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Special Issue: The ENIGMA Consortium: the first 10 years., 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., European Community's Seventh Framework Programme, Grant/Award Numbers: 278948, 602450, 603016, 602805; US National Institute of Child Health and Human Development, Grant/Award Numbers: RO1HD050735, 1009064, 496682; QIMR Berghofer Medical Research Institute and the Centre for Advanced Imaging, University of Queensland; ICTSI NIH/NCRR, Grant/Award Number: RR025761; European Community's Horizon 2020 Programme, Grant/Award Numbers: 667302, 643051; Vici Innovation Program, Grant/Award Numbers: #91619115, 016-130-669; NWO Brain & Cognition Excellence Program, Grant/Award Number: 433-09-229; Biobanking and Biomolecular Resources Research Infrastructure (Netherlands) (BBMRI-NL); Spinozapremie, Grant/Award Number: NWO-56-464-14192; Biobanking and Biomolecular Resources Research Infrastructure, Grant/Award Numbers: 184.033.111, 184.021.007; Netherlands Organization for Health Research and Development (ZonMW), Grant/Award Numbers: 480-15-001/674, 024.001.003, 911-09-032, 056-32-010, 481-08-011, 016-115-035, 31160008, 400-07-080, 400-05-717, 451-04-034, 463-06-001, 480-04-004, 904-61-193, 912-10-020, 985-10-002, 904-61-090; NIMH, Grant/Award Number: R01 MH090553; Geestkracht programme of the Dutch Health Research Council, Grant/Award Number: 10-000-1001; FP7 Ideas: European Research Council; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Grant/Award Numbers: NWO/SPI 56-464-14192, NWO-MagW 480-04-004, 433-09-220, NWO 51.02.062, NWO 51.02.061; National Center for Advancing Translational Sciences, National Institutes of Health, Grant/Award Number: UL1 TR000153; National Center for Research Resources; National Center for Research Resources at the National Institutes of Health, Grant/Award Numbers: NIH 1U24 RR025736-01, NIH 1U24 RR021992; NIH Institutes contributing to the Big Data to Knowledge; U.S. National Institutes of Health, Grant/Award Numbers: R01 CA101318, P30 AG10133, R01 AG19771; Medical Research Council, Grant/Award Numbers: U54EB020403, G0500092; National Institute of Mental Health, Grant/Award Numbers: R01MH117014, R01MH042191; Fundación Instituto de Investigación Marqués de Valdecilla, Grant/Award Numbers: API07/011, NCT02534363, NCT0235832; Instituto de Salud Carlos III, Grant/Award Numbers: PI14/00918, PI14/00639, PI060507, PI050427, PI020499; Swedish Research Council, Grant/Award Numbers: 523-2014-3467, 2017-00949, 521-2014-3487; South-Eastern Norway Health Authority; the Research Council of Norway, Grant/Award Number: 223273; South Eastern Norway Regional Health Authority, Grant/Award Numbers: 2017-112, 2019107; Icahn School of Medicine at Mount Sinai; Seventh Framework Programme (FP7/2007-2013), Grant/Award Number: 602450; National Institutes of Health, Grant/Award Numbers: R01 MH116147, R01 MH113619, R01 MH104284; South London and Maudsley NHS Foundation Trust; the National Institute for Health Research (NIHR)

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., De‐identification methods remove or blur facial characteristics on anatomical MRI scans, but also have a small effect on FreeSurfer brain measures. Furthermore, some methods are too invasive in younger participants or do not always succeed to remove all facial characteristics.

Special Issue: The ENIGMA Consortium: the first 10 years.-- Karolinska Schizophrenia Project (KaSP)., 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., National Institute of Mental Health, Grant/Award Numbers: MH104284, MH116147, R01MH113619, R01 MH090553, R01MH117014, R01MH042191; Karolinska Institutet; Stockholm County Council; Southern and Eastern Norway Regional Health Authority; German Centre for Cardiovascular Research; DZHK; Siemens Healthineers; University of Queensland; US National Institute of Child Health and Human Development, Grant/Award Number: RO1HD050735; Australian National Health and Medical Research Council; Eunice Kennedy Shriver National Institute of Child Health & Human Development; National Institute on Drug Abuse, Grant/Award Numbers: UL1 TR000153, 1 U24 RR025736-01, 1 U24 RR021992; Brain Injury Fund Research Grant Program; Indiana State Department of Health Spinal Cord; Parents Against Childhood Epilepsy; Epilepsy Therapy Project, Fight Against Childhood Epilepsy and Seizures; Epilepsy Foundation; American Epilepsy Society; Knut and Alice Wallenberg Foundation; European Community's Horizon 2020 Programme; Vici Innovation Program; NWO Brain & Cognition Excellence Program; Netherlands Organization for Scientific Research; Hersenstichting Nederland; Netherlands Organization for Health Research and Development; Geestkracht Programme of the Dutch Health Research Council, Grant/Award Number: 10-000-1001; Universiteit Utrecht; FP7 Ideas: European Research Council; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; National Center for Advancing Translational Sciences; National Institutes of Health; National Center for Research Resources; Consortium grant, Grant/Award Number: U54 EB020403; U.S. National Institutes of Health, Grant/Award Numbers: R01 CA101318, P30 AG10133, R01 AG19771; Medical Research Council, Grant/Award Number: G0500092; Fundación Instituto de Investigación Marqués de Valdecilla, Grant/Award Numbers: API07/011, NCT02534363, NCT0235832; Instituto de Salud Carlos III, Grant/Award Numbers: PI14/00918, PI14/00639, PI060507, PI050427, PI020499; the Research Council, Grant/Award Number: 223273; South Eastern Norway Regional Health Authority, Grant/Award Numbers: 2017-112, 2019107; Swedish Research Council; European Community's Seventh Framework Programme, Grant/Award Number: 602450; King's College London; South London and Maudsley NHS Foundation Trust; Biomedical Research Centre; National Institute for Health Research.

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 (h2) 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.

Hippocampal volume deficits have been linked to life stress. However, the degree to which genes and environment influence the association between hippocampal volume and life events is largely unknown. In total, 123 healthy twins from monozygotic and dizygotic twin pairs underwent magnetic resonance imaging (MRI), and 57 healthy twins were interviewed with the Life Events and Difficulties Schedule (LEDS), with an overlap of 54 twins undergoing both MRI and the life events interview. Hippocampal volumes were segmented with Freesurfer software. Data were analyzed with OpenMx software. Smaller hippocampal volume was associated with higher severe life event load (rph = -0.39), where shared environmental factors influencing both measures fully explained the association. Hippocampal volume was not associated with total or mild life event load. Hippocampal volume showed high heritability (range, h(2) : 57%-81%) whereas life event measures were influenced by shared (c(2) ) and unique (e(2) ) environmental factors only (range, c(2) :40%-64%, e(2) : 36%-60%). The results suggested that shared environmental factors influenced the relationship between smaller hippocampal volume and severe (but not mild) stress. This indicated that particularly severe life events that were shared between twins were associated with smaller hippocampal volume. Furthermore, it is suggested to distinguish between mild and severe life events in life event research. © 2016 Wiley Periodicals, Inc.

r r Abstract: It has been shown that brain volume and general intellectual ability are to a significant extent influenced by the same genetic factors. Several cortical regions of the brain also show a genetic correla- tion with intellectual ability, demonstrating that intellectual functioning is probably represented in a her- itable distributed network of cortical regions throughout the brain. This study is the first to investigate a genetic association between subcortical volumes and intellectual ability, taking into account the thalamus, caudate nucleus, putamen, globus pallidus, hippocampus, amygdala, and nucleus accumbens using an extended twin design. Genetic modeling was performed on a healthy adult twin sample consisting of 106 twin pairs and 30 of their siblings, IQ data was obtained from 132 subjects. Our results demonstrate that of all subcortical volumes measured, only thalamus volume is significantly correlated with intellec- tual functioning. Importantly, the association found between thalamus volume and intellectual ability is significantly influenced by a common genetic factor. This genetic factor is also implicated in cerebral brain volume. The thalamus, with its widespread cortical connections, may thus play a key role in human intelligence. Hum Brain Mapp 35:2632-2642, 2014. V C 2013 Wiley Periodicals, Inc.

Objective We investigated the association between mean corpuscular volume (MCV), carbohydrate-deficient transferrin (CDT), and gamma-glutamyl transferase (GGT) levels and gray and white brain matter in male drinkers to find out which if any of these biomarkers of alcohol consumption is indicative for alcohol-related differences in brain volume. Method Plasma levels of CDT, GGT, and MCV and magnetic resonance imaging-determined brain gray and white matter volumes were assessed in 55 male drinkers. Current alcohol intake and lifetime alcohol intake were determined by self-report measures. The relationship between MCV, CDT, and GGT and brain volumes was explored using multiple linear regression analyses. Results There was a significant negative relationship between plasma GGT and MCV levels and gray matter volumes. Middle-aged male drinkers with highly elevated GGT and MCV levels (twice the standard deviation above the mean) have 4–12% less parietal and occipital gray matter than males with average GGT and MCV levels. There was no association between CDT levels and brain gray or white matter. Conclusions Elevated GGT and MCV levels may be indicative of alcohol-related gray-matter decline in male drinkers. The link with GGT may reflect that elevated GGT levels are a sign of increased oxidative stress. The link with MCV levels may reflect a decreased oxygen transport to the brain. Copyright © 2012 John Wiley & Sons, Ltd.

Numerous diffusion tensor imaging (DTI) studies have implicated white matter brain tissue abnormalities in schizophrenia. However, the vast majority of these studies included patient populations that use antipsychotic medication. Previous research showed that medication intake can affect brain morphology and the question therefore arises to what extent the reported white matter aberrations can be attributed to the disease rather than to the use of medication. In this study we included 16 medication-naive patients with schizophrenia and compared them to 23 healthy controls to exclude antipsychotic medication use as a confounding factor. For each subject DTI scans and magnetization transfer imaging (MTI) scans were acquired. A new tract-based analysis was used that combines fractional anisoptropy (FA), mean diffusivity (MD) and magnetization transfer ratio (MTR) to examine group differences in 12 major white matter fiber bundles. Significant group differences in combined FA, MD, MTR values were found for the right uncinate fasciculus and the left arcuate fasciculus. Additional analysis revealed that the largest part of both tracts showed an increase in MTR in combination with an increase in MD for patients with schizophrenia. We interpret these group-related differences as disease-related axonal or glial aberrations that cannot be attributed to antipsychotic medication use.

Frontostriatal brain areas have been implicated in the neurobiology of attention deficit/hyperactivity disorder (ADHD), but little work has directly addressed the white matter connections between these regions. The present study investigates the microstructural organization and myelination of frontostriatal white matter in children with ADHD and controls. Diffusion tensor imaging and magnetization transfer imaging scans were acquired in 30 children with ADHD and 34 controls. A study specific volume of interest (VOI) of frontostriatal white matter was created using a tractography based statistical group map. Fractional anisotropy (FA, indexing microstructural organization) and magnetization transfer ratio (MTR, indexing macromolecular content, myelin in particular) were computed for the frontostriatal VOI and for total cerebral white matter. Exploratory analyses were conducted investigating the effect of stimulant use on these measures. Frontostriatal FA but not MTR was decreased in ADHD compared with controls. There were no differences in FA or MTR for total cerebral white matter. Frontostriatal FA correlated negatively with teacher‐rated attention problems in controls but not children with ADHD. The duration of stimulant use did not affect the main results. Changes in frontostriatal connectivity in ADHD appear to be related to changes in microstructural organization rather than myelination per se. A correlation with attention problems for controls suggests that frontostriatal organization is relevant to ADHD‐related behaviors. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc

Multicenter structural MRI studies can have greater statistical power than single‐center studies. However, across‐center differences in contrast sensitivity, spatial uniformity, etc., may lead to tissue classification or image registration differences that could reduce or wholly offset the enhanced statistical power of multicenter data. Prior work has validated volumetric multicenter MRI, but robust methods for assessing reliability and power of multisite analyses with voxel‐based morphometry (VBM) and cortical thickness measurement (CORT) are not yet available. We developed quantitative methods to investigate the reproducibility of VBM and CORT to detect group differences and estimate heritability when MRI scans from different scanners running different acquisition protocols in a multicenter setup are included. The method produces brain maps displaying information such as lowest detectable effect size (or heritability) and effective number of subjects in the multicenter study. We applied the method to a five‐site multicenter calibration study using scanners from four different manufacturers, running different acquisition protocols. The reliability maps showed an overall good comparability between the sites, providing a reasonable gain in sensitivity in most parts of the brain. In large parts of the cerebrum and cortex scan pooling improved heritability estimates, with “effective‐N” values upto the theoretical maximum. For some areas, “optimal‐pool” maps indicated that leaving out a site would give better results. The reliability maps also reveal which brain regions are in any case difficult to measure reliably (e.g., around the thalamus). These tools will facilitate the design and analysis of multisite VBM and CORT studies for detecting group differences and estimating heritability. Hum Brain Mapp, 2010. © 2010 Wiley‐Liss, Inc.

Objective. So far, there have been no attempts to integrate the growing number of all brain volumetric magnetic resonance imaging studies in depression. In this comprehensive meta-analysis the magnitude and extent of brain volume differences between 2,418 patients with major depressive disor- der and 1,974 healthy individuals from 64 studies was determined. Methods. A systematic research was conducted for volumetric magnetic resonance imaging studies of patients with major depressive disorder in relation to healthy control subjects. Studies had to report sufficient data for computation of effect sizes. For each study, the Cohen's d was calculated. All analyses were performed using the ran- dom effects model. Additionally, meta-regression analyses were done to explore the effects of potential sources of heterogeneity. Results. Patients showed large volume reductions in frontal regions, especially in the anterior cingulate and orbitofrontal cortex with smaller reductions in the prefrontal cortex. The hippocampus, the putamen and caudate nucleus showed moderate volume reductions. Conclusions. This is the first comprehensive meta-analysis in major depressive disorder demonstrating structural brain abnormalities, particularly in those brain areas that are involved in emotion processing and stress-regulation. Hum Brain Mapp 30:3719-3735, 2009. V C 2009 Wiley-Liss, Inc.

Purpose To evaluate cerebral abnormalities in childhood idiopathic anatomic megalencephaly (MC) by means of different magnetic resonance (MR) modalities. Materials and Methods MRI, volumetry, spectroscopy, and cerebropetal blood flowmetry were performed in six children with idiopathic anatomic MC, and seven volunteers. Results MRI revealed an increased ventricular system in five of six patients. A thalamic hamartoma was found in one patient and a Chiari I malformation was found in two. Volumetric analysis showed a disproportional increase of ventricular volume but normal subarachnoid cerebrospinal fluid (CSF) volume. Supratentorial volume was disproportionally increased compared to cerebellar volume. Intracranial volume correlated significantly with skull circumference. MR spectroscopy (MRS) N-acetyl aspartate/choline (NAA/Cho) peak ratios in WM were significantly higher in patients than in controls. Choline/creatine (Cho/Cr) peak ratios in WM were significantly lower in patients. Cortical gray matter (GM) MRS ratios were unaltered. Cerebropetal flow was increased in MC, possibly related to increased brain volume. Conclusion This study reveals associated developmental anomalies for idiopathic anatomic MC. A relative ventriculomegaly was found, which should not be misinterpreted as true hydrocephalus. In contrast to metabolic MC, MRS showed no severe disturbances. Total intracranial volume is correlated to skull circumference and cerebropetal blood flow. J. Magn. Reson. Imaging 2006. © 2006 Wiley-Liss, Inc.

Background: Alcohol-dependent individuals have brain volume loss. Possibly, moderate drinkers who are not alcohol dependent have similar but less prominent brain damage. The authors investigated whether current or lifetime alcohol intake is related to volumes of total brain, cerebellum, ventricles, peripheral cerebrospinal fluid, and cerebral gray and white matter in moderate drinkers. Methods: The relation between current or lifetime alcohol intake and brain volumes of 47 male moderate drinkers (current alcohol intake 20 drinks per week, lifetime alcohol intake 240 kg) and 44 female moderate drinkers (current alcohol intake 15 drinks per week, lifetime alcohol intake 170 kg), all without a personal or family history of alcohol dependence, was determined using high-resolution magnetic resonance images, corrected for intracranial volume, age, and sex. Results: In males, mean lifetime alcohol intake was positively associated with cerebral white matter volume, particularly in the frontal region. In females, mean lifetime alcohol intake was not associated with brain volumes. Current alcohol intake was unrelated to brain volumes in either males or females. Conclusions: Neither current nor lifetime alcohol intake is associated with decreases in brain volumes in male or female moderate drinkers. Because all participants had a negative personal and family history of alcohol dependence, the current results relatively purely concern the effects of moderate alcohol intake on brain volumes.

Multicenter studies can provide additional information over single center studies because of their increased statistical power. Because similar acquisition protocols are being used internationally for structural magnetic resonance imaging (MRI) studies of the human brain, volumetric MRI data studies seem suitable for this purpose. Possible systematic differences between sites should be avoided, however, particularly when subtle differences in tissue volume are being searched for, such as in neuropsychiatric diseases. In this calibration study, the brains of six healthy volunteers were (re)scanned with MR scanners from four different manufacturers at five different sites, using the local acquisition protocols. The images were segmented at a central reference site. The intraclass correlation coefficient (ICC) was determined for the whole brain, gray and white matter, cerebellum, and lateral and third ventricle volumes. When required, the processing algorithms were calibrated for each site. Calibration of the histogram analysis was needed for segmentation of total brain volume at one site and for gray and white matter volume at all sites. No (additional) calibration was needed for cerebellum and ventricle volumes. The ICCs were ≥0.96 for total brain, ≥0.92 for cerebellum, ≥0.96 for lateral ventricle, ≥0.21 for third ventricle, ≥0.84 for gray matter, and ≥0.78 for white matter volume. Calibration of segmentation procedures allows morphologic MRI data acquired at different research sites to be combined reliably in multicenter studies. Hum. Brain Mapping 22:312–320, 2004. © 2004 Wiley‐Liss, Inc.

The pathophysiology of auditory verbal hallucinations (AVH) is still unclear. Cognitive as well as electrophysiological studies indicate that a defect in sensory feedback (corollary discharge) may contribute to the experience of AVH. This could result from disruption of the arcuate fasciculus, the major tract connecting frontal and temporo‐parietal language areas. Previous diffusion tensor imaging studies indeed demonstrated abnormalities of this tract in schizophrenia patients with AVH. It is, however, difficult to disentangle specific associations with AVH in this patient group as many other factors, such as other positive and negative symptoms, medication or halted education could likewise have affected tract integrity. We therefore investigated AVH in relative isolation and studied a group of non‐psychotic individuals with AVH as well as patients with AVH and non‐hallucinating matched controls. We compared tract integrity of the arcuate fasiculus and of three other control tracts, between 35 non‐psychotic individuals with AVH, 35 schizophrenia patients with AVH, and 36 controls using diffusion tensor imaging and magnetization transfer imaging. Both groups with AVH showed an increase in magnetization transfer ratio (MTR) in the arcuate fasciculus, but not in the other control tracts. In addition, a general decrease in fractional anisotropy (FA) for almost all bundles was observed in the patient group, but not in the non‐psychotic individuals with AVH. As increased MTR in the arcuate fasciculus was present in both hallucinating groups, a specific association with AVH seems plausible. Decreases in FA, on the other hand, seem to be related to other disease processes of schizophrenia. Hum Brain Mapp, 2013. © 2011 Wiley Periodicals, Inc.