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Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Accurate detection of depression at an individual level using structural magnetic resonance imaging (sMRI) remains a challenge. Brain volumetric changes at a structural level appear to have importance in depression biomarkers studies. An automated algorithm is developed to select brain sMRI volumetric features for the detection of depression.

Methods

A feature selection (FS) algorithm called degree of contribution (DoC) is developed for selection of sMRI volumetric features. This algorithm uses an ensemble approach to determine the degree of contribution in detection of major depressive disorder. The DoC is the score of feature importance used for feature ranking. The algorithm involves four stages: feature ranking, subset generation, subset evaluation, and DoC analysis. The performance of DoC is evaluated on the Duke University Multi-site Imaging Research in the Analysis of Depression sMRI dataset. The dataset consists of 115 brain sMRI scans of 88 healthy controls and 27 depressed subjects. Forty-four sMRI volumetric features are used in the evaluation.

Results

The DoC score of forty-four features was determined as the accuracy threshold (Acc_Thresh) was varied. The DoC performance was compared with that of four existing FS algorithms. At all defined Acc_Threshs, DoC outperformed the four examined FS algorithms for the average classification score and the maximum classification score.

Conclusion

DoC has a good ability to generate reduced-size subsets of important features that could yield high classification accuracy. Based on the DoC score, the most discriminant volumetric features are those from the left-brain region.

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References

  1. Kipli K, Kouzani A, Williams L (2013) Towards automated detection of depression from brain structural magnetic resonance images. Neuroradiology, pp. 1–18. doi:10.1007/s00234-013-1139-8

  2. Kipli K, Kouzani AZ, Joordens M (2012) Computer-aided detection of depression from magnetic resonance images. In: International conference on complex medical engineering (CME), 2012 ICME, 1–4 July 2012. pp 500–505. doi:10.1109/iccme.2012.6275745

  3. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182

    Google Scholar 

  4. Daelemans W, Hoste V, Meulder F, Naudts B (2003) Combined optimization of feature selection and algorithm parameters in machine learning of language. In: Lavrač N, Gamberger D, Blockeel H, Todorovski L (eds) Machine learning: ECML 2003, vol 2837. Lecture notes in computer science. Springer, Berlin, pp 84–95. doi:10.1007/978-3-540-39857-8_10

  5. Ruiz R, Riquelme J, Aguilar-Ruiz J (2005) Heuristic search over a ranking for feature selection. In: Cabestany J, Prieto A, Sandoval F (eds) Computational intelligence and bioinspired systems, vol 3512. Lecture Notes in Computer Science. Springer, Berlin, pp 742–749. doi:10.1007/11494669_91

  6. Costafreda SG, Chu C, Ashburner J, Fu CHY (2009) Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS ONE 4(7):e6353. doi:10.1371/journal.pone.0006353

    Article  PubMed Central  PubMed  Google Scholar 

  7. Mwangi B, Ebmeier KP, Matthews K, Douglas Steele J (2012) Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder. Brain 135(5):1508–1521. doi:10.1093/brain/aws084

    Article  PubMed  Google Scholar 

  8. Fu CHY, Mourao-Miranda J, Costafreda SG, Khanna A, Marquand AF, Williams SCR, Brammer MJ (2008) Pattern classification of sad facial processing: toward the development of neurobiological markers in depression. Biol Psychiatry 63(7):656–662. doi:10.1016/j.biopsych.2007.08.020

    Article  PubMed  Google Scholar 

  9. Marquand AF, Mourão-Miranda J, Brammer MJ, Cleare AJ, Fu CHY (2008) Neuroanatomy of verbal working memory as a diagnostic biomarker for depression. NeuroReport 19(15):1507–1511

    Article  PubMed  Google Scholar 

  10. Chyzhyk D, Graña M, Savio A, Maiora J (2012) Hybrid dendritic computing with kernel-LICA applied to Alzheimer’s disease detection in MRI. Neurocomputing 75(1):72–77. doi:10.1016/j.neucom.2011.02.024

    Article  Google Scholar 

  11. De Martino F, Valente G, Staeren N, Ashburner J, Goebel R, Formisano E (2008) Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. NeuroImage 43(1):44–58. doi:10.1016/j.neuroimage.2008.06.037

    Article  PubMed  Google Scholar 

  12. Craddock RC, Holtzheimer PE, Hu XP, Mayberg HS (2009) Disease state prediction from resting state functional connectivity. Magn Reson Med 62(6):1619–1628. doi:10.1002/mrm.22159

    Article  PubMed Central  PubMed  Google Scholar 

  13. Zeng L-L, Shen H, Liu L, Wang L, Li B, Fang P, Zhou Z, Li Y, Hu D (2012) Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. Brain 135(5):1498–1507. doi:10.1093/brain/aws059

    Article  PubMed  Google Scholar 

  14. Nouretdinov I, Costafreda SG, Gammerman A, Chervonenkis A, Vovk V, Vapnik V, Fu CHY (2011) Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression. NeuroImage 56(2):809–813. doi:10.1016/j.neuroimage.2010.05.023

    Article  PubMed  Google Scholar 

  15. Gong Q, Wu Q, Scarpazza C, Lui S, Jia Z, Marquand A, Huang X, McGuire P, Mechelli A (2011) Prognostic prediction of therapeutic response in depression using high-field MR imaging. NeuroImage 55(4):1497–1503. doi:10.1016/j.neuroimage.2010.11.079

    Article  PubMed  Google Scholar 

  16. Mwangi B, Matthews K, Steele JD (2012) Prediction of illness severity in patients with major depression using structural MR brain scans. J Magn Reson Imaging 35(1):64–71. doi:10.1002/jmri.22806

    Article  PubMed  Google Scholar 

  17. Bao F, Ghosh S, Giard J, Parsey R, Klein A (2011) Brain shape analysis for predicting treatment remission in major depressive disorder. Paper presented at the 41st annual meeting for the society for neuroscience

  18. Kipli K, Kouzani AZ, Hamid IRA (2013) Investigating machine learning techniques for detection of depression using structural MRI volumetric features. Int J Biosci Biochem Bioinform 3(5):444–448

    Google Scholar 

  19. Kipli K, Kouzani AZ, Joordens M (2013) Evaluation of feature selection algorithms for detection of depression from brain sMRI scans. In: Complex medical engineering (CME), 2013 ICME international conference on, 25–28 May 2013. pp 64–69. doi:10.1109/ICCME.2013.6548213

  20. Kipli K, Kouzani AZ (2013) An algorithm for determination of rank and degree of contribution of sMRI volumetric features in depression detection. Paper presented at the 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC’13), Osaka, Japan

  21. Saeys Y, Abeel T, de Peer Y (2008) Towards robust feature selection techniques. In: Proceedings of Benelearn, pp 45–46

  22. Saeys Y, Inza I, Larrañaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507–2517

  23. Kabir MM, Shahjahan M, Murase K (2012) A new hybrid ant colony optimization algorithm for feature selection. Expert Syst Appl 39(3):3747–3763. doi:10.1016/j.eswa.2011.09.073

  24. Whitney AW (1971) A direct method of nonparametric measurement selection. IEEE Trans Comput C–20(9):1100–1103. doi:10.1109/T-C.1971.223410

    Article  Google Scholar 

  25. Marill T, Green DM (1963) On the effectiveness of receptors in recognition systems. IEEE Trans Inf Theory 9(1):11–17

    Article  Google Scholar 

  26. Stearns SD (1976) On selecting features for pattern classifiers. In: Proceedings of the 3rd international conference on pattern recognition (ICPR 1976), pp 71–75

  27. Kittler J (1978) An introduction to feature extraction, pp 41–60

  28. Pudil P, Novovičová J, Kittler J (1994) Floating search methods in feature selection. Pattern Recognit Lett 15(11):1119–1125. doi:10.1016/0167-8655(94)90127-9

  29. Peng H, Fulmi L, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238. doi:10.1109/TPAMI.2005.159

    Article  PubMed  Google Scholar 

  30. Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1–3):389–422. doi:10.1023/a:1012487302797

    Article  Google Scholar 

  31. Tibshirani R (2011) Regression shrinkage and selection via the lasso: a retrospective. J R Stat Soc Ser B (Statistical Methodology) 73(3):273–282

    Article  Google Scholar 

  32. Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B (Statistical Methodology) 67(2):301–320

    Article  Google Scholar 

  33. McIntosh AR, Lobaugh NJ (2004) Partial least squares analysis of neuroimaging data: applications and advances. NeuroImage 23:S250–S263

    Article  PubMed  Google Scholar 

  34. Mwangi B, Tian T, Soares J (2013) A review of feature reduction techniques in neuroimaging. Neuroinform, pp 1–16. doi:10.1007/s12021-013-9204-3

  35. Belanche L, González F (2011) Review and evaluation of feature selection algorithms in synthetic problems. arXiv preprint arXiv:11012320

  36. Huan L, Lei Y (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17(4):491–502. doi:10.1109/tkde.2005.66

    Article  Google Scholar 

  37. Santana LA, Canuto AP (2012) Bi-objective genetic algorithm for feature selection in ensemble systems. In: Villa AP, Duch W, Érdi P, Masulli F, Palm G (eds) Artificial neural networks and machine learning—ICANN 2012, vol 7552. Lecture notes in computer Science. Springer, Berlin, pp 701–709. doi:10.1007/978-3-642-33269-2_88

  38. Abeel T, Helleputte T, Van de Peer Y, Dupont P, Saeys Y (2010) Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics 26(3):392–398. doi:10.1093/bioinformatics/btp630

    Article  CAS  PubMed  Google Scholar 

  39. Saeys Y, Abeel T, Peer Y (2008) Robust feature selection using ensemble feature selection techniques. In: Daelemans W, Goethals B, Morik K (eds) Machine learning and knowledge discovery in databases, vol 5212. Lecture notes in computer science. Springer, Berlin, pp 313–325. doi:10.1007/978-3-540-87481-2_21

  40. Ben Brahim A, Limam M (2013) Robust ensemble feature selection for high dimensional data sets. In: High performance computing and simulation (HPCS), 2013 international conference on. IEEE, pp 151–157

  41. Yang P, Liu W, Zhou BB, Chawla S, Zomaya AY (2013) Ensemble-based wrapper methods for feature selection and class imbalance learning. In: Advances in knowledge discovery and data mining. Springer, Berlin, pp 544–555

  42. Das R, Turkoglu I, Sengur A (2009) Effective diagnosis of heart disease through neural networks ensembles. Expert Syst Appl 36(4):7675–7680. doi:10.1016/j.eswa.2008.09.013

  43. Netzer M, Millonig G, Osl M, Pfeifer B, Praun S, Villinger J, Vogel W, Baumgartner C (2009) A new ensemble-based algorithm for identifying breath gas marker candidates in liver disease using ion molecule reaction mass spectrometry. Bioinformatics 25(7):941–947. doi:10.1093/bioinformatics/btp093

    Article  CAS  PubMed  Google Scholar 

  44. Tsymbal A, Puuronen S, Patterson DW (2003) Ensemble feature selection with the simple Bayesian classification. Inf Fusion 4(2):87–100. doi:10.1016/S1566-2535(03)00004-6

  45. Xu J, Sun L, Gao Y, Xu T (2013) An ensemble feature selection technique for cancer recognition. Biomed Mater Eng 23:S1053–S1060

    Google Scholar 

  46. Tsai C-F, Hsiao Y-C (2010) Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches. Decision Support Syst 50(1):258–269. doi:10.1016/j.dss.2010.08.028

  47. Brendel M, Zaccarelli R, Devillers L (2010) A quick sequential forward floating feature selection algorithm for emotion detection from speech. In: INTERSPEECH, pp 1157–1160

  48. NIRL Imaging Database http://nirlarc.duhs.duke.edu/nirle/

  49. Zhao Z, Taylor WD, Styner M, Steffens DC, Krishnan KRR, MacFall JR (2008) Hippocampus shape analysis and late-life depression. PLoS ONE 3(3):e1837. doi:10.1371/journal.pone.0001837

    Article  PubMed Central  PubMed  Google Scholar 

  50. Greenberg DL, Payne ME, MacFall JR, Steffens DC, Krishnan RR (2008) Hippocampal volumes and depression subtypes. Psychiatry Res Neuroimaging 163(2):126–132. doi:10.1016/j.pscychresns.2007.12.009

    Article  Google Scholar 

  51. Taylor WD, MacFall JR, Steffens DC, Payne ME, Provenzale JM, Krishnan KRR (2003) Localization of age-associated white matter hyperintensities in late-life depression. Prog Neuro-Psychopharmacol Biol Psychiatry 27(3):539–544. doi:10.1016/s0278-5846(02)00358-5

    Article  Google Scholar 

  52. Steffens DC, Tupler LA, Krishnan KRR (1998) Magnetic resonance imaging signal hypointensity and iron content of putamen nuclei in elderly depressed patients. Psychiatry Res Neuroimaging 83(2):95–103. doi:10.1016/s0925-4927(98)00032-8

    Article  CAS  Google Scholar 

  53. Lai T-J, Payne ME, Byrum CE, Steffens DC, Krishnan KRR (2000) Reduction of orbital frontal cortex volume in geriatric depression. Biol Psychiatry 48(10):971–975. doi:10.1016/s0006-3223(00)01042-8

    Article  CAS  PubMed  Google Scholar 

  54. Steffens DC, Byrum CE, McQuoid DR, Greenberg DL, Payne ME, Blitchington TF, MacFall JR, Krishnan KRR (2000) Hippocampal volume in geriatric depression. Biol Psychiatry 48(4):301–309. doi:10.1016/s0006-3223(00)00829-5

    Article  CAS  PubMed  Google Scholar 

  55. Tupler LA, Krishnan KRR, McDonald WM, Dombeck CB, D’Souza S, Steffens DC (2002) Anatomic location and laterality of MRI signal hyperintensities in late-life depression. J Psychosom Res 53(2):665–676. doi:10.1016/s0022-3999(02)00425-7

    Article  PubMed  Google Scholar 

  56. Lee S-H, Payne ME, Steffens DC, McQuoid DR, Lai T-J, Provenzale JM, Krishnan KRR (2003) Subcortical lesion severity and orbitofrontal cortex volume in geriatric depression. Biol Psychiatry 54(5):529–533. doi:10.1016/s0006-3223(03)00063-5

    Article  PubMed  Google Scholar 

  57. Steffens DC, Trost WT, Payne ME, Hybels CF, MacFall JR (2003) Apolipoprotein E genotype and subcortical vascular lesions in older depressed patients and control subjects. Biol Psychiatry 54(7):674–681. doi:10.1016/s0006-3223(02)01782-1

    Article  CAS  PubMed  Google Scholar 

  58. Steffens DC, McQuoid DR, Welsh-Bohmer KA, Krishnan KRR (2003) Left orbital frontal cortex volume and performance on the benton visual retention test in older depressives and controls. Neuropsychopharmacology 28(12):2179–2183

    PubMed  Google Scholar 

  59. Taylor WD, Steffens DC, McQuoid DR, Payne ME, Lee S-H, Lai T-J, Krishnan KRR (2003) Smaller orbital frontal cortex volumes associated with functional disability in depressed elders. Biol Psychiatry 53(2):144–149. doi:10.1016/s0006-3223(02)01490-7

    Article  PubMed  Google Scholar 

  60. Taylor WD, MacFall JR, Payne ME, McQuoid DR, Steffens DC, Provenzale JM, Krishnan RR (2005) Greater MRI lesion volumes in elderly depressed subjects than in control subjects. Psychiatry Res Neuroimaging 139(1):1–7. doi:10.1016/j.pscychresns.2004.08.004

    Article  Google Scholar 

  61. Taylor WD, Steffens DC, Payne ME, MacFall JR, Marchuk DA, Svenson IK, Krishnan KRR (2005) Influence of serotonin transporter promoter region polymorphisms on hippocampal volumes in late-life depression. Arch Gen Psychiatry 62(5):537–544. doi:10.1001/archpsyc.62.5.537

    Article  CAS  PubMed  Google Scholar 

  62. Bae JN, MacFall JR, Krishnan KRR, Payne ME, Steffens DC, Taylor WD (2006) Dorsolateral prefrontal cortex and anterior cingulate cortex white matter alterations in late-life depression. Biol Psychiatry 60(12):1356–1363. doi:10.1016/j.biopsych.2006.03.052

    Article  PubMed  Google Scholar 

  63. Chen PS, McQuoid DR, Payne ME, Steffens DC (2006) White matter and subcortical gray matter lesion volume changes and late-life depression outcome: a 4-year magnetic resonance imaging study. Int Psychogeriatr 18(3):445–456. doi:10.1017/s1041610205002796

    Article  PubMed  Google Scholar 

  64. MacFall JR, Taylor WD, Rex DE, Pieper S, Payne ME, McQuoid DR, Steffens DC, Kikinis R, Toga AW, Krishnan KRR (2005) Lobar distribution of lesion volumes in late-life depression: the biomedical informatics research network (BIRN). Neuropsychopharmacology 31(7):1500–1507

    Article  PubMed  Google Scholar 

  65. Potter GG, Blackwell AD, McQuoid DR, Payne ME, Steffens DC, Sahakian BJ, Welsh-Bohmer KA, Krishnan KRR (2007) Prefrontal white matter lesions and prefrontal task impersistence in depressed and nondepressed elders. Neuropsychopharmacology 32(10):2135–2142

    Article  PubMed  Google Scholar 

  66. Taylor WD, MacFall JR, Payne ME, McQuoid DR, Steffans DC, Provenzale JM, Krishnan KRR (2007) Orbitofrontal cortex volume in late life depression: influence of hyperintense lesions and genetic polymorphisms. Psychol Med 37(12):1763–1773. doi:10.1017/S0033291707000128

    PubMed  Google Scholar 

  67. Steffens DC, Taylor WD, McQuoid DR, Krishnan KRR (2008) Short/long heterozygotes at 5HTTLPR and white matter lesions in geriatric depression. Int J Geriatr Psychiatry 23(3):244–248. doi:10.1002/gps.1869

    Article  PubMed  Google Scholar 

  68. Taylor WD, Zchner S, McQuoid DR, Payne ME, MacFall JR, Steffens DC, Speer MC, Krishnan KRR (2008) The brain-derived neurotrophic factor val66met polymorphism and cerebral white matter hyperintensities in late-life depression. Am J Geriatr Psychiatry 16(4):263–271. doi:10.1097/JGP.0b013e3181591c30

    Article  PubMed  Google Scholar 

  69. Taylor WD, Zhao Z, Ashley-Koch A, Payne ME, Steffens DC, Krishnan RR, Hauser E, Macfall JR (2011) Fiber tract-specific white matter lesion severity findings in late-life depression and by AGTR1 A1166C genotype. Hum Brain Mapp. doi:10.1002/hbm.21445

  70. Pan C-C, McQuoid DR, Taylor WD, Payne ME, Ashley-Koch A, Steffens DC (2009) Association analysis of the COMT/MTHFR genes and geriatric depression: an MRI study of the putamen. Int J Geriatr Psychiatry 24(8):847–855. doi:10.1002/gps.2206

  71. Qiu A, Taylor WD, Zhao Z, MacFall JR, Miller MI, Key CR, Payne ME, Steffens DC, Krishnan KRR (2009) APOE related hippocampal shape alteration in geriatric depression. NeuroImage 44(3):620–626. doi:10.1016/j.neuroimage.2008.10.010

    Article  PubMed Central  PubMed  Google Scholar 

  72. Taylor WD, Steffens DC, Ashley-Koch A, Payne ME, MacFall JR, Potocky CF, Krishnan KRR (2010) Angiotensin receptor gene polymorphisms and 2-year change in hyperintense lesion volume in men. Mol Psychiatry 15(8):816–822. http://www.nature.com/mp/journal/v15/n8/suppinfo/mp200926s1.html

  73. Steffens DC, Krishnan KRR (1998) Structural neuroimaging and mood disorders: recent findings, implications for classification, and future directions. Biol Psychiatry 43(10):705–712. doi:10.1016/s0006-3223(98)00084-5

    Article  CAS  PubMed  Google Scholar 

  74. Payne ME, Fetzer DL, MacFall JR, Provenzale JM, Byrum CE, Krishnan KRR (2002) Development of a semi-automated method for quantification of MRI gray and white matter lesions in geriatric subjects. Psychiatry Res Neuroimaging 115(1–2):63–77. doi:10.1016/s0925-4927(02)00009-4

    Article  Google Scholar 

  75. Frank E, Hall M, Trigg L, Holmes G, Witten IH (2004) Data mining in bioinformatics using Weka. Bioinformatics 20(15):2479–2481. doi:10.1093/bioinformatics/bth261

    Article  CAS  PubMed  Google Scholar 

  76. Somol P, Novovičová J (2008) Evaluating the stability of feature selectors that optimize feature subset cardinality. In: Vitoria Lobo N, Kasparis T, Roli F et al (eds) Structural, syntactic, and statistical pattern Recognition, vol 5342. Lecture notes in computer science. Springer, Berlin, pp 956–966. doi:10.1007/978-3-540-89689-0_99

  77. Han Y, Yu L (2012) A variance reduction framework for stable feature selection. Stat Anal Data Min 5(5):428–445. doi:10.1002/sam.11152

    Article  Google Scholar 

  78. Yu L, Liu H (2004) Efficient feature selection via analysis of relevance and redundancy. J Mach Learn Res 5:1205–1224

    Google Scholar 

  79. Dernoncourt D, Hanczar B, Zucker J-D (2014) Analysis of feature selection stability on high dimension and small sample data. Comput Stat Data Anal 71:681–693. doi:10.1016/j.csda.2013.07.012

  80. Kalousis A, Prados J, Hilario M (2007) Stability of feature selection algorithms: a study on high-dimensional spaces. Knowl Inf Syst 12(1):95–116

    Article  Google Scholar 

  81. Bermejo P, Gámez JA, Puerta JM (2014) Speeding up incremental wrapper feature subset selection with Naive Bayes classifier. Knowl-Based Syst 55:140–147. doi:10.1016/j.knosys.2013.10.016

  82. Bermejo P, Gámez JA, Puerta JM (2011) A GRASP algorithm for fast hybrid (filter-wrapper) feature subset selection in high-dimensional datasets. Pattern Recognit Lett 32(5):701–711. doi:10.1016/j.patrec.2010.12.016

  83. Bermejo P, de la Ossa L, Gámez JA, Puerta JM (2012) Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking. Knowl-Based Syst 25(1):35–44. doi:10.1016/j.knosys.2011.01.015

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Acknowledgments

We thank the Duke University Neuropsychiatric Imaging Research Laboratory for making the Multisite Imaging Research in the Analysis of Depression (MIRIAD) MRI data available. The first author would like to acknowledge the funding from Ministry of Education Malaysia (MoE) and Universiti Malaysia Sarawak (UNIMAS).

Conflict of interest

There is no conflict of interest.

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Authors and Affiliations

  1. School of Engineering, Deakin University, Waurn Ponds, VIC, 3216, Australia

    Kuryati Kipli & Abbas Z. Kouzani

  2. Department of Electronic, Faculty of Engineering, Universiti Malaysia Sarawak, 94300, Kota Samarahan, Sarawak, Malaysia

    Kuryati Kipli

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Correspondence to Kuryati Kipli.

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Table 5 Volumetric features descriptions
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Kipli, K., Kouzani, A.Z. Degree of contribution (DoC) feature selection algorithm for structural brain MRI volumetric features in depression detection. Int J CARS 10, 1003–1016 (2015). https://doi.org/10.1007/s11548-014-1130-9

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