Correction of Distortion in Flattened Representations of the Cortical Surface Allows Prediction of V1-V3 Functional Organization from Anatomy
Open accessAbstract
Several domains of neuroscience offer map-like models that link location on the cortical surface to properties of sensory representation. Within cortical visual areas V1, V2, and V3, algebraic transformations can relate position in the visual field to the retinotopic representation on the flattened cortical sheet. A limit to the practical application of this structure-function model is that the cortex, while topologically a two-dimensional surface, is curved. Flattening of the curved surface to a plane unavoidably introduces local geometric distortions that are not accounted for in idealized models. Here, we show that this limitation is overcome by correcting the geometric distortion induced by cortical flattening. We use a mass-spring-damper simulation to create a registration between functional MRI retinotopic mapping data of visual areas V1, V2, and V3 and an algebraic model of retinotopy. This registration is then applied to the flattened cortical surface anatomy to create an anatomical template that is linked to the algebraic retinotopic model. This registered cortical template can be used to accurately predict the location and retinotopic organization of these early visual areas from cortical anatomy alone. Moreover, we show that prediction accuracy remains when extrapolating beyond the range of data used to inform the model, indicating that the registration reflects the retinotopic organization of visual cortex. We provide code for the mass-spring-damper technique, which has general utility for the registration of cortical structure and function beyond the visual cortex.
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It is currently possible to accurately predict human cortical receptive field mapping based on anatomical scans or other retinotopic mapping strategies that do not rely on visual input.
“it is currently possible to accurately predict human cortical receptive field mapping based on anatomical scans (Benson et al., 2012; Benson et al., 2014), or other retinotopic mapping strategies that do not rely on visual input (Goebel et al., 2022; Bock et al., 2015).”
From Towards biologically plausible phosphene simulation for the differentiable optimization of visual cortical prostheses (2024) · cites this paper as Benson et al., 2014
Many aspects of the model, including the transformation from visual space to cortical surface, will easily generalize to higher visual areas.
“Many aspects of our model, including the transformation from visual space to cortical surface63,65,92, will easily generalize to these higher visual areas.”
From A virtual patient simulation modeling the neural and perceptual effects of human visual cortical stimulation, from pulse trains to percepts (2024) · cites this paper as [92]
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Cites
Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910 · see citing works
Measuring the thickness of the human cerebral cortex from magnetic resonance images
Fischl, B. R., & Dale, A. M. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.200033797 · Cited by 5,789 · see citing works
Thinning of the Cerebral Cortex in Aging
Salat, D. H. (2004) · Cerebral Cortex · doi:10.1093/cercor/bhh032 · Cited by 1,851 · see citing works
Retinotopic organization in human visual cortex and the spatial precision of functional MRI
Engel, S. A. (1997) · Cerebral Cortex · doi:10.1093/cercor/7.2.181 · Cited by 1,606 · see citing works
Visual field maps in human cortex.
Wandell, B. A., Dumoulin, S. O., & Brewer, A. A. (2007) · Neuron · doi:10.1016/j.neuron.2007.10.012 · Cited by 1,231 · see citing works
The Variability of Human, BOLD Hemodynamic Responses
Aguirre, G. K., Zarahn, E., & D’Esposito, M. (1998) · NeuroImage · doi:10.1006/nimg.1998.0369 · Cited by 1,224 · see citing works
Self-citationPopulation receptive field estimates in human visual cortex
Dumoulin, S. O., & Wandell, B. A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.09.034 · Cited by 1,183 · see citing works
Visual field representations and locations of visual areas V1/2/3 in human visual cortex
Dougherty, R. F., Koch, V. M., Brewer, A. A., Fischer, B., Jan Modersitzki, C., & Wandell, B. A. (2003) · Journal of Vision · doi:10.1167/3.10.1 · Cited by 538 · see citing works
Accurate prediction of V1 location from cortical folds in a surface coordinate system
Hinds, O., Rajendran, N., Polimeni, J. R., Augustinack, J. C., Wiggins, G. C., Wald, L. L., Rosas, H. D., Potthast, A., Schwartz, E., & Fischl, B. R. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.10.033 · Cited by 238 · see citing works
The Retinotopic Organization of Striate Cortex Is Well Predicted by Surface Topology
Benson, N. C., Butt, O. H., Datta, R., Radoeva, P. D., Brainard, D. H., & Aguirre, G. K. (2012) · Current Biology · doi:10.1016/j.cub.2012.09.014 · Cited by 227 · see citing works
Self-citationA Surface-based Analysis of Language Lateralization and Cortical Asymmetry
Greve, D. N., der Haegen, L. V., Cai, Q., Stufflebeam, S. M., Sabuncu, M. R., Fischl, B. R., & Brysbaert, M. (2013) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00405 · Cited by 220 · see citing works
Modeling of Tool-Tissue Interactions for Computer-Based Surgical Simulation: A Literature Review
Misra, S., Ramesh, K. T., & Okamura, A. M. (2008) · PRESENCE Virtual and Augmented Reality · doi:10.1162/pres.17.5.463 · Cited by 220 · see citing works
Reorganization of human cortical maps caused by inherited photoreceptor abnormalities
Baseler, H. A., Brewer, A. A., Sharpe, L. T., Morland, A. B., Jägle, H., & Wandell, B. A. (2002) · Nature Neuroscience · doi:10.1038/nn817 · Cited by 172 · see citing works
Mass–spring–damper modelling of the human body to study running and hopping – an overview
Nikooyan, A. A., & ZADPOOR, A. A. (2011) · Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · doi:10.1177/0954411911424210 · Cited by 111 · see citing works
Modeling Magnification and Anisotropy in the Primate Foveal Confluence
Schira, M. M., Tyler, C. W., Špehar, B., & Breakspear, M. J. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000651 · Cited by 69 · see citing works
Henriksson, L., Karvonen, J., Salminen‐Vaparanta, N., Railo, H., & Vanni, S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036859 · Cited by 62 · see citing works
Verstynen, T., & Deshpande, V. S. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.11.090 · Cited by 58 · see citing works
The V1–V2–V3 complex: quasiconformal dipole maps in primate striate and extra-striate cortex
Balasubramanian, M., Polimeni, J. R., & Schwartz, E. L. (2002) · Neural Networks · doi:10.1016/s0893-6080(02)00094-1 · Cited by 55 · see citing works
Neural Activity within Area V1 Reflects Unconscious Visual Performance in a Case of Blindsight
Radoeva, P. D., Prasad, S., Brainard, D. H., et al. (2008) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2008.20139 · Cited by 47 · see citing works
Self-citationComputational fluid-structure interaction model for parachute inflation
Benney, R., & Stein, K. (1996) · Journal of Aircraft · doi:10.2514/3.47008 · Cited by 45 · see citing works
Brain mapping: the (un)folding of striate cortex
Schira, M. M., Tyler, C. W., & Rosa, M. G. P. (2012) · Current Biology · doi:10.1016/j.cub.2012.11.003 · Cited by 22 · see citing works
Predicted Selective Increase of Cortical Magnification Due to Cortical Folding
Dahlem, M. A., & Tusch, J. (2012) · The Journal of Mathematical Neuroscience · doi:10.1186/2190-8567-2-14 · Cited by 8 · see citing works
High-resolution intersubject averaging and a coordinate system for the cortical surface
Fischl, B., Sereno, M. I., Tootell, R. B. H., & Dale, A. M. (1999) · Human Brain Mapping · doi:10.1002/(sici)1097-0193(1999)8:4
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Spitschan, M., Bock, A. S., Ryan, J., Frazzetta, G., Brainard, D. H., & Aguirre, G. K. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1711522114 · Cited by 116 · see citing works
Self-citationWinawer, J., & Parvizi, J. (2016) · Neuron · doi:10.1016/j.neuron.2016.11.008 · Cited by 95 · see citing works
A cross-validated cytoarchitectonic atlas of the human ventral visual stream
Rosenke, M., Weiner, K. S., Barnett, M. A., Zilles, K., Amunts, K., Goebel, R., & Grill-Spector, K. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.040 · Cited by 97 · see citing works
van der Grinten, M., de Ruyter van Steveninck, J., Lozano, A., Pijnacker, L., Rueckauer, B., Roelfsema, P., van Gerven, M., van Wezel, R., Güçlü, U., & Güçlütürk, Y. (2024) · eLife · doi:10.7554/elife.85812 · Cited by 39 · see citing works
“…et al., 2021 ) and it is currently possible to accurately predict human cortical receptive field mapping based on anatomical scans ( Benson et al., 2012 ; Benson et al., 2014 ), or other retinotopic mapping strategies that do not rely on visual input ( Goebel et al., 2022 ; Bock et al., 2015 ).”
Background1024-channel electrophysiological recordings in macaque V1 and V4 during resting state
Chen, X., Morales-Gregorio, A., Sprenger, J., Kleinjohann, A., Sridhar, S., van Albada, S. J., Grün, S., & Roelfsema, P. R. (2022) · Scientific Data · doi:10.1038/s41597-022-01180-1 · Cited by 32 · see citing works
Visual temporal frequency preference shows a distinct cortical architecture using fMRI
Chai, Y., Handwerker, D. A., Marrett, S., Gonzalez-Castillo, J., Merriam, E. P., Hall, A., Molfese, P. J., & Bandettini, P. A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.048 · Cited by 22 · see citing works
Fine, I., & Boynton, G. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-65337-1 · Cited by 19 · see citing works
van der Grinten, M., de Ruyter van Steveninck, J., Lozano, A., Pijnacker, L., Rückauer, B., Roelfsema, P., van Gerven, M., van Wezel, R., Güçlü, U., & Güçlütürk, Y. (2022) · doi:10.1101/2022.12.23.521749 · Cited by 39 · see citing works
- Authors
- Benson, N. C., Butt, O. H., Brainard, D. H., & Aguirre, G. K.
- Affiliations
- University of Pennsylvania (US)
- Corresponding author
- Aguirre, Geoffrey K.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-03-27
- Volume
- 10
- Issue
- 3
- Pages
- e1003538
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC3967932
- OpenAlex
- W1970167670
- Cited by
- 222
- References
- 23
- Citations by year
- 2026: 20 · 2025: 27 · 2024: 27 · 2023: 16 · 2022: 23 · 2021: 26 · 2020: 23 · 2019: 13 · 2018: 15 · 2017: 12 · 2016: 7 · 2015: 7 · 2014: 5
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Pennsylvania Department of Health
- National Institutes of Health (EY001583, NS045839)
- National Eye Institute (R01EY020516, P30EY001583)
- National Institute of Neurological Disorders and Stroke (P30NS045839)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Keywords
- retinotopy, geometric distortion correction, visual cortex, structure-function mapping, anatomical template, visual field mapping
- MeSH terms
- Adult, Algorithms, Brain Mapping, Computer Simulation, Female, Humans, Magnetic Resonance Imaging, Male, Models, Neurological, Models, Theoretical, Occipital Lobe, Surface Properties, Visual Cortex, Visual Fields, Visual Pathways, Reproducibility of Results, Young Adult
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