Region Role Detection in Autism Spectrum Disorder using Graph Theoretical Approaches

Authors

  • Geetha Ramani R  Department of Information Science and Technology, Anna University, Chennai, Tamilnadu, India
  • Sivaselvi K  Department of Information Science and Technology, Anna University, Chennai, Tamilnadu, India

Keywords:

Autism Spectrum Disorder, Connectome, Magnetic Resonance Imaging, Centrality

Abstract

Connectome analysis has received increased attention in the field of neurological research. Graph theoretical measures are extensively applied to understand the intricate structure of the brain. In this work, resting state functional connectome of Autism Spectrum Disorder and Typically Developing brain are investigated to reveal the influential regions in the brain. Centrality measures are involved in the detection of global region role identification and they are compared against functional cartography. Then, the modular region role is determined from both individual functional connectome and group averaged connectome of both Autism Spectrum Disorder and Typically Developing subjects. The modular roles are compared using supervised association rule mining. The major alterations are identified mostly in visual and frontal regions of Autism Spectrum Disorder functional connectome.

References

  1. Wing, L 1997, ‘The autistic spectrum’, Lancet, vol. 350, no.9093, pp. 1761-1766
  2. P. Szatmari, S. Georgiades, E. Duku, T.A. Bennett, S. Bryson, E. Fombonne, et. al., ‘Developmental trajectories of symptom severity and adaptive functioning in an inception cohort of preschool children with autism spectrum disorder’, JAMA psychiatry, vol. 72(3), 2015,  pp. 276-283
  3. C. Gillberg, ‘Autism and related behaviours’, Journal of Intellectual Disability Research, vol. 37(4), 1993, pp. 343-372
  4. C. Lord, M. Rutter, and A. Couteur, ‘Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders’, Journal of autism and developmental disorders, vol. 24(5), 1994, pp. 659-685
  5. K. Gotham, A. Pickles,and C. Lord, ‘Trajectories of autism severity in children using standardized ADOS scores’, Pediatrics, vol. 130(5), 2012,pp. e1278-e1284
  6. C. Lord, S. Risi, L. Lambrecht, E.H. Cook, B.L. Leventhal, P.C. DiLavore, et. al., ‘The Autism Diagnostic Observation Schedule—Generic: A standard measure of social and communication deficits associated with the spectrum of autism’, Journal of autism and developmental disorders,  vol. 30(3), 2000, pp. 205-223
  7. C. Lord, and R.M. Jones, ‘Annual Research Review: Re?thinking the classification of autism spectrum disorders’, Journal of Child Psychology and Psychiatry, vol. 53(5), 2012, pp. 490-509
  8. M.P. van den Heuvel and O. Sporns, ‘Network hubs in the human brain’, Trends in cognitive sciences, vol. 17(12), 2013, pp. 683-696
  9. L.E. Libero, T.P. DeRamus, A.C. Lahti, G. Deshpande, and R.K. Kana, ‘Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates’, Cortex, vol. 66, 2015,  pp. 46-59
  10. G. Deshpande, L.E. Libero, K.R. Sreenivasan, H.D. Deshpande, and R.K. Kana, ‘Identification of neural connectivity signatures of autism using machine learning’, vol.7, 2013.
  11. C. Ecker, V. Rocha-Rego,  P. Johnston, J. Mourao-Miranda, A. Marquand, E.M. Daly, et. al. ‘Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach’, Neuroimage, vol. 49(1), 2010, pp. 44-56
  12. Plitt, M, Barnes, KA & Marlin, A 2014,  ‘Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards’, Neuroimage : Clinical, vol.7, pp. 359-366
  13. Y. Zhou, F. Yu and T. Duong, ‘Multiparameteric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning’, PLOS ONE, vol. 9(6), 2014.
  14. Rudie, JD, Brown, JA, Beck-Pancer, D, Hernandez, LM, Dennis, EL, Thompson, PM, Bookheimer, SY & Dapretto, M 2013, ‘Altered Functional and Structural Brain Network Organization in Autism’, Neuroimage : Clinical,  vol. 2, pp. 79-94.
  15. Wechsler, D 1991, Wechsler Intelligence Scale for Children, San Antonio, TX.
  16. Wechsler, 1999, Wechsler Abbreviated Scale of Intelligence, San Antonio, TX.
  17. American Psychiatric Association 2000, Task Force on DSM-IV, Diagnostic and statistical manual of mental disorders: DSM-IV-TR., American Psychiatric Pub.
  18. Woolrich, MW, Jbabdi, S, Patenaude, B, Chappell, M, Makni, S, . Behrens, T, Beckmann, C, Jenkinson, M & Smith, SM  2009, ‘Bayesian analysis of neuroimaging data in FSL’, Neuroimage, vol. 45, no.S1, pp. 173-186.
  19. Smith, SM 2002, ‘Fast robust automated brain extraction’, Human Brain Mapping, vol. 17, no. 3,  pp. 143-155.
  20. Cox, RW 1996, ‘AFNI: software for analysis and visualization of functional magnetic resonance neuroimages’, Computers Biomedical Research, vol. 29, no. 3,  pp. 162-173.
  21. M. Rubinov, and O. Sporns, ‘Complex network measures of brain connectivity: uses and interpretations’, Neuroimage, vol. 5 (3), 2010, pp. 1059-1069
  22. Hwang, K, Hallquist, MN & Luna, B 2013, ‘The Development of Hub Architecture in the Human Functional Brain Network’, Cerebral Cortex, vol. 23, no. 10,  pp. 2380-2393.
  23. Ruhnau, B 2000, ‘Eigenvector centrality-a node centrality’, Social Networks, vol. 22, no. 4,  pp. 357-365.
  24. K.E. Joyce, P.J., Laurienti, J.H., Burdette and S. Hayasaka, ‘A new measure of centrality for brain networks’, PLoS One, 2010,  e12200
  25. Jain, AK, Murty, MN & Flynn, PJ 1999, ‘Data Clustering: A Review’, ACM computing surveys (CSUR), vol. 31, no. 3,  pp. 264-323.
  26. Newman, MEJ 2006, ‘Finding community structure in networks using eigenvector of matrices’, Phys Rev E, vol. 74:036104.
  27. Bonacich, P 1972, ‘Factoring and weighting approaches to status scores and clique identification’, The Journal of Mathematical Sociology, vol. 2,  no.1, pp. 113-120.
  28. Barabasi, AL & Albert, R 1999, ‘Emergence of scaling in random networks’, Science, vol. 286,  pp. 509-512.
  29. Bassett, DS, Bullmore, E, Verchinski, BA, Mattay, VS, Weinberger, DR & Meyer-Lindenberg, AA 2008, ‘Hierarchical organization of human cortical networks in health and Schizophrenia’, The Journal of Neuroscience : the official journal of the Society for Neuroscience, vol. 28, no. 37,  pp. 9239-9248.
  30. Guimera, R & Amaral, LAN 2005a, ‘Cartography of complex networks: modules and universal roles’, Journal of Statistical Mechanics,  pp. 1-17.
  31. Guimera, R & Amaral, LA 2005b, ‘ Functional cartography of complex metabolic networks’, Nature, vol. 433,  pp. 895-900.
  32. Hartigan, JA 1975, Clustering Algorithms, John Wiley & Sons Inc, United States of  America.
  33. Hartwell, LH, Hopfield, JJ, Leibler, S & Murray, AW 1999, ‘From molecular to modular cell biology’, Nature,  pp. 47-52.
  34. Dichter, GS 2012, ‘Functional Magnetic Resonance Imaging of Autism Spectrum Disorders’, Dialogues in Clinical Neuroscience,
    vol. 14, no. 3,  pp. 319-351.
  35. Lainhart, JE 2015, ‘Brain Imaging Research in Autism Spectrum Disorders: In Search of Neuropathology and Health across the Lifespan’, Current opinion in psychiatry, vol. 28, no. 2,  pp. 76-82.
  36. Leekam, SR, Nieto, C, Libby, SJ, Wing, L & Gould, J 2007, ‘Describing the sensory abnormalities of children and adults with autism’, Journal of autism and developmental disorders, vol. 37, no.5,  pp. 894-910.
  37. Hua, X, Thompson, PM, Leow, AD, Madsen, SK, Caplan, R, Alger, JR, Neill, JO, Joshi, K, Smalley, SL, Toga, AW & Levitt, JG 2013, ‘Brain growth rate abnormalities visualized in adolescents with autism’, Human Brain Mapping, vol. 34, no.2, pp. 425-436.
  38. Hardan, AY, Libove, RA, Keshavan, MS, Melhem, NM & Minshew, NJ 2009, ‘A preliminary longitudinal magnetic resonance imaging study of brain volume and cortical thickness in autism’, Biological Psychiatry, vol. 66, no. 4, pp. 320-326.
  39. Redcay, E, Moran JM, Mavros, PL, Tager-Flusberg, H, Gabrieli, JDE & Whitfield-Gabrieli, S 2013, ‘Intrinsic functional network organization in high-functioning adolescents with autism spectrum disorder’, Frontiers in Human Neuroscience, vol. 7, pp.573.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2017-10-31

Issue

Volume 3, Issue 6, September-October-2017

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Geetha Ramani R, Sivaselvi K, " Region Role Detection in Autism Spectrum Disorder using Graph Theoretical Approaches, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 6, pp.413-427, September-October-2017.