Multiscale cortical thickness changes associated with Friedreich ataxia, as revealed by eigenmodes
Poster No:
180
Submission Type:
Abstract Submission
Authors:
Zuitian Tao1, Gille Naeije2,3, Fuad Noman4, Ian Harding5, Nellie Georgiou-Karistianis6, Alex Fornito6, Trang Cao6, Susmita Saha1,7
Institutions:
1School of Psychological Sciences, Monash University, Victoria, Australia, 2Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, Université libre de Bruxelles (ULB), Brussels, Belgium, 3Department of Neurology, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium, 4Monash University Malaysia, Subang, Selangor, 5QIMR Berghofer Medical Research Institute, Brisbane, Queensland, 6School of Psychological Sciences, Monash University, Clayton, Victoria, 7School of Translational Medicine, Monash University, Victoria, Australia
First Author:
Co-Author(s):
Gille Naeije
Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, Université libre de Bruxelles (ULB)|Department of Neurology, CUB Hôpital Erasme, Université libre de Bruxelles (ULB)
Brussels, Belgium|Brussels, Belgium
Laboratoire de Neuroanatomie et Neuroimagerie translationnelles, Université libre de Bruxelles (ULB)|Department of Neurology, CUB Hôpital Erasme, Université libre de Bruxelles (ULB)
Brussels, Belgium|Brussels, Belgium
Nellie Georgiou-Karistianis, PhD
School of Psychological Sciences, Monash University
Clayton, Victoria
School of Psychological Sciences, Monash University
Clayton, Victoria
Susmita Saha
School of Psychological Sciences, Monash University|School of Translational Medicine, Monash University
Victoria, Australia|Victoria, Australia
School of Psychological Sciences, Monash University|School of Translational Medicine, Monash University
Victoria, Australia|Victoria, Australia
Introduction:
Friedreich ataxia (FA) is a rare neurodegenerative disease associated with widespread neuropathological changes in cerebral and cerebellar cortex (Selvadurai et al., 2016). Voxel-based morphometry (VBM) and surface-based morphometry are two widely used neuroimaging techniques for describing brain anatomy. However, they are restricted to a single spatial resolution scale, precluding the opportunity to distinguish anatomical variations that are expressed across multiple scales. Drawing on concepts from classical physics, we applied mode-based morphometry (MBM) to characterize cortical thickness differences between FA patients and healthy controls in spatial frequency, i.e., at multiple spatial scales. This approach decomposes the cortical thickness map of each subject into fundamental, resonant eigenmodes of brain geometry, each corresponding to a specific spatial scale and revealing distinct patterns of cortical thickness variations in the brain(Cao et al., 2024; Pang et al., 2023).
Methods:
T1-weighted MRI data from 30 individuals with genetically proven FA and 29 healthy participants matched for age were analysed in this study after excluding 3 FA participants due to insufficient data quality, as assessed by MRIQC (Esteban et al., 2017). Cortical thickness maps were derived from the T1w-MRI data using FreeSurfer (Dale et al., 1999). Geometric eigenmodes are derived from population-averaged template of the cortical midthickness surface (fsaverage 164k) by solving the eigendecomposition of the Laplace–Beltrami operator (LBO) on the cortical surface. The resulting eigenmodes represent an orthogonal basis set describing spatial patterns of variations in cortical geometry at different spatial scales (Fig. 1A). The cortical thickness map of each hemisphere for each subject was decomposed as a weighted sum of geometric eigenmodes (Fig. 1B). A two-sample t-test was conducted to compare the weights of each eigenmode between the FA and control groups.
Results:
Significant differences (p-value < 0.05, two-sample t-test) in eigenmode weights were observed in geometric eigenmodes with low spatial frequencies in both the left and right hemispheres. Figure 1C & D show three significant eigenmode examples (mode 2, 4, 5) with low spatial frequencies in the left hemisphere. These low-frequency modes, with lower absolute mean weights compared to controls, revealed a disruption or reduction in the consistency of global cortical thickness patterns in the FA group, indicating large-scale cortical thickness change rather than localized abnormalities. This finding contrast with classical approaches that focus solely on focal, isolated areas, suggesting that such methods capture only the 'tip of the iceberg', failing to account for the potential broader global pattern changes associated with FA.
Conclusions:
Our findings demonstrate that mode-based morphometry provides a more comprehensive view of cortical thickness changes in FA by capturing global pattern alterations across multiple spatial scales. Significant differences in low-frequency geometrical eigenmode weights reveal macro-level structural changes, surpassing the insights of classical methods. This approach offers a novel perspective on neurodegenerative disease pathophysiology and hold promise for identifying valuable biomarkers for FA and similar conditions.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Classification and Predictive Modeling
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling
Keywords:
Computational Neuroscience
Cortex
Degenerative Disease
FUNCTIONAL MRI
Machine Learning
Modeling
Movement Disorder
MRI
Neurological
STRUCTURAL MRI
1|2Indicates the priority used for review
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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.
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Provide references using APA citation style.
Cao, T., Pang, J. C., Segal, A., Chen, Y. C., Aquino, K. M., Breakspear, M., & Fornito, A. (2024). Mode‐based morphometry: A multiscale approach to mapping human neuroanatomy. Human Brain Mapping, 45(4). https://doi.org/10.1002/hbm.26640
Dale, A. M., Fischl, B., & Sereno, M. I. (1999). Cortical surface-based analysis: I. Segmentation and surface reconstruction. NeuroImage, 9(2), 179-194.
Esteban, O., Birman, D., Schaer, M., Koyejo, O. O., Poldrack, R. A., & Gorgolewski, K. J. (2017). MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites. PloS one, 12(9), e0184661.
Pang, J. C., Aquino, K. M., Oldehinkel, M., Robinson, P. A., Fulcher, B. D., Breakspear, M., & Fornito, A. (2023). Geometric constraints on human brain function. Nature, 618(7965), 566-574. https://pmc.ncbi.nlm.nih.gov/articles/PMC10266981/pdf/41586_2023_Article_6098.pdf
Selvadurai, L. P., Harding, I. H., Corben, L. A., Stagnitti, M. R., Storey, E., Egan, G. F., Delatycki, M. B., & Georgiou-Karistianis, N. (2016). Cerebral and cerebellar grey matter atrophy in Friedreich ataxia: the IMAGE-FRDA study. Journal of Neurology, 263(11), 2215-2223. https://doi.org/10.1007/s00415-016-8252-7