Abstract
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.
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Friston, K.J., Harrison, L., Penny, W.: Dynamic causal modelling. Neuroimage 19(4), 1273–1302 (2003)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Felleman, D.J., Van Essen, D.C.: Distributed Hierarchical Processing in the Primate Cerebral Cortex. Cereb Cortex 1(1), 1–47 (1991)
Lamme, V.A., Roelfsema, P.R.: The distinct modes of vision offered by feedforward and recurrent processing. Trends Neurosci. 23(11), 571–579 (2000)
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)
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)
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)
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)
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)
Mori, S., van Zijl, P.C.: Fiber tracking: principles and strategies - a technical review. NMR Biomed. 15(7-8), 468–480 (2002)
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)
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)
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Leow, A.D. et al. (2012). Hierarchical Structural Mapping for Globally Optimized Estimation of Functional Networks. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33418-4_29
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DOI: https://doi.org/10.1007/978-3-642-33418-4_29
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