Course catalogue doctoral education - VT21

  • Ansökan kan ske mellan 2020-10-15 och 2020-11-16
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Title Network Theory and Brain Imaging
Course number 5233
Programme Neurovetenskap
Language English
Credits 1.5
Date 2020-11-03 -- 2020-11-26
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.

Holme, P. (2015). Modern temporal network theory: a colloquium. European Physical Journal B, 88(9).
Number of students 8 - 30
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 3rd, 5th, 10th, 12th November. kl 10-15. Presentations on 26th November, 10-16.
Additional course leader Peter Fransson
Latest course evaluation Not available
Course responsible William Thompson
Institutionen för klinisk neurovetenskap
Contact person William Thompson
Institutionen för klinisk neurovetenskap