Course catalogue doctoral education - VT24
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Title | Network Theory and Brain Imaging |
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Course number | 5233 |
Programme | Neurovetenskap |
Language | English |
Credits | 1.5 |
Date | 2022-02-02 -- 2022-02-25 | Responsible KI department | Institutionen för klinisk neurovetenskap |
Specific entry requirements | No advanced mathematical knowledge is required. However, being comfortable with reading linear equations (once they are explained) is highly recommended. |
Purpose of the course | Network theory is an interdisciplinary subject of how networks are organized and function. On multiple different spatial scales (from neurons to brain regions) the brain is organized as a network of connected components. For several decades network theory has been applied to study the brain's structure and function revealing new knowledge about the brain.
The purpose of this course is to provide students with the foundations of network theory. This course will cover all aspects of creating network models from neuroimaging data (theory, assumptions, visualization, and quantifying models). |
Intended learning outcomes | After the course, the doctoral student shall have obtained a thorough knowledge about core concepts about network neuroscience. This includes: 1) be able to create network models; 2) be able to apply and interpret network measures calculated from models (centrality measures, shortest paths, community detection etc); 3) implement a network analysis and visualize the results using python, R or graphical interfaces; 4) gain knowledge about how network models have been applied within the neurosciences; 5) understand how network models relate to theory; 6) gain an overview of recent developments within network neuroscience. |
Contents of the course | The content consists of lectures, seminars and demonstrations to provide the students with knowledge about network neuroscience. This will include the basics of network models, measures to quantify networks, history of network science and applications of network models in neuroscience, programming exercises in how to construct network models (R, python, and graphical software packages – students can choose one), and recent developments in network neuroscience. Each student will also do an individual research project applying elements from the course onto real data. |
Teaching and learning activities | Lectures, seminars, laboratory exercises (that will require using R, Python or graphical software (Gephi)), oral presentation. |
Compulsory elements | Mandatory attendance to lectures and presentation. Absence during lectures will require completing supplementary written tasks. |
Examination | Individual mini research project. This can be carried out on the student’s own data or open data provided. The analysis should be presented in a 10 minute presentation. The students will also submit their analysis (e.g. Jupyter notebook of analysis or 1-2 pages describing/motivating analysis steps). |
Literature and other teaching material | Recommended literature:
Newman, M. E. J. (2010). Networks. An introduction. Oxford University Press. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52(3), 1059-1069. Bassett, D. S., & Sporns, O. (2017). Network neuroscience. Nature neuroscience, 20(3), 353. Fortunato, S., & Hric, D. (2016). Community detection in networks: A user guide. Physics Reports, 659, 1–44. https://doi.org/10.1016/j.physrep.2016.09.002 Holme, P. (2015). Modern temporal network theory: a colloquium. European Physical Journal B, 88(9). https://doi.org/10.1140/epjb/e2015-60657-4 |
Number of students | 8 - 25 |
Selection of students | Selection will be based on 1) the relevance of the course syllabus for the applicant's doctoral project (according to written motivation), 2) start date of doctoral studies (priority given to earlier start date) |
More information | Lectures/workshop: 2nd, 3rd, 9th, 10th February 2022. (10-15). Presentations: Friday 25th February (9-15). |
Additional course leader | |
Latest course evaluation | Course evaluation report |
Course responsible |
William Thompson Institutionen för klinisk neurovetenskap william.thompson@ki.se |
Contact person |
William Thompson Institutionen för klinisk neurovetenskap william.thompson@ki.se |
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