Eccentricity-Based Mapping of Transcallosal Fiber Connectivity in Human Early Visual Cortex
Poster No:
1219
Submission Type:
Abstract Submission
Authors:
William Neaves1, Mark Schira2
Institutions:
1The University of Wollongong, Port Kembla, New South Wales, 2University of Wollongong, Wollongong, NSW
First Author:
Co-Author:
Introduction:
The human visual cortex processes information from the contralateral visual field, yet our perception of the visual world is unified into a single, seamless image. This integration is facilitated by interhemispheric connectivity via transcallosal fibers in the splenium of the corpus callosum. These fibres are hypothesized to exhibit an eccentricity-based topographic organization [5] (Saenz & Fine, 2010). However, technological limitations have historically hindered detailed study of this region [4] (Rokem et al., 2017), particularly due to insufficient scanning resolution and coarse fiber tracking algorithms that are ill-suited for regions like the splenium with complex fiber configurations [2][7] (Calamante, 2019; Tournier, 2012). Consequently, the mechanisms of interhemispheric visual integration remain poorly understood.
Methods:
This study leveraged high-resolution 7T diffusion imaging from the Human Connectome Project (HCP) [9] (Van Essen et al., 2013) combined with constrained spherical deconvolution (CSD) [6] (Tournier, 2007) to resolve previously problematic complex fiber configurations within the splenium. Analyses were performed using MRtrix 3.0.2 module on Neurodesk [3] (Renton et al., 2024).
First, a template was generated from the 182 subjects with 7T diffusion data using the template generation script from the MRtrix 3.0.2 [8] (Tournier et al, 2019). Twenty subjects were then randomly selected from the dataset, and V1 in each hemisphere was generated using a Bayesian predictive model as described by [1] Benson (2018). This model utilizes an atlas derived from voxel-wise functional measurements as a prior to guide the mapping process. The resulting V1 maps demonstrated strong alignment with existing functional maps in the dataset, capturing individual variations in brain anatomy (Figure 1).
V1 regions were subdivided into central (0-3°), and peripheral (3-10°) regions based on visual field eccentricity. Fifty-Thousand streamlines were seeded in each V1 subdivision, with tracking constrained by anatomical boundaries to ensure biologically plausible connectivity. Fibers were tracked to V1 in the opposite hemisphere, generating detailed maps of transcallosal visual connectivity.
First, a template was generated from the 182 subjects with 7T diffusion data using the template generation script from the MRtrix 3.0.2 [8] (Tournier et al, 2019). Twenty subjects were then randomly selected from the dataset, and V1 in each hemisphere was generated using a Bayesian predictive model as described by [1] Benson (2018). This model utilizes an atlas derived from voxel-wise functional measurements as a prior to guide the mapping process. The resulting V1 maps demonstrated strong alignment with existing functional maps in the dataset, capturing individual variations in brain anatomy (Figure 1).
V1 regions were subdivided into central (0-3°), and peripheral (3-10°) regions based on visual field eccentricity. Fifty-Thousand streamlines were seeded in each V1 subdivision, with tracking constrained by anatomical boundaries to ensure biologically plausible connectivity. Fibers were tracked to V1 in the opposite hemisphere, generating detailed maps of transcallosal visual connectivity.
Results:
A consistent fovea-to-periphery organization of transcallosal fibers was identified in every individual subject and in the average (Figure 2), with distinct spatial gradients observed across the ventral-most region of the splenium. Peripheral visual field regions were positioned more dorsally compared to central/foveal regions. This organization was maintained despite variations in individual anatomy. To increase confidence in these findings, data were analyzed after alignment to the group template. These results support the hypothesis of eccentricity-based organization in transcallosal fibers and provide a robust framework for future investigations into interhemispheric joining of visual information.
Conclusions:
We find a clear eccentricity-based organization of transcallosal fibers connecting human V1, with central/foveal visual field representations mapped to more dorsal positions in the splenium compared to peripheral regions. These findings provide direct evidence for a topographic framework underlying interhemispheric visual integration, supporting the idea that the visual system maintains retinotopic organization even at the level of interhemispheric connections. Such organization may play a critical role in the seamless processing of the visual field across hemispheres, offering a potential neural basis for the unified nature of human visual perception. These findings open new avenues for investigating how disruptions in this connectivity may contribute to perceptual deficits in neurological conditions like stroke or amblyopia. Future work will further quantify fiber density and explore genetic contributions to this organizational pattern using sibling data from the HCP dataset.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Diffusion MRI Modeling and Analysis 2
Keywords:
MRI
Vision
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - interhemispheric connectivity
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
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.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
[2] Calamante, F. (2019). The seven deadly sins of measuring brain structural connectivity using diffusion MRI streamlines fibre-tracking. Diagnostics, 9(3), 115. https://doi.org/10.3390/diagnostics9030115
[3] Renton, A. I., Dao, T. T., Johnstone, T., Civier, O., Sullivan, R. P., White, D. J., Lyons, P., Slade, B. M., Abbott, D. F., Amos, T. J., Bollmann, S., Botting, A., Campbell, M. E. J., Chang, J., Close, T. G., Dörig, M., Eckstein, K., Egan, G. F., Evas, S., Flandin, G., … Bollmann, S. (2024). Neurodesk: An accessible, flexible, and portable data analysis environment for reproducible neuroimaging. Nature Methods, 21(5), 804–808. https://doi.org/10.1038/s41592-023-02145-x
[4] Rokem, A., Takemura, H., Bock, A. S., Scherf, K. S., Behrmann, M., Wandell, B. A., Fine, I., Bridge, H., & Pestilli, F. (2017). The visual white matter: The application of diffusion MRI and fiber tractography to vision science. Journal of Vision, 17(2), 4. https://doi.org/10.1167/17.2.4
[5] Saenz, M., & Fine, I. (2010). Topographic organization of V1 projections through the corpus callosum in humans. NeuroImage, 52(4), 1224–1229. https://doi.org/10.1016/j.neuroimage.2010.05.060
[6] Tournier, J. D., Calamante, F., & Connelly, A. (2007). Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution. NeuroImage, 35, 1459–1472. https://doi.org/10.1016/j.neuroimage.2007.02.016
[7] Tournier, J. D., Calamante, F., & Connelly, A. (2012). MRtrix: Diffusion tractography in crossing fiber regions. International Journal of Imaging Systems and Technology, 22(1), 53–66. https://doi.org/10.1002/ima.22005
[8] Tournier, J.-D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C.-H., & Connelly, A. (2019). MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage, 202, 116137. https://doi.org/10.1016/j.neuroimage.2019.116137
[9] Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E., Yacoub, E., Ugurbil, K., & WU-Minn HCP Consortium. (2013). The WU-Minn Human Connectome Project: An overview. NeuroImage, 80, 62–79. https://doi.org/10.1016/j.neuroimage.2013.05.041