Search

Prior studies have shown strong genetic effects on cortical thickness (CT), structural covariance, and neurodevelopmental trajectories in childhood and adolescence. However, the importance of genetic factors on the induction of spatiotemporal variation during neurodevelopment remains poorly understood. Here, we explore the genetics of maturational coupling by examining 308 MRI-derived regional CT measures in a longitudinal sample of 677 twins and family members. We find dynamic inter-regional genetic covariation in youth, with the emergence of regional subnetworks in late childhood and early adolescence. Three critical neurodevelopmental epochs in genetically-mediated maturational coupling were identified, with dramatic network strengthening near eleven years of age. These changes are associated with statistically-significant (empirical p-value <0.0001) increases in network strength as measured by average clustering coefficient and assortativity. We then identify genes from the Allen Human Brain Atlas with similar co-expression patterns to genetically-mediated structural covariation in children. This set was enriched for genes involved in potassium transport and dendrite formation. Genetically-mediated CT-CT covariance was also strongly correlated with expression patterns for genes located in cells of neuronal origin., (© 2024. The Author(s).)

Genetic influences on cortical thickness (CT) and surface area (SA) are known to vary across the life span. Little is known about the extent to which genetic factors influence CT and SA in infancy and toddlerhood. We performed the first longitudinal assessment of genetic influences on variation in CT and SA in 501 twins who were aged 0-2 years. We observed substantial additive genetic influences on both average CT (0.48 in neonates, 0.37 in 1-year-olds, and 0.44 in 2-year-olds) and total SA (0.59 in neonates, 0.74 in 1-year-olds, and 0.73 in 2-year-olds). In addition, we found strong heritability of the change in average CT (0.49) from neonates to 1-year-olds, but not from 1- to 2-year-olds. Moreover, we found strong genetic correlations for average CT (rG = 0.92) between 1- and 2-year-olds and strong genetic correlations for total SA across all timepoints (rG = 0.96 between neonates and 1-year-olds, rG = 1 between 1- and 2-year-olds). In addition, we found CT and SA are strongly genetic correlated at birth, but weaken over time. Overall, results suggest a dynamic genetic relationship between CT and SA during first 2 years of life and provide novel insights into how genetic influences shape the cortical structure during early brain development., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Rationale and Objectives: To determine the metabolic effects of developmental venous anomalies (DVAs) and to correlate those effects with conventional magnetic resonance imaging (MRI) findings., Materials and Methods: We conducted a retrospective review of MRI and brain 18F-fluorodeoxyglucose positron emission tomography ( 18 F-FDG-PET) examinations in subjects with DVAs. Conventional MRI was used to determine DVA number, location, size, and associated parenchymal findings such as atrophy, hemorrhage, cavernoma, capillary telangiectasia, cortical dysplasia/polymicrogyria, and white matter signal abnormality. Qualitative and quantitative measures of relative metabolism in the drainage territory of the DVA were measured on 18 F-FDG-PET., Results: Fifty-four subjects with 57 DVAs were included in the analysis. 38% were associated with qualitative and quantitative metabolic abnormalities on 18 F-FDG-PET, with decreased metabolism in the parenchyma surrounding all but one of these DVAs. DVAs draining gray matter were significantly more likely to be hypometabolic than those draining only white matter, suggesting that the metabolic effects of DVAs may be underestimated on 18 F-FDG-PET., Conclusion: Altered metabolism is seen in the drainage territory of a significant proportion of DVAs, suggesting that these anomalies are vascular lesions with abnormal physiologic features., (Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Data quality is increasingly recognized as one of the most important confounding factors in brain imaging research. It is particularly important for studies of brain development, where age is systematically related to in-scanner motion and data quality. Prior work has demonstrated that in-scanner head motion biases estimates of structural neuroimaging measures. However, objective measures of data quality are not available for most structural brain images. Here we sought to identify quantitative measures of data quality for T1-weighted volumes, describe how these measures relate to cortical thickness, and delineate how this in turn may bias inference regarding associations with age in youth. Three highly-trained raters provided manual ratings of 1840 raw T1-weighted volumes. These images included a training set of 1065 images from Philadelphia Neurodevelopmental Cohort (PNC), a test set of 533 images from the PNC, as well as an external test set of 242 adults acquired on a different scanner. Manual ratings were compared to automated quality measures provided by the Preprocessed Connectomes Project's Quality Assurance Protocol (QAP), as well as FreeSurfer's Euler number, which summarizes the topological complexity of the reconstructed cortical surface. Results revealed that the Euler number was consistently correlated with manual ratings across samples. Furthermore, the Euler number could be used to identify images scored "unusable" by human raters with a high degree of accuracy (AUC: 0.98-0.99), and out-performed proxy measures from functional timeseries acquired in the same scanning session. The Euler number also was significantly related to cortical thickness in a regionally heterogeneous pattern that was consistent across datasets and replicated prior results. Finally, data quality both inflated and obscured associations with age during adolescence. Taken together, these results indicate that reliable measures of data quality can be automatically derived from T1-weighted volumes, and that failing to control for data quality can systematically bias the results of studies of brain maturation., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background and Purpose: Hyperintensity on T1-weighted imaging in perilesional vasogenic edema has been reported as a useful sign for differentiating cavernous malformation from other hemorrhagic intra-axial masses. In this study, we investigated the frequency of perilesional hyperintensity on T1-weighted imaging in patients with intra-axial hemorrhagic and nonhemorrhagic brain masses., Methods: The study was performed with the approval of the institutional review board. Magnetic resonance images of 218 patients with 282 intra-axial brain masses (129 metastases, 46 gliomas, 18 primary central nervous system lymphomas [PCNSLs], 25 intracerebral hemorrhages, 50 cavernous malformations, and 14 patients with brain abscesses) were evaluated. The signal intensity in perilesional area was qualitatively evaluated on T1-weighted sequences. In addition, signal intensity in perilesional area was quantitatively measured on T1-weighted sequences and normalized to the contralateral white matter., Results: Hyperintensity on T1-weighted imaging in perilesional vasogenic edema was found in 12 (9%) of 129 metastases, 8 (16%) of 50 cavernous malformations, 1 (4%) in 25 nonneoplastic intracerebral hemorrhages, and none of the patients with high-grade glioma, PCNSL, or abscess. All of the lesions with perilesional hyperintensity showed either acute or subacute hemorrhage. Pairwise comparison of qualitative hyperintensity on T1-weighted imaging demonstrated no significant difference between the groups. Perilesional hyperintensity on T1-weighted imaging showed high specificity in both metastasis and cavernous malformation groups (94%)., Conclusion: Perilesional hyperintensity on T1-weighted imaging is not limited to cavernous malformations and frequently evident with melanoma and other hemorrhagic metastasis to the brain. In our experience, it was not seen in high-grade glioma, PCNSL, and brain abscess., (Copyright © 2017 by the American Society of Neuroimaging.)

Cortical surface area measures appear to be functionally relevant and distinct in etiology, development, and behavioral correlates compared with other size characteristics, such as cortical thickness. Little is known about genetic and environmental influences on individual differences in regional surface area in humans. Using a large sample of adult twins, we determined relative contributions of genes and environment on variations in regional cortical surface area as measured by magnetic resonance imaging before and after adjustment for genetic and environmental influences shared with total cortical surface area. We found high heritability for total surface area and, before adjustment, moderate heritability for regional surface areas. Compared with other lobes, heritability was higher for frontal lobe and lower for medial temporal lobe. After adjustment for total surface area, regionally specific genetic influences were substantially reduced, although still significant in most regions. Unlike other lobes, left frontal heritability remained high after adjustment. Thus, global and regionally specific genetic factors both influence cortical surface areas. These findings are broadly consistent with results from animal studies regarding the evolution and development of cortical patterning and may guide future research into specific environmental and genetic determinants of variation among humans in the surface area of particular regions.

Using high resolution magnetic resonance imaging data, we examined the interrelationships between eight cerebral lobar volumetric measures via both exploratory and confirmatory factor analyses in a large sample (N = 484) of pediatric twins and singletons. These analyses suggest the presence of strong genetic correlations between cerebral structures, particularly between regions of like tissue type or in spatial proximity. Structural modeling estimated that most of the variance in all structures is associated with highly correlated lobar latent factors, with differences in genetic covariance and heritability driven by a common genetic factor that influenced gray and white matter differently. Reanalysis including total brain volume as a covariate dramatically reduced the total residual variance and disproportionately influenced the additive genetic variance in all regions of interest.

This article reviews the extant twin studies employing magnetic resonance imaging data (MRI), with an emphasis on studies of population-based samples. There have been approximately 75 twin reports using MRI, with somewhat under half focusing on typical brain structure. Of these, most are samples of adults. For large brain regions such as lobar volumes, the heritabilities of large brain volumes are consistently high, with genetic factors accounting for at least half of the phenotypic variance. The role of genetics in generating individual differences in the volumes of small brain regions is less clear, mostly due to a dearth of information, but rarely because of disagreement between studies. Multivariate analyses show strong genetic relationships between brain regions. Cortical regions involved in language, executive function, and emotional regulation appear to be more heritable than other areas. Studies of brain shape also show significant, albeit lower, genetic effects on population variance. Finally, there is evidence of significant genetically mediated relationships between intelligence and brain structure. At present, the majority of twin imaging studies are limited by sample sizes small by the standards of behavioral genetics; nevertheless the literature at present represents a pioneering effort in the pursuit of answers to many challenging neurobiological questions.

Velo-cardio-facial syndrome (VCFS), also known as 22q11.2 deletion syndrome, is a common genetic condition associated with increased risk for developing schizophrenia. Given that cortical malformations play an integral role in the pattern of neuroanatomical alterations associated with VCFS, the aim of the present study was to quantify and localize gyral abnormalities. Magnetic resonance images were obtained on a 1.5 T scanner. The gyrification index (GI), a measure of the degree of cortical complexity, was differentially calculated for each lobe using a semi-automated protocol. The GI was calculated for 37 patients affected by VCFS as well as for 36 comparison individuals group-matched for age, handedness, and gender. The subjects affected by VCFS showed a significant decrease in the GI in the frontal and parietal lobes compared with the control group. The pattern of decreased gyrification in the frontal and parietal lobes further defines the structural changes associated with the syndrome and suggests underlying abnormalities in neural connectivity. Aberrant connectivity may be partially responsible for the cognitive and behavioral impairments in the syndrome, as well as the high incidence of schizophrenia among affected individuals.

Williams syndrome (WS), a neurogenetic developmental disorder, is characterized by a rare fractionation of higher cortical functioning: selective preservation of certain complex faculties (language, music, face processing, and sociability) in contrast to marked and severe deficits in nearly every other cognitive domain (reasoning, spatial ability, motor coordination, arithmetic, problem solving). WS people are also known to suffer from hyperacusis and to experience heightened emotional reactions to music and certain classes of noise. We used functional magnetic resonance imaging to examine the neural basis of auditory processing of music and noise in WS patients and age-matched controls and found strikingly different patterns of neural organization between the groups. Those regions supporting music and noise processing in normal subjects were found not to be consistently activated in the WS participants (e.g., superior temporal and middle temporal gyri). Instead, the WS participants showed significantly reduced activation in the temporal lobes coupled with significantly greater activation in the right amygdala. In addition, WS participants (but not controls) showed a widely distributed network of activation in cortical and subcortical structures, including the brain stem, during music processing. Taken together with previous ERP and cytoarchitectonic studies, this first published report of WS using fMRI provides additional evidence of a different neurofunctional organization in WS people than normal people, which may help to explain their atypical reactions to sound. These results constitute an important first step in drawing out the links between genes, brain, cognition, and behavior in Williams syndrome.

Understanding patterns of gyrification in neurogenetic disorders helps to uncover the neurodevelopmental etiology underlying behavioral phenotypes. This is particularly true in Williams syndrome (WS), a condition caused by de novo deletion of approximately 1 to 2 Mb in the 7q11.23 region. Individuals with WS characteristically possess an unusual dissociation between deficits in visual-spatial ability and relative preservations in language, music, and social drive. A preliminary postmortem study reported anomalous gyri and sulci in individuals with WS. The present study examined gyrification patterns in 17 participants with WS (10 females, 7 males; mean age 28 years 11 months, SD 8 years 6 months) and 17 age- and sex-matched typically developing control participants (mean age 29 years 1 month, SD 8 years 1 month) using new automated techniques in MRI. Significantly increased cortical gyrification was found globally with abnormalities being more marked in the right parietal (p=0.0227), right occipital (p=0.0249), and left frontal (p=0.0086) regions. These results suggest that one or more genes in the 7q11.23 region are involved during the critical period when cortical folding occurs, and may be related to the hypothesized dorsal/ventral dissociation in this condition.

22q11.2 deletion syndrome (22q11DS) results from a hemizygous deletion that typically spans 46 protein-coding genes and is associated with widespread alterations in brain morphology. The specific genetic mechanisms underlying these alterations remain unclear. In the 22q11.2 ENIGMA Working Group, we characterized cortical alterations in individuals with 22q11DS (n = 232) versus healthy individuals (n = 290) and conducted spatial convergence analyses using gene expression data from the Allen Human Brain Atlas to prioritize individual genes that may contribute to altered surface area (SA) and cortical thickness (CT) in 22q11DS. Total SA was reduced in 22q11DS (Z-score deviance = -1.04), with prominent reductions in midline posterior and lateral association regions. Mean CT was thicker in 22q11DS (Z-score deviance = +0.64), with focal thinning in a subset of regions. Regional expression of DGCR8 was robustly associated with regional severity of SA deviance in 22q11DS; AIFM3 was also associated with SA deviance. Conversely, P2RX6 was associated with CT deviance. Exploratory analysis of gene targets of microRNAs previously identified as down-regulated due to DGCR8 deficiency suggested that DGCR8 haploinsufficiency may contribute to altered corticogenesis in 22q11DS by disrupting cell cycle modulation. These findings demonstrate the utility of combining neuroanatomic and transcriptomic datasets to derive molecular insights into complex, multigene copy number variants.

The 22q11.2 deletion (22q11DS) is a common chromosomal microdeletion and a potent risk factor for psychotic illness. Prior studies reported widespread cortical changes in 22q11DS, but were generally underpowered to characterize neuroanatomic abnormalities associated with psychosis in 22q11DS, and/or neuroanatomic effects of variability in deletion size. To address these issues, we developed the ENIGMA (Enhancing Neuro Imaging Genetics Through Meta-Analysis) 22q11.2 Working Group, representing the largest analysis of brain structural alterations in 22q11DS to date. The imaging data were collected from 10 centers worldwide, including 474 subjects with 22q11DS (age = 18.2 ± 8.6; 46.9% female) and 315 typically developing, matched controls (age = 18.0 ± 9.2; 45.9% female). Compared to controls, 22q11DS individuals showed thicker cortical gray matter overall (left/right hemispheres: Cohen's d = 0.61/0.65), but focal thickness reduction in temporal and cingulate cortex. Cortical surface area (SA), however, showed pervasive reductions in 22q11DS (left/right hemispheres: d = -1.01/-1.02). 22q11DS cases vs. controls were classified with 93.8% accuracy based on these neuroanatomic patterns. Comparison of 22q11DS-psychosis to idiopathic schizophrenia (ENIGMA-Schizophrenia Working Group) revealed significant convergence of affected brain regions, particularly in fronto-temporal cortex. Finally, cortical SA was significantly greater in 22q11DS cases with smaller 1.5 Mb deletions, relative to those with typical 3 Mb deletions. We found a robust neuroanatomic signature of 22q11DS, and the first evidence that deletion size impacts brain structure. Psychotic illness in this highly penetrant deletion was associated with similar neuroanatomic abnormalities to idiopathic schizophrenia. These consistent cross-site findings highlight the homogeneity of this single genetic etiology, and support the suitability of 22q11DS as a biological model of schizophrenia.

Objective22q11.2 deletion syndrome (22q11DS) is among the strongest known genetic risk factors for schizophrenia. Previous studies have reported variable alterations in subcortical brain structures in 22q11DS. To better characterize subcortical alterations in 22q11DS, including modulating effects of clinical and genetic heterogeneity, the authors studied a large multicenter neuroimaging cohort from the ENIGMA 22q11.2 Deletion Syndrome Working Group.MethodsSubcortical structures were measured using harmonized protocols for gross volume and subcortical shape morphometry in 533 individuals with 22q11DS and 330 matched healthy control subjects (age range, 6-56 years; 49% female).ResultsCompared with the control group, the 22q11DS group showed lower intracranial volume (ICV) and thalamus, putamen, hippocampus, and amygdala volumes and greater lateral ventricle, caudate, and accumbens volumes (Cohen's d values, -0.90 to 0.93). Shape analysis revealed complex differences in the 22q11DS group across all structures. The larger A-D deletion was associated with more extensive shape alterations compared with the smaller A-B deletion. Participants with 22q11DS with psychosis showed lower ICV and hippocampus, amygdala, and thalamus volumes (Cohen's d values, -0.91 to 0.53) compared with participants with 22q11DS without psychosis. Shape analysis revealed lower thickness and surface area across subregions of these structures. Compared with subcortical findings from other neuropsychiatric disorders studied by the ENIGMA consortium, significant convergence was observed between participants with 22q11DS with psychosis and participants with schizophrenia, bipolar disorder, major depressive disorder, and obsessive-compulsive disorder.ConclusionsIn the largest neuroimaging study of 22q11DS to date, the authors found widespread alterations to subcortical brain structures, which were affected by deletion size and psychotic illness. Findings indicate significant overlap between 22q11DS-associated psychosis, idiopathic schizophrenia, and other severe neuropsychiatric illnesses.

The neural substrates of intelligence represent a fundamental but largely uncharted topic in human developmental neuroscience. Prior neuroimaging studies have identified modest but highly dynamic associations between intelligence and cortical thickness (CT) in childhood and adolescence. In a separate thread of research, quantitative genetic studies have repeatedly demonstrated that most measures of intelligence are highly heritable, as are many brain regions associated with intelligence. In the current study, we integrate these 2 streams of prior work by examining the genetic contributions to CT-intelligence relationships using a genetically informative longitudinal sample of 813 typically developing youth, imaged with high-resolution MRI and assessed with Wechsler Intelligence Scales (IQ). In addition to replicating the phenotypic association between multimodal association cortex and language centers with IQ, we find that CT-IQ covariance is nearly entirely genetically mediated. Moreover, shared genetic factors drive the rapidly evolving landscape of CT-IQ relationships in the developing brain.

The genetics of cortical arealization in youth is not well understood. In this study, we use a genetically informative sample of 677 typically developing children and adolescents (mean age 12.72 years), high-resolution MRI, and quantitative genetic methodology to address several fundamental questions on the genetics of cerebral surface area. We estimate that >85% of the phenotypic variance in total brain surface area in youth is attributable to additive genetic factors. We also observed pronounced regional variability in the genetic influences on surface area, with the most heritable areas seen in primary visual and visual association cortex. A shared global genetic factor strongly influenced large areas of the frontal and temporal cortex, mirroring regions that are the most evolutionarily novel in humans relative to other primates. In contrast to studies on older populations, we observed statistically significant genetic correlations between measures of surface area and cortical thickness (r G = 0.63), suggestive of overlapping genetic influences between these endophenotypes early in life. Finally, we identified strong and highly asymmetric genetically mediated associations between Full-Scale Intelligence Quotient and left perisylvian surface area, particularly receptive language centers. Our findings suggest that spatially complex and temporally dynamic genetic factors are influencing cerebral surface area in our species.SIGNIFICANCE STATEMENT Over evolution, the human cortex has undergone massive expansion. In humans, patterns of neurodevelopmental expansion mirror evolutionary changes. However, there is a sparsity of information on how genetics impacts surface area maturation. Here, we present a systematic analysis of the genetics of cerebral surface area in youth. We confirm prior research that implicates genetics as the dominant force influencing individual differences in global surface area. We also find evidence that evolutionarily novel brain regions share common genetics, that overlapping genetic factors influence both area and thickness in youth, and the presence of strong genetically mediated associations between intelligence and surface area in language centers. These findings further elucidate the complex role that genetics plays in brain development and function.

Although prior studies have demonstrated that genetic factors play the dominant role in the patterning of the pediatric brain, it remains unclear how these patterns change over time. Using 1748 longitudinal anatomic MRI scans from 792 healthy twins and siblings, we quantified how genetically mediated inter-regional associations change over time via multivariate longitudinal structural equation modeling. These analyses found that genetic correlations for both lobar volumes and cortical thickness are dynamic, with relatively static effects on surface area. While genetic correlations for lobar volumes decrease over childhood and adolescence, in general they increase for cortical thickness in the second decade of life. Quantification of how genetic factors influence maturational coupling improves our understanding of typical neurodevelopment and informs future molecular genetic analyses.

Chronic cigarette use has been consistently associated with differences in the neuroanatomy of smokers relative to nonsmokers in case-control studies. However, the etiology underlying the relationships between brain structure and cigarette use is unclear. A community-based sample of male twin pairs ages 51-59 (110 monozygotic pairs, 92 dizygotic pairs) was used to determine the extent to which there are common genetic and environmental influences between brain structure and average lifetime cigarette use. Brain structure was measured by high-resolution structural magnetic resonance imaging, from which subcortical volume and cortical volume, thickness and surface area were derived. Bivariate genetic models were fitted between these measures and average lifetime cigarette use measured as cigarette pack-years. Widespread, negative phenotypic correlations were detected between cigarette pack-years and several cortical as well as subcortical structures. Shared genetic and unique environmental factors contributed to the phenotypic correlations shared between cigarette pack-years and subcortical volume as well as cortical volume and surface area. Brain structures involved in many of the correlations were previously reported to play a role in specific aspects of networks of smoking-related behaviors. These results provide evidence for conducting future research on the etiology of smoking-related behaviors using measures of brain morphology.

The article presents a study that investigated the possible correlation between genetic factors and the volumes of subcortical brain structures. Several models for genetic factors namely a Basal Ganglia/Thalamus factor, a Ventricular factor, a Limbic factor, and a Nucleus Accumbens factor are found to fit the data generated. Genetic patterning among structures that differentiate between brain, different subcortical regions, and cerebrospinal fluid spaces was observed.

Presents a study undertaken to confirm previously reported abnormalities of regional brain volumes in Down's syndrome by using high-resolution magnetic resonance imaging. Determination whether the volumetric abnormalities are present in childhood; Relationship between neuroanatomic abnormalities and the cognitive profile of Down's syndrome.

Williams syndrome (WMS), a genetic condition resulting from a contiguous deletion on the long arm of chromosome 7, is associated with a relatively consistent profile of neurocognitive and neurobehavioral features. The distinctiveness and regularity of the profile of learning and behavioral characteristics in this genetic condition suggests that underlying neurobiological correlates may be identifiable. In this initial study, we report findings derived from a high-resolution neuroimaging study of 14 young adult subjects with WMS and an individually matched normal control group. Compared to controls, subjects with WMS were noted to have decreased overall brain and cerebral volumes, relative preservation of cerebellar and superior temporal gyrus (STG) volumes, and disproportionate volume reduction of the brainstem. Analyses also suggested that the pattern of cerebral lobe proportions in WMS may be altered compared to normal controls with a greater ratio of frontal to posterior (parietal+occipital) tissue. Assessment of tissue composition indicated that, relative to controls, individuals with WMS have relative preservation of cerebral gray matter volume and disproportionate reduction in cerebral white matter volume. However, within the cerebral gray matter tissue compartment, the right occipital lobe was noted to have excess volume loss. Combined with our growing knowledge of the function of genes in the commonly deleted region for WMS, more detailed information regarding the structure and function of the WMS brain will provide a unique opportunity for elucidating meaningful correlations amongst genetic, neurobiological, and neurobehavioral factors in humans. [ABSTRACT FROM AUTHOR]

The cerebral cortex contains a significant quantity of intracortical myelin, but the genetics of cortical myelination (CM) in humans is not well understood. Relatively novel MRI-derived measures now enable the investigation of cortical myelination in large samples. In this study, we use a genetically-informative neuroimaging sample of 1096 young adult subjects from the Human Connectome Project in order to investigate genetic and environmental variation in CM and its relationships with cerebral surface area (SA) and cortical thickness (CT). We found that genetic factors account for approximately 50% of the observed individual differences in mean cortical myelin, 75% of the variation in total SA, and 85% of the variance in global mean CT. Although significant genetic influences were found throughout the cortex, both CM and SA demonstrated a posterior predominance, with disproportionately strong effects in the parietal and occipital lobes and significantly overlapping heritability maps (p < 0.001). Yet despite showing similar spatial heritability patterns, we found evidence that CM is genetically independent from SA at both global and vertex levels; genetically-mediated relationships between CM and CT were similarly small in magnitude. We also found small but statistically significant genetic associations between NIH Toolbox Total Cognition score and CM in the temporal lobe and insula. SA-cognition and CT-cognition correlations were less widespread compared to CM and both patterns were similar to those reported in prior studies. • We examined the genetics of cortical myelination using a twin and family design. • Cortical myelination was heritable, particularly in the posterior cerebrum. • Myelination was genetically independent from surface area and cortical thickness. • Cortical myelin was weakly correlated with intelligence via genetic factors. [ABSTRACT FROM AUTHOR]