Genetic topography of brain morphology

Abstract

Significance How diverse functional cortical regions develop is an important neuroscience question. Animal experiments show that regional differentiation is controlled by genes that express in a graded and regionalized pattern; however, such investigation in humans is scarce. Using noninvasive imaging techniques to acquire brain structure data of genetically related subjects (i.e., twins), we estimated the spatial pattern of genetic influences on cortical structure. We developed a genetic parcellation of cortical thickness to delineate the boundaries of cortical divisions that are—within each division—maximally under control of shared genetic influences. We also found differences in genetic influences on cortical surface area and thickness along two orthogonal axes. The concept of gradations is crucial for understanding the organization of the human brain.

Neurodevelopmental origins of lifespan changes in brain and cognition

“…This finding fits with the notion that GCA is supported by distributed brain networks (44,45). Using genetically defined cortical clusters (6,35), there was evidence that especially prefrontal and medial and posterolateral temporal clusters related more strongly to GCA. Most previous studies have focused on cortical volume or thickness, but the present results correspond with previously reported findings on areacognition relationships (24) in a sample not overlapping with the current developmental cohort wherein the region was currently identified.…”

“…As described above, we have recently shown that developmental and adult age-related changes in cortical thickness follow this genetic organization of the cerebral cortex (6). The most fundamental genetic influence on cortical surface area goes along an anterior-posterior axis (35). However, analyses in the current subsample 1 showed that the relationship between GCA and cortical area did not differ between the anterior (r = 0.24) vs. posterior (r = 0.22) genetic cluster (Fig.…”

“…However, when directly contrasted, the GCA-cortical area relationship was significantly (P < 0.00001) stronger for the GCA region than for the rest of the cortex or the total cortical surface area (Supporting Information). To further explore regional differences in surface area-GCA relationships, we tested whether the GCA-area relationship in subsample 1 differed between regions of the cortex established to be influenced by genetic differences (35). Previous research has shown that the cortex can be divided into regions of maximum shared genetic variance, and these regions can further be organized into superordinate clusters based on genetic similarity.…”

Studying neuroanatomy using MRI

“…Here the inside and outside surfaces of the cortex are segmented using deformable models and measures such as cortical thickness and surface area are extracted [12][13][14] . The diverse anatomical metrics that can be extracted from threedimensional models of the cortical surface capture different developmental processes, and show dissociable correlations with demographic 15,16 , genetic 17,18 , environmental 19 , and clinical 20,21 variables -highlighting the value in moving classical volumetric approaches to anatomical analysis. Surface based methods also provide an improved coordinate system for the cerebral cortex, allowing for smoothing of signal on the cortical sheet and surface based alignment to bring individuals into closer correspondence for statistical comparisons 13,22 .…”

“…For example, modern methods for cortical morphometry measure cortical thickness, surface area, volume, curvature and sulcal depth relative to the brain hull at tens of thousands of points across the cortical mantle in a single scan. It has become clear that these diverse metrics follow distinct developmental trajectories in health 163 , which reflect non-overlapping sets of genetic and environmental influences 18 , but can be inter-related in a spatiotemporally specific manner 164 . These normative observations carry major consequences for the optimal design of structural neuroimaging analysis in clinical populations, because conclusions regarding the presence and regional distribution of cortical abnormalities in a given genetic disorder can vary greatly across different morphometric features 159 .…”