Article Figures & Data
Figures
- Figure 1.
Degree of within-hemispheric connectivity and cross-hemispheric connectivity estimated in 1000 healthy subjects. The maps were based on the correlation threshold of 0.25. The difference map (the third row) indicates that within-hemispheric connectivity is much stronger than cross-hemispheric connectivity in the association areas.
- Figure 2.
Functional autonomy, defined as the difference between within- and cross-hemispheric connectivity, is displayed for the Discovery (N = 500) and Replication (N = 500) samples. Each individual brain was registered nonlinearly to the FreeSurfer surface template, which has 10,242 vertices in each hemisphere. Autonomy indices were computed at each vertex by subtracting the degree of cross-hemisphere connectivity from within-hemisphere connectivity (see Eq. 1) The connectivity degree was normalized by the total number of vertices in each hemisphere; therefore, the AI is denoted as a percentage. Regions with higher within-hemisphere connectivity than cross-hemisphere connectivity are shown in warm colors. Regions with higher cross-hemispheric connectivity are shown in cold colors. In the left hemisphere, strong autonomy was observed in inferior prefrontal and temporal regions overlapping with traditional language processing regions (see arrows). In the right hemisphere, strong autonomy was observed in lateral frontal, insula, and angular gyrus regions that are associated with attention (see arrows). Minimal autonomy was found in the sensorimotor, auditory, and visual cortices. The patterns of hemispheric autonomy largely replicate between the Discovery and Replication samples and are insensitive to the selection of correlation threshold.
- Figure 3.
Intrinsic hemispheric specialization during rest predicts language lateralization during task. The regions showing strongest autonomy in the 1000 subjects (a) and the regions activated by a semantic decision task (modified from Desmond et al., 1995) in 55 subjects (b) are plotted on the brain surface, with boundaries of functional connectivity networks from Yeo et al. (2011), illustrated by black lines (for the networks, also see Fig. 5). The regions showing strong autonomy (>6%) in the left hemisphere were taken as a mask. The AI within the mask was then averaged for each subject. A task-based language lateralization index was calculated for each subject based on the asymmetric activation in the two hemispheres. A significant correlation (Spearman rank correlation r = 0.47, p < 0.001) was found between the AI and language lateralization (c), indicating that intrinsic hemispheric specialization was associated with language lateralization during task.
- Figure 4.
Regions activated in the semantic decision task are displayed for 8 subjects to elaborate the results in Figure 3. The left hemisphere autonomy and language lateralization indices are displayed at the upper left and right corner of each panel, respectively. Two subjects with strong left hemisphere autonomy show language activation along the left inferior prefrontal gyrus, as well as in the right Crus I/II of cerebellum (a, b). Six subjects with the lowest left hemisphere autonomy either show bilateral task activation in both cerebrum and cerebellum (c, e, f, g) or lateralized activation in the right inferior prefrontal cortex and the left cerebellum (d, h).
- Figure 5.
Hemispheric specialization was quantified across seven cerebral networks in the left (a) and right (b) hemispheres. The analysis was based on our prior parcellation of the cerebrum (Yeo et al., 2011) into seven functional networks (a & b; top right), namely, the FPN, vATN, dATN, DN, limbic (LMB), sensorimotor (Mot), and visual (Vis) networks. The AI values were plotted on the brain surface (a & b; top left) with boundaries of functional connectivity networks illustrated by black lines. The autonomy indices from 1000 subjects were averaged within each network within each hemisphere. The bars illustrate mean AI in these networks with SE. Strong specialization was found in the left DN and FPN. In the right hemisphere, strong specialization was also seen in the FPN, along with the attention networks.
- Figure 6.
A map of intrinsic hemispheric autonomy in the human cerebellum based on 1000 individuals. Individual cerebellar volumes were nonlinearly registered to the FreeSurfer template (voxel size: 2 mm × 2 mm × 2 mm; 14,888 voxels in left hemisphere and 15,250 voxels in right hemisphere). Autonomy was calculated based on within- and cross-hemispheric functional coupling within the cerebellum, independent of the cerebral cortex. Regions with strongest autonomy were localized mainly in the right posterior Crus I/II (yellow), as well as the left lobules VI, VIIB, and the left anterior portion of Crus I/II (blue). The sections display coronal (left), sagittal (middle), and transverse (right) images. Major fissures are demarcated on the left, and lobules are labeled on the right (Buckner et al., 2011; Wang et al., 2013). PF, Primary fissure; SPF, superior posterior fissure; HF, horizontal fissure; AF, ansoparamedian fissure; PbF, prepyramidal/prebiventer fissure; IbF, intrabiventer fissure; SF, secondary fissure. IcF, intraculminate fissure; PLF, posterolateral fissure; PrcF, preculminate fissure; A, anterior; P, posterior; L, left; R, right; S, superior; I, inferior. The coordinates at the bottom right of each panel represent the section level in the MNI atlas space.
- Figure 7.
Functional autonomy was quantified in seven cerebellar networks in the right hemisphere (a) and left hemisphere (b). The computation of cerebellar specialization was independent of the cerebral cortex. The cerebellar segmentation based on coupling to cerebral networks from Buckner et al. (2011) is displayed in the bottom row. Each color in the cerebellar segmentation indicates which cerebral network shows the greatest coupling. In the right cerebellar hemisphere, strong autonomy was observed in regions linked to the DN and FPN. In the left cerebellar hemisphere, strongest autonomy was found in regions linked to the FPN and attention networks. The results are highly consistent with the autonomy distribution across cerebral networks but in the contralateral hemisphere (for a comparison, see Fig. 5). Note that the visual signal from the cerebrum was regressed from the cerebellum to mitigate partial volume effects.
- Figure 8.
Specialization of the FPN is preferentially coupled with different networks in two hemispheres. In the left hemisphere, a significant correlation was found between the autonomy in the FPN and the default network (Spearman rank correlation r = 0.42), but weaker correlations between the FPN and the attention networks (r = 0.37 with dATN and r = 0.14 with vATN; ANCOVA, p < 0.005 and p < 0.001 for the comparison of regression slopes, respectively). In the right hemisphere, autonomy in the FPN showed strongest correlation with the attention network (r = 0.47 with dATN and r = 0.44 with vATN) but a weaker correlation with the default network (r = 0.36; ANCOVA, p < 0.005 for both comparisons of the regression slopes). The different correlation patterns in two hemispheres suggest that subdivisions of FPN are specialized in each hemisphere.
- Figure 9.
Comparison of hemispheric specialization (a) and evolutionary cortical expansion (b). Evolutionary cortical expansion was estimated from the comparison between an adult macaque and the average human adult PALS-B12 atlas. Data were provided by Van Essen and Dierker (2007) and Hill et al. (2010a). Results are displayed for the right hemisphere. At the whole-brain level, hemispheric specialization tracks evolutionary cortical expansion (Spearman rank correlation r = 0.49, p < 0.001). The correlation between AI and cortical expansion is shown in the scatter plot, where each 100th vertex on the brain surface is represented by a small circle (c).
- Figure 10.
Spatial distribution of hemispheric specialization map is insensitive to the selection of correlation threshold. Specialization maps were computed based on 10 different thresholds (from r = 0.1 to r = 0.55 at the increment of 0.05). The Spearman rank correlations among these maps range from 0.73 to 0.99, indicating that the specialization patterns are relatively insensitive to the selection of thresholds. The specialization maps corresponding to three thresholds were normalized (z-transformed) and displayed (a). Spatial distribution of hemispheric specialization in the cerebellum is also insensitive to the selection of correlation threshold. Specialization maps were computed based on five different thresholds (from r = 0.1 to r = 0.3 at the increment of 0.05). The specialization maps were normalized (z-transformed) and displayed (b).
