Joan Falco Roget, Luca Gherardini / j.roget@sanoscience.org
github JoanSano
Abstract:
With adequate mathematical and computational methods brain networks can be reconstructed from Magnetic Resonance Images (MRIs) opening the gates for meaningful statistical studies about several brain deseases (e.g. stroke, tumor, alzheimer, ...) In this project we will focus on finding structural similarities between brain networks suffering from stroke and tumors. Possible approaches include network statistics, statistical connectomics and topological data analysis. Next steps would include inferences on common cognitive impairments between the groups.
List of materials:
[1] - Jeurissen, B., Descoteaux, M., Mori, S., & Leemans, A. (2019). Diffusion MRI fiber tractography of the brain. NMR in Biomedicine, 32(4), e3785.
[2] - Faskowitz, Joshua, Richard F. Betzel, and Olaf Sporns. "Edges in brain networks: Contributions to models of structure and function." Network Neuroscience 6.1 (2022): 1-28.
[3] - Rubinov, Mikail, and Olaf Sporns. "Complex network measures of brain connectivity: uses and interpretations." Neuroimage 52.3 (2010): 1059-1069.
[4] - Chung, Jaewon, et al. "Statistical connectomics." Annual Review of Statistics and Its Application 8 (2021): 463-492.
[5] - Centeno, Eduarda Gervini Zampieri, et al. "A hands-on tutorial on network and topological neuroscience." Brain Structure and Function (2022): 1-22.
List of requirements for taking part in the project:
Python
Mathematics
Complex Systems (optional)
Maximal allowed number of team members: 5
Added as an issue for book keeping
Source: https://www.brainhack-krakow.org/projects
Team Leaders:
Joan Falco Roget, Luca Gherardini / j.roget@sanoscience.org github JoanSano
Abstract:
With adequate mathematical and computational methods brain networks can be reconstructed from Magnetic Resonance Images (MRIs) opening the gates for meaningful statistical studies about several brain deseases (e.g. stroke, tumor, alzheimer, ...) In this project we will focus on finding structural similarities between brain networks suffering from stroke and tumors. Possible approaches include network statistics, statistical connectomics and topological data analysis. Next steps would include inferences on common cognitive impairments between the groups. List of materials:
[1] - Jeurissen, B., Descoteaux, M., Mori, S., & Leemans, A. (2019). Diffusion MRI fiber tractography of the brain. NMR in Biomedicine, 32(4), e3785. [2] - Faskowitz, Joshua, Richard F. Betzel, and Olaf Sporns. "Edges in brain networks: Contributions to models of structure and function." Network Neuroscience 6.1 (2022): 1-28. [3] - Rubinov, Mikail, and Olaf Sporns. "Complex network measures of brain connectivity: uses and interpretations." Neuroimage 52.3 (2010): 1059-1069. [4] - Chung, Jaewon, et al. "Statistical connectomics." Annual Review of Statistics and Its Application 8 (2021): 463-492. [5] - Centeno, Eduarda Gervini Zampieri, et al. "A hands-on tutorial on network and topological neuroscience." Brain Structure and Function (2022): 1-22. List of requirements for taking part in the project:
Python Mathematics Complex Systems (optional) Maximal allowed number of team members: 5