Frontal cortical regions associated with attention connect more strongly to central than peripheral V1

The functionality of central vision is different from peripheral vision. Central vision is used for fixation and has higher acuity, making it useful for everyday activities such as reading and object identification. The central and peripheral representations in primary visual cortex (V1) also differ in how higher-order processing areas modulate their responses. For example, attention and expectation are top-down processes (i.e., high-order cognitive functions) that influence visual information processing during behavioral tasks. This top-down control is different for central vs. peripheral vision. Since functional networks can influence visual information processing in different ways, networks (such as the Fronto-Parietal (FPN), Default Mode (DMN), and Cingulo-Opercular (CON)) likely differ in how they connect to representations of the visual field across V1. Prior work indicated the central representing portion of V1 was more functionally connected to regions belonging to the FPN, and the far-peripheral representing portion of V1 was more functionally connected to regions belonging to the DMN. Our goals were (1) Assess the reproducibility and generalizability of retinotopic effects on functional connections between V1 and functional networks. (2) Extend this work to understand structural connections of central vs. peripheral representations in V1. (3) Examine the overlapping eccentricity differences in functional and structural connections of V1. (4) Examine the major white matter tracks connecting central V1 to frontal regions. We used resting-state BOLD fMRI and DWI to examine whether portions of V1 that represent different visual eccentricities differ in their functional and structural connectivity to functional networks. All data were acquired and minimally preprocessed by the Human Connectome Project. We identified central and far-peripheral representing regions from a retinotopic template. Functional connectivity was measured by correlated activity between V1 and functional networks, and structural connectivity was measured by probabilistic tractography and converted to track probability. In both modalities, differences between V1 eccentricity segment connections were compared by paired, two-tailed t-test. A spatial permutation approach was used to determine the statistical significance of the spatial overlap between modalities. The identified spatial overlap was then used in a deterministic tractography approach to identify the white matter pathways connecting the overlap to central V1. We found (1) Centrally representing portions of V1 are more strongly functionally connected to frontal regions than are peripherally representing portions of V1, (2) Structural connections also show stronger connections between central V1 and frontal regions, (3) Patterns of structural and functional connections overlaps in the lateral frontal cortex, (4) This lateral frontal overlap is connected to central V1 via the IFOF. In summary, the work’s main contribution is a greater understanding of higher-order functional networks’ connectivity to V1. There are stronger structural connections to central representations in V1, particularly for lateral frontal regions, implying that the functional relationship between central V1 and frontal regions is built upon direct, long-distance connections via the IFOF. Overlapping structural and functional connections reflect differences in V1 eccentricities, with central V1 preferentially connected to attention-associated regions. Understanding how V1 is functionally and structurally connected to higher-order brain areas contributes to our understanding of how the human brain processes visual information and forms a baseline for understanding any modifications in processing that might occur with training or experience.