Presented During:
Monday, June 24, 2024: 5:45 PM - 7:00 PM
COEX
Room:
ASEM Ballroom 202
Poster No:
2106
Submission Type:
Abstract Submission
Authors:
Maiko Uesaki1,2, Toshikazu Miyata1,3, Noah Benson4, Jonathan Winawer5, Hiromasa Takemura1,3,6
Institutions:
1Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, NICT, Suita, Japan, 2Graduate School of Frontier Biosciences, Osaka University, Suita, Japan, 3National Institute for Physiological Sciences, Okazaki, Japan, 4University of Washington, Seattle, WA, United States, 5New York University, New York City, NY, United States, 6The Graduate Institute of Advanced Studies, SOKENDAI, Hayama, Japan
First Author:
Maiko Uesaki
Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, NICT|Graduate School of Frontier Biosciences, Osaka University
Suita, Japan|Suita, Japan
Co-Author(s):
Toshikazu Miyata
Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, NICT|National Institute for Physiological Sciences
Suita, Japan|Okazaki, Japan
Noah Benson
University of Washington
Seattle, WA, United States
Hiromasa Takemura
Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, NICT|National Institute for Physiological Sciences|The Graduate Institute of Advanced Studies, SOKENDAI
Suita, Japan|Okazaki, Japan|Hayama, Japan
Introduction:
Over the past several decades, neuroanatomy and neuroimaging research has revealed large individual differences in the size of the human visual cortex (Stensaas et al. 1974; Andrews et al. 1997; Dougherty et al. 2003). Further studies have documented covariance amongst the size of visual areas, and between the size of visual areas and properties of other visual structures (Benson et al. 2022; Miyata et al. 2022). We wondered whether there was also substantial variability in the grey matter tissue microstructure of the visual cortex across individuals, and how such measures covary throughout the multiple cortical maps.. Recent advances in structural neuroimaging provide opportunities for characterising tissue properties of cortical areas using MRI. The ratio of T1- to T2-weighted signal intensity (T1w/T2w) has become a widely used semi-quantitative measure of tissue microstructure of the brain (Glasser & van Essen, 2011; Berman et al. 2022). Here, we analysed the Human Connectome Project 7T retinotopy dataset (Benson et al. 2018), to evaluate individual differences and covariance of T1w/T2w amongst early visual areas V1, V2, and V3.
Methods:
Analyses were performed on the 3T structural and 7T functional MRI data acquired from 160 subjects who participated in the retinotopic mapping experiments (Benson et al. 2018) as part of the Human Connectome Project (van Essen et al. 2012). Borders of V1, V2, and V3 (regions of interest; ROIs) were manually drawn by four researchers based on polar angle reversals in the retinotopic map (Benson et al. 2022). We identified the mid-grey surface of ROIs for each subject using neuropythy (https://github.com/noahbenson/neuropythy), and averaged T1w/T2w across voxels belonging to each ROI. We first calculated inter-subject correlations between T1w/T2w of ROIs. Subsequently, we computed how much of the individual variability in T1w/T2w of each ROI could be predicted by that of the entire cortex using linear regression. We then used the residuals to evaluate whether, and the extent to which, T1w/T2w covaried amongst ROIs once the cortex-wide variability was partialled out.
Results:
Figure 1 depicts correlations between T1w/T2w of ROIs across subjects for each hemisphere. Strong correlations were found between V1 and V2 (left: r = 0.92; right: r = 0.92), V2 and V3 (left: r = 0.93; right: r = 0.95), and V1 and V3 (left: r = 0.89; right: r = 0.91). T1w/T2w tended to be higher in V1, compared with V2 and V3, consistent with known anatomical differences in grey matter microstructure between V1 and V2/V3. We note that T1w/T2w of corresponding ROIs were also highly correlated between hemispheres (left vs right hemispheres for V1: r = 0.90; V2: r = 0.90; V3: r = 0.84). The high correlations were not simply due to some subjects having overall high or low T1w/T2w values in all of cortex. Figure 2 shows correlations between the residuals of ROIs after T1w/T2w of the whole-brain grey matter was regressed out. Strong correlations remained between V1 and V2 (left: r = 0.87; right: r = 0.86), V2 and V3 (left: r = 0.89; right: r = 0.90), and V1 and V3 (left: r = 0.82; right: r = 0.83).
Conclusions:
We examined whether tissue properties, reflected in T1w/T2w, covaried between areas V1, V2, and V3 across 160 individuals. Results showed strong correlations between the border areas V1 and V2, as well as V2 and V3, and also between V1 and V3 though to a lesser extent, in both hemispheres, which persisted even when adjusted for cortex-wide T1w/T2w. T1w/T2w of corresponding areas in the two hemispheres were also highly correlated. Our findings generally align with the finding that the surface areas of V1, V2, and V3 are highly correlated within and across hemispheres (Benson et al. 2022), and suggest that structural covariance between early visual areas is not limited to their surface area, but also found in grey matter tissue microstructure.
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 1
Perception, Attention and Motor Behavior:
Perception: Visual 2
Keywords:
STRUCTURAL MRI
Vision
1|2Indicates the priority used for review
Provide references using author date format
Andrews, T. J. et al. (1997), ‘Correlated size variations in human visual cortex, lateral geniculate nucleus, and optic tract’, The Journal of Neuroscience, vol. 17, no. 8, pp. 2859-2868.
Benson, N. C. et al. (2018), ‘The Human Connectome Project 7 Tesla retinotopy dataset: Description and population receptive field analysis’, Journal of Vision, vol. 18, no. 13, pp. 23-23.
Benson, N. C. et al. (2022), ‘Variability of the surface area of the V1, V2, and V3 maps in a large sample of human observers’, The Journal of Neuroscience, vol. 42, no. 46, pp. 8629-8646.
Berman, S. et al. (2022), ‘Spatial profiles provide sensitive MRI measures of the midbrain micro- and macrostructure’, NeuroImage, vol. 264, pp. 119660.
Dougherty, R. F. et al. (2003), ‘Visual field representations and locations of visual areas V1/2/3 in human visual cortex’, Journal of Vision, vol. 3, no. 10, pp. 1.
Glasser, M. F., & van Essen, D. C. (2011), ‘Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-Weighted MRI’, The Journal of Neuroscience, vol. 21, no. 32, pp. 11597-11616.
Miyata, T. et al. (2022), ‘Structural covariance and heritability of the optic tract and primary visual cortex in living human brains’, The Journal of Neuroscience, vol. 42, no. 35, pp. 6761-6769.
Stensaas, S. S. et al. (1974), ‘The topography and variability of the primary visual cortex in man’, The Journal of Neurosurgery, vol. 40, no. 6, pp. 747-755.