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The presence of a 22q11.2 microdeletion (22q11.2 deletion syndrome [22q11DS]) ranks among the greatest known genetic risk factors for the development of psychotic disorders. There is emerging evidence that the cerebellum is important in the pathophysiology of psychosis. However, there is currently limited information on cerebellar neuroanatomy in 22q11DS specifically.High-resolution 3T magnetic resonance imaging was acquired in 79 individuals with 22q11DS and 70 typically developing control subjects (N = 149). Lobar and lobule-level cerebellar volumes were estimated using validated automated segmentation algorithms, and subsequently group differences were compared. Hierarchical clustering, principal component analysis, and graph theoretical models were used to explore intercerebellar relationships. Cerebrocerebellar structural connectivity with cortical thickness was examined via linear regression models.Individuals with 22q11DS had, on average, 17.3% smaller total cerebellar volumes relative to typically developing subjects (p.0001). The lobules of the superior posterior cerebellum (e.g., VII and VIII) were particularly affected in 22q11DS. However, all cerebellar lobules were significantly smaller, even after adjusting for total brain volumes (all cerebellar lobules p.0002). The superior posterior lobule was disproportionately associated with cortical thickness in the frontal lobes and cingulate cortex, brain regions known be affected in 22q11DS. Exploratory analyses suggested that the superior posterior lobule, particularly Crus I, may be associated with psychotic symptoms in 22q11DS.The cerebellum is a critical but understudied component of the 22q11DS neuroendophenotype.

Twin studies yield valuable insights into the sources of variation, covariation and causation in human traits. The ABCD Study® (abcdstudy.org) was designed to take advantage of four universities known for their twin research, neuroimaging, population-based sampling, and expertise in genetic epidemiology so that representative twin studies could be performed. In this paper we use the twin data to: (i) provide initial estimates of heritability for the wide range of phenotypes assessed in the ABCD Study using a consistent direct variance estimation approach, assuring that both data and methodology are sound; and (ii) provide an online resource for researchers that can serve as a reference point for future behavior genetic studies of this publicly available dataset. Data were analyzed from 772 pairs of twins aged 9–10 years at study inception, with zygosity determined using genotypic data, recruited and assessed at four twin hub sites. The online tool provides twin correlations and both standardized and unstandardized estimates of additive genetic, and environmental variation for 14,500 continuously distributed phenotypic features, including: structural and functional neuroimaging, neurocognition, personality, psychopathology, substance use propensity, physical, and environmental trait variables. The estimates were obtained using an unconstrained variance approach, so they can be incorporated directly into meta-analyses without upwardly biasing aggregate estimates. The results indicated broad consistency with prior literature where available and provided novel estimates for phenotypes without prior twin studies or those assessed at different ages. Effects of site, self-identified race/ethnicity, age and sex were statistically controlled. Results from genetic modeling of all 53,172 continuous variables, including 38,672 functional MRI variables, will be accessible via the user-friendly open-access web interface we have established, and will be updated as new data are released from the ABCD Study. This paper provides an overview of the initial results from the twin study embedded within the ABCD Study, an introduction to the primary research domains in the ABCD study and twin methodology, and an evaluation of the initial findings with a focus on data quality and suitability for future behavior genetic studies using the ABCD dataset. The broad introductory material is provided in recognition of the multidisciplinary appeal of the ABCD Study. While this paper focuses on univariate analyses, we emphasize the opportunities for multivariate, developmental and causal analyses, as well as those evaluating heterogeneity by key moderators such as sex, demographic factors and genetic background.

BackgroundBrain MRIs acquired in clinical settings represent a valuable and underutilized scientific resource for investigating neurodevelopment. Utilization of these clinical scans has been limited because of their clinical acquisition and technical heterogeneity. These barriers have curtailed the interpretability and scientific value of retrospective studies of clinically acquired brain MRIs, compared to studies of prospectively acquired research quality brain MRIs.PurposeTo develop a scalable and rigorous approach to generate clinical brain growth chart models, to benchmark neuroanatomical differences in clinical MRIs, and to validate clinically-derived brain growth charts against those derived from large-scale research studies.Materials and MethodsWe curated a set of clinical MRIScans withLimitedImagingPathology (SLIP) – so-called “clinical controls” – from an urban pediatric healthcare system acquired between 2005 and 2020. The curation process included manual review of signed radiology reports, as well as automated and manual quality review of images without gross pathology. We measured global and regional volumetric imaging phenotypes in the SLIP sample using two alternative, advanced image processing pipelines, and quantitatively compared clinical brain growth charts to research brain growth charts derived from >123,000 MRIs.ResultsThe curated SLIP dataset included 372 patients scanned between the ages of 28 days post-birth and 22.2 years across nine 3T MRI scanners. Clinical brain growth charts were highly similar to growth charts derived from large-scale research datasets, in terms of the normative developmental trajectories predicted by the models. The clinical indication of the scans did not significantly bias the output of clinical brain charts. Tens of thousands of additional healthcare system scans meet inclusion criteria to be included in future brain growth charts.ConclusionBrain charts derived from clinical-controls are highly similar to brain charts from research-controls, suggesting that curated clinical scans could be used to supplement research datasets.Summary StatementBrain growth charts of pediatric clinical MRIs with limited imaging pathology (N=372) are highly correlated with charts from a large aggregated set of research controls (N>120,000).Key ResultsA cohort of brain MRI scans with limited reported imaging pathology (N=372, 186 female; ages 0.07 - 22.2 years, median = 10.2) were identified using signed radiology reports and processed using two segmentation pipelines. Growth charts generated from these scans are highly correlated with growth charts from a large aggregated set of research controls (r range 0.990 - 0.999). There was no evidence of bias due to the reason for each scan.

The mechanisms underlying cortical folding are incompletely understood. Prior studies have suggested that individual differences in sulcal depth are genetically mediated, with deeper and ontologically older sulci more heritable than others. In this study, we examine FreeSurfer-derived estimates of average convexity and mean curvature as proxy measures of cortical folding patterns using a large (N = 1096) genetically informative young adult subsample of the Human Connectome Project. Both measures were significantly heritable near major sulci and primary fissures, where approximately half of individual differences could be attributed to genetic factors. Genetic influences near higher order gyri and sulci were substantially lower and largely nonsignificant. Spatial permutation analysis found that heritability patterns were significantly anticorrelated to maps of evolutionary and neurodevelopmental expansion. We also found strong phenotypic correlations between average convexity, curvature, and several common surface metrics (cortical thickness, surface area, and cortical myelination). However, quantitative genetic models suggest that correlations between these metrics are largely driven by nongenetic factors. These findings not only further our understanding of the neurobiology of gyrification, but have pragmatic implications for the interpretation of heritability maps based on automated surface-based measurements.

Objective: 22q11.2 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. Methods: Subcortical structures were measured using harmonized protocols for gross volume and subcortical shape morphometry in 533 individualswith 22q11DS and 330matched healthy control subjects (age range, 6-56 years; 49% female). Results: Compared 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, 20.90 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, 20.91 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. Conclusions: In 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.

Twin studies yield valuable insights into the sources of variation, covariation and causation in human traits. The ABCD Study® (abcdstudy.org) was designed to take advantage of four universities known for their twin research, neuroimaging, population-based sampling, and expertise in genetic epidemiology so that representative twin studies could be performed. In this paper we use the twin data to: (i) provide initial estimates of heritability for the wide range of phenotypes assessed in the ABCD Study using a consistent direct variance estimation approach, assuring that both data and methodology are sound; and (ii) provide an online resource for researchers that can serve as a reference point for future behavior genetic studies of this publicly available dataset. Data were analyzed from 772 pairs of twins aged 9-10 years at study inception, with zygosity determined using genotypic data, recruited and assessed at four twin hub sites. The online tool provides twin correlations and both standardized and unstandardized estimates of additive genetic, and environmental variation for 14,500 continuously distributed phenotypic features, including: structural and functional neuroimaging, neurocognition, personality, psychopathology, substance use propensity, physical, and environmental trait variables. The estimates were obtained using an unconstrained variance approach, so they can be incorporated directly into meta-analyses without upwardly biasing aggregate estimates. The results indicated broad consistency with prior literature where available and provided novel estimates for phenotypes without prior twin studies or those assessed at different ages. Effects of site, self-identified race/ethnicity, age and sex were statistically controlled. Results from genetic modeling of all 53,172 continuous variables, including 38,672 functional MRI variables, will be accessible via the user-friendly open-access web interface we have established, and will be updated as new data are released from the ABCD Study. This paper provides an overview of the initial results from the twin study embedded within the ABCD Study, an introduction to the primary research domains in the ABCD study and twin methodology, and an evaluation of the initial findings with a focus on data quality and suitability for future behavior genetic studies using the ABCD dataset. The broad introductory material is provided in recognition of the multidisciplinary appeal of the ABCD Study. While this paper focuses on univariate analyses, we emphasize the opportunities for multivariate, developmental and causal analyses, as well as those evaluating heterogeneity by key moderators such as sex, demographic factors and genetic background.

Although professional societies now support MRI in patients with nonconditional (legacy) cardiac implanted electronic devices (CIEDs), concern remains regarding potential cumulative effects of serial examinations. We evaluated 481 patients with CIEDs who underwent 599 1.5-T MRI examinations (44.6% cardiac examinations), including 68 patients who underwent multiple examinations (maximum, seven examinations). No major events occurred. The minor adverse event rate was 5.7%. Multiple statistical evaluations showed no increase in adverse event rate with increasing number of previous examinations.

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 (18F-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 18F-FDG-PET. Results Fifty-four subjects with 57 DVAs were included in the analysis. 38% were associated with qualitative and quantitative metabolic abnormalities on 18F-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 18F-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.

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.

Psychiatric and cognitive phenotypes have been associated with a range of specific, rare copy number variants (CNVs). Moreover, IQ is strongly associated with CNV risk scores that model the predicted risk of CNVs across the genome. But the utility of CNV risk scores for psychiatric phenotypes has been sparsely examined.To determine how CNV risk scores, common genetic variation indexed by polygenic scores (PGSs), and environmental factors combine to associate with cognition and psychopathology in a community sample.The Philadelphia Neurodevelopmental Cohort is a community-based study examining genetics, psychopathology, neurocognition, and neuroimaging. Participants were recruited through the Children's Hospital of Philadelphia pediatric network. Participants with stable health and fluency in English underwent genotypic and phenotypic characterization from November 5, 2009, through December 30, 2011. Data were analyzed from January 1 through July 30, 2021.The study examined (1) CNV risk scores derived from models of burden, predicted intolerance, and gene dosage sensitivity; (2) PGSs from genomewide association studies related to developmental outcomes; and (3) environmental factors, including trauma exposure and neighborhood socioeconomic status.The study examined (1) neurocognition, with the Penn Computerized Neurocognitive Battery; (2) psychopathology, with structured interviews based on the Schedule for Affective Disorders and Schizophrenia for School-Age Children; and (3) brain volume, with magnetic resonance imaging.Participants included 9498 youths aged 8 to 21 years; 4906 (51.7%) were female, and the mean (SD) age was 14.2 (3.7) years. After quality control, 18 185 total CNVs greater than 50 kilobases (10 517 deletions and 7668 duplications) were identified in 7101 unrelated participants genotyped on Illumina arrays. In these participants, elevated CNV risk scores were associated with lower overall accuracy on cognitive tests (standardized β = 0.12; 95% CI, 0.10-0.14; P = 7.41 × 10-26); lower accuracy across a range of cognitive subdomains; increased overall psychopathology; increased psychosis-spectrum symptoms; and higher deviation from a normative developmental model of brain volume. Statistical models of developmental outcomes were significantly improved when CNV risk scores were combined with PGSs and environmental factors.In this study, elevated CNV risk scores were associated with lower cognitive ability, higher psychopathology including psychosis-spectrum symptoms, and greater deviations from normative magnetic resonance imaging models of brain development. Together, these results represent a step toward synthesizing rare genetic, common genetic, and environmental factors to understand clinically relevant outcomes in youth.

22q11.2 deletion syndrome (22q11DS)—a neurodevelopmental condition caused by a hemizygous deletion on chromosome 22—is associated with an elevated risk of psychosis and other developmental brain disorders. Prior single-site diffusion magnetic resonance imaging (dMRI) studies have reported altered white matter (WM) microstructure in 22q11DS, but small samples and variable methods have led to contradictory results. Here we present the largest study ever conducted of dMRI-derived measures of WM microstructure in 22q11DS (334 22q11.2 deletion carriers and 260 healthy age- and sex-matched controls; age range 6–52 years). Using harmonization protocols developed by the ENIGMA-DTI working group, we identified widespread reductions in mean, axial and radial diffusivities in 22q11DS, most pronounced in regions with major cortico-cortical and cortico-thalamic fibers: the corona radiata, corpus callosum, superior longitudinal fasciculus, posterior thalamic radiations, and sagittal stratum (Cohen’s d’s ranging from −0.9 to −1.3). Only the posterior limb of the internal capsule (IC), comprised primarily of corticofugal fibers, showed higher axial diffusivity in 22q11DS. 22q11DS patients showed higher mean fractional anisotropy (FA) in callosal and projection fibers (IC and corona radiata) relative to controls, but lower FA than controls in regions with predominantly association fibers. Psychotic illness in 22q11DS was associated with more substantial diffusivity reductions in multiple regions. Overall, these findings indicate large effects of the 22q11.2 deletion on WM microstructure, especially in major cortico-cortical connections. Taken together with findings from animal models, this pattern of abnormalities may reflect disrupted neurogenesis of projection neurons in outer cortical layers. Sin financiación 15.992 JCR (2020) Q1, 10/295 Biochemistry & Molecular Biology 5.071 SJR (2020) Q1, 5/85 Cellular and Molecular Neuroscience No data IDR 2020 UEM

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.

OBJECTIVE: To determine whether demyelinating lesions attributable to multiple sclerosis (MS) occur more commonly in regions of pre-existing cervical stenosis (CS). DESIGN/METHODS: One hundred comorbid MS/CS patients and 100 MS-only controls were identified via ICD codes and radiology reports from a retrospective chart review of the records of the University of Pennsylvania Hospital System (UPHS) from January 1(st), 2009 to December 31(st), 2018. For each patient, axial and sagittal T2 sequences of cervical MRI scans were examined. The cervical cord was split into 7 equal segments comprising the disc space and half of each adjacent vertebral body. Each segment was assessed for the presence of MS lesions and grade 2 CS or higher by previously published criteria. Lesions which were concerning for spondylotic-related signal change based on imaging characteristics were excluded (n=6, 3.2%). Clinical data was extracted from the electronic medical record. RESULTS: Average age at the time of MRI was 57.0 +/− 10.5 years and average time with MS diagnosis was 15.3 +/− 9.2 years. The majority of patients had a diagnosis of relapse-remitting MS (81.0%) and the F:M ratio was 3.5. Eighty-five percent of patients were on treatment at the time of MRI, most often glatiramer acetate (35.0%). Spinal segments with at least grade 2 stenosis were significantly associated with the presence of an MS lesion in the same segment (χ(2) = 19.0, p < 0.001, OR = 2.6, 95% CI 1.8–3.7). CONCLUSIONS: Our data suggest there is a significant association between segments of spinal cord with at least moderate CS and segments with MS lesions. Further analysis is required to assess if cervical stenosis is a causative or aggravating factor in multiple sclerosis.

As data collection costs fall and vast quantities of data are collected, data analysis time can become a bottleneck. For massively parallel analyses, cloud computing offers the short-term rental of ample processing power. Recent software innovations have reduced the offort needed to take advantage of cloud computing. To demonstrate, we replicate a voxel-wise examination of the genetic contributions to cortical development by age using evidence from 1,748 MRI scans. Specifically, we employ off-the-shelf Kubernetes software that permits us to re-run our analyses using almost the same computer code as was published in the original article. Large, well funded institutions may continue to maintain their own computing clusters. However, the modest cost of renting and ease of utilizing cloud computing services makes unprecedented compute power available to all researchers, whether or not affliated with a research institution. We expect this to spur innovation in the sophisticated modeling of large datasets.

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

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 (rG= 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 STATEMENTOver 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.

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.

Diffusion MRI (dMRI) is widely used to study the brain’s white matter (WM) microstructure in a range of psychiatric and neurological diseases. As the diffusion tensor model has limitations in brain regions with crossing fibers, novel diffusion MRI reconstruction models may offer more accurate measures of tissue properties, and a better understanding of the brain abnormalities in specific diseases. Here we studied a large sample of 249 participants with 22q11.2 deletion syndrome (22q11DS), a neurogenetic condition associated with high rates of developmental neuropsychiatric disorders, and 224 age-matched healthy controls (HC) (age range: 8-35 years). Participants were scanned with dMRI at eight centers worldwide. Using a meta-analytic approach, we assessed the profile of group differences in four diffusion anisotropy measures to better understand the patterns of WM microstructural abnormalities and evaluate their consistency across alternative measures. When assessed in atlas-defined regions of interest, we found statistically significant differences for all anisotropy measures, all showing a widespread but not always coinciding pattern of effects. The tensor distribution function fractional anisotropy (TDF-FA) showed largest effect sizes all in the same direction (greater anisotropy in 22q11DS than HC). Fractional anisotropy based on the tensor model (FA) showed the second largest effect sizes after TDF-FA; some regions showed higher mean values in 22q11DS, but others lower. Generalized fractional anisotropy (GFA) showed the opposite pattern to TDF-FA with most regions showing lower anisotropy in 22q11DS versus HC. Anisotropic power maps (AP) showed the lowest effect sizes also with a mixed pattern of effects across regions. These results were also consistent across skeleton projection methods, with few differences when projecting anisotropy values from voxels sampled on the FA map or projecting values from voxels sampled from each anisotropy map. This study highlights that different mathematical definitions of anisotropy may lead to different profiles of group differences, even in large, well-powered population studies. Further studies of biophysical models derived from multi-shell dMRI and histological validations may help to understand the sources of these differences. 22q11DS is a promising model to study differences among novel anisotropy/dMRI measures, as group differences are relatively large and there exist animal models suitable for histological validation.

To determine the metabolic effects of developmental venous anomalies (DVAs) and to correlate those effects with conventional magnetic resonance imaging (MRI) findings.We conducted a retrospective review of MRI and brain 18F-fluorodeoxyglucose positron emission tomography (Fifty-four subjects with 57 DVAs were included in the analysis. 38% were associated with qualitative and quantitative metabolic abnormalities onAltered metabolism is seen in the drainage territory of a significant proportion of DVAs, suggesting that these anomalies are vascular lesions with abnormal physiologic features.

Rationale and Objectives The advent of the ACGME's Next Accreditation System represents a significant new challenge for residencies and fellowships, owing to its requirements for more complex and detailed information. Material and Methods We developed a system of online assessment tools to provide comprehensive coverage of the twelve ACGME Milestones and digitized them using freely available cloud-based productivity tools. These tools include a combination of point-of-care procedural assessments, electronic quizzes, online modules, and other data entry forms. Using free statistical analytic tools, we also developed an automated system for management, processing, and data reporting. Results After one year of use, our Milestones project has resulted in the submission of over 20,000 individual data points. The use of automated statistical methods to generate resident-specific profiles has allowed for dynamic reports of individual residents' progress. These profiles both summarize data and also allow program directors access to more granular information as needed. Conclusion Informatics-driven strategies for data assessment and processing represent feasible solutions to Milestones assessment and analysis, reducing the potential administrative burden for program directors, residents, and staff.

Background There is increased risk of developing psychosis in 22q11.2 deletion syndrome (22q11DS). Although this condition is associated with morphologic brain abnormalities, simultaneous examination of multiple high-resolution measures of cortical structure has not been performed. Methods Fifty-three patients with 22q11DS, 30 with psychotic symptoms, were compared with demographically matched nondeleted youths: 53 typically developing and 53 with psychotic symptoms. High-resolution magnetic resonance imaging measures of cerebral volume, cortical thickness, surface area, and an index of local gyrification were obtained and compared between groups. Results Patients with 22q11DS demonstrated global increases in cortical thickness associated with reductions in surface area, reduced index of local gyrification, and lower cerebral volumes relative to typically developing controls. Findings were principally in the frontal lobe, superior parietal lobes, and in the paramedian cerebral cortex. Focally decreased thickness was seen in the superior temporal gyrus and posterior cingulate cortex in 22q11DS relative to nondeleted groups. Patterns between nondeleted participants with psychotic symptoms and 22q11DS were similar but with important differences in several regions implicated in schizophrenia. Post hoc analysis suggested that like the 22q11DS group, cortical thickness in nondeleted individuals with psychotic symptoms differed from typically developing controls in the superior frontal gyrus and superior temporal gyrus, regions previously linked to schizophrenia. Conclusions Simultaneous examination of multiple measures of cerebral architecture demonstrates that differences in 22q11DS localize to regions of the frontal, superior parietal, superior temporal, and paramidline cerebral cortex. The overlapping patterns between nondeleted participants with psychotic symptoms and 22q11DS suggest partially shared neuroanatomic substrates.

Data quality is increasingly recognized as one of the most important confounders 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. Yet, objective measures of data quality are not available for most structural brain images. Here we sought to identify reliable, quantitative measures of data quality for T1-weighted volumes, describe how such measures of quality relate to common measures of brain structure, and delineate how this in turn may bias inference regarding brain development in youth. Three highly-trained raters provided manual ratings for 1601 T1-weighted volumes acquired as part of the Philadelphia Neurodevelopmental Cohort. Expert manual ratings were compared to automated quality measures, which derived measures from the Preprocessed Connectomes Project9s Quality Assurance Protocol (QAP). Generalized linear mixed-effects models using the automated quality measures were constructed in a training sample (n = 1067) to: 1) identify unusable images with significant artifacts, and 2) quantify subtle artifacts in usable images. These models were then tested in an independent validation dataset (n = 534). Results reveal that unusable images can be detected with a high degree of accuracy: a model including background kurtosis and skewness achieved an AUC of 0.95 in the training dataset and 0.94 in the independent validation dataset. While identification of subtle artifact was more challenging, an 8-parameter model achieved an AUC of 0.80 in the training dataset, and 0.92 in the validation dataset. Notably, quantitative measures of image quality were related to cortical thickness and gray matter density; measures of cortical volume were less affected by artifact. Furthermore, these quantitative measures of image quality demonstrated comparable or superior performance to estimates of motion derived from other imaging sequences acquired during the same protocol. Finally, data quality significantly altered structural brain maturation occurring during adolescent development. 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 development.

To analyze Internet search data to characterize the temporal and geographic interest of Internet users in the United States in varicose vein treatment.From January 1, 2004, to September 1, 2012, the Google Trends tool was used to analyze query data for "varicose vein treatment" to identify individuals seeking treatment information for varicose veins. The term "varicose vein treatment" returned a search volume index (SVI), representing the search frequency relative to the total search volume during a specific time interval and region. Linear regression analysis and Kruskal-Wallis one-way analysis of variance were employed to characterize search results.Search traffic for varicose vein treatment increased by 520% over the 104-month study period. There was an annual mean increase of 28% (range, -18%-100%; standard deviation [SD], 35%), with a statistically significant linear increase in average yearly SVI over time (R(2) = 0.94, P.0001). All years showed positive growth in mean SVI except for 2008 (18% decrease). There were statistically significant differences in SVI by month (Kruskal-Wallis, P.0001) with significantly higher mean SVI compared with other months in May (190% increase; range, 26%-670%; SD, 15%) and June (209% increase; range, 35%-700%; SD, 20%). The southern United States showed significantly higher search traffic than all other regions (Tukey-Kramer, P.00001).There have been significant increases in Internet search traffic related to varicose vein treatment in the past 8 years. Reflected in this trend is an annual peak in search traffic in the late spring months with an overall geographic bias toward southern states. Rigorous analysis of Internet search queries for medical procedures may prove useful to guide the efficient use of limited resources and marketing dollars.

Talk given during the "Introduction to Imaging Genetics" workshop at the 2012 Organization for Human Brain Mapping (OHBM) conference in in Beijing, 10-14 June.

To examine filter characteristics at preretrieval computed tomography (CT) that are associated with complicated inferior vena cava (IVC) filter retrieval procedures.This study was HIPAA compliant, and informed consent was waived. Institutional review board-approved retrospective review of IVC filter retrievals between January 2002 and July 2011 was performed to identify patients with preretrieval CT in whom a complicated retrieval was performed, as defined by use of nonstandard techniques, filter fracture, filter tip incorporation into the IVC wall, and retrieval failure. Age- and sex-matched control subjects with standard IVC filter retrieval were used for comparison. Preretrieval CT images were evaluated for tilt angle in mediolateral and anteroposterior directions, CT appearance of tip embedding, degree of filter strut perforation, and distance of filter tip from the nearest renal vein. Dwell time was also recorded. Statistical analysis was performed by using the Fisher exact test, Student t test, and Wilcoxon signed-rank test, depending on the variables being evaluated, as well as multivariate logistic regression.Forty-eight patients with complicated retrievals and 48 control subjects with uncomplicated retrievals were evaluable for preretrieval CT characteristics. Mediolateral and anteroposterior tilt angle, degree of perforation, and dwell time were higher for the complicated versus non-complicated retrieval group (P.01). Odds of complicated retrieval were increased 129-fold with CT appearance of tip embedding (P.0001), with an odds ratio of 33 with a tilt angle of more than 15° in any direction (P.0001), while perforation and dwell time increased risk of a complicated retrieval by 10.7 (P.0001) and 2.3 (P.05) times, respectively. Distance from renal veins was noncontributory.CT appearance of tip embedding, increased tilt angle, higher-grade perforation, and longer dwell times are associated with complicated IVC filter retrieval. Therefore, preretrieval CT may be warranted in select patients for identification of these characteristics to tailor retrieval approach or to arrange a referral to a tertiary center if necessary.

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.

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.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.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%).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.

Little is known about genetic influences on the volume of subcortical brain structures in adult humans, particularly whether there is regional specificity of genetic effects. Understanding patterns of genetic covariation among volumes of subcortical structures may provide insight into the development of individual differences that have consequences for cognitive and emotional behavior and neuropsychiatric disease liability. We measured the volume of 19 subcortical structures (including brain and ventricular regions) in 404 twins (110 monozygotic and 92 dizygotic pairs) from the Vietnam Era Twin Study of Aging and calculated the degree of genetic correlation among these volumes. We then examined the patterns of genetic correlation through hierarchical cluster analysis and by principal components analysis. We found that a model with four genetic factors best fit the data: a Basal Ganglia/Thalamus factor; a Ventricular factor; a Limbic factor; and a Nucleus Accumbens factor. Homologous regions from each hemisphere loaded on the same factors. The observed patterns of genetic correlation suggest the influence of multiple genetic influences. There is a genetic organization among structures which distinguishes between brain and cerebrospinal fluid spaces and between different subcortical regions. Further study is needed to understand this genetic patterning and whether it reflects influences on early development, functionally dependent patterns of growth or pruning, or regionally specific losses due to genes involved in aging, stress response, or disease.

Background Although global brain structure is highly heritable, there is still variability in the magnitude of genetic influences on the size of specific regions. Yet, little is known about the patterning of those genetic influences, i.e., whether the same genes influence structure throughout the brain or whether there are regionally specific sets of genes. Methods We mapped the heritability of cortical thickness throughout the brain using three-dimensional structural magnetic resonance imaging in 404 middle-aged male twins. To assess the amount of genetic overlap between regions, we then mapped genetic correlations between three selected seed points and all other points comprising the continuous cortical surface. Results There was considerable regional variability in the magnitude of genetic influences on cortical thickness. The primary visual (V1) seed point had strong genetic correlations with posterior sensory and motor areas. The anterior temporal seed point had strong genetic correlations with anterior frontal regions but not with V1. The middle frontal seed point had strong genetic correlations with inferior parietal regions. Conclusions These results provide strong evidence of regionally specific patterns rather than a single, global genetic factor. The patterns are largely consistent with a division between primary and association cortex, as well as broadly defined patterns of brain gene expression, neuroanatomical connectivity, and brain maturation trajectories, but no single explanation appears to be sufficient. The patterns do not conform to traditionally defined brain structure boundaries. This approach can serve as a step toward identifying novel phenotypes for genetic association studies of psychiatric disorders and normal and pathological cognitive aging.

Ordered latent class analysis (OLCA) can be used to approximate unidimensional latent distributions. The main objective of this study is to evaluate the method of OLCA in detecting non-normality of an unobserved continuous variable (i.e., a common factor) used to explain the covariation between dichotomous item-level responses. Using simulation, we compared a model in which probabilities of class membership were estimated to a restricted submodel in which class memberships were fixed to normal Gauss-Hermite quadrature values. Our results indicate that the likelihood ratio statistic follows a predictable chi-square distribution for a wide range of sample sizes (N = 500-12,000) and test instrument characteristics, and has reasonable power to detect non-normality in cases of moderate effect sizes. Furthermore, under situations of large sample sizes, large numbers of items, or centrally located item difficulties, simulations suggest that it may be possible to describe the shape of latent trait distributions. Application to data on the symptoms of major depression, assessed in the National Comorbidity Survey, suggests that the latent trait does not depart from normality in men but does so to a small but significant extent in women.

This article reviews the extant twin studies employing magnetic resonance imaging data (MRI), with an emphasis on studies of populationbased 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.

© 2015, Springer Science+Business Media New York. 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.

Background: Longitudinal pediatric neuroimaging studies have demonstrated increasing volumes of white matter and regionally-specific inverted U shaped developmental trajectories of gray matter volumes during childhood and adolescence. Studies of monozygotic and dyzygotic twins during this developmental period allow exploration of genetic and non-genetic influences on these developmental trajectories. Method: Magnetic resonance imaging brain scans were acquired on a pediatric sample of 90 monozygotic twin pairs, 38 same-sex dyzygotic twin pairs, and 158 unrelated typically developing singletons. Structural equation modeling was used to estimate the additive genetic, common environment, and unique environment effects, as well as age by heritability interactions, on measures of brain volumes from these images. Results: Consistent with previous adult studies, additive genetic effects accounted for a substantial portion of variability in nearly all brain regions with the notable exception of the cerebellum. Significant age by heritability interactions were observed with gray matter volumes showing a reduction in heritability with increasing age, while white matter volume heritability increased with greater age. Conclusion: Understanding the relative contributions of genetic and nongenetic factors on developmental brain trajectories may have implications for better understanding brain-based disorders and typical cognitive development.

The advent of the ACGME's Next Accreditation System represents a significant new challenge for residencies and fellowships, owing to its requirements for more complex and detailed information.We developed a system of online assessment tools to provide comprehensive coverage of the twelve ACGME Milestones and digitized them using freely available cloud-based productivity tools. These tools include a combination of point-of-care procedural assessments, electronic quizzes, online modules, and other data entry forms. Using free statistical analytic tools, we also developed an automated system for management, processing, and data reporting.After one year of use, our Milestones project has resulted in the submission of over 20,000 individual data points. The use of automated statistical methods to generate resident-specific profiles has allowed for dynamic reports of individual residents' progress. These profiles both summarize data and also allow program directors access to more granular information as needed.Informatics-driven strategies for data assessment and processing represent feasible solutions to Milestones assessment and analysis, reducing the potential administrative burden for program directors, residents, and staff.

As the largest interhemispheric commissure in the brain, the corpus callosum is of particular interest in disorders that may preferentially affect white matter development such as Williams syndrome (WS). Individuals with WS possess a remarkable array of neurobehavioral peaks and valleys, including deficits in visuospatial ability, mathematics, and attention, but with relative preservation of language and affect. Our study measured the corpus callosum and its primary subdivisions using high-resolution MRI in 20 individuals with WS (13 females and seven males; mean age 28.5, SD 8.3 years; range 19 to 44 years) and 20 age- and sex-matched control participants (mean age 28.5, SD 8.2 years; range 19 to 48 years). Total midsagittal corpus callosum area was reduced (F=4.5, p=0.04, df=36) in the WS population. The area of the splenium (F=12.4, p=0.001, df=36) and isthmus (F=9.4, p=0.004, df=36) were disproportionately reduced in WS beyond the absolute reduction of the entire corpus callosum. These reductions are in concordance with other neuroanatomical findings of decreased parietooccipital volumes as well as the observed visuospatial problems associated with WS.