Brain maturation: Predicting individual BrainAGE in children and adolescents using structural MRI

NeuroImage

Volumn 63, Issue 3, 2012, Pages 1305-1312

  Franke, Katja ^a   Luders, Eileen ^b   May, Arne ^c   Wilke, Marko ^d,e   Gaser, Christian ^a  

^a JENA UNIVERSITY HOSPITAL   (Germany)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF HAMBURG   (Germany)

^d Children's Hospital   (Germany)

^e UNIVERSITY OF TÜBINGEN   (Germany)

Author keywords

Brain maturation; BrainAGE; Developmental delay; Gray matter; Prediction; Structural MRI; White matter

Indexed keywords

ACCURACY; ADOLESCENCE; ADOLESCENT; ADULT; AGE; ANALYTICAL ERROR; ARTICLE; BRAIN MATURATION; CHILD; CHILDHOOD; CLINICAL ARTICLE; CONCEPTUAL FRAMEWORK; CONTROLLED STUDY; FEMALE; GESTATIONAL AGE; HUMAN; MALE; NUCLEAR MAGNETIC RESONANCE IMAGING; PREDICTION; PREMATURITY; PRESCHOOL CHILD; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; RELIABILITY; SCHOOL CHILD;

ADOLESCENT; BRAIN; BRAIN MAPPING; CHILD; CHILD, PRESCHOOL; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; REFERENCE VALUES;

EID: 84866152087     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2012.08.001     Document Type: Article

References (54)

1. Allen M.C. Neurodevelopmental outcomes of preterm infants. Curr. Opin. Neurol. 2008, 21:123-128.
2. Altaye M., Holland S.K., Wilke M., Gaser C. Infant brain probability templates for MRI segmentation and normalization. NeuroImage 2008, 43:721-730.
3. Ashburner J., Friston K.J. Voxel-based morphometry-the methods. NeuroImage 2000, 11:805-821.
4. Ashburner J., Friston K.J. Unified segmentation. NeuroImage 2005, 26:839-851.
5. Bennett K.P., Campbell C. Support vector machines: hype or hallelujah?. SIGKDD Explor. 2003, 2:1-13.
6. Bishop C.M. Pattern Recognition and Machine Learning 2006, Springer, New York, NY.
7. Cao K., Chen-Plotkin A.S., Plotkin J.B., Wang L.S. Age-correlated gene expression in normal and neurodegenerative human brain tissues. PLoS One 2010, 5.
8. Cuadra M.B., Cammoun L., Butz T., Cuisenaire O., Thiran J.P. Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images. IEEE Trans. Med. Imaging 2005, 24:1548-1565.
9. Dosenbach N.U., Nardos B., Cohen A.L., Fair D.A., Power J.D., Church J.A., Nelson S.M., Wig G.S., Vogel A.C., Lessov-Schlaggar C.N., Barnes K.A., Dubis J.W., Feczko E., Coalson R.S., Pruett J.R., Barch D.M., Petersen S.E., Schlaggar B.L. Prediction of individual brain maturity using fMRI. Science 2010, 329:1358-1361.
10. Driscoll I., Davatzikos C., An Y., Wu X., Shen D., Kraut M., Resnick S.M. Longitudinal pattern of regional brain volume change differentiates normal aging from MCI. Neurology 2009, 72:1906-1913.
11. Dukart J., Schroeter M.L., Mueller K. Age correction in dementia-matching to a healthy brain. PLoS One 2011, 6:e22193.
12. Evans A.C. The NIH MRI study of normal brain development. NeuroImage 2006, 30:184-202.
13. Faul A., Tipping M.E. Analysis of sparse Bayesian learning. Advances in Neural Information Processing Systems 2002, 14:383-389. MIT Press. T.G. Dietterich, S. Becker, Z. Ghahramani (Eds.).
14. Franke K., Ziegler G., Klöppel S., Gaser C., Initiative, t.A.s.D.N. Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. NeuroImage 2010, 50:883-892.
15. Gaser C. Partial volume segmentation with adaptive maximum a posteriori (MAP) approach. NeuroImage 2009, 47:S121.
16. Ghosh S., Mujumdar P.P. Statistical downscaling of GCM simulations to streamflow using relevance vector machine. Adv. Water Resour. 2008, 31:132-146.
17. Giedd J.N., Snell J.W., Lange N., Rajapakse J.C., Casey B.J., Kozuch P.L., Vaituzis A.C., Vauss Y.C., Hamburger S.D., Kaysen D., Rapoport J.L. Quantitative magnetic resonance imaging of human brain development: ages 4-18. Cereb. Cortex 1996, 6:551-560.
18. Giedd J.N., Blumenthal J., Jeffries N.O., Castellanos F.X., Liu H., Zijdenbos A., Paus T., Evans A.C., Rapoport J.L. Brain development during childhood and adolescence: a longitudinal MRI study. Nat. Neurosci. 1999, 2:861-863.
19. Giedd J.N., Lalonde F.M., Celano M.J., White S.L., Wallace G.L., Lee N.R., Lenroot R.K. Anatomical brain magnetic resonance imaging of typically developing children and adolescents. J. Am. Acad. Child Adolesc. Psychiatry 2009, 48:465-470.
20. Giorgio A., Watkins K.E., Douaud G., James A.C., James S., De Stefano N., Matthews P.M., Smith S.M., Johansen-Berg H. Changes in white matter microstructure during adolescence. NeuroImage 2008, 39:52-61.
21. Giorgio A., Watkins K.E., Chadwick M., James S., Winmill L., Douaud G., De Stefano N., Matthews P.M., Smith S.M., Johansen-Berg H., James A.C. Longitudinal changes in grey and white matter during adolescence. NeuroImage 2010, 49:94-103.
22. Gogtay N., Thompson P.M. Mapping gray matter development: implications for typical development and vulnerability to psychopathology. Brain Cogn. 2010, 72:6-15.
23. Gogtay N., Giedd J.N., Lusk L., Hayashi K.M., Greenstein D., Vaituzis A.C., Nugent T.F., Herman D.H., Clasen L.S., Toga A.W., Rapoport J.L., Thompson P.M. Dynamic mapping of human cortical development during childhood through early adulthood. Proc. Natl. Acad. Sci. U. S. A. 2004, 101:8174-8179.
24. Gogtay N., Lu A., Leow A.D., Klunder A.D., Lee A.D., Chavez A., Greenstein D., Giedd J.N., Toga A.W., Rapoport J.L., Thompson P.M. Three-dimensional brain growth abnormalities in childhood-onset schizophrenia visualized by using tensor-based morphometry. Proc. Natl. Acad. Sci. U. S. A. 2008, 105:15979-15984.
25. Groeschel S., Vollmer B., King M.D., Connelly A. Developmental changes in cerebral grey and white matter volume from infancy to adulthood. Int. J. Dev. Neurosci. 2010, 28:481-489.
26. Harbord M.G., Finn J.P., Hall-Craggs M.A., Robb S.A., Kendall B.E., Boyd S.G. Myelination patterns on magnetic resonance of children with developmental delay. Dev. Med. Child Neurol. 1990, 32:295-303.
27. Jones D.T., Machulda M.M., Vemuri P., McDade E.M., Zeng G., Senjem M.L., Gunter J.L., Przybelski S.A., Avula R.T., Knopman D.S., Boeve B.F., Petersen R.C., Jack C.R. Age-related changes in the default mode network are more advanced in Alzheimer disease. Neurology 2011, 77:1524-1531.
28. Kirkpatrick B., Messias E., Harvey P.D., Fernandez-Egea E., Bowie C.R. Is schizophrenia a syndrome of accelerated aging?. Schizophr. Bull. 2008, 34:1024-1032.
29. Klöppel S., Stonnington C.M., Chu C., Draganski B., Scahill R.I., Rohrer J.D., Fox N.C., Jack C.R., Ashburner J., Frackowiak R.S. Automatic classification of MR scans in Alzheimer's disease. Brain 2008, 131:681-689.
30. Lebel C., Beaulieu C. Longitudinal development of human brain wiring continues from childhood into adulthood. J. Neurosci. 2011, 31:10937-10947.
31. Lenroot R.K., Giedd J.N. Brain development in children and adolescents: insights from anatomical magnetic resonance imaging. Neurosci. Biobehav. Rev. 2006, 30:718-729.
32. May A. Experience-dependent structural plasticity in the adult human brain. Trends Cogn. Sci. 2011, 15:475-482.
33. McLaughlin K.A., Fox N.A., Zeanah C.H., Sheridan M.A., Marshall P., Nelson C.A. Delayed maturation in brain electrical activity partially explains the association between early environmental deprivation and symptoms of attention-deficit/hyperactivity disorder. Biol. Psychiatry 2010, 68:329-336.
34. Paus T., Collins D.L., Evans A.C., Leonard G., Pike B., Zijdenbos A. Maturation of white matter in the human brain: a review of magnetic resonance studies. Brain Res. Bull. 2001, 54:255-266.
35. Paus T., Keshavan M., Giedd J.N. Why do many psychiatric disorders emerge during adolescence?. Nat. Rev. Neurosci. 2008, 9:947-957.
36. Rajapakse J.C., Giedd J.N., Rapoport J.L. Statistical approach to segmentation of single-channel cerebral MR images. IEEE Trans. Med. Imaging 1997, 16:176-186.
37. Ramenghi L.A., Martinelli A., De Carli A., Brusati V., Mandia L., Fumagalli M., Triulzi F., Mosca F., Cetin I. Cerebral maturation in IUGR and appropriate for gestational age preterm babies. Reprod. Sci. 2011, 18:469-475.
38. Saetre P., Jazin E., Emilsson L. Age-related changes in gene expression are accelerated in Alzheimer's disease. Synapse 2011, 65:971-974.
39. Schölkopf B., Smola A. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond 2002, MIT Press, Cambridge, Mass.
40. Silk T.J., Wood A.G. Lessons about neurodevelopment from anatomical magnetic resonance imaging. J. Dev. Behav. Pediatr. 2011, 32:158-168.
41. Smith K.M., Mecoli M.D., Altaye M., Komlos M., Maitra R., Eaton K.P., Egelhoff J.C., Holland S.K. Morphometric differences in the Heschl's gyrus of hearing impaired and normal hearing infants. Cereb. Cortex 2011, 21:991-998.
42. Spulber G., Niskanen E., MacDonald S., Smilovici O., Chen K., Reiman E.M., Jauhiainen A.M., Hallikainen M., Tervo S., Wahlund L.O., Vanninen R., Kivipelto M., Soininen H. Whole brain atrophy rate predicts progression from MCI to Alzheimer's disease. Neurobiol. Aging 2010, 31:1601-1605.
43. Thompson P.M., Vidal C., Giedd J.N., Gochman P., Blumenthal J., Nicolson R., Toga A.W., Rapoport J.L. Mapping adolescent brain change reveals dynamic wave of accelerated gray matter loss in very early-onset schizophrenia. Proc. Natl. Acad. Sci. U. S. A. 2001, 98:11650-11655.
44. Tipping M. The relevance vector machine. Advances in Neural Information Processing Systems 2000, 12:652-658. MIT Press. S.A. Solla, T.K. Leen, K.-R. Müller (Eds.).
45. Tipping M.E. Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 2001, 1:211-244.
46. Tipping M.E., Faul A.C. Fast marginal likelihood maximisation for sparse Bayesian models. Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, Key West, FL 2003, C.M. Bishop, B.J. Frey (Eds.).
47. Tohka J., Zijdenbos A., Evans A. Fast and robust parameter estimation for statistical partial volume models in brain MRI. NeuroImage 2004, 23:84-97.
48. Van Dijk K.R., Sabuncu M.R., Buckner R.L. The influence of head motion on intrinsic functional connectivity MRI. NeuroImage 2012, 59:431-438.
49. Verbruggen K.T., Meiners L.C., Sijens P.E., Lunsing R.J., van Spronsen F.J., Brouwer O.F. Magnetic resonance imaging and proton magnetic resonance spectroscopy of the brain in the diagnostic evaluation of developmental delay. Eur. J. Paediatr. Neurol. 2009, 13:181-190.
50. Wilke M., Holland S.K. Variability of gray and white matter during normal development: a voxel-based MRI analysis. Neuroreport 2003, 14:1887-1890.
51. Wilke M., Schmithorst V.J., Holland S.K. Normative pediatric brain data for spatial normalization and segmentation differs from standard adult data. Magn. Reson. Med. 2003, 50:749-757.
52. Wilke M., Holland S.K., Altaye M., Gaser C. Template-O-Matic: a toolbox for creating customized pediatric templates. NeuroImage 2008, 41:903-913.
53. Yuan W., Altaye M., Ret J., Schmithorst V., Byars A.W., Plante E., Holland S.K. Quantification of head motion in children during various fMRI language tasks. Hum. Brain Mapp. 2009, 30:1481-1489.
54. Zheng Y.-T., Neo S.-Y., Chua T.-S., Tian Q. Probabilistic optimized ranking for multimedia semantic concept detection via RVM. Proceedings of the 2008 international conference on Content-based image and video retrieval 2008, 161-168. ACM, Niagara Falls, Canada.