Abstract
We propose a novel probabilistic framework to merge information from DWI tractography and resting-state fMRI correlations. In particular, we model the interaction of latent anatomical and functional connectivity templates between brain regions and present an intuitive extension to population studies. We employ a mean-field approximation to fit the new model to the data. The resulting algorithm identifies differences in latent connectivity between the groups. We demonstrate our method on a study of normal controls and schizophrenia patients.
Chapter PDF
Similar content being viewed by others
Keywords
- Functional Connectivity
- Schizophrenia Patient
- Superior Temporal Gyrus
- Linear Support Vector Machine
- Anatomical Connectivity
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Buckner, R.L., Vincent, J.L.: Unrest at rest: Default activity and spontaneous network correlations. NeuroImage 37(4), 1091–1096 (2007)
Basser, P., Pierpaoli, C.: Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor mri. Journal of Magnetic Resonance, Series B 111, 209–219 (1996)
Honey, C., et al.: Predicting human resting-state functional connectivity from structural connectivity. PNAS 106(6), 2035–2040 (2009)
Koch, M.A., et al.: An investigation of functional and anatomical connectivity using magnetic resonance imaging. NeuroImage 16(1), 241–250 (2002)
Sporns, O., et al.: Theoretical neuroanatomy: Relating anatomical and functional connectivity in graphs and cortical connection matrices. Cerebral Cortex 10(2), 127–141 (2000)
Honey, C., et al.: Network structure of cerebral cortex shapes functional connectivity on multiple time scales. PNAS 104(24), 10240–10245 (2007)
Gabrieli-Whitfield, S., et al.: Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. PNAS 106(4), 1279–1284 (2009)
Burns, J., et al.: Structural disconnectivity in schizophrenia: A diffusion tensor magnetic resonance imaging study. Br. J. Psychiatry 182, 439–443 (2003)
Ke, M., et al.: Combined analysis for resting state fmri and dti data reveals abnormal development of function-structure in early-onset schizophrenia. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.) RSKT 2008. LNCS (LNAI), vol. 5009, pp. 628–635. Springer, Heidelberg (2008)
Zhou, Y., et al.: Altered resting-state functional connectivity and anatomical connectivity of hippocampus in schizophrenia. Schiz. Res. 100(1-3), 120–132 (2008)
Jordan, M., et al.: An introduction to variational methods for graphical models. Machine Learning 37(2), 183–233 (1999)
Fischl, B., et al.: Sequence-independent segmentation of magnetic resonance images. NeuroImage 23, 69–84 (2004)
Malcolm, J., Shenton, M., Rathi, Y.: Neural tractography using an unscented kalman filter. In: IPMI, pp. 126–138 (2009)
Smith, S.M., et al.: Advances in functional and structural mr image analysis and implementation as fsl. NeuroImage 23(51), 208–219 (2004)
Kubicki, M., et al.: A review of diffusion tensor imaging studies in schizophrenia. Journal of Psychiatric Research 41(1-2), 15–30 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Venkataraman, A., Rathi, Y., Kubicki, M., Westin, CF., Golland, P. (2010). Joint Generative Model for fMRI/DWI and Its Application to Population Studies. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15705-9_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-15705-9_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15704-2
Online ISBN: 978-3-642-15705-9
eBook Packages: Computer ScienceComputer Science (R0)