Measuring apparent cortical thickness changes with T1234
Poster No:
1995
Submission Type:
Late-Breaking Abstract Submission
Authors:
Chung (Kenny) Kan1, Renzo Huber1, Rüdiger Stirnberg2, A. Tyler Morgan1, Peter Bandettini1
Institutions:
1NIH, Bethesda, MD, 2German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
First Author:
Co-Author(s):
Late Breaking Reviewer(s):
Rosanna Olsen
Rotman Research Institute, Baycrest Academy for Research and Education
Toronto, Ontario
Rotman Research Institute, Baycrest Academy for Research and Education
Toronto, Ontario
Introduction:
Previously, we developed a fast EPI-based structural scan technique that can provide MP2RAGE-like structural scans without distortions and without typical EPI phase artifacts with TRs of just 20 seconds: T1234 (Kan 2024). This method allows structural scans at the time scale of typical fMRI activation blocks, allowing researchers to investigate functionally induced changes in GM structure.
Previous studies could investigate functionally-induced GM thickness changes (Zaretskaya 2023, Tardif 2017) and GM displacements (Zoraghi 2021) in the time scales of 5-10 minutes. Here we aim to investigate such structural changes at more typical fMRI timescales of 20-40 seconds (Renvall 2014). The purpose of this is to obtain knowledge of cortical fine-scale volume redistribution mechanisms between GM and CSF with potential implications of interpreting VASO layer-fMRI results.
Previous studies could investigate functionally-induced GM thickness changes (Zaretskaya 2023, Tardif 2017) and GM displacements (Zoraghi 2021) in the time scales of 5-10 minutes. Here we aim to investigate such structural changes at more typical fMRI timescales of 20-40 seconds (Renvall 2014). The purpose of this is to obtain knowledge of cortical fine-scale volume redistribution mechanisms between GM and CSF with potential implications of interpreting VASO layer-fMRI results.
Methods:
T1234: T1-weighted 2-inversion 3D-EPI (Stirnberg 2021) with 4 combinations spatial encoding polarities for high-resolution fMRI uses inverse read polarities (Stirnberg 2024) to account for Fuzzy Ripple artifacts (Huber 2024) and geometric distortions with the sequence structure depicted in Fig 1A. Due to its high sampling efficiency, this method enables us to capture structural brain metrics-such as Freesurfer-estimated apparent gray matter (GM) thickness changes-with conventional fMRI task blocks. We used a block design of 40 seconds (two TRs per block) with a flickering checkerboard. Each TR was converted to MP2RAGE-like (Marques 2010) UNI images, which were then analysed with Freesurfer using the layer-fMRI toolbox wrapper (Barilari 2025).
·Fig. 1: acquisition and analysis procedure for EPI-BASED structural scanning at fMRI task time scales
Results:
Preliminary results of two participants indicate that task-induced activity increases the estimated volume of activated brain areas by 10-20% (left in figure 1B), while frontal brain regions (control, right in figure 1B) maintain stable tissue boundaries.
·Fig. 2: apparent GM thickness changes during functional activation
Conclusions:
Interestingly, our findings reveal that most of the detected GM expansion occurs at the GM/white matter (WM) boundary, with no noticeable changes at the GM/cerebrospinal fluid (CSF) interface. In the near future, we aim to reduce the TR further to explore underlying contrast mechanisms and their implications with respect to volume redistribution between GM, blood and CSF. We think that our results suggest that apparent GM thickness increases need to be considered as a potential vascular bias in traditional voxel-based morphometry plasticity studies.
Novel Imaging Acquisition Methods:
Anatomical MRI 2
Non-BOLD fMRI 1
Keywords:
fMRI CONTRAST MECHANISMS
FUNCTIONAL MRI
HIGH FIELD MR
NORMAL HUMAN
Plasticity
1|2Indicates the priority used for review
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Provide references using APA citation style.
● Kan, et al., 2024. https://doi.org/10.1101/2024.09.19.613939.
● Huber, et al., 2024. https://doi.org/10.1101/2024.09.04.611294.
● Marques, et al., 2010. https://doi.org/10.1016/j.neuroimage.2009.10.002,
● Stirnberg et al., 2021. https://doi.org/10.1101/2020.06.08.140699.
● Stirnberg, et al., 2024. https://doi.org/10.1002/mrm.30216,
● Renvall, et al. 2014. ISMRM. #1488.
● Tardif, et al., 2017. https://doi.org/10.1016/j.neuroimage.2017.01.025,
● Zaretskaya, et al., https://doi.org/10.1093/cercor/bhac244,
● Zoraghi, et al. 2021. https://doi.org/10.3389/fnins.2021.722366.