Poster No:
1095
Submission Type:
Abstract Submission
Authors:
Emin Serin1, Lea Schill2, Christoph Bärtl3, Marina Giglberger3, Julian Konzok3, Hannah Peter3, Nina Speicher3, Ludwig Kreuzpointner3, Brigitte Kudielka3, Stefan Wüst3, Henrik Walter2, Gina-Isabelle Henze4
Institutions:
1Charité – Universitätsmedizin Berlin, Berlin, Germany, 2Charité–Universitätsmedizin Berlin, Berlin, Germany, 3University of Regensburg, Regensburg, Germany, 4Charité Universitätsmedizin Berlin, Berlin, Berlin
First Author:
Emin Serin
Charité – Universitätsmedizin Berlin
Berlin, Germany
Co-Author(s):
Lea Schill
Charité–Universitätsmedizin Berlin
Berlin, Germany
Stefan Wüst
University of Regensburg
Regensburg, Germany
Introduction:
Alterations in stress response are associated with several mental disorders (Van Oort et al., 2020; Zorn et al., 2017). Research has focused on the neural substrates of the (acute) cortisol stress response, primarily using fMRI. However, structural brain measures, particularly in healthy individuals, have yet to be significantly noticed. As individual differences in the functional architecture of the human brain are significantly reflected in structural architecture (Smith et al., 2019), investigating how structural brain measures may influence acute stress is crucial for a comprehensive understanding of brain-stress relationships. While a few studies have investigated the relationship between brain structure and stress response, they have been limited by small sample sizes of healthy individuals (Barry et al., 2017; Henze et al., 2023; Pruessner et al., 2007) and a focus on a limited number of limbic areas, hindering the reliability and generalizability of their findings. Although Henze et al. (2023) made progress by examining a larger number of brain regions and a significantly larger sample size (~67 adults), further advancements in sample size and analytical methods are critically needed. This study aims to comprehensively investigate the relationship between structural brain measures and acute cortisol stress response using a large-scale sample of 289 healthy subjects and various advanced methods, including machine learning.
Methods:
Our study consisted of two parts: confirmatory (hypothesis-driven) and exploratory (data-driven). In the confirmatory part, we examined the associations between the thickness of 6 cortical and volume of 6 subcortical areas from each hemisphere (24 measures in total) and individual cortisol responses to confirm the results of a previous study (Henze et al., 2023). In the exploratory portion, we performed whole-brain surface-based analyses associating vertices with acute cortisol response. This allowed us to identify more reliable cortisol-related brain structures on a broader scale without limiting the analysis scope. Since the analysis was done on the cortical surface level, subcortical regions were not included in this whole-brain analysis. We then used advanced machine learning to predict individuals' cortisol responses from cortical and subcortical measures (e.g., thickness, volume, and surface area). This step is crucial as machine learning enables inherently out-of-sample evaluation, which is critical for testing the generalizability of findings and cannot be achieved with traditional statistical analyses (Kriegeskorte et al., 2009). In all steps, we carefully controlled for confounding variables (e.g., sex, age).
Results:
We found that caudate volume, particularly in males, is associated with acute cortisol response, as Henze et al. (2023) reported. Moreover, the thickness of all six cortical regions (rostral and caudal, ACC, PCC, parahippocampus, lOF, and mOFC) exhibited a significant association with stress-induced cortisol increases. Supporting the confirmatory analysis, the whole-brain analysis of the cortical surface showed that rostral ACC across sex and caudal ACC in males were significantly linked to individuals' cortisol response, indicating that the effect in ACC is strong enough to survive corrections on a whole-brain scale. After careful confound correction, Partial Least Squares (PLS) Regression significantly predicted individuals' cortisol response (r = .127, p = 0.037); however, the variance explained by the model is very small, so readers should exercise caution when evaluating the prediction results.
Conclusions:
This study, using the largest sample to date, provides valuable insights into the interplay between structural measures of the human brain and the cortisol stress response. We believe our findings will serve as a significant step forward in studying the neural underpinnings of stress on a multi-sample population level in the future.
Modeling and Analysis Methods:
Classification and Predictive Modeling 1
Univariate Modeling
Novel Imaging Acquisition Methods:
Anatomical MRI 2
Keywords:
Machine Learning
MRI
NORMAL HUMAN
STRUCTURAL MRI
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Other
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
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Not applicable
Please indicate which methods were used in your research:
Structural MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Other, Please list
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HALFpipe
Provide references using APA citation style.
Barry, T. J., Murray, L., Fearon, P., Moutsiana, C., Johnstone, T., & Halligan, S. L. (2017). Amygdala volume and hypothalamic-pituitary-adrenal axis reactivity to social stress. Psychoneuroendocrinology, 85, 96–99. https://doi.org/10.1016/j.psyneuen.2017.07.487
Henze, G., Konzok, J., Kudielka, B. M., Wüst, S., Nichols, T. E., & Kreuzpointner, L. (2023). Associations between cortisol stress responses and limbic volume and thickness in young adults: An exploratory study. European Journal of Neuroscience, 58(9), 3962–3980. https://doi.org/10.1111/ejn.16161
Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. F., & Baker, C. I. (2009). Circular analysis in systems neuroscience: The dangers of double dipping. Nature Neuroscience, 12(5), Article 5. https://doi.org/10.1038/nn.2303
Pruessner, M., Pruessner, J. C., Hellhammer, D. H., Bruce Pike, G., & Lupien, S. J. (2007). The associations among hippocampal volume, cortisol reactivity, and memory performance in healthy young men. Psychiatry Research: Neuroimaging, 155(1), 1–10. https://doi.org/10.1016/j.pscychresns.2006.12.007
Smith, S., Duff, E., Groves, A., Nichols, T. E., Jbabdi, S., Westlye, L. T., Tamnes, C. K., Engvig, A., Walhovd, K. B., Fjell, A. M., Johansen-Berg, H., & Douaud, G. (2019). Structural Variability in the Human Brain Reflects Fine-Grained Functional Architecture at the Population Level. The Journal of Neuroscience, 39(31), 6136–6149. https://doi.org/10.1523/JNEUROSCI.2912-18.2019
Van Oort, J., Tendolkar, I., Hermans, E. J., Mulders, P. C., Beckmann, C. F., Schene, A. H., Fernández, G., & Van Eijndhoven, P. F. (2017). How the brain connects in response to acute stress: A review at the human brain systems level. Neuroscience & Biobehavioral Reviews, 83, 281–297. https://doi.org/10.1016/j.neubiorev.2017.10.015
Zorn, J. V., Schür, R. R., Boks, M. P., Kahn, R. S., Joëls, M., & Vinkers, C. H. (2017). Cortisol stress reactivity across psychiatric disorders: A systematic review and meta-analysis. Psychoneuroendocrinology, 77, 25–36. https://doi.org/10.1016/j.psyneuen.2016.11.036
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