The Urban Brain Connectome: Harmonisation of Structural Connectivity Data in the iMAP Study.
Poster No:
1304
Submission Type:
Abstract Submission
Authors:
Govinda Poudel1, Anthony Barnett1, Ester Cerin1
Institutions:
1Australian Catholic University, Melbourne, VIC
First Author:
Co-Author(s):
Introduction:
Emerging evidence suggests that the urban neighbourhood environment plays a significant role in shaping the human brain connectome, particularly in older adults (Poudel et al., 2022). The International Mind, Activities, and Urban Places (iMAP) study provides a unique opportunity to investigate these effects using personalized imaging and geographical information data from diverse urban populations across three cities: Melbourne, Barcelona, and Hong Kong (Cerin et al., 2020). However, differences in acquisition protocols and imaging platforms across sites introduce variability, which may reduce the reliability of cross-cohort analyses of brain connectome data. This study addresses these challenges by developing a harmonisation approach to standardize diffusion MRI (dMRI)-derived structural connectivity data, enabling robust comparisons and insights into the urban brain connectome.
Methods:
T1-weighted structural (1 mm isotropic) and dMRI data (>45 directions) were obtained from 650 participants across three sites. The dMRI data were pre-processed using MRtrix3.0 software to correct for noise, eddy currents, and inhomogeneities. Anatomically constrained tractography was performed using constrained spherical deconvolution. Individual structural connectomes were generated for 86 brain regions based on the Desikan-Killiany atlas and were then normalized for differences in brain volume. Connectome data were thresholded using a consistency-based thresholding approach (30%) to ensure the removal of spurious connections.
Furthermore, graph theoretical measures, including total strength, global efficiency, and characteristic path length, were derived to examine the impact of harmonisation on graph measures of the connectomes. For harmonisation, modified log versions of two well-established retrospective harmonisation approaches, ComBat and CovBat, were used (Shen et al., 2024, Johnson et al., 2007, Chen et al 2022).
To assess the impact of harmonisation, we evaluated site-related biases in the covariance of the connectome using the Average Frobenius distance and site effects on graph theoretical measures using one-way ANOVA.
Furthermore, graph theoretical measures, including total strength, global efficiency, and characteristic path length, were derived to examine the impact of harmonisation on graph measures of the connectomes. For harmonisation, modified log versions of two well-established retrospective harmonisation approaches, ComBat and CovBat, were used (Shen et al., 2024, Johnson et al., 2007, Chen et al 2022).
To assess the impact of harmonisation, we evaluated site-related biases in the covariance of the connectome using the Average Frobenius distance and site effects on graph theoretical measures using one-way ANOVA.
Results:
Harmonisation of the structural connectome data using the ComBat and CovBat methods reduced the covariance in connectivity matrices across sites (Figure 1a), improving consistency across datasets. Furthermore, the three graph theoretical measures showed significant site effects before harmonisation (p < 0.05, ANOVA) (Figure 1b). The largest effect size (>1.0) for site effects was observed in the total strength measure, followed by characteristic path length (and global efficiency. Both ComBat and CovBat harmonisation reduced site-related effects to non-significant levels (p>0.05) for all three graph theoretical measures.
·Figure 1: The impact of harmonisation on connectome covariance and graph theoretical measures. The average Frobenius distance and Site-related effects on graph measures are shown.
Conclusions:
This study demonstrates the feasibility and importance of harmonising structural connectivity data in multicenter studies. By reducing variability, the harmonisation pipeline enables reliable exploration of how urban environments may shape the human connectome. These methods and insights pave the way for future research on brain-environment interactions within the iMAP study.
Lifespan Development:
Aging 2
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 1
Keywords:
Data analysis
Other - Diffusion MRI, Urban Brain Health, dMRI Harmonisation, Structural Connectivity
1|2Indicates the priority used for review
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Provide references using APA citation style.
2. Cerin, E., (2020). International Mind, Activities and Urban Places (iMAP) study: Methods of a cohort study on environmental and lifestyle influences on brain and cognitive health. BMJ open, 10(3), e036607.
3. Shen, R. S. (2024). Harmonization of Diffusion MRI-based Structural Connectomes for Multi-site Studies. bioRxiv, https://doi.org/10.1101/2024.10.08.617340, 2024-10.
4. Johnson, W. E. (2007). Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8(1), 118-127.
5. Chen, A.A. (2022), Mitigating site effects in covariance for machine learning in neuroimaging data. Human brain mapping, 43(4): p. 1179-1195.