Predictive models of autism spectrum disorder based on brain regional cortical thickness

NeuroImage

Volumn 50, Issue 2, 2010, Pages 589-599

  Jiao, Yun ^a,b,c   Chen, Rong ^c   Ke, Xiaoyan ^a,d   Chu, Kangkang ^d   Lu, Zuhong ^a,b   Herskovits, Edward H ^c  

^a Southeast University   (China)

^b SOUTHEAST UNIVERSITY   (China)

^c UNIVERSITY OF PENNSYLVANIA   (United States)

^d NANJING MEDICAL UNIVERSITY   (China)

Author keywords

Autism spectrum disorder; Cortical thickness; Diagnostic model; Surface based morphometry

Indexed keywords

ARTICLE; AUTISM; BRAIN CORTEX; BRAIN SIZE; CHILD; CINGULATE GYRUS; CLINICAL ARTICLE; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; FEMALE; FRONTAL LOBE; HUMAN; INTERMETHOD COMPARISON; MACHINE LEARNING; MALE; MORPHOMETRICS; NUCLEAR MAGNETIC RESONANCE IMAGING; ORBITAL CORTEX; PERCEPTRON; PREDICTION; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SCHOOL CHILD; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; SUBICULUM; SUPPORT VECTOR MACHINE; VOLUMETRY; VOLUNTEER; ADOLESCENT; BIOLOGICAL MODEL; BRAIN; BRAIN MAPPING; COMPUTER ASSISTED DIAGNOSIS; METHODOLOGY; PATHOLOGY;

ADOLESCENT; BRAIN; BRAIN MAPPING; CHILD; CHILD DEVELOPMENT DISORDERS, PERVASIVE; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MODELS, NEUROLOGICAL;

EID: 77952298475     PISSN: 10538119     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2009.12.047     Document Type: Article

References (57)

1. Akshoomoff, N., Lord, C., Lincoln, A.J., Courchesne, R.Y., Carper, R.A., Townsend, J., Courchesne, E., 2004. Outcome classification of preschool children with autism spectrum disorders using MRI brain measures. J. Am. Acad. Child Adolesc. Psych. 43 (3), 349-357.
2. Altmann, A., Beerenwinkel, N., et al., 2007. Improved prediction of response to antiretroviral combination therapy using the genetic barrier to drug resistance. Antivir. Ther. 12 (2), 169-178.
3. Amaral, D.G., Schumann, C.M., Nordahl, C.W., 2008. Neuroanatomy of autism. Trends Neurosci. 31 (3), 137-145.
4. Aires, R., Manfrin, A., Aluisio, S., Santos, D., 2004. Which classification algorithm works best with stylistic features of Portuguese in order to classify web texts according to users needs? Technical Report NILC-TR-04-09. University de Sao Paulo, Brasil. 2004.
5. Aylward, E.H., Minshew, N.J., Goldstein, G., Honeycutt, N.A., Augustine, A.M., Yates, K.O., Barta, P.E., Pearlson, G.D., 1999. MRI volumes of amygdala and hippocampus in nonmentally retarded autistic adolescents and adults. Neurology 53 (9), 2145-2150.
6. Aylward, E.H., Minshew, N.J., Field, K., Sparks, B.F., Singh, N., 2002. Effects of age on brain volume and head circumference in autism. Neurology 59 (2), 175-183.
7. Bakkour, A., Morris, J.C., Dickerson, B.C., 2009. The cortical signature of prodromal AD: regional thinning predicts mild AD dementia. Neurology 72 (12), 1048-1055.
8. Belmonte, M.K., Cook, E.H., Anderson, G.M., Rubenstein, J.L.R., Greenough, W.T., Beckel-Mitchener, A., Courchesne, E., Boulanger, L.M., Powell, S.B., Levitt, P.R., et al., 2004. Autism as a disorder of neural information processing: directions for research and targets for therapy. Mol. Psychiatry 9 (7), 646-663.
9. Benjamini, Y., Hochberg, Y., 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc., Series B (Methodological) 57 (1), 289-300.
10. Cavanna, A.E., Trimble, M.R., 2006. The precuneus: a review of its functional anatomy and behavioural correlates. Brain 129 (Pt 3), 564-583.
11. Cheng, L., Ge, Q., Xiao, P., Sun, B., Ke, X., Bai, Y., Lu, Z., 2009. Association study between BDNF gene polymorphisms and autism by three-dimensional gel-based microarray. Int. J. Mol. Sci. 10 (6), 2487-2500.
12. Courchesne, E., Karns, C.M., Davis, H.R., Ziccardi, R., Carper, R.A., Tigue, Z.D., Chisum, H.J., Moses, P., Pierce, K., Lord, C., et al., 2001. Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study. Neurology 57 (2), 245-254.
13. Dale, A.M., Fischl, B., Sereno, M.I., 1999. Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage 9 (2), 179-194.
14. Desikan, R.S., Ségonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., Buckner, R.L., Dale, A.M., Maguire, R.P., Hyman, B.T., et al., 2006. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31 (3), 968-980.
15. Dickerson, B.C., Bakkour, A., Salat, D.H., Feczko, E., Pacheco, J., Greve, D.N., Grodstein, F., Wright, C.I., Blacker, D., Rosas, H.D., et al., 2009. The cortical signature of Alzheimer's disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals 10.1093/cercor/bhn113. Cereb. Cortex 19 (3), 497-510.
16. Ecker, C., Rocha-Rego, V., Johnston, P., Mourao-Miranda, J., Marquand, A., Daly, E.M., Brammer, M.J., Murphy, C., Murphy, D.G., 2009. Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. NeuroImage 49 (1), 44-56.
17. Fischl, B., Dale, A.M., 2000. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. U. S. A. 97 (20), 11050-11055.
18. Fischl, B., Sereno, M.I., Dale, A.M., 1999. Cortical surface-based analysis. II. Inflation, flattening, and a surface-based coordinate system. NeuroImage 9 (2), 195-207.
19. Fischl, B., van der Kouwe, A., Destrieux, C., Halgren, E., Segonne, F., Salat, D.H., Busa, E., Seidman, L.J., Goldstein, J., Kennedy, D., et al., 2004. Automatically parcellating the human cerebral cortex. Cereb. Cortex 14 (1), 11-22.
20. Friedman, J., Hastie, T., Tibshirani, R., 2000. Additive logistic regression: a statistical view of boosting. Ann. Stat. 28 (2), 337-374.
21. Gama, J., 2004. Functional trees. Mach. Learn. 55 (3), 219-250.
22. Gardner, M.W., Dorling, S.R., 1998. Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmos. Environ. 32 (14-15), 2627-2636.
23. Geschwind, D.H., 2009. Advances in autism. Annu. Rev. Med. 60, 367-380.
24. Gmitrowicz, A., Kucharska, A., 1994. Developmental disorders in the fourth edition of the American classification: diagnostic and statistical manual of mental disorders (DSM IV - optional book). Psychiatr. Pol. 28 (5), 509-521.
25. Gray, J.R., Braver, T.S., 2002. Personality predicts working-memory-related activation in the caudal anterior cingulate cortex. Cogn. Affect Behav. Neurosci. 2 (1), 64-75.
26. Hadjikhani, N., Joseph, R.M., Snyder, J., Tager-Flusberg, H., 2006. Anatomical differences in the mirror neuron system and social cognition network in autism. Cereb. Cortex 16 (9), 1276-1282.
27. Hadjikhani, N., Joseph, R.M., Snyder, J., Tager-Flusberg, H., 2007. Abnormal activation of the social brain during face perception in autism. Hum. Brain Mapp. 28 (5), 441-449.
28. Han, X., Jovicich, J., Salat, D., van der Kouwe, A., Quinn, B., Czanner, S., Busa, E., Pacheco, J., Albert, M., Killiany, R., et al., 2006. Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer. NeuroImage 32 (1), 180-194.
29. Hardan, A.Y., Muddasani, S., Vemulapalli, M., Keshavan, M.S., Minshew, N.J., 2006. An MRI study of increased cortical thickness in autism. Am. J. Psychiatry 163 (7), 1290-1292.
30. Hashimoto, T., Tayama, M., Murakawa, K., Yoshimoto, T., Miyazaki, M., Harada, M., Kuroda, Y., 1995. Development of the brainstem and cerebellum in autistic patients. J. Autism Dev. Disord. 25 (1), 1-18.
31. Ke, X., Hong, S., Tang, T., Zou, B., Li, H., Hang, Y., Zhou, Z., Ruan, Z., Lu, Z., Tao, G., et al., 2008. Voxel-based morphometry study on brain structure in children with highfunctioning autism. NeuroReport 19 (9), 921-925.
32. Kemper, T.L., Bauman, M.L., 1993. The contribution of neuropathologic studies to the understanding of autism. Neurol. Clin. 11 (1), 175-187.
33. Kim, J.S., Singh, V., Lee, J.K., Lerch, J., Ad-Dab'bagh, Y., MacDonald, D., Lee, J.M., Kim, S.I., Evans, A.C., 2005. Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. NeuroImage 27 (1), 210-221.
34. Kirk, G.R., Haynes, M.R., Palasis, S., Brown, C., Burns, T.G., McCormick, M., Jones, R.A., 2009. Regionally specific cortical thinning in children with sickle cell disease. Cereb. Cortex 19 (7), 1549-1556.
35. Kloppel, S., Stonnington, C.M., Chu, C., Draganski, B., Scahill, R.I., Rohrer, J.D., Fox, N.C., Jack Jr., C.R., Ashburner, J., Frackowiak, R.S., 2008. Automatic classification of MR scans in Alzheimer's disease. Brain 131 (Pt 3), 681-689.
36. Kringelbach, M.L., 2005. The human orbitofrontal cortex: linking reward to hedonic experience. Nat. Rev., Neurosci. 6 (9), 691-702.
37. Landwehr, N., Hall, M., Frank, E., 2003. Logistic model trees. Mach. Learn.: Ecml 2003 (2837), 241-252.
38. Landwehr, N., Hall, M., Frank, E., 2005. Logistic model trees. Mach. Learn. 59 (1-2), 161-205.
39. Lenroot, R.K., Gogtay, N., Greenstein, D.K., Wells, E.M., Wallace, G.L., Clasen, L.S., Blumenthal, J.D., Lerch, J., Zijdenbos, A.P., Evans, A.C., et al., 2007. Sexual dimorphism of brain developmental trajectories during childhood and adolescence. NeuroImage 36 (4), 1065-1073.
40. Lord, C., Rutter, M., Le Couteur, A., 1994. Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J. Autism Dev. Disord. 24 (5), 659-685.
41. Makris, N., Kaiser, J., Haselgrove, C., Seidman, L.J., Biederman, J., Boriel, D., Valera, E.M., Papadimitriou, G.M., Fischl, B., Caviness Jr., V.S., et al., 2006. Human cerebral cortex: a system for the integration of volume-and surface-based representations. NeuroImage 33 (1), 139-153.
42. Muller, R.A., 2007. The study of autism as a distributed disorder. Ment. Retard. Dev. Disabil. Res. Rev. 13 (1), 85-95.
43. Nordahl, C.W., Dierker, D., Mostafavi, I., Schumann, C.M., Rivera, S.M., Amaral, D.G., Van Essen, D.C., 2007. Cortical folding abnormalities in autism revealed by surfacebased morphometry. J. Neurosci. 27 (43), 11725-11735.
44. Okuda, J., Fujii, T., Ohtake, H., Tsukiura, T., Tanji, K., Suzuki, K., Kawashima, R., Fukuda, H., Itoh, M., Yamadori, A., 2003. Thinking of the future and past: the roles of the frontal pole and the medial temporal lobes. NeuroImage 19 (4), 1369-1380.
45. Petrova, N.V., Wu, C.H., 2006. Prediction of catalytic residues using Support Vector Machinewith selected protein sequence andstructural properties. BMC Bioinformatics 7, 312.
46. Platt, J.C., 1999. Fast training of support vector machines using sequential minimal optimization Advances in kernel methods: support vector learning. MIT Press, pp. 185-208.
47. Rapin, I., 1997. Autism. N. Engl. J. Med. 337 (2), 97-104.
48. Rojas, D.C., Peterson, E., Winterrowd, E., Reite, M.L., Rogers, S.J., Tregellas, J.R., 2006. Regional gray matter volumetric changes in autism associated with social and repetitive behavior symptoms. BMC Psychiatry 6, 56.
49. Sajda, P., 2006. Machine learning for detection and diagnosis of disease. Annu. Rev. Biomed. Eng. 8, 537-565.
50. Schopler, E., Reichler, R.J., DeVellis, R.F., Daly, K., 1980. Toward objective classification of childhood autism: Childhood Autism Rating Scale (CARS). J. Autism Dev. Disord. 10 (1), 91-103.
51. Shen, L., Ford, J., Makedon, F., Saykin, A., 2004. Surface-based approach for classification of 3d neuroanatomical structures. Intell. Data Anal. 8, 519-542.
52. Singh, V., Mukherjee, L., Chung, M.K., 2008. Cortical surface thickness as a classifier: boosting for autism classification. Med. Image Comput. Comput. Assist. Interv. Int. Conf. Med. Image Comput. Comput. Assist. Interv. 11 (Pt. 1), 999-1007.
53. Sparks, B.F., Friedman, S.D., Shaw, D.W., Aylward, E.H., Echelard, D., Artru, A.A., Maravilla, K.R., Giedd, J.N., Munson, J., Dawson, G., et al., 2002. Brain structural abnormalities in young children with autism spectrum disorder. Neurology 59 (2), 184-192.
54. Sumner, M., Frank, E., Hall, M., 2005. Speeding up logistic model tree induction. Knowl. Discov. Databases: Pkdd 2005 (3721), 675-683.
55. Ursu, S., Clark, K.A., Aizenstein, H.J., Stenger, V.A., Carter, C.S., 2009. Conflict-related activity in the caudal anterior cingulate cortex in the absence of awareness. Biol. Psychol. 80 (3), 279-286.
56. Voets, N.L., Hough, M.G., Douaud, G., Matthews, P.M., James, A., Winmill, L., Webster, P., Smith, S., 2008. Evidence for abnormalities of cortical development in adolescentonset schizophrenia. NeuroImage 43 (4), 665-675.
57. Witten, I.H., Frank, E., 2005. Data Mining: practical machine learning tools and techniques, 2005. Morgan Kaufmann, San Francisco, p. 525.