Mapping Asymmetries in Structural Connectivity of the Visual Network
Poster No:
1240
Submission Type:
Abstract Submission
Authors:
Gabriele Amorosino1, Junbeom Kwon1, Marisa Carrasco2, Bradley Caron1, Franco Pestilli1
Institutions:
1The University of Texas at Austin, Austin, TX, 2New York University, New York, NY
First Author:
Co-Author(s):
Introduction:
Visual performance exhibits well-established asymmetries dependent on visual field location, such as horizontal-vertical asymmetry (HVA) and vertical-meridian asymmetry (VMA). These perceptual asymmetries are consistent across tasks measuring visual acuity, contrast sensitivity, and spatial resolution, where performance is superior along the horizontal meridian compared to the vertical meridian (HVA) and along the lower versus upper vertical meridian (VMA) (Himmelberg et al., 2023; Barbot et al., 2021). Despite robust functional and structural evidence linking these asymmetries to cortical activity and morphometry (Benson et al., 2021; Liu et al., 2006), their presence in visual white matter (WM) connectivity remains largely unexplored. Structural connectivity, quantified using diffusion magnetic resonance imaging (dMRI), offers unique insights into the long-range myelinated tracts supporting visual behavior. This study investigates HVA and VMA within visual WM using a dataset of 1800 subjects sampled from 3 public sources: Human Connectome Project (HCP) (Van Essen et al., 2012), Pediatric Imaging Neurocognition and Genetics (PING) (Jernigan et al., 2016), and Cambridge Centre for Ageing and Neuroscience (Cam-CAN) (Shafto et al., 2014).
Methods:
We developed VISCONTI (VISual CONnectome and Tracts Information), a novel open-source pipeline implemented on brainlife.io (Hayashi et al., 2024) (see Figure 1). VISCONTI combines tractography with automated population receptive field (pRF) mapping (Benson et al., 2012) to analyze structural connectivity patterns within the early visual cortex. The visual cortical surfaces were segmented into polar angle meridians (e.g., horizontal, vertical, and diagonal meridians) and iso-eccentric bins (0-2°, 2-4°, 4-6°, 6-8°) using automated pRF mapping methods. Streamlines were segmented as: short-range connections, where both endpoints terminated within the same polar angle wedge (intra-occipital connections), and long-range connections, where only one endpoint terminated in a polar angle wedge with the other extending beyond the occipital lobe. Twelve visual areas of interest were generated for each visual wedge: v1, v2, v3, v3a, v3b, hv4, lO1, lO2, tO1, tO2, vO1, and vO2 (Benson et al., 2012). Streamline density, defined as the number of streamlines normalized by the average volume of connected regions, was computed to estimate structural connectivity for each visual wedge. Statistical analyses included repeated measures ANOVA to test for significant effects of streamline density asymmetries across polar angle meridians, followed by post-hoc two-tailed t-tests for pairwise comparisons.
Results:
We observed significant asymmetries in visual white matter (WM) connectivity. Streamline density was consistently higher along the horizontal meridian compared to the vertical meridians (HVA; p < 0.001) and along the lower vertical meridian compared to the upper vertical meridian (VMA; p < 0.001) for intra-occipital connections (Figure 2.a). In contrast, connections between visual areas and the rest of the brain (inbound/outbound connections) showed significant asymmetry only for the HVA (p < 0.01) (see Figure 2.b). This highlights the localized nature of HVA and VMA within early visual processing regions. These asymmetries were robust across visual areas and participant cohorts.
Conclusions:
This study provides the first evidence of HVA and VMA in human visual WM connectivity, linking structural asymmetries to well-established perceptual phenomena. The persistence of these asymmetries across participants, visual areas, and demographic groups suggests an innate neural architecture underlying visual field performance. VISCONTI, validated through extensive analyses and released as open-source software, offers a robust and reproducible tool for investigating the visual connectome. These findings emphasize the role of WM connectivity in shaping perceptual asymmetries.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 2
Neuroinformatics and Data Sharing:
Workflows
Perception, Attention and Motor Behavior:
Perception: Visual
Keywords:
Tractography
Vision
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Benson, N. C., Butt, O. H., Datta, R., Radoeva, P. D., Brainard, D. H., & Aguirre, G. K. (2012). The retinotopic organization of striate cortex is well predicted by surface topology. Current Biology, 22(21), 2081-2085.
Benson, N. C., Kupers, E. R., Barbot, A., Carrasco, M., & Winawer, J. (2021). Cortical magnification in human visual cortex parallels task performance around the visual field. eLife, 10, e67685.
Carrasco, M., Roberts, M., Myers, C., & Shukla, L. (2022). Visual field asymmetries vary between children and adults. Current Biology, 32(11), R509-R510.
Hayashi, S., Caron, B. A., Heinsfeld, A. S., Vinci-Booher, S., McPherson, B., Bullock, D. N., ... & Pestilli, F. (2024). brainlife.io: a decentralized and open-source cloud platform to support neuroscience research. Nature Methods, 21(5), 809-813.
Himmelberg, M. M., Winawer, J., & Carrasco, M. (2023). Polar angle asymmetries in visual perception and neural architecture. Trends in Neurosciences, 46(6), 445-458.
Jernigan, T. L., Brown, T. T., Hagler Jr, D. J., Akshoomoff, N., Bartsch, H., Newman, E., ... & Dale, A. M. (2016). The pediatric imaging, neurocognition, and genetics (PING) data repository. NeuroImage, 124, 1149-1154.
Liu, T., Heeger, D. J., & Carrasco, M. (2006). Neural correlates of the visual vertical meridian asymmetry. Journal of Vision, 6(11), 12-12.
Shafto, M. A., Tyler, L. K., Dixon, M., Taylor, J. R., Rowe, J. B., Cusack, R., ... & Cam-CAN. (2014). The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC Neurology, 14, 1-25.
Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E., Bucholz, R., ... & WU-Minn HCP Consortium. (2012). The Human Connectome Project: a data acquisition perspective. NeuroImage, 62(4), 2222-2231.