Hierarchical Structural Mapping for Globally Optimized Estimation of Functional Networks

  • Alex D. Leow
  • Liang Zhan
  • Donatello Arienzo
  • Johnson J. GadElkarim
  • Aifeng F. Zhang
  • Olusola Ajilore
  • Anand Kumar
  • Paul M. Thompson
  • Jamie D. Feusner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7511)


In this study, we propose a framework to map functional MRI (fMRI) activation signals using DTI-tractography. This framework, which we term functional by structural hierarchical (FSH) mapping, models the regional origin of fMRI brain activation to construct “N-step reachable structural maps”. Linear combinations of these N-step reachable maps are then used to predict the observed fMRI signals. Additionally, we constructed a utilization matrix, which numerically estimates whether the inclusion of a specific structural connection better predicts fMRI, using simulated annealing. We applied this framework to a visual fMRI task in a sample of body dysmorphic disorder (BDD) subjects and comparable healthy controls. Group differences were inferred by comparing the observed utilization differences against 10,000 permutations under the null hypothesis. Results revealed that BDD subjects under-utilized several key local connections in the visual system, which may help explain previously reported fMRI findings and further elucidate the underlying pathophysiology of BDD.


DTI HARDI fMRI network Simulated Annealing 


  1. 1.
    Friston, K.J., Harrison, L., Penny, W.: Dynamic causal modelling. Neuroimage 19(4), 1273–1302 (2003)CrossRefGoogle Scholar
  2. 2.
    Saygin, Z.M., Osher, D.E., Koldewyn, K., Reynolds, G., Gabrieli, J.D., Saxe, R.R.: Anatomical connectivity patterns predict face selectivity in the fusiform gyrus. Nat. Neurosci. 15(2), 321–327 (2011)CrossRefGoogle Scholar
  3. 3.
    Johansen-Berg, H., Behrens, T.E., Robson, M.D., Drobnjak, I., Rushworth, M.F., Brady, J.M., Smith, S.M., Higham, D.J., Matthews, P.M.: Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex. Proc. Natl. Acad. Sci. USA 101, 13335–13340 (2004)CrossRefGoogle Scholar
  4. 4.
    Passingham, R.E., Stephan, K.E., Kotter, R.: The anatomical basis of functional localization in the cortex. Nat. Rev. Neurosci. 3(8), 606–616 (2002)Google Scholar
  5. 5.
    Lim, C., Li, X., Li, K.M., Guo, L., Liu, T.: Brain state change detection via fiber-centered functional connectivity analysis. In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (2011)Google Scholar
  6. 6.
    Skudlarski, P., Jagannathan, K., Anderson, K., Stevens, M.C., Calhoun, V.D., Skudlarska, B.A., Pearlson, G.: Brain Connectivity Is Not Only Lower but Different in Schizophrenia: A Combined Anatomical and Functional Approach. Biol. Psychiatry 68(1), 61–69 (2010)CrossRefGoogle Scholar
  7. 7.
    Deligianni, F., Robinson, E.C., Bechmann, C.F., Sharp, D., Edwards, A.D., Rueckert, D.: Inference of functional connectivity from structural brain connectivity. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (2010)Google Scholar
  8. 8.
    Honey, C.J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J.P., Meuli, R., Hagmann, P.: Predicting human resting-state functional connectivity from structural connectivity. Proc. Natl. Acad. Sci. USA 106(6), 2035–2040 (2009)CrossRefGoogle Scholar
  9. 9.
    Varkuti, B., Cavusoglu, M., Kullik, A., Schiffler, B., Veit, R., Yilmaz, O., Rosenstiel, W., Braun, C., Uludag, K., Birbaumer, N., Sitaram, R.: Quantifying the Link between Anatomical Connectivity, Gray Matter Volume and Regional Cerebral Blood Flow: An Integrative MRI Study. PLoS One 6(4), e14801 (2011)Google Scholar
  10. 10.
    Sporns, O., Tononi, G., Edelman, G.M.: Theoretical neuroanatomy: Relating anatomical and functional connectivity in graphs and cortical connection matrices. Cereb Cortex 10, 127–141 (2000)CrossRefGoogle Scholar
  11. 11.
    Koch, M.A., Norris, D.G., Hund-Georgiadis, M.: An investigation of functional and anatomical connectivity using magnetic resonance imaging. Neuroimage 16, 241–250 (2002)CrossRefGoogle Scholar
  12. 12.
    Venkataraman, A., Rathi, Y., Kubicki, M., Westin, C.F., Golland, P.: Joint Modeling of Anatomical and Functional Connectivity for Population Studies. IEEE Trans. Med. Imaging 31(2), 164–182 (2012)CrossRefGoogle Scholar
  13. 13.
    Felleman, D.J., Van Essen, D.C.: Distributed Hierarchical Processing in the Primate Cerebral Cortex. Cereb Cortex 1(1), 1–47 (1991)CrossRefGoogle Scholar
  14. 14.
    Lamme, V.A., Roelfsema, P.R.: The distinct modes of vision offered by feedforward and recurrent processing. Trends Neurosci. 23(11), 571–579 (2000)CrossRefGoogle Scholar
  15. 15.
    American Psychiatric Association: Diagnostic and statistical manual of mental disorders: DSM-IV-TR, 4th edn., vol. xxxvii, p. 943. American Psychiatric Association, Washington, DC (2000)Google Scholar
  16. 16.
    Feusner, J.D., Moody, T., Hembacher, E., Townsend, J., Mckinley, M., Moller, H., Bookheimer, S.: Abnormalities of visual processing and frontostriatal systems in body dysmorphic disorder. Arch. Gen. Psychiatry 67(2), 197–205 (2010)CrossRefGoogle Scholar
  17. 17.
    Feusner, J.D., Townsend, J., Bystritsky, A., Bookheimer, S.: Visual information processing of faces in body dysmorphic disorder. Arch. Gen. Psychiatry 64(12), 1417–1425 (2007)CrossRefGoogle Scholar
  18. 18.
    Feusner, J.D., Hembacher, E., Moller, H., Moddy, T.D.: Abnormalities of object visual processing in body dysmorphic disorder. Psychol. Med. 41(11), 2385–2397 (2011)CrossRefGoogle Scholar
  19. 19.
    Iidaka, T., Yamashita, K., Kashikura, K., Yonekura, Y.: Spatial frequency of visual image modulates neural responses in the temporo-occipital lobe. An investigation with event-related fMRI. Cogn. Brain Res. 18(2), 196–204 (2004)CrossRefGoogle Scholar
  20. 20.
    Mori, S., van Zijl, P.C.: Fiber tracking: principles and strategies - a technical review. NMR Biomed. 15(7-8), 468–480 (2002)CrossRefGoogle Scholar
  21. 21.
    Morgan, V.L., Mishra, A., Newton, A.T., Gore, J.C., Ding, Z.H.: Integrating functional and diffusion magnetic resonance imaging for analysis of structure-function relationship in the human language network. PLoS One 4(8), e6660 (2009)Google Scholar
  22. 22.
    Jenkinson, M., Bannister, P., Brady, M., Smith, S.: Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17(2), 825–841 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alex D. Leow
    • 1
    • 2
    • 3
  • Liang Zhan
    • 4
  • Donatello Arienzo
    • 5
  • Johnson J. GadElkarim
    • 1
    • 6
  • Aifeng F. Zhang
    • 1
  • Olusola Ajilore
    • 1
  • Anand Kumar
    • 1
  • Paul M. Thompson
    • 4
  • Jamie D. Feusner
    • 5
  1. 1.Department of PsychiatryUniversity of IllinoisChicagoUSA
  2. 2.Department of BioengineeringUniversity of IllinoisChicagoUSA
  3. 3.Community Psychiatry AssociatesSacramentoUSA
  4. 4.Laboratory of Neuro Imaging, Department of NeurologyUCLAUSA
  5. 5.UCLA Semel Institute for Neuroscience and Human Behavior, UCLAUSA
  6. 6.Department of Electrical and Computer EngineeringUICUSA

Personalised recommendations