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

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.This study presents independent research funded by multiple agen-cies. The funding sources had no role in the study design, data collection, analysis, and interpretation of the data. The views expressed inthe manuscript are those of the authors and do not necessarily repre-sent those of any of the funding agencies. Dr. Dima received fundingfrom the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS FoundationTrust and King's College London, the Psychiatry Research Trust and2014 NARSAD Young Investigator Award. Dr. Frangou received sup-port from the National Institutes of Health (R01 MH104284,R01MH113619, R01 MH116147), the European Community's Sev-enth Framework Programme (FP7/2007–2013) (grant agreementn 602450). This work was supported in part through the computa-tional resources and staff expertise provided by Scientific Computingat the Icahn School of Medicine at Mount Sinai, USA. Dr. Agartz wassupported by the Swedish Research Council (grant numbers:521-2014-3487 and 2017-00949). Dr. Alnæs was supported by theSouth Eastern Norway Regional Health Authority (grant number:2019107). Dr. O Andreasen was supported by the Research Councilof Norway (grant number: 223273) and South-Eastern Norway HealthAuthority (grant number: 2017-112). Dr. Cervenka was supported bythe Swedish Research Council (grant number 523-2014-3467).Dr. Crespo-Facorro was supported by the IDIVAL Neuroimaging Unitfor imaging acquisition; Instituto de Salud Carlos III (grant numbers:PI020499, PI050427, PI060507, PI14/00639 and PI14/00918) andthe Fundación Instituto de Investigación Marqués de Valdecilla (grantnumbers: NCT0235832, NCT02534363, and API07/011). Dr. Gurwas supported by the National Institute of Mental Health (grant num-bers: R01MH042191 and R01MH117014). Dr. James was supportedby the Medical Research Council (grant no G0500092). Dr. Saykinreceived support from U.S. National Institutes of Health grants R01AG19771, P30 AG10133 and R01 CA101318. Dr. Thompson,Dr. Jahanshad, Dr. Wright, Dr. Medland, Dr. O Andreasen, Dr. Rinker,Dr. Schmaal, Dr. Veltam, Dr. van Erp, and D.P.H. were supported inpart by a Consortium grant (U54 EB020403 to P.M.T.) from the NIHInstitutes contributing to the Big Data to Knowledge (BD2K) Initiative.FBIRN sample: Data collection and analysis was supported by the National Center for Research Resources at the National Institutes ofHealth (grant numbers: NIH 1 U24 RR021992 (Function BiomedicalInformatics Research Network) and NIH 1 U24 RR025736-01(Biomedical Informatics Research Network Coordinating Center;http://www.birncommunity.org). FBIRN data was processed by theUCI High Performance Computing cluster supported by the NationalCenter for Research Resources and the National Center for AdvancingTranslational Sciences, National Institutes of Health, through GrantUL1 TR000153. Brainscale: This work was supported by NederlandseOrganisatie voor Wetenschappelijk Onderzoek (NWO 51.02.061 toH.H., NWO 51.02.062 to D.B., NWO- NIHC Programs of excellence433-09-220 to H.H., NWO-MagW 480-04-004 to D.B., andNWO/SPI 56-464-14192 to D.B.); FP7 Ideas: European ResearchCouncil (ERC-230374 to D.B.); and Universiteit Utrecht (High Poten-tial Grant to H.H.). UMCU-1.5T: This study is partially funded throughthe Geestkracht Programme of the Dutch Health Research Council(Zon-Mw, grant No 10-000-1001), and matching funds from partici-pating pharmaceutical companies (Lundbeck, AstraZeneca, Eli Lilly,Janssen Cilag) and universities and mental health care organizations(Amsterdam: Academic Psychiatric Centre of the Academic MedicalCenter and the mental health institutions: GGZ Ingeest, Arkin, Dijk enDuin, GGZ Rivierduinen, Erasmus Medical Centre, GGZ Noord Hol-land Noord. Groningen: University Medical Center Groningen and themental health institutions: Lentis, GGZ Friesland, GGZ Drenthe, Dim-ence, Mediant, GGNet Warnsveld, Yulius Dordrecht and Parnassiapsycho-medical center The Hague. Maastricht: Maastricht UniversityMedical Centre and the mental health institutions: GGzE, GGZBreburg, GGZ Oost-Brabant, Vincent van Gogh voor GeestelijkeGezondheid, Mondriaan, Virenze riagg, Zuyderland GGZ, MET ggz,Universitair Centrum Sint-Jozef Kortenberg, CAPRI University of Ant-werp, PC Ziekeren Sint-Truiden, PZ Sancta Maria Sint-Truiden, GGZOverpelt, OPZ Rekem. Utrecht: University Medical Center Utrechtand the mental health institutions Altrecht, GGZ Centraal and Delta.).UMCU-3T: This study was supported by NIMH grant number: R01MH090553 (to RAO). The NIMH had no further role in study design,in the collection, analysis and interpretation of the data, in the writingof the report, and in the decision to submit the paper for publication.Netherlands Twin Register: Funding was obtained from the Nether-lands Organization for Scientific Research (NWO) and The NetherlandsOrganization for Health Research and Development (ZonMW) grants904-61-090, 985-10-002, 912-10-020, 904-61-193,480-04-004,463-06-001, 451-04-034, 400-05-717, 400-07-080, 31160008,016-115-035, 481-08-011, 056-32-010, 911-09-032, 024-001-003,480-15-001/674, Center for Medical Systems Biology (CSMB, NWOGenomics), Biobanking and Biomolecular Resources Research Infra-structure (BBMRI-NL, 184.021.007 and 184.033.111); Spinozapremie(NWO- 56-464-14192), and the Neuroscience Amsterdam researchinstitute (former NCA). The BIG database, established in Nijmegen in2007, is now part of Cognomics, a joint initiative by researchers of theDonders Centre for Cognitive Neuroimaging, the Human Genetics andCognitive Neuroscience departments of the Radboud University Medi-cal Centre, and the Max Planck Institute for Psycholinguistics. TheCognomics Initiative is supported by the participating departments and centers and by external grants, including grants from the Biobankingand Biomolecular Resources Research Infrastructure (Netherlands)(BBMRI-NL) and the Hersenstichting Nederland. The authors alsoacknowledge grants supporting their work from the Netherlands Orga-nization for Scientific Research (NWO), that is, the NWO Brain & Cog-nition Excellence Program (grant 433-09-229), the Vici InnovationProgram (grant 016-130-669 to BF) and #91619115. Additional sup-port is received from the European Community's Seventh FrameworkProgramme (FP7/2007–2013) under grant agreements n 602805(Aggressotype), n 603016 (MATRICS), n 602450 (IMAGEMEND), andn 278948 (TACTICS), and from the European Community's Horizon2020 Programme (H2020/2014–2020) under grant agreements n 643051 (MiND) and n 667302 (CoCA). Betula sample: Data collectionfor the BETULA sample was supported by a grant from Knut and AliceWallenberg Foundation (KAW); the Freesurfer segmentations wereperformed on resources provided by the Swedish National Infrastruc-ture for Computing (SNIC) at HPC2N in Umeå, Sweden. Indiana sample:This sample was supported in part by grants to BCM from SiemensMedical Solutions, from the members of the Partnership for PediatricEpilepsy Research, which includes the American Epilepsy Society, theEpilepsy Foundation, the Epilepsy Therapy Project, Fight Against Child-hood Epilepsy and Seizures (F.A.C.E.S.), and Parents Against ChildhoodEpilepsy (P.A.C.E.), from the Indiana State Department of Health SpinalCord and Brain Injury Fund Research Grant Program, and by a ProjectDevelopment Team within the ICTSI NIH/NCRR Grant NumberRR025761. MHRC study: It was supported in part by RFBR grant20-013-00748. PING study: Data collection and sharing for the Pediat-ric Imaging, Neurocognition and Genetics (PING) Study (National Insti-tutes of Health Grant RC2DA029475) were funded by the NationalInstitute on Drug Abuse and the Eunice Kennedy Shriver National Insti-tute of Child Health & Human Development. A full list of PING investi-gators is at http://pingstudy.ucsd.edu/investigators.html. QTIM sample:The authors are grateful to the twins for their generosity of time andwillingness to participate in our study and thank the many researchassistants, radiographers, and other staff at QIMR Berghofer MedicalResearch Institute and the Centre for Advanced Imaging, University ofQueensland. QTIM was funded by the Australian National Health andMedical Research Council (Project Grants No. 496682 and 1009064)and US National Institute of Child Health and Human Development(RO1HD050735). Lachlan Strike was supported by a University ofQueensland PhD scholarship. Study of Health in Pomerania (SHIP): thisis part of the Community Medicine Research net (CMR) (http://www.medizin.uni-greifswald.de/icm) of the University Medicine Greifswald,which is supported by the German Federal State of Mecklenburg- WestPomerania. MRI scans in SHIP and SHIP-TREND have been supportedby a joint grant from Siemens Healthineers, Erlangen, Germany and theFederal State of Mecklenburg-West Pomerania. This study was furthersupported by the DZHK (German Centre for Cardiovascular Research),the German Centre of Neurodegenerative Diseases (DZNE) and theEU-JPND Funding for BRIDGET (FKZ:01ED1615). TOP study: this wassupported by the European Community's Seventh Framework Pro-gramme (FP7/2007–2013), grant agreement n 602450. The Southernand Eastern Norway Regional Health Authority supported Lars T. Westlye (grant no. 2014-097) and STROKEMRI (grantno. 2013-054). HUBIN sample: HUBIN was supported by the SwedishResearch Council (K2007-62X-15077-04-1, K2008-62P-20597-01-3,K2010-62X-15078-07-2, K2012-61X-15078-09-3), the regional agree-ment on medical training and clinical research between StockholmCounty Council, and the Karolinska Institutet, and the Knut and AliceWallenberg Foundation. The BIG database: this was established in Nij-megen in 2007, is now part of Cognomics, a joint initiative byresearchers of the Donders Centre for Cognitive Neuroimaging, theHuman Genetics and Cognitive Neuroscience departments of theRadboud university medical centre, and the Max Planck Institute forPsycholinguistics. The Cognomics Initiative is supported by the partici-pating departments and centres and by external grants, including grantsfrom the Biobanking and Biomolecular Resources Research Infrastruc-ture (Netherlands) (BBMRI-NL) and the Hersenstichting Nederland. Theauthors also acknowledge grants supporting their work from the Neth-erlands Organization for Scientific Research (NWO), that is, the NWOBrain & Cognition Excellence Program (grant 433-09-229), the ViciInnovation Program (grant 016-130-669 to BF) and #91619115. Addi-tional support is received from the European Community's SeventhFramework Programme (FP7/2007–2013) under grant agreements n 602805 (Aggressotype), n 603016 (MATRICS), n 602450(IMAGEMEND), and n 278948 (TACTICS), and from the EuropeanCommunity's Horizon 2020 Programme (H2020/2014–2020) undergrant agreements n 643051 (MiND) and n 667302 (CoCA)

The effect of excess weight on circulating inflammatory cytokines in drug-naïve first-episode psychosis individuals

Background: Low-grade inflammation has been repeatedly associated with both excess weight and psychosis. However, no previous studies have addressed the direct effect of body mass index (BMI) on basal serum cytokines in individuals with first-episode psychosis (FEP). Objectives: The aim of this study is to analyze the effect of BMI on basal serum cytokine levels in FEP patients and control subjects, separating the total sample into two groups: normal-weight and overweight individuals. Methods: This is a prospective and open-label study. We selected 75 FEP patients and 75 healthy controls with similar characteristics to patients according to the following variables: sex, age, and cannabis and tobacco consumption. Both controls and patients were separated into two groups according to their BMI: subjects with a BMI under 25 were considered as normal weight and those with a BMI equal to or more than 25 were considered as overweight. Serum levels of 21 cytokines/chemokines were measured at baseline using the Human High Sensitivity T Cell Magnetic Bead Panel protocol from the Milliplex® Map Kit. We compared the basal serum levels of the 21 cytokines between control and patient groups according to their BMI. Results: In the normal-weight group, IL-8 was the only cytokine that was higher in patients than in the control group (p = 0.001), whereas in the overweight group, serum levels of two pro-inflammatory cytokines (IL-6, p = 0.000; IL-1?, p = 0.003), two chemokines (IL-8, p = 0.001; MIP-1?, p = 0.001), four Th-1 and Th-2 cytokines (IL-13, p = 0.009; IL-2, p = 0.001; IL-7, p = 0.001; IL-12p70, p = 0.010), and one Type-3 cytokine (IL-23, p = 0.010) were higher in patients than in controls. Conclusions: Most differences in the basal serum cytokine levels between patients and healthy volunteers were found in the overweight group. These findings suggest that excess weight can alter the homeostasis of the immune system and therefore may have an additive pro-inflammatory effect on the one produced by psychosis in the central nervous system.Funding: The present study was carried out at the Hospital Marqués de Valdecilla, University of Cantabria, Santander, Spain, under the following grant support from MINECO SAF2013-46292-R, Instituto de Salud Carlos III, and Fundación Marqués de Valdecilla. No pharmaceutical company has participated in the study concept and design, data collection, analysis and interpretation of the results, and drafting of the manuscript. We thank the Valdecilla Biobank for blood sampling handling and storage. We also wish to thank the participants and their families for enrolling in this study. The study, designed and directed by B C-F, conformed to international standards for research ethics and was approved by the local institutional review board

Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium

Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18–72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18–73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years (95% CI: 2.91, 4.19; I2 = 57.53%) compared to controls, after adjusting for age, sex and site (Cohen’s d = 0.48). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions

Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium

BACKGROUND Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia (N = 11,095), using a single image analysis protocol. METHODS We included T1-weighted data from 46 datasets (5,080 affected individuals and 6,015 controls) from the ENIGMA Consortium. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Analyses were also performed with respect to the use of antipsychotic medication and other clinical variables, as well as age and sex. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029). RESULTS Small average differences between cases and controls were observed for asymmetries in cortical thickness, specifically of the rostral anterior cingulate (d = −0.08, pFDR = 0.047) and the middle temporal gyrus (d = −0.07, pFDR = 0.048), both driven primarily by thinner cortices in the left hemisphere in schizophrenia. These asymmetries were not significantly associated with the use of antipsychotic medication or other clinical variables. Older individuals with schizophrenia showed a stronger average leftward asymmetry of pallidum volume than older controls (d = 0.08, pFDR = 9.0 × 10−3). The multivariate analysis revealed that 7% of the variance across all structural asymmetries was explained by case-control status (F = 1.87, p = 1.25 × 10−5). CONCLUSIONS Altered trajectories of asymmetrical brain development and/or lifespan asymmetry may contribute to schizophrenia pathophysiology. Small case-control differences of brain macro-structural asymmetry may manifest due to more substantial differences at the molecular, cytoarchitectonic or circuit levels, with functional relevance for lateralized cognitive processes

Genetic variants associated with longitudinal changes in brain structure across the lifespan

Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging

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

Genetic variants associated with longitudinal changes in brain structure across the lifespan

Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging

Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium

Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia