Fig 1.
Procedure to extract surface-based resting-state functional connectivity properties.
A N nodes x R regions connectivity matrix was constructed by calculating Pearson’s correlation coefficient from all the nodes in the sphere and all the regions in the parcellation map. From the connectivity matrix, functional connectivity features were extracted.
Fig 2.
Group PCA procedure of extracting individual principal components (PCs) corresponding to each other and across species as functional connectivity modes.
PCA of individual subjects’ PCs was conducted to find group-common PCs in humans and macaques. Those group-common PCs were projected to each individual. These PCs are driven from N x R functional connectivity matrices, not from conventional fMRI time series. From the individual PCs, group-average PCs for humans and macaques were generated. To these group-average PCs, spherical registration of each individual was conducted.
Fig 3.
Six group average principal components (modes) of functional connectivity matrix for the human and macaque.
Fig 4.
The proposed spherical registration algorithm between macaque and human hemisphere.
For each iteration of the proposed algorithm, curvature, sulcus depth, myelination, node degree, and six group principal components based on functional connectivity matrix were generated and used in the demons registration.
Fig 5.
Spherical demons registration results of nonlinear warping simulation.
Spheres on the top row show the target features (warped from the source sphere using a ground-truth deformation field). The spheres on the second row show the source features. The last two rows show the registration results only with structural features and with both structural and functional features, respectively. Warping with structural and functional properties makes the source sphere highly aligned with the target sphere.
Fig 6.
(a) Registration errors defined by the nodes’ geodesic distance are displayed at different iterations. (b) Registration errors defined by the number of wrong-labeled nodes are displayed. The first iteration error was calculated after demons registration only with structural data. In contrast, the registration at the second to the fourth iterations was conducted with both structural and functional properties, weighting functional properties along with the increased iteration.
Fig 7.
Cortical surface area changes in macaques and humans after structural registration and functional (combined structural and functional features) registration.
The left/right hemisphere of human and macaque subjects were registered to the left/right hemisphere of the human template and the macaque template, respectively. The first two top rows are the average of ACI over 13 individuals. The bottom two rows are the standard deviation of ACI. The blue color in the average figure indicates where the area after registration to the group template was less than that of the group template.
Fig 8.
Group interhemispheric asymmetry results: Humans and macaques (13 each).
A and B show mean and standard deviation of AAI evaluated after registration of the right hemisphere to the left hemisphere in humans using structural features (left column) and using combined (structural and functional) features (right column) were computed on each parcellation. C and D show the results of the macaque’s interhemispheric registration using structural and functional features. E displays two-sample t-test result of the absolute value of human AAI and macaque AAI with intensity = −log10p*sign(t).
Table 1.
Interhemispheric areal asymmetry.
Table 2.
Group differences in the interhemispheric areal asymmetry between humans and macaques.
Fig 9.
Parcellation maps of the human atlas and macaque atlas.
(a) Parcellation map with 180 ROI labels of the human atlas. (b) Parcellation maps on the macaque atlas after registration using only structural features and (c) structural and functional network features.