Attentional bias towards and away from fearful faces is modulated by developmental amygdala damage

The amygdala is believed to play a major role in orienting attention towards threat-related stimuli. However, behavioral studies on amygdala-damaged patients have given inconsistent results-variously reporting decreased, persisted, and increased attention towards threat. Here we aimed to characterize the impact of developmental amygdala damage on emotion perception and the nature and time-course of spatial attentional bias towards fearful faces. We investigated SF, a 14-year-old with selective bilateral amygdala damage due to Urbach-Wiethe disease (UWD), and ten healthy controls. Participants completed a fear sensitivity questionnaire, facial expression classification task, and dot-probe task with fearful or neutral faces for spatial cueing. Three cue durations were used to assess the time-course of attentional bias. SF expressed significantly lower fear sensitivity, and showed a selective impairment in classifying fearful facial expressions. Despite this impairment in fear recognition, very brief (100 msec) fearful cues could orient SF's spatial attention. In healthy controls, the attentional bias emerged later and persisted longer. SF's attentional bias was due solely to facilitated engagement to fear, while controls showed the typical phenomenon of difficulty in disengaging from fear. Our study is the first to demonstrate the separable effects of amygdala damage on engagement and disengagement of spatial attention. The findings indicate that multiple mechanisms contribute in biasing attention towards fear, which vary in their timing and dependence on amygdala integrity. It seems that the amygdala is not essential for rapid attention to emotion, but probably has a role in assessment of biological relevance

Cortical Thickness and Subcortical Volume Trajectories Across the Lifespan: Data From 14,600 Healthy Individuals Aged 6-90 Years

Background: Age is a critical determinant of brain morphometry. We formed the Lifespan Working group of the Enhancing Neuroimaging Genetics through Meta-Analysis(ENIGMA) Consortium to perform a large-scale analysis of cortical thickness data across more than 79 samples and14,600 healthy individuals aged 6-90 years. Methods: We used fractional polynomial (FP) regression to characterize age-related trajectories in brain morphometry. Normalized growth centiles were constructed using the para-metric Lambda, Mu, and Sigma (LMS) method. Inter-individual variability was analyzed using meta-analysis and one-way analysis of variance. Results: The volume of subcortical regions followed an inverted U-shaped trajectory while cortical thickness in almost all regions decreased during the first two to three decades of life and showed an attenuated or plateaued slope afterwards. Exceptions were the entorhinal, anterior cingulate and temporopolar cortices that followed attenuated age-related U-shaped trajectories. Age and its FP combinations explained up to 48% variance in morphometry. Sample, strength of the MRI scanner magnetic field and FreeSurfer version jointly accounted for a median of 17% of the variance. Conclusions: Our results could shed light on the uncertainties about age-related developmental trajectories of cortical thickness. The estimated centile values will provide scientists and clinicians with more efficient tools to detect brain morphological deviations and their association with behavioral, cognitive and clinical outcome

Advantageous developmental outcomes of advancing paternal age

AbstractAdvanced paternal age (APA) at conception has been associated with negative outcomes in offspring, raising concerns about increasing age at fatherhood. Evidence from evolutionary and psychological research, however, suggests possible link between APA and a phenotypic advantage. We defined such advantage as educational success, which is positively associated with future socioeconomic status. We hypothesised that high IQ, strong focus on the subject of interest and little concern about ‘fitting in’ will be associated with such success. Although these traits are continuously distributed in the population, they cluster together in so-called ‘geeks’. We used these measures to compute a ‘geek index’ (GI), and showed it to be strongly predictive of future academic attainment, beyond the independent contribution of the individual traits. GI was associated with paternal age in male offspring only, and mediated the positive effects of APA on education outcomes, in a similar sexually dimorphic manner. The association between paternal age and GI was partly mediated by genetic factors not correlated with age at fatherhood, suggesting contribution of de novo factors to the ‘geeky’ phenotype. Our study sheds new light on the multifaceted nature of the APA effects and explores the intricate links between APA, autism and talent.</jats:p

Linked patterns of biological and environmental covariation with brain structure in adolescence: a population-based longitudinal study

Adolescence is a period of major brain reorganization shaped by biologically timed and by environmental factors. We sought to discover linked patterns of covariation between brain structural development and a wide array of these factors by leveraging data from the IMAGEN study, a longitudinal population-based cohort of adolescents. Brain structural measures and a comprehensive array of non-imaging features (relating to demographic, anthropometric, and psychosocial characteristics) were available on 1476 IMAGEN participants aged 14 years and from a subsample reassessed at age 19 years (n = 714). We applied sparse canonical correlation analyses (sCCA) to the cross-sectional and longitudinal data to extract modes with maximum covariation between neuroimaging and non-imaging measures. Separate sCCAs for cortical thickness, cortical surface area and subcortical volumes confirmed that each imaging phenotype was correlated with non-imaging features (sCCA r range: 0.30-0.65, all PFDR < 0.001). Total intracranial volume and global measures of cortical thickness and surface area had the highest canonical cross-loadings (|rho| = 0.31-0.61). Age, physical growth and sex had the highest association with adolescent brain structure (|rho = 0.24-0.62); at baseline, further significant positive associations were noted for cognitive measures while negative associations were observed at both time points for prenatal parental smoking, life events, and negative affect and substance use in youth (|rho| = 0.10-0.23). Sex, physical growth and age are the dominant influences on adolescent brain development. We highlight the persistent negative influences of prenatal parental smoking and youth substance use as they are modifiable and of relevance for public health initiatives

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 Effectiveness of Pharmacological and Non-Pharmacological Interventions for Improving Glycaemic Control in Adults with Severe Mental Illness: A Systematic Review and Meta-Analysis

People with severe mental illness (SMI) have reduced life expectancy compared with the general population, which can be explained partly by their increased risk of diabetes. We conducted a meta-analysis to determine the clinical effectiveness of pharmacological and non-pharmacological interventions for improving glycaemic control in people with SMI (PROSPERO registration: CRD42015015558). A systematic literature search was performed on 30/10/2015 to identify randomised controlled trials (RCTs) in adults with SMI, with or without a diagnosis of diabetes that measured fasting blood glucose or glycated haemoglobin (HbA1c). Screening and data extraction were carried out independently by two reviewers. We used random effects meta-analysis to estimate effectiveness, and subgroup analysis and univariate meta-regression to explore heterogeneity. The Cochrane Collaboration’s tool was used to assess risk of bias. We found 54 eligible RCTs in 4,392 adults (40 pharmacological, 13 behavioural, one mixed intervention). Data for meta-analysis were available from 48 RCTs (n = 4052). Both pharmacological (mean difference (MD), -0.11mmol/L; 95% confidence interval (CI), [-0.19, -0.02], p = 0.02, n = 2536) and behavioural interventions (MD, -0.28mmol//L; 95% CI, [-0.43, -0.12], p<0.001, n = 956) were effective in lowering fasting glucose, but not HbA1c (pharmacological MD, -0.03%; 95% CI, [-0.12, 0.06], p = 0.52, n = 1515; behavioural MD, 0.18%; 95% CI, [-0.07, 0.42], p = 0.16, n = 140) compared with usual care or placebo. In subgroup analysis of pharmacological interventions, metformin and antipsychotic switching strategies improved HbA1c. Behavioural interventions of longer duration and those including repeated physical activity had greater effects on fasting glucose than those without these characteristics. Baseline levels of fasting glucose explained some of the heterogeneity in behavioural interventions but not in pharmacological interventions. Although the strength of the evidence is limited by inadequate trial design and reporting and significant heterogeneity, there is some evidence that behavioural interventions, antipsychotic switching, and metformin can lead to clinically important improvements in glycaemic measurements in adults with SMI

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

Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation

The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3–90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain