Hippocampal Morphology and Stress Levels in Medical and Non-Medical Students: Implications for Learn
Poster No:
1058
Submission Type:
Abstract Submission
Authors:
Ashwag Alruwaili1, Sumyah Alnajashi1, Afnan Alwasedi2, Sara Albraihi2, Ross Keenan3, Tracy Melzer3, Mustafa Almuqbel4
Institutions:
1King Saud University, Riyadh, Ryiadh, 2King Saud University, Riyadh, Riyadh, 3New Zealand Brain Research Institute, Christchurch, Christchurch, 4Pacific Radiology Group, Christchurch, Christchurch
First Author:
Co-Author(s):
Introduction:
The presence of hippocampal atrophy has been consistently identified in studies involving stress imaging. Maintaining overall mental health is crucial for sustaining this ability. Even seemingly trivial factors such as apprehensive thoughts regarding potential threats or anxieties about the future have the potential to disrupt our well-being (WHO, 2004). In this study, we investigate the impact of academic stress on hippocampal volume. This study aimed to investigate the role of stress on academic students.
Methods:
A cohort of 20 medical and 20 non-medical female students underwent assessment for stress and anxiety levels during the examination period using the Kessler Psychological Distress Questionnaire (K10). Following this assessment, magnetic resonance imaging (MRI) scans of the brain were conducted on our 3-Tesla (Siemens Spectra) MRI scanner (Iselin, NJ). For each participant, we acquired a three-dimensional T1-weighted MPRAGE dataset. The data were collected in sagittal plane with a FOV=256mm, voxel size = 1.0x1.0x1.0mm, TR=1900ms, TE=2.42ms, TI=900ms, rBW=220Hz, FA=90, and an acquisition time of 04:23 minutes. Hippocampal segmentation and volumetric quantification were assessed using the FreeSurfer software package. All participants' T1-weighted MR images were processed using the freely available FreeSurfer 7.1.0 (http://surfer.nmr.mgh.harvard.edu) standard cross-sectional processing pipeline. Following that, the dedicated FreeSurfer hippocampal subfields pipeline was employed on all data sets. Briefly, the algorithm for segmentation of individual sub-regions uses Bayesian inference based on observed image intensities and a probabilistic atlas built from a library of in vivo manual segmentations and ultra-high resolution (~0.1 mm isotropic) ex-vivo labelled MRI data. Details of these steps are described by Iglesias and colleagues (Lee et al. 2009, Iglesias et al. 2015, Brown et al. 2020). The hippocampal subfield segmentation module resulted in volumetric estimation of the hippocampal head, body, and tail for each hemisphere. Estimated total intracranial volume (eTIV) generated by FreeSurfer was used as an estimate for intracranial volume (ICV) in this study. All the processed data were visually inspected for artifacts; no manual editing was performed on the data. The estimated right and left volumes of the hippocampal head, body, and tail were used in the statistical analysis. Figure 1 demonstrates the hippocampal subfields segmentation in one of the study subjects. Demographic data and imaging metrics were subjected to rigorous statistical analysis using the R software°
Results:
Our findings revealed a notable disparity in stress levels between medical and non-medical students, with the former experiencing significantly higher stress level. Contrary to expectations, medical students had larger hippocampal head volumes, with no significant differences in hippocampal body and tail volumes between the two groups. Discussion: Medical students' rich learning environment and cognitive demands may have contributed to larger hippocampal volumes, as head region plays a vital role in encoding information and building associations based on learning and memorization. The hippocampal head region has been associated with declarative memory, learning, and visual associations, which are highly relevant to medical students who constantly face novel information and body organ imagery. The lack of a significant correlation between hippocampal volume and stress may be due to a shorter duration of stress exposure, as chronic stress may require an extended period to induce structural changes.
Conclusions:
This research underscores the potential role of learning over stress on brain morphology and, subsequently, the overall well-being of students. As a result, educators should consider implementing strategies to equip students with effective stress-coping mechanisms to mitigate the risk of enduring long-term and potentially irreversible consequences.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Learning and Memory:
Learning and Memory Other 1
Keywords:
Anxiety
Learning
Memory
MRI
Plasticity
Segmentation
STRUCTURAL MRI
Other - education, stress
1|2Indicates the priority used for review
Provide references using author date format
Iglesias, J. E., J. C. Augustinack, K. Nguyen, C. M. Player, A. Player, M. Wright, N. Roy, M. P. Frosch, A. C. McKee, L. L. Wald, B. Fischl and K. Van Leemput (2015). "A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI." NeuroImage 115: 117-137.
Kessler, R. and D. Mroczek (1992). "An update of the development of mental health screening scales for the US national health interview study." Ann Arbor: University of Michigan, Survey Research Center of the Institute for Social Research: 31115-31118.
Lee, T., T. Jarome, S.-J. Li, J. J. Kim and F. J. Helmstetter (2009). "Chronic stress selectively reduces hippocampal volume in rats: a longitudinal magnetic resonance imaging study." Neuroreport 20(17): 1554-1558.
Reuter, M., Schmansky, N.J., Rosas, H.D., Fischl, B. 2012. Within-Subject Template Estimation for Unbiased Longitudinal Image Analysis. Neuroimage 61 (4), 1402-1418. http://reuter.mit.edu/papers/reuter-long12.pdf
WHO, W. H. O. (2004). The International Statistical Classification of Diseases and Health Related Problems World Health Organization.