Alteration in macro- and micro-structures of the adolescent hippocampus after the COVID-19 pandemic

Presented During:

Thursday, June 27, 2024: 11:30 AM - 12:45 PM
COEX  
Room: Grand Ballroom 103  

Poster No:

1310 

Submission Type:

Abstract Submission 

Authors:

Lin Cai1, Norihide Maikusa1, Yinghan Zhu1, Atsushi Nishida2, Shuntaro Ando1, Naohiro Okada3, Kiyoto Kasai1, Yuko Nakamura1, Shinsuke Koike1

Institutions:

1The University of Tokyo, Tokyo, Japan, 2Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan, 3University of Tokyo Institutes for Advanced Study, Tokyo, Japan

First Author:

Lin Cai  
The University of Tokyo
Tokyo, Japan

Co-Author(s):

Norihide Maikusa  
The University of Tokyo
Tokyo, Japan
Yinghan Zhu  
The University of Tokyo
Tokyo, Japan
Atsushi Nishida  
Tokyo Metropolitan Institute of Medical Science
Tokyo, Japan
Shuntaro Ando  
The University of Tokyo
Tokyo, Japan
Naohiro Okada  
University of Tokyo Institutes for Advanced Study
Tokyo, Japan
Kiyoto Kasai  
The University of Tokyo
Tokyo, Japan
Yuko Nakamura  
The University of Tokyo
Tokyo, Japan
Shinsuke Koike  
The University of Tokyo
Tokyo, Japan

Introduction:

Although a SARS-CoV-2 infection has been revealed to result in changes in human brain structure (1), the impact of the COVID-19 pandemic on the brain among uninfected individuals is still underexplored. Since adolescence is a sensitive period for the development of mental illnesses caused by stressful life events (2), it is important to understand the effect of stressful experiences on the adolescent brain, particularly the hippocampus. Thus, the COVID-19 pandemic, as one of extremely stressful life events, enabled us to examine the impact of stress on the adolescent hippocampus.

Methods:

We analyzed 1,149 longitudinal brain structural scans from 479 participants (mean ± SD =14.5 ± 2.2 years; 214 girls) who were from a longitudinal population-neuroscience Tokyo TEEN Cohort study (3). At the end of wave 3, due to the first state of emergency (SoE), data collection was suspended between the end of March 2020 and July 30, 2020.

For the four waves of data collection, three acquisition procedures were performed. Only T1-weighted images were acquired for Procedure 1 and 2, while T1-weighted, T2-weighted, and diffusion images were acquired for Procedure 3. The HCP pipeline was utilized for image preprocessing (4). Then, bilateral hippocampal volumes were extracted from the FreeSurfer's aseg.stats file. For Procedure 3, 12 hippocampal subfield volumes were obtained using the hippocampal subfield segmentation algorithm in FreeSurfer v6.0.0 (5). After raw diffusion scans were preprocessed, the microstructural metrics were estimated using the diffusion kurtosis imaging (DKI) model (6). The DKI model could produce 7 microstructural indices, namely fractional anisotropy (FA), mean diffusivity, axial diffusivity, radial diffusivity, mean kurtosis, axial kurtosis, and radial kurtosis. For each scan, volumetric estimates for the bilateral hippocampi and hippocampal subfields, and the 7 microstructural indices for the bilateral hippocampi were averaged across hemispheres as indices in the following statistical analysis.

To examine whether the hippocampus during the COVID-19 pandemic differed from that collected in other dates, we set a one-year time interval from 2020/07/29 to 2021/07/29 after the first SoE (2020/04/07 ~ 2020/05/25). Relative to July 29, 2020, the date of MRI scans during this one-year interval was converted into relative values using the log transformations denoted as RV.log. Subsequently, RV.log entered the generalized additive mixed models (GAMMs) or generalized linear mixed models (GLMMs) to examine how the SoE impacted the hippocampal structures. We included age as a smooth term, sex, SES, IQ, and ICV as linear terms, as well as a tensor interaction term between age and sex in the GAMMs. Additionally, the participant ID was introduced as the random intercept. For hippocampal subfield volumes and DKI indices, we used GLMMs to examine the relationships between subfield volumes and SoE. False discovery rate correction was used to account for multiple comparisons.

Results:

The GAMM showed that there was a significant main effect of SoE on the mean hippocampal volume (β = 102.19, 95% CI [0.61, 203.77], p = 0.049). The GLMMs showed main effects of SoE on mean volumes in the granule cell and molecular layer of the dentate gyrus (β = 18.19, 95% CI [2.97, 33.41], uncorrected p = 0.02), CA4 (β = 12.75, 95% CI [0.38, 25.12], uncorrected p = 0.04), and hippocampus-amygdala transition area (β = 5.67, 95% CI [1.18, 10.17], uncorrected p = 0.01). A main effect of SoE on the FA values in the mean hippocampus was found (β = 0.03,95% CI [1.93e-03, 0.06], uncorrected p = 0.04).

Conclusions:

Our findings revealed that the COVID-19 pandemic resulted in a transient increase in adolescent hippocampal volumes. Similar but less robust findings were observed for hippocampal subfields and microstructure. These findings provide new insight that a major life event might alter the adolescent hippocampal development.

Education, History and Social Aspects of Brain Imaging:

Education, History and Social Aspects of Brain Imaging 2

Lifespan Development:

Normal Brain Development: Fetus to Adolescence 1

Keywords:

Development
MRI
Plasticity
Sub-Cortical

1|2Indicates the priority used for review

Provide references using author date format

1 Douaud, G.(2022), 'SARS-CoV-2 is associated with changes in brain structure in UK Biobank', Nature 604, 697-707
2 Fuhrmann, D. (2015), 'Adolescence as a sensitive period of brain development', Trends Cogn Sci 19, 558-566
3 Okada, N. (2019), 'Population‐neuroscience study of the Tokyo TEEN Cohort (pn‐TTC): Cohort longitudinal study to explore the neurobiological substrates of adolescent psychological and behavioral development', Psychiatry and Clinical Neurosciences 73, 231-242
4 Glasser, M. F. (2013), 'The minimal preprocessing pipelines for the Human Connectome Project', Neuroimage 80, 105-124
5 Iglesias, J. E. (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
6 Jensen, J. H. (2005), 'Diffusional kurtosis imaging: the quantification of non‐gaussian water diffusion by means of magnetic resonance imaging', Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 53, 1432-1440