Thesis Project Form
Title (tentative): Investigate the function-structure coupling with ultra-high field MRI (magnetic resonance imaging)| Thesis advisor(s): Chiappalone Michela, Maura Casadio, Riccardo Iandolo (Norwegian University of Science and Technology) | E-mail: |
| Address: Via Opera Pia 13, 16145 Genova | Phone: |
Description
Motivation and application domain
Structure-function coupling is an ubiquitous property of biological systems [1]. In the human brain this link is not fully understood. For example, there are complex multi-synaptic interactions in the structural connectome, that makes this relationship difficult to unveil. To disentangle the structure-function relationship, statistical [2], biophysical [3], network communication models [4], and sophisticated graph theory techniques [5] have been adopted and declined across scales, species and neuroimaging techniques.
General objectives and main activities
The goal, within this project, is to try to provide new insights about the structure-function coupling using ultra-high field (7T) neuroimaging. 7T neuroimaging has recently received clearance for use in clinical population [6]. Hence the MSc student will have the chance to work with this unique and new dataset on a cutting-edge topic in human neuroscience [1]. In sum, the project objectives are:
i) to estimate the orientation of the white matter bundles connecting gray matter regions (tractography) from the 7T diffusion imaging sequence.
ii) to study the structure-function coupling with new and previously existing methods (e.g., graph filtering, biophysical model, among others [1]) adapted for use in 7T MRI framework. Functional connectivity estimates will be provided separately, in the context of another project.
References:
[1]Suárez, L. E., Markello, R. D., Betzel, R. F., & Misic, B. (2020). Linking structure and function in macroscale brain networks. Trends in Cognitive Sciences, 24(4), 302-315.
[2]Sarwar, T., Tian, Y., Yeo, B. T., Ramamohanarao, K., & Zalesky, A. (2021). Structure-function coupling in the human connectome: A machine learning approach. NeuroImage, 226, 117609.
[3] Cabral, J., Kringelbach, M. L., & Deco, G. (2017). Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms. NeuroImage, 160, 84-96.
[4] Avena-Koenigsberger, A., Yan, X., Kolchinsky, A., van den Heuvel, M. P., Hagmann, P., & Sporns, O. (2019). A spectrum of routing strategies for brain networks. PLoS computational biology, 15(3), e1006833.
[5] Preti, M. G., & Van De Ville, D. (2019). Decoupling of brain function from structure reveals regional behavioral specialization in humans. Nature communications, 10(1), 1-7.
[6] Cosottini, M., & Roccatagliata, L. (2021). Neuroimaging at 7 T: are we ready for clinical transition? European Radiology Experimental.
i) to estimate the orientation of the white matter bundles connecting gray matter regions (tractography) from the 7T diffusion imaging sequence.
ii) to study the structure-function coupling with new and previously existing methods (e.g., graph filtering, biophysical model, among others [1]) adapted for use in 7T MRI framework. Functional connectivity estimates will be provided separately, in the context of another project.
References:
[1]Suárez, L. E., Markello, R. D., Betzel, R. F., & Misic, B. (2020). Linking structure and function in macroscale brain networks. Trends in Cognitive Sciences, 24(4), 302-315.
[2]Sarwar, T., Tian, Y., Yeo, B. T., Ramamohanarao, K., & Zalesky, A. (2021). Structure-function coupling in the human connectome: A machine learning approach. NeuroImage, 226, 117609.
[3] Cabral, J., Kringelbach, M. L., & Deco, G. (2017). Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms. NeuroImage, 160, 84-96.
[4] Avena-Koenigsberger, A., Yan, X., Kolchinsky, A., van den Heuvel, M. P., Hagmann, P., & Sporns, O. (2019). A spectrum of routing strategies for brain networks. PLoS computational biology, 15(3), e1006833.
[5] Preti, M. G., & Van De Ville, D. (2019). Decoupling of brain function from structure reveals regional behavioral specialization in humans. Nature communications, 10(1), 1-7.
[6] Cosottini, M., & Roccatagliata, L. (2021). Neuroimaging at 7 T: are we ready for clinical transition? European Radiology Experimental.
Training Objectives (technical/analytical tools, experimental methodologies)
1)To learn how to use software packages for neuroimaging analysis.
2)To learn the methods for estimation of the function-structure coupling and apply them to both a population of healthy and stroke subjects.
3)Improve knowledge in the field of human and clinical neuroscience.
Software tools: Python, MatLab, neuroimaging packages (FSL, FreeSurfer, SPM, AFNI), Unix-based operating systems, bash scripting.
2)To learn the methods for estimation of the function-structure coupling and apply them to both a population of healthy and stroke subjects.
3)Improve knowledge in the field of human and clinical neuroscience.
Software tools: Python, MatLab, neuroimaging packages (FSL, FreeSurfer, SPM, AFNI), Unix-based operating systems, bash scripting.
Place(s) where the thesis work will be carried out: Norwegian University of Science and Technology
Additional information
Maximum number of students: 1