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 crosssectional 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

Neural correlates of anxious distress in depression:A neuroimaging study of reactivity to emotional faces and resting-state functional connectivity

Background: Comorbid anxiety disorders and anxious distress are highly prevalent in major depressive disorder (MDD). The presence of the DSM-5 anxious distress specifier (ADS) has been associated with worse treatment outcomes and chronic disease course. However, little is known about the neurobiological correlates of anxious distress in MDD. Methods: We probed the relation between the DSM-5 ADS and task-related reactivity to emotional faces, as well as resting-state functional connectivity patterns of intrinsic salience and basal ganglia networks in unmedicated MDD patients with (MDD/ADS+, N = 24) and without ADS (MDD/ADS−, N = 48) and healthy controls (HC, N = 59). Both categorical and dimensional measures of ADS were investigated. Results: MDD/ADS+ patients had higher left amygdala responses to emotional faces compared to MDD/ADS− patients (p =.015)—part of a larger striato-limbic cluster. MDD/ADS+ did not differ from MDD/ADS− or controls in resting-state functional connectivity of the salience or basal ganglia networks. Conclusions: Current findings suggest that amygdala and striato-limbic hyperactivity to emotional faces may be a neurobiological hallmark specific to MDD with anxious distress, relative to MDD without anxious distress. This may provide preliminary indications of the underlying mechanisms of anxious distress in depression, and underline the importance to account for heterogeneity in depression research

Cortical and subcortical brain structure in generalized anxiety disorder: findings from 28 research sites in the ENIGMA-Anxiety Working Group

The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5–90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology

Default Mode Network Connectivity and Social Dysfunction in Major Depressive Disorder

Though social functioning is often hampered in Major Depressive Disorder (MDD), we lack a complete and integrated understanding of the underlying neurobiology. Connectional disturbances in the brain’s Default Mode Network (DMN) might be an associated factor, as they could relate to suboptimal social processing. DMN connectional integrity, however, has not been explicitly studied in relation to social dysfunctioning in MDD patients. Applying Independent Component Analysis and Dual Regression on resting-state fMRI data, we explored DMN intrinsic functional connectivity in relation to social dysfunctioning (i.e. composite of loneliness, social disability, small social network) among 74 MDD patients (66.2% female, Mean age = 36.9, SD = 11.9). Categorical analyses examined whether DMN connectivity differs between high and low social dysfunctioning MDD groups, dimensional analyses studied linear associations between social dysfunction and DMN connectivity across MDD patients. Threshold-free cluster enhancement (TFCE) with family-wise error (FWE) correction was used for statistical thresholding and multiple comparisons correction (P < 0.05). The analyses cautiously linked greater social dysfunctioning among MDD patients to diminished DMN connectivity, specifically within the rostromedial prefrontal cortex and posterior superior frontal gyrus. These preliminary findings pinpoint DMN connectional alterations as potentially germane to social dysfunction in MDD, and may as such improve our understanding of the underlying neurobiology

White matter architecture in major depression with anxious distress symptoms

Background: Comorbid anxious distress is common in Major Depressive Disorder (MDD), and associated with significantly worse clinical course and treatment response. While DSM-5 recently introduced the Anxious Distress (AD) specifier as a potentially useful symptom-based subtyping scheme for MDD, its neurobiological underpinnings remain unclear. The current study hence uniquely probed whether MDD with co-occurring AD (MDD/AD+) relates to distinct perturbations in frontolimbic white matter (WM) pathways tentatively theorized in MDD/AD+ pathophysiology. Methods: Tract-based spatial statistics (TBSS) was therefore used to analyze diffusion tensor imaging data on WM microstructure, in MDD/AD+ patients (N = 20) relative to MDD patients without AD (MDD/AD-; N = 29) and healthy controls (HC; N = 39). Using TBSS, we probed fractional anisotropy and axial/radial/mean diffusivity as proxies for WM integrity. Categorical (between-groups) and dimensional (within-patients) analyses subsequently assessed how Anxious Distress in MDD impacts frontolimbic WM connectivity. Receiver-Operating Characteristics additionally assessed classification capabilities of between-groups WM effects. Results: Compared to MDD/AD- and HC participants, MDD/AD+ patients exhibited diminished integrity within the anterior thalamic radiation (ATR). Higher AD specifier scores within MDD patients additionally related to diminished integrity of the uncinate fasciculus and cingulum pathways. These effects were not confounded by key clinical (e.g., comorbid anxiety disorder) and sociodemographic (e.g., age/sex) factors, with altered ATR integrity moreover successfully classifying MDD/AD+ patients from MDD/AD- and HC participants (90% sensitivity vertical bar 73% specificity vertical bar 77% accuracy). Conclusions: These findings collectively link MDD/AD+ to distinct WM anomalies in frontolimbic tracts important to adaptive emotional functioning, and may as such provide relevant, yet preliminary, clues on MDD/AD+ pathophysiology

Contributing factors to advanced brain aging in depression and anxiety disorders

Depression and anxiety are common and often comorbid mental health disorders that represent risk factors for aging-related conditions. Brain aging has shown to be more advanced in patients with major depressive disorder (MDD). Here, we extend prior work by investigating multivariate brain aging in patients with MDD, anxiety disorders, or both, and examine which factors contribute to older-appearing brains. Adults aged 18–57 years from the Netherlands Study of Depression and Anxiety underwent structural MRI. A pretrained brain-age prediction model based on >2000 samples from the ENIGMA consortium was applied to obtain brain-predicted age differences (brain PAD, predicted brain age minus chronological age) in 65 controls and 220 patients with current MDD and/or anxiety. Brain-PAD estimates were associated with clinical, somatic, lifestyle, and biological factors. After correcting for antidepressant use, brain PAD was significantly higher in MDD (+2.78 years, Cohen’s d = 0.25, 95% CI −0.10-0.60) and anxiety patients (+2.91 years, Cohen’s d = 0.27, 95% CI −0.08-0.61), compared with controls. There were no significant associations with lifestyle or biological stress systems. A multivariable model indicated unique contributions of higher severity of somatic depression symptoms (b = 4.21 years per unit increase on average sum score) and antidepressant use (−2.53 years) to brain PAD. Advanced brain aging in patients with MDD and anxiety was most strongly associated with somatic depressive symptomatology. We also present clinically relevant evidence for a potential neuroprotective antidepressant effect on the brain-PAD metric that requires follow-up in future research

The neuroscience of sadness: A multidisciplinary synthesis and collaborative review

Sadness is typically characterized by raised inner eyebrows, lowered corners of the mouth, reduced walking speed, and slumped posture. Ancient subcortical circuitry provides a neuroanatomical foundation, extending from dorsal periaqueductal grey to subgenual anterior cingulate, the latter of which is now a treatment target in disorders of sadness. Electrophysiological studies further emphasize a role for reduced left relative to right frontal asymmetry in sadness, underpinning interest in the transcranial stimulation of left dorsolateral prefrontal cortex as an antidepressant target. Neuroimaging studies – including meta-analyses – indicate that sadness is associated with reduced cortical activation, which may contribute to reduced parasympathetic inhibitory control over medullary cardioacceleratory circuits. Reduced cardiac control may – in part – contribute to epidemiological reports of reduced life expectancy in affective disorders, effects equivalent to heavy smoking. We suggest that the field may be moving toward a theoretical consensus, in which different models relating to basic emotion theory and psychological constructionism may be considered as complementary, working at different levels of the phylogenetic hierarchy.Fil: Arias, Juan A.. Swansea University; Reino Unido. Universidad de Santiago de Compostela; EspañaFil: Williams, Claire. Swansea University; Reino UnidoFil: Raghvani, Rashmi. Swansea University; Reino UnidoFil: Aghajani, Moji. No especifíca;Fil: Baez, Sandra. Universidad de los Andes; ColombiaFil: Belzung, Catherine. Universite de Tours; FranciaFil: Booij, Linda. Concordia University Montreal; CanadáFil: Busatto, Geraldo. Universidade de Sao Paulo; BrasilFil: Chiarella, Julian. Concordia University Montreal; CanadáFil: Fu, Cynthia. University Of East London; Reino UnidoFil: Ibañez, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Instituto de Neurología Cognitiva. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt | Fundación Favaloro. Instituto de Neurociencia Cognitiva y Traslacional. Fundación Ineco Rosario Sede del Incyt; Argentina. Universidad Adolfo Ibañez; Chile. Universidad Autónoma del Caribe; ColombiaFil: Liddell, Belinda J.. University of New South Wales; AustraliaFil: Lowe, Leroy. No especifíca;Fil: Penninx, Brenda W.J.H.. No especifíca;Fil: Rosa, Pedro. Universidade de Sao Paulo; BrasilFil: Kemp, Andrew H.. Universidade de Sao Paulo; Brasil. Swansea University; Reino Unid

Cross-disorder and disorder-specific deficits in social functioning among schizophrenia and Alzheimer's disease patients

BACKGROUND: Social functioning is often impaired in schizophrenia (SZ) and Alzheimer's disease (AD). However, commonalities and differences in social dysfunction among these patient groups remain elusive.MATERIALS AND METHODS: Using data from the PRISM study, behavioral (all subscales and total score of the Social Functioning Scale) and affective (perceived social disability and loneliness) indicators of social functioning were measured in patients with SZ (N = 56), probable AD (N = 50) and age-matched healthy controls groups (HC, N = 29 and N = 28). We examined to what extent social functioning differed between disease and age-matched HC groups, as well as between patient groups. Furthermore, we examined how severity of disease and mood were correlated with social functioning, irrespective of diagnosis.RESULTS: As compared to HC, both behavioral and affective social functioning seemed impaired in SZ patients (Cohen's d's 0.81-1.69), whereas AD patients mainly showed impaired behavioral social function (Cohen's d's 0.65-1.14). While behavioral indices of social functioning were similar across patient groups, SZ patients reported more perceived social disability than AD patients (Cohen's d's 0.65). Across patient groups, positive mood, lower depression and anxiety levels were strong determinants of better social functioning (p's &lt;0.001), even more so than severity of disease.CONCLUSIONS: AD and SZ patients both exhibit poor social functioning in comparison to age- and sex matched HC participants. Social dysfunction in SZ patients may be more severe than in AD patients, though this may be due to underreporting by AD patients. Across patients, social functioning appeared as more influenced by mood states than by severity of disease.</p

ENIGMA-anxiety working group : rationale for and organization of large-scale neuroimaging studies of anxiety disorders

Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA-Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA-Anxiety database contains information about more than 100 unique samples, from 16 countries and 59 institutes. Future directions include examining additional imaging modalities, integrating imaging and genetic data, and collaborating with other ENIGMA working groups. The ENIGMA consortium creates synergy at the intersection of global mental health and clinical neuroscience, and the ENIGMA-Anxiety Working Group extends the promise of this approach to neuroimaging research on anxiety disorders

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)