ALZHEIMER'S DISEASE; GROUP SPARSITIES; LONGITUDINAL DATA; MILD COGNITIVE IMPAIRMENTS (MCI); SPARSE LEARNING; TEMPORAL SMOOTHNESS;

NEURODEGENERATIVE DISEASES;

AGED; ALGORITHM; ALZHEIMER DISEASE; ARTICLE; BRAIN REGION; CONTROLLED STUDY; DATA ANALYSIS; FEMALE; HUMAN; LEARNING; LEARNING DISORDER; LINEAR REGRESSION ANALYSIS; LONGITUDINAL STUDY; MAJOR CLINICAL STUDY; MALE; NEUROIMAGING; AGING; BRAIN; COGNITIVE DEFECT; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; IMAGE SUBTRACTION; MACHINE LEARNING; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

AGING; ALGORITHMS; ALZHEIMER DISEASE; BRAIN; COGNITIVE DYSFUNCTION; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LONGITUDINAL STUDIES; MACHINE LEARNING; MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 85008600022     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2016.2553663     Document Type: Article

References (60)

1. B. Ron et al., "Forecasting the global burden of Alzheimer's disease," Alzheimer's Dementia: J. Alzheimer's Assoc., vol. 3, no. 3, pp. 186-191, 2007.
2. M. J. de Leon et al., "Longitudinal CSF isoprostane and MRI atrophy in the progression to AD," J. Neurol., vol. 254, no. 12, pp. 1666-1675, Dec. 2007.
3. A. M. Fjell et al., "CSF biomarkers in prediction of cerebral and clinical change in mild cognitive impairment and Alzheimer's disease," J. Neurosci., vol. 30, no. 6, pp. 2088-2101, 2010.
4. A. T. Du et al., "Different regional patterns of cortical thinning in Alzheimer's disease and frontotemporal dementia," Brain, vol. 130, no. Pt 4, pp. 1159-1166, Apr. 2007.
5. L. K. McEvoy et al., "Alzheimer disease: Quantitative structural neuroimaging for detection and prediction of clinical and structural changes in mild cognitive impairment," Radiology, vol. 251, no. 1, pp. 195-205, Apr. 2009.
6. S. De Santi et al., "Hippocampal formation glucose metabolism and volume losses in MCI and AD," Neurobiology Aging, vol. 22, no. 4, pp. 529-539, Jul./Aug. 2001.
7. J. C. Morris et al., "Mild cognitive impairment represents early-stage Alzheimer disease," Archives Neurol., vol. 58, no. 3, pp. 397-405, 2001.
8. L.M. Shaw et al., "Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects," Ann. Neurol., vol. 65, no. 4, pp. 403-413, Apr. 2009.
9. N. Mattsson et al., "CSF biomarkers and incipient Alzheimer disease in patients with mild cognitive impairment," JAMA, vol. 302, no. 4, pp. 385-393, 2009.
10. F. H. Bouwman et al., "Longitudinal changes of CSF biomarkers in memory clinic patients," Neurology, vol. 69, no. 10, pp. 1006-1011, 2007.
11. M. Liu et al., "Ensemble sparse classification of Alzheimer's disease," Neuroimage, vol. 60, no. 2, pp. 1106-1116, 2012.
12. R. Tibshirani, "Regression shrinkage and selection via the Lasso," J. Royal Statist. Soc. Series B-Methodological, vol. 58, no. 1, pp. 267-288, 1996.
13. L. Shen et al., "Identifying neuroimaging and proteomic biomarkers for MCI and AD via the elastic net," Lect. Notes Comput. Sci., vol. 7012, pp. 27-34, 2011.
14. H. Zou and T. Hastie, "Regularization and variable selection via the elastic net," J. Roy. Statist. Soc. Series B-Statistical Methodol., vol. 67, pp. 301-320, 2005.
15. B. Ng and R. Abugharbieh, "Generalized sparse regularization with application to fMRI brain decoding," in Proc. 22nd Int. Conf. Inf. Process Med. Imaging, 2011, pp. 612-623.
16. M. Yuan and Y. Lin, "Model selection and estimation in regression with grouped variables," J. Royal Statist. Soc. Series B-Statistical Methodol., vol. 68, pp. 49-67, 2006.
17. H. Wang et al., "Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression," in Proc. 14th Int. Conf. Med. Image Comput. Comput. Assist. Interv., 2011, pp. 115-123.
18. D. Zhang and D. Shen, "Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease," Neuroimage, vol. 59, no. 2, pp. 895-907, 2012.
19. J. Liu et al., "An efficient algorithm for a class of fused lasso problems," in Proc. 16th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2010, pp. 323-332.
20. R. Tibshirani et al., "Sparsity and smoothness via the fused lasso," J. Royal Statist. Soc. Series B-Statist. Methodol., vol. 67, pp. 91-108, 2005.
21. A. Caroli and G. B. Frisoni, "The dynamics of Alzheimer's disease biomarkers in the Alzheimer's Disease Neuroimaging Initiative cohort," Neurobiol. Aging, vol. 31, no. 8, pp. 1263-1274, Aug. 2010.
22. C. R. Jack, Jr et al., "The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods," J. Magn. Reson. Imag., vol. 27, no. 4, pp. 685-691, Apr. 2008.
23. J. Zhou et al., "Modeling disease progression via multi-task learning," Neuroimage, vol. 78, pp. 233-248, Sep. 2013.
24. D. Zhang et al., "Multimodal classification of Alzheimer's disease and mild cognitive impairment," Neuroimage, vol. 55, no. 3, pp. 856-867, 2011.
25. J. G. Sled et al., "A nonparametric method for automatic correction of intensity nonuniformity in MRI data," IEEE Trans. Med.Imag., vol. 17, no. 1, pp. 87-97, Feb. 1998.
26. F. Shi et al., "LABEL: Pediatric brain extraction using learning-based meta-algorithm," Neuroimage, vol. 62, no. 3, pp. 1975-1986, Sep. 2012.
27. D. W. Shattuck et al., "Magnetic resonance image tissue classification using a partial volume model," Neuroimage, vol. 13, no. 5, pp. 856-876, May 2001.
28. S. M. Smith, "Fast robust automated brain extraction," Human Brain Mapping, vol. 17, no. 3, pp. 143-155, Nov. 2002.
29. Y. Zhang et al., "Segmentation of brain MR images through a hidden Markov random field model and the expectationmaximization algorithm," IEEE Trans. Med. Imag., vol. 20, no. 1, pp. 45-57, 2001.
30. D. Shen et al., "4D HAMMER image registrationmethod for longitudinal study of brain changes," in Proc. Human Brain Mapping, 2003, pp. 1-8.
31. N. Kabani et al., "A 3D atlas of the human brain," Neuroimage, vol. 7, no. 4, p. S717, 1998.
32. M. Liu et al., "View-centralized multi-atlas classification for Alzheimer's disease diagnosis," Hum Brain Mapp., vol. 36, no. 5, pp. 1847-1865, May 2015.
33. B. Jie et al., "Manifold regularized multitask feature learning for multimodality disease classification," Hum Brain Mapp., vol. 36, no. 2, pp. 489-507, Feb. 2015.
34. A. Beck and M. Teboulle, "A fast iterative shrinkage-thresholding algorithm for linear inverse problems," SIAM J. Img. Sci., vol. 2, no. 1, pp. 183-202, 2009.
35. C. C. Chang and C. J. Lin, LIBSVM: A Library for Support Vector Machines, 2001. Online. Available: http://www.csie.ntu.edu.tw/cjlin/
36. D. Zhang and D. Shen, "Predicting future clinical changes ofMCI patients using longitudinal and multimodal biomarkers," PLoS One, vol. 7, no. 3, p. e33182, 2012.
37. G. Chetelat et al., "Mapping gray matter loss with voxel-based morphometry in mild cognitive impairment," Neuroreport, vol. 13, no. 15, pp. 1939-1943, 2002.
38. C. Misra et al., "Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI," Neuroimage, vol. 44, no. 4, pp. 1415-1422, 2009.
39. N. C. Fox and J. M. Schott, "Imaging cerebral atrophy: normal ageing to Alzheimer's disease," Lancet, vol. 363, no. 9406, pp. 392-394, 2004.
40. A. Chincarini et al., "Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer's disease," Neuroimage, vol. 58, no. 2, pp. 469-480, 2011.
41. R. La Joie et al., "Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer's disease and semantic dementia," Neuroimage Clin., vol. 3, pp. 155-162, 2013.
42. A. Convit et al., "Atrophy of the medial occipitotemporal, inferior, and middle temporal gyri in non-demented elderly predict decline to Alzheimer's disease," Neurobiol. Aging, vol. 21, no. 1, pp. 19-26, 2000.
43. D. P. Devanand et al., "Hippocampal and entorhinal atrophy in mild cognitive impairment: Prediction of Alzheimer disease," Neurology, vol. 68, no. 11, pp. 828-836, 2007.
44. J. G. Csernansky et al., "Correlations between antemortem hippocampal volume and postmortem neuropathology in AD subjects," Alzheimer Disease Associated Disorders, vol. 18, no. 4, pp. 190-195, Oct.-Dec. 2004.
45. P. M. Thompson et al., "Mapping hippocampal and ventricular change in Alzheimer disease," Neuroimage, vol. 22, no. 4, pp. 1754-1766, Aug. 2004.
46. C. M. Stonnington et al., "Predicting clinical scores from magnetic resonance scans in Alzheimer's disease," Neuroimage, vol. 51, no. 4, pp. 1405-1413, Jul. 15.
47. G. B. Frisoni et al., "The clinical use of structural MRI in Alzheimer disease," Nature Reviews Neurol., vol. 6, no. 2, pp. 67-77, Feb. 2010.
48. S. Duchesne et al., "Relating one-year cognitive change in mild cognitive impairment to baseline MRI features," Neuroimage, vol. 47, no. 4, pp. 1363-1370, 2009.
49. Y. Fan et al., "Joint estimation of multiple clinical variables of neurological diseases from imaging patterns," in Proc. 7th IEEE Int. Symp. Biomed. Imaging: From Nano Macro, 2010, pp. 852-855.
50. J. Wan et al., "Identifying the neuroanatomical basis of cognitive impairment in Alzheimer's disease by correlation-and nonlinearity-aware sparse Bayesian learning," IEEE Trans. Med. Imag., vol. 33, no. 7, pp. 1475-1487, Jul. 2014.
51. J. Yan et al., "Cortical surface biomarkers for predicting cognitive outcomes using group l2,1 norm," Neurobiol. Aging, vol. 36, no. Suppl. 1, pp. S185-S193, Jan. 2015.
52. C. R. Jack, Jr et al., "Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade," Lancet Neurol., vol. 9, no. 1, pp. 119-128, Jan. 2010.
53. M. Tondelli et al., "Structural MRI changes detectable up to ten years before clinical Alzheimer's disease," Neurobiol. Aging, vol. 33, no. 4, pp. e25-e36, Apr. 2012.
54. C. Aguilar et al., "Differentmultivariate techniques for automated classification ofMRI data in Alzheimer's disease and mild cognitive impairment," Psychiatry Res., vol. 212, no. 2, pp. 89-98, 2013.
55. R. Cuingnet et al., "Automatic classification of patients with Alzheimer's disease from structuralMRI: a comparison of ten methods using the ADNI database," Neuroimage, vol. 56, no. 2, pp. 766-781, 2011.
56. S. Duchesne and A. Mouiha, "Morphological factor estimation via highdimensional reduction: Prediction of MCI conversion to probable AD," Int. J. Alzheimers Dis., vol. 2011, pp. 1-8, 2011.
57. C. Davatzikos et al., "Prediction ofMCI to AD conversion, viaMRI, CSF biomarkers, and pattern classification," Neurobiol. Aging, vol. 32, no. 12, pp. 2322 e19-27, Dec. 2011.
58. M. Lehmann et al., "Visual ratings of atrophy in MCI: Prediction of conversion and relationship with CSF biomarkers," Neurobiol. Aging, vol. 34, no. 1, pp. 73-82, Jan. 2013.
59. Y. Cho et al., "Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data," Neuroimage, vol. 59, no. 3, pp. 2217-2230, 2012.
60. X. Liu et al., "Locally linear embedding (LLE) forMRI basedAlzheimer's disease classification," Neuroimage, vol. 83, pp. 148-157, Dec. 2013.