Normative brain size variation and brain shape diversity in humans

See allHide authors and affiliations

Science  15 Jun 2018:
Vol. 360, Issue 6394, pp. 1222-1227
DOI: 10.1126/science.aar2578
  • Fig. 1 Nonlinear areal scaling of the cortex with normative brain size variation in PNC, NIH, and HCP cohorts.

    (A) Unthresholded vertex maps showing local surface area scaling with naturally occurring variations in total cortical area. Red, relative expansion in larger cortices (“positive scaling”); blue, relative contraction (“negative scaling”). The observed cross-vertex correlation in scaling between PNC/NIH cohorts is greater than that in 1000 surface-based rotations of the NIH scaling map (i.e., pSPIN < 0.001, density plot). (B) Categorical scaling maps showing regions of statistically significant positive and negative areal scaling (i.e., Ho: scaling coefficient = 1 rejected) after correction for multiple comparisons across vertices. (C) Conjunction of PNC and NIH maps from Fig. 1B. (D) Regional scaling in a third independent dataset (Human Connectome Project, n = 1113), across two different FWHM (full width at half maximum) smoothing kernels (for maps for all five kernels, see fig. S4). Density plot shows that observed alignment of HCP scaling with PNC(solid)/NIH(dashed) (r > 0.3) exceeds chance (pSPIN < 0.002). (E) Scatter plots of raw (top row) and proportional (bottom row) surface area in regions of nonlinear scaling from Fig. 1B versus total cortical area (SA). (F) Interrelationships between age and sex residualized scaling index (SI, with and without residualization for SA), SA, and IQ in the NIH cohort.

  • Fig. 2 Areal scaling aligns with patterns of cortical evolution, development, functional network topography, and cytoarchitecture.

    (A) Area expansion map in humans relative to macaques (2), with PNC/NIH scaling maps for comparison. Density plot shows that observed spatial correlation of scaling maps and evolutionary expansion is greater than chance for PNC (solid) and NIH (dashed) cohorts. (B) Conjunction between regions of positive areal scaling in PNC/NIH cohorts (Fig. 1B) and vertices with evolutionary expansion values above the 50th centile. (C and D) Identical analyses as Fig. 2, A and B, except for areal expansion map in human adults relative to human infants (2). (E) Parcellation of cortex into seven canonical resting state functional connectivity networks (8), with bar plots and conjunction maps showing differential representation of positive versus negative scaling in each network (**pSPIN < 0.001, *pSPIN < 0.05). Regions of positive scaling localize to the default mode network (DMN) and regions of negative scaling to the limbic network (Lim). (F) Cortical parcellation into seven different laminar types according to a classical cytoarchitectonic atlas (12), with bar plots and conjunction maps showing that regions of positive scaling localize to von Economo Type 3 cortex, and negative to Type 6 (**pSPIN < 0.001, *pSPIN < 0.05).

  • Fig. 3 Areal scaling aligns with cortical gene expression and metabolism.

    (A) Vertex scaling coefficients from PNC and NIH maps (Fig. 1A) were uniquely assigned to each of ~2k cortical samples from the Allen Institute for Brain Sciences (AIBS) (13) by spatial proximity, allowing ranking of the ~16,000 genes measured across all AIBS samples by their spatial correlation with areal scaling. (B) Extreme-ranking genes for the PNC and NIH scaling maps. (C) Visualization of GO cellular component terms in semantic space showing statistically significant enrichment of mitochondria- and synapse-related terms among top-ranking genes. (D) Areal scaling is positively correlated with two neuroimaging-based proxies for cortical energy consumption at rest: arterial spin labeling (ASL) measures of arterial blood flow (15) and positron emission tomography measures of glucose uptake (CMRGlu) (16) (density plots, PNC solid, NIH dashed, black null, red observed, mean pSPIN < 0.03).

Supplementary Materials

  • Normative brain size variation and brain shape diversity in humans

    P. K. Reardon, Jakob Seidlitz, Simon Vandekar, Siyuan Liu, Raihaan Patel, M. T. M. Park, Aaron Alexander-Bloch, Liv S. Clasen, Jonathan D. Blumenthal, Francois M. Lalonde, Jay N. Giedd, Ruben Gur, Raquel Gur, Jason P. Lerch, M. Mallar Chakravarty, Theodore Satterthwaite, Russell T. Shinohara, Armin Raznahan

    Materials/Methods, Supplementary Text, Tables, Figures, and/or References

    Download Supplement
    • Materials and Methods
    • Figs. S1 to S7
    • Tables S1 and S3
    • References
    Table S2
    Table S4
    Table S5
    Table S6
    Table S7

Stay Connected to Science

Navigate This Article