Accurate prediction of V1 location from cortical folds in a surface coordinate system

Abstract

Previous studies demonstrated substantial variability of the location of primary visual cortex (V1) in stereotaxic coordinates when linear volume-based registration is used to match volumetric image intensities [Amunts, K., Malikovic, A., Mohlberg, H., Schormann, T., and Zilles, K. (2000). Brodmann's areas 17 and 18 brought into stereotaxic space-where and how variable? Neuroimage, 11(1):66-84]. However, other qualitative reports of V1 location [Smith, G. (1904). The morphology of the occipital region of the cerebral hemisphere in man and the apes. Anatomischer Anzeiger, 24:436-451; Stensaas, S.S., Eddington, D.K., and Dobelle, W.H. (1974). The topography and variability of the primary visual cortex in man. J Neurosurg, 40(6):747-755; Rademacher, J., Caviness, V.S., Steinmetz, H., and Galaburda, A.M. (1993). Topographical variation of the human primary cortices: implications for neuroimaging, brain mapping, and neurobiology. Cereb Cortex, 3(4):313-329] suggested a consistent relationship between V1 and the surrounding cortical folds. Here, the relationship between folds and the location of V1 is quantified using surface-based analysis to generate a probabilistic atlas of human V1. High-resolution (about 200 microm) magnetic resonance imaging (MRI) at 7 T of ex vivo human cerebral hemispheres allowed identification of the full area via the stria of Gennari: a myeloarchitectonic feature specific to V1. Separate, whole-brain scans were acquired using MRI at 1.5 T to allow segmentation and mesh reconstruction of the cortical gray matter. For each individual, V1 was manually identified in the high-resolution volume and projected onto the cortical surface. Surface-based intersubject registration [Fischl, B., Sereno, M.I., Tootell, R.B., and Dale, A.M. (1999b). High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp, 8(4):272-84] was performed to align the primary cortical folds of individual hemispheres to those of a reference template representing the average folding pattern. An atlas of V1 location was constructed by computing the probability of V1 inclusion for each cortical location in the template space. This probabilistic atlas of V1 exhibits low prediction error compared to previous V1 probabilistic atlases built in volumetric coordinates. The increased predictability observed under surface-based registration suggests that the location of V1 is more accurately predicted by the cortical folds than by the shape of the brain embedded in the volume of the skull. In addition, the high quality of this atlas provides direct evidence that surface-based intersubject registration methods are superior to volume-based methods at superimposing functional areas of cortex and therefore are better suited to support multisubject averaging for functional imaging experiments targeting the cerebral cortex.

Figure 1

The same coronal slice through volumes acquired at high and standard resolution. The high-resolution acquisition allowed delineation of V1 via the stria of Gennari, which is visible as a dark stripe in layer IV of the calcarine sulcus in the left panel. The manually located V1/V2 boundary is shown in both panels as the center of the yellow circles. The standard-resolution acquisition allowed reconstruction of a whole-brain surface mesh, which is superimposed on the slice shown in the right panel with portions of the surface that are identified as V1 shown in green.

Figure 2

The similarity measures at the optimal parameter values for each iteration of the template construction process with the dashed line indicating values for the left hemispheres and the solid line indicating right hemisphere values. Substantial increase in alignment quality is evident from the first to the third iterations, after which there is no consistent increase or decrease in alignment quality.

Figure 3

Alignment quality of V1 computed for several values of λ_A and λ_d after three iterations of the template generation process. The top row represents left hemispheres and the bottom row represents right hemispheres. The average kernel size is shown in the first column. Lower sizes represent better V1 alignment, as indicated by the color bars. The Jaccard similarity coefficient for the same parameter values is shown in the second column, with higher values representing better alignment. The third column shows the percent overlap of V1 for all individuals P_o(10). In all images, the location of the gray star indicates the parameter values that produced the best V1 alignment for that measure. The location of the black star indicates the parameter value pair commonly employed using this registration method.

Figure 4

A view of the medial surface of the inflated average left hemisphere cortical surface is shown on the top left, and an oblique posterior view is shown below it. A medial view and an oblique posterior view of the inflated average right hemisphere cortical surface is shown to the right. V1 probability is indicated by the color of the vertex, with yellow indicating high probability and red indicating low probability, as indicated by the color bars.

Figure 5

Average percent overlap of V1 over all possible groups of R left hemispheres is shown on the left, and right hemispheres on the right.

Figure 6

The cumulative V1 probability for the atlas presented here generated using surface-based registration (red) and an atlas generated using nonlinear volume-based registration (blue). The ratio of the cumulative probability using surface-based and volume-based registration is indicated above the bar for each probability.

Figure 7

Linear regression of V1 alignment quality average rank over parameter values with respect to calcarine alignment average rank for each group of hemispheres. The line corresponding to the coefficients β₀ and β₁ is drawn, and the r² value for the regression is shown for each hemisphere to the top left.