Beyond the Global Brain Differences:Intraindividual Variability Differences in 1q21.1 Distal and 15q11.2 BP1-BP2 Deletion Carriers

BACKGROUND: Carriers of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants exhibit regional and globalbrain differences compared with noncarriers. However, interpreting regional differences is challenging if a globaldifference drives the regional brain differences. Intraindividual variability measures can be used to test for regionaldifferences beyond global differences in brain structure.METHODS: Magnetic resonance imaging data were used to obtain regional brain values for 1q21.1 distal deletion (n =30) and duplication (n = 27) and 15q11.2 BP1-BP2 deletion (n = 170) and duplication (n = 243) carriers and matchednoncarriers (n = 2350). Regional intra-deviation scores, i.e., the standardized difference between an individual’sregional difference and global difference, were used to test for regional differences that diverge from the globaldifference.RESULTS: For the 1q21.1 distal deletion carriers, cortical surface area for regions in the medial visual cortex, posterior cingulate, and temporal pole differed less and regions in the prefrontal and superior temporal cortex differedmore than the global difference in cortical surface area. For the 15q11.2 BP1-BP2 deletion carriers, cortical thicknessin regions in the medial visual cortex, auditory cortex, and temporal pole differed less and the prefrontal andsomatosensory cortex differed more than the global difference in cortical thickness.CONCLUSIONS: We find evidence for regional effects beyond differences in global brain measures in 1q21.1 distaland 15q11.2 BP1-BP2 copy number variants. The results provide new insight into brain profiling of the 1q21.1 distaland 15q11.2 BP1-BP2 copy number variants, with the potential to increase understanding of the mechanismsinvolved in altered neurodevelopment

Beyond the global brain differences: Intraindividual variability differences in 1q21.1 distal and 15q11.2 BP1-BP2 deletion carriers

Rune Boen, et al.[Background]: Carriers of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants exhibit regional and global brain differences compared with noncarriers. However, interpreting regional differences is challenging if a global difference drives the regional brain differences. Intraindividual variability measures can be used to test for regional differences beyond global differences in brain structure.[Methods]: Magnetic resonance imaging data were used to obtain regional brain values for 1q21.1 distal deletion (n = 30) and duplication (n = 27) and 15q11.2 BP1-BP2 deletion (n = 170) and duplication (n = 243) carriers and matched noncarriers (n = 2350). Regional intra-deviation scores, i.e., the standardized difference between an individual's regional difference and global difference, were used to test for regional differences that diverge from the global difference.[Results]: For the 1q21.1 distal deletion carriers, cortical surface area for regions in the medial visual cortex, posterior cingulate, and temporal pole differed less and regions in the prefrontal and superior temporal cortex differed more than the global difference in cortical surface area. For the 15q11.2 BP1-BP2 deletion carriers, cortical thickness in regions in the medial visual cortex, auditory cortex, and temporal pole differed less and the prefrontal and somatosensory cortex differed more than the global difference in cortical thickness.[Conclusions]: We find evidence for regional effects beyond differences in global brain measures in 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants. The results provide new insight into brain profiling of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants, with the potential to increase understanding of the mechanisms involved in altered neurodevelopment.1000BRAINS. This work was supported by the Institute of Neuroscience and Medicine (INM-1), Research Centre Julich, European Union’s Horizon 2020 Research and Innovation Programme (Grant No. 945539 [HBP SGA3; SC]), and Joint Lab Supercomputing and Modeling for the Human Brain. We thank the Heinz Nixdorf Foundation for the generous support of the Heinz Nixdorf Study. We also thank the scientists and the study staff of the Heinz Nixdorf Recall Study and 1000BRAINS. We gratefully acknowledge the computing time granted through JARA-HPC on the supercomputer JURECA at Forschungszentrum Julich. TOP: This work was supported by Research Council of Norway (Grant Nos. 23273 (Centre of Excellence, Norwegian Centre for Mental Disorders, NORMENT), Grant Nos. 324252 and 226971). ENIGMA-CNV Working Group. This work was supported by the Research Council of Norway (Grant No. 223273 [to OAA]), South-Eastern Norway Regional Health Authority (Grant No. 2020060 [to IES, RB]), European Union’s Horizon 2020 Research and Innovation Programme (CoMorMent project; Grant No. 847776 [to IES]), Kristian Gerhard Jebsen Stiftelsen (Grant No. SKGJ-MED-021 [to IES]), and National Institute of Mental Health (NIMH) (Grant Nos. U01MH119736, R21MH116473, R01MH085953 [to CEB]), and 1R01MH129858-01A1 [to SJ]. This work was performed on Services for Sensitive Data, University of Oslo, with resources provided by UNINETT Sigma2, the national infrastructure for high performance computing and data storage in Norway. Australian Schizophrenia Research Bank: This work was supported by the Australian National Health and Medical Research Council (NHMRC) (Enabling Grant No. 386500, L3 Investigator Grant No. 1196508 [to CP]), and Program Grant No. APP1150083 [to CP]), Pratt Foundation, Ramsay Health Care, Viertel Charitable Foundation, and Schizophrenia Research Institute. Chief Investigators for the Australian Schizophrenia Research Bank were Vaughan Carr, US, RJS, Assen Jablensky, BJM, Patricia Michie, Stanley Catts, FAH, and CP. ECHO-DEFINE: This work was supported by the Wellcome Trust (Institutional Strategic Support Fund [to MBMvdB] and Clinical Research Training Fellowship Grant No. 102003/Z/13/Z [to JLD]), Waterloo Foundation (Grant No. WF 918-1234 [to MBMvdB]), Baily Thomas Charitable Fund (Grant No. 2315/1 [to MBMvdB]), NIMH (Grant Nos. 5UO1MH101724 and U01MH119738 [to MBMvdB]), IMAGINE-ID and IMAGINE-2 studies (funded by Medical Research Council Grant Nos. MR/N022572/1 and MR/T033045/1 [to MBMvdB]), and Medical Research Council (Centre Grant No. MR/P005748/1 [to MJO]). The DEFINE study was supported by a Wellcome Trust Strategic Award (Grant No. 100202/Z/12/Z [to MJO]). FOR2107 Marburg and Münster: This work is part of the German multicenter consortium “Neurobiology of Affective Disorders: A Translational Perspective on Brain Structure and Function,” supported by the German Research Foundation (Deutsche Forschungsgemeinschaft; Forschungsgruppe/Research Unit FOR2107). Principal investigators with respective areas of responsibility in the FOR2107 consortium: Work Package WP1, FOR2107/MACS cohort and brainimaging: TK (speaker FOR2107; DFG Grant Nos. KI 588/14-1, KI 588/14-2, KI 588/20-1, and KI 588/22-1), UD (co-speaker FOR2107; Grant Nos. DA 1151/5-1, DA 1151/5-2, and DA 1151/6-1), Axel Krug (Grant Nos. KR 3822/5-1 and KR 3822/7-2), IN (Grant Nos. NE 2254/1-2, NE 2254/3-1, and NE 2254/4-1), Carsten Konrad (Grant No. KO 4291/3-1). WP5, genetics: Marcella Rietschel (Grant Nos. RI 908/11-1 and RI 908/11-2), MMN (Grant Nos. NO 246/10-1 and NO 246/10-2), Stephanie Witt (Grant Nos. WI 3439/3-1 and WI 3439/3-2). WP6, multi-method data analytics: AJ (Grant Nos. JA 1890/7-1 and JA 1890/7-2), TH (Grant No. HA 7070/2-2). We are deeply indebted to all study participants and staff. A list of acknowledgments can be found at: www.for2107.de/acknowledgements. UCLA-Utrecht: This work was supported by the NIMH (Grant No. R01 MH090553 [to RAO]). The NIMH had no further role in study design, in the collection, analysis, and interpretation of the data, in the writing of the report, and in the decision to submit the paper for publication. QTIM: This work was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (Grant No. R01 HD050735) and the NHMRC (Grant Nos. 486682 and 1009064). Genotyping was supported by the NHMRC (Grant No. 389875). BETULA: This work was supported by the Knut and Alice Wallenberg Foundation (scholar grant [to LN]). Freesurfer calculations were enabled by resources provided by the Swedish National Infrastructure for Computing at HPC2N, Umeå. SHIP (Study of Health in Pomerania): This work is part of the Community Medicine Research net of the University of Greifswald, Germany, which is supported by the Federal Ministry of Education and Research (Grant Nos. 01ZZ9603, 01ZZ0103, and 01ZZ0403), Ministry of Cultural Affairs, and Social Ministry of the Federal State of Mecklenburg-West Pomerania. Genome-wide data in SHIP have been supported by the Federal Ministry of Education and Research (Grant No. 03ZIK012) and a joint grant from Siemens Healthineers and the Federal State of Mecklenburg-West Pomerania. MRI scans in SHIP and SHIP-TREND have been supported by a joint grant from Siemens Healthineers and the Federal State of Mecklenburg-West Pomerania. PAFIP (Programa de Atención a las Fases Iniciales de Psicosis): This work was supported by the Instituto de Salud Carlos III (Grant Nos. 00/3095, 01/3129, PI020499, PI14/00639, PI17/01056, and PI14/00918), SENY Fundació (Research Grant No. CI2005 0308007), and Fundación Marqués de Valdecilla Instituto de Investigación Sanitaria Valdecilla (Grant Nos. A/02/07, NCT0235832, and NCT02534363). Osaka: This work was supported by the Japan Agency for Medical Research and Development (Grant Nos. JP21wm0425012, JP18dm0307002, JP22wm0525019, and JP22dk0207060) and Japan Society for the Promotion of Science KAKENHI (Grant Nos. JP20H03611, JP22H04926, and 20K15778). Some computations were performed at the Research Center for Computational Science, Okazaki, Japan (Project: NIPS, 18-IMS-C162, 19-IMS-C181, 20-IMS-C162, 21-IMS-C179, 22-IMS-C195). IMAGEN: This work was supported by the European Union–funded FP6 Integrated Project IMAGEN (Reinforcement-Related Behavior in Normal Brain Function and Psychopathology) (Grant No. LSHM-CT-2007-037286), Horizon 2020–funded ERC Advanced Grant STRATIFY (Brain Network Based Stratification of Reinforcement-Related Disorders) (Grant No. 695313), Medical Research Foundation and Medical Research Council (Grants Nos. MR/R00465X/1 and MRF-058-0004-RG-DESRI [Neurobiological Underpinning of Eating Disorders: Integrative Biopsychosocial Longitudinal Analyses in Adolescents] and MR/S020306/1 and MRF-058-0009-RG-DESR-C0759 [Establishing Causal Relationships Between Biopsychosocial Predictors and Correlates of Eating Disorders and Their Mediation by Neural Pathways]), National Institutes of Health (NIH) (Consortium Grant No. U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence, and Grant No. 1R56AG058854-01), National Institute for Health Research Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, ERANID (Understanding the Interplay Between Cultural, Biological and Subjective Factors in Drug Use Pathways) (Grant No. PR-ST-0416-10004), BRIDGET (JPND: BRain Imaging, cognition Dementia and next generation GEnomics) (Grant No. MR/N027558/1), Human Brain Project (HBP SGA 2) (Grant No. 785907), FP7 project MATRICS (Grant No. 603016), Medical Research Council Grant Consortium on Vulnerability to Externalizing Disorders and Addictions (Grant No. MR/N000390/1), Bundesministerium für Bildung und Forschung (Grant Nos. 01GS08152, 01EV0711, Forschungsnetz AERIAL 01EE1406A, and 01EE1406B), Deutsche Forschungsgemeinschaft (Grant Nos. SM 80/7-2, SFB 940/2, and NE 1383/14-1), Agence Nationale de la Recherche (ANR) (Grant Nos. ANR-12-SAMA-0004 and AAPG2019 [GeBra]), Eranet Neuron (Grant Nos. AF12-NEUR0008-01 [WM2NA], ANR-18-NEUR00002-01 [ADORe], and ANR-12-SAMA-0004), Fondation de France (Grant No. 00081242), Fondation pour la Recherche Médicale (Grant No. DPA20140629802), Mission Interministérielle de Lutte contre les Drogues et les Conduites Addictives, Assistance Publique Hôpitaux de Paris and Institut National de la Santé et de la Recherche Médicale (Interface Grant), Paris Sud University IDEX 2012, Fondation de l’Avenir (Grant No. AP-RM-17-013), and Fédération pour la Recherche sur le Cerveau. MCIC (Mind Clinical Imaging Consortium): This work was supported by the NIH (NIH/National Center for Research Resources Grant No. P41RR14075 [to VC] and Grant No. R01EB005846 [to VC]), Department of Energy (Grant No. DE-FG02-99ER62764), Mind Research Network, Morphometry Biomedical Informatics Research Network (Grant Nos. 1U24 and RR021382A), Function Biomedical Informatics Research Network (Grant No. U24RR021992-01 [to VC], NIH/National Center for Research Resources Grant No. MO1 RR025758-01 [to VC], and NIMH Grant No. 1RC1MH089257 [to VC]), Deutsche Forschungsgemeinschaft (research fellowship [to SE]), and Brain and Behavior Research Foundation (NARSAD Young Investigator Award [to SE]). NTR (Netherlands Twin Register): This work was supported by the Netherlands Organization for Scientific Research (NWO) and Netherlands Organisation for Health Research and Development (Grant Nos. 904-61-090, 985-10-002, 912-10-020, 904-61-193, 480-04-004,463-06-001, 451-04-034, 400-05-717, Addiction-31160008, 016-115-035, 481-08-011, 056-32-010, Middelgroot-911-09-032, OCW_NWO Gravity programme—024.001.003, and NWO-Groot 480-15-001/674), Center for Medical Systems Biology (NWO Genomics), NBIC/BioAssist/RK (Grant No. 2008.024), Biobanking and Biomolecular Resources Research Infrastructure The Netherlands (Grant Nos. 184.021.007 and 184.033.111), Spinozapremie (Grant No. NWO-56-464-14192), Royal Netherlands Academy of Arts and Sciences Academy Professor Award (Grant No. PAH/6635) and University Research Fellow grant (to DIB), Amsterdam Public Health research institute (former EMGO+ Institute for Health and Care Research), Amsterdam Neuroscience (former Neuoscience Campus Amsterdam), European Science Foundation (Grant No. EU/QLRT-2001-01254), European Community’s Seventh Framework Programme (FP7- HEALTH-F4-2007-2013, Grant Nos. 01413: ENGAGE and 602768: ACTION), European Research Council (Grant Nos. ERC Starting 284167, ERC Consolidator 771057, and ERC Advanced 230374), Rutgers University Cell and DNA Repository (NIMH Grant No. U24 MH068457-06), NIH (Grant Nos. R01D0042157-01A1, R01MH58799-03, MH081802, DA018673, and R01 DK092127-04 and Grand Opportunity Grant Nos. 1RC2 MH089951 and 1RC2 MH089995), and Avera Institute for Human Genetics. The genotyping and analyses were partly funded by the Genetic Association Information Network of the Foundation for the National Institutes of Health. Computing was supported by NWO (Grant No. 2018/EW/00408559), BiG Grid, Dutch e-Science Grid, and SURFsara. OATS (Older Australian Twins Study): This work was supported by the NHMRC and Australian Research Council Strategic Award Grant of the Ageing Well, Ageing Productively Program (Grant No. 401162), NHMRC project (seed) grants (Grant Nos. 1024224 and 1025243), NHMRC project grants (Grant Nos. 1045325 and 1085606), and NHMRC program grants (Grant Nos. 568969 and 1093083). The OATS Study was facilitated through access to Twins Research Australia, a national resource supported by a Centre of Research Excellence Grant (Grant No. 1079102) from the NHMRC. We thank the participants for their time and generosity in contributing to this research. We acknowledge the contribution of the OATS research team (https://cheba.unsw.edu.au/project/older-australian-twins-study) to this study. PING (Pediatric Imaging, Neurocognition, and Genetics): This work was supported by the National Institute on Drug Abuse and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (Grant Nos. RC2DA029475 and R01 HD061414). EPIGEN-London/University College London: The work was partly undertaken at University College London Hospitals NHS Foundation Trust/University College London, which received a proportion of funding from the UK Department of Health National Institute for Health Research Biomedical Research Centres funding scheme. We thank the Wolfson Trust and the Epilepsy Society for supporting the Epilepsy Society MRI scanner. OSR (Istituto di Ricovero e Cura a Carattere Scientifico Ospedale San Raffele)-Milan: This work was supported by the European Union H2020 (EU.3.1.1 Grant No. 754740 MOODSTRATIFICATION.EU), Italian Ministry of Health (Grant No. RF-2018-12367249) and Italian Ministry of University and Scientific Research (Grant No. A_201779W93T). Dublin: This work was supported by the European Research Council (Grant No. ERC-2015-STG-677467 [to GD]) and Science Foundation Ireland (Grant No. SFI-16/ERCS/3787 [to GD]). Brain Imaging Genetics (BIG): This work makes use of the BIG database, first established in Nijmegen, the Netherlands, in 2007. This resource is now part of Cognomics (www.cognomics.nl), a joint initiative by researchers from the Donders Centre for Cognitive Neuroimaging, Human Genetics and Cognitive Neuroscience departments of Radboud University Medical Centre, and Max Planck Institute for Psycholinguistics in Nijmegen. The Cognomics Initiative is supported by the participating departments and centers and Biobanking and Biomolecular Resources Research Infrastructure (Netherlands), Hersenstichting Nederland, and NWO. The research leading to these results also receives funding from the NWO (Gravitation Grant No. 024.001.006 Language in Interaction), European Community’s Seventh Framework Programme (FP7/2007-2013) (Grant Nos. 602450 IMAGEMEND, 278948 TACTICS, and 602805 Aggressotype), European Community’s Horizon 2020 programme (Grant Nos. 643051 MiND, and ERC-2010-AdG 268800-NEUROSCHEMA). In addition, the work was supported by the ENIGMA Consortium (Grant No. U54 EB020403) from the BD2K Initiative of a cross-NIH partnership. UK Biobank: This research has been conducted using the UK Biobank Resource under Application Number 27412. OAA has received speaker’s honorarium from Lundbeck, Janssen, and Sunovion and is a consultant to Coretechs.ai. TRM reports personal fees from Pfizer, Lundbeck, Astellas, Janssen, and Angelini outside the submitted work. He is an employee and shareholder of Pasithea Therapeutics. CRKC has received partial research support from Biogen for work unrelated to the topic of this article (principal investigator, PMT). PMT has received partial research support from Biogen for work unrelated to the topic of this article. MBMvdB and MJO report grants from Takeda Pharmaceuticals outside the submitted work. MJO reports a grant from Akrivia Health outside the submitted work. HJG has received travel grants and speaker’s honoraria from Fresenius Medical Care, Neuraxpharm, Servier, and Janssen as well as research funding from Fresenius Medical Care. GS has received honoraria for participating at advisory board meetings from Roche and Biogen regarding new Alzheimer’s disease drugs. KKS has received consultant and speaker’s honoraria from Roche and OrionPharma, reimbursement of travel and accommodation costs at a meeting from Kolpharma, and sponsorships for arranging conferences from Desitin and Eisai AB. IG has received speaker’s honorarium from Lundbeck. MMN has received fees for membership in an advisory board from HMG Systems Engineering GmbH, for membership in the Medical-Scientific Editorial Office of the Deutsches Ärzteblatt, and for serving as a consultant for Everis Belgium in a project of the European Commission (REFORM/SC2020/029). MMN also receives salary payments from Life & Brain GmbH and holds shares in Life & Brain GmbH. All these concerned activities outside the submitted work. CP received honoraria for talks from Lundbeck, Australia Pty Ltd. outside the submitted work. DJS has received consultancy honoraria from Discovery Vitality, Johnson & Johnson, Kanna, L’Oreal, Lundbeck, Orion, Sanofi, Servier, Takeda and Vistagen. JH has received speaker’s honorarium from Medice and Takeda outside the submitted work. KP reports work with Novo Nordisk and Roche clinical trials outside the submitted work. All other authors report no biomedical financial interests or potential conflicts of interest.Peer reviewe

Age-related differences in the error-related negativity and error positivity in children and adolescents are moderated by sample and methodological characteristics: A meta-analysis

The error-related negativity (ERN) and the error positivity (Pe) are electrophysiological components associated with error processing that are thought to exhibit distinctive developmental trajectories from childhood to adulthood. To investigate the age and age moderation effects on the ERN and the Pe strength during development, we conducted a preregistered three-level meta-analysis synthesizing 120 and 41 effect sizes across 18 group comparison studies and 19 correlational studies, respectively. The meta-analysis included studies with mean age range between 3.6 to 28.7 years for age-group comparisons and 6.1 to 18.7 years for age correlations. Results showed that age was associated with a more negative ERN (SMD = -.433, r = -.230). No statistically significant association between age and the Pe was found (SMD = .059, r = -.091), except for in a group comparison between younger and older adolescents. The age effects were not significantly moderated by whether a Flanker or a Go/No-Go task was used, whereas studies that used other tasks found larger age effects on the Pe. There was a moderating effect of electrode site, whereby the Fz and Cz electrode sites yielded stronger negative associations between age and the ERN and the Pe, respectively. The results confirm that the ERN and the Pe show differential development courses and suggest that sample and methodological characteristics influence the age effects, and lay the foundation for investigations of developmental patterns of error processing in relation to psychopathology and early genetic and environmental risk factors

Age-related differences in the error-related negativity and error positivity in children and adolescents: a systematic review and meta-analysis protocol

Background: The error-related negativity (ERN) and the error positivity (Pe) are electrophysiological signals linked to error processing, a crucial aspect of self-monitoring and regulation. Previous research suggests different developmental trajectories for the ERN and Pe, with the ERN increasing in strength during the course of childhood and adolescence, while the Pe appears to reach a plateau by late childhood. There are, however, reports that are discrepant with this pattern, and effects of participant, task and methodological characteristics are poorly understood. The main objectives of this systematic review and meta-analysis are to evaluate the effect of age on ERN and Pe magnitude in children and adolescents, and to examine potential moderators of these effects, including age, sex, experimental task, task difficulty, and topography and quantification of the ERN and Pe. Methods/design: Studies that report group differences between age-groups or associations with age for the ERN and/or Pe magnitude in typically developing children and/or adolescents will be identified. The literature search will be conducted through PubMed and Scopus, all abstracts will be screened, and reference lists of relevant articles cross-checked for inclusion. The present protocol will also be disseminated on social media platforms to call for unpublished data. The data will be extracted from the eligible studies and will be included in random-effect meta-analyses in R. The results will include the estimation of age and age-group effect sizes, heterogeneity, risk of publication bias, and effects of moderating variables. Discussion: The study will include a systematic literature search and meta-analyses to better understand age-related differences in the ERN and Pe magnitudes. The results will provide estimates of effect sizes that are relevant for calculating statistical power and sample sizes for future studies. In addition, it will provide benchmark effect sizes for typical development of the ERN and the Pe that could be used for comparison purposes in developmental studies of clinical or at-risk groups

Perceived coach behavior in training and competition predicts collective efficacy in female elite handball players

The purpose of the present study was to explore the relationships between athletes’ perceived coach behaviors during training and competition, and collective efficacy. Elite female handball players in Norway (N = 113) completed the Leadership Scale for Sport (LSS), the Coaching Behavior Questionnaire (CBQ), and the Collective Efficacy Questionnaire for Sports (CEQS). Relationships were identified between perceived coach behaviors in training and competition environments and with athlete collective efficacy. Hierarchical multiple regression analyses revealed that prediction of variance in collective efficacy improved when perceptions of coach behaviour during competition were entered in addition to perceptions of coach leadership behavior during training. Furthermore, the results indicated that greater levels of perceived training and instructional behavior, positive feedback, social support, and supportiveness predicted greater collective efficacy. In contrast, higher perceptions of negative activation predicted lower levels of collective efficacy. The results highlight the important relationships between coach behavior in both training and competition, and their combined impact upon collective efficacy in elite female handball teams.status: publishe

A team fares well with a fair coach: Predictors of social loafing in interactive female sport teams

The present research aimed to develop and test a theoretical model that links players' perceived justice of the coach to a more optimal motivational climate, which in turn increases players' team identification and cohesion, and results in lower levels of social loafing in female sport teams. Belgian elite female basketball, volleyball, and football players (study 1; N = 259; Mage = 22.6) and Norwegian world-class female handball players (study 2; N = 110; Mage = 22.8) completed questionnaires assessing players' perceived justice (distributive and procedural), motivational climate, team identification, team cohesion (task and social), and social loafing (perceived and self-reported). In both studies, confirmatory and exploratory path analyses indicated that perceived justice was positively related to a mastery climate (P < 0.05) and negatively to a performance climate (P < 0.05). In turn, a mastery climate was linked to increased levels of team identification (P < 0.05) and task cohesion (P < 0.05). Consequently, players' perceived and self-reported social loafing decreased (P < 0.05). The findings of both independent studies demonstrated the impact of coaches' fairness, and consequently, the motivational climate created by the coach on the optimal functioning of female sport teams.status: publishe

The effects of oxytocin administration on social and repetitive behaviors in autism: A systematic review and meta-analysis protocol

Oxytocin administration has demonstrated considerable promise for providing additional support for people with autism who have a desire for such interventions. However, results from studies evaluating the effects of oxytocin administration on autistic characteristics have been mixed. Here we describe a protocol for a planned systematic review and meta-analysis of studies on the effect of oxytocin administration on social and repetitive behaviors in autism, which adopts recently devel-oped methods to more precisely assess the potential impact of effect size dependency and publica-tion bias

Beyond the Global Brain Differences:Intraindividual Variability Differences in 1q21.1 Distal and 15q11.2 BP1-BP2 Deletion Carriers

Background: Carriers of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants exhibit regional and global brain differences compared with noncarriers. However, interpreting regional differences is challenging if a global difference drives the regional brain differences. Intraindividual variability measures can be used to test for regional differences beyond global differences in brain structure. Methods: Magnetic resonance imaging data were used to obtain regional brain values for 1q21.1 distal deletion (n = 30) and duplication (n = 27) and 15q11.2 BP1-BP2 deletion (n = 170) and duplication (n = 243) carriers and matched noncarriers (n = 2350). Regional intra-deviation scores, i.e., the standardized difference between an individual's regional difference and global difference, were used to test for regional differences that diverge from the global difference. Results: For the 1q21.1 distal deletion carriers, cortical surface area for regions in the medial visual cortex, posterior cingulate, and temporal pole differed less and regions in the prefrontal and superior temporal cortex differed more than the global difference in cortical surface area. For the 15q11.2 BP1-BP2 deletion carriers, cortical thickness in regions in the medial visual cortex, auditory cortex, and temporal pole differed less and the prefrontal and somatosensory cortex differed more than the global difference in cortical thickness. Conclusions: We find evidence for regional effects beyond differences in global brain measures in 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants. The results provide new insight into brain profiling of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants, with the potential to increase understanding of the mechanisms involved in altered neurodevelopment.</p

The effects of oxytocin administration on social and routinized behaviors in autism: A preregistered systematic review and meta-analysis

Oxytocin administration has demonstrated considerable promise for providing individualized support for people with autism. However, studies evaluating the effects of oxytocin administration on autistic characteristics have yielded inconsistent results. This systematic review and meta-analysis investigates the effect of oxytocin administration on social and routinized behaviors in autism using advanced methods to accurately assess potential impact of effect size dependency and publication bias. Our frequentist meta-analysis yielded a significant summary estimate for the effect of oxytocin administration on social outcomes in autism (d = 0.22, p &lt; 0.001), and an effect that was on the threshold of statistical significance for routinized behavior outcomes (d = 0.16, p = 0.07). Frequentist and Bayesian assessments for publication bias, as well as results from Robust Bayesian meta-analysis of oxytocin effects on social outcomes in autism, indicated that the summary effect size might be inflated due to publication bias. Future studies should aim to reduce bias by preregistering analysis plans and implementing rigorous study designs, and to increase precision with larger sample sizes