Mapping directed information flow between homotopic regions of the human brain
Poster No:
1175
Submission Type:
Abstract Submission
Authors:
Annie Bryant1, James Shine1, Joseph Lizier1, Ben Fulcher1
Institutions:
1The University of Sydney, Sydney, NSW
First Author:
Co-Author(s):
Introduction:
Homotopic connectivity is an integral component of the brain's functional architecture, typically quantified using the Pearson correlation between mirrored voxels or parcels (Mancuso et al. 2019). Despite mounting evidence for left-right structural asymmetry in health (Wan et al. 2024; Kong et al. 2022) and disease (Chen et al. 2024; Roe et al. 2021), interhemispheric information flow is seldom directly assessed.
Methods:
We quantified homotopic directed information (DI) from functional magnetic resonance imaging (fMRI) to measure directed interhemispheric coupling for the first time (Lizier et al. 2011). All resting-state fMRI data were obtained the Human Connectome Project (N=100) and were preprocessed and parcellated into the Desikan-Killiany cortical atlas in (Fallon et al. 2020). For each of 34 cortical regions (one per hemisphere), we computed DI with a Gaussian density estimator using pyspi (Cliff et al. 2023) in Python.
Results:
Homotopic DI magnitudes varied across the brain, ranging from the lowest average magnitude in the entorhinal cortex (1.23 × 10-2 ± 8.59 × 10-3) to the highest in the lateral occipital cortex (2.39 ± 0.96). Generally, medial occipital and superior parietal regions exhibited higher homotopic DI while inferior frontal and temporal regions exhibited lower homotopic DI. We also compared left-right asymmetries, finding that lateral regions exhibit greater DI from left to right while medial regions exhibit greater DI from right to left.
Conclusions:
Our findings suggest that there are gradients of both the magnitude and direction of interhemispheric information flow, warranting further characterization of the functional implications for health and disease.
Modeling and Analysis Methods:
Classification and Predictive Modeling
Connectivity (eg. functional, effective, structural) 1
fMRI Connectivity and Network Modeling 2
Methods Development
Keywords:
Computational Neuroscience
Data analysis
FUNCTIONAL MRI
Hemispheric Specialization
Open Data
Open-Source Code
Statistical Methods
1|2Indicates the priority used for review
Abstract Information
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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.
Cliff, O. M., et al. Unifying pairwise interactions in complex dynamics. Nature Computational Science, 1–11. https://doi.org/10.1038/s43588-023-00519-x
Fallon, J., et al. (2020). Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain. Network Neuroscience, 4(3), Article 3. https://doi.org/10.1162/netn_a_00151
Kong, X.-Z., et al. (2022). Mapping brain asymmetry in health and disease through the ENIGMA consortium. Human Brain Mapping, 43(1), 167–181. https://doi.org/10.1002/hbm.25033
Lizier, J. T., et al. (2011). Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity. Journal of Computational Neuroscience, 30(1), 85–107. https://doi.org/10.1007/s10827-010-0271-2
Mancuso, L., et al. (2019). The homotopic connectivity of the functional brain: A meta-analytic approach. Scientific Reports, 9(1), 3346. https://doi.org/10.1038/s41598-019-40188-3
Roe, J. M., et al. (2021). Asymmetric thinning of the cerebral cortex across the adult lifespan is accelerated in Alzheimer’s disease. Nature Communications, 12(1), 721. https://doi.org/10.1038/s41467-021-21057-y
Wan, B., et al. (2024). Microstructural asymmetry in the human cortex. https://doi.org/10.1101/2024.04.08.587194