Understanding human brain function in health and psychiatric disorders remains to be a fundamental challenge in science and medicine. Recent advances in functional (e.g. fMRI, EEG, MEG) and structural (e.g. DTI) neuroimaging enabled precise mapping of brain’s functional networks as well as its anatomical connections, called “connectome”. These advances provide a unique opportunity towards understanding the link between the human connectome and the complex dynamics underlying brain function. However, despite abundant and rapidly growing structural and functional neuroimaging data, e.g. made publicly available by various large-scale, international projects such as the Human Connectome project, how the human connectome shapes the functional activity patterns in health and neuropsychiatric disorders remains to be an open question.

This half-day tutorial will include:

  • introduction to structural and functional connectivity,
  • review of the state-of-the-art structural and functional neuroimaging techniques and dynamical models of brain activity,
  • discussion on how medical image analysis and machine learning techniques can be applied to better understand healthy and diseased brain function.


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  • Arslan, S. and Rueckert, D.(2015) Multi-level parcellation of the cerebral cortex using resting-state fMRI, MICCAI 2015.
  • Parisot, S., Arslan, S., Passerat-Palmbach, J., Wells III, W. M., & Rueckert, D. (2015). Tractography-driven groupwise multi-scale parcellation of the cortex. In Information Processing in Medical Imaging (pp. 600-612). Springer International Publishing.
  • Arslan, S., Parisot, S. and Rueckert, D. (2015) Joint spectral decomposition for the parcellation of the cerebral cortex using resting-state fMRI, IPMI 2015.
  • Parisot, S., Rajchl, M., Passerat-Palmbach, J. and Rueckert, D. A. (2015) Continuous Flow-Maximisation Approach to Connectivity-driven Cortical Parcellation. MICCAI 2015.
  • Parisot S., and Rueckert, D. (2015) Multi-Scale Spectral Parcellation of the Cortex Based on Structural Connectivity, OHBM 2015.
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  • Deco, G., McIntosh, A. R., Shen, K., Hutchison, R. M., Menon, R. S., Everling, S., et al. (2014). Identification of Optimal Structural Connectivity Using Functional Connectivity and Neural Modeling. Journal of Neuroscience, 34(23), 7910–7916.
  • Deco, G., Jirsa, V. K., & McIntosh, A. R. (2013). Resting brains never rest: computational insights into potential cognitive architectures. Trends in Neurosciences, 36(5), 268–274.
  • Mueller S, Wang D, Fox MD, Yeo BT, Sepulcre J, Sabuncu MR, Shafee R, Lu J, Liu H. (2013). Individual variability in functional connectivity architecture of the human brain. Neuron.
  • Sepulcre, J., Sabuncu, M. R., Yeo, T. B., Liu, H., & Johnson, K. A. (2012). Stepwise connectivity of the modal cortex reveals the multimodal organization of the human brain. The Journal of Neuroscience, 32(31), 10649-10661.
  • Deco, G., Jirsa, V. K., & McIntosh, A. R. (2011). Emerging concepts for the dynamical organization of resting-state activity in the brain. Nature Reviews Neuroscience, 12(1), 43–56.
  • Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., et al. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165.
  • Deco, G., Jirsa, V., McIntosh, A. R., Sporns, O., & Kotter, R. (2009). Key role of coupling, delay, and noise in resting brain fluctuations. Proceedings of the National Academy of Sciences, 106(25), 10302–10307.