From Infancy to Old Age: Exploring Claustrum Volume Changes Throughout the Lifespan
Poster No:
1000
Submission Type:
Abstract Submission
Authors:
Sevilay Ayyildiz1, Antonia Neubauer2, Melissa Thalhammer3, Hongwei Li4, Jil Wendt1, Aurore Menegaux5, Rebecca Hippen1, Benita Schmitz-Koep6, David Schinz1, Claus Zimmer3, Dennis Hedderich1, Christian Sorg7
Institutions:
1Technical University of Munich, Munich, Bavaria, 2Ludwig-Maximilians-Universität Munich, Munich, Bavaria, 3Institute for Neuroradiology, TUM University Hospital, Munich, Bavaria, 4Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, 55. TUM-Neuroimaging Center of Klinikum rechts der Isar, Technische Universität München, Munich, Bavaria, 6TUM University Hospital, Technical University of Munich, School of Medicine and Health, Munich, Bavaria, 7Department of Psychiatry, Klinikum Rechts der Isar, Technische Universität München, Munich, Bavaria
First Author:
Co-Author(s):
Hongwei Li
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA
Aurore Menegaux
5. TUM-Neuroimaging Center of Klinikum rechts der Isar, Technische Universität München
Munich, Bavaria
5. TUM-Neuroimaging Center of Klinikum rechts der Isar, Technische Universität München
Munich, Bavaria
Benita Schmitz-Koep
TUM University Hospital, Technical University of Munich, School of Medicine and Health
Munich, Bavaria
TUM University Hospital, Technical University of Munich, School of Medicine and Health
Munich, Bavaria
Christian Sorg
Department of Psychiatry, Klinikum Rechts der Isar, Technische Universität München
Munich, Bavaria
Department of Psychiatry, Klinikum Rechts der Isar, Technische Universität München
Munich, Bavaria
Introduction:
The claustrum is a small grey matter structure located between the insula and striatum (Jackson, Smith, & Lee, 2020). It is the most extensively connected brain structure relative to its volume, forming networks with cortical and subcortical regions (Crick & Koch, 2005; Torgerson, Irimia, Goh, & Van Horn, 2015). Alterations in claustrum volume are observed across various clinical conditions and age groups, indicating a potential role of the claustrum in neurodevelopmental and neurodegenerative disorders (Ayyildiz et al., 2023; Davis, 2008). Complementary to cross-sectional changes for certain clinical conditions, it remains unclear how the claustrum changes along development and aging in the healthy population. The current study, therefore, investigates the whole trajectory of the claustrum volume across the lifespan and investigates potential influences of hemisphere and sex.
Methods:
The study comprises data from six datasets, encompassing a cohort of 3474 healthy participants aged 1–80 years, with nearly equal representation of men (48%) and women (52%). The datasets include anonymized conventional 3D T1-weighted (T1w) MRI images sourced from the following: the UNC/UMN Lifespan Baby Connectome Project, the Calgary Preschool MRI Dataset, a selected subset from the Adolescent Brain Cognitive Development Study (5.1 release), and the Lifespan Human Connectome Project (HCP) dataset. Participants without mental disorders, cognitive impairments, or significant medical conditions and with T1w scans of adequate resolution for precise claustrum segmentation were included in the analysis. All T1w scans underwent a preprocessing in the FSL toolbox, a protocol that included brain extraction, denoising, and resampling to 1mm3 to standardize the spatial resolution across images. The claustrum segmentation process employed a developed in-house deep learning algorithm and used transfer learning from adult scans (Li et al., 2021; Neubauer et al., 2022). We used warped Bayesian Linear Regression (BLR) within the PCNtoolkit framework (cite 10.1016/j.neuroimage.2021.118715 and https://doi.org/10.1038/s41596-022-00696-5) to model normative trajectories of claustrum development across the lifespan, including age, sex, scanner type, and total intracranial volume as covariates. Developmental milestones and sex differences in the claustrum volume trajectory were assessed and delineated based on the resulting normative models.
Results:
Lifespan trajectories indicated that bilateral claustrum volume increased until adolescence, peaking at an average age of 18.5 years (CI 18.4–18.7) for the right (Figure 1 A1) and 16.7 years (CI 16.6–16.9) for the left claustrum (Figure 1 A2), followed by a near-linear decline. Growth velocity reached its highest at approximately 1.1 years (CI 1–1.4). Age-stratified analyses of bilateral lateralization revealed significant gender differences, with males exhibiting larger left claustrum volumes during late adolescence (ages 16–17, p < 0.05) and early adulthood (ages 26–29, p < 0.05). For the right claustrum, males consistently showed larger volumes compared to females from mid-adolescence through early adulthood (ages 15–37, p < 0.001) and persisted into later adulthood (ages 62–68, p < 0.05).
·Figure 1: The 5th, 25th, 50th, 75th, and 95th percentiles quantify the range of variation among lifespan-healthy individuals
Conclusions:
The application of deep learning-based techniques for claustrum segmentation, coupled with normative modeling to generate reference plots for claustrum volume, represents a robust methodology for identifying the lifelong trajectory of the claustrum volumes. Investigating volumetric changes across various age intervals is essential for accurately delineating the distinctions between normal and pathological development of the claustrum.
Higher Cognitive Functions:
Higher Cognitive Functions Other
Lifespan Development:
Lifespan Development Other 1
Modeling and Analysis Methods:
Bayesian Modeling
Segmentation and Parcellation 2
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Aging
Data analysis
Data Organization
Morphometrics
MRI
Open Data
STRUCTURAL MRI
1|2Indicates the priority used for review
Abstract Information
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2. Crick, F. C., & Koch, C. (2005). What is the function of the claustrum? Philosophical Transactions of the Royal Society B: Biological Sciences.
3. Davis, W. (2008). The Claustrum in Autism and Typically Developing Male Children: A Quantitative MRI study.
4. Jackson, J., Smith, J. B., & Lee, A. K. (2020). The Anatomy and Physiology of Claustrum-Cortex Interactions. Annual Review of Neuroscience, 43, 231–247.
5. Li, H., Menegaux, A., Schmitz-Koep, B., Neubauer, A., Bäuerlein, F. J. B., Shit, S., et al. (2021). Automated claustrum segmentation in human brain MRI using deep learning. Human Brain Mapping, 42(18), 5862–5872.
6. Neubauer, A., Li, H. B., Wendt, J., Schmitz-Koep, B., Menegaux, A., Schinz, D., et al. (2022). Efficient Claustrum Segmentation in T2-weighted Neonatal Brain MRI Using Transfer Learning from Adult Scans. Clinical Neuroradiology, 1–12.
7. Torgerson, C. M., Irimia, A., Goh, S. Y. M., & Van Horn, J. D. (2015). The DTI connectivity of the human claustrum. Human Brain Mapping, 36(3), 827–838.