While many studies have reported that individual differences in personality traits are genetically influenced, the neurobiological bases mediating these influences have not yet been well characterized. To advance understanding concerning the pathway from genetic variation to personality, here we examined whether measures of heritable variation in neuroanatomical size in candidate regions (amygdala and medial orbitofrontal cortex) were associated with heritable effects on personality. A sample of 486 middle-aged (mean=55 years) male twins (complete MZ pairs=120; complete DZ pairs=84) underwent structural brain scans and also completed measures of two core domains of personality: positive and negative emotionality. After adjusting for estimated intracranial volume, significant phenotypic (r(p)) and genetic (r(g)) correlations were observed between left amygdala volume and positive emotionality (r(p)=.16, p<.01; r(g)=.23, p<.05, respectively). In addition, after adjusting for mean cortical thickness, genetic and nonshared-environmental correlations (r(e)) between left medial orbitofrontal cortex thickness and negative emotionality were also observed (r(g)=.34, p<.01; r(e)=-.19, p<.05, respectively). These findings support a model positing that heritable bases of personality are, at least in part, mediated through individual differences in the size of brain structures, although further work is still required to confirm this causal interpretation.

Self-esteem and well-being are important for successful aging, and some evidence suggests that self-esteem and well-being are associated with hippocampal volume, cognition and stress responsivity. Whereas most of this evidence is based on studies on older adults, we investigated self-esteem, well-being and hippocampal volume in 474 male middle-aged twins. Self-esteem was significantly positively correlated with hippocampal volume (0.09, P = 0.03 for left hippocampus, 0.10, P = 0.04 for right). Correlations for well-being were not significant (Ps > 0.05). There were strong phenotypic correlations between self-esteem and well-being (0.72, P < 0.001) and between left and right hippocampal volume (0.72, P < 0.001). In multivariate genetic analyses, a two-factor additive genetic and unique environmental (AE) model with well-being and self-esteem on one factor and left and right hippocampal volumes on the other factor fits the data better than Cholesky, independent pathway or common pathway models. The correlation between the two genetic factors was 0.12 (P = 0.03); the correlation between the environmental factors was 0.09 (P > 0.05). Our results indicate that largely different genetic and environmental factors underlie self-esteem and well-being on one hand and hippocampal volume on the other.

Self-esteem and well-being are important for successful aging, and some evidence suggests that self-esteem and well-being are associated with hippocampal volume, cognition and stress responsivity. Whereas most of this evidence is based on studies on older adults, we investigated self-esteem, well-being and hippocampal volume in 474 male middle-aged twins. Self-esteem was significantly positively correlated with hippocampal volume (0.09, P = 0.03 for left hippocampus, 0.10, P = 0.04 for right). Correlations for well-being were not significant (Ps > 0.05). There were strong phenotypic correlations between self-esteem and well-being (0.72, P < 0.001) and between left and right hippocampal volume (0.72, P < 0.001). In multivariate genetic analyses, a two-factor additive genetic and unique environmental (AE) model with well-being and self-esteem on one factor and left and right hippocampal volumes on the other factor fits the data better than Cholesky, independent pathway or common pathway models. The correlation between the two genetic factors was 0.12 (P = 0.03); the correlation between the environmental factors was 0.09 (P > 0.05). Our results indicate that largely different genetic and environmental factors underlie self-esteem and well-being on one hand and hippocampal volume on the other. © 2012 The Authors. Genes, Brain and Behavior © 2012 Blackwell Publishing Ltd and International Behavioural and Neural Genetics Society.

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight¹. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories² of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones³, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.