NAD Fellow year: 2024

Emma Kragelund Christensen

MSc in Neuroscience, University of Copenhagen

Lab rotations - pre-PhD year

Lab Rotation 1

Analysis Group, Center of Functionally Integrative Neuroscience, Aarhus University w/PI Professor Diego Vidaurre

One of the major goals in society at present is to achieve a more personalised medicine,  e.g. characterising trajectories of disease on a patient-by-patient basis. When we analyse brain data from patients and controls, the variability we observe is due to at least three factors: evolution in time of the patient (due to disease or ageing), intrinsic differences between subjects, and measurement noise. To be able to bring machine learning to clinical decision making, we need to disentangle these aspects, for which we typically need longitudinal data. Emma is exploring, in the context of brain imaging, a novel machine learning technique developed in a different field (molecular biology) to help us characterise changes in time (e.g. due to disease) using purely cross-sectional data. For this purpose, she is using a public MRI data set with Alzheimer’s patients and controls.

Meet Emma Kragelund Christensen

Emma Kragelund Christensen is as keen on data analysis as she is on experiments, and she is looking forward to learning new techniques and experiencing different research environments during the pre-PhD year.

Emma Kragelund Christensen graduated from the University of Copenhagen with a master’s degree in Neuroscience in 2022. Since then, she has been working as a research assistant in the Kiehn and Bellardita Labs at the Department of Neuroscience. Emma’s previous work has centered on spinal cord injuries and spasticity in mouse models, and she has been especially focused on locomotor function and muscle activity measurements in mice. She also has a keen interest in data analysis:

“I have found that I am actually very interested in what we can use all this data that we have generated for; I am interested in gaining as much knowledge from it as possible,” Emma explains.

This interest is what has led Emma choose the Analysis Lab led by Diego Vidaurre at Aarhus University for her first lab rotation as the group has a strong focus on data analysis, model development and machine learning:

“I think it would be great to gain a deeper understanding of how to either develop a data analysis model or implement an existing model that have been used to analyse one type of data on a different type of dataset (…) Moving forward, I would like to have a stronger focus on the computational aspects of neuroscience,” Emma says.

While Emma has always been drawn to the natural sciences, her specific interest in neuroscience was strengthened through her work in a facility for people with physical and mental disabilities, many of them also suffering from additional neurological disorders such as neurodegenerative diseases:

“I saw first-hand how challenging these diseases are to treat and how challenged patients suffering from these types of diseases can be (…) Because I worked there for a long time, I also witnessed both improvement and progression in the different diseases”, Emma says.

As such, Emma’s interest in neuroscience comes from a combination of love of experiments and analysis as well as a desire to contribute to future treatment of neurodegenerative diseases.

Emma is enthusiastic about entering the NAD PhD programme, particularly because it offers her the chance to work in various laboratories and immerse herself in the diverse Danish neuroscience community. She highlights the programme’s uniqueness in Denmark, pointing out how it allows for the creation of a broad network in a distinct manner:

”This opportunity to engage with various research settings and methodologies enables me to combine different skills,” Emma explains.

Emma believes this experience will not only enhance her scientific knowledge but also provide insight into her own scientific identity. She also anticipates that experiencing different research cultures will be beneficial for her personal and professional growth.