Skip to main content

Advertisement

SpringerLink
Log in

ConnectViz: Accelerated Approach for Brain Structural Connectivity Using Delaunay Triangulation

  • Original Research Article
  • Published:
Interdisciplinary Sciences: Computational Life Sciences Aims and scope Submit manuscript

Abstract

Stroke is a cardiovascular disease with high mortality and long-term disability in the world. Normal functioning of the brain is dependent on the adequate supply of oxygen and nutrients to the brain complex network through the blood vessels. Stroke, occasionally a hemorrhagic stroke, ischemia or other blood vessel dysfunctions can affect patients during a cerebrovascular incident. Structurally, the left and the right carotid arteries, and the right and the left vertebral arteries are responsible for supplying blood to the brain, scalp and the face. However, a number of impairment in the function of the frontal lobes may occur as a result of any decrease in the flow of the blood through one of the internal carotid arteries. Such impairment commonly results in numbness, weakness or paralysis. Recently, the concepts of brain’s wiring representation, the connectome, was introduced. However, construction and visualization of such brain network requires tremendous computation. Consequently, previously proposed approaches have been identified with common problems of high memory consumption and slow execution. Furthermore, interactivity in the previously proposed frameworks for brain network is also an outstanding issue. This study proposes an accelerated approach for brain connectomic visualization based on graph theory paradigm using compute unified device architecture, extending the previously proposed SurLens Visualization and computer aided hepatocellular carcinoma frameworks. The accelerated brain structural connectivity framework was evaluated with stripped brain datasets from the Department of Surgery, University of North Carolina, Chapel Hill, USA. Significantly, our proposed framework is able to generate and extract points and edges of datasets, displays nodes and edges in the datasets in form of a network and clearly maps data volume to the corresponding brain surface. Moreover, with the framework, surfaces of the dataset were simultaneously displayed with the nodes and the edges. The framework is very efficient in providing greater interactivity as a way of representing the nodes and the edges intuitively, all achieved at a considerably interactive speed for instantaneous mapping of the datasets’ features. Uniquely, the connectomic algorithm performed remarkably fast with normal hardware requirement specifications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Dibajnia P, Morshead CM (2013) Role of neural precursor cells in promoting repair following stroke. Acta Pharmacol Sin 34:78–90

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  2. Ovbiagele B, Goldstein LB, Higashida RT, Howard VJ, Johnston SC, Khavjou OA, Lackland DT, Lichtman JH, Mohl S, Sacco RL, Saver JL, Trogdon JG (2013) Forecasting the future of stroke in the United States: a policy statement from the American Heart Association and American Stroke Association. Stroke 44:2361–2375

    Article  PubMed  Google Scholar 

  3. Watts LT, Lloyd R, Garling RJ, Duong T (2013) Stroke neuroprotection: targeting mitochondria. Brain Sci 2013(3):540–560

    Article  Google Scholar 

  4. Guo Y., Wang Y., Fang S., Chao H., Saykin A.J., Shen L. (2012) Pattern visualization of human connectome data. In: Eurographics conference on visualization (EuroVis)

  5. Pfister H, Kaynig V, Botha CP, Bruckner S, Dercksen VJ, Hege H-C, Roerdink JBTM (2012) Visualization in connectomics. arXiv:1206.1428v2 [cs.GR]

  6. His W (1888) Zur Geschichte des Gehirns sowie der centralen und peripherischen nervenbahnen beim meschlichen Embryo. Abh d math-phys Kl d Königl Sächs Gesel d Wiss 14:341–392

    Google Scholar 

  7. Sporns O, Tononi G, Kotter R (2005) The human connectome: a Structural description of the human brain. PLos Comput Biol 1:e42

    Article  PubMed  PubMed Central  Google Scholar 

  8. Wang Y, Xu M, Ren L, Zhang X, Wu D, He Y, Xu N, Yang H (2011) A heterogeneous accelerator platform for multi-subject voxel-based brain network analysis. In: IEEE/ACM international conference on computer-aided design (ICCAD), pp. 339–344

  9. Van Essen DC, Drury HA (1997) Structural and functional analyses of human cerebral cortex using a surface-based atlas. J Neurosci 17:7079–7102

    PubMed  Google Scholar 

  10. Kaiser M (2011) A tutorial in connectome analysis: topological and spatial features of brain networks. arXiv:1105.4705v1 [q-bio.NC]

  11. Mueller K, Chen M, Kaufman A (eds) (2001) Volume graphics. Springer, London

    Google Scholar 

  12. Kaufman A, Mueller K (2005) Overview of volume rendering. In: Johnson C, Hansen C (eds) The visualization handbook. Academic Press, London

    Google Scholar 

  13. Adeshina AM, Hashim R, Khalid NEA, Abidin SZZ (2012) Medical imaging modalities: a conceptual review for volume visualization. Glob J Technol 1(2012):115–121 (Formerly AWERProcedia Information Technology and Computer Science)

    Google Scholar 

  14. Adeshina AM, Hashim R, Khalid NEA, Abidin SZZ (2012) Medical volume visualization: decades of review. Glob J Technol 1(2012):152–157 (Formerly AWERProcedia Information Technology and Computer Science)

    Google Scholar 

  15. Petrella JR (2011) Use of graph theory to evaluate brain networks: a clinical tool for a small world? Radiology 259(2):317–320

    Article  PubMed  Google Scholar 

  16. Catani M, Ffytche DH (2005) The rises and falls of disconnection syndromes. Brain 128(pt 10):2224–2239

    Article  PubMed  Google Scholar 

  17. Geschwind N (1965a) Disconnexion syndromes in animals and man. I. Brain 88(2):237–294

    Article  PubMed  CAS  Google Scholar 

  18. Geschwind N (1965b) Disconnexion syndromes in animals and man. II. Brain 88(3):585–644

    Article  PubMed  CAS  Google Scholar 

  19. Van den Heuvel MP, Stam CJ, Kahn RS, Pol HEH (2009) Efficiency of functional brain networks and intellectual performance. J Neurosci 29(23):7619–7624

    Article  PubMed  Google Scholar 

  20. Achard S, Salvador R, Whitcher B, Suckling J, Bullmore E (2006) A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci 26:63–72

    Article  PubMed  CAS  Google Scholar 

  21. Stam CJ, Reijneveld JC (2007) Graph theoretical analysis of complex networks in the brain. Nonlinear Biomed Phys 1:3

    Article  PubMed  PubMed Central  Google Scholar 

  22. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186–198

    Article  PubMed  CAS  Google Scholar 

  23. Van den Heuvel MP, Stam CJ, Boersma M, Hulshoff Pol HE (2008) Small world and scale-free organization of voxel based resting-state functional connectivity in the human brain. Neuroimage 43:528–539

    Article  PubMed  Google Scholar 

  24. Peng B, Zhang L, Zhang D (2013) A survey of graph theoretical approaches to image segmentation. Pattern Recognit 46:1020–1038

    Article  Google Scholar 

  25. Morris OJ, Lee MDJ, Constantinides AG (1986) Graph theory for image analysis: an approach based on the shortest spanning tree. IEE Proc Commun Radar Signal Process 133:146–152

    Article  Google Scholar 

  26. Zahn CT (1971) Graph-theoretic methods for detecting and describing gestalt clusters. IEEE Trans Comput 20(1971):68–86

    Article  Google Scholar 

  27. Kwok SH, Constantinides AG (1997) A fast recursive shortest spanning tree for image segmentation and edge detection. IEEE Trans Image Process 6(2):328–332

    Article  PubMed  CAS  Google Scholar 

  28. Wu Z, Leahy R (1990) Tissue classification in MR images using hierarchical segmentation. Proc IEEE Int Conf Med Imaging 12(1):81–85

    Google Scholar 

  29. Grady L (2005) Multi label random walker segmentation using prior models. IEEE Conf Comput Vis Pattern Recognit 1:763–770

    Google Scholar 

  30. Pavan M, Pelillo M (2003) A new graph-theoretic approach to clustering and segmentation. IEEE Conf Comput Vis Pattern Recognit 1:145–152

    Google Scholar 

  31. Wu QF, Zhang CS, Chen Q, Yu SG (2012) On feasibility of researching acupoint combination by using complex network analysis techniques. Zhen Ci Yan Jiu 37(3):252–255

    PubMed  Google Scholar 

  32. Lee S-H, Kim C-E, Lee I-S, Jung W-M, Kim H-G, Jang H, Kim S-J, Lee H, Park H-J, Chae Y (2013) Network analysis of acupuncture points used in the treatment of low back pain. In: Evidence-based complementary and alternative medicine, vol 2013. doi:10.1155/2013/402180

  33. Peters JM, Taquet M, Vega C, Jeste SS, Fernández IS, Tan J, Nelson CA, Sahin M, Warfield SK (2013) Brain functional networks in syndromic and non-syndromic autism: a graph theoretical study of EEG connectivity. BMC Med 11:54

    Article  PubMed  PubMed Central  Google Scholar 

  34. Sato JR, Hoexter MQ, Fujita A, Rohde LA (2012) Evaluation of pattern recognition and feature extraction methods in ADHD prediction. Front Syst Neurosci 6:68

    Article  PubMed  PubMed Central  Google Scholar 

  35. Bassett DS, Nelson BG, Mueller BA, Camchong J, Lim KO (2012) Altered resting state complexity in schizophrenia. Neuroimage 59:2196–2207

    Article  PubMed  PubMed Central  Google Scholar 

  36. Dey S, Rao AR, Shah M (2012) Exploiting the brain’s network structure in Identifying ADHD subjects. Font Syst Neurosci 6:75

    Google Scholar 

  37. Zhang J, Cheng W, Wang Z, Zhang Z, Lu G, Feng J (2012) Pattern classification of large-scale functional brain networks: identification of informative neuroimaging markers for epilepsy. Plos One 7:e36733

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  38. Craddock RC, Holtzheimer PE III, Hu XP, Mayberg HS (2009) Disease state prediction from resting state functional connectivity. Magn Reson Med 62:1619–1628

    Article  PubMed  PubMed Central  Google Scholar 

  39. Conturo TE, Lori NF, Cull TS, Akbudak E, Snyder AZ, Shimony JS, McKinstry RC, Burton H, Raichle ME (1999) Tracking neuronal fiber pathways in the living human brain. Proc Natl Acad Sci USA 96:10422–10427

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  40. Mori S, Crain BJ, Chacko VP, Zijl VPC (1999) Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 45:265–269

    Article  PubMed  CAS  Google Scholar 

  41. Kapri AV, Rick T, Caspers S, Eickhoff SB, Zilles K, Kuhlen T (2010) Evaluating a visualization of uncertainty in probabilistic tractography. In: Proceedings of SPIE medical imaging 2010: visualization, image-guided procedures, and modeling, p. 7625

  42. Berres A, Goldau M, Tittgemeyer M, Scheuermann G, Hagen H (2012) Tractography in context: multimodal visualization of probabilistic tractograms in anatomical context. Eurographics workshop on visual computing for biology and medicine, pp. 9–16

  43. Chen B, Moreland J, Zhang J (2011) Human brain functional MRI and DTI visualization with virtual reality. Quant Imaging Med Surg 1:11–16

    PubMed  PubMed Central  Google Scholar 

  44. Rick T, Kapri VA, Caspers S, Amunts K, Zilles K, Kuhlen T (2011) Visualization of probabilistic fiber tracts in virtual reality. Stud Health Technol Inform 163:486–492

    PubMed  Google Scholar 

  45. Adeshina AM, Hashim R, Khalid NEA, Abidin SZZ (2012c) Locating abnormalities in brain blood vessels using parallel computing architecture. Interdiscip Sci Comput Life Sci 4:161–172

    Article  CAS  Google Scholar 

  46. Labra N, Figueroa M, Guevara P, Duclap D, Hoeunou J, Poupon C, Mangin J-F (2013) GPU-based acceleration of an automatic white matter segmentation algorithm using CUDA. In: 35th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp. 89–92

  47. Qin AK, Raimondo F, Fobes F, Ong YS (2012) An improved CUDA-based implementation of differential evolution on GPU. In: ACM genetic and evolutionary computation conference. GECCO, Philadelphia, USA

  48. Qureshi MNI, Lee J-E, Lee SW (2012) Robust classification techniques for connection pattern analysis with adaptive decision boundaries using CUDA. In: IEEE international conference on cloud computing and social networking (ICCCSN)

  49. Adeshina AM, Hashim R, Khalid NEA, Abidin SZZ (2013) Multimodal 3-D reconstruction of human anatomical structures using surlens visualization system. Interdiscip Sci Comput Life Sci 4:161–172

    Article  Google Scholar 

  50. Adeshina AM, Hashim R, Khalid NEA, Abidin SZZ (2011) Hardware-accelerated raycasting: towards an effective brain MRI visualization. J Comput 3:36–42

    Google Scholar 

  51. Adeshina AM, Hashim R, Khalid NEA, Abidin SZZ (2012) Infrared-modified V-gear talk-cam tracer for image processing. Glob J Technol 1(2012):175–180 (Formerly AWERProcedia Information Technology and Computer Science)

    Google Scholar 

  52. Adeshina AM, Lau S-H, Loo C-K (2009) Real-time facial expression recognitions: a review. In: Senanayake A (ed) Innovative technologies in intelligent systems and industrial applications (CITISIA). Monash University, Kuala Lumpur, Malaysia, pp 375–378

    Google Scholar 

  53. Adeshina AM, Hashim R, Khalid NEA (2014) CAHECA: computer aided hepatocellular carcinoma therapy planning. Interdiscip Sci Comput Life Sci 6:222–234

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This study is supported by Universiti Tun Hussein Onn Malaysia. Many thanks to the Department of Surgery, University of North Carolina, Chapel Hill, United States for all the datasets made available for this study.

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia

    A. M. Adeshina & R. Hashim

Authors
  1. A. M. Adeshina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Hashim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. M. Adeshina.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Adeshina, A.M., Hashim, R. ConnectViz: Accelerated Approach for Brain Structural Connectivity Using Delaunay Triangulation. Interdiscip Sci Comput Life Sci 8, 53–64 (2016). https://doi.org/10.1007/s12539-015-0274-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12539-015-0274-9

Keywords

Search

Navigation