Multiclass classification for the differential diagnosis on the ADHD subtypes using recursive feature elimination and hierarchical extreme learning machine: Structural MRI study

PLoS ONE

Volumn 11, Issue 8, 2016, Pages

  Qureshi, Muhammad Naveed Iqbal ^a   Min, Beomjun ^a   Jo, Hang Joon ^b   Lee, Boreom ^a  

^a Gwangju Institute of Science and Technology (GIST)   (South Korea)

^b MAYO CLINIC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ATTENTION DEFICIT DISORDER; ATTENTION DEFICIT DISORDER COMBINED TYPE; ATTENTION DEFICIT DISORDER INATTENTIVE TYPE; BRAIN CORTEX; BRAIN SIZE; CHILD; CHILD DEVELOPMENT; CLINICAL FEATURE; CONTROLLED STUDY; CORTICAL THICKNESS (BRAIN); DIAGNOSTIC TEST ACCURACY STUDY; DIAGNOSTIC VALUE; DIFFERENTIAL DIAGNOSIS; DISEASE CLASSIFICATION; FEMALE; FRONTAL LOBE; HIERARCHICAL EXTREME LEARNING MACHINE; HUMAN; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; RECURSIVE FEATURE ELIMINATION SUPPORT VECTOR MACHINE ALGORITHM; ADOLESCENT; ATTENTION DEFICIT DISORDER WITH HYPERACTIVITY; BRAIN; CLASSIFICATION; DIAGNOSTIC IMAGING; PATHOLOGY; PROCEDURES; RANDOMIZED CONTROLLED TRIAL; SOFTWARE; SUPPORT VECTOR MACHINE;

ADOLESCENT; ATTENTION DEFICIT DISORDER WITH HYPERACTIVITY; BRAIN; CHILD; DIAGNOSIS, DIFFERENTIAL; FEMALE; HUMANS; MACHINE LEARNING; MAGNETIC RESONANCE IMAGING; MALE; NEUROIMAGING; SOFTWARE; SUPPORT VECTOR MACHINE;

EID: 84983401426     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0160697     Document Type: Article

References (58)

1. Polanczyk GV, Salum GA, Sugaya LS, Caye A, Rohde LA. Annual Research Review: A meta-analysis of the worldwide prevalence of mental disorders in children and adolescents. Journal of Child Psychology and Psychiatry. 2015; 56(3):345-365. doi: 10.1111/jcpp.12381 PMID: 25649325
2. Guilherme Polanczyk BLHJB Mauricio Silva de Lima, Rohde LA. The Worldwide Prevalence of ADHD: A Systematic Review and Metaregression Analysis. American Journal of Psychiatry. 2007; 164 (6):942-948. doi: 10.1176/ajp.2007.164.6.942 PMID: 17541055
3. Diamond A. Attention-deficit disorder (attention-deficit/ hyperactivity disorder without hyperactivity): A neurobiologically and behaviorally distinct disorder from attention-deficit/hyperactivity disorder (with hyperactivity). Development and Psychopathology. 2005;p. 807-825. doi: 10.1017/S0954579405050388 PMID: 16262993
4. Shaw P, Eckstrand K, Sharp W, Blumenthal J, Lerch JP, Greenstein D, et al. Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation. Proceedings of the National Academy of Sciences. 2007; 104(49):19649-19654. doi: 10.1073/pnas.0707741104
5. American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-5. 5th ed.; 2013.
6. Castellanos TR F Xavier. Neuroscience of attention-deficit/hyperactivity disorder: the search for endophenotypes. Nature Reviews Neuroscience. 2002; 3:617-628. doi: 10.1038/nrn896 PMID: 12154363
7. Solanto MV. The Predominantly Inattentive Subtype of Attention-Deficit/Hyperactivity Disorder. CNS Spectr. 2000; 5:45-51. PMID: 18268458
8. Willcutt EG, Nigg JT, Pennington BF, Solanto MV, Rohde LA, Tannock R, et al. Validity of DSM-IV attention deficit/hyperactivity disorder symptom dimensions and subtypes. Journal of abnormal psychology. 2012; 121(4):991-1010. doi: 10.1037/a0027347 PMID: 22612200
9. Pineda D, Restrepo M, Sarmiento R, Gutierrez J, Vargas S, Quiroz Y, et al. Statistical analyses of structural magnetic resonance imaging of the head of the caudate nucleus in Colombian children with attention-deficit hyperactivity disorder. Journal of Child Neurology. 2002; 17(2):97-105. doi: 10.1177/088307380201700202 PMID: 11952084
10. Miller SR, Miller CJ, Bloom JS, Hynd GW, Craggs JG. Right Hemisphere Brain Morphology, Attention-Deficit Hyperactivity Disorder (ADHD) Subtype, and Social Comprehension. Journal of Child Neurology. 2006; 21(2):139-144. doi: 10.1177/08830738060210021901 PMID: 16566878
11. Zou Q, Zeng J, Cao L, Ji R. A novel features ranking metric with application to scalable visual and bioinformatics data classification. Neurocomputing. 2016; 173:346-354. doi: 10.1016/j.neucom.2014.12.123
12. Li D, Ju Y, Zou Q. Protein Folds Prediction with Hierarchical Structured SVM. Current Proteomics. 2016; 13(2):79-85. doi: 10.2174/157016461302160514000940
13. Lin C, Zou Y, Qin J, Liu X, Jiang Y, Ke C, et al. Hierarchical classification of protein folds using a novel ensemble classifier. PloS one. 2013; 8(2):e56499. doi: 10.1371/journal.pone.0056499 PMID: 23437146
14. Klouppel S, Abdulkadir A, Jr CRJ, Koutsouleris N, Mourão-Miranda J, Vemuri P. Diagnostic neuroimaging across diseases. NeuroImage. 2012; 61(2):457-463. doi: 10.1016/j.neuroimage.2011.11.002
15. Castro E, Gómez-Verdejo V, Martínez-Ramón M, Kiehl KA, Calhoun VD. A multiple kernel learning approach to perform classification of groups from complex-valued fMRI data analysis: Application to schizophrenia. NeuroImage. 2014; 87:1-17. doi: 10.1016/j.neuroimage.2013.10.065 PMID: 24225489
16. Schnack HG, Nieuwenhuis M, van Haren NEM, Abramovic L, Scheewe TW, Brouwer RM, et al. Can structural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects. NeuroImage. 2014; 84:299-306. doi: 10.1016/j.neuroimage.2013.08.053 PMID: 24004694
17. Tang J, Deng C, Huang GB. Extreme Learning Machine for Multilayer Perceptron. Neural Networks and Learning Systems, IEEE Transactions on. 2015;PP( 99):1-1.
18. Li S, Wang S, Li X, Li Q, Li X. Abnormal surface morphology of the central sulcus in children with attention-deficit/hyperactivity disorder. Frontiers in Neuroanatomy. 2015; 9(114).
19. Peng X, Lin P, Zhang T, Wang J. Extreme Learning Machine-Based Classification of ADHD Using Brain Structural MRI Data. PLoS ONE. 2013; 8(11):e79476. doi: 10.1371/journal.pone.0079476 PMID: 24260229
20. Grant JA, Duerden EG, Courtemanche J, Cherkasova M, Duncan GH, Rainville P. Cortical thickness, mental absorption and meditative practice: Possible implications for disorders of attention. Biological Psychology. 2013; 92(2):275-281. doi: 10.1016/j.biopsycho.2012.09.007 PMID: 23046904
21. Hyatt CJ, Haney-Caron E, Stevens MC. Cortical Thickness and Folding Deficits in Conduct-Disordered Adolescents. Biological Psychiatry. 2012; 72(3):207-214. Oxytocin and Social Bonds in Development doi: 10.1016/j.biopsych.2011.11.017 PMID: 22209639
22. Dai D, Wang J, Hua J, He H. Classification of ADHD children through multimodal Magnetic Resonance Imaging. Frontiers in Systems Neuroscience. 2012; 6(63). doi: 10.3389/fnsys.2012.00063 PMID: 22969710
23. Guyon I, Weston J, Barnhill S, Vapnik V. Gene Selection for Cancer Classification using Support Vector Machines. Machine Learning. 2002; 46(1):389-422. doi: 10.1023/A:1012487302797
24. Klein A, Tourville J. 101 labeled brain images and a consistent human cortical labeling protocol. Frontiers in Neuroscience. 2012; 6(171). doi: 10.3389/fnins.2012.00171 PMID: 23227001
25. Colby JB, Rudie JD, Brown JA, Douglas PK, Cohen MS, Shehzad Z. Insights into multimodal imaging classification of ADHD. Frontiers in Systems Neuroscience. 2012; 6(59). doi: 10.3389/fnsys.2012.00059 PMID: 22912605
26. Yusta SC. Different metaheuristic strategies to solve the feature selection problem. Pattern Recognition Letters. 2009; 30(5):525-534. doi: 10.1016/j.patrec.2008.11.012
27. Dale A, Fischl B, Sereno MI. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction. NeuroImage. 1999; 9(2):179-194. doi: 10.1006/nimg.1998.0395 PMID: 9931268
28. Fischl B, Sereno MI, Dale A. Cortical Surface-Based Analysis: II: Inflation, Flattening, and a Surface-Based Coordinate System. NeuroImage. 1999; 9(2):195-207. doi: 10.1006/nimg.1998.0396 PMID: 9931269
29. Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences. 2000; 97(20):11050-11055. doi: 10.1073/pnas.200033797
30. Fischl B, Liu A, Dale AM. Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex. IEEE Medical Imaging. 2001; 20(1):70-80. doi: 10.1109/42.906426 PMID: 11293693
31. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002; 33:341-355. doi: 10.1016/S0896-6273(02)00569-X PMID: 11832223
32. Cox RW. AFNI: Software for Analysis and Visualization of Functional Magnetic Resonance Neuroimages. Comput Biomed Res. 1996; 29(3):162-173. doi: 10.1006/cbmr.1996.0014 PMID: 8812068
33. Huang GB, Zhu QY, Siew CK. Extreme learning machine: Theory and applications. Neurocomputing. 2006;70(1-3):489-501. Neural NetworksSelected Papers from the 7th Brazilian Symposium on Neural Networks (SBRN'04)7th Brazilian Symposium on Neural Networks.
34. Termenon M, Graña M, Barrós-Loscertales A, Ávila C. Extreme Learning Machines for Feature Selection and Classification of Cocaine Dependent Patients on Structural MRI Data. Neural Processing Letters. 2013; 38(3):375-387. doi: 10.1007/s11063-013-9277-x
35. Zhang W, Shen H, Ji Z, Meng G, Wang B. Identification of Mild Cognitive Impairment Using Extreme Learning Machines Model. In: Huang DS, Jo KH, Hussain A, editors. Intelligent Computing Theories and Methodologies: 11th International Conference, ICIC 2015, Fuzhou, China, August 20-23, 2015, Proceedings, Part II. Cham: Springer International Publishing; 2015. p. 589-600.
36. Cortes C, Vapnik V. Support-Vector Networks. Mach Learn. 1995; 20(3):273-297. doi: 10.1023/A:1022627411411
37. Bellman RE. Adaptive control processes - A guided tour. Princeton, New Jersey, U.S.A.: Princeton University Press; 1961.
38. Gori I, Giuliano A, Muratori F, Saviozzi I, Oliva P, Tancredi R, et al. Gray matter alterations in young children with autism spectrum disorders: comparing morphometry at the voxel and regional level. Journal of Neuroimaging. 2015; 25(6):866-874. doi: 10.1111/jon.12280 PMID: 26214066
39. Golland P, Fischl B. Permutation Tests for Classification: Towards Statistical Significance in Image-Based Studies. In: Taylor C, Noble JA, editors. Information Processing in Medical Imaging: 18th International Conference, IPMI 2003, Ambleside, UK, July 20-25, 2003. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg; 2003. p. 330-341.
40. Cohen J. A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement. 1960; 20(1):37. doi: 10.1177/001316446002000104
41. Wang D, Miao D, Blohm G. Multi-class motor imagery EEG decoding for brain-computer interfaces. Frontiers in Neuroscience. 2012; 6(151).
42. Hart H, Chantiluke K, Cubillo AI, Smith AB, Simmons A, Brammer MJ, et al. Pattern classification of response inhibition in ADHD: Toward the development of neurobiological markers for ADHD. Human Brain Mapping. 2014; 35(7):3083-3094. doi: 10.1002/hbm.22386 PMID: 24123508
43. Anderson dos Santos Siqueira WECLAR Claudinei Eduardo Biazoli Junior, Sato JR. Abnormal Functional Resting-State Networks in ADHD: Graph Theory and Pattern Recognition Analysis of fMRI Data. BioMed Research International. 2014;.
44. Solmaz B, Dey S, Rao AR, Shah M. ADHD classification using bag of words approach on network features; 2012.
45. Deshpande G, Wang P, Rangaprakash D, Wilamowski B. Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification From Functional Magnetic Resonance Imaging Data. Cybernetics, IEEE Transactions on. 2015;( 99):1-1.
46. Fair D, Nigg JT, Iyer S, Bathula D, Mills KL, Dosenbach NU, et al. Distinct Neural Signatures Detected for ADHD Subtypes After Controlling for Micro-Movements in Resting State Functional Connectivity MRI Data. Frontiers in Systems Neuroscience. 2013; 6(80). doi: 10.3389/fnsys.2012.00080 PMID: 23382713
47. Lim L, Marquand A, Cubillo AA, Smith AB, Chantiluke K, Simmons A, et al. Disorder-Specific Predictive Classification of Adolescents with Attention Deficit Hyperactivity Disorder (ADHD) Relative to Autism Using Structural Magnetic Resonance Imaging. PLoS ONE. 2013; 8(5):e63660. doi: 10.1371/journal.pone.0063660 PMID: 23696841
48. Durston S. A review of the biological bases of ADHD: what have we learned from imaging studies? Mental retardation and developmental disabilities research reviews. 2003; 9(3):184-195. doi: 10.1002/mrdd.10079 PMID: 12953298
49. Cubillo A, Halari R, Smith A, Taylor E, Rubia K. A review of fronto-striatal and fronto-cortical brain abnormalities in children and adults with Attention Deficit Hyperactivity Disorder (ADHD) and new evidence for dysfunction in adults with ADHD during motivation and attention. Cortex. 2012; 48(2):194-215. doi: 10.1016/j.cortex.2011.04.007 PMID: 21575934
50. Ehlis AC, Bähne CG, Jacob CP, Herrmann MJ, Fallgatter AJ. Reduced lateral prefrontal activation in adult patients with attention-deficit/hyperactivity disorder (ADHD) during a working memory task: a functional near-infrared spectroscopy (fNIRS) study. Journal of psychiatric research. 2008; 42(13):1060-1067. doi: 10.1016/j.jpsychires.2007.11.011 PMID: 18226818
51. Makris N, Biederman J, Valera EM, Bush G, Kaiser J, Kennedy DN, et al. Cortical thinning of the attention and executive function networks in adults with attention-deficit/hyperactivity disorder. Cerebral Cortex. 2007; 17(6):1364-1375. doi: 10.1093/cercor/bhl047 PMID: 16920883
52. Rubia K, Overmeyer S, Taylor E, Brammer M, Williams SC, Simmons A, et al. Hypofrontality in attention deficit hyperactivity disorder during higher-order motor control: a study with functional MRI. American Journal of Psychiatry. 1999;. doi: 10.1176/ajp.156.6.891 PMID: 10360128
53. Rubia K, Smith AB, Brammer MJ, Toone B, Taylor E. Abnormal brain activation during inhibition and error detection in medication-naive adolescents with ADHD. American Journal of Psychiatry. 2005; 162(6):1067-1075. doi: 10.1176/appi.ajp.162.6.1067 PMID: 15930054
54. Ströhle A, Stoy M, Wrase J, Schwarzer S, Schlagenhauf F, Huss M, et al. Reward anticipation and outcomes in adult males with attention-deficit/hyperactivity disorder. Neuroimage. 2008; 39(3):966-972. doi: 10.1016/j.neuroimage.2007.09.044 PMID: 17996464
55. Fernández-Jaén A, López-Martín S, Albert J, Fernández-Mayoralas DM, Fernández-Perrone AL, Tapia DQ, et al. Cortical thinning of temporal pole and orbitofrontal cortex in medication-naïve children and adolescents with ADHD. Psychiatry Research: Neuroimaging. 2014; 224(1):8-13. doi: 10.1016/j. pscychresns.2014.07.004 PMID: 25085707
56. Mwangi B, Tian TS, Soares JC. A review of feature reduction techniques in neuroimaging. Neuroinformatics. 2014; 12(2):229-244. doi: 10.1007/s12021-013-9204-3 PMID: 24013948
57. Valera EM, Faraone SV, Murray KE, Seidman LJ. Meta-analysis of structural imaging findings in attention-deficit/hyperactivity disorder. Biological psychiatry. 2007; 61(12):1361-1369. doi: 10.1016/j. biopsych.2006.06.011 PMID: 16950217
58. Sowell ER, Thompson PM, Leonard CM, Welcome SE, Kan E, Toga AW. Longitudinal Mapping of Cortical Thickness and Brain Growth in Normal Children. The Journal of Neuroscience. 2004; 24(38):8223-8231. doi: 10.1523/JNEUROSCI.1798-04.2004 PMID: 15385605