Insights into multimodal imaging classification of ADHD

Frontiers in Systems Neuroscience

Volumn , Issue AUG 2012, 2012, Pages 1-18

  Colby, John B ^a   Rudie, Jeffrey D ^a   Brown, Jesse A ^b   Douglas, Pamela K ^b   Cohen, Mark S ^b   Shehzad, Zarrar ^c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c YALE UNIVERSITY   (United States)

Author keywords

ADHD 200; Attention deficit hyperactivity disorder; Classification; Feature selection; fMRI; Graph theory; Machine learning

Indexed keywords

AGE; ARTICLE; ATTENTION DEFICIT DISORDER; CLASSIFICATION ALGORITHM; CLASSIFIER; CLINICAL FEATURE; CONTROLLED STUDY; CORRELATION COEFFICIENT; DIAGNOSTIC ACCURACY; DIAGNOSTIC IMAGING; DIAGNOSTIC TEST ACCURACY STUDY; DIFFUSION TENSOR IMAGING; DISEASE CLASSIFICATION; FEMALE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; FUNCTIONAL NEUROIMAGING; GLOBAL GRAPH MEASURE; HUMAN; INTELLIGENCE QUOTIENT; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; NEUROANATOMY; NEUROIMAGING; NODAL GRAPH MEASURE; NODAL POWER SPECTRUM; PREDICTION; RADIAL BASIS FUNCTION KERNEL SUPPORT VECTOR MACHINE; RESTING STATE FUNCTIONAL CONNECTIVITY MATRIX; SENSITIVITY AND SPECIFICITY; SEX DIFFERENCE; STATISTICAL PARAMETERS; SUPPORT VECTOR MACHINE RECURSIVE FEATURE ELIMINATION ALGORITHM; VOXELWISE GLOBAL CONNECTIVITY; VOXELWISE REGIONAL HOMOGENEITY;

EID: 84865966989     PISSN: 16625137     EISSN: None     Source Type: Journal    
DOI: 10.3389/fnsys.2012.00059     Document Type: Article

References (84)

1. Almeida, L. G., Ricardo-Garcell, J., Prado, H., Barajas, L., Fernández-Bouzas, A., Avila, D., and Martínez, R. B. (2010). Reduced right frontal cortical thickness in children, adolescents and adults with ADHD and its correlation to clinical variables: a cross-sectional study. J. Psychiatr. Res. 44, 1214-1223.
2. Ambroise, C., and McLachlan, G. J. (2002). Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Natl. Acad. Sci. U.S.A. 99, 6562-6566.
3. American Psychiatric Association. (2000). Diagnostic, and Statistical Manual of Mental Disorders: DSM-IV-TR. Washington, DC: American Psychiatric Association.
4. Archer, T., Oscar-Berman, M., and Blum, K. (2011). Epigenetics in developmental disorder: ADHD and endophenotypes. J. Genet. Syndr. Gene Ther. 2, 1000104.
5. Biswal, B. B., Mennes, M., Zuo, X.-N., Gohel, S., Kelly, C., Smith, S. M., Beckmann, C. F., Adelstein, J. S., Buckner, R. L., Colcombe, S., Dogonowski, A.-M., Ernst, M., Fair, D., Hampson, M., Hoptman, M. J., Hyde, J. S., Kiviniemi, V. J., Kötter, R., Li, S.-J., Lin, C.-P., Lowe, M. J., Mackay, C., Madden, D. J., Madsen, K. H., Margulies, D. S., Mayberg, H. S., McMahon, K., Monk, C. S., Mostofsky, S. H., Nagel, B. J., Pekar, J. J., Peltier, S. J., Petersen, S. E., Riedl, V., Rombouts, S. A. R. B., Rypma, B., Schlaggar, B. L., Schmidt, S., Seidler, R. D., Siegle, G. J., Sorg, C., Teng, G.-J., Veijola, J., Villringer, A., Walter, M., Wang, L., Weng, X.-C., Whitfield-Gabrieli, S., Williamson, P., Windischberger, C., Zang, Y.-F., Zhang, H.-Y., Castellanos, F. X., and Milham, M. (2010). Toward discovery science of human brain function. Proc. Natl. Acad. Sci. U.S.A. 107, 4734-4739.
6. Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008). Fast unfolding of communities in large networks. J. Stat. Mech. Theor. Exp. 2008, P10008.
7. Brown, R. T., Freeman, W. S., Perrin, J. M., Stein, M. T., Amler, R. W., Feldman, H. M., Pierce, K., and Wolraich, M. L. (2001). Prevalence and assessment of attention-deficit/hyperactivity disorder in primary care settings. Pediatrics 107, E43.
8. Broyd, S. J., Demanuele, C., Debener, S., Helps, S. K., James, C. J., and Sonuga-Barke, E. J. S. (2009). Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci. Biobehav. Rev. 33, 279-296.
9. Buckner, R. L., Andrews-Hanna, J. R., and Schacter, D. L. (2008). The brain's default network: anatomy, function, and relevance to disease. Ann. N.Y. Acad. Sci. 1124, 1-38.
10. Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., Andrews-Hanna, J. R., Sperling, R. A., and Johnson, K. A. (2009). Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. J. Neurosci. 29, 1860-1873.
11. Bullmore, E., and Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186-198.
12. Bush, G. (2011). Cingulate, frontal, and parietal cortical dysfunction in attention-deficit/hyperactivity disorder. Biol. Psychiatry 69, 1160-1167.
13. Castellanos, F. X., Kelly, C., and Milham, M. P. (2009). The restless brain: attention-deficit hyperactivity disorder, resting-state functional connectivity, and intra-subject variability. Can. J. Psychiatry 54, 665-672.
14. Castellanos, F. X., Margulies, D. S., Kelly, C., Uddin, L. Q., Ghaffari, M., Kirsch, A., Shaw, D., Shehzad, Z., Di Martino, A., Biswal, B., Sonuga-Barke, E. J. S., Rotrosen, J., Adler, L. A., and Milham, M. P. (2008). Cingulate-precuneus interactions: a new locus of dysfunction in adult attention-deficit/hyperactivity disorder. Biol. Psychiatry 63, 332-337.
15. Castellanos, F. X., and Proal, E. (2009). Location, location, and thickness: volumetric neuroimaging of attention-deficit/hyperactivity disorder comes of age. J. Am. Acad. Child Adolesc. Psychiatry 48, 979-981.
16. Cherkasova, M. V., and Hechtman, L. (2009). Neuroimaging in attention-deficit hyperactivity disorder: beyond the frontostriatal circuitry. Can. J. Psychiatry 54, 651-664.
17. Christakou, A., Murphy, C. M., Chantiluke, K., Cubillo, A. I., Smith, A. B., Giampietro, V., Daly, E., Ecker, C., Robertson, D., Murphy, D. G., and Rubia, K. (2012). Disorder-specific functional abnormalities during sustained attention in youth with Attention Deficit Hyperactivity Disorder (ADHD) and with Autism. Mol. Psychiatry. (in press).
18. Cole, M. W., and Schneider, W. (2007). The cognitive control network: integrated cortical regions with dissociable functions. Neuroimage 37, 343-360.
19. Cortes, C., and Vapnik, V. (1995). Support-vector networks. Mach. Learn. 20, 273-297.
20. Cortese, S. (2012). The neurobiology and genetics of Attention-Deficit/Hyperactivity Disorder (ADHD): What every clinician should know. Eur. J. Paediatr. Neurol. (in press)
21. Craddock, R. C., Holtzheimer, P. E. 3rd, Hu, X. P., and Mayberg, H. S. (2009). Disease state prediction from resting state functional connectivity. Magn. Reson. Med. 62, 1619-1628.
22. Craddock, R. C., James, G. A., Holtzheimer, P. E. 3rd, Hu, X. P., and Mayberg, H. S. (2011). A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum. Brain Mapp. 33, 1914-1928.
23. Davatzikos, C., Shen, D., Gur, R. C., Wu, X., Liu, D., Fan, Y., Hughett, P., Turetsky, B. I., and Gur, R. E. (2005). Whole-brain morphometric study of schizophrenia revealing a spatially complex set of focal abnormalities. Arch. Gen. Psychiatry 62, 1218-1227.
24. De Martino, F., Valente, G., Staeren, N., Ashburner, J., Goebel, R., and Formisano, E. (2008). Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. Neuroimage 43, 44-58.
25. Depue, B. E., Burgess, G. C., Bidwell, L. C., Willcutt, E. G., and Banich, M. T. (2010). Behavioral performance predicts grey matter reductions in the right inferior frontal gyrus in young adults with combined type ADHD. Psychiatry Res. 182, 231-237.
26. Deshpande, G., Li, Z., Santhanam, P., Coles, C. D., Lynch, M. E., Hamann, S., and Hu, X. (2010). Recursive cluster elimination based support vector machine for disease state prediction using resting state functional and effective brain connectivity. PLoS ONE 5:e14277. doi: 10.1371/journal.pone.0014277
27. Duan, K.-B., Rajapakse, J. C., Wang, H., and Azuaje, F. (2005). Multiple SVM-RFE for gene selection in cancer classification with expression data. IEEE Trans. Nanobioscience 4, 228-234.
28. Ecker, C., Marquand, A., Mourão-Miranda, J., Johnston, P., Daly, E. M., Brammer, M. J., Maltezos, S., Murphy, C. M., Robertson, D., Williams, S. C., and Murphy, D. G. M. (2010). Describing the brain in autism in five dimensions-magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach. J. Neurosci. 30, 10612-10623.
29. Ellison-Wright, I., Ellison-Wright, Z., and Bullmore, E. (2008). Structural brain change in Attention Deficit Hyperactivity Disorder identified by meta-analysis. BMC Psychiatry 8, 51.
30. Emond, V., Joyal, C., and Poissant, H. (2009). Structural and functional neuroanatomy of attention-deficit hyperactivity disorder (ADHD). Encephale 35, 107-114.
31. Fair, D. A., Bathula, D., Nikolas, M. A., and Nigg, J. T. (2012). Distinct neu-ropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD. Proc. Natl. Acad. Sci. U.S.A. 109, 6769-6774.
32. Fair, D. A., Posner, J., Nagel, B. J., Bathula, D., Dias, T. G. C., Mills, K. L., Blythe, M. S., Giwa, A., Schmitt, C. F., and Nigg, J. T. (2010). Atypical default network connectivity in youth with attention-deficit/hyperactivity disorder. Biol. Psychiatry 68, 1084-1091.
33. Farahat, A. K., Ghodsi, A., and Kamel, M. S. (2011). "An efficient greedy method for unsupervised feature selection," in IEEE (Vancouver, BC), 161-170.
34. Faraone, S. V., and Mick, E. (2010). Molecular genetics of attention deficit hyperactivity disorder. Psychiatr. Clin. North Am. 33, 159-180.
35. Fassbender, C., Zhang, H., Buzy, W. M., Cortes, C. R., Mizuiri, D., Beckett, L., and Schweitzer, J. B. (2009). A lack of default network suppression is linked to increased distractibility in ADHD. Brain Res. 1273, 114-128.
36. Fischl, B., and Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc. Natl. Acad. Sci. U.S.A. 97, 11050-11055.
37. Fox, M. D., and Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700-711.
38. Froehlich, T. E., Epstein, J. N., Nick, T. G., Melguizo Castro, M. S., Stein, M. A., Brinkman, W. B., Graham, A. J., Langberg, J. M., and Kahn, R. S. (2011). Pharmacogenetic predictors of methylphenidate dose-response in attention-deficit/hyperactivity disorder. J. Am. Acad. Child Adolesc. Psychiatry 50, 1129-1139+e2.
39. Goldman, L. S., Genel, M., Bezman, R. J., and Slanetz, P. J. (1998). Diagnosis and treatment of attention-deficit/hyperactivity disorder in children and adolescents. Council on Scientific Affairs, American Medical Association. JAMA 279, 1100-1107.
40. Guyon, I. (2003). An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157-1182.
41. Guyon, I., Hur, A. B., Gunn, S., and Dror, G. (2005). "Result analysis of the NIPS 2003. feature selection challenge," in Advances in Neural Information Processing Systems, Vol. 17. eds L. Saul, Y. Weiss, and L. Bottou (Cambridge, MA: MIT Press), 545-552.
42. Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. J. Mach. Learn. 46, 389-422.
43. Hanke, M., Halchenko, Y. O., Haxby, J. V., and Pollmann, S. (2010). Statistical learning analysis in neuroscience: aiming for transparency. Front. Neurosci. 4:38. doi: 10.3389/neuro.01.007.2010
44. Hughes, G. (1968). On the mean accuracy of statistical pattern recognizers. Inf. Theor. IEEE Trans. 14, 55-63.
45. Ingalhalikar, M., Parker, D., Bloy, L., Roberts, T. P. L., and Verma, R. (2011). Diffusion based abnormality markers of pathology: toward learned diagnostic prediction of ASD. Neuroimage 57, 918-927.
46. Ivanov, I., Bansal, R., Hao, X., Zhu, H., Kellendonk, C., Miller, L., Sanchez-Pena, J., Miller, A. M., Chakravarty, M. M., Klahr, K., Durkin, K., Greenhill, L. L., and Peterson, B. S. (2010). Morphological abnormalities of the thalamus in youths with attention deficit hyperactivity disorder. Am. J. Psychiatry 167, 397-408.
47. Johannes, M., Brase, J. C., Fröhlich, H., Gade, S., Gehrmann, M., Fälth, M., Sültmann, H., and Beissbarth, T. (2010). Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients. Bioinformatics 26, 2136-2144.
48. Klöppel, S., Stonnington, C. M., Chu, C., Draganski, B., Scahill, R. I., Rohrer, J. D., Fox, N. C., Jack, C. R. Jr, Ashburner, J., and Frackowiak, R. S. J. (2008). Automatic classification of MR scans in Alzheimer's disease. Brain 131, 681-689.
49. Kohavi, R., and John, G. H. (1997). Wrappers for feature subset selection. Artif. Intell. 97, 273-324.
50. Kuntsi, J., Eley, T. C., Taylor, A., Hughes, C., Asherson, P., Caspi, A., and Moffitt, T. E. (2004). Cooccurrence of ADHD and low IQ has genetic origins. Am. J. Med. Genet. B Neuropsychiatr. Genet. 124B, 41-47.
51. Larisch, R., Sitte, W., Antke, C., Nikolaus, S., Franz, M., Tress, W., and Müller, H.-W. (2006). Striatal dopamine transporter density in drug naive patients with attention-deficit/hyperactivity disorder. Nucl. Med. Commun. 27, 267-270.
52. Linden, D. E. J. (2012). The challenges and promise of neuroimaging in psychiatry. Neuron 73, 8-22.
53. Liston, C., Malter Cohen, M., Teslovich, T., Levenson, D., and Casey, B. J. (2011). Atypical prefrontal connectivity in attention-deficit/hyperactivity disorder: pathway to disease or pathological end point? Biol. Psychiatry 69, 1168-1177.
54. Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI. Nature 453, 869-878.
55. McGough, J. J. (2012). Attention deficit hyperactivity disorder pharmacogenetics: the dopamine transporter and D4 receptor. Pharmacogenomics 13, 365-368.
56. Melgani, F., and Bruzzone, L. (2004). Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 42, 1778-1790.
57. Milham, M. P. (2012). Open neuroscience solutions for the connectome-wide association era. Neuron 73, 214-218.
58. Morbidity and Mortality Weekly Report. (2010). Increasing prevalence of parent-reported attention-deficit/hyperactivity disorder among children - United States, 2003 and 2007. MMWR Morb. Mortal. Wkly. Rep. 59, 1439-1443.
59. Narr, K. L., Woods, R. P., Lin, J., Kim, J., Phillips, O. R., Del'Homme, M., Caplan, R., Toga, A. W., McCracken, J. T., and Levitt, J. G. (2009). Widespread cortical thinning is a robust anatomical marker for attention-deficit/hyperactivity disorder. J. Am. Acad. Child Adolesc. Psychiatry 48, 1014-1022.
60. Nyman, E. S., Ogdie, M. N., Loukola, A., Varilo, T., Taanila, A., Hurtig, T., Moilanen, I. K., Loo, S. K., McGough, J. J., Järvelin, M.-R., Smalley, S. L., Nelson, S. F., and Peltonen, L. (2007). ADHD candidate gene study in a population-based birth cohort: association with DBH and DRD2. J. Am. Acad. Child Adolesc. Psychiatry 46, 1614-1621.
61. O'Toole, A. J., Jiang, F., Abdi, H., Pénard, N., Dunlop, J. P., and Parent, M. A. (2007). Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data. J. Cogn. Neurosci. 19, 1735-1752.
62. Pelham, W. E., Foster, E. M., and Robb, J. A. (2007). The economic impact of attention-deficit/hyperactivity disorder in children and adolescents. J. Pediatr. Psychol. 32, 711-727.
63. Power, T. J., Costigan, T. E., Leff, S. S., Eiraldi, R. B., and Landau, S. (2001). Assessing ADHD across settings: contributions of behavioral assessment to categorical decision making. J. Clin. Child Psychol. 30, 399-412.
64. Qiu, M., Ye, Z., Li, Q., Liu, G., Xie, B., and Wang, J. (2011). Changes of brain structure and function in ADHD children. Brain Topogr. 24, 243-252.
65. Raichle, M. E. (2001). Inaugural article: a default mode of brain function. Proc. Nat. Acad. Sci. 98, 676-682.
66. Rubinov, M., and Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059-1069.
67. Shaw, P., Lerch, J., Greenstein, D., Sharp, W., Clasen, L., Evans, A., Giedd, J., Castellanos, F. X., and Rapoport, J. (2006). Longitudinal mapping of cortical thickness and clinical outcome in children and adolescents with attention-deficit/hyperactivity disorder. Arch. Gen. Psychiatry 63, 540-549.
68. Shaw, P., Sharp, W. S., Morrison, M., Eckstrand, K., Greenstein, D. K., Clasen, L. S., Evans, A. C., and Rapoport, J. L. (2009). Psychostimulant treatment and the developing cortex in attention deficit hyperactivity disorder. Am. J. Psychiatry 166, 58-63.
69. Shi, P., Ray, S., Zhu, Q., and Kon, M. A. (2011). Top scoring pairs for feature selection in machine learning and applications to cancer outcome prediction. BMC Bioinformatics 12, 375.
70. Supekar, K., Menon, V., Rubin, D., Musen, M., and Greicius, M. D. (2008). Network analysis of intrinsic functional brain connectivity in Alzheimer's disease. PLoS Comput. Biol. 4:e1000100. doi: 10.1371/jour-nal.pcbi.1000100
71. Swanson, J. M., Sunohara, G. A., Kennedy, J. L., Regino, R., Fineberg, E., Wigal, T., Lerner, M., Williams, L., LaHoste, G. J., and Wigal, S. (1998). Association of the dopamine receptor D4 (DRD4) gene with a refined phenotype of attention deficit hyperactivity disorder (ADHD): a family-based approach. Mol. Psychiatry 3, 38-41.
72. Tomasi, D., and Volkow, N. D. (2012). Abnormal functional connectivity in children with attention-deficit/hyperactivity disorder. Biol. Psychiatry 71, 443-450.
73. Uddin, L. Q., Kelly, A. M. C., Biswal, B. B., Margulies, D. S., Shehzad, Z., Shaw, D., Ghaffari, M., Rotrosen, J., Adler, L. A., Castellanos, F. X., and Milham, M. P. (2008). Network homogeneity reveals decreased integrity of default-mode network in ADHD. J. Neurosci. Methods 169, 249-254.
74. Uddin, L. Q., Menon, V., Young, C. B., Ryali, S., Chen, T., Khouzam, A., Minshew, N. J., and Hardan, A. Y. (2011). Multivariate searchlight classification of structural magnetic resonance imaging in children and adolescents with autism. Biol. Psychiatry 70, 833-841.
75. Vafaie, H., and Imam, I. F. (1994). "Feature selection methods: genetic algorithms vs greedy-like search," in Proceedings Of The International Conference On Fuzzy And Intelligent Control Systems. (Louisville, KY).
76. Vapnik, V., and Lerner, A. (1963). Pattern recognition using generalized portrait method. Autom. Remote Control 24, 774-780.
77. Verkhliutov, V. M., Gapienko, G. V., Ushakov, V. L., Portnova, G. V., Verkhliutova, I. A., Anisimov, N. V., and Pirogov, I. A. (2009). MRI morphometry of the brain ventricles in patients with attention-deficit hyperactivity disorder. Zh. Vyssh. Nerv. Deiat. Im. I P Pavlova 59, 34-44.
78. Wang, L., Zhu, C., He, Y., Zang, Y., Cao, Q., Zhang, H., Zhong, Q., and Wang, Y. (2009). Altered small-world brain functional networks in children with attention-deficit/hyperactivity disorder. Hum. Brain Mapp. 30, 638-649.
79. Watts, D. J., and Strogatz, S. H. (1998). Collective dynamics of "small-world" networks. Nature 393, 440-442.
80. Wolraich, M., Brown, L., Brown, R. T., DuPaul, G., Earls, M., Feldman, H. M., Ganiats, T. G., Kaplanek, B., Meyer, B., Perrin, J., Pierce, K., Reiff, M., Stein, M. T., and Visser, S. (2011). ADHD: clinical practice guideline for the diagnosis, evaluation, and treatment of attention-deficit/hyperactivity disorder in children and adolescents. Pediatrics 128, 1007-1022.
81. Wolraich, M. L., Hannah, J. N., Pinnock, T. Y., Baumgaertel, A., and Brown, J. (1996). Comparison of diagnostic criteria for attention-deficit hyperactivity disorder in a county-wide sample. J. Am. Acad. Child Adolesc. Psychiatry 35, 319-324.
82. Zametkin, A. J., Nordahl, T. E., Gross, M., King, A. C., Semple, W. E., Rumsey, J., Hamburger, S., and Cohen, R. M. (1990). Cerebral glucose metabolism in adults with hyperactivity of childhood onset. N. Engl. J. Med. 323, 1361-1366.
83. Zang, Y., Jiang, T., Lu, Y., He, Y., and Tian, L. (2004). Regional homogeneity approach to fMRI data analysis. Neuroimage 22, 394-400.
84. Zang, Y.-F., He, Y., Zhu, C.-Z., Cao, Q.-J., Sui, M.-Q., Liang, M., Tian, L.-X., Jiang, T.-Z., and Wang, Y.-F. (2007). Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev. 29, 83-91.