VertexWiseR: a R package for simplified vertex-wise analyses of whole-brain and hippocampal surfaces
Poster No:
1506
Submission Type:
Abstract Submission
Authors:
Charly Billaud1, Junhong Yu1
Institutions:
1Nanyang Technological University, Singapore, NA
First Author:
Co-Author:
Introduction:
Currently, whole-brain vertex-wise analyses on brain surfaces commonly require specially configured operating systems/environments to run and are largely inaccessible to R users. We present VertexWiseR, a user-friendly R package, to run cortical and hippocampal surface vertex-wise analyses, in just about any computer, requiring minimal technical expertise and computational resources. The package allows cohort-wise anatomical surface data to be highly compressed into a single, compact, easy-to-share file. Users can run a range of vertex-wise statistical analyses with that single file without requiring a special operating system/environment and direct access to the preprocessed file directories. The package includes a suite of tools for extracting, manipulating, analysing, and visualizing vertex-wise data, and is designed to be easy for beginners to use. It also includes advanced functionalities such as hippocampal surface analyses, meta-analytic decoding, threshold-free cluster enhancement (TFCE), and mixed effects models that would appeal to experienced researchers as well.
·Summary of the VertexWiseR analysis workflow
Methods:
We showcase analyses of two publicly accessible datasets. For whole-brain surface analysis, we used a public dataset of healthy younger (N=154; age=22.29y±3.12; F56%) and older adults (N=84; age=67.54y±5.69; F56%) (Spreng et al., 2022) with T1w scans preprocessed in FreeSurfer (Fischl, 2012). Using VertexWiseR's tools, all participants' vertex-wise cortical thickness (CT) values were extracted and converted to a single R object. Surface smoothing was applied, and the effect of age on whole-brain cortical thickness, controlling for sex, was tested via two vertex-wise linear models: one with random field theory (RFT)-based cluster correction and one with TFCE (5000 permutations).
For hippocampal surface analysis, we used a public dataset of 48 healthy young adults scanned at 3 time-points of a running intervention, given to one group between t1 and t2 (N=21; age=23.24y±3.18; F57%), and a second between t2 and t3 (N=27; age=22.78y±2.58; F63%) (Fink et al., 2021), with T1w scans preprocessed with HippUnfold (DeKraker et al., 2023). Likewise, using VertexWiseR's tools, hippocampal thickness values were compiled into a single cohort-wise object, and smoothed. The interaction effect of session x group on the vertex-wise hippocampal thickness was tested using a mixed effects model. RFT and TFCE corrections (1000 permutations) were applied to the results subsequently.
For hippocampal surface analysis, we used a public dataset of 48 healthy young adults scanned at 3 time-points of a running intervention, given to one group between t1 and t2 (N=21; age=23.24y±3.18; F57%), and a second between t2 and t3 (N=27; age=22.78y±2.58; F63%) (Fink et al., 2021), with T1w scans preprocessed with HippUnfold (DeKraker et al., 2023). Likewise, using VertexWiseR's tools, hippocampal thickness values were compiled into a single cohort-wise object, and smoothed. The interaction effect of session x group on the vertex-wise hippocampal thickness was tested using a mixed effects model. RFT and TFCE corrections (1000 permutations) were applied to the results subsequently.
Results:
For the whole-brain analysis, widespread negative clusters with both cluster correction approaches, however, a small temporal polar positive cluster survived RFT but not TFCE correction. Meta-analytic decoding was also used to identify keywords from a database of brain activation maps linked to the negative clusters. The meta-analytic decoding found small correlations between the clusters and memory-related and aging keywords. For hippocampal surface analysis, a positive cluster in the left subiculum survived both TFCE and RFT correction. Additionally, a positive cluster in the right cornu ammonis (CA1), and 2 small negative clusters in the left CA1 and right subiculum survived TFCE but not RFT correction.
Conclusions:
The first example analysis reinforced the widespread effect of age on cortical thinning in older adults and how thickness can be a sensitive measure of neurodegeneration. In the second example, we were also able to reproduce the results of Fink and colleagues, pertaining to intervention-related gains in hippocampal volume (Fink et al., 2021), demonstrating the complementary information from the hippocampal surface-based thickness measurements. These example analyses highlighted the ease at which one can run analyses to identify significant clusters as a function of individual differences, and longitudinal changes. Overall, VertexWiseR opens up new frontiers for the R's user base/community and makes such neuroimaging analyses accessible to the masses.
Modeling and Analysis Methods:
Methods Development 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 2
Keywords:
Computational Neuroscience
Cortex
Data analysis
Informatics
Modeling
Open-Source Code
Open-Source Software
Statistical Methods
STRUCTURAL MRI
Other
1|2Indicates the priority used for review
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Fink, A., Koschutnig, K., Zussner, T., Perchtold-Stefan, C. M., Rominger, C., Benedek, M., & Papousek, I. (2021). A two-week running intervention reduces symptoms related to depression and increases hippocampal volume in young adults. Cortex, 144, 70–81. https://doi.org/10.1016/j.cortex.2021.08.010
Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774–781. https://doi.org/10.1016/j.neuroimage.2012.01.021
Spreng, R. N., Setton, R., Alter, U., Cassidy, B. N., Darboh, B., DuPre, E., Kantarovich, K., Lockrow, A. W., Mwilambwe-Tshilobo, L., Luh, W.-M., Kundu, P., & Turner, G. R. (2022). Neurocognitive aging data release with behavioral, structural and multi-echo functional MRI measures. Scientific Data, 9(1), Article 1. https://doi.org/10.1038/s41597-022-01231-7