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. 2023 Jun 19;12:e84685. doi: 10.7554/eLife.84685

Figure 1. Population-level asymmetry of the cerebral cortex.

(A) Mean areal and (B) thickness asymmetry in each dataset. Warm and cold colours depict leftward and rightward asymmetry, respectively. (C) Spatial overlap (Pearson’s r) of the unthresholded maps between datasets for areal (lower matrix) and thickness asymmetry (upper). (D) Overlapping effects across datasets were used to delineate clusters exhibiting population-level areal (lower threshold = 5%) and (E) thickness asymmetry (lower threshold = 1%) based on a minimum 6-dataset overlap (black outlined clusters). (F, H) Raw distribution of the individual-level asymmetry index (AI) in adults extracted from clusters exhibiting areal and thickness asymmetry, respectively. Mean AI’s are in black, Raw distributions are shown for the LCBC (18–55 years) dataset with mixed effects data (cluster-wise outliers defined in lifespan analysis removed on a region-wise basis; Methods; Supplementary file 1E-F). X-axis denotes the AI of the average thickness and area of a vertex within the cluster. (G, I) Proportion of individuals with the expected directionality of asymmetry within each cluster exhibiting areal and thickness asymmetry, respectively, shown for the three largest adult datasets. The X-axes in G and I are ordered according to the clusters shown in F and H, respectively. Lat = lateral; Med = medial; Post = posterior; Ant = anterior; Sup = superior; Inf = inferior.

Figure 1.

Figure 1—figure supplement 1. Significance of asymmetry effects across samples.

Figure 1—figure supplement 1.

Significance for (A) areal and (B) thickness asymmetry across all samples. Note that because differences in sample size and test power affect the overall level of significance and consequently the FDR-correction, the visualization threshold is set to match the FDR-corrected significance level for each cortical metric in the first sample (LCBC; p<0.001). Warm and cold colours depict significance of leftward and rightward asymmetry, respectively. To permit more fine grained interpretation of anatomical correspondence, an outline of the (Destrieux et al., 2010) cortical atlas is overlain. Compare with effect sizes in Figure 1 in main paper.
Figure 1—figure supplement 2. Unthresholded maps.

Figure 1—figure supplement 2.

Completely unthresholded significance maps of areal (leftmost 3 columns) and thickness (rightmost 3 columns) asymmetry ‘effects’ for the three largest samples. Desikan-Killiany atlas is overlain.
Figure 1—figure supplement 3. Comparison of vertex-wise and atlas-based asymmetry estimates.

Figure 1—figure supplement 3.

To assess to what extent vertex-wise areal and thickness asymmetry estimates adhere to the anatomical boundaries of the Desikan-Killany (DK) atlas, we derived AI maps per subject and extracted a vertex × subject matrix. Then, within each of the 34 DK parcels we correlated the AI at each vertex with the parcel mean (i.e. the mean asymmetry across parcel vertices) and computed the mean correlation for each parcel. High correlations would be expected where atlas-derived parcels fit well to the underlying vertex-wise structure of cortical asymmetry. We then repeated this analysis using our set of robust asymmetry clusters. As a formal test, for each cortical metric the resulting coefficients were used as response variable in linear regressions with Cluster Type (DK parcels vs. Robust clusters) as predictor, controlling for cluster size (nVertices) and the Cluster Type × nVertices interaction. For areal asymmetry, the average vertex-mean correlations across all Desikan-Killiany (DK) parcels were r=0.53 ± [CI = 0.14] [LCBC], r=0.51 ± .14 [UK Biobank], and r=0.49 ± 0.15 [HCP], which respectively increased to r=0.65 ± 0.18, r=0.64 ± 0.18, and r=0.62 ± 0.19 for areal asymmetry clusters. For thickness, the average vertex-mean correlations across all DK parcels were r=0.36 ± 0.12 [LCBC], r=0.39 ± 0.12 [UK Biobank] and r=0.38 ± 0.12 [HCP], which respectively increased to r=0.47 ± 0.11, r=0.50 ± 0.11 and r=0.48 ± 0.12 for thickness asymmetry clusters. As would be expected, within parcel/cluster vertex-mean correlations were significantly lower in larger parcels/clusters. Linear regressions (size controlled) revealed a significant main effect of Cluster Type in all but one test (see Supplementary file 1D), confirming the visual impression that DK parcels conform poorly to the underlying asymmetry of cortex. (A–B) Average correlation between vertex-wise estimates of asymmetry within DK parcels to the mean across each parcel (top rows) and between vertex-wise asymmetry estimates within our robust clusters to the mean across each cluster (bottom rows), for areal (A) and thickness (B) asymmetry. The results on the surface are the average across the LCBC, UKB, and HCP datasets used in the vertex-wise analysis in main paper. The complete set of raw vertex-mean correlation coefficients correlated highly between all dataset-pairs for both areal [min r=0.97] and thickness asymmetry [min r=0.95]. (C–D) Vertex-mean correlation coefficients for areal asymmetry in DK parcels and robust asymmetry clusters, shown as raw values and after correcting for number of vertices in the parcel/cluster, for areal (C) and thickness asymmetry (D). Lat = lateral; Med = medial; Post = posterior; Ant = anterior.
Figure 1—figure supplement 4. HCP pipeline.

Figure 1—figure supplement 4.

Thickness asymmetry results in the HCP dataset vary depending on the preprocessing pipeline. (A) Results from using the –hires argument to recon-all (as in main paper). We used this method to best harmonize preprocessing across cohorts whilst accounting for the higher resolution of HCP data. Shown again for comparison, the top row in A is akin to the results in the main paper (analyzed using cross-hemispheric registration methods) whereas the bottom row is the same data analyzed using standard parcellation methods (as in Figure 1—figure supplement 5). (B) Unthresholded thickness asymmetry results using the HCP preprocessed data subject to extra preprocessing steps and inputs, analyzed using cross-hemispheric registration methods (top row) and standard parcellation methods (bottom). (C) Results using the HCP preprocessed data when calculating thickness asymmetry on the fs_LR template. Warm and cold colours depict leftward and rightward asymmetry, respectively.
Figure 1—figure supplement 5. Unthresholded asymmetry effects analyzed using a standard brain atlas with no cross-hemispheric registration.

Figure 1—figure supplement 5.

Mean (A) areal and (B) thickness asymmetry in each dataset analyzed using standard methods on the fsaverage template within parcels from the HCP multimodal brain atlas (Glasser et al., 2016). We used this atlas here because it appeared best suited to assess parcels that are homotopic. Warm and cold colours depict leftward and rightward asymmetry, respectively. Post = posterior; Lat = lateral; Med = medial; Ant = anterior; Sup = superior; Inf = inferior.