Prediction of pediatric unipolar depression using multiple neuromorphometric measurements: A pattern classification approach

Journal of Psychiatric Research

Volumn 62, Issue , 2015, Pages 84-91

  Wu, Mon Ju ^a   Wu, Hanjing Emily ^a   Mwangi, Benson ^a   Sanches, Marsal ^a   Selvaraj, Sudhakar ^a   Zunta Soares, Giovana B ^a   Soares, Jair C ^a  

^a UNIVERSITY OF TEXAS MEDICAL SCHOOL   (United States)

Author keywords

Machine learning; Neuroimaging; Pediatric unipolar depression; Support vector machine

Indexed keywords

ADOLESCENT; ARTICLE; BRAIN SIZE; CHILD; CLINICAL ARTICLE; CONTROLLED STUDY; CORTICAL THICKNESS (BRAIN); DEMOGRAPHY; DIAGNOSTIC TEST ACCURACY STUDY; FEMALE; HUMAN; INVESTIGATIVE PROCEDURES; MAJOR DEPRESSION; MALE; MENTAL PATIENT; MORPHOMETRICS; NEUROMORPHOMETRIC; PREDICTIVE VALIDITY; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; THALAMUS; VOLUMETRY; ANALYSIS OF VARIANCE; BRAIN; BRAIN MAPPING; DEPRESSIVE DISORDER; IMAGE PROCESSING; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY;

ADOLESCENT; ANALYSIS OF VARIANCE; BRAIN; BRAIN MAPPING; CHILD; DEPRESSIVE DISORDER; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; SUPPORT VECTOR MACHINE;

EID: 84924284045     PISSN: 00223956     EISSN: 18791379     Source Type: Journal    
DOI: 10.1016/j.jpsychires.2015.01.015     Document Type: Article

References (70)

1. Almeida L.G., Ricardo-Garcell J., Prado H., Barajas L., Fernández-Bouzas A., Ávila D., et al. Reduced right frontal cortical thickness in children, adolescents and adults with ADHD and its correlation to clinical variables: a cross-sectional study. JPsychiatr Res 2010, 44:1214-1223.
2. Anderson A., Douglas P.K., Kerr W.T., Haynes V.S., Yuille A.L., Xie J., et al. Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD. Neuroimage November 2014, 102:207-219. Part 1.
3. Atmaca M., Yildirim H., Ozdemir H., Tezcan E., Kursad Poyraz A. Volumetric MRI study of key brain regions implicated in obsessive-compulsive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2007, 31:46-52.
4. Bishop C.M. Pattern recognition and machine learning 2006, Springer, New York.
5. Brady E.U., Kendall P.C. Comorbidity of anxiety and depression in children and adolescents. Psychol Bull 1992, 111:244.
6. Bray S., Chang C., Hoeft F. Applications of multivariate pattern classification analyses in developmental neuroimaging of healthy and clinical populations. Front Hum Neurosci 2009, 3.
7. Brown T.A., Campbell L.A., Lehman C.L., Grisham J.R., Mancill R.B. Current and lifetime comorbidity of the DSM-IV anxiety and mood disorders in a large clinical sample. JAbnorm Psychol 2001, 110:585.
8. Caetano S.C., Fonseca M., Hatch J.P., Olvera R.L., Nicoletti M., Hunter K., et al. Medial temporal lobe abnormalities in pediatric unipolar depression. Neurosci Lett 2007, 427:142-147.
9. Caetano S.C., Silveira C.M., Kaur S., Nicoletti M., Hatch J.P., Brambilla P., et al. Abnormal corpus callosum myelination in pediatric bipolar patients. JAffect Disord 2008, 108:297-301.
10. Carballo J.J., Muñoz-Lorenzo L., Blasco-Fontecilla H., Lopez-Castroman J., García-Nieto R., Dervic K., et al. Continuity of depressive disorders from childhood and adolescence to adulthood: a naturalistic study in community mental health centers. Prim Care Companion CNS Disord 2011, 13.
11. Colby J.B., Rudie J.D., Brown J.A., Douglas P.K., Cohen M.S., Shehzad Z. Insights into multimodal imaging classification of ADHD. Front Syst Neurosci 2012, 6.
12. Costafreda S.G., Chu C., Ashburner J., Fu C.H. Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS One 2009, 4:e6353.
13. Craddock R.C., Holtzheimer P.E., Hu X.P., Mayberg H.S. Disease state prediction from resting state functional connectivity. Magn Reson Med 2009, 62:1619-1628.
14. Cui Y., Sachdev P.S., Lipnicki D.M., Jin J.S., Luo S., Zhu W., et al. Predicting the development of mild cognitive impairment: a new use of pattern recognition. Neuroimage 2012, 60:894-901.
15. De Martino F., Valente G., Staeren N., Ashburner J., Goebel R., Formisano E. Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. Neuroimage 2008, 43:44-58.
16. Desikan R.S., Ségonne F., Fischl B., Quinn B.T., Dickerson B.C., Blacker D., et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006, 31:968-980.
17. Ecker C., Marquand A., Mourão-Miranda J., Johnston P., Daly E.M., Brammer M.J., et al. Describing the brain in autism in five dimensions-magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach. JNeurosci 2010, 30:10612-10623.
18. Fischl B. FreeSurfer. Neuroimage 2012, 62:774-781.
19. Fischl B., Dale A.M. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A 2000, 97:11050-11055.
20. Fischl B., Salat D.H., 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.
21. Fu C.H., Mourao-Miranda J., Costafreda S.G., Khanna A., Marquand A.F., Williams S.C., et al. Pattern classification of sad facial processing: toward the development of neurobiological markers in depression. Biol Psychiatry 2008, 63:656-662.
22. Gaffrey M.S., Barch D.M., Singer J., Shenoy R., Luby J.L. Disrupted amygdala reactivity in depressed 4-to 6-year-old children. JAm Acad Child Adolesc Psychiatry 2013, 52:737-746.
23. Gilbert A.R., Moore G.J., Keshavan M.S., Paulson L.A.D., Narula V., Mac Master F.P., et al. Decrease in thalamic volumes of pediatric patients with obsessive-compulsive disorder who are taking paroxetine. Arch Gen Psychiatry 2000, 57:449-456.
24. Good C.D., Johnsrude I.S., Ashburner J., Henson R.N., Fristen K., Frackowiak R.S. Avoxel-based morphometric study of ageing in 465 normal adult human brains. Biomed Imaging 2002, 16. 2002 5th IEEE EMBS International Summer School on: IEEE.
25. Gray A., Abbena E., Salamon S. Modern differential geometry of curves and surfaces with Mathematica 1997, 373-380. Boca Raton, FL.
26. Guyon I., Elisseeff A. An introduction to variable and feature selection. JMach Learn Res 2003, 3:1157-1182.
27. Harrington R., Fudge H., Rutter M., Pickles A., Hill J. Adult outcomes of childhood and adolescent depression: I. Psychiatric status. Arch Gen Psychiatry 1990, 47:465-473.
28. Huttenlocher P.R. Morphometric study of human cerebral cortex development. Neuropsychologia 1990, 28:517-527.
29. Jacob L., Vert J.-P. Protein-ligand interaction prediction: an improved chemogenomics approach. Bioinformatics 2008, 24:2149-2156.
30. Johnston B., Mwangi B., Matthews K., Coghill D., Steele J. Predictive classification of individual magnetic resonance imaging scans from children and adolescents. Eur Child Adolesc Psychiatry 2013, 22:733-744.
31. Kim M.J., Hamilton J.P., Gotlib I.H. Reduced caudate gray matter volume in women with major depressive disorder. Psychiatry Res 2008, 164:114-122.
32. Konarski J.Z., McIntyre R.S., Kennedy S.H., Rafi-Tari S., Soczynska J.K., Ketter T.A. Volumetric neuroimaging investigations in mood disorders: bipolar disorder versus major depressive disorder. Bipolar Disord 2008, 10:1-37.
33. Kowalczyk A. Classification of anti-learnable biological and synthetic data 2007, 176-187. Knowledge Discovery in Databases: PKDD 2007, Springer.
34. Kowalczyk A., Chapelle O. An analysis of the anti-learning phenomenon for the class symmetric polyhedron. Algorithmic learning theory 2005, 78-91. Springer.
35. Lewinsohn P.M., Clarke G.N., Seeley J.R., Rohde P. Major depression in community adolescents: age at onset, episode duration, and time to recurrence. JAm Acad Child Adolesc Psychiatry 1994, 33:809-818.
36. Ma N., Li L., Shu N., Liu J., Gong G., He Z., et al. White matter abnormalities in first-episode, treatment-naive young adults with major depressive disorder. Am J Psychiatry 2007, 164:823-826.
37. MacMaster F.P., Kusumakar V. Hippocampal volume in early onset depression. BMC Med 2004, 2:2.
38. Matsuo K., Rosenberg D.R., Easter P.C., MacMaster F.P., Chen H.-H., Nicoletti M., et al. Striatal volume abnormalities in treatment-naive patients diagnosed with pediatric major depressive disorder. JChild Adolesc Psychopharmacol 2008, 18:121-131.
39. McCauley S.R., Wilde E.A., Merkley T.L., Schnelle K.P., Bigler E.D., Hunter J.V., et al. Patterns of cortical thinning in relation to event-based prospective memory performance three months after moderate to severe traumatic brain injury in children. Dev Neuropsychol 2010, 35:318-332.
40. Mwangi B., Ebmeier K.P., Matthews K., Steele J.D. Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder. Brain 2012, 135:1508-1521.
41. Mwangi B., Hasan K.M., Soares J.C. Prediction of individual subject's age across the human lifespan using diffusion tensor imaging: a machine learning approach. Neuroimage 2013, 75:2013, 58-67.
42. Mwangi B., Spiker D., Zunta-Soares G.B., Soares J.C. Prediction of pediatric bipolar disorder using neuroanatomical signatures of the amygdala. Bipolar Disord Nov.2014, 16(7):713-721.
43. Mwangi B., Tian T.S., Soares J.C. Areview of feature reduction techniques in neuroimaging. Neuroinformatics April 2014, 12(2):229-244.
44. Nolan C.L., Moore G.J., Madden R., Farchione T., Bartoi M., Lorch E., et al. Prefrontal cortical volume in childhood-onset major depression: preliminary findings. Arch Gen Psychiatry 2002, 59:173-179.
45. Nouretdinov I., Costafreda S.G., Gammerman A., Chervonenkis A., Vovk V., Vapnik V., et al. Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression. Neuroimage 2011, 56:809-813.
46. Olson I.R., Plotzker A., Ezzyat Y. The enigmatic temporal pole: a review of findings on social and emotional processing. Brain 2007, 130:1718-1731.
47. Orrù G., Pettersson-Yeo W., Marquand A.F., Sartori G., Mechelli A. Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev 2012, 36:1140-1152.
48. Palaniyappan L., Mallikarjun P., Joseph V., White T.P., Liddle P.F. Folding of the prefrontal cortex in schizophrenia: regional differences in gyrification. Biol Psychiatry 2011, 69:974-979.
49. Peng X., Lin P., Zhang T., Wang J. Extreme learning machine-based classification of ADHD using brain structural MRI data. PLoS One 2013, 8:e79476.
50. Pienaar R., Fischl B., Caviness V., Makris N., Grant P.E. Amethodology for analyzing curvature in the developing brain from preterm to adult. Int J Imaging Syst Technol 2008, 18:42-68.
51. Prasad K.M., Goradia D., Eack S., Rajagopalan M., Nutche J., Magge T., et al. Cortical surface characteristics among offspring of schizophrenia subjects. Schizophr Res 2010, 116:143-151.
52. Price J.L., Drevets W.C. Neural circuits underlying the pathophysiology of mood disorders. Trends Cogn Sci 2012, 16:61-71.
53. Rao U., Chen L.-A. Characteristics, correlates, and outcomes of childhood and adolescent depressive disorders. Dialogues Clin Neurosci 2009, 11:45.
54. Rao U., Chen L.-A., Bidesi A.S., Shad M.U., Thomas M.A., Hammen C.L. Hippocampal changes associated with early-life adversity and vulnerability to depression. Biol Psychiatry 2010, 67:357-364.
55. Rimol L.M., Hartberg C.B., Nesvåg R., Fennema-Notestine C., Hagler D.J., Pung C.J., et al. Cortical thickness and subcortical volumes in schizophrenia and bipolar disorder. Biol Psychiatry 2010, 68:41-50.
56. Roadknight C., Aickelin U., Qiu G., Scholefield J., Durrant L. Supervised learning and anti-learning of colorectal cancer classes and survival rates from cellular biology parameters. Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on: IEEE 2012, 797-802.
57. Ronan L., Pienaar R., Williams G., Bullmore E., Crow T.J., Roberts N., et al. Intrinsic curvature: a marker of millimeter-scale tangential cortico-cortical connectivity?. Int J Neural Syst 2011, 21:351-366.
58. Rosso I.M., Cintron C.M., Steingard R.J., Renshaw P.F., Young A.D., Yurgelun-Todd D.A. Amygdala and hippocampus volumes in pediatric major depression. Biol Psychiatry 2005, 57:21-26.
59. Schwaighofer A. SVM toolbox 2001.
60. Shad M.U., Muddasani S., Rao U. Gray matter differences between healthy and depressed adolescents: a voxel-based morphometry study. JChild Adolesc Psychopharmacol 2012, 22:190-197.
61. Sled J.G., Zijdenbos A.P., Evans A.C. Anonparametric method for automatic correction of intensity nonuniformity in MRI data. Med Imaging IEEE Trans 1998, 17:87-97.
62. Sun D., van Erp T.G., Thompson P.M., Bearden C.E., Daley M., Kushan L., et al. Elucidating a magnetic resonance imaging-based neuroanatomic biomarker for psychosis: classification analysis using probabilistic brain atlas and machine learning algorithms. Biol Psychiatry 2009, 66:1055-1060.
63. Theodoridis S., Pikrakis A., Koutroumbas K., Cavouras D. Introduction to pattern recognition: a Matlab approach 2010, Academic Press.
64. Van Essen D., Drury H. Structural and functional analyses of human cerebral cortex using a surface-based atlas. JNeurosci 1997, 17:7079-7102.
65. Vapnik V.N. An overview of statistical learning theory. Neural Netw IEEE Trans 1999, 10:988-999.
66. Wang J., Li W., Miao W., Dai D., Hua J., He H. Age estimation using cortical surface pattern combining thickness with curvatures. Med Biol Eng Comput 2014, 52:331-341.
67. Westman E., Aguilar C., Muehlboeck J.-S., Simmons A. Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment. Brain Topogr 2013, 26:9-23.
68. Winkler A.M., Kochunov P., Blangero J., Almasy L., Zilles K., Fox P.T., et al. Cortical thickness or grey matter volume? the importance of selecting the phenotype for imaging genetics studies. Neuroimage 2010, 53:1135-1146.
69. Wolosin S.M., Richardson M.E., Hennessey J.G., Denckla M.B., Mostofsky S.H. Abnormal cerebral cortex structure in children with ADHD. Hum Brain Mapp 2009, 30:175-184.
70. Zeng L.-L., Shen H., Liu L., Wang L., Li B., Fang P., et al. Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain 2012, 135:1498-1507.