Functional magnetic resonance imaging (fMRI) attracts increasing attention in attempts to map brain activities and construct brain networks that accurately describe the interconnected regions of the brain. It has been shown that graphs are a natural way to model the brain due to its interconnected complexity. Time series obtained from neuroimaging are the underlying data for constructing functional brain networks. So far, many works discuss the inference of static graphs from the time series. After constructing a graph, global and local graph measures and properties can be evaluated to obtain further insights into the functional brain network.

Recent works extend these approaches by including dynamic changes of the brain network into the graphs. They discuss the inference of temporal instead of static graphs that can mirror changes in the activities of neural connections with changing topology of the graph. A temporal graph consists of a set of vertices and a set of temporal edges. Each temporal edge has a discrete timestamp determining when it is available in the network. Hence, temporal graphs are well suited for modeling dynamic brain networks. Many recent works discuss centrality measures and global graph properties designed explicitly for temporal graphs, which might be helpful for obtaining insights into the dynamically changing brain networks.

The goals of the lab are to implement a framework for temporal graph inference from fMRI data. After obtaining temporal brain graphs from real-life data sets, temporal and static centrality measures and graph properties are applied and evaluated. The evaluation should discuss the possible (dis-)advantages of the temporal over the static approaches. Furthermore, we are interested in identifying temporal centrality measures or graph properties that have a strong correlation to specific brain properties, e.g., the quality of episodic memory.

The lab is in collaboration with Xenia Kobeleva.

The initial meeting will be on Tuesday, April 5, 1 pm online via Zoom.

Attending the initial meeting is mandatory for joining the lab.

If you are interested in joining this lab, write an email to lutz.oettershagen [add] cs.uni-bonn.de by Monday, April 4, 6 pm.

In the first meeting, we will give a short introduction to the topic of the lab. Furthermore, we answer all organizational questions. Official registration of the students to the lab can be done in the following weeks (the exact date of the deadline will be announced later).

We start off with a mini-seminar phase in which each participant presents an assigned paper or topic. An oral presentation of the work and the results, as well as a written report, is expected at the end of the lab.

Important dates:

The assignment of the papers will be on 12.04.22, and the presentations on 26.04.22. We will have the following talks.

• Thompson, William Hedley, Per Brantefors, and Peter Fransson. “From static to temporal network theory: Applications to functional brain connectivity.” Network Neuroscience 1.2 (2017): 69-99.
• Farahani, Farzad V., Waldemar Karwowski, and Nichole R. Lighthall. “Application of graph theory for identifying connectivity patterns in human brain networks: a systematic review.” frontiers in Neuroscience 13 (2019): 585.
• Holme, Petter, and Jari Saramäki. “Temporal networks.” Physics reports 519.3 (2012): 97-125.
• Fornito, Alex, Andrew Zalesky, and Edward Bullmore. Fundamentals of brain network analysis. Academic Press, 2016.