This study provides insight on the influence of population structure on brain shape

Knowing how the human brain is shaped by migration and admixture is a critical step in studying human evolution [], as well as in preventing the bias of hidden population structure in brain research []. Yet, the neuroanatomical differences engendered by population history are still poorly understood. Most of the inference relies on craniometric measurements, because morphology of the brain is presumed to be the neurocranium’s main shaping force before bones are fused and ossified []. Although studies have shown that the shape variations of cranial bones are consistent with population history [], it is unknown how much human ancestry information is retained by the human cortical surface. In our group’s previous study, we found that area measures of cortical surface and total brain volumes of individuals of European descent in the United States correlate significantly with their ancestral geographic locations in Europe []. Here, we demonstrate that the three-dimensional geometry of cortical surface is highly predictive of individuals’ genetic ancestry in West Africa, Europe, East Asia, and America, even though their genetic background has been shaped by multiple waves of migratory and admixture events. The geometry of the cortical surface contains richer information about ancestry than the areal variability of the cortical surface, independent of total brain volumes. Besides explaining more ancestry variance than other brain imaging measurements, the 3D geometry of the cortical surface further characterizes distinct regional patterns in the folding and gyrification of the human brain associated with each ancestral lineage.

The following regions are labeled at the top, as defined in a previous publication []: 1, central region; 2, occipital cortex; 3, posterolateral temporal region; 4, superior parietal region; 5, orbitofrontal region; 6, superior temporal region; 7, inferior parietal region; 8, dorsomedial frontal region; 9, anteromedial temporal region; 10, precuneus;11, dorsolateral prefrontal cortex; 12, pars opercularis. The Euclidean distances between cortical surface of 100% ancestry and neutral ancestry were calculated for each vertex. The mean and standard deviations of the Euclidean distances for different cortical regions are shown in the bar plots.

We then reconstructed the 3D geometry of the cortical surface based on the linear relationship we observed between cortical surface geometry and proportion of genetic ancestry. This allowed us to visualize how the geometry of the cortical surface changes as a function of increasing proportion of genetic ancestry in each ancestral component. The morphing of 3D cortical surfaces from neutral ancestry (25% of genetic ancestry in all four components) to 100% ancestry in each component is demonstrated in Figure 2 (for dynamic morphing of surface geometry, see Movies S1 S3 , and S4 ). As Figure 2 illustrates, the textural contrasts between regions of the cortical surface indicate that the morphing process has complex, unique patterns for each ancestral component, while the intensity varies from region to region. For example, as the proportion of the YRI component increases, the temporal surfaces move posteriorly and inward. The proportion of the CEU component is associated with protrusion of the occipital and frontal surfaces. Increases in the proportion of the EA component are accompanied by variations in temporal-parietal regions. The NA component is associated with flattening of the frontal and occipital surfaces.

The still image illustrates how each vertex on the cortical surface morphs from an ancestry-neutral 3D cortical surface (a 25% proportion of genetic ancestry in all ancestral components) to a 3D cortical surface with a 100% proportion of genetic ancestry in a specific ancestral component. The morphing coefficients were estimated from the PING sample. Here, the colors represent the direction of the morphing process. Movement along the medial-lateral axis is coded in red, along the anterior-posterior axis in green, and along the dorsal-ventral axis in blue. The final color is the combination of these three, depending on which direction the vertices move. For each viewing perspective, the coloring frame of reference is rendered on the top of each column. The length of each morphing line is the actual distance between two 3D cortical surfaces. For dynamic morphing animations, see Movies S1 S2 , and S3

As Figure 1 shows, the geometry of the cortical surface has good predictive value for each of the ancestry components. The variances explained by the models are 66% for ancestry in YRI, 55% for ancestry in CEU, 49% for ancestry in EA, and 47% for ancestry in NA. To determine to what degree the geometric differences reflect variation in area expansion of cortical surface, comparable models were computed using vertex-wise surface area ( Table 1 ). Also, to examine possible roles in the prediction of simpler morphological attributes, such as aspect ratios of the cerebrum and volumes of subcortical structures, we conducted comparable analyses predicting ancestry from these measures. None had as much information about ancestry as the geometry of cortical surface did ( Table 1 ).

Cortical surface geometry and cortical surface area were sampled in icosahedral level 4, which contains 642 vertices in each hemisphere. All models were fit with the same setting and evaluated with leave-one-out cross-validation (LOOCV). Nuisance covariates gender, age, age squared, total brain volumes, and scanner were regressed out before calculating the variance explained in LOOCV.

YRI: Yoruban, as a proxy for West African ancestry; CEU: Utah residents with Northern and Western European ancestry; EA: East Asian; NA: Native American. In all predictive models, the variables have been residualized with respect to age, age squared, gender, total brain volumes, and scanner used. All models excluded individuals with a 0% proportion of genetic ancestry to that specific component. The colors of the data points are determined by the proportion of genetic ancestry as illustrated in the key in the upper left panel. LOOCV: leave-one-out cross-validation.

We first tested whether the surface geometry of the cerebral cortex predicted the proportion of genetic ancestry among participants. To characterize variation in the geometry, we reconstructed the cortical surfaces from all individuals’ T1-weighted scans and then represented the positions of the corresponding surface vertices using standard 3D Cartesian coordinates. The reconstruction and registration processes ensure that each vertex on the reconstructed cortical surface is located in a homologous position with respect to the curvature patterns for individuals []. Taking the coordinates of all vertices as a whole, we then have information about shape variation of the cortical surface, including aspect ratios, sulcal depth, and gyrification. The prediction models were fit with ridge regression while treating gender, age, age squared, total brain volumes, and the scanner on which the image data were acquired as nuisance covariates. The model performance was evaluated using leave-one-out cross-validation (LOOCV).

The proportions of genetic ancestry were estimated using principal component (PC) analysis with whole-genome SNP reference panels for ancestry []. Four continental populations were used as ancestral references: West Africa (YRI, Yoruba in Ibadan), Europe (CEU, Utah residents with Northern and Western European ancestry), East Asia (EA), and America (NA, Native American). The metrics for summarizing genetic ancestry in each ancestral component were standardized as proportions ranging from 0% to 100%. These proportions represent how genetically similar an individual is to the reference population [].

The participants were recruited as part of the Pediatric Imaging, Neurocognition, and Genetics (PING) study. A detailed overview of the study can be found in previous publications (e.g., []), and research protocols and data are publicly available online []. Briefly, PING was a multisite project recruiting children and adolescents from ages 3 to 21 at ten sites in the United States. All participants were screened for history of major developmental, psychiatric, and neurological disorders; brain injury; and other medical conditions that affect development. Participants then received neurodevelopmental assessments, standardized multimodal neuroimaging, and genome-wide genotyping. The overall PING sample consisted of 1,493 participants; 1,152 individuals remained after quality control of the genotyping and neuroimaging data (for quality-control processes and demographics of the participants, see Supplemental Experimental Procedures and Table S1 ). We focused our analyses on 562 individuals older than 12 years (289 males, mean age 16.6 years, standard deviation 2.6 years). Considering that the morphological features of cortical surface change little after age 12 [], this stratified approach further reduced the residual confounds of developmental effects.

Discussion

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et al. Modeling 3D facial shape from DNA. Our data indicate that the unique folding patterns of gyri and sulci are closely aligned with genetic ancestry. The geometry robustly predicts each individual’s genetic background even though the population has been shaped by waves of migration and admixtures []. A previous study, using only facial features, achieved 64% explained variance in YRI ancestry among African Americans []. Our 3D representation of cortical surface geometry performs similarly in predicting YRI ancestry and also performs well for the other three continental ancestries. As data in Table 1 show, the explanatory power is not due to the differences in total brain volumes, nor to the differences in areal expansion of the cortical surface. Instead, regional folding patterns characterize each ancestral lineage.

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Halperin E. A model-based approach for analysis of spatial structure in genetic data. 26 Cavalli-Sforza L.L. The Human Genome Diversity Project: past, present and future. At first glance, these results are surprising because our model is based on the contemporary United States population, which is the historical product of migrations, slave trades, and local admixture events []. Nevertheless, the coordinates of reference-inferred PC space reflect information about individuals’ ancestral origins ( Figure S1 ) []. Our group’s previous study also showed that individuals’ positions in PC space are matched with their ancestral locations, rather than their current geographic locations []. Therefore, our 3D representation might to a certain degree reflect the neuroanatomical and/or neurocranial changes along the human migratory path in the dispersal from Africa []. Based on our current model, we simulated what might be expected from the “out of Africa” scenario in the Supplemental Experimental Procedures Movie S5 ). More precise characterization of an individual’s ancestral origins would require more complex estimates of ancestry based on global-scale reference panels []. Further understanding of neuroanatomical change associated with the “out of Africa” scenario based on brain imaging data will require future studies using sampling methods similar to those of the Human Genome Diversity Project [].

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Long-term influence of normal variation in neonatal characteristics on human brain development. 28 Roseman C.C. Detecting interregionally diversifying natural selection on modern human cranial form by using matched molecular and morphometric data. It is important to note that these ancestry-related geometric features of the cortical surface are not substantially attributable to variation in cortical surface area. Previous studies of ancient crania often interpreted the shape differences as evidence of relative size alterations of different cortical functional domains []. Our results suggest that in the case of the contemporary United States population, the differences in cortical surface geometry might not reflect variation in the relative surface area of different functional cortical regions. In prior studies, regionalization of the cortex was linked to cognitive differences in humans []. Any functional significance of the cortical surface geometry per se remains to be established. The effects reported here might be mediated by neutral drift of the phenotypic variations []. They could also result from a complex interaction between the brain and neurocranium, with the former expanding while the latter acts as physical resistance. Nevertheless, the causal relationships between the observed shapes and crania are beyond the scope of our current study.