At the Center for Lifespan Changes in Brain and Cognition (LCBC, https://www.oslobrains.no/), we study how the human brain develops over the whole lifespan. We’re particularly interested in how physical changes in the brain are related to changes in cognitive function (e.g., decline in memory), and if it’s possible for people to improve their own brain health, e.g., through memory training or physical exercise. All of our work requires large datasets and complex statistical models, and this document describes some possible master thesis projects which would be of high interest to us.
Project description
Electroencephalography (EEG) is a way of measuring electrical activity in the brain by placing electrodes on the surface of the skull. EEG data take the form of dense time series, as illustrated in Figure 1, with a distinct cyclic nature, seen by the repeating patterns. For example, at LCBC we have studies in which the participants have electrodes attached to the skull during a whole night, so that we can analyze the brain activity during sleep, and particularly how the EEG patterns are related to learning and formation of memories.
Scientific questions of interest often relate to the distribution power across frequencies, the spatial correlation between signals from different electrodes, and how the voltage at different frequencies is synchronized. For example, it has been shown that during sleep, the synchronization of signal power at certain low and high frequency bands are related to formation of memories (Helfrich et al., 2018). In contrast, less synchronization of these signals, typical in old people, is associated with less formation of memories during sleep.
Analyzing these data requires taking into account their cyclic nature, since patterns keep repeating themselves. The field of directional statistics (Pewsey & García-Portugués, 2021) is devoted to this type of analysis, and in this project we are looking for a student who is interested in learning more about directional statistics, and figure out how it can be applied to a scientific question involving EEG data. In particular, we have data where participants learn a task in the evening, then EEG measurements recorded through the night, and then new measurements in the morning of how well the participants remember what they learned. While the paper by Helfrich et al. (2018) computed cyclic correlation coefficients, we want to take a step further and do regression analyses, e.g., with mixed models or generalized additive models, to understand how brain activity during sleep can explain how well the participants learned the task.
To help us explore this, we are seeking a motivated students who is interested in exploring new statistical tools and work with interesting applications involving how the human brain works. The exact nature of the project will depend on the student’s interest, but will likely involve the following points:
- Learn about directional statistics and summarize how it can be used in analysis of EEG data.
- Find existing software packages, and explore how they can be used.
- Analyze some of our EEG data using what has been learned in the previous two points.
References
Helfrich, R. F., Mander, B. A., Jagust, W. J., Knight, R. T., & Walker, M. P. (2018). Old Brains Come Uncoupled in Sleep: Slow Wave-Spindle Synchrony, Brain Atrophy, and Forgetting. Neuron, 97(1), 221-230.e4. https://doi.org/10.1016/j.neuron.2017.11.020
Pewsey, A., & García-Portugués, E. (2021). Recent advances in directional statistics. TEST, 30(1), 1–58. https://doi.org/10.1007/s11749-021-00759-x