Age-related change in structural features between multimodal HCP pipeline and T1w HCP legacy mode
Poster No:
1018
Submission Type:
Abstract Submission
Authors:
Shinsuke Koike1, Norihide Maikusa1, Issei Ueda1, Lin Cai1, Shuhei Shibukawa1, Shinichiro Nakajima2, Saori Tanaka3, Takuya Hayashi4
Institutions:
1The University of Tokyo, Tokyo, NA, 2Keio University School of Medicine, Tokyo, NA, 3Nara Institute of Science and Technology, Nara, NA, 4RIKEN Center for Biosystems Dynamics Research, Kobe, NA
First Author:
Co-Author(s):
Introduction:
Life-course trajectory analysis has revealed unique characteristics of brain magnetic resonance imaging (MRI) features (Bethlehem et al., 2022). For example, surface area (SA) and cortical thickness (CT) features in most regions typically begin to decrease from childhood, with accelerated decline during adolescence. However, these analyses are based on the assumption that brain image preprocessing is minimally affected by demographic characteristics like age. Given that CT changes during adolescence due to cortical foldings and myelination (Natu et al., 2019), we investigated whether brain features vary with measurement procedures and age at scan using large-sample life-course and traveling subject (TS) datasets.
Methods:
We utilized 1281 multimodal brain images (median age at scan: 25.3 years [range: 14.1 - 80], females: 602) from the Brain/MINDS Beyond Human Brain MRI (BMB-HBM) study project in Japan (Koike et al., 2021), acquired using Siemens Prisma and the Connectome Related Human Disease (CRHD) protocol, including 0.8 mm isotropic resolution T1-weighted, T2-weighted, and 2.0 mm resolution filedmap images. We also analyzed 167 scans from 35 TS participants across 12 procedures, including the CRHD and conventional T1-weighted scan protocols (1.0 mm isotropic resolution). The original Human Connectome Project (HCP) pipeline was performed for multimodal images in CRHD, and the legacy mode for all T1-weighted images. From the preprocessed images, 68 SA, 68 CT, and 14 subcortical volume (SV) features were extracted based on Desikan Killiany atlas.
Discrepancies of procedures and preprocessing methods were examined for TS images. We also tested whether the discrepancies would vary across age using for 1281 multimodal images and general linear models (GLMs). Finally, we compared three GLMs for HCP preprocessed metrics as the dependent variable, with the following independent variables for each feature:
Model 1. Legacy mode metrics only.
Model 2. Legacy mode metrics and age at scan.
Model 3. Legacy mode metrics, age at scan, and their interaction.
Discrepancies of procedures and preprocessing methods were examined for TS images. We also tested whether the discrepancies would vary across age using for 1281 multimodal images and general linear models (GLMs). Finally, we compared three GLMs for HCP preprocessed metrics as the dependent variable, with the following independent variables for each feature:
Model 1. Legacy mode metrics only.
Model 2. Legacy mode metrics and age at scan.
Model 3. Legacy mode metrics, age at scan, and their interaction.
Results:
Using multimodal HCP preprocessed metrics as a reference, mean absolute errors (MAEs) of the legacy mode preprocessed metrics for the same T1-weighted images were 22.1 (1.02% of mean), 0.087 (3.14%), and 138.9 (4.93%) for the SA, CT, and SV features, respectively (Figure 1). MAEs of the legacy mode for a different T1-weighted 1.0 mm isotropic protocol on the same Prisma machine were 58.7 (3.35%), 0.23 (7.55%), and 118.3 (6.44%), respectively, suggesting that CT features are likely to be affected by protocol and preprocessing variations. MAEs of the legacy mode for different machines and conventional protocols were 83.0 (3.66%), 0.25 (9.69%), and 187.3 (6.64%), respectively.
GLMs showed that age was associated with discrepancies between the HCP and legacy mode preprocessed metrics in 33 SA, 68 CT, and 11 SV features (p < .05). Legacy preprocessing underestimated metrics in younger population for 105 significant features (Figure 2), while overestimateing for 6 SA features and left hippocampal volume. Model comparison indicated that Model 2 was chosen for 15 SA, 26 CT, and 8 SV features and Model 3 for 20 SA, 42 CT, and 3 SA, while Model 1 was not selected for any of CT features.
GLMs showed that age was associated with discrepancies between the HCP and legacy mode preprocessed metrics in 33 SA, 68 CT, and 11 SV features (p < .05). Legacy preprocessing underestimated metrics in younger population for 105 significant features (Figure 2), while overestimateing for 6 SA features and left hippocampal volume. Model comparison indicated that Model 2 was chosen for 15 SA, 26 CT, and 8 SV features and Model 3 for 20 SA, 42 CT, and 3 SA, while Model 1 was not selected for any of CT features.
·Figure 1
·Figure 2
Conclusions:
Age was associated with discrepancies between multimodal HCP and T1-weighted legacy mode preprocessing methods, particularly in the CT features. Life-course trajectory analysis may be carefully interpreted because of the differences in measurement and preprocessing. Future studies should investigate the relationships between CT, myelination, and age-related brain factors.
Lifespan Development:
Early life, Adolescence, Aging
Normal Brain Development: Fetus to Adolescence 1
Modeling and Analysis Methods:
Methods Development
Segmentation and Parcellation 2
Keywords:
MRI
Segmentation
STRUCTURAL MRI
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Koike, S., Tanaka, S. C., Okada, T., Aso, T., Yamashita, A., Yamashita, O., Asano, M., Maikusa, N., Morita, K., Okada, N., Fukunaga, M., Uematsu, A., Togo, H., Miyazaki, A., Murata, K., Urushibata, Y., Autio, J., Ose, T., Yoshimoto, J., … Hayashi, T. (2021). Brain/MINDS Beyond Human Brain MRI project: A protocol for multi-level harmonization across brain disorders throughout the lifespan. NeuroImage: Clinical, 30, 102600. https://doi.org/10.1016/j.nicl.2021.102600
Natu, V. S., Gomez, J., Barnett, M., Jeska, B., Kirilina, E., Jaeger, C., Zhen, Z., Cox, S., Weiner, K. S., Weiskopf, N., & Grill-Spector, K. (2019). Apparent thinning of human visual cortex during childhood is associated with myelination. Proceedings of the National Academy of Sciences, 116(41), 20750–20759. https://doi.org/10.1073/pnas.1904931116