Predicting the age of healthy adults from structural MRI by sparse representation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7751 LNCS, Issue , 2013, Pages 271-279

  Su, Longfei ^a   Wang, Lubin ^a   Hu, Dewen ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

Author keywords

Age prediction; Biomarker of brain age; Healthy brain aging; MAE; MRI; voxel selection

Indexed keywords

ABNORMALITY DETECTION; AGE PREDICTIONS; CLINICAL DIAGNOSIS; MAE; MEAN ABSOLUTE ERROR; REGRESSION MODEL; SPARSE REPRESENTATION; VOXEL SELECTIONS;

BRAIN; DIAGNOSIS; MAGNETIC RESONANCE IMAGING; REGRESSION ANALYSIS;

FORECASTING;

EID: 84892925549     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-36669-7_34     Document Type: Conference Paper

References (26)

1. Heemels, M.-T.: Ageing. Nature 464, 503 (2010)
2. Frisoni, G.B., Fox, N.C., Jack Jr., C.R., Scheltens, P., Thompson, P.M.: The clinical use of structural MRI in Alzheimer disease. Nature Reviews Neurology 6, 67-77 (2010)
3. Fan, Y., Batmanghelich, N., Clark, C.M., Davatzikos, C.: Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage 39, 1731-1743 (2008)
4. Franke, K., Ziegler, G., Kloppel, S., Gaser, C.: Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. Neuroimage 50, 883-892 (2010)
5. Brickman, A.M., Habeck, C., Zarahn, E., Flynn, J., Stern, Y.: Structural MRI covariance patterns associated with normal aging and neuropsychological functioning. Neurobiol. Aging. 28, 284-295 (2007)
6. Wang, B., Pham, T.D.: MRI-based age prediction using hidden Markov models. J. Neurosci Methods 199, 140-145 (2011)
7. Brown, T.T., Kuperman, J.M., Chung, Y., Erhart, M., McCabe, C., et al.: Neuroanatomical Assessment of Biology Maturity. Current Biology 22, 1-6 (2012)
8. Taki, Y., Kinomura, S., Sato, K., Goto, R., Kawashima, R., et al.: A longitudinal study of gray matter volume decline with age and modifying factors. Neurobiol. Aging. 32, 907-915 (2009)
9. Salat, D.H., Lee, S.Y., van der Kouwe, A.J., Greve, D.N., Fischl, B., et al.: Age-associated alterations in cortical gray and white matter signal intensity and gray to white matter contrast. NeuroImage 48, 21-28 (2009)
10. Tisserand, D.J., van Boxtel, M.P.J., Pruessner, J.C., Hofman, P., Evans, A.C., et al.: A Voxel-based morphometric study to determine individual differences in gray matter density associated with age and cognitive change over time. Cereb. Cortex 14, 966-973 (2004)
11. Galluzzi, S., Beltramello, A., Filippi, M., Frisoni, G.B.: Aging. Neurol Sci. 29, s296-s300 (2008)
12. Ge, Y., Grossman, R.I., Babb, J.S., Rabin, M.L., Mannon, L.J., et al.: Age-related total gray matter and white matter changes in normal adult brain. Part I: volumetric MR Imaging analysis. Am J. Neuroradiol. 23, 1327-1333 (2002)
13. Giorgio, A., Santelli, L., Tomassini, V., Bosnell, R., Smith, S., et al.: Age-related changes in grey and white matter structure throughout adulthood. NeuroImage 51, 943-951 (2010)
14. Good, C.D., Johnsrude, I.S., Ashburner, J., Henson, R.N.A., Friston, K.J., et al.: A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14, 21-36 (2001)
15. Smith, C.D., Chebrolu, H., Wekstein, D.R., Schmitt, F.A., Markesbery, W.R.: Age and gender effects on human brain anatomy: A voxel-based morphometric study in healthy elderly. Neurobiol. Aging. 28, 1075-1087 (2007)
16. Shen, H., Wang, L., Liu, Y., Hu, D.: Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI. Neuroimage 49, 3110-3121 (2010)
17. Robinson, E.C., Hammers, A., Ericsson, A., Edwards, A.D., Rueckert, D.: Identifying population differences in whole-brain structural networks: a machine learning approach. Neuroimage 50, 910-919 (2010)
18. Su, L., Wang, L., Chen, F., Shen, H., Li, B., et al.: Sparse representation of brain aging: extracting covariance patterns from structural MRI. PLoS One 7, e36147 (2012)
19. Daubechies, I., Roussos, E., Takerkart, S., Benharrosh, M., Golden, C., et al.: Independent component analysis for brain fMRI does not select for independence. Proc. Natl. Acad. Sci. USA 106, 10415-10422 (2009)
20. Marcus, D.S., Wang, T.H., Parker, J., Csernansky, J.G., Morris, J.C., et al.: Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J. Cognitive NeuroSci. 19, 1498-1507 (2007)
21. Ashburner, J.: A fast diffeomorphic image registration algorithm. NeuroImage 38, 95-113 (2007)
22. Li, Y., Namburi, P., Yu, Z., Guan, C., Feng, J., et al.: Voxel selection in fMRI data analysis based on sparse representation. IEEE T. Bio.-Med. Eng. 56, 2439-2451 (2009)
23. Ecker, C., Rocha-Rego, V., Johnston, P., Mourao-Miranda, J., Marquand, A., et al.: Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. Neuroimage 49, 44-56 (2010)
24. Fan, Y., Shen, D., Gur, R.C., Gur, R.E., Davatzikos, C.: COMPARE: classification of morphological patterns using adaptive regional elements. IEEE Trans. Med. Imaging 26, 93-105 (2007)
25. Duara, R., Loewenstein, D.A., Potter, E., Appel, J., Greig, M.T., et al.: Medial temporal lobe atrophy on MRI scans and the diagnosis of Alzheimer disease. Neurology 71, 1986-1992 (2008)
26. Li, S., Xia, M., Pu, F., Li, D., Fan, Y., et al.: Age-related changes in the surface morphology of the central sulcus. Neuroimage 58, 381-390 (2011)