BACKGROUND:

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Ever heard about the phenomenon of a “pregnancy brain” or a “mom brain”? While many pregnant women report diffuse symptoms like forgetfulness, confusion, and reduced concentration, little research has taken place to investigate and better understand these symptoms.

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My PhD research delves into understanding how pregnancy affects the human brain, building on the findings of Hoekzema et al. (2017). They revealed widespread gray matter volume reductions after pregnancy, particularly in brain areas linked to the theory-of-mind network reflecting social processes. Recent work by Hoekzema et al. (2022) further illuminates these changes, showing an association between pregnancy-related brain alterations and hormonal levels of estradiol during the second and third trimester. In addition, the pregnancy-induced brain changes relate to a pregnant woman’s nesting behavior, physiological reactions to infant cues, and maternal-fetal attachment, suggesting that those brain changes help preparing a woman for motherhood. Looking at functional resting-state data, they discovered an increase in the connectivity of the default mode network in women who were pregnant between sessions.

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My fascination with the human brain dates back to high school, evolving from a general interest in Alzheimer's disease to a more specific curiosity about the fundamental changes that happen in the brain over the lifespan. Now, my PhD gives me the opportunity to merge my research interests with the incredibly important research on women’s health. Given my background in psychology, I am especially interested in investigating the neural mechanisms underlying mental disorders like postpartum depression or perinatal anxiety.

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METHODOLOGY:

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I am currently focusing on analyzing structural and functional MRI data. Structural MRI data gives us insights about brain volumes in specific areas and how those volumes change during/after pregnancy. fMRI data gives us information about the activation in specific brain areas while being exposed to certain stimuli or during rest. With fMRI, we can for example see if the reaction of the brain to seeing a baby changes after pregnancy. In combination with behavioral data and questionnaires, this results in many options that can be explored. 

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Since fMRI data is inheritably very noisy due to e.g. movement during the scan, I currently use a preprocessing approach where I manually remove the noise from the data. This allows me to look at more detail at the fMRI signal and draw more nuanced conclusions. One analysis approach I’m currently investigating is to study the variability of brain activity over the period of the scan (instead of looking at the mean).

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RESULTS:

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As the manual preprocessing of the data is very time-consuming, I am just at the beginning of my data analysis. However, preliminary results already show that the variability of neural activity changes over the course of a pregnancy.

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FUTURE WORK: 

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In the short term, I hope that I can build on my first analyses and find out more about the variability of neural activity in relation to pregnancy but also possible symptoms or cognitive tasks.

Looking ahead, I would like to further improve my programming and machine learning skills to create new perspectives for our data analysis. Collaborating with my colleagues, I hope to contribute to a comprehensive understanding of the neural underpinnings of pregnancy.

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