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Bi-objective approach for computer-aided diagnosis of schizophrenia patients using fMRI data

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Abstract

Computer-aided diagnosis (CAD) of schizophrenia based on the analysis of brain images, captured using functional Magnetic Resonance Imaging (fMRI) technique, is an active area of research. The main problem lies in the identification of brain regions that contribute to differentiating between a healthy subject and a schizophrenia affected subject. The problem becomes complex due to the high dimensionality of the fMRI data on the one hand and the availability of data for only a small number of subjects on the other hand. In this paper, we propose a three-stage evolutionary based framework for feature selection. It comprises application of general linear model, followed by statistical hypothesis testing, and finally application of Non-dominated Sorting Genetic Algorithm (NSGA-II) to arrive at a small set of about fifty features. Experiments show that the feature set generated by the proposed approach yields accuracy as high as 99.5% in classifying fMRI dataset of healthy and schizophrenia subjects, and can identify the relevant brain regions that are affected in schizophrenia.

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Notes

  1. SPM Version 8: http://www.fil.ion.ucl.ac.uk/spm/software/spm8

References

  1. Åberg MB, Löken L, Wessberg J (2008) An evolutionary approach to multivariate feature selection for fmri pattern analysis. In: BIOSIGNALS, vol 2, pp 302–307

  2. Agarwal M, Kumar N, Vig L (2014) Non-additive multi-objective robot coalition formation. Expert Syst Appl 41(8):3736–3747

    Article  Google Scholar 

  3. Aliakbaryhosseinabadi S, Kamavuako EN, Jiang N, Farina D, Mrachacz-Kersting N (2017) Influence of dual-tasking with different levels of attention diversion on characteristics of the movement-related cortical potential. Brain Res 1674:10–19

    Article  Google Scholar 

  4. Arribas JI, Calhoun VD, Adali T (2010) Automatic bayesian classification of healthy controls, bipolar disorder, and schizophrenia using intrinsic connectivity maps from fmri data. IEEE Trans Biomed Eng 57(12):2850–2860

    Article  Google Scholar 

  5. Bellman RE (1961) Adaptive Control Processes: A Guided Tour. Princeton University Press, Princeton

    Book  MATH  Google Scholar 

  6. Bhattacharyya A (1946) On a measure of divergence between two multinomial populations. Sankhyā: Indian J Statist, 401–406

  7. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the fifth annual workshop on Computational learning theory. ACM, pp 144–152

  8. Caprihan A, Pearlson GD, Calhoun VD (2008) Application of principal component analysis to distinguish patients with schizophrenia from healthy controls based on fractional anisotropy measurements. Neuroimage 42(2):675–682

    Article  Google Scholar 

  9. Castro E, Martínez-Ramón M, Pearlson G, Sui J, Calhoun VD (2011) Characterization of groups using composite kernels and multi-source fmri analysis data: application to schizophrenia. Neuroimage 58(2):526–536

    Article  Google Scholar 

  10. Castro E, Gómez-Verdejo V, Martínez-Ramón M, Kiehl KA, Calhoun VD (2014) A multiple kernel learning approach to perform classification of groups from complex-valued fmri data analysis: application to schizophrenia. NeuroImage 87:1–17

    Article  Google Scholar 

  11. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2:27:1–27:27. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm

    Article  Google Scholar 

  12. Chen J, Xu Y, Zhang J, Liu Z, Xu C, Zhang K, Shen Y, Xu Q (2013) A combined study of genetic association and brain imaging on the daoa gene in schizophrenia. Amer J Med Gen Part B: Neuropsych Gen 162(2):191–200

    Article  Google Scholar 

  13. Chyzhyk D, Savio A, Graña M (2015) Computer aided diagnosis of schizophrenia on resting state fmri data by ensembles of elm. Neural Netw 68:23–33

    Article  Google Scholar 

  14. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evolut Comput 6(2):182–197

    Article  Google Scholar 

  15. Demirci O, Clark VP, Magnotta VA, Andreasen NC, Lauriello J, Kiehl KA, Pearlson GD, Calhoun VD (2008) A review of challenges in the use of fmri for disease classification/characterization and a projection pursuit application from a multi-site fmri schizophrenia study. Brain Imag Behav 2(3):207–226

    Article  Google Scholar 

  16. Du W, Calhoun VD, Li H, Ma S, Eichele T, Kiehl KA, Pearlson GD, Adali T (2012) High classification accuracy for schizophrenia with rest and task fmri data. Front Human Neurosci 6:145

    Article  Google Scholar 

  17. Ford J, Shen L, Makedon F, Flashman LA, Saykin AJ (2002) A combined structural-functional classification of schizophrenia using hippocampal volume plus fmri activation. In: Engineering in medicine and biology, 2002. 24th Annual conference and the annual fall meeting of the biomedical engineering society EMBS/BMES conference, 2002. Proceedings of the second joint, vol 1. IEEE, pp 48-49

  18. Ford J, Farid H, Makedon F, Flashman LA, McAllister TW, Megalooikonomou V, Saykin AJ (2003) Patient classification of fmri activation maps. In: Medical image computing and computer-assisted intervention-MICCAI 2003. Springer, pp 58–65

  19. Fortin FA, Grenier S, Parizeau M (2013) Generalizing the improved run-time complexity algorithm for non-dominated sorting. In: Proceedings of the 15th annual conference on Genetic and evolutionary computation. ACM, pp 615–622

  20. Frances A et al. (1994) Diagnostic and statistical manual of mental disorders. DSM-IV. American Psychiatric Association

  21. Friston KJ, Holmes AP, Worsley KJ, Poline JP, Frith CD, Frackowiak RS (1994) Statistical parametric maps in functional imaging: a general linear approach. Hum Brain Mapp 2(4):189–210

    Article  Google Scholar 

  22. Garrity AG, Pearlson GD, McKiernan K, Lloyd D, Kiehl KA, Calhoun VD (2007) Aberrant “default mode” functional connectivity in schizophrenia. Amer J Psych 164(3):450–457

    Article  Google Scholar 

  23. Gur RE, Gur RC (2010) Functional magnetic resonance imaging in schizophrenia. Dial Clin Neurosci 12(3):333

    Google Scholar 

  24. Honea RA, Meyer-Lindenberg A, Hobbs KB, Pezawas L, Mattay VS, Egan MF, Verchinski B, Passingham RE, Weinberger DR, Callicott JH (2008) Is gray matter volume an intermediate phenotype for schizophrenia? A voxel-based morphometry study of patients with schizophrenia and their healthy siblings. Biolog Psych 63(5):465–474

    Article  Google Scholar 

  25. Iragui VJ, Kutas M, Mitchiner MR, Hillyard SA (1993) Effects of aging on event-related brain potentials and reaction times in an auditory oddball task. Psychophysiology 30(1):10–22

    Article  Google Scholar 

  26. Juneja A, Rana B, Agrawal RK (2014) A novel approach for computer aided diagnosis of schizophrenia using auditory oddball functional mri. In: Proceedings of the 2014 Indian conference on computer vision graphics and image processing, ICVGIP ’14, pp 37:1–37:6

  27. Juneja A, Rana B, Agrawal R (2016) A combination of singular value decomposition and multivariate feature selection method for diagnosis of schizophrenia using fmri. Biomed Signal Process Control 27:122–133

    Article  Google Scholar 

  28. Juneja A, Rana B, Agrawal R (2017) fmri based computer aided diagnosis of schizophrenia using fuzzy kernel feature extraction and hybrid feature selection. Multimed Tools Appl, 1–27

  29. Kiehl KA, Liddle PF (2001) An event-related functional magnetic resonance imaging study of an auditory oddball task in schizophrenia. Schizophren Res 48 (2):159–171

    Article  Google Scholar 

  30. Kim DI, Mathalon D, Ford J, Mannell M, Turner J, Brown G, Belger A, Gollub R, Lauriello J, Wible C et al (2009) Auditory oddball deficits in schizophrenia: an independent component analysis of the fmri multisite function birn study. Schizophren Bull 35(1):67–81

    Article  Google Scholar 

  31. Kim J, Calhoun VD, Shim E, Lee JH (2016) Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. Neuroimage 124:127–146

    Article  Google Scholar 

  32. Koolschijn PCM, van Haren NE, Pol HEH, Kahn RS (2008) Hypothalamus volume in twin pairs discordant for schizophrenia. Eur Neuropsychopharmacol 18 (4):312–315

    Article  Google Scholar 

  33. Kriegeskorte N, Simmons WK, Bellgowan PS, Baker CI (2009) Circular analysis in systems neuroscience: the dangers of double dipping. Nat Neurosci 12 (5):535–540

    Article  Google Scholar 

  34. Lancaster JL, Woldorff MG, Parsons LM, Liotti M, Freitas CS, Rainey L, Kochunov PV, Nickerson D, Mikiten SA, Fox PT (2000) Automated talairach atlas labels for functional brain mapping. Hum Brain Mapp 10(3):120–131

    Article  Google Scholar 

  35. Lancaster JL, Laird AR, Eickhoff SB, Martinez MJ, Fox PM, Fox PT (2012) Automated regional behavioral analysis for human brain images. Front Neuroinform 6:23

    Article  Google Scholar 

  36. Linden DE, Prvulovic D, Formisano E, Völlinger M, Zanella FE, Goebel R, Dierks T (1999) The functional neuroanatomy of target detection: an fmri study of visual and auditory oddball tasks. Cereb Cortex 9(8):815–823

    Article  Google Scholar 

  37. Ma X, Chou CA, Sayama H, Chaovalitwongse WA (2016) Brain response pattern identification of fmri data using a particle swarm optimization-based approach. Brain Inform, 1–12

  38. Mitchell TM, Hutchinson R, Niculescu RS, Pereira F, Wang X, Just M, Newman S (2004) Learning to decode cognitive states from brain images. Mach Learn 57(1-2):145–175

    Article  MATH  Google Scholar 

  39. Niiniskorpi T, Åberg MB, Wessberg J (2009) Particle swarm feature selection for fmri pattern classification. In: BIOSIGNALS, pp 279–284

  40. O’Brien JL, Lister JJ, Fausto BA, Clifton GK, Edwards JD (2017) Cognitive training enhances auditory attention efficiency in older adults. Front Aging Neurosci 9:322

    Article  Google Scholar 

  41. Ogawa S, Lee TM, Kay AR, Tank DW (1990) Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci 87(24):9868–9872

    Article  Google Scholar 

  42. Riaz A, Asad M, Al-Arif SMR, Alonso E, Dima D, Corr P, Slabaugh G (2017) Fcnet: a convolutional neural network for calculating functional connectivity from functional mri. In: International workshop on connectomics in neuroimaging. Springer, pp 70–78

  43. Savio A, Graña M (2015) Local activity features for computer aided diagnosis of schizophrenia on resting-state fmri. Neurocomputing 164:154–161

    Article  Google Scholar 

  44. Shahamat H, Pouyan AA (2015) Feature selection using genetic algorithm for classification of schizophrenia using fmri data. J AI Data Min 3(1):30–37

    Google Scholar 

  45. Shi F, Liu Y, Jiang T, Zhou Y, Zhu W, Jiang J, Liu H, Liu Z (2007) Regional homogeneity and anatomical parcellation for fmri image classification: application to schizophrenia and normal controls. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 136–143

  46. Smart O, Burrell L (2015) Genetic programming and frequent itemset mining to identify feature selection patterns of ieeg and fmri epilepsy data. Eng Appl Artif Intell 39:198–214

    Article  Google Scholar 

  47. Ülker CC, Aytekin T (2013) Improving the performance of active voxel selection in the analysis of fmri data using genetic algorithms. In: Proceedings of the 6th Balkan conference in informatics. ACM, pp 129–136

  48. Ungar L, Nestor PG, Niznikiewicz MA, Wible CG, Kubicki M (2010) Color stroop and negative priming in schizophrenia: an fmri study. Psychiatry Res Neuroimaging 181(1):24–29

    Article  Google Scholar 

  49. Viviani R, Grön G, Spitzer M (2005) Functional principal component analysis of fmri data. Hum Brain Mapp 24(2):109–129

    Article  Google Scholar 

  50. Welch BL (1947) The generalization of student’s’ problem when several different population variances are involved. Biometrika 34(1/2):28–35

    Article  MathSciNet  MATH  Google Scholar 

  51. Williams M, Galvin K, O’Sullivan B, MacDonald C, Ching E, Turkheimer F, Howes O, Pearce R, Hirsch S, Maier M (2014) Neuropathological changes in the substantia nigra in schizophrenia but not depression. Eur Arch Psychiatry Clin Neurosci 264(4):285–296

    Article  Google Scholar 

  52. Wu L, Shen C, van den Hengel A (2017) Deep linear discriminant analysis on fisher networks: a hybrid architecture for person re-identification. Pattern Recogn 65:238–250

    Article  Google Scholar 

  53. Yoon JH, Minzenberg MJ, Raouf S, D’Esposito M, Carter CS (2013) Impaired prefrontal-basal ganglia functional connectivity and substantia nigra hyperactivity in schizophrenia. Biol Psych 74(2):122–129

    Article  Google Scholar 

  54. Zang Y, Jiang T, Lu Y, He Y, Tian L (2004) Regional homogeneity approach to fmri data analysis. Neuroimage 22(1):394–400

    Article  Google Scholar 

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Acknowledgements

We are thankful to Prof. R. K. Agrawal, School of Computer & Systems Sciences, Jawaharlal Nehru University, Delhi, India for his insightful comments. Indranath Chatterjee is thankful to the Council of Scientific & Industrial Research (CSIR), India for his research fellowship with grant number 09/045(1323)/2014-EMR-I. Naveen Kumar is thankful to University of Delhi for the research grant RC/2015/9677. Data used here for this study were downloaded from the Function BIRN Data Repository (http://fbirnbdr.birncommunity.org:8080/BDR/), i.e., Biomedical Informatics Research Network under the following support: for function data, U24-RR021992, Function BIRN and U24 GM104203, Bio-Informatics Research Network Coordinating Center (BIRN-CC). These data were obtained from the Function BIRN Data Repository, Project Accession Number 2007-BDR-6UHZ1.

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Authors and Affiliations

  1. Department of Computer Science, University of Delhi, Delhi, 110007, India

    Indranath Chatterjee & Naveen Kumar

  2. Department of Computer Science, Hans Raj College, University of Delhi, Delhi, 110007, India

    Manoj Agarwal & Bharti Rana

  3. Department of MRI, Saral Diagnostics, New Delhi, 110034, India

    Navin Lakhyani

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  1. Indranath Chatterjee
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Correspondence to Indranath Chatterjee.

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Chatterjee, I., Agarwal, M., Rana, B. et al. Bi-objective approach for computer-aided diagnosis of schizophrenia patients using fMRI data. Multimed Tools Appl 77, 26991–27015 (2018). https://doi.org/10.1007/s11042-018-5901-0

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