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Claustrum Microstructure and Structural Connectivity in Neonates, Children, and Adults

Poster No:

965

Submission Type:

Abstract Submission

Authors:

Sevilay Ayyildiz¹^,2, Rebecca Hippen¹^,3, Aurore Menegaux³, Jil Wendt¹^,3, Antonia Neubauer⁴, Hongwei Li⁵, Benita Schmitz-Koep¹^,6, David Schinz¹^,3, Claus Zimmer¹^,6, Dennis Hedderich¹^,6, Christian Sorg⁷^,6

Institutions:

¹Institute for Neuroradiology, TUM University Hospital, Munich, Germany, ²TUM-Neuroimaging Center of Klinikum rechts der Isar, Technische Universität München, Munchen, Germany, ³TUM-Neuroimaging Center of Klinikum rechts der Isar, Technische Universität München, Munich, Germany, ⁴Ludwig-Maximilians-Universität Munich, Center for Neuropathology and Prion Research, Munich, Germany, ⁵Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, ⁶TUM-Neuroimaging Center of Klinikum rechts der Isar, Technische Universität, Munich, Germany, ⁷Department of Psychiatry, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany

First Author:

Sevilay Ayyildiz
Institute for Neuroradiology, TUM University Hospital|TUM-Neuroimaging Center of Klinikum rechts der Isar, Technische Universität München
Munich, Germany|Munchen, Germany

Co-Author(s):

Rebecca Hippen
Institute for Neuroradiology, TUM University Hospital|TUM-Neuroimaging Center of Klinikum rechts der Isar, Technische Universität München
Munich, Germany|Munich, Germany

Aurore Menegaux
TUM-Neuroimaging Center of Klinikum rechts der Isar, Technische Universität München
Munich, Germany

Jil Wendt
Institute for Neuroradiology, TUM University Hospital|TUM-Neuroimaging Center of Klinikum rechts der Isar, Technische Universität München
Munich, Germany|Munich, Germany

Antonia Neubauer
Ludwig-Maximilians-Universität Munich, Center for Neuropathology and Prion Research
Munich, Germany

Hongwei Li
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Boston, MA

Benita Schmitz-Koep
Institute for Neuroradiology, TUM University Hospital|TUM-Neuroimaging Center of Klinikum rechts der Isar, Technische Universität
Munich, Germany|Munich, Germany

David Schinz
Institute for Neuroradiology, TUM University Hospital|TUM-Neuroimaging Center of Klinikum rechts der Isar, Technische Universität München
Munich, Germany|Munich, Germany

Claus Zimmer
Institute for Neuroradiology, TUM University Hospital|TUM-Neuroimaging Center of Klinikum rechts der Isar, Technische Universität
Munich, Germany|Munich, Germany

Dennis Hedderich
Institute for Neuroradiology, TUM University Hospital|TUM-Neuroimaging Center of Klinikum rechts der Isar, Technische Universität
Munich, Germany|Munich, Germany

Christian Sorg
Department of Psychiatry, Klinikum Rechts der Isar, Technische Universität München|TUM-Neuroimaging Center of Klinikum rechts der Isar, Technische Universität
Munich, Germany|Munich, Germany

Introduction:

The claustrum is a thin, irregular, sheet-like neuronal structure located beneath the insular cortex (Smith, Lee, & Jackson, 2020). The claustrum is the most densely connected brain structure by size - it sends and receives connections to and from almost all regions of the cortex (Pathak & Fernandez-Miranda, 2014; Torgerson et al., 2015). This widespread connectivity suggests that the claustrum is critical in various functions, including attention and cognitive processes (Crick & Koch, 2005; Jackson et al., 2018). Diffusion-weighted imaging (DWI) reliably demonstrates the claustrum's cortical and subcortical connections across populations and protocols (Wendt et al., 2024). This study aims to investigate and compare the microstructure and structural connectivity of the claustrum to the cortex in neonates, children, and adults, providing insight into its developmental trajectory.

Methods:

We conducted a study involving 64 neonates (scanned at 40 gestational weeks) from the Developing Human Connectome Project (HCP), 65 children aged 6–9 years from the HCP Development, and 70 healthy adults aged 22–35 years from the HCP Young Adults. Diffusion-weighted images were preprocessed using optimized pipelines tailored for these datasets, including correction for motion, susceptibility-induced distortions, and eddy currents, as well as generating a synthetic undistorted b0 (Bastiani et al., 2019; Glasser et al., 2013). These preprocessing steps are specifically designed to account for the significant variations in brain tissue properties among adults, children, and neonates. The delineation of the bilateral claustra was achieved through a deep learning-based automated segmentation approach and transfer learning from adult scans (Li et al., 2021; Neubauer et al., 2022). Fractional anisotropy (FA) and mean diffusivity (MD) maps were calculated using the DTIFIT tool implemented in FSL to assess the microstructure of the claustrum. Structural connectivity between the claustrum and primary, associative, and cingulate cortices was evaluated using probabilistic tractography in the FSL toolbox, and connection density (CD) was computed (Behrens et al., 2007). NeuroCombat via Python was used to harmonize DTI measurements such as FA, MD, and CD to account for inter-scanner differences across datasets. Inter-group comparisons of microstructural and tractography parameters were conducted using ANCOVA, controlling for sex.

Results:

It was observed that FA values increased from the neonatal to the adult group, while MD values decreased (p < .05) (Figure1 A-B). Ipsilaterally, the highest density of streamlines seed from the claustrum and reached the prefrontal associative cortex across all groups. Although CD between the claustrum and all primary and associative cortical regions was found to be higher ipsilaterally than contralaterally, CD for the cingulate cortices tends to be more contralateral. CD from the claustrum to cingulate, visual, associative (occipital and parietal), and sensory cortices increased across development (adults > children > neonates; p < .05) (Figure1 C). In contrast, connectivity to motor cortices decreased with age (adults < children < neonates; p < .05) (Figure1 D).

·Figure1: Claustrum microstructure and structural connectivity newborn, children, and adult groups comparison

Conclusions:

The results suggest that the claustrum microstructure gets more organized, and its connectivity to most brain regions strengthens from newborns to adulthood. Tractography analyses identified extensive ipsilateral connectivity of the claustrum, with cortical targets, while revealing less dense yet widespread contralateral connectivity. The observed variations in the structural connectivity of the claustrum may reflect its adaptive capacity to modify connections based on the individual's developmental stage and the corresponding sensory and cognitive demands.

Lifespan Development:

Early life, Adolescence, Aging ¹

Modeling and Analysis Methods:

Segmentation and Parcellation

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

White Matter Anatomy, Fiber Pathways and Connectivity

Novel Imaging Acquisition Methods:

Anatomical MRI

Diffusion MRI ²

Keywords:

Aging

Cognition

Data analysis

MRI

Open Data

STRUCTURAL MRI

WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC

Other - Claustrum

^1|2Indicates the priority used for review

Abstract Information

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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?

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.

Yes

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.

Not applicable

Please indicate which methods were used in your research:

Structural MRI

Diffusion MRI

For human MRI, what field strength scanner do you use?

1.5T

Which processing packages did you use for your study?

FSL

Provide references using APA citation style.

1. Bastiani, M., Andersson, J. L. R., Cordero-Grande, L., Murgasova, M., Hutter, J., Price, A. N., et al. (2019). Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project. Neuroimage, 185, 750. https://doi.org/10.1016/J.NEUROIMAGE.2018.05.064
2. Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., & Woolrich, M. W. (2007). Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage, 34(1), 144–155. https://doi.org/10.1016/J.NEUROIMAGE.2006.09.018
3. Crick, F. C., & Koch, C. (2005). What is the function of the claustrum? Philosophical Transactions of the Royal Society B: Biological Sciences. https://doi.org/10.1098/rstb.2005.1661
4. Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., et al. (2013). The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage, 80, 105–124. https://doi.org/10.1016/J.NEUROIMAGE.2013.04.127
5. Jackson, J., Karnani, M. M., Zemelman, B. V., Burdakov, D., & Lee, A. K. (2018). Inhibitory Control of Prefrontal Cortex by the Claustrum. Neuron, 99(5), 1029-1039.e4. https://doi.org/10.1016/j.neuron.2018.07.031
6. Pathak, S., & Fernandez-Miranda, J. C. (2014). Structural and Functional Connectivity of the Claustrum in the Human Brain. In The Claustrum: Structural, Functional, and Clinical Neuroscience. Elsevier Inc. https://doi.org/10.1016/B978-0-12-404566-8.00007-6
7. Smith, J. B., Lee, A. K., & Jackson, J. (2020). The claustrum. Current Biology : CB, 30(23), R1401–R1406. https://doi.org/10.1016/J.CUB.2020.09.069
8. 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. https://doi.org/10.1002/HBM.22667
9. Wendt, J., Neubauer, A., Hedderich, D. M., Schmitz-Koep, B., Ayyildiz, S., Schinz, D., et al. (2024). Human Claustrum Connections: Robust In Vivo Detection by DWI-Based Tractography in Two Large Samples. Human Brain Mapping, 45(14), e70042. https://doi.org/10.1002/HBM.70042

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