Publications by authors named "Rachel M Brouwer"

75 Publications

De-identification procedures for magnetic resonance images and the impact on structural brain measures at different ages.

Hum Brain Mapp 2021 May 11. Epub 2021 May 11.

UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.

Surface rendering of MRI brain scans may lead to identification of the participant through facial characteristics. In this study, we evaluate three methods that overwrite voxels containing privacy-sensitive information: Face Masking, FreeSurfer defacing, and FSL defacing. We included structural T1-weighted MRI scans of children, young adults and older adults. For the young adults, test-retest data were included with a 1-week interval. The effects of the de-identification methods were quantified using different statistics to capture random variation and systematic noise in measures obtained through the FreeSurfer processing pipeline. Face Masking and FSL defacing impacted brain voxels in some scans especially in younger participants. FreeSurfer defacing left brain tissue intact in all cases. FSL defacing and FreeSurfer defacing preserved identifiable characteristics around the eyes or mouth in some scans. For all de-identification methods regional brain measures of subcortical volume, cortical volume, cortical surface area, and cortical thickness were on average highly replicable when derived from original versus de-identified scans with average regional correlations >.90 for children, young adults, and older adults. Small systematic biases were found that incidentally resulted in significantly different brain measures after de-identification, depending on the studied subsample, de-identification method, and brain metric. In young adults, test-retest intraclass correlation coefficients (ICCs) were comparable for original scans and de-identified scans with average regional ICCs >.90 for (sub)cortical volume and cortical surface area and ICCs >.80 for cortical thickness. We conclude that apparent visual differences between de-identification methods minimally impact reliability of brain measures, although small systematic biases can occur.
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http://dx.doi.org/10.1002/hbm.25459DOI Listing
May 2021

Accelerated aging in the brain, epigenetic aging in blood, and polygenic risk for schizophrenia.

Schizophr Res 2021 Apr 17;231:189-197. Epub 2021 Apr 17.

Center for Neurobehavioral Genetics, University of California, Los Angeles, United States; Department of Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands.

Schizophrenia patients show signs of accelerated aging in cognitive and physiological domains. Both schizophrenia and accelerated aging, as measured by MRI brain images and epigenetic clocks, are correlated with increased mortality. However, the association between these aging measures have not yet been studied in schizophrenia patients. In schizophrenia patients and healthy subjects, accelerated aging was assessed in brain tissue using a longitudinal MRI (N = 715 scans; mean scan interval 3.4 year) and in blood using two epigenetic age clocks (N = 172). Differences ('gaps') between estimated ages and chronological ages were calculated, as well as the acceleration rate of brain aging. The correlations between these aging measures as well as with polygenic risk scores for schizophrenia (PRS; N = 394) were investigated. Brain aging and epigenetic aging were not significantly correlated. Polygenic risk for schizophrenia was significantly correlated with brain age gap, brain age acceleration rate, and negatively correlated with DNAmAge gap, but not with PhenoAge gap. However, after controlling for disease status and multiple comparisons correction, these effects were no longer significant. Our results imply that the (accelerated) aging observed in the brain and blood reflect distinct biological processes. Our findings will require replication in a larger cohort.
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http://dx.doi.org/10.1016/j.schres.2021.04.005DOI Listing
April 2021

1q21.1 distal copy number variants are associated with cerebral and cognitive alterations in humans.

Transl Psychiatry 2021 Mar 22;11(1):182. Epub 2021 Mar 22.

Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

Low-frequency 1q21.1 distal deletion and duplication copy number variant (CNV) carriers are predisposed to multiple neurodevelopmental disorders, including schizophrenia, autism and intellectual disability. Human carriers display a high prevalence of micro- and macrocephaly in deletion and duplication carriers, respectively. The underlying brain structural diversity remains largely unknown. We systematically called CNVs in 38 cohorts from the large-scale ENIGMA-CNV collaboration and the UK Biobank and identified 28 1q21.1 distal deletion and 22 duplication carriers and 37,088 non-carriers (48% male) derived from 15 distinct magnetic resonance imaging scanner sites. With standardized methods, we compared subcortical and cortical brain measures (all) and cognitive performance (UK Biobank only) between carrier groups also testing for mediation of brain structure on cognition. We identified positive dosage effects of copy number on intracranial volume (ICV) and total cortical surface area, with the largest effects in frontal and cingulate cortices, and negative dosage effects on caudate and hippocampal volumes. The carriers displayed distinct cognitive deficit profiles in cognitive tasks from the UK Biobank with intermediate decreases in duplication carriers and somewhat larger in deletion carriers-the latter potentially mediated by ICV or cortical surface area. These results shed light on pathobiological mechanisms of neurodevelopmental disorders, by demonstrating gene dose effect on specific brain structures and effect on cognitive function.
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http://dx.doi.org/10.1038/s41398-021-01213-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7985307PMC
March 2021

Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years.

Hum Brain Mapp 2021 Feb 17. Epub 2021 Feb 17.

Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil.

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
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http://dx.doi.org/10.1002/hbm.25364DOI Listing
February 2021

Reliability modelling of resting-state functional connectivity.

Neuroimage 2021 05 11;231:117842. Epub 2021 Feb 11.

Brain Center Rudolf Magnus and Department of Psychiatry, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands.

Resting-state functional magnetic resonance imaging (rs-fMRI) has an inherently low signal-to-noise ratio largely due to thermal and physiological noise that attenuates the functional connectivity (FC) estimates. Such attenuation limits the reliability of FC and may bias its association with other traits. Low reliability also limits heritability estimates. Classical test theory can be used to obtain a true correlation estimate free of random measurement error from parallel tests, such as split-half sessions of a rs-fMRI scan. We applied a measurement model to split-half FC estimates from the resting-state fMRI data of 1003 participants from the Human Connectome Project (HCP) to examine the benefit of reliability modelling of FC in association with traits from various domains. We evaluated the efficiency of the measurement model on extracting a stable and reliable component of FC and its association with several traits for various sample sizes and scan durations. In addition, we aimed to replicate our previous findings of increased heritability estimates when using a measurement model in a longitudinal adolescent twin cohort. The split-half measurement model improved test-retest reliability of FC on average with +0.33 points (from +0.49 to +0.82), improved strength of associations between FC and various traits on average 1.2-fold (range 1.09-1.35), and increased heritability estimates on average with +20% points (from 39% to 59%) for the full HCP dataset. On average, about half of the variance in split-session FC estimates was attributed to the stable and reliable component of FC. Shorter scan durations showed greater benefit of reliability modelling (up to 1.6-fold improvement), with an additional gain for smaller sample sizes (up to 1.8-fold improvement). Reliability modelling of FC based on a split-half using a measurement model can benefit genetic and behavioral studies by extracting a stable and reliable component of FC that is free from random measurement error and improves genetic and behavioral associations.
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http://dx.doi.org/10.1016/j.neuroimage.2021.117842DOI Listing
May 2021

Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years.

Hum Brain Mapp 2021 Feb 11. Epub 2021 Feb 11.

Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA.

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.
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http://dx.doi.org/10.1002/hbm.25320DOI Listing
February 2021

Diffusion MRI derived free-water imaging measures in patients with schizophrenia and their non-psychotic siblings.

Prog Neuropsychopharmacol Biol Psychiatry 2021 Jul 2;109:110238. Epub 2021 Jan 2.

Department of Psychiatry, University Medical Center Utrecht (UMCU), UMCU Brain Center, Utrecht, the Netherlands; Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Boston, USA.

Free-water imaging is a diffusion MRI technique that separately models water diffusion hindered by fiber tissue and water that disperses freely in the extracellular space. Studies using this technique have shown that schizophrenia is characterized by a lower level of fractional anisotropy of the tissue compartment (FA) and higher free-water fractional volume (FW). It is unknown, however, whether such abnormalities are an expression of pre-existing (genetic) risk for schizophrenia or a manifestation of the illness. To investigate the contribution of familial risk factors to white matter abnormalities, we used the free-water imaging technique to assess FA and FW in a large cohort of 471 participants including 161 patients with schizophrenia, 182 non-psychotic siblings, and 128 healthy controls. In this sample, patients did not show significant differences in FA as compared to controls, but did exhibit a higher level of FW relative to both controls and siblings in the left uncinate fasciculus, superior corona radiata and fornix / stria terminalis. This increase in FW was found to be related to, though not solely explained by, ventricular enlargement. Siblings did not show significant FW abnormalities. However, siblings did show a higher level of FA as compared to controls and patients, in line with results of a previous study on the same data using conventional DTI. Taken together, our findings suggest that extracellular free-water accumulation in patients is likely a manifestation of established disease rather than an expression of familial risk for schizophrenia and that super-normal levels of FA in unaffected siblings may reflect a compensatory process.
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http://dx.doi.org/10.1016/j.pnpbp.2020.110238DOI Listing
July 2021

The YOUth study: Rationale, design, and study procedures.

Dev Cogn Neurosci 2020 12 7;46:100868. Epub 2020 Oct 7.

UMC Utrecht Brain Center, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands; Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, the Netherlands; Developmental Psychology, Utrecht University, Utrecht, the Netherlands.

Behavioral development in children shows large inter-individual variation, and is driven by the interplay between biological, psychological, and environmental processes. However, there is still little insight into how these processes interact. The YOUth cohort specifically focuses on two core characteristics of behavioral development: social competence and self-regulation. Social competence refers to the ability to engage in meaningful interactions with others, whereas self-regulation is the ability to control one's emotions, behavior, and impulses, to balance between reactivity and control of the reaction, and to adjust to the prevailing environment. YOUth is an accelerated population-based longitudinal cohort study with repeated measurements, centering on two groups: YOUth Baby & Child and YOUth Child & Adolescent. YOUth Baby & Child aims to include 3,000 pregnant women, their partners and children, wheras YOUth Child & Adolescent aims to include 2,000 children aged between 8 and 10 years old and their parents. All participants will be followed for at least 6 years, and potentially longer. In this paper we describe in detail the design of this study, the population included, the determinants, intermediate neurocognitive measures and outcomes included in the study. Furthermore, we describe in detail the procedures of inclusion, informed consent, and study participation.
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http://dx.doi.org/10.1016/j.dcn.2020.100868DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7575850PMC
December 2020

The Speed of Development of Adolescent Brain Age Depends on Sex and Is Genetically Determined.

Cereb Cortex 2021 Jan;31(2):1296-1306

Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX Utrecht, the Netherlands.

Children and adolescents show high variability in brain development. Brain age-the estimated biological age of an individual brain-can be used to index developmental stage. In a longitudinal sample of adolescents (age 9-23 years), including monozygotic and dizygotic twins and their siblings, structural magnetic resonance imaging scans (N = 673) at 3 time points were acquired. Using brain morphology data of different types and at different spatial scales, brain age predictors were trained and validated. Differences in brain age between males and females were assessed and the heritability of individual variation in brain age gaps was calculated. On average, females were ahead of males by at most 1 year, but similar aging patterns were found for both sexes. The difference between brain age and chronological age was heritable, as was the change in brain age gap over time. In conclusion, females and males show similar developmental ("aging") patterns but, on average, females pass through this development earlier. Reliable brain age predictors may be used to detect (extreme) deviations in developmental state of the brain early, possibly indicating aberrant development as a sign of risk of neurodevelopmental disorders.
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http://dx.doi.org/10.1093/cercor/bhaa296DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8204942PMC
January 2021

Greater male than female variability in regional brain structure across the lifespan.

Hum Brain Mapp 2020 Oct 12. Epub 2020 Oct 12.

FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain.

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.
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http://dx.doi.org/10.1002/hbm.25204DOI Listing
October 2020

The YOUth cohort study: MRI protocol and test-retest reliability in adults.

Dev Cogn Neurosci 2020 10 8;45:100816. Epub 2020 Jul 8.

UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands. Electronic address:

The YOUth cohort study is a unique longitudinal study on brain development in the general population. As part of the YOUth study, 2000 children will be included at 8, 9 or 10 years of age and planned to return every three years during adolescence. Magnetic resonance imaging (MRI) brain scans are collected, including structural T1-weighted imaging, diffusion-weighted imaging (DWI), resting-state functional MRI and task-based functional MRI. Here, we provide a comprehensive report of the MR acquisition in YOUth Child & Adolescent including the test-retest reliability of brain measures derived from each type of scan. To measure test-retest reliability, 17 adults were scanned twice with a week between sessions using the full YOUth MRI protocol. Intraclass correlation coefficients were calculated to quantify reliability. Global brain measures derived from structural T1-weighted and DWI scans were reliable. Resting-state functional connectivity was moderately reliable, as well as functional brain measures for both the inhibition task (stop versus go) and the emotion task (face versus house). Our results complement previous studies by presenting reliability results of regional brain measures collected with different MRI modalities. YOUth facilitates data sharing and aims for reliable and high-quality data. Here we show that using the state-of-the art YOUth MRI protocol brain measures can be estimated reliably.
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http://dx.doi.org/10.1016/j.dcn.2020.100816DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7365929PMC
October 2020

Intelligence, educational attainment, and brain structure in those at familial high-risk for schizophrenia or bipolar disorder.

Hum Brain Mapp 2020 Oct 7. Epub 2020 Oct 7.

Neuroscience Research Australia, Sydney, Australia.

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d = -0.42, p = 3 × 10 ), with weak evidence of IQ reductions among BD-FDRs (d = -0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment.
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http://dx.doi.org/10.1002/hbm.25206DOI Listing
October 2020

Heritability of specific cognitive functions and associations with schizophrenia spectrum disorders using CANTAB: a nation-wide twin study.

Psychol Med 2020 Aug 11:1-14. Epub 2020 Aug 11.

Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS) and Center for Neuropsychiatric Schizophrenia Research, Mental Health Center Glostrup, Glostrup, Denmark.

Background: Many cognitive functions are under strong genetic control and twin studies have demonstrated genetic overlap between some aspects of cognition and schizophrenia. How the genetic relationship between specific cognitive functions and schizophrenia is influenced by IQ is currently unknown.

Methods: We applied selected tests from the Cambridge Neuropsychological Test Automated Battery (CANTAB) to examine the heritability of specific cognitive functions and associations with schizophrenia liability. Verbal and performance IQ were estimated using The Wechsler Adult Intelligence Scale-III and the Danish Adult Reading Test. In total, 214 twins including monozygotic (MZ = 32) and dizygotic (DZ = 22) pairs concordant or discordant for a schizophrenia spectrum disorder, and healthy control pairs (MZ = 29, DZ = 20) were recruited through the Danish national registers. Additionally, eight twins from affected pairs participated without their sibling.

Results: Significant heritability was observed for planning/spatial span (h2 = 25%), self-ordered spatial working memory (h2 = 64%), sustained attention (h2 = 56%), and movement time (h2 = 47%), whereas only unique environmental factors contributed to set-shifting, reflection impulsivity, and thinking time. Schizophrenia liability was associated with planning/spatial span (rph = -0.34), self-ordered spatial working memory (rph = -0.24), sustained attention (rph = -0.23), and set-shifting (rph = -0.21). The association with planning/spatial span was not driven by either performance or verbal IQ. The remaining associations were shared with performance, but not verbal IQ.

Conclusions: This study provides further evidence that some cognitive functions are heritable and associated with schizophrenia, suggesting a partially shared genetic etiology. These functions may constitute endophenotypes for the disorder and provide a basis to explore genes common to cognition and schizophrenia.
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http://dx.doi.org/10.1017/S0033291720002858DOI Listing
August 2020

What we learn about bipolar disorder from large-scale neuroimaging: Findings and future directions from the ENIGMA Bipolar Disorder Working Group.

Hum Brain Mapp 2020 Jul 29. Epub 2020 Jul 29.

Division of Mental Health and Addicition, Oslo University Hospital, Oslo, Norway.

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness.
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http://dx.doi.org/10.1002/hbm.25098DOI Listing
July 2020

Changes in the intracranial volume from early adulthood to the sixth decade of life: A longitudinal study.

Neuroimage 2020 10 24;220:116842. Epub 2020 Apr 24.

UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, the Netherlands. Electronic address:

Normal brain-aging occurs at all structural levels. Excessive pathophysiological changes in the brain, beyond the normal one, are implicated in the etiology of brain disorders such as severe forms of the schizophrenia spectrum and dementia. To account for brain-aging in health and disease, it is critical to study the age-dependent trajectories of brain biomarkers at various levels and among different age groups. The intracranial volume (ICV) is a key biological marker, and changes in the ICV during the lifespan can teach us about the biology of development, aging, and gene X environment interactions. However, whether ICV changes with age in adulthood is not resolved. Applying a semi-automatic in-house-built algorithm for ICV extraction on T1w MR brain scans in the Dutch longitudinal cohort (GROUP), we measured ICV changes. Individuals between the ages of 16 and 55 years were scanned up to three consecutive times with 3.32±0.32 years between consecutive scans (N = 482, 359, 302). Using the extracted ICVs, we calculated ICV longitudinal aging-trajectories based on three analysis methods; direct calculation of ICV differences between the first and the last scan, fitting all ICV measurements of individuals to a straight line, and applying a global linear mixed model fitting. We report statistically significant increase in the ICV in adulthood until the fourth decade of life (average change +0.03%/y, or about 0.5 ml/y, at age 20), and decrease in the ICV afterward (-0.09%/y, or about -1.2 ml/y, at age 55). To account for previous cross-sectional reports of ICV changes, we analyzed the same data using a cross-sectional approach. Our cross-sectional analysis detected ICV changes consistent with the previously reported cross-sectional effect. However, the reported amount of cross-sectional changes within this age range was significantly larger than the longitudinal changes. We attribute the cross-sectional results to a generational effect. In conclusion, the human intracranial volume does not stay constant during adulthood but instead shows a small increase during young adulthood and a decrease thereafter from the fourth decade of life. The age-related changes in the longitudinalmeasure are smaller than those reported using cross-sectional approaches and unlikely to affect structural brain imaging studies correcting for intracranial volume considerably. As to the possible mechanisms involved, this awaits further study, although thickening of the meninges and skull bones have been proposed, as well as a smaller amount of brain fluids addition above the overall loss of brain tissue.
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http://dx.doi.org/10.1016/j.neuroimage.2020.116842DOI Listing
October 2020

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

Transl Psychiatry 2020 03 20;10(1):100. Epub 2020 Mar 20.

Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.
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http://dx.doi.org/10.1038/s41398-020-0705-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7083923PMC
March 2020

The genetic architecture of the human cerebral cortex.

Science 2020 03;367(6484)

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.
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http://dx.doi.org/10.1126/science.aay6690DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295264PMC
March 2020

Association of Copy Number Variation of the 15q11.2 BP1-BP2 Region With Cortical and Subcortical Morphology and Cognition.

JAMA Psychiatry 2020 04;77(4):420-430

Department of Biological Psychology and Netherlands Twin Register, VU University Amsterdam, Amsterdam, the Netherlands.

Importance: Recurrent microdeletions and duplications in the genomic region 15q11.2 between breakpoints 1 (BP1) and 2 (BP2) are associated with neurodevelopmental disorders. These structural variants are present in 0.5% to 1.0% of the population, making 15q11.2 BP1-BP2 the site of the most prevalent known pathogenic copy number variation (CNV). It is unknown to what extent this CNV influences brain structure and affects cognitive abilities.

Objective: To determine the association of the 15q11.2 BP1-BP2 deletion and duplication CNVs with cortical and subcortical brain morphology and cognitive task performance.

Design, Setting, And Participants: In this genetic association study, T1-weighted brain magnetic resonance imaging were combined with genetic data from the ENIGMA-CNV consortium and the UK Biobank, with a replication cohort from Iceland. In total, 203 deletion carriers, 45 247 noncarriers, and 306 duplication carriers were included. Data were collected from August 2015 to April 2019, and data were analyzed from September 2018 to September 2019.

Main Outcomes And Measures: The associations of the CNV with global and regional measures of surface area and cortical thickness as well as subcortical volumes were investigated, correcting for age, age2, sex, scanner, and intracranial volume. Additionally, measures of cognitive ability were analyzed in the full UK Biobank cohort.

Results: Of 45 756 included individuals, the mean (SD) age was 55.8 (18.3) years, and 23 754 (51.9%) were female. Compared with noncarriers, deletion carriers had a lower surface area (Cohen d = -0.41; SE, 0.08; P = 4.9 × 10-8), thicker cortex (Cohen d = 0.36; SE, 0.07; P = 1.3 × 10-7), and a smaller nucleus accumbens (Cohen d = -0.27; SE, 0.07; P = 7.3 × 10-5). There was also a significant negative dose response on cortical thickness (β = -0.24; SE, 0.05; P = 6.8 × 10-7). Regional cortical analyses showed a localization of the effects to the frontal, cingulate, and parietal lobes. Further, cognitive ability was lower for deletion carriers compared with noncarriers on 5 of 7 tasks.

Conclusions And Relevance: These findings, from the largest CNV neuroimaging study to date, provide evidence that 15q11.2 BP1-BP2 structural variation is associated with brain morphology and cognition, with deletion carriers being particularly affected. The pattern of results fits with known molecular functions of genes in the 15q11.2 BP1-BP2 region and suggests involvement of these genes in neuronal plasticity. These neurobiological effects likely contribute to the association of this CNV with neurodevelopmental disorders.
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http://dx.doi.org/10.1001/jamapsychiatry.2019.3779DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6822096PMC
April 2020

Genetic architecture of subcortical brain structures in 38,851 individuals.

Nat Genet 2019 11 21;51(11):1624-1636. Epub 2019 Oct 21.

Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.

Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.
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http://dx.doi.org/10.1038/s41588-019-0511-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055269PMC
November 2019

The Association Between Familial Risk and Brain Abnormalities Is Disease Specific: An ENIGMA-Relatives Study of Schizophrenia and Bipolar Disorder.

Biol Psychiatry 2019 10 13;86(7):545-556. Epub 2019 Jun 13.

Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, Connecticut; Tommy Fuss Center for Neuropsychiatric Disease Research, Boston Children's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts.

Background: Schizophrenia and bipolar disorder share genetic liability, and some structural brain abnormalities are common to both conditions. First-degree relatives of patients with schizophrenia (FDRs-SZ) show similar brain abnormalities to patients, albeit with smaller effect sizes. Imaging findings in first-degree relatives of patients with bipolar disorder (FDRs-BD) have been inconsistent in the past, but recent studies report regionally greater volumes compared with control subjects.

Methods: We performed a meta-analysis of global and subcortical brain measures of 6008 individuals (1228 FDRs-SZ, 852 FDRs-BD, 2246 control subjects, 1016 patients with schizophrenia, 666 patients with bipolar disorder) from 34 schizophrenia and/or bipolar disorder family cohorts with standardized methods. Analyses were repeated with a correction for intracranial volume (ICV) and for the presence of any psychopathology in the relatives and control subjects.

Results: FDRs-BD had significantly larger ICV (d = +0.16, q < .05 corrected), whereas FDRs-SZ showed smaller thalamic volumes than control subjects (d = -0.12, q < .05 corrected). ICV explained the enlargements in the brain measures in FDRs-BD. In FDRs-SZ, after correction for ICV, total brain, cortical gray matter, cerebral white matter, cerebellar gray and white matter, and thalamus volumes were significantly smaller; the cortex was thinner (d < -0.09, q < .05 corrected); and third ventricle was larger (d = +0.15, q < .05 corrected). The findings were not explained by psychopathology in the relatives or control subjects.

Conclusions: Despite shared genetic liability, FDRs-SZ and FDRs-BD show a differential pattern of structural brain abnormalities, specifically a divergent effect in ICV. This may imply that the neurodevelopmental trajectories leading to brain anomalies in schizophrenia or bipolar disorder are distinct.
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http://dx.doi.org/10.1016/j.biopsych.2019.03.985DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7068800PMC
October 2019

Genetic and environmental influences on functional connectivity within and between canonical cortical resting-state networks throughout adolescent development in boys and girls.

Neuroimage 2019 11 3;202:116073. Epub 2019 Aug 3.

University Medical Center Utrecht, UMC Brain Center, Department of Psychiatry, Utrecht, Netherlands.

The human brain is active during rest and hierarchically organized into intrinsic functional networks. These functional networks are largely established early in development, with reports of a shift from a local to more distributed organization during childhood and adolescence. It remains unknown to what extent genetic and environmental influences on functional connectivity change throughout adolescent development. We measured functional connectivity within and between eight cortical networks in a longitudinal resting-state fMRI study of adolescent twins and their older siblings on two occasions (mean ages 13 and 18 years). We modelled the reliability for these inherently noisy and head-motion sensitive measurements by analyzing data from split-half sessions. Functional connectivity between resting-state networks decreased with age whereas functional connectivity within resting-state networks generally increased with age, independent of general cognitive functioning. Sex effects were sparse, with stronger functional connectivity in the default mode network for girls compared to boys, and stronger functional connectivity in the salience network for boys compared to girls. Heritability explained up to 53% of the variation in functional connectivity within and between resting-state networks, and common environment explained up to 33%. Genetic influences on functional connectivity remained stable during adolescent development. In conclusion, longitudinal age-related changes in functional connectivity within and between cortical resting-state networks are subtle but wide-spread throughout adolescence. Genes play a considerable role in explaining individual variation in functional connectivity with mostly stable influences throughout adolescence.
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http://dx.doi.org/10.1016/j.neuroimage.2019.116073DOI Listing
November 2019

Homogenizing Estimates of Heritability Among SOLAR-Eclipse, OpenMx, APACE, and FPHI Software Packages in Neuroimaging Data.

Front Neuroinform 2019 12;13:16. Epub 2019 Mar 12.

Big Data Institute, University of Oxford, Oxford, United Kingdom.

Imaging genetic analyses use heritability calculations to measure the fraction of phenotypic variance attributable to additive genetic factors. We tested the agreement between heritability estimates provided by four methods that are used for heritability estimates in neuroimaging traits. SOLAR-Eclipse and OpenMx use iterative maximum likelihood estimation (MLE) methods. Accelerated Permutation inference for ACE (APACE) and fast permutation heritability inference (FPHI), employ fast, non-iterative approximation-based methods. We performed this evaluation in a simulated twin-sibling pedigree and phenotypes and in diffusion tensor imaging (DTI) data from three twin-sibling cohorts, the human connectome project (HCP), netherlands twin register (NTR) and BrainSCALE projects provided as a part of the enhancing neuro imaging genetics analysis (ENIGMA) consortium. We observed that heritability estimate may differ depending on the underlying method and dataset. The heritability estimates from the two MLE approaches provided excellent agreement in both simulated and imaging data. The heritability estimates for two approximation approaches showed reduced heritability estimates in datasets with deviations from data normality. We propose a data homogenization approach (implemented in solar-eclipse; www.solar-eclipse-genetics.org) to improve the convergence of heritability estimates across different methods. The homogenization steps include consistent regression of any nuisance covariates and enforcing normality on the trait data using inverse Gaussian transformation. Under these conditions, the heritability estimates for simulated and DTI phenotypes produced converging heritability estimates regardless of the method. Thus, using these simple suggestions may help new heritability studies to provide outcomes that are comparable regardless of software package.
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http://dx.doi.org/10.3389/fninf.2019.00016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6422938PMC
March 2019

Correction: Dose response of the 16p11.2 distal copy number variant on intracranial volume and basal ganglia.

Mol Psychiatry 2020 Mar;25(3):692-695

Department of Psychiatry and Mental Health, Anzio Road, 7925, Cape Town, South Africa.

Prior to and following the publication of this article the authors noted that the complete list of authors was not included in the main article and was only present in Supplementary Table 1. The author list in the original article has now been updated to include all authors, and Supplementary Table 1 has been removed. All other supplementary files have now been updated accordingly. Furthermore, in Table 1 of this Article, the replication cohort for the row Close relative in data set, n (%) was incorrect. All values have now been corrected to 0(0%). The publishers would like to apologise for this error and the inconvenience it may have caused.
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http://dx.doi.org/10.1038/s41380-019-0358-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7608381PMC
March 2020

Running in the Family? Structural Brain Abnormalities and IQ in Offspring, Siblings, Parents, and Co-twins of Patients with Schizophrenia.

Schizophr Bull 2019 10;45(6):1209-1217

Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.

Structural brain abnormalities and cognitive deficits have been reported in patients with schizophrenia and to a lesser extent in their first-degree relatives (FDRs). Here we investigated whether brain abnormalities in nonpsychotic relatives differ per type of FDR and how these abnormalities are related to intelligent quotient (IQ). Nine hundred eighty individuals from 5 schizophrenia family cohorts (330 FDRs, 432 controls, 218 patients) were included. Effect sizes were calculated to compare brain measures of FDRs and patients with controls, and between each type of FDR. Analyses were repeated with a correction for IQ, having a nonpsychotic diagnosis, and intracranial volume (ICV). FDRs had significantly smaller ICV, surface area, total brain, cortical gray matter, cerebral white matter, cerebellar gray and white matter, thalamus, putamen, amygdala, and accumbens volumes as compared with controls (ds < -0.19, q < 0.05 corrected). Offspring showed the largest effect sizes relative to the other FDRs; however, none of the effects in the different relative types survived correction for multiple comparisons. After IQ correction, all effects disappeared in the FDRs after correction for multiple comparisons. The findings in FDRs were not explained by having a nonpsychotic disorder and were only partly explained by ICV. FDRs show brain abnormalities that are strongly covarying with IQ. On the basis of consistent evidence of genetic overlap between schizophrenia, IQ, and brain measures, we suggest that the brain abnormalities in FDRs are at least partly explained by genes predisposing to both schizophrenia risk and IQ.
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http://dx.doi.org/10.1093/schbul/sby182DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6811835PMC
October 2019

Dose response of the 16p11.2 distal copy number variant on intracranial volume and basal ganglia.

Mol Psychiatry 2020 03 3;25(3):584-602. Epub 2018 Oct 3.

Department of Psychiatry and Mental Health, Anzio Road, 7925, Cape Town, South Africa.

Carriers of large recurrent copy number variants (CNVs) have a higher risk of developing neurodevelopmental disorders. The 16p11.2 distal CNV predisposes carriers to e.g., autism spectrum disorder and schizophrenia. We compared subcortical brain volumes of 12 16p11.2 distal deletion and 12 duplication carriers to 6882 non-carriers from the large-scale brain Magnetic Resonance Imaging collaboration, ENIGMA-CNV. After stringent CNV calling procedures, and standardized FreeSurfer image analysis, we found negative dose-response associations with copy number on intracranial volume and on regional caudate, pallidum and putamen volumes (β = -0.71 to -1.37; P < 0.0005). In an independent sample, consistent results were obtained, with significant effects in the pallidum (β = -0.95, P = 0.0042). The two data sets combined showed significant negative dose-response for the accumbens, caudate, pallidum, putamen and ICV (P = 0.0032, 8.9 × 10, 1.7 × 10, 3.5 × 10 and 1.0 × 10, respectively). Full scale IQ was lower in both deletion and duplication carriers compared to non-carriers. This is the first brain MRI study of the impact of the 16p11.2 distal CNV, and we demonstrate a specific effect on subcortical brain structures, suggesting a neuropathological pattern underlying the neurodevelopmental syndromes.
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http://dx.doi.org/10.1038/s41380-018-0118-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7042770PMC
March 2020

Understanding hallucinations in probable Alzheimer's disease: Very low prevalence rates in a tertiary memory clinic.

Alzheimers Dement (Amst) 2018 21;10:358-362. Epub 2018 Apr 21.

Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.

Introduction: Averaging at 13.4%, current literature reports widely varying prevalence rates of hallucinations in patients with probable Alzheimer's disease (AD), and is still inconclusive on contributive factors to hallucinations in AD.

Methods: This study assessed prevalence, associated factors and clinical characteristics of hallucinations in 1227 patients with probable AD, derived from a tertiary memory clinic specialized in early diagnosis of dementia. Hallucinations were assessed with the Neuropsychiatric Inventory.

Results: Hallucination prevalence was very low, with only 4.5% (n = 55/1227) affected patients. Hallucinations were mostly visual (n = 40/55) or auditory (n = 12/55). Comorbid delusions were present in over one-third of cases (n = 23/55).Hallucinations were associated with increased dementia severity, neuropsychiatric symptoms, and a lifetime history of hallucination-evoking disease (such as depression and sensory impairment), but not with age or gender.

Discussion: In the largest sample thus far, we report a low prevalence of hallucinations in probable AD patients, comparable to rates in non-demented elderly. Our results suggest that hallucinations are uncommon in early stage AD. Clinicians that encounter hallucinations in patients with early AD should be sensitive to hallucination-evoking comorbidity.
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http://dx.doi.org/10.1016/j.dadm.2018.03.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6019263PMC
April 2018

Genetic Influences on the Development of Cerebral Cortical Thickness During Childhood and Adolescence in a Dutch Longitudinal Twin Sample: The Brainscale Study.

Cereb Cortex 2019 03;29(3):978-993

Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, 5384 CX Utrecht, the Netherlands.

Previous studies have demonstrated that cortical thickness (CT) is under strong genetic control across the life span. However, little is known about genetic influences that cause changes in cortical thickness (ΔCT) during brain development. We obtained 482 longitudinal MRI scans at ages 9, 12, and 17 years from 215 twins and applied structural equation modeling to estimate genetic influences on (1) cortical thickness between regions and across time, and (2) changes in cortical thickness between ages. Although cortical thickness is largely mediated by the same genetic factor throughout late childhood and adolescence, we found evidence for influences of distinct genetic factors on regions across space and time. In addition, we found genetic influences for cortical thinning during adolescence that is mostly due to fluctuating influences from the same genetic factor, with evidence of local influences from a second emerging genetic factor. This fluctuating core genetic factor and emerging novel genetic factor might be implicated in the rapid cognitive and behavioral development during childhood and adolescence, and could potentially be targets for investigation into the manifestation of psychiatric disorders that have their origin in childhood and adolescence.
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http://dx.doi.org/10.1093/cercor/bhy005DOI Listing
March 2019

Detailed T1-Weighted Profiles from the Human Cortex Measured in Vivo at 3 Tesla MRI.

Neuroinformatics 2018 04;16(2):181-196

Brain Center Rudolf Magnus, Department of Psychiatry, Brain Division, University Medical Center Utrecht, Utrecht University, HPNR A01.126, Heidelberglaan 100, 3584, CG, Utrecht, The Netherlands.

Studies into cortical thickness in psychiatric diseases based on T1-weighted MRI frequently report on aberrations in the cerebral cortex. Due to limitations in image resolution for studies conducted at conventional MRI field strengths (e.g. 3 Tesla (T)) this information cannot be used to establish which of the cortical layers may be implicated. Here we propose a new analysis method that computes one high-resolution average cortical profile per brain region extracting myeloarchitectural information from T1-weighted MRI scans that are routinely acquired at a conventional field strength. To assess this new method, we acquired standard T1-weighted scans at 3 T and compared them with state-of-the-art ultra-high resolution T1-weighted scans optimised for intracortical myelin contrast acquired at 7 T. Average cortical profiles were computed for seven different brain regions. Besides a qualitative comparison between the 3 T scans, 7 T scans, and results from literature, we tested if the results from dynamic time warping-based clustering are similar for the cortical profiles computed from 7 T and 3 T data. In addition, we quantitatively compared cortical profiles computed for V1, V2 and V7 for both 7 T and 3 T data using a priori information on their relative myelin concentration. Although qualitative comparisons show that at an individual level average profiles computed for 7 T have more pronounced features than 3 T profiles the results from the quantitative analyses suggest that average cortical profiles computed from T1-weighted scans acquired at 3 T indeed contain myeloarchitectural information similar to profiles computed from the scans acquired at 7 T. The proposed method therefore provides a step forward to study cortical myeloarchitecture in vivo at conventional magnetic field strength both in health and disease.
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http://dx.doi.org/10.1007/s12021-018-9356-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5984962PMC
April 2018

Association between structural brain network efficiency and intelligence increases during adolescence.

Hum Brain Mapp 2018 02 14;39(2):822-836. Epub 2017 Nov 14.

Brain Center Rudolf Magnus, Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands.

Adolescence represents an important period during which considerable changes in the brain take place, including increases in integrity of white matter bundles, and increasing efficiency of the structural brain network. A more efficient structural brain network has been associated with higher intelligence. Whether development of structural network efficiency is related to intelligence, and if so to which extent genetic and environmental influences are implicated in their association, is not known. In a longitudinal study, we mapped FA-weighted efficiency of the structural brain network in 310 twins and their older siblings at an average age of 10, 13, and 18 years. Age-trajectories of global and local FA-weighted efficiency were related to intelligence. Contributions of genes and environment were estimated using structural equation modeling. Efficiency of brain networks changed in a non-linear fashion from childhood to early adulthood, increasing between 10 and 13 years, and leveling off between 13 and 18 years. Adolescents with higher intelligence had higher global and local network efficiency. The dependency of FA-weighted global efficiency on IQ increased during adolescence (r =0.007 at age 10; 0.23 at age 18). Global efficiency was significantly heritable during adolescence (47% at age 18). The genetic correlation between intelligence and global and local efficiency increased with age; genes explained up to 87% of the observed correlation at age 18. In conclusion, the brain's structural network differentiates depending on IQ during adolescence, and is under increasing influence of genes that are also associated with intelligence as it develops from late childhood to adulthood.
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http://dx.doi.org/10.1002/hbm.23885DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6866576PMC
February 2018

Genetic influences on individual differences in longitudinal changes in global and subcortical brain volumes: Results of the ENIGMA plasticity working group.

Hum Brain Mapp 2017 09 5;38(9):4444-4458. Epub 2017 Jun 5.

Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.

Structural brain changes that occur during development and ageing are related to mental health and general cognitive functioning. Individuals differ in the extent to which their brain volumes change over time, but whether these differences can be attributed to differences in their genotypes has not been widely studied. Here we estimate heritability (h ) of changes in global and subcortical brain volumes in five longitudinal twin cohorts from across the world and in different stages of the lifespan (N = 861). Heritability estimates of brain changes were significant and ranged from 16% (caudate) to 42% (cerebellar gray matter) for all global and most subcortical volumes (with the exception of thalamus and pallidum). Heritability estimates of change rates were generally higher in adults than in children suggesting an increasing influence of genetic factors explaining individual differences in brain structural changes with age. In children, environmental influences in part explained individual differences in developmental changes in brain structure. Multivariate genetic modeling showed that genetic influences of change rates and baseline volume significantly overlapped for many structures. The genetic influences explaining individual differences in the change rate for cerebellum, cerebellar gray matter and lateral ventricles were independent of the genetic influences explaining differences in their baseline volumes. These results imply the existence of genetic variants that are specific for brain plasticity, rather than brain volume itself. Identifying these genes may increase our understanding of brain development and ageing and possibly have implications for diseases that are characterized by deviant developmental trajectories of brain structure. Hum Brain Mapp 38:4444-4458, 2017. © 2017 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/hbm.23672DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5572837PMC
September 2017