Identifying reproducible neuroanatomical signatures of schizophrenia using mode-based morphometry
Poster No:
485
Submission Type:
Abstract Submission
Authors:
Trang Cao1, James Pang1, Alex Fornito1
Institutions:
1Monash University, Clayton, Victoria
First Author:
Co-Author(s):
Introduction:
Surface-based morphometry (SBM) (Fischl & Dale, 2000) has been used extensively to study anatomical differences between people with schizophrenia (SCZ) and healthy controls but the results of these studies have often been difficult to replicate (Alnæs et al., 2019; Brugger & Howes, 2017). This poor reproducibility may arise due to small sample sizes and limitations of existing methods for capturing robust phenotypes in the face of the well-known clinical heterogeneity of the disorder. Here, we investigated how increasing the sample size and using a novel mapping method, called mode-based morphometry (MBM) (Cao et al., 2024), can improve our ability to find robust cortical thickness changes in SCZ.
Methods:
We used 12 open-source neuroimaging datasets of T1-weighted images. After performing quality control checks, our total sample consisted of 2296 healthy controls (HC) and 1424 SCZ patients with ages 18-60 years. Images were processed using FreeSurfer 7.1.0 (Fischl & Dale, 2000).
With this data pool, we randomly subsampled 2 pseudo-sites with the number of subjects assigned in each group of patients and controls taken from a logarithmically-increasing range (10, 16, 25, 40, 63, 100, 158, 251, 398, 631). For each sample size, we drew 100 random sub-samples.
For each pseudo-site, we estimated case-control differences in cortical thickness using SBM, implemented via FreeSurfer and MBM. SBM performs statistical inference at each of thousands of surface mesh vertices and is thus restricted to a single spatial resolution scale (Figure 1a). MBM decomposes point-wise cortical thickness difference maps into a linear combination of geometric eigenmodes of the cortex, allowing a succinct representation of thickness differences across multiple spatial resolution scales (Cao et al., 2024; Pang et al., 2023) (Figure 1b, c).
To examine the consistency of cortical thickness differences using SBM, we calculated spatial correlations of the resulting thresholded and unthresholed statistical maps for every pair of pseudo-sites. To evaluate the consistency of results using MBM, we calculated correlations of the resulting eigenmode loading (beta) coefficient spectra (Figure 1d), which quantify the contribution of each eigenmode to the cortical thickness difference map. We also correlated the maps reconstructed using only the modes that were deemed to make a statistically significant contribution via permutation tests (Figure 1e).
With this data pool, we randomly subsampled 2 pseudo-sites with the number of subjects assigned in each group of patients and controls taken from a logarithmically-increasing range (10, 16, 25, 40, 63, 100, 158, 251, 398, 631). For each sample size, we drew 100 random sub-samples.
For each pseudo-site, we estimated case-control differences in cortical thickness using SBM, implemented via FreeSurfer and MBM. SBM performs statistical inference at each of thousands of surface mesh vertices and is thus restricted to a single spatial resolution scale (Figure 1a). MBM decomposes point-wise cortical thickness difference maps into a linear combination of geometric eigenmodes of the cortex, allowing a succinct representation of thickness differences across multiple spatial resolution scales (Cao et al., 2024; Pang et al., 2023) (Figure 1b, c).
To examine the consistency of cortical thickness differences using SBM, we calculated spatial correlations of the resulting thresholded and unthresholed statistical maps for every pair of pseudo-sites. To evaluate the consistency of results using MBM, we calculated correlations of the resulting eigenmode loading (beta) coefficient spectra (Figure 1d), which quantify the contribution of each eigenmode to the cortical thickness difference map. We also correlated the maps reconstructed using only the modes that were deemed to make a statistically significant contribution via permutation tests (Figure 1e).
·Figure 1: Workflow for surface-based morphometry (SBM) and mode-based morphometry (MBM).
Results:
Figure 2 shows the mean and standard deviation of the correlations across 100 repeated subsamples for each sample size. Between-site consistency for both SBM and MBM increases as a function of sample size. Consistency is highest for MBM beta coefficient spectra. Consistency is intermediate for unthresholded SBM maps, with reconstructed MBM maps converging to a similar level of consistency when sample sizes exceed ~400. Consistency is lowest for thresholded SBM maps.
·Figure 2: Correlations of SBM and MBM measures across repeat subsampling for different sample sizes. The solid lines represent the mean and the shaded areas represent the standard deviation.
Conclusions:
Our results suggest that the traditional reliance on thresholded SBM maps for identifying group differences in neuroanatomical phenotypes is associated with the lowest reproducibility. Increasing sample sizes and using MBM can facilitate more reliable inferences.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 2
Keywords:
Morphometrics
Schizophrenia
STRUCTURAL MRI
Other - eigenmodes
1|2Indicates the priority used for review
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Provide references using APA citation style.
Brugger, S. P., & Howes, O. D. (2017). Heterogeneity and Homogeneity of Regional Brain Structure in Schizophrenia: A Meta-analysis. JAMA Psychiatry, 74(11), 1104–1111. https://doi.org/10.1001/jamapsychiatry.2017.2663
Cao, T., Pang, J. C., Segal, A., Chen, Y., Aquino, K. M., Breakspear, M., & Fornito, A. (2024). Mode‐based morphometry: A multiscale approach to mapping human neuroanatomy. Human Brain Mapping, 45(4), e26640. https://doi.org/10.1002/hbm.26640
Fischl, B., & Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences, 97(20), 11050–11055. https://doi.org/10.1073/pnas.200033797
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://doi.org/10.1038/s41586-023-06098-1