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Rowe LI, Hattie J, Munro J. High-performing teams: Is collective intelligence the answer? PLoS One 2024; 19:e0307945. [PMID: 39133757 PMCID: PMC11318883 DOI: 10.1371/journal.pone.0307945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 07/16/2024] [Indexed: 08/15/2024] Open
Abstract
BACKGROUND/OBJECTIVES The concept of a general factor of collective intelligence, proposed by Woolley et al. in 2010, has spurred interest in understanding collective intelligence within small groups. This study aims to extend this investigation by examining the validity of a general collective intelligence factor, assessing its underlying factor structure, and evaluating its utility in predicting performance on future group problem-solving tasks and academic outcomes. METHODS Employing a correlational study design, we engaged 85 university students in a series of complex cognitive tasks designed to measure collective intelligence through individual, group, and predictive phases. RESULTS Contrary to the hypothesized single-factor model, our findings favor a two-factor model influenced by Cattell's theory of crystalized and fluid intelligence. These two factors accounted for substantial variance in group performance outcomes, challenging the prevailing single-factor model. Notably, the predictive validity of these factors on group assignments was statistically significant, with both individual and collective intelligence measures correlating moderately with group assignment scores (rs = .40 to .47, p < .05). CONCLUSIONS Our research suggests that collective intelligence in small group settings may not be uniformly governed by a single factor but rather by multiple dimensions that reflect established theories of individual intelligence. This nuanced understanding of collective intelligence could have significant implications for enhancing group performance in both educational and organizational contexts. Future research should explore these dimensions and their independent contributions to group dynamics and outcomes.
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Affiliation(s)
- Luke I. Rowe
- National School of Education, Australian Catholic University, East Melbourne, Victoria, Australia
| | - John Hattie
- Science of Learning Research Centre, The University of Melbourne, Parkville, Victoria, Australia
| | - John Munro
- National School of Education, Australian Catholic University, East Melbourne, Victoria, Australia
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Wang Y, Wildschut T, Sedikides C, Wu M, Cai H. Trajectory of Nostalgia in Emerging Adulthood. PERSONALITY AND SOCIAL PSYCHOLOGY BULLETIN 2024; 50:629-644. [PMID: 36601905 DOI: 10.1177/01461672221143241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
We examined the change and stability of nostalgia in emerging adulthood. We followed 327 students through their 4 university years with six assessments. Nostalgia demonstrated moderate rank stability (r = .25-.79). A Trait-State-Occasion model analysis indicated that the stable trait component, slowing-change trait component, and state component explained 37% to 43%, 10% to 27%, and 29% to 49% of variation in nostalgia on specific occasions, respectively. Longitudinal multilevel analysis revealed that the mean nostalgia level declined across university years. Greater intensity of negative life events at the start of university was associated with higher initial nostalgia and slower decline of it, while the emotion intensified when experiencing more negative life events. Nostalgia in emerging adulthood displays moderate stability, with negative life events contributing to the shape of its trajectory.
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Affiliation(s)
- Yuqi Wang
- Zhejiang Gongshang University, Hangzhou, China
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Jonas KG, Cannon TD, Docherty AR, Dwyer D, Gur RC, Gur RE, Nelson B, Reininghaus U, Kotov R. Psychosis superspectrum I: Nosology, etiology, and lifespan development. Mol Psychiatry 2024; 29:1005-1019. [PMID: 38200290 DOI: 10.1038/s41380-023-02388-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 12/05/2023] [Accepted: 12/15/2023] [Indexed: 01/12/2024]
Abstract
This review describes the Hierarchical Taxonomy of Psychopathology (HiTOP) model of psychosis-related psychopathology, the psychosis superspectrum. The HiTOP psychosis superspectrum was developed to address shortcomings of traditional diagnoses for psychotic disorders and related conditions including low reliability, arbitrary boundaries between psychopathology and normality, high symptom co-occurrence, and heterogeneity within diagnostic categories. The psychosis superspectrum is a transdiagnostic dimensional model comprising two spectra-psychoticism and detachment-which are in turn broken down into fourteen narrow components, and two auxiliary domains-cognition and functional impairment. The structure of the spectra and their components are shown to parallel the genetic structure of psychosis and related traits. Psychoticism and detachment have distinct patterns of association with urbanicity, migrant and ethnic minority status, childhood adversity, and cannabis use. The superspectrum also provides a useful model for describing the emergence and course of psychosis, as components of the superspectrum are relatively stable over time. Changes in psychoticism predict the onset of psychosis-related psychopathology, whereas changes in detachment and cognition define later course. Implications of the superspectrum for genetic, socio-environmental, and longitudinal research are discussed. A companion review focuses on neurobiology, treatment response, and clinical utility of the superspectrum, and future research directions.
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Affiliation(s)
- Katherine G Jonas
- Department of Psychiatry & Behavioral Health, Stony Brook University, Stony Brook, NY, USA.
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Anna R Docherty
- Huntsman Mental Health Institute, Department of Psychiatry, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
| | - Ruben C Gur
- Brain Behavior Laboratory, Department of Psychiatry and the Penn-CHOP Lifespan Brain Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Raquel E Gur
- Brain Behavior Laboratory, Department of Psychiatry and the Penn-CHOP Lifespan Brain Institute, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Barnaby Nelson
- Centre for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
| | - Ulrich Reininghaus
- Department of Public Mental Health, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- ESRC Centre for Society and Mental Health and Centre for Epidemiology and Public Health, Health Service and Population Research Department, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Roman Kotov
- Department of Psychiatry & Behavioral Health, Stony Brook University, Stony Brook, NY, USA
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Bognár Z, Turcsán B, Faragó T, Szabó D, Iotchev IB, Kubinyi E. Age-related effects on a hierarchical structure of canine cognition. GeroScience 2024:10.1007/s11357-024-01123-1. [PMID: 38512580 DOI: 10.1007/s11357-024-01123-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 03/07/2024] [Indexed: 03/23/2024] Open
Abstract
The current study investigates whether there are statistically independent age-related influences on the canine cognitive structure and how individual factors moderate cognitive aging on both cross-sectional and longitudinal samples. A battery of seven tasks was administered to 129 pet dogs, on which exploratory and confirmatory factor analyses were employed to unveil the correlational structure underlying individual differences in cognitive performance. The best-fitting model featured a hierarchical structure with two first-order cognitive domains (individual problem solving, learning) and a second-order common factor. These higher order factors exhibited consistency over a period of at least 2.5 years. External validation linked the common factor positively to discrimination and reversal learning performance, exploration, neophilia, activity/excitability, and training level while negatively to cognitive dysfunction symptoms, suggesting that it is a good candidate for a general cognitive factor (canine g). Structural equation models identified three distinct age-related influences, operating on associative learning, on memory, and on canine g. Health status moderated the negative age-canine g relationship, with a stronger association observed in dogs with poorer health status, and no relationship for dogs in good health. On a longitudinal sample (N = 99), we showed that the direction and magnitude of change in canine g over up to 3 years is affected by various interactions between the dogs' age, communication score, baseline performance, and time elapsed since the baseline measurement. These findings underscore the presence of a general cognitive factor in dogs and reveal intriguing parallels between human and canine aging, affirming the translational value of dogs in cognition and aging research.
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Affiliation(s)
- Zsófia Bognár
- Department of Ethology, ELTE Eötvös Loránd University, Budapest, Hungary
- MTA-ELTE Lendület "Momentum" Companion Animal Research Group, Budapest, Hungary
| | - Borbála Turcsán
- Department of Ethology, ELTE Eötvös Loránd University, Budapest, Hungary.
- MTA-ELTE Lendület "Momentum" Companion Animal Research Group, Budapest, Hungary.
| | - Tamás Faragó
- Department of Ethology, ELTE Eötvös Loránd University, Budapest, Hungary
| | - Dóra Szabó
- Department of Ethology, ELTE Eötvös Loránd University, Budapest, Hungary
| | | | - Enikő Kubinyi
- Department of Ethology, ELTE Eötvös Loránd University, Budapest, Hungary
- MTA-ELTE Lendület "Momentum" Companion Animal Research Group, Budapest, Hungary
- ELTE NAP Canine Brain Research Group, Budapest, Hungary
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Day DB, LeWinn KZ, Karr CJ, Loftus CT, Carroll KN, Bush NR, Zhao Q, Barrett ES, Swan SH, Nguyen RHN, Trasande L, Moore PE, Adams Ako A, Ji N, Liu C, Szpiro AA, Sathyanarayana S. Subpopulations of children with multiple chronic health outcomes in relation to chemical exposures in the ECHO-PATHWAYS consortium. ENVIRONMENT INTERNATIONAL 2024; 185:108486. [PMID: 38367551 PMCID: PMC10961192 DOI: 10.1016/j.envint.2024.108486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 02/01/2024] [Accepted: 02/05/2024] [Indexed: 02/19/2024]
Abstract
A multimorbidity-focused approach may reflect common etiologic mechanisms and lead to better targeting of etiologic agents for broadly impactful public health interventions. Our aim was to identify clusters of chronic obesity-related, neurodevelopmental, and respiratory outcomes in children, and to examine associations between cluster membership and widely prevalent chemical exposures to demonstrate our epidemiologic approach. Early to middle childhood outcome data collected 2011-2022 for 1092 children were harmonized across the ECHO-PATHWAYS consortium of 3 prospective pregnancy cohorts in six U.S. cities. 15 outcomes included age 4-9 BMI, cognitive and behavioral assessment scores, speech problems, and learning disabilities, asthma, wheeze, and rhinitis. To form generalizable clusters across study sites, we performed k-means clustering on scaled residuals of each variable regressed on study site. Outcomes and demographic variables were summarized between resulting clusters. Logistic weighted quantile sum regressions with permutation test p-values associated odds of cluster membership with a mixture of 15 prenatal urinary phthalate metabolites in full-sample and sex-stratified models. Three clusters emerged, including a healthier Cluster 1 (n = 734) with low morbidity across outcomes; Cluster 2 (n = 192) with low IQ and higher levels of all outcomes, especially 0.4-1.8-standard deviation higher mean neurobehavioral outcomes; and Cluster 3 (n = 179) with the highest asthma (92 %), wheeze (53 %), and rhinitis (57 %) frequencies. We observed a significant positive, male-specific stratified association (odds ratio = 1.6; p = 0.01) between a phthalate mixture with high weights for MEP and MHPP and odds of membership in Cluster 3 versus Cluster 1. These results identified subpopulations of children with co-occurring elevated levels of BMI, neurodevelopmental, and respiratory outcomes that may reflect shared etiologic pathways. The observed association between phthalates and respiratory outcome cluster membership could inform policy efforts towards children with respiratory disease. Similar cluster-based epidemiology may identify environmental factors that impact multi-outcome prevalence and efficiently direct public policy efforts.
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Affiliation(s)
- Drew B Day
- Center for Child Health, Behavior, and Development, Seattle Children's Research Institute, 1920 Terry Avenue, Seattle, Washington 98101, USA.
| | - Kaja Z LeWinn
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, 675 18th Street, San Francisco, CA 94143, USA
| | - Catherine J Karr
- Department of Environmental and Occupational Health, University of Washington, 4245 Roosevelt Way NE, Seattle, WA 98105, USA; Department of Epidemiology, University of Washington, 4245 Roosevelt Way NE, Seattle, WA 98105, USA; Department of Pediatrics, University of Washington, 4245 Roosevelt Way NE, Seattle, WA 98105, USA
| | - Christine T Loftus
- Department of Environmental and Occupational Health, University of Washington, 4245 Roosevelt Way NE, Seattle, WA 98105, USA
| | - Kecia N Carroll
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029, USA; Department of Pediatrics, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029, USA
| | - Nicole R Bush
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, 675 18th Street, San Francisco, CA 94143, USA; Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA
| | - Qi Zhao
- Department of Preventive Medicine, Division of Preventive Medicine, University of Tennessee Health Science Center, 66 North Pauline Street, Memphis, TN 38163, USA
| | - Emily S Barrett
- Department of Biostatistics and Epidemiology, Rutgers School of Public Health, 683 Hoes Lane West, Piscataway, NJ 08854, USA; Environmental and Occupational Health Sciences Institute, Rutgers University, 170 Frelinghuysen Road, Piscataway, NJ 08854, USA
| | - Shanna H Swan
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029, USA
| | - Ruby H N Nguyen
- Department of Epidemiology and Community Health, University of Minnesota, 420 Delaware Street Southeast, Minneapolis, Minnesota 55455, USA
| | - Leonardo Trasande
- Department of Pediatrics, New York University Grossman School of Medicine, 550 First Avenue, New York, NY 10016, USA
| | - Paul E Moore
- Division of Allergy, Immunology, and Pulmonary Medicine, Department of Pediatrics, Vanderbilt University Medical Center, 2200 Children's Way, Nashville, TN 37232, USA
| | - Ako Adams Ako
- Department of Pediatrics, Children's Hospital at Montefiore, 3415 Bainbridge Avenue, Bronx, NY 10467, USA
| | - Nan Ji
- Division of Environmental Health, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, 1845 N Soto St, MC 9239, Los Angeles, CA, 90039, USA
| | - Chang Liu
- Department of Psychology, Washington State University, Johnson Tower, Pullman, WA 99164, USA
| | - Adam A Szpiro
- Department of Biostatistics, University of Washington, 3980 15th Avenue NE, Seattle, WA 98195, USA
| | - Sheela Sathyanarayana
- Center for Child Health, Behavior, and Development, Seattle Children's Research Institute, 1920 Terry Avenue, Seattle, Washington 98101, USA; Department of Environmental and Occupational Health, University of Washington, 4245 Roosevelt Way NE, Seattle, WA 98105, USA; Department of Epidemiology, University of Washington, 4245 Roosevelt Way NE, Seattle, WA 98105, USA; Department of Pediatrics, University of Washington, 4245 Roosevelt Way NE, Seattle, WA 98105, USA
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Eichelberger DA, Latal B, Kakebeeke TH, Caflisch JA, Jenni OG, Wehrle FM. The influence of preschool IQ on the individual-order stability of intelligence into adulthood. Acta Paediatr 2023; 112:2161-2163. [PMID: 37517051 DOI: 10.1111/apa.16925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 06/17/2023] [Accepted: 07/26/2023] [Indexed: 08/01/2023]
Affiliation(s)
| | - Beatrice Latal
- Child Development Center, University Children's Hospital Zurich, Zurich, Switzerland
- Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Tanja H Kakebeeke
- Child Development Center, University Children's Hospital Zurich, Zurich, Switzerland
- Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland
| | - Jon A Caflisch
- Child Development Center, University Children's Hospital Zurich, Zurich, Switzerland
- Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland
| | - Oskar G Jenni
- Child Development Center, University Children's Hospital Zurich, Zurich, Switzerland
- Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland
- University of Zurich, Zurich, Switzerland
| | - Flavia M Wehrle
- Child Development Center, University Children's Hospital Zurich, Zurich, Switzerland
- Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland
- Department of Neonatology and Intensive Care, University Children's Hospital Zurich, Zurich, Switzerland
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Genç E, Metzen D, Fraenz C, Schlüter C, Voelkle MC, Arning L, Streit F, Nguyen HP, Güntürkün O, Ocklenburg S, Kumsta R. Structural architecture and brain network efficiency link polygenic scores to intelligence. Hum Brain Mapp 2023; 44:3359-3376. [PMID: 37013679 PMCID: PMC10171514 DOI: 10.1002/hbm.26286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 02/15/2023] [Accepted: 03/01/2023] [Indexed: 04/05/2023] Open
Abstract
Intelligence is highly heritable. Genome-wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here, we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as the surface of brain areas partly located in parieto-frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence.
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Affiliation(s)
- Erhan Genç
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany
| | - Dorothea Metzen
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Christoph Fraenz
- Department of Psychology and Neuroscience, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany
| | - Caroline Schlüter
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Manuel C Voelkle
- Psychological Research Methods Department of Psychology, Humboldt University, Berlin, Germany
| | - Larissa Arning
- Department of Human Genetics, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Fabian Streit
- Department Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Huu Phuc Nguyen
- Department of Human Genetics, Faculty of Medicine, Ruhr University Bochum, Bochum, Germany
| | - Onur Güntürkün
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Sebastian Ocklenburg
- Biopsychology, Institute for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
- Department of Psychology, Medical School Hamburg, Hamburg, Germany
- ICAN Institute for Cognitive and Affective Neuroscience, Medical School Hamburg, Hamburg, Germany
| | - Robert Kumsta
- Genetic Psychology, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
- Department of Behavioural and Cognitive Sciences, Laboratory for Stress and Gene-Environment Interplay, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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Liu Z, Su Y, Huang R, Li R, Wei Z, He L, Pei Y, Min Y, Hu X, Peng X. Depression Trajectories, Genetic Risk, and Cognitive Performance in Older Adults: Multilevel Model with a 10-Year Longitudinal Cohort. Gerontology 2023; 69:899-909. [PMID: 36944316 DOI: 10.1159/000530200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 03/13/2023] [Indexed: 03/23/2023] Open
Abstract
INTRODUCTION Cognitive performance in older ages is strongly affected by individuals' genetic predispositions. We investigated whether depression trajectories were associated with subsequent cognitive performance independent of participants' genetic predispositions. METHODS Participants from the Health and Retirement Study with European ancestry and aged over 50 were included in the analysis. Depressive symptoms were evaluated using the Center for Epidemiologic Studies Depression Scale, and the 6-year trajectories were fitted using latent class linear mixed models. Linear multilevel regression was applied to model the associations between depression trajectory and subsequent cognitive performance. Stratified analyses were performed to investigate these associations in participants with different genetic predispositions of cognitive performance and APOE ε4 allelic status. RESULTS A total of 5,942 eligible participants were included in the study. Four depression trajectories were identified. Compared with the nondepression trajectory, all other depression trajectories were associated with worse cognitive performance (β [95% CI]: mild-depression trajectory: -0.20 [-0.56, -0.06], p = 0.007; worsening-depression trajectory: -0.29 [-0.47, -0.12], p = 0.001; persistent-depression trajectory: -0.32 [-0.53, -0.13], p = 0.001). Although these associations were independent of participants' inherent genetic risk, the participants with a low polygenetic score for cognitive performance were more likely to have an enhanced association between depression trajectories and cognitive decline. Similar relationships were also found in APOE ε4 noncarriers. CONCLUSION Among older participants with European ancestry, even a mild-depression trajectory was associated with worse cognitive performance. Early intervention in participants with any degree of depression might benefit regarding preventing cognitive performance decline.
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Affiliation(s)
- Zheran Liu
- Department of Biotherapy and National Clinical Research Center for Geriatrics, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yonglin Su
- West China Hospital, Sichuan University, Chengdu, China
| | - Rendong Huang
- School of Nursing, Hangzhou Medical College, Hangzhou, China
| | - Ruidan Li
- Department of Biotherapy and National Clinical Research Center for Geriatrics, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhigong Wei
- Department of Biotherapy and National Clinical Research Center for Geriatrics, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Ling He
- Department of Biotherapy and National Clinical Research Center for Geriatrics, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yiyan Pei
- Department of Biotherapy and National Clinical Research Center for Geriatrics, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Min
- Department of Biotherapy and National Clinical Research Center for Geriatrics, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaolin Hu
- West China School of Nursing, West China Hospital, Sichuan University, Chengdu, China
| | - Xingchen Peng
- Department of Biotherapy and National Clinical Research Center for Geriatrics, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
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Mattie LJ, Loveall SJ, Channell MM, Rodgers DB. Perspectives on adaptive functioning and intellectual functioning measures for intellectual disabilities behavioral research. Front Psychol 2023; 14:1084576. [PMID: 36993885 PMCID: PMC10040585 DOI: 10.3389/fpsyg.2023.1084576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 02/14/2023] [Indexed: 03/14/2023] Open
Abstract
IntroductionIntellectual disability (ID) is a significant limitation in both intellectual ability and adaptive functioning, but many studies of participants with ID only include a measure of overall intellectual functioning when describing their samples. The purpose of this perspective article was to provide a starting point for future research regarding the utility of including measures of both intellectual and adaptive functioning in research focused on ID. In this article, we discuss the differences and similarities between the constructs of intellectual and adaptive functioning, how they are measured, and the benefits of using both measures to describe participant abilities. Data are presented to demonstrate that intellectual and adaptive functioning measures capture separate but related skills in a sample of individuals with ID (i.e., children with Down syndrome [DS]; the leading genetic cause of ID).MethodsThirty children with DS (7–31 months) were administered the Mullen Scales of Early Learning and their mothers were interviewed using the Vineland Adaptive Behavior Scales.ResultsAt the group level, Vineland and Mullen composite scores were relatively normally distributed and positively correlated. At the individual level, a concordance correlation coefficient indicated moderate agreement between Vineland and Mullen composite scores.DiscussionAlthough many children showed consistency between measures, others did not. Our discussion and findings, though preliminary, highlight that intellectual and adaptive functioning are separate but related skills and that there are benefits to including both measures when describing samples with ID. We discuss considerations for including adaptive functioning measures to enhance future research on individuals with ID.
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Affiliation(s)
- Laura J. Mattie
- Department of Speech and Hearing Science, University of Illinois at Urbana-Champaign, Champaign, IL, United States
- *Correspondence: Laura J. Mattie,
| | - Susan J. Loveall
- Department of Special Education and Communication Disorders, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Marie Moore Channell
- Department of Speech and Hearing Science, University of Illinois at Urbana-Champaign, Champaign, IL, United States
| | - Derek B. Rodgers
- Department of Teaching and Learning, University of Iowa, Iowa City, IA, United States
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Corley J, Conte F, Harris SE, Taylor AM, Redmond P, Russ TC, Deary IJ, Cox SR. Predictors of longitudinal cognitive ageing from age 70 to 82 including APOE e4 status, early-life and lifestyle factors: the Lothian Birth Cohort 1936. Mol Psychiatry 2023; 28:1256-1271. [PMID: 36481934 PMCID: PMC10005946 DOI: 10.1038/s41380-022-01900-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 11/17/2022] [Accepted: 11/21/2022] [Indexed: 12/13/2022]
Abstract
Discovering why some people's cognitive abilities decline more than others is a key challenge for cognitive ageing research. The most effective strategy may be to address multiple risk factors from across the life-course simultaneously in relation to robust longitudinal cognitive data. We conducted a 12-year follow-up of 1091 (at age 70) men and women from the longitudinal Lothian Birth Cohort 1936 study. Comprehensive repeated cognitive measures of visuospatial ability, processing speed, memory, verbal ability, and a general cognitive factor were collected over five assessments (age 70, 73, 76, 79, and 82 years) and analysed using multivariate latent growth curve modelling. Fifteen life-course variables were used to predict variation in cognitive ability levels at age 70 and cognitive slopes from age 70 to 82. Only APOE e4 carrier status was found to be reliably informative of general- and domain-specific cognitive decline, despite there being many life-course correlates of cognitive level at age 70. APOE e4 carriers had significantly steeper slopes across all three fluid cognitive domains compared with non-carriers, especially for memory (β = -0.234, p < 0.001) and general cognitive function (β = -0.246, p < 0.001), denoting a widening gap in cognitive functioning with increasing age. Our findings suggest that when many other candidate predictors of cognitive ageing slope are entered en masse, their unique contributions account for relatively small proportions of variance, beyond variation in APOE e4 status. We conclude that APOE e4 status is important for identifying those at greater risk for accelerated cognitive ageing, even among ostensibly healthy individuals.
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Affiliation(s)
- Janie Corley
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK.
| | - Federica Conte
- Department of Psychology, University of Milano-Bicocca, Milan, Italy
| | - Sarah E Harris
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Adele M Taylor
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Paul Redmond
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Tom C Russ
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
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Eichelberger DA, Sticca F, Kübler DR, Kakebeeke TH, Caflisch JA, Jenni OG, Wehrle FM. Stability of mental abilities and physical growth from 6 months to 65 years: Findings from the Zurich Longitudinal Studies. INTELLIGENCE 2023. [DOI: 10.1016/j.intell.2023.101730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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12
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Sandalic D, Tran Y, Arora M, Middleton J, McBain C, Myles D, Pozzato I, Craig A. Improving Assessment of Cognitive Impairment after Spinal Cord Injury: Methods to Reduce the Risk of Reporting False Positives. J Clin Med 2022; 12:jcm12010068. [PMID: 36614869 PMCID: PMC9820950 DOI: 10.3390/jcm12010068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 12/16/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
Adults with spinal cord injury (SCI) are reported to have heightened risk of cognitive impairment, notably mild cognitive impairment (MCI). Reports of increased risk of MCI are almost exclusively based on cross-sectional assessments of cognitive function using norm-referenced scores. Norm-referenced single-point assessments do not reflect cognitive decline at the individual level but rather represent between group differences in cognitive function. The practice of relying solely on norm-referenced assessment to study MCI after SCI is therefore problematic as it lends to potential misclassification of MCI. Premorbid intelligence estimates permit comparison of people's actual versus expected cognitive function and thereby can be used to validate the presence of genuine cognitive decline. These are not utilized in the assessment of MCI after SCI. This study simulated data for 500,000 adults with SCI to compare norm-referenced and premorbid-intelligence methods of screening for MCI to examine the potential extent of MCI misclassification after SCI resulting from the overreliance on norm-referenced methods and exclusion of premorbid intelligence methods. One in five to one in 13 simulated adults with SCI were potentially misclassified as having MCI showing that measures of premorbid cognitive function must be included in assessment of cognitive function after SCI.
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Affiliation(s)
- Danielle Sandalic
- John Walsh Centre Rehabilitation Research, The Kolling Institute, Royal North Shore Hospital, St Leonards, Sydney, NSW 2065, Australia
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia
- SCI Unit, Royal North Shore Hospital, St Leonards, Sydney, NSW 2065, Australia
- Correspondence:
| | - Yvonne Tran
- Australian Institute of Health Innovation, Macquarie University, North Ryde, Sydney, NSW 2113, Australia
| | - Mohit Arora
- John Walsh Centre Rehabilitation Research, The Kolling Institute, Royal North Shore Hospital, St Leonards, Sydney, NSW 2065, Australia
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia
| | - James Middleton
- John Walsh Centre Rehabilitation Research, The Kolling Institute, Royal North Shore Hospital, St Leonards, Sydney, NSW 2065, Australia
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia
- Spinal Outreach Service, Royal Rehab, Ryde, Sydney, NSW 2112, Australia
| | - Candice McBain
- John Walsh Centre Rehabilitation Research, The Kolling Institute, Royal North Shore Hospital, St Leonards, Sydney, NSW 2065, Australia
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia
| | - Daniel Myles
- John Walsh Centre Rehabilitation Research, The Kolling Institute, Royal North Shore Hospital, St Leonards, Sydney, NSW 2065, Australia
| | - Ilaria Pozzato
- John Walsh Centre Rehabilitation Research, The Kolling Institute, Royal North Shore Hospital, St Leonards, Sydney, NSW 2065, Australia
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia
| | - Ashley Craig
- John Walsh Centre Rehabilitation Research, The Kolling Institute, Royal North Shore Hospital, St Leonards, Sydney, NSW 2065, Australia
- Faculty of Medicine and Health, Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia
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13
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Banfi JT, Randall JG. A meta-analysis of trait mindfulness: Relationships with the big five personality traits, intelligence, and anxiety. JOURNAL OF RESEARCH IN PERSONALITY 2022. [DOI: 10.1016/j.jrp.2022.104307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
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14
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Conte FP, Okely JA, Hamilton OK, Corley J, Page D, Redmond P, Taylor AM, Russ TC, Deary IJ, Cox SR. Cognitive Change Before Old Age (11 to 70) Predicts Cognitive Change During Old Age (70 to 82). Psychol Sci 2022; 33:1803-1817. [PMID: 36113037 PMCID: PMC9660354 DOI: 10.1177/09567976221100264] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Identifying predictors of cognitive decline in old age helps us understand its mechanisms and identify those at greater risk. Here, we examined how cognitive change from ages 11 to 70 is associated with cognitive change at older ages (70 to 82 years) in the Lothian Birth Cohort 1936 longitudinal study (N = 1,091 at recruitment). Using latent-growth-curve models, we estimated rates of change from ages 70 to 82 in general cognitive ability (g) and in three cognitive domains: visuospatial, memory, and processing speed. We found that g accounted for 71.3% of interindividual change variance. Greater cognitive gain from ages 11 to 70 predicted slower decline in g over 12 subsequent years (β = 0.163, p = .001), independently of cognitive level in childhood and at age 70, and domain-specific change beyond g. These results contribute to the goal of identifying people at higher risk of age-related cognitive decline.
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Affiliation(s)
- Federica P. Conte
- Department of Psychology, University of
Milano-Bicocca
- Lothian Birth Cohorts Group, The
University of Edinburgh
- Federica P. Conte, University of
Milano-Bicocca, Department of Psychology
| | - Judith A. Okely
- Lothian Birth Cohorts Group, The
University of Edinburgh
- Department of Psychology, The
University of Edinburgh
| | - Olivia K. Hamilton
- Lothian Birth Cohorts Group, The
University of Edinburgh
- Department of Psychology, The
University of Edinburgh
| | - Janie Corley
- Lothian Birth Cohorts Group, The
University of Edinburgh
- Department of Psychology, The
University of Edinburgh
| | - Danielle Page
- Lothian Birth Cohorts Group, The
University of Edinburgh
- Department of Psychology, The
University of Edinburgh
| | - Paul Redmond
- Lothian Birth Cohorts Group, The
University of Edinburgh
- Department of Psychology, The
University of Edinburgh
| | - Adele M. Taylor
- Lothian Birth Cohorts Group, The
University of Edinburgh
- Department of Psychology, The
University of Edinburgh
| | - Tom C. Russ
- Lothian Birth Cohorts Group, The
University of Edinburgh
- Alzheimer Scotland Dementia Research
Centre, The University of Edinburgh
- Division of Psychiatry, Centre for
Clinical Brain Sciences, The University of Edinburgh
| | - Ian J. Deary
- Lothian Birth Cohorts Group, The
University of Edinburgh
- Department of Psychology, The
University of Edinburgh
| | - Simon R. Cox
- Lothian Birth Cohorts Group, The
University of Edinburgh
- Department of Psychology, The
University of Edinburgh
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15
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Almeida-Meza P, Richards M, Cadar D. Moderating Role of Cognitive Reserve Markers Between Childhood Cognition and Cognitive Aging: Evidence From the 1946 British Birth Cohort. Neurology 2022; 99:e1239-e1250. [PMID: 35922143 PMCID: PMC9576292 DOI: 10.1212/wnl.0000000000200928] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 05/19/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES As the population ages, differences in cognitive abilities become more evident. We investigated key genetic and life course influences on cognitive state at age 69 years, building on previous work using the longitudinal Medical Research Council National Survey of Health and Development (the British 1946 birth cohort). METHODS Multivariable regressions investigated the association between 4 factors: (1) childhood cognition at age 8 years; (2) a Cognitive Reserve Index (CRI) composed of 3 markers: (i) educational attainment by age 26 years, (ii) engagement in leisure activities at age 43 years, and (iii) occupation up to age 53 years; (3) reading ability assessed by the National Adult Reading Test (NART) at age 53 years; and (4) APOE genotype in relation to cognitive state measured at age 69 years with Addenbrooke's Cognitive Examination, third edition (ACE-III). We then investigated the modifying role of the CRI, NART, and APOE in the association between childhood cognition and the ACE-III. RESULTS The analytical sample comprised 1,184 participants. Higher scores in childhood cognition, CRI, and NART were associated with higher scores in the ACE-III. We found that the CRI and NART modified the association between childhood cognition and the ACE-III: for 30 additional points in the CRI or 20 additional points in the NART, the simple slope of childhood cognition decreased by approximately 0.10 points (CRI = 70: marginal effects (MEs) 0.22, 95% CI 0.12-0.32, p < 0.001 vs CRI = 100: MEs 0.12, 95% CI 0.06-0.17, p < 0.001; NART = 15: MEs 0.22, 95% CI 0.09-0.35, p = 0.001, vs NART = 35: MEs 0.11, 95% CI 0.05-0.17, p < 0.001). The association between childhood cognition and the ACE-III was nonsignificant at high levels of the CRI or NART. Furthermore, the e4 allele of the APOE gene was associated with lower scores in the ACE-III (β = -0.71, 95% CI -1.36 to -0.06, p = 0.03) but did not modify the association between childhood cognition and cognitive state in later life. DISCUSSION The CRI and NART are independent measures of cognitive reserve because both modify the association between childhood cognition and cognitive state.
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Affiliation(s)
- Pamela Almeida-Meza
- From the Department of Behavioural Science and Health (P.A.-M., D.C.), University College London; MRC Unit for Lifelong Health and Ageing, University College London; and Centre for Dementia Studies (D.C.), Department of Neuroscience, Brighton and Sussex Medical School, UK.
| | - Marcus Richards
- From the Department of Behavioural Science and Health (P.A.-M., D.C.), University College London; MRC Unit for Lifelong Health and Ageing, University College London; and Centre for Dementia Studies (D.C.), Department of Neuroscience, Brighton and Sussex Medical School, UK
| | - Dorina Cadar
- From the Department of Behavioural Science and Health (P.A.-M., D.C.), University College London; MRC Unit for Lifelong Health and Ageing, University College London; and Centre for Dementia Studies (D.C.), Department of Neuroscience, Brighton and Sussex Medical School, UK
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16
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Okely JA, Overy K, Deary IJ. Experience of Playing a Musical Instrument and Lifetime Change in General Cognitive Ability: Evidence From the Lothian Birth Cohort 1936. Psychol Sci 2022; 33:1495-1508. [PMID: 36031803 DOI: 10.1177/09567976221092726] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
We tested whether experience of playing a musical instrument was associated with lifetime change in cognitive ability. Participants were 366 older adults from the Lothian Birth Cohort 1936 who had completed general cognitive-ability assessments at ages 11 and 70 and reported their lifetime experience of playing a musical instrument at age 82. This sample included 117 participants with musical-instrument experience, mostly at a beginner or an intermediate level. There was a small, statistically significant positive association between experience of playing a musical instrument and change in general cognitive ability between ages 11 and 70; specifically, individuals with more musical-instrument experience were likely to show greater gains in general cognitive ability. This association was reduced but remained statistically significant following adjustment for covariates (childhood and adulthood socioeconomic status, years of education, and disease history). These findings suggest that playing a musical instrument is associated with a long-term cognitive advantage.
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Affiliation(s)
- Judith A Okely
- Lothian Birth Cohort Studies, Department of Psychology, University of Edinburgh
- Department of Psychology, Edinburgh Napier University
| | - Katie Overy
- Reid School of Music, University of Edinburgh
- Edinburgh Neuroscience, University of Edinburgh
| | - Ian J Deary
- Lothian Birth Cohort Studies, Department of Psychology, University of Edinburgh
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17
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Orben A, Lucas RE, Fuhrmann D, Kievit RA. Trajectories of adolescent life satisfaction. ROYAL SOCIETY OPEN SCIENCE 2022; 9:211808. [PMID: 35937913 DOI: 10.6084/m9.figshare.c.6108470] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 05/27/2022] [Indexed: 05/25/2023]
Abstract
Increasing global policy interest in measuring and improving population wellbeing has prompted academic investigations into the dynamics of lifespan life satisfaction. Yet little research has assessed the complete adolescent age range, although it harbours developmental changes that could affect wellbeing far into adulthood. This study investigates how life satisfaction develops throughout the whole of adolescence, and compares this development to that in adulthood, by applying exploratory and confirmatory latent growth curve modelling to UK and German data, respectively (37 076 participants, 10-24 years). We find a near universal decrease in life satisfaction during adolescence. This decrease is steeper than at any other point across adulthood. Further, our findings suggest that adolescent girls' life satisfaction is lower than boys', but that this difference does not extend into adulthood. The study highlights the importance of studying adolescent subjective wellbeing trajectories to inform research, policy and practice.
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Affiliation(s)
- Amy Orben
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Richard E Lucas
- Department of Psychology, Michigan State University, East Lansing, MI, USA
| | - Delia Fuhrmann
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Department of Psychology, King's College London, London, UK
| | - Rogier A Kievit
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Gelderland, The Netherlands
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
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18
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Orben A, Lucas RE, Fuhrmann D, Kievit RA. Trajectories of adolescent life satisfaction. ROYAL SOCIETY OPEN SCIENCE 2022; 9:211808. [PMID: 35937913 PMCID: PMC9346371 DOI: 10.1098/rsos.211808] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 05/27/2022] [Indexed: 05/10/2023]
Abstract
Increasing global policy interest in measuring and improving population wellbeing has prompted academic investigations into the dynamics of lifespan life satisfaction. Yet little research has assessed the complete adolescent age range, although it harbours developmental changes that could affect wellbeing far into adulthood. This study investigates how life satisfaction develops throughout the whole of adolescence, and compares this development to that in adulthood, by applying exploratory and confirmatory latent growth curve modelling to UK and German data, respectively (37 076 participants, 10-24 years). We find a near universal decrease in life satisfaction during adolescence. This decrease is steeper than at any other point across adulthood. Further, our findings suggest that adolescent girls' life satisfaction is lower than boys', but that this difference does not extend into adulthood. The study highlights the importance of studying adolescent subjective wellbeing trajectories to inform research, policy and practice.
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Affiliation(s)
- Amy Orben
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Richard E. Lucas
- Department of Psychology, Michigan State University, East Lansing, MI, USA
| | - Delia Fuhrmann
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Department of Psychology, King's College London, London, UK
| | - Rogier A. Kievit
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Gelderland, The Netherlands
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19
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Wright AJ, Jackson JJ. Childhood temperament and adulthood personality differentially predict life outcomes. Sci Rep 2022; 12:10286. [PMID: 35717439 PMCID: PMC9206675 DOI: 10.1038/s41598-022-14666-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 05/17/2022] [Indexed: 11/09/2022] Open
Abstract
Debate has long surrounded whether temperament and personality are distinct sets of individual differences or are rather two sides of the same coin. To the extent that there are differences, it could indicate important developmental insights concerning the mechanisms responsible for linking traits with outcomes. One way to test this is to examine the joint and incremental predictive validity of temperament and personality in the same individuals across time. Using a longitudinal sample spanning 3 decades starting at infancy and followed up to 37 years old (N = 7081), we ran a series of Bayesian generalized linear models with measures of childhood temperament and adult-based personality to predict outcomes in several life domains. Results indicated that while each set of individual differences were often related to the same outcomes, there were instances in which temperament provided incremental validity above adult personality, ranging from 2 to 10% additional variance explained. Personality in childhood explained the most variance for outcomes such as cognitive ability and educational attainment whereas personality performed best for outcomes such as health status, substance use, and most internalizing outcomes. These findings indicate childhood and adulthood assessments of personality are not redundant and that a lifespan approach is needed to understand fully understand life outcomes.
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20
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Deary IJ, Cox SR, Hill WD. Genetic variation, brain, and intelligence differences. Mol Psychiatry 2022; 27:335-353. [PMID: 33531661 PMCID: PMC8960418 DOI: 10.1038/s41380-021-01027-y] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 12/28/2020] [Accepted: 01/11/2021] [Indexed: 01/30/2023]
Abstract
Individual differences in human intelligence, as assessed using cognitive test scores, have a well-replicated, hierarchical phenotypic covariance structure. They are substantially stable across the life course, and are predictive of educational, social, and health outcomes. From this solid phenotypic foundation and importance for life, comes an interest in the environmental, social, and genetic aetiologies of intelligence, and in the foundations of intelligence differences in brain structure and functioning. Here, we summarise and critique the last 10 years or so of molecular genetic (DNA-based) research on intelligence, including the discovery of genetic loci associated with intelligence, DNA-based heritability, and intelligence's genetic correlations with other traits. We summarise new brain imaging-intelligence findings, including whole-brain associations and grey and white matter associations. We summarise regional brain imaging associations with intelligence and interpret these with respect to theoretical accounts. We address research that combines genetics and brain imaging in studying intelligence differences. There are new, though modest, associations in all these areas, and mechanistic accounts are lacking. We attempt to identify growing points that might contribute toward a more integrated 'systems biology' account of some of the between-individual differences in intelligence.
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Affiliation(s)
- Ian J. Deary
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ UK
| | - Simon R. Cox
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ UK
| | - W. David Hill
- grid.4305.20000 0004 1936 7988Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ UK
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21
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Cave SN, Wright M, von Stumm S. Change and stability in the association of parents' education with children's intelligence. INTELLIGENCE 2022. [DOI: 10.1016/j.intell.2021.101597] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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22
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Lechner CM, Gauly B, Miyamoto A, Wicht A. Stability and change in adults' literacy and numeracy skills: Evidence from two large-scale panel studies. PERSONALITY AND INDIVIDUAL DIFFERENCES 2021. [DOI: 10.1016/j.paid.2021.110990] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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23
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Hamilton O, Cox SR, Ballerini L, Bastin ME, Corley J, Gow AJ, Muñoz Maniega S, Redmond P, Valdés Hernández MDC, Wardlaw JM, Deary IJ. Associations between total MRI-visible small vessel disease burden and domain-specific cognitive abilities in a community-dwelling older-age cohort. Neurobiol Aging 2021; 105:25-34. [PMID: 34022536 PMCID: PMC8345313 DOI: 10.1016/j.neurobiolaging.2021.04.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 01/27/2021] [Accepted: 04/13/2021] [Indexed: 01/08/2023]
Abstract
Cerebral small vessel disease (SVD) is a leading cause of vascular cognitive impairment, however the precise nature of SVD-related cognitive deficits, and their associations with structural brain changes, remain unclear. We combined computational volumes and visually-rated MRI markers of SVD to quantify total SVD burden, using data from the Lothian Birth Cohort 1936 (n = 540; age: 72.6 ± 0.7 years). We found negative associations between total SVD burden and general cognitive ability (standardized β: -0.363; 95%CI: [-0.49, -0.23]; p(FDR) < 0.001), processing speed (-0.371 [-0.50, -0.24]; p(FDR) < 0.001), verbal memory (-0.265; [-0.42, -0.11]; p(FDR) = 0.002), and visuospatial ability (-0.170; [-0.32, -0.02]; p(FDR) = 0.029). Only the association between SVD burden and processing speed remained after accounting for covariance with general cognitive ability (-0.325; [-0.61, -0.04]; p(FDR) = 0.029). This suggests that SVD's association with poorer processing speed is not driven by, but is independent of its association with poorer general cognitive ability. Tests of processing speed may be particularly sensitive to the cognitive impact of SVD, but all major cognitive domains should be tested to determine the full range of SVD-related cognitive characteristics.
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Affiliation(s)
- Okl Hamilton
- Centre for Clinical Brain Sciences, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, UK, EH16 4SB; Dementia Research Institute, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, UK, EH16 4SB; Lothian Birth Cohorts, University of Edinburgh, 7 George Square, Edinburgh, UK, EH8 9JZ
| | - S R Cox
- Lothian Birth Cohorts, University of Edinburgh, 7 George Square, Edinburgh, UK, EH8 9JZ
| | - L Ballerini
- Centre for Clinical Brain Sciences, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, UK, EH16 4SB; Dementia Research Institute, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, UK, EH16 4SB; Lothian Birth Cohorts, University of Edinburgh, 7 George Square, Edinburgh, UK, EH8 9JZ
| | - M E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, UK, EH16 4SB; Lothian Birth Cohorts, University of Edinburgh, 7 George Square, Edinburgh, UK, EH8 9JZ
| | - J Corley
- Lothian Birth Cohorts, University of Edinburgh, 7 George Square, Edinburgh, UK, EH8 9JZ
| | - A J Gow
- Department of Psychology and the Centre for Applied Behavioural Sciences, School of Social Sciences, Heriot-Watt University, Edinburgh, UK, EH14 4AS
| | - S Muñoz Maniega
- Centre for Clinical Brain Sciences, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, UK, EH16 4SB; Dementia Research Institute, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, UK, EH16 4SB; Lothian Birth Cohorts, University of Edinburgh, 7 George Square, Edinburgh, UK, EH8 9JZ
| | - P Redmond
- Lothian Birth Cohorts, University of Edinburgh, 7 George Square, Edinburgh, UK, EH8 9JZ
| | - M Del C Valdés Hernández
- Centre for Clinical Brain Sciences, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, UK, EH16 4SB; Dementia Research Institute, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, UK, EH16 4SB
| | - J M Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, UK, EH16 4SB; Dementia Research Institute, University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, UK, EH16 4SB; Lothian Birth Cohorts, University of Edinburgh, 7 George Square, Edinburgh, UK, EH8 9JZ.
| | - I J Deary
- Lothian Birth Cohorts, University of Edinburgh, 7 George Square, Edinburgh, UK, EH8 9JZ.
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Cerebral small vessel disease burden and longitudinal cognitive decline from age 73 to 82: the Lothian Birth Cohort 1936. Transl Psychiatry 2021; 11:376. [PMID: 34226517 PMCID: PMC8257729 DOI: 10.1038/s41398-021-01495-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/14/2021] [Accepted: 06/22/2021] [Indexed: 12/15/2022] Open
Abstract
Slowed processing speed is considered a hallmark feature of cognitive decline in cerebral small vessel disease (SVD); however, it is unclear whether SVD's association with slowed processing might be due to its association with overall declining general cognitive ability. We quantified the total MRI-visible SVD burden of 540 members of the Lothian Birth Cohort 1936 (age: 72.6 ± 0.7 years; 47% female). Using latent growth curve modelling, we tested associations between total SVD burden at mean age 73 and changes in general cognitive ability, processing speed, verbal memory and visuospatial ability, measured at age 73, 76, 79 and 82. Covariates included age, sex, vascular risk and childhood cognitive ability. In the fully adjusted models, greater SVD burden was associated with greater declines in general cognitive ability (standardised β: -0.201; 95% CI: [-0.36, -0.04]; pFDR = 0.022) and processing speed (-0.222; [-0.40, -0.04]; pFDR = 0.022). SVD burden accounted for between 4 and 5% of variance in declines of general cognitive ability and processing speed. After accounting for the covariance between tests of processing speed and general cognitive ability, only SVD's association with greater decline in general cognitive ability remained significant, prior to FDR correction (-0.222; [-0.39, -0.06]; p = 0.008; pFDR = 0.085). Our findings do not support the notion that SVD has a specific association with declining processing speed, independent of decline in general cognitive ability (which captures the variance shared across domains of cognitive ability). The association between SVD burden and declining general cognitive ability supports the notion of SVD as a diffuse, whole-brain disease and suggests that trials monitoring SVD-related cognitive changes should consider domain-specific changes in the context of overall, general cognitive decline.
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25
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Ahmed A, Kramer MS, Bernard JY, Perez Trejo ME, Martin RM, Oken E, Yang S. Early childhood growth trajectory and later cognitive ability: evidence from a large prospective birth cohort of healthy term-born children. Int J Epidemiol 2021; 49:1998-2009. [PMID: 32743654 DOI: 10.1093/ije/dyaa105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 05/14/2020] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Most studies of associations between child growth and cognitive ability were based on size at one or two ages and a single measure of cognition. We aimed to characterize different aspects of early growth and their associations with cognitive outcomes in childhood through adolescence. METHODS In a sample of 12 368 Belarusian children born at term, we examined associations of length/height and weight trajectories over the first 6.5 years of life with cognitive ability at 6.5 and 16 years and its change over time. We estimated growth trajectories using two random-effects models-the SuperImposition by Translation and Rotation to model overall patterns of growth and the Jenss-Bayley to distinguish growth in infancy from post infancy. Cognitive ability was measured using the Wechsler Abbreviated Scales of Intelligence at 6.5 years and the computerized NeuroTrax test at 16 years. RESULTS Higher length/height between birth and 6.5 years was associated with higher cognitive scores at 6.5 and 16 years {2.7 points [95% confidence interval (CI): 2.1, 3.2] and 2.5 points [95% CI: 1.9, 3.0], respectively, per standard deviation [SD] increase}. A 1-SD delay in the childhood height-growth spurt was negatively associated with cognitive scores [-2.4 (95% CI: -3.0, -1.8) at age 6.5; -2.2 (95% CI: -2.7, -1.6) at 16 years]. Birth size and post-infancy growth velocity were positively associated with cognitive scores at both ages. Height trajectories were not associated with the change in cognitive score. Similar results were observed for weight trajectories. CONCLUSIONS Among term infants, the overall size, timing of the childhood growth spurt, size at birth and post-infancy growth velocity were all associated with cognitive ability at early-school age and adolescence.
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Affiliation(s)
- Asma Ahmed
- Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Michael S Kramer
- Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.,Department of Pediatrics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Jonathan Y Bernard
- Inserm, Centre for Research in Epidemiology and Statistics (CRESS), Research Team on Early Life Origins of Health, Villejuif, France.,Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore
| | | | - Richard M Martin
- Bristol Medical School, Population Health Sciences, University of Bristol, Bristol, UK.,University Hospitals Bristol NHS Foundation Trust National Institute for Health Research (NIHR) Bristol Biomedical Research Centre, University of Bristol, Bristol, UK
| | - Emily Oken
- Division of Chronic Disease Research Across the Lifecourse (CoRAL), Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Seungmi Yang
- Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
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26
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Guez A, Peyre H, Williams C, Labouret G, Ramus F. The epidemiology of cognitive development. Cognition 2021; 213:104690. [PMID: 33931198 DOI: 10.1016/j.cognition.2021.104690] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 03/12/2021] [Accepted: 03/17/2021] [Indexed: 12/26/2022]
Abstract
The epidemiology of cognitive development is an approach essentially based on large observational studies, which examines individual differences in cognitive abilities throughout childhood and their determinants. Although different in terms of methodology and main interests from developmental psychology, cognitive epidemiology offers complementary viewpoints on cognitive development and addresses fundamental research questions of interest to developmental psychologists. The present paper depicts the contributions of the epidemiological approach to the field of cognitive development and highlights the methodological advances that have made such contributions possible. We discuss the stability and developmental trajectories of cognitive functions, their main predictors, the complex interplay between environmental and genetic predictors, and the relationships between the different domains of cognition from birth to adulthood.
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Affiliation(s)
- Ava Guez
- Laboratoire de sciences cognitives et psycholinguistique, ENS, EHESS, PSL University, CNRS, Paris, France.
| | - Hugo Peyre
- Laboratoire de sciences cognitives et psycholinguistique, ENS, EHESS, PSL University, CNRS, Paris, France; Neurodiderot. INSERM UMR 1141, Paris Diderot University, Paris, France; Department of Child and Adolescent Psychiatry, Robert Debré Hospital, APHP, France
| | - Camille Williams
- Laboratoire de sciences cognitives et psycholinguistique, ENS, EHESS, PSL University, CNRS, Paris, France
| | - Ghislaine Labouret
- Laboratoire de sciences cognitives et psycholinguistique, ENS, EHESS, PSL University, CNRS, Paris, France
| | - Franck Ramus
- Laboratoire de sciences cognitives et psycholinguistique, ENS, EHESS, PSL University, CNRS, Paris, France.
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27
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Deary IJ, Sternberg RJ. Ian Deary and Robert Sternberg answer five self-inflicted questions about human intelligence. INTELLIGENCE 2021. [DOI: 10.1016/j.intell.2021.101539] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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28
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Deary IJ, Hill WD, Gale CR. Intelligence, health and death. Nat Hum Behav 2021; 5:416-430. [PMID: 33795857 DOI: 10.1038/s41562-021-01078-9] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 02/15/2021] [Indexed: 02/06/2023]
Abstract
The field of cognitive epidemiology studies the prospective associations between cognitive abilities and health outcomes. We review research in this field over the past decade and describe how our understanding of the association between intelligence and all-cause mortality has consolidated with the appearance of new, population-scale data. To try to understand the association better, we discuss how intelligence relates to specific causes of death, diseases/diagnoses and biomarkers of health through the adult life course. We examine the extent to which mortality and health associations with intelligence might be attributable to people's differences in education, other indicators of socioeconomic status, health literacy and adult environments and behaviours. Finally, we discuss whether genetic data provide new tools to understand parts of the intelligence-health associations. Social epidemiologists, differential psychologists and behavioural and statistical geneticists, among others, contribute to cognitive epidemiology; advances will occur by building on a common cross-disciplinary knowledge base.
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Affiliation(s)
- Ian J Deary
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK.
| | - W David Hill
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Catharine R Gale
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK.,MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton, UK
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29
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Abstract
Dementia is a major contributor to dependence and disability in older people, with aging societies characterized by growing numbers of people living with the condition. Dementia rates are highest in those with low education early in life, midlife hypertension, midlife hearing loss, depression, obesity, loneliness, a sedentary lifestyle, or sustained exposure to smoking or diabetes. Tooth loss is a putative risk factor for dementia which has received increasing research attention, but systematic review findings are mixed. Three main mechanisms have been proposed, involving 1) tooth loss leading to compromised nutrition and then leading to poorer central nervous system (CNS) function; 2) tooth loss resulting in fewer interocclusal contacts and so less somatosensory feedback to the CNS, leading to impaired cognition; and (3) chronic periodontitis resulting in tooth loss, but not before the inflammation has affected the CNS, impairing cognition. None of these is supported by compelling empirical evidence. Here, we use the life course approach to propose a plausible, empirically supported explanation for the associations between missing teeth and poorer cognitive function in older people. Evidence from longstanding cohort studies demonstrates that the putative association arises from cognitive function much earlier in life, in childhood. People with better childhood cognitive function have better oral health and access to routine dental care as they go through life, losing fewer teeth along the life course. They are also much more likely to have better cognitive function in old age. Their less cognitively able childhood counterparts will experience higher disease rates and poorer access to care, resulting in greater incremental tooth loss. Comparison of the 2 groups at any age from the mid-20s on will show greater numbers of missing teeth in the group who were less cognitively able in childhood. Those differences will be most pronounced in old age.
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Affiliation(s)
- W M Thomson
- Department of Oral Sciences, Sir John Walsh Research Institute, School of Dentistry, The University of Otago, Dunedin, New Zealand
| | - Y Barak
- Department of Psychological Medicine, Dunedin School of Medicine, The University of Otago, Dunedin, New Zealand
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30
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Crawford DW, Bendrath S, Manzano MD, Mehta A, Patel HM, Piela MC, Sauce B, Matzel LD. Intelligence demands flexibility: Individual differences in attentional disengagement strongly predict the general cognitive ability of mice. LEARNING AND MOTIVATION 2020. [DOI: 10.1016/j.lmot.2020.101657] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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31
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Mansukoski L, Bogin B, Galvez-Sobral JA, Furlán L, Johnson W. Differences and secular trends in childhood IQ trajectories in Guatemala City. INTELLIGENCE 2020; 80:101438. [PMID: 32508371 PMCID: PMC7263732 DOI: 10.1016/j.intell.2020.101438] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 03/04/2020] [Accepted: 03/05/2020] [Indexed: 11/20/2022]
Abstract
This study documents differences in childhood IQ trajectories of Guatemala City children, aged 6-15 years and born 1961-1993, according to school attended, height-for-age Z-scores (HAZ) and over time (Flynn effect). IQ data come from the Universidad del Valle de Guatemala Longitudinal Study of Child and Adolescent Development. IQ was measured using standardised tests from the Otis-Lennon Mental Ability Test-series. A multilevel model was developed to describe 60,986 IQ observations (level 1), in 22,724 children (level 2), in five schools representing students of different socioeconomic status (SES) (level 3). Average IQ trajectories differed by school. The difference in average IQ at age 11 years between the students of high and low SES schools was 28.7 points. A one-unit increase in HAZ was associated with a 1.42 (0.72, 2.11) unit higher IQ if HAZ was <0, this association was stronger in public compared to private schools. Conversely, one unit increase in HAZ was only associated with a 0.3 (0.001, 0.5) unit higher IQ if HAZ was ≥0. With each birth year increase, IQ at age 11 years increased by 0.14 (95% CI 0.12, 0.16) units, although this Flynn effect attenuated slightly across adolescence. We found no evidence of secular change in the inequality in IQ trajectories (according to school or HAZ). Shorter children from disadvantaged schools in Guatemala City have lower IQ than their taller and wealthier peers, possibly reflecting the damaging effects of poor early life environments both for linear growth and cognitive development.
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Affiliation(s)
- Liina Mansukoski
- Centre for Global Child Health, The Hospital for Sick Children, Toronto, Canada
| | - Barry Bogin
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
- UCSD/Salk Center for Academic Research and Training in Anthropogeny (CARTA), USA
| | - J. Andres Galvez-Sobral
- Centro de Investigaciones Educativas, Universidad del Valle de Guatemala, Guatemala City, Guatemala
| | - Luis Furlán
- Centro de Estudios en Informática Aplicada, Universidad del Valle de Guatemala, Guatemala City, Guatemala
| | - William Johnson
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
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32
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Preckel F, Golle J, Grabner R, Jarvin L, Kozbelt A, Müllensiefen D, Olszewski-Kubilius P, Schneider W, Subotnik R, Vock M, Worrell FC. Talent Development in Achievement Domains: A Psychological Framework for Within- and Cross-Domain Research. PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2020; 15:691-722. [DOI: 10.1177/1745691619895030] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Achievement in different domains, such as academics, music, or visual arts, plays a central role in all modern societies. Different psychological models aim to describe and explain achievement and its development in different domains. However, there remains a need for a framework that guides empirical research within and across different domains. With the talent-development-in-achievement-domains (TAD) framework, we provide a general talent-development framework applicable to a wide range of achievement domains. The overarching aim of this framework is to support empirical research by focusing on measurable psychological constructs and their meaning at different levels of talent development. Furthermore, the TAD framework can be used for constructing domain-specific talent-development models. With examples for the application of the TAD framework to the domains of mathematics, music, and visual arts, the review provided supports the suitability of the TAD framework for domain-specific model construction and indicates numerous research gaps and open questions that should be addressed in future research.
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Affiliation(s)
| | - Jessika Golle
- Hector Research Institute of Education Sciences and Psychology, University of Tuebingen
| | | | | | - Aaron Kozbelt
- Department of Psychology, Brooklyn College, City University of New York
| | | | - Paula Olszewski-Kubilius
- Center for Talent Development and School of Education and Social Policy, Northwestern University
| | | | - Rena Subotnik
- Center for Psychology in Schools and Education, American Psychological Association, Washington, DC
| | - Miriam Vock
- Department of Educational Sciences, University of Potsdam
| | - Frank C. Worrell
- Graduate School of Education, University of California, Berkeley
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33
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Cortical gyrification in relation to age and cognition in older adults. Neuroimage 2020; 212:116637. [PMID: 32081782 DOI: 10.1016/j.neuroimage.2020.116637] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 01/31/2020] [Accepted: 02/12/2020] [Indexed: 12/21/2022] Open
Abstract
Gyrification of the cerebral cortex changes with aging and relates to development of cognitive function during early life and midlife. Little is known about how gyrification relates to age and cognitive function later in life. We investigated this in 4397 individuals (mean age: 63.5 years, range: 45.7 to 97.9) from the Rotterdam Study, a population-based cohort. Global and local gyrification were assessed from T1-weighted images. A measure for global cognition, the g-factor, was calculated from five cognitive tests. Older age was associated with lower gyrification (mean difference per year = -0.0021; 95% confidence interval = -0.0025; -0.0017). Non-linear terms did not improve the models. Age related to lower gyrification in the parietal, frontal, temporal and occipital regions, and higher gyrification in the medial prefrontal cortex. Higher levels of the g-factor were associated with higher global gyrification (mean difference per g-factor unit = 0.0044; 95% confidence interval = 0.0015; 0.0073). Age and the g-factor did not interact in relation to gyrification (p > 0.05). The g-factor bilaterally associated with gyrification in three distinct clusters. The first cluster encompassed the superior temporal gyrus, the insular cortex and the postcentral gyrus, the second cluster the lingual gyrus and the precuneus, and the third cluster the orbitofrontal cortex. These clusters largely remained statistically significant after correction for cortical surface area. Overall, the results support the notion that gyrification varies with aging and cognition during and after midlife, and suggest that gyrification is a potential marker for age-related brain and cognitive decline beyond midlife.
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34
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Abstract
Intelligence is a crucial psychologic construct for understanding human behavioral differences. This construct is based on one of the most replicated findings in psychology (the positive manifold): individuals can be reliably ordered according to their cognitive performance. Those showing high levels in ability X are more likely to show high levels in the remaining abilities, while those showing low levels in ability X are more likely to show low levels in the remaining abilities. Intelligence is characterized as a general cognitive ability integrating more than 80 distinguishable but related abilities. The mainstream definition states that intelligence is a general mental ability for reasoning, planning, solving problems, think abstractly, comprehending complex ideas, and learning. Intellectual abilities are measured by standardized tests showing highly reliable and valid indices. Intelligence is a highly stable psychologic trait, but different abilities change following disparate trends across the life span. These average trends, however, (a) hide the wide range of individual differences in the rates of change and (b) are consistent with the fact that abilities show orchestrated changes, which is consistent with the positive manifold. This chapter presents examples of the conventional testing paradigm along with recent developments based on cognitive psychology and cognitive neuroscience.
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Affiliation(s)
- Roberto Colom
- Department of Biological and Health Psychology, Universidad Autónoma de Madrid, Madrid, Spain.
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35
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Frøyland LR, Soest T. Adolescent boys' physical fighting and adult life outcomes: Examining the interplay with intelligence. Aggress Behav 2020; 46:72-83. [PMID: 31631354 DOI: 10.1002/ab.21871] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 09/19/2019] [Accepted: 10/03/2019] [Indexed: 12/28/2022]
Abstract
Although it is well known that adolescent delinquent behavior is related to poor outcomes in adulthood, longitudinal research on specific acts of delinquency and their interplay with important individual characteristics in predicting future outcomes is scarce. We aimed to examine how physical fighting-one of the most common acts of violent delinquency among adolescent boys-is related to adult life success in several domains, and how intelligence influences these associations. The study used data from 1,083 boys that participated in the population-based longitudinal Young in Norway Study, following adolescents from 1992 to 2015, by combining self-reports at four time points with comprehensive information from registers. Results showed that adolescent boys' physical fighting was associated with poor adult outcomes in the domains of employment, education, and criminal behavior. Associations remained significant even after controlling for conduct problems in general-which isolated the effects of fighting from other delinquent acts-as well as from a variety of other potential confounders. Detailed analyses on the interplay of physical fighting and intelligence showed that some parts of the associations between adolescent boys' fighting and several adverse adult outcomes could be ascribed to lower intelligence among the fighters. Moreover, intelligence moderated the relationship between physical fighting and adult education. Adolescent fighting was not related to educational attainment among boys with high intelligence, whereas boys with lower intelligence experienced detrimental effects of adolescent fighting. The analyses show the importance of considering adolescent boys' physical fighting as a potential risk factor for future social marginalization.
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Affiliation(s)
- Lars Roar Frøyland
- NOVA—Norwegian Social ResearchOsloMet—Oslo Metropolitan University Oslo Norway
| | - Tilmann Soest
- NOVA—Norwegian Social ResearchOsloMet—Oslo Metropolitan University Oslo Norway
- PROMENTA Research Center, Department of PsychologyUniversity of Oslo Oslo Norway
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36
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Comes AL, Senner F, Budde M, Adorjan K, Anderson-Schmidt H, Andlauer TFM, Gade K, Hake M, Heilbronner U, Kalman JL, Reich-Erkelenz D, Klöhn-Saghatolislam F, Schaupp SK, Schulte EC, Juckel G, Dannlowski U, Schmauß M, Zimmermann J, Reimer J, Reininghaus E, Anghelescu IG, Arolt V, Baune BT, Konrad C, Thiel A, Fallgatter AJ, Nieratschker V, Figge C, von Hagen M, Koller M, Becker T, Wigand ME, Jäger M, Dietrich DE, Stierl S, Scherk H, Spitzer C, Folkerts H, Witt SH, Degenhardt F, Forstner AJ, Rietschel M, Nöthen MM, Wiltfang J, Falkai P, Schulze TG, Papiol S. The genetic relationship between educational attainment and cognitive performance in major psychiatric disorders. Transl Psychiatry 2019; 9:210. [PMID: 31462630 PMCID: PMC6713703 DOI: 10.1038/s41398-019-0547-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 06/03/2019] [Accepted: 07/17/2019] [Indexed: 11/13/2022] Open
Abstract
Cognitive deficits are a core feature of psychiatric disorders like schizophrenia and bipolar disorder. Evidence supports a genome-wide polygenic score (GPS) for educational attainment (GPSEDU) can be used to explain variability in cognitive performance. We aimed to identify different cognitive domains associated with GPSEDU in a transdiagnostic clinical cohort of chronic psychiatric patients with known cognitive deficits. Bipolar and schizophrenia patients from the PsyCourse cohort (N = 730; 43% female) were used. Likewise, we tested whether GPSs for schizophrenia (GPSSZ) and bipolar disorder (GPSBD) were associated with cognitive outcomes. GPSEDU explained 1.5% of variance in the backward verbal digit span, 1.9% in the number of correctly recalled words of the Verbal Learning and Memory Test, and 1.1% in crystallized intelligence. These effects were robust to the influences of treatment and diagnosis. No significant associations between GPSSZ or GPSBD with cognitive outcomes were found. Furthermore, these risk scores did not confound the effect of GPSEDU on cognitive outcomes. GPSEDU explains a small fraction of cognitive performance in adults with psychiatric disorders, specifically for domains related to linguistic learning and working memory. Investigating such a proxy-phenotype longitudinally, could give intriguing insight into the disease course, highlighting at what time genes play a more influential role on cognitive performance. Better understanding the origin of these deficits might help identify those patients at risk for lower levels of functioning and poor social outcomes. Polygenic estimates may in the future be part of predictive models for more personalized interventions.
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Affiliation(s)
- Ashley L Comes
- Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, 80336, Germany.
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, 80804, Germany.
| | - Fanny Senner
- Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, 80336, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, 80336, Germany
| | - Monika Budde
- Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, 80336, Germany
| | - Kristina Adorjan
- Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, 80336, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, 80336, Germany
| | - Heike Anderson-Schmidt
- Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, 80336, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, 37075, Germany
| | - Till F M Andlauer
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, 81675, Germany
| | - Katrin Gade
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, 37075, Germany
| | - Maria Hake
- Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, 80336, Germany
| | - Urs Heilbronner
- Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, 80336, Germany
| | - Janos L Kalman
- Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, 80336, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, 80804, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, 80336, Germany
| | - Daniela Reich-Erkelenz
- Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, 80336, Germany
| | - Farah Klöhn-Saghatolislam
- Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, 80336, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, 80336, Germany
| | - Sabrina K Schaupp
- Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, 80336, Germany
| | - Eva C Schulte
- Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, 80336, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, 80336, Germany
| | - Georg Juckel
- Department of Psychiatry, Ruhr University Bochum, LWL University Hospital, Bochum, 44791, Germany
| | - Udo Dannlowski
- Department of Psychiatry, University of Münster, Münster, 48149, Germany
| | - Max Schmauß
- Department of Psychiatry and Psychotherapy, Bezirkskrankenhaus Augsburg, Augsburg, 86156, Germany
| | - Jörg Zimmermann
- Psychiatrieverbund Oldenburger Land gGmbH, Karl-Jaspers-Klinik, Bad Zwischenahn, 26160, Germany
| | - Jens Reimer
- Department of Psychiatry and Psychotherapy, University Medical Centre Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany
| | - Eva Reininghaus
- Department of Psychiatry and Psychotherapeutic Medicine, Research Unit for Bipolar Affective Disorder, Medical University of Graz, Graz, 8036, Austria
| | | | - Volker Arolt
- Department of Psychiatry, University of Münster, Münster, 48149, Germany
| | - Bernhard T Baune
- Department of Psychiatry, University of Münster, Münster, 48149, Germany
- Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Carsten Konrad
- Department of Psychiatry and Psychotherapy, Agaplesion Diakonieklinikum, Rotenburg, 27356, Germany
| | - Andreas Thiel
- Department of Psychiatry and Psychotherapy, Agaplesion Diakonieklinikum, Rotenburg, 27356, Germany
| | - Andreas J Fallgatter
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, 72076, Germany
| | - Vanessa Nieratschker
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, 72076, Germany
| | - Christian Figge
- Karl-Jaspers Clinic, European Medical School Oldenburg-Groningen, Oldenburg, 26160, Germany
| | - Martin von Hagen
- Clinic for Psychiatry and Psychotherapy, Clinical Center Werra-Meißner, Eschwege, 37269, Germany
| | - Manfred Koller
- Asklepios Specialized Hospital, Göttingen, 37081, Germany
| | - Thomas Becker
- Department of Psychiatry II, Ulm University, Bezirkskrankenhaus Günzburg, Günzburg, 89312, Germany
| | - Moritz E Wigand
- Department of Psychiatry II, Ulm University, Bezirkskrankenhaus Günzburg, Günzburg, 89312, Germany
| | - Markus Jäger
- Department of Psychiatry II, Ulm University, Bezirkskrankenhaus Günzburg, Günzburg, 89312, Germany
| | - Detlef E Dietrich
- AMEOS Clinical Center Hildesheim, Hildesheim, 31135, Germany
- Center für Systems Neuroscience (ZSN) Hannover, Hannover, 30559, Germany
- Dept. of Psychiatry, Medical School of Hannover, Hannover, 30625, Germany
| | | | - Harald Scherk
- AMEOS Clinical Center Osnabrück, Osnabrück, 49088, Germany
| | - Carsten Spitzer
- ASKLEPIOS Specialized Hospital Tiefenbrunn, Rosdorf, 37124, Germany
- Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, 18051, Germany
| | - Here Folkerts
- Department of Psychiatry, Psychotherapy and Psychosomatics, Clinical Center Wilhelmshaven, Wilhelmshaven, 26389, Germany
| | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, 68159, Germany
| | - Franziska Degenhardt
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, 53127, Germany
| | - Andreas J Forstner
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, 53127, Germany
- Center for Human Genetics, University of Marburg, Marburg, 35033, Germany
- Department of Biomedicine, University of Basel, Basel, 4031, Switzerland
- Department of Psychiatry (UPK), University of Basel, Basel, 4002, Switzerland
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, 68159, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, 53127, Germany
| | - Jens Wiltfang
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, 37075, Germany
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, 37075, Germany
- iBiMED, Medical Sciences Department, University of Aveiro, Aveiro, 3810-193, Portugal
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, 80336, Germany
| | - Thomas G Schulze
- Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, 80336, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, 80336, Germany
| | - Sergi Papiol
- Institute of Psychiatric Phenomics and Genomics, University Hospital, LMU Munich, Munich, 80336, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, 80336, Germany
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Taylor AM, Pattie A, Deary IJ. Cohort Profile Update: The Lothian Birth Cohorts of 1921 and 1936. Int J Epidemiol 2019; 47:1042-1042r. [PMID: 29546429 PMCID: PMC6124629 DOI: 10.1093/ije/dyy022] [Citation(s) in RCA: 140] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/29/2018] [Indexed: 01/02/2023] Open
Affiliation(s)
- Adele M Taylor
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Alison Pattie
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Department of Psychology, University of Edinburgh, Edinburgh, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
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Instability in longitudinal childhood IQ scores of Guatemalan high SES individuals born between 1941-1953. PLoS One 2019; 14:e0215828. [PMID: 31022230 PMCID: PMC6483529 DOI: 10.1371/journal.pone.0215828] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Accepted: 04/09/2019] [Indexed: 11/19/2022] Open
Abstract
Childhood IQ has been used to predict later life outcomes across disciplines in epidemiology, education, and psychology. Most often only a single childhood IQ test is available or is used for these purposes in the belief that IQ is stable across the life course. The primary aim of this study was to examine the longitudinal stability of individuals' IQ test scores derived from school-age tests. The secondary aim was to investigate the association of the pre-adult scores with later life intelligence scores. The longitudinal pre-adult IQ scores of 42 high socioeconomic status Guatemalans born 1941-1953 were analysed and showed low stability of longitudinal test scores. Fluctuations of >1SD were found for 59.5% of the sample. The same participants, aged 64-76 years, were re-assessed and average pre-adult IQ explained 12% of variance in the older age intelligence score. The reasons behind the longitudinal instability in test scores reported in this study remains unknown but the results suggest single point measurements of intelligence before adulthood should be regarded with some caution.
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Girn M, Mills C, Christoff K. Linking brain network reconfiguration and intelligence: Are we there yet? Trends Neurosci Educ 2019; 15:62-70. [PMID: 31176472 DOI: 10.1016/j.tine.2019.04.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 02/22/2019] [Accepted: 04/04/2019] [Indexed: 01/08/2023]
Abstract
Recent applications of dynamic network analyses to functional neuroimaging data have revealed relationships between a number of cognition conditions and the dynamic reconfiguration of brain networks. Here we critically review such applications of network neuroscience to intelligence. After providing an overview of network neuroscience, we center our discussion around the recently proposed Network Neuroscience Theory of Intelligence (Barbey, 2017). We evaluate and review existing empirical support for the theses made by this theory and argue that while studies strongly suggest their plausibility, evidence to date has largely been indirect. We propose avenues for future research to directly evaluate these theses by overcoming the methodological and analytical shortcomings of previous studies. In doing so, our goal is to stimulate future empirical investigations and present valuable ways forward in the network neuroscience of intelligence.
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Affiliation(s)
- Manesh Girn
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Department of Psychology, University of British Columbia, Vancouver, British Columbia.
| | - Caitlin Mills
- Department of Psychology, University of New Hampshire, Durham, New Hampshire
| | - Kalina Christoff
- Department of Psychology, University of British Columbia, Vancouver, British Columbia; Centre for Brain Health, University of British Columbia, Vancouver, British Columbia
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40
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Cheng H, Furnham A. Correlates of Adult Vocabulary Task Performance: Findings from a British Cohort. J Intell 2019; 7:jintelligence7010002. [PMID: 31162381 PMCID: PMC6526404 DOI: 10.3390/jintelligence7010002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Revised: 12/19/2018] [Accepted: 01/03/2019] [Indexed: 11/16/2022] Open
Abstract
This study explored a longitudinal data set of 4361 adults (2119 males and 2239 females) to examine factors that influence adult vocabulary task performance. Data were collected at birth, in childhood (age 10 years), during teenage years (age 16 years), and in adulthood (ages 30, 34, and 42 years) to examine the effects of family social status, childhood cognitive ability, teenager locus of control, psychological distress, educational qualifications, and occupational prestige in adulthood on an adult vocabulary task-an index of crystallized intelligence. Structural equation modeling showed that childhood cognitive ability, teenager locus of control, education, and occupation were all significant and direct predictors of adult vocabulary task performance. Parental social status affected the outcome variable mediated through educational qualifications. The strongest predictor of adult vocabulary task performance was childhood cognitive ability, followed by educational qualifications and locus of control. Finally, limitations were acknowledged.
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Affiliation(s)
- Helen Cheng
- Department of Psychology, University College London, London WC1E 6BT, UK.
- ESRC Centre for Learning and Life Chances in Knowledge Economies and Societies, Institute of Education, University of London, London WC1H 0AL, UK.
| | - Adrian Furnham
- BI: Norwegian Business School, Nydalsveien 37, 0484 Oslo, Norway.
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41
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Shen X, Cox SR, Adams MJ, Howard DM, Lawrie SM, Ritchie SJ, Bastin ME, Deary IJ, McIntosh AM, Whalley HC. Resting-State Connectivity and Its Association With Cognitive Performance, Educational Attainment, and Household Income in the UK Biobank. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 3:878-886. [PMID: 30093342 PMCID: PMC6289224 DOI: 10.1016/j.bpsc.2018.06.007] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Revised: 06/18/2018] [Accepted: 06/18/2018] [Indexed: 01/01/2023]
Abstract
Background Cognitive ability is an important predictor of lifelong physical and mental well-being, and impairments are associated with many psychiatric disorders. Higher cognitive ability is also associated with greater educational attainment and increased household income. Understanding neural mechanisms underlying cognitive ability is of crucial importance for determining the nature of these associations. In the current study, we examined the spontaneous activity of the brain at rest to investigate its relationships with not only cognitive ability but also educational attainment and household income. Methods We used a large sample of resting-state neuroimaging data from the UK Biobank (n = 3950). Results First, analysis at the whole-brain level showed that connections involving the default mode network (DMN), frontoparietal network (FPN), and cingulo-opercular network (CON) were significantly positively associated with levels of cognitive performance assessed by a verbal-numerical reasoning test (standardized β cingulo-opercular values ranged from 0.054 to 0.097, pcorrected < .038). Connections associated with higher levels of cognitive performance were also significantly positively associated with educational attainment (r = .48, n = 4160) and household income (r = .38, n = 3793). Furthermore, analysis on the coupling of functional networks showed that better cognitive performance was associated with more positive DMN–CON connections, decreased cross-hemisphere connections between the homotopic network in the CON and FPN, and stronger CON–FPN connections (absolute βs ranged from 0.034 to 0.063, pcorrected < .045). Conclusions The current study found that variation in brain resting-state functional connectivity was associated with individual differences in cognitive ability, largely involving the DMN and lateral prefrontal network. In addition, we provide evidence of shared neural associations of cognitive ability, educational attainment, and household income.
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Affiliation(s)
- Xueyi Shen
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom.
| | - Simon R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom; Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark J Adams
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - David M Howard
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Stephen M Lawrie
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Stuart J Ritchie
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom; Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom; Brain Research Imaging Centre, University of Edinburgh, Edinburgh, United Kingdom
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom; Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom
| | - Heather C Whalley
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
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42
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Yu H, McCoach DB, Gottfried AW, Gottfried AE. Stability of intelligence from infancy through adolescence: An autoregressive latent variable model. INTELLIGENCE 2018. [DOI: 10.1016/j.intell.2018.03.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Davies G, Lam M, Harris SE, Trampush JW, Luciano M, Hill WD, Hagenaars SP, Ritchie SJ, Marioni RE, Fawns-Ritchie C, Liewald DCM, Okely JA, Ahola-Olli AV, Barnes CLK, Bertram L, Bis JC, Burdick KE, Christoforou A, DeRosse P, Djurovic S, Espeseth T, Giakoumaki S, Giddaluru S, Gustavson DE, Hayward C, Hofer E, Ikram MA, Karlsson R, Knowles E, Lahti J, Leber M, Li S, Mather KA, Melle I, Morris D, Oldmeadow C, Palviainen T, Payton A, Pazoki R, Petrovic K, Reynolds CA, Sargurupremraj M, Scholz M, Smith JA, Smith AV, Terzikhan N, Thalamuthu A, Trompet S, van der Lee SJ, Ware EB, Windham BG, Wright MJ, Yang J, Yu J, Ames D, Amin N, Amouyel P, Andreassen OA, Armstrong NJ, Assareh AA, Attia JR, Attix D, Avramopoulos D, Bennett DA, Böhmer AC, Boyle PA, Brodaty H, Campbell H, Cannon TD, Cirulli ET, Congdon E, Conley ED, Corley J, Cox SR, Dale AM, Dehghan A, Dick D, Dickinson D, Eriksson JG, Evangelou E, Faul JD, Ford I, Freimer NA, Gao H, Giegling I, Gillespie NA, Gordon SD, Gottesman RF, Griswold ME, Gudnason V, Harris TB, Hartmann AM, Hatzimanolis A, Heiss G, Holliday EG, Joshi PK, Kähönen M, Kardia SLR, Karlsson I, Kleineidam L, Knopman DS, Kochan NA, Konte B, Kwok JB, Le Hellard S, Lee T, Lehtimäki T, Li SC, Lill CM, Liu T, Koini M, London E, Longstreth WT, Lopez OL, Loukola A, Luck T, Lundervold AJ, Lundquist A, Lyytikäinen LP, Martin NG, Montgomery GW, Murray AD, Need AC, Noordam R, Nyberg L, Ollier W, Papenberg G, Pattie A, Polasek O, Poldrack RA, Psaty BM, Reppermund S, Riedel-Heller SG, Rose RJ, Rotter JI, Roussos P, Rovio SP, Saba Y, Sabb FW, Sachdev PS, Satizabal CL, Schmid M, Scott RJ, Scult MA, Simino J, Slagboom PE, Smyrnis N, Soumaré A, Stefanis NC, Stott DJ, Straub RE, Sundet K, Taylor AM, Taylor KD, Tzoulaki I, Tzourio C, Uitterlinden A, Vitart V, Voineskos AN, Kaprio J, Wagner M, Wagner H, Weinhold L, Wen KH, Widen E, Yang Q, Zhao W, Adams HHH, Arking DE, Bilder RM, Bitsios P, Boerwinkle E, Chiba-Falek O, Corvin A, De Jager PL, Debette S, Donohoe G, Elliott P, Fitzpatrick AL, Gill M, Glahn DC, Hägg S, Hansell NK, Hariri AR, Ikram MK, Jukema JW, Vuoksimaa E, Keller MC, Kremen WS, Launer L, Lindenberger U, Palotie A, Pedersen NL, Pendleton N, Porteous DJ, Räikkönen K, Raitakari OT, Ramirez A, Reinvang I, Rudan I, Dan Rujescu, Schmidt R, Schmidt H, Schofield PW, Schofield PR, Starr JM, Steen VM, Trollor JN, Turner ST, Van Duijn CM, Villringer A, Weinberger DR, Weir DR, Wilson JF, Malhotra A, McIntosh AM, Gale CR, Seshadri S, Mosley TH, Bressler J, Lencz T, Deary IJ. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat Commun 2018; 9:2098. [PMID: 29844566 PMCID: PMC5974083 DOI: 10.1038/s41467-018-04362-x] [Citation(s) in RCA: 380] [Impact Index Per Article: 63.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Accepted: 04/23/2018] [Indexed: 11/15/2022] Open
Abstract
General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N = 300,486; age 16-102) and find 148 genome-wide significant independent loci (P < 5 × 10-8) associated with general cognitive function. Within the novel genetic loci are variants associated with neurodegenerative and neurodevelopmental disorders, physical and psychiatric illnesses, and brain structure. Gene-based analyses find 709 genes associated with general cognitive function. Expression levels across the cortex are associated with general cognitive function. Using polygenic scores, up to 4.3% of variance in general cognitive function is predicted in independent samples. We detect significant genetic overlap between general cognitive function, reaction time, and many health variables including eyesight, hypertension, and longevity. In conclusion we identify novel genetic loci and pathways contributing to the heritability of general cognitive function.
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Affiliation(s)
- Gail Davies
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Max Lam
- Institute of Mental Health, Singapore, 539747, Singapore
| | - Sarah E Harris
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Medical Genetics Section, Centre for Genomic & Experimental Medicine, MRC Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, UK
| | - Joey W Trampush
- BrainWorkup, LLC, Los Angeles, 90033, CA, USA
- Department of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, 90033, CA, USA
| | - Michelle Luciano
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - W David Hill
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Saskia P Hagenaars
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK
| | - Stuart J Ritchie
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Riccardo E Marioni
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Medical Genetics Section, Centre for Genomic & Experimental Medicine, MRC Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, UK
| | - Chloe Fawns-Ritchie
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - David C M Liewald
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Judith A Okely
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Ari V Ahola-Olli
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, 20520, Finland
- Department of Internal Medicine, Satakunta Central Hospital, Pori, 28100, Finland
| | - Catriona L K Barnes
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland
| | - Lars Bertram
- Max Planck Institute for Molecular Genetics, Berlin, 14195, Germany
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, 98101, Washington, USA
| | - Katherine E Burdick
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 3), James J. Peters VA Medical Center, Bronx, 10468, NY, USA
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, MA, USA
| | - Andrea Christoforou
- NORMENT, K.G. Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, 5021, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, 5020, Norway
| | - Pamela DeRosse
- Institute of Mental Health, Singapore, 539747, Singapore
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, 11030, NY, USA
| | - Srdjan Djurovic
- NORMENT, K.G. Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, 5021, Norway
- Department of Medical Genetics, Oslo University Hospital, University of Bergen, Oslo, 0424, Norway
| | - Thomas Espeseth
- Department of Psychology, University of Oslo, Oslo, 0373, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, 0315, Norway
| | - Stella Giakoumaki
- Department of Psychology, University of Crete, Crete, GR-74100, Greece
| | - Sudheer Giddaluru
- NORMENT, K.G. Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, 5021, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, 5020, Norway
| | - Daniel E Gustavson
- Department of Psychiatry, University of California, San Diego, 92093, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, 92093, CA, USA
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Generation Scotland, Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Edith Hofer
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University of Graz, Graz, 8036, Austria
- Institute of Medical Informatics Statistics and Documentation, Medical University of Graz, Graz, 8036, Austria
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3015, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, 3015, The Netherlands
- Department of Neurology, Erasmus University Medical Center, Rotterdam, xxxxxx, The Netherlands
| | - Robert Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SE-171 77, Sweden
| | - Emma Knowles
- Department of Psychiatry, Yale University School of Medicine, New Haven, 06511, CT, USA
| | - Jari Lahti
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, 00014, Finland
- Helsinki Collegium for Advanced Studies, University of Helsinki, Helsinki, 00014, Finland
| | - Markus Leber
- Department of Psychiatry and Psychotherapy, University of Cologne, Cologne, D-50937, Germany
| | - Shuo Li
- Department of Biostatistics, Boston University School of Public Health, Boston, 02118, MA, USA
| | - Karen A Mather
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, 2031, Australia
| | - Ingrid Melle
- NORMENT, K.G. Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, 5021, Norway
- Department of Psychology, University of Oslo, Oslo, 0373, Norway
| | - Derek Morris
- Neuroimaging, Cognition & Genomics (NICOG) Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland Galway, Galway, H91 TK33, Ireland
| | - Christopher Oldmeadow
- Medical Research Institute and Faculty of Health, University of Newcastle, New South Wa0les, 2308, Australia
| | - Teemu Palviainen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, FI-00014, Finland
| | - Antony Payton
- Centre for EpidemiologyDivision of Population Health, Health Services Research & Primary Care, The University of Manchester, Manchester, M13 9PL, UK
| | - Raha Pazoki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Katja Petrovic
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University of Graz, Graz, 8036, Austria
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, 92521, CA, USA
| | - Muralidharan Sargurupremraj
- University of Bordeaux, Bordeaux Population Health Research Center, INSERM UMR 1219, F-33000, Bordeaux, France
| | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, 04107, Germany
- LIFE-Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, 04107, Germany
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, 48104, USA
| | - Albert V Smith
- Icelandic Heart Association, Kopavogur, IS-201, Iceland
- University of Iceland, Reykjavik, 101, Iceland
| | - Natalie Terzikhan
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3015, The Netherlands
- Department of Respiratory Medicine, Ghent University Hospital, De Pintelaan 185, 9000, Ghent, Belgium
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, 2031, Australia
| | - Stella Trompet
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, 2333, The Netherlands
| | - Sven J van der Lee
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3015, The Netherlands
| | - Erin B Ware
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, 48104, USA
| | - B Gwen Windham
- Department of Medicine, Division of Geriatrics, University of Mississippi Medical Center, Jackson, 39216, MS, USA
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, 4072, Australia
- Centre for Advanced Imaging, University of Queensland, Brisbane, 4072, Australia
| | - Jingyun Yang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, 60612, IL, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, 60612, IL, USA
| | - Jin Yu
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, 11030, NY, USA
| | - David Ames
- National Ageing Research Institute, Royal Melbourne Hospital, Victoria, 3052, Australia
- Academic Unit for Psychiatry of Old Age, University of Melbourne, St George's Hospital, Kew, 3010, Australia
| | - Najaf Amin
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3015, The Netherlands
| | - Philippe Amouyel
- Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-LabEx DISTALZ, F-59000, Lille, France
| | - Ole A Andreassen
- Department of Psychology, University of Oslo, Oslo, 0373, Norway
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, 0372, Norway
| | | | - Amelia A Assareh
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, 2031, Australia
| | - John R Attia
- Hunter Medical Research Institute and Faculty of Health, University of Newcastle, New South Wales, 2305, Australia
| | - Deborah Attix
- Department of NeurologyBryan Alzheimer's Disease Research Center, and Center for Genomic and Computational Biology, Duke University Medical Center, Durham, 27708, NC, USA
- Psychiatry and Behavioral Sciences, Division of Medical Psychology, and Department of Neurology, Duke University Medical Center, Durham, 27708, NC, USA
| | - Dimitrios Avramopoulos
- Department of Psychiatry, Johns Hopkins University School of Medicine, MD, Baltimore, 21287, USA
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, MD, Baltimore, 21287, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, 60612, IL, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, 60612, IL, USA
| | - Anne C Böhmer
- Institute of Human Genetics, University of Bonn, Bonn, 53113, Germany
- Department of Genomics, Life and Brain Center, University of Bonn, Bonn, 53113, Germany
| | - Patricia A Boyle
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, 60612, IL, USA
- Departments of Behavioral Sciences, Rush University Medical Center, Chicago, 60612, IL, USA
| | - Henry Brodaty
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, 2031, Australia
- Dementia Centre for Research Collaboration, University of New South Wales, Sydney, 2031, NSW, Australia
| | - Harry Campbell
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, 06520, CT, USA
| | | | - Eliza Congdon
- UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, 90024, CA, USA
| | | | - Janie Corley
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Simon R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Anders M Dale
- Department of Psychiatry, University of California, San Diego, 92093, CA, USA
- Department of Cognitive Science, University of California, San Diego, La Jolla, 92093, CA, USA
- Department of Neurosciences, University of California, San Diego, La Jolla, 92093, CA, USA
- Department of Radiology, University of California, San Diego, La Jolla, 92093, CA, USA
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
- MRC-PHE Centre for Environment, School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Danielle Dick
- Department of Psychology, Virginia Commonwealth University, Richmond, 23284, VA, USA
| | - Dwight Dickinson
- Clinical and Translational Neuroscience Branch, Intramural Research Program, National Institute of Mental Health, National Institute of Health, Bethesda, 20892, MD, USA
| | - Johan G Eriksson
- National Institute for Health and Welfare, Helsinki, FI-00271, Finland
- Department of General Practice and Primary Health Care, University of Helsinki, Helsinki, 00290, Finland
- Helsinki University Central Hospital, Unit of General Practice, Helsinki, FI-00029, Finland
- Folkhälsan Research Centre, Helsinki, 2018, Finland
| | - Evangelos Evangelou
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
- National Institute for Health and Welfare, Helsinki, FI-00271, Finland
| | - Jessica D Faul
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, 48104, USA
| | - Ian Ford
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
| | - Nelson A Freimer
- UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, 90024, CA, USA
| | - He Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Ina Giegling
- Department of Psychiatry, Martin Luther University of Halle-Wittenberg, Halle, 06108, Germany
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, 23298, VA, USA
| | - Scott D Gordon
- QIMR Berghofer Medical Research Institute, Brisbane, 4029, Australia
| | - Rebecca F Gottesman
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, 21287, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, 21205, MD, USA
| | - Michael E Griswold
- Department of Data Science, University of Mississippi Medical Center, Jackson, 39216, MS, USA
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, IS-201, Iceland
- University of Iceland, Reykjavik, 101, Iceland
| | - Tamara B Harris
- Intramural Research Program National Institutes on Aging, National Institutes of Health, Bethesda, 20892, MD, USA
| | - Annette M Hartmann
- Department of Psychiatry, Martin Luther University of Halle-Wittenberg, Halle, 06108, Germany
| | - Alex Hatzimanolis
- Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, 11528, Greece
- University Mental Health Research Institute, Athens, GR-156 01, Greece
- Neurobiology Research Institute, Theodor-Theohari Cozzika Foundation, Athens, 11521, Greece
| | - Gerardo Heiss
- Department of Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, 27599, NC, USA
| | - Elizabeth G Holliday
- Hunter Medical Research Institute and Faculty of Health, University of Newcastle, New South Wales, 2305, Australia
| | - Peter K Joshi
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital, and Finnish Cardiovascular Research Center, Tampere, FI-33014, Finland
- Faculty of Medicine and Life Sciences, University of Tampere, Tampere, 33521, Finland
- Department of Clinical Physiology, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, 33014, Finland
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Ida Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SE-171 77, Sweden
| | - Luca Kleineidam
- Department of Psychiatry Medical Faculty, University of Cologne, Cologne, 50923, Germany
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, 53127, Germany
- German Center for Neurodegenerative Diseases (DZNE), Bonn, 53127, Germany
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, 53127, Germany
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, 55905, MN, USA
| | - Nicole A Kochan
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, 2031, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, 2031, Australia
| | - Bettina Konte
- Department of Psychiatry, Martin Luther University of Halle-Wittenberg, Halle, 06108, Germany
| | - John B Kwok
- Brain and Mind Centre-The University of Sydney, Camperdown, NSW, 2050, Australia
- School of Medical Sciences, University of New South Wales, Sydney, 2052, Australia
| | - Stephanie Le Hellard
- NORMENT, K.G. Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, 5021, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, 5020, Norway
| | - Teresa Lee
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, 2031, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, 2031, Australia
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, 33014, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, 33014, Finland
| | - Shu-Chen Li
- Max Planck Institute for Human Development, Berlin, 14195, Germany
- Technische Universität Dresden, Dresden, 01187, Germany
| | - Christina M Lill
- Genetic and Molecular Epidemiology Group, Lübeck Interdisciplinary Platform for Genome Analytics, Institutes of Neurogenetics & Cardiogenetics, University of Lübeck, Lübeck, Germany
| | - Tian Liu
- Max Planck Institute for Molecular Genetics, Berlin, 14195, Germany
- Max Planck Institute for Human Development, Berlin, 14195, Germany
| | - Marisa Koini
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University of Graz, Graz, 8036, Austria
| | - Edythe London
- UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, 90024, CA, USA
| | - Will T Longstreth
- Department of Neurology, School of Medicine, University of Washington, Seattle, 98195-6465, WA, USA
- Department of Epidemiology, University of Washington, Seattle, 98195, WA, USA
| | - Oscar L Lopez
- Department of Neurology and Psychiatry, University of Pittsburgh, Pittsburgh, 15213, PA, USA
| | - Anu Loukola
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, FI-00014, Finland
| | - Tobias Luck
- LIFE-Leipzig Research Center for Civilization Diseases, University of Leipzig, Leipzig, 04107, Germany
- Institute of Social Medicine, Occupational Health and Public Health (ISAP), University of Leipzig, Leipzig, 04103, Germany
| | - Astri J Lundervold
- Department of Biological and Medical Psychology, University of Bergen, Bergen, 5009, Norway
- K. G. Jebsen Center for Neuropsychiatry, University of Bergen, Bergen, N-5009, Norway
| | - Anders Lundquist
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, SE-901 87, Sweden
- Department of Statistics, USBE Umeå University, S-907 97, Umeå, Sweden
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, 33014, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere, 33014, Finland
| | - Nicholas G Martin
- QIMR Berghofer Medical Research Institute, Brisbane, 4029, Australia
| | - Grant W Montgomery
- QIMR Berghofer Medical Research Institute, Brisbane, 4029, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, 4072, Australia
| | - Alison D Murray
- Generation Scotland, Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
- The Institute of Medical Sciences, Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, AB25 2ZD, UK
| | - Anna C Need
- Division of Brain Sciences, Department of Medicine, Imperial College, London, SW7 2AZ, UK
| | - Raymond Noordam
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, 2333, The Netherlands
| | - Lars Nyberg
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, Umeå, SE-901 87, Sweden
- Department of Radiation Sciences, Umeå University, Umeå, SE-901 87, Sweden
- Department of Integrative Medical Biology, Umeå University, Umeå, SE-901 87, Sweden
| | - William Ollier
- Centre for Integrated Genomic Medical Research, Institute of Population Health, University of Manchester, Manchester, M13 9PT, UK
| | - Goran Papenberg
- Max Planck Institute for Human Development, Berlin, 14195, Germany
- Karolinska Institutet, Aging Research Center, Stockholm University, Stockholm, SE-113 30, Sweden
| | - Alison Pattie
- Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Ozren Polasek
- Gen-Info LLC, Zagreb, 10000, Croatia
- Faculty of Medicine, University of Split, Split, 21000, Croatia
| | - Russell A Poldrack
- Department of Psychology, Stanford University, Palo Alto, 94305-2130, CA, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, 98101, Washington, USA
- Deparment of Health Services, University of Washington, Seattle, 98195-7660, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, 98101, WA, USA
| | - Simone Reppermund
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, 2031, Australia
- Department of Developmental Disability Neuropsychiatry, School of Psychiatry, University of New South Wales, Sydney, 2052, Australia
| | - Steffi G Riedel-Heller
- Institute of Social Medicine, Occupational Health and Public Health (ISAP), University of Leipzig, Leipzig, 04103, Germany
| | - Richard J Rose
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405-7007, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
- Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, 90509, CA, USA
| | - Panos Roussos
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA
- Department of Genetics and Genomic Science and Institute for Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2), James J. Peters VA Medical Center, Bronx, 10468, NY, USA
| | - Suvi P Rovio
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, 20520, Finland
| | - Yasaman Saba
- Institute of Molecular Biology and Biochemistry, Centre for Molecular Medicine, Medical University of Graz, Graz, 8036, Austria
| | - Fred W Sabb
- Robert and Beverly Lewis Center for Neuroimaging, University of Oregon, Eugene, 97403, OR, USA
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, 2031, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, 2031, Australia
| | - Claudia L Satizabal
- Department of Neurology, Boston University School of Medicine, Boston, 02118, MA, USA
- The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, 01702-5827, MA, USA
| | - Matthias Schmid
- Department of Medical Biometry, Informatics and Epidemiology, University Hospital, Bonn, D-53012, Germany
| | - Rodney J Scott
- Hunter Medical Research Institute and Faculty of Health, University of Newcastle, New South Wales, 2305, Australia
| | - Matthew A Scult
- Laboratory of NeuroGenetics, Department of Psychology & Neuroscience, Duke University, Durham, 27708-0086, NC, USA
| | - Jeannette Simino
- Department of Data Science, University of Mississippi Medical Center, Jackson, 39216, MS, USA
| | - P Eline Slagboom
- Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, 2333, The Netherlands
| | - Nikolaos Smyrnis
- Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, 11528, Greece
- University Mental Health Research Institute, Athens, GR-156 01, Greece
| | - Aïcha Soumaré
- University of Bordeaux, Bordeaux Population Health Research Center, INSERM UMR 1219, F-33000, Bordeaux, France
| | - Nikos C Stefanis
- Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Eginition Hospital, Athens, 11528, Greece
- University Mental Health Research Institute, Athens, GR-156 01, Greece
- Neurobiology Research Institute, Theodor-Theohari Cozzika Foundation, Athens, 11521, Greece
| | - David J Stott
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, G12 8QQ, United Kingdom
| | - Richard E Straub
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, 21205, MD, USA
| | - Kjetil Sundet
- Department of Psychology, University of Oslo, Oslo, 0373, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, 0315, Norway
| | - Adele M Taylor
- Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
- Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, 90509, CA, USA
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
- MRC-PHE Centre for Environment, School of Public Health, Imperial College London, London, W2 1PG, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, 45110, Greece
| | - Christophe Tzourio
- University of Bordeaux, Bordeaux Population Health Research Center, INSERM UMR 1219, F-33000, Bordeaux, France
- Department of Public Health, University Hospital of Bordeaux, Bordeaux, 33076, France
| | - André Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3015, The Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, 3015, The Netherlands
| | - Veronique Vitart
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Aristotle N Voineskos
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, M5T 1L8, Canada
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, FI-00014, Finland
- National Institute for Health and Welfare, Helsinki, FI-00271, Finland
- Department of Public Health, University of Helsinki, Helsinki, 00014, Finland
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, 53127, Germany
- Department for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, 53127, Germany
| | - Holger Wagner
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, 53127, Germany
| | - Leonie Weinhold
- Department of Medical Biometry, Informatics and Epidemiology, University Hospital, Bonn, D-53012, Germany
| | - K Hoyan Wen
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3015, The Netherlands
| | - Elisabeth Widen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, FI-00014, Finland
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, 02118, MA, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Hieab H H Adams
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3015, The Netherlands
- Department of Radiology, Erasmus MC, Rotterdam, 3015, The Netherlands
| | - Dan E Arking
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, MD, Baltimore, 21287, USA
| | - Robert M Bilder
- UCLA Semel Institute for Neuroscience and Human Behavior, Los Angeles, 90024, CA, USA
| | - Panos Bitsios
- Department of Psychiatry and Behavioral Sciences, Faculty of Medicine, University of Crete, Heraklion, GR-71003, Greece
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, 77030, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, 77030-3411, TX, USA
| | - Ornit Chiba-Falek
- Department of NeurologyBryan Alzheimer's Disease Research Center, and Center for Genomic and Computational Biology, Duke University Medical Center, Durham, 27708, NC, USA
| | - Aiden Corvin
- Neuropsychiatric Genetics Research Group, Department of Psychiatry and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, DO2 AY89, Ireland
| | - Philip L De Jager
- Center for Translational and Systems Neuroimmunology, Department of Neurology, Columbia University Medical Center, New York, 10032, NY, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, 02142, MA, USA
| | - Stéphanie Debette
- University of Bordeaux, Bordeaux Population Health Research Center, INSERM UMR 1219, F-33000, Bordeaux, France
- Department of Neurology, University Hospital of Bordeaux, Bordeaux, 33000, France
| | - Gary Donohoe
- Neuroimaging, Cognition & Genomics (NICOG) Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland Galway, Galway, H91 TK33, Ireland
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, W2 1PG, UK
- MRC-PHE Centre for Environment, School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Annette L Fitzpatrick
- Department of Epidemiology, University of Washington, Seattle, 98195, WA, USA
- Department of Global Health, University of Washington, Seattle, 98104, WA, USA
| | - Michael Gill
- Neuropsychiatric Genetics Research Group, Department of Psychiatry and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, DO2 AY89, Ireland
| | - David C Glahn
- Department of Psychiatry, Yale University School of Medicine, New Haven, 06511, CT, USA
| | - Sara Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SE-171 77, Sweden
| | - Narelle K Hansell
- Queensland Brain Institute, University of Queensland, Brisbane, 4072, Australia
| | - Ahmad R Hariri
- Laboratory of NeuroGenetics, Department of Psychology & Neuroscience, Duke University, Durham, 27708-0086, NC, USA
| | - M Kamran Ikram
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3015, The Netherlands
- Department of Neurology, Erasmus University Medical Center, Rotterdam, xxxxxx, The Netherlands
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, 2333, The Netherlands
| | - Eero Vuoksimaa
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, FI-00014, Finland
- Department of Public Health, University of Helsinki, Helsinki, 00014, Finland
| | - Matthew C Keller
- Institute for Behavioral Genetics, University of Colorado, Boulder, 80309, CO, USA
| | - William S Kremen
- Department of Psychiatry, University of California, San Diego, 92093, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, 92093, CA, USA
| | - Lenore Launer
- Intramural Research Program National Institutes on Aging, National Institutes of Health, Bethesda, 20892, MD, USA
| | | | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, FI-00014, Finland
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, CB10 1SA, UK
- Department of Medical Genetics, University of Helsinki and University Central Hospital, Helsinki, 00014, Finland
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, SE-171 77, Sweden
| | - Neil Pendleton
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Manchester Academic Health Science Centre, and Manchester Medical School, Institute of Brain, Behaviour, and Mental Health, University of Manchester, Manchester, M13 9PL, UK
| | - David J Porteous
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Medical Genetics Section, Centre for Genomic & Experimental Medicine, MRC Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, UK
- Generation Scotland, Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Katri Räikkönen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, 00014, Finland
| | - Olli T Raitakari
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, 20520, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, 20520, Finland
| | - Alfredo Ramirez
- Department of Psychiatry and Psychotherapy, University of Cologne, Cologne, D-50937, Germany
- Institute of Human Genetics, University of Bonn, Bonn, 53113, Germany
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, 53127, Germany
| | - Ivar Reinvang
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, 0315, Norway
| | - Igor Rudan
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland
| | - Dan Rujescu
- Department of Psychiatry, Martin Luther University of Halle-Wittenberg, Halle, 06108, Germany
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University of Graz, Graz, 8036, Austria
| | - Helena Schmidt
- Institute of Molecular Biology and Biochemistry, Centre for Molecular Medicine, Medical University of Graz, Graz, 8036, Austria
| | - Peter W Schofield
- School of Medicine and Public Health, University of Newcastle, New South Wales, 2308, Australia
| | - Peter R Schofield
- Neuroscience Research Australia, Sydney, 2031, Australia
- Faculty of Medicine, University of New South Wales, Sydney, 2052, Australia
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Vidar M Steen
- NORMENT, K.G. Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, 5021, Norway
- Dr. Einar Martens Research Group for Biological Psychiatry, Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, 5020, Norway
| | - Julian N Trollor
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, 2031, Australia
- Department of Developmental Disability Neuropsychiatry, School of Psychiatry, University of New South Wales, Sydney, 2052, Australia
| | - Steven T Turner
- Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Rochester, MN, 55905, USA
| | - Cornelia M Van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3015, The Netherlands
| | - Arno Villringer
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04103, Germany
- Day Clinic for Cognitive Neurology, University Hospital Leipzig, Leipzig, 04103, Germany
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins University Medical Campus, Baltimore, 21205, MD, USA
| | - David R Weir
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, 48104, USA
| | - James F Wilson
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, Scotland
- Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Anil Malhotra
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, 11030, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, 11004, NY, USA
- Department of Psychiatry, Hofstra Northwell School of Medicine, Hempstead, 11549, NY, USA
| | - Andrew M McIntosh
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Division of Psychiatry, University of Edinburgh, Edinburgh, EH10 5HF, UK
| | - Catharine R Gale
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, SO16 6YD, UK
| | - Sudha Seshadri
- Robert and Beverly Lewis Center for Neuroimaging, University of Oregon, Eugene, 97403, OR, USA
- Department of Neurology, Boston University School of Medicine, Boston, 02118, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, 78229, TX, USA
| | - Thomas H Mosley
- Department of Medicine, Division of Geriatrics, University of Mississippi Medical Center, Jackson, 39216, MS, USA
| | - Jan Bressler
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, 77030, TX, USA
| | - Todd Lencz
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, 11030, NY, USA
- Division of Psychiatry, University of Edinburgh, Edinburgh, EH10 5HF, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK.
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Abstract
In the face of shifting demographics and an increase in human longevity, it is important to examine carefully what is known about cognitive ageing, and to identify and promote possibly malleable lifestyle and health-related factors that might mitigate age-associated cognitive decline. The Lothian Birth Cohorts of 1921 (LBC1921, n = 550) and 1936 (LBC1936, n = 1091) are longitudinal studies of cognitive and brain ageing based in Scotland. Childhood IQ data are available for these participants, who were recruited in later life and then followed up regularly. This overview summarises some of the main LBC findings to date, illustrating the possible genetic and environmental contributions to cognitive function (level and change) and brain imaging biomarkers in later life. Key associations include genetic variation, health and fitness, psychosocial and lifestyle factors, and aspects of the brain's structure. It addresses some key methodological issues such as confounding by early-life intelligence and social factors and emphasises areas requiring further investigation. Overall, the findings that have emerged from the LBC studies highlight that there are multiple correlates of cognitive ability level in later life, many of which have small effects, that there are as yet few reliable predictors of cognitive change, and that not all of the correlates have independent additive associations. The concept of marginal gains, whereby there might be a cumulative effect of small incremental improvements across a wide range of lifestyle and health-related factors, may offer a useful way to think about and promote a multivariate recipe for healthy cognitive and brain ageing.
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Affiliation(s)
- J Corley
- Department of Psychology,The University of Edinburgh,Edinburgh,UK
| | - S R Cox
- Department of Psychology,The University of Edinburgh,Edinburgh,UK
| | - I J Deary
- Department of Psychology,The University of Edinburgh,Edinburgh,UK
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Wong TTY. Is conditional reasoning related to mathematical problem solving? Dev Sci 2017; 21:e12644. [PMID: 29266674 DOI: 10.1111/desc.12644] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 10/25/2017] [Indexed: 11/29/2022]
Abstract
The current study aimed to investigate the relation between conditional reasoning, which is a common type of logical reasoning, and children's mathematical problem solving. A sample of 124 fourth graders was tested for their conditional reasoning skills and their mathematical problem solving skills, as well as a list of control variables (e.g., IQ, working memory, reading) and potential mediators (number sentence construction and computation). The children's ability to make modus ponens (MP) inferences significantly predicted their mathematical problem solving skills, even after controlling for the potential confounding variables. The relation was mediated by the number sentence construction skills. The findings, in addition to supporting the link between conditional reasoning and mathematics, further indicate that the ability to process relations may be the mechanism underlying the relation. Theoretical and practical implications are discussed.
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Affiliation(s)
- Terry Tin-Yau Wong
- Department of Psychology, The Education University of Hong Kong, Hong Kong.,Psychological Assessment and Clinical Research Unit, The Education University of Hong Kong, Hong Kong
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The unique and shared contributions of arithmetic operation understanding and numerical magnitude representation to children’s mathematics achievement. J Exp Child Psychol 2017; 164:68-86. [DOI: 10.1016/j.jecp.2017.07.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2016] [Revised: 05/18/2017] [Accepted: 07/12/2017] [Indexed: 11/18/2022]
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Barbey AK. Network Neuroscience Theory of Human Intelligence. Trends Cogn Sci 2017; 22:8-20. [PMID: 29167088 DOI: 10.1016/j.tics.2017.10.001] [Citation(s) in RCA: 133] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Revised: 10/05/2017] [Accepted: 10/06/2017] [Indexed: 10/18/2022]
Abstract
An enduring aim of research in the psychological and brain sciences is to understand the nature of individual differences in human intelligence, examining the stunning breadth and diversity of intellectual abilities and the remarkable neurobiological mechanisms from which they arise. This Opinion article surveys recent neuroscience evidence to elucidate how general intelligence, g, emerges from individual differences in the network architecture of the human brain. The reviewed findings motivate new insights about how network topology and dynamics account for individual differences in g, represented by the Network Neuroscience Theory. According to this framework, g emerges from the small-world topology of brain networks and the dynamic reorganization of its community structure in the service of system-wide flexibility and adaptation.
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Affiliation(s)
- Aron K Barbey
- Decision Neuroscience Laboratory, University of Illinois at Urbana-Champaign, IL, USA; Department of Psychology, University of Illinois at Urbana-Champaign, IL, USA; Department of Bioengineering, University of Illinois at Urbana-Champaign, IL, USA; Neuroscience Program, University of Illinois at Urbana-Champaign, IL, USA; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, IL, USA; https://www.DecisionNeuroscienceLab.org.
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Brain cortical characteristics of lifetime cognitive ageing. Brain Struct Funct 2017; 223:509-518. [PMID: 28879544 PMCID: PMC5772145 DOI: 10.1007/s00429-017-1505-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Accepted: 08/20/2017] [Indexed: 12/02/2022]
Abstract
Regional cortical brain volume is the product of surface area and thickness. These measures exhibit partially distinct trajectories of change across the brain’s cortex in older age, but it is unclear which cortical characteristics at which loci are sensitive to cognitive ageing differences. We examine associations between change in intelligence from age 11 to 73 years and regional cortical volume, surface area, and thickness measured at age 73 years in 568 community-dwelling older adults, all born in 1936. A relative positive change in intelligence from 11 to 73 was associated with larger volume and surface area in selective frontal, temporal, parietal, and occipital regions (r < 0.180, FDR-corrected q < 0.05). There were no significant associations between cognitive ageing and a thinner cortex for any region. Interestingly, thickness and surface area were phenotypically independent across bilateral lateral temporal loci, whose surface area was significantly related to change in intelligence. These findings suggest that associations between regional cortical volume and cognitive ageing differences are predominantly driven by surface area rather than thickness among healthy older adults. Regional brain surface area has been relatively underexplored, and is a potentially informative biomarker for identifying determinants of cognitive ageing differences.
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Rinaldi L, Karmiloff-Smith A. Intelligence as a Developing Function: A Neuroconstructivist Approach. J Intell 2017; 5:E18. [PMID: 31162409 PMCID: PMC6526422 DOI: 10.3390/jintelligence5020018] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Revised: 04/21/2017] [Accepted: 04/27/2017] [Indexed: 11/16/2022] Open
Abstract
The concept of intelligence encompasses the mental abilities necessary to survival and advancement in any environmental context. Attempts to grasp this multifaceted concept through a relatively simple operationalization have fostered the notion that individual differences in intelligence can often be expressed by a single score. This predominant position has contributed to expect intelligence profiles to remain substantially stable over the course of ontogenetic development and, more generally, across the life-span. These tendencies, however, are biased by the still limited number of empirical reports taking a developmental perspective on intelligence. Viewing intelligence as a dynamic concept, indeed, implies the need to identify full developmental trajectories, to assess how genes, brain, cognition, and environment interact with each other. In the present paper, we describe how a neuroconstructivist approach better explains why intelligence can rise or fall over development, as a result of a fluctuating interaction between the developing system itself and the environmental factors involved at different times across ontogenesis.
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Affiliation(s)
- Luca Rinaldi
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia 27100, Italy.
- Milan Center for Neuroscience, Milano 20126, Italy.
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Haring L, Mõttus R, Kajalaid K, Koch K, Uppin K, Maron E, Vasar E. The course of cognitive functioning after first-episode of psychosis: A six month follow-up study. Schizophr Res 2017; 182:31-41. [PMID: 27746055 DOI: 10.1016/j.schres.2016.10.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2016] [Revised: 09/30/2016] [Accepted: 10/10/2016] [Indexed: 12/22/2022]
Abstract
Our aim with the present study was to evaluate rank-order and mean-level cognitive functioning stability among first-episode psychosis (FEP) patients, measured using the Cambridge Neuropsychological Test Automated Battery (CANTAB), over a six month period. We also aimed to examine longitudinal measurement invariance and identify factors-such as age, gender, educational level, treatment and psychopathological change scores-potentially linked to cognitive change among patients. In addition, correlations between objectively measured and subjectively evaluated cognitive functioning were estimated. Neuropsychological assessments were administered to 85 patients after the initial stabilisation of their psychosis; 82 of the patients were retested. Subjectively perceived cognitive functioning was measured using a subscale derived from the Estonian version of the Subjective Well-Being Under Neuroleptic Scale (SWN-K-E). On average, executive functioning and processing speed improved significantly, while memory test scores decreased significantly, over time. Very high rank-order stability (r=0.80 to 0.94, p<0.001) was observed with all measured ability scores. Confirmatory factor analysis revealed the loadings of a single (broad ability) factor model were equal across both measurement occasions, but the lack of intercept invariance suggested that mean-level comparisons are more appropriately carried out at a subtest level. On average psychopathology scores and antipsychotics doses declined over time, with the latter also significantly correlating with better executive functioning. Gender was a significant moderator of some domains of cognitive performance, and decline tended to be somewhat more pronounced for women. The results also indicated the lack of any relationship between objective and subjective measurements of cognitive functioning.
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Affiliation(s)
- Liina Haring
- Psychiatry Clinic of Tartu University Hospital, Tartu, Estonia.
| | - René Mõttus
- Department of Psychology, University of Edinburgh, Edinburgh, UK; Department of Psychology, University of Tartu, Tartu, Estonia.
| | | | - Kadri Koch
- Psychiatry Clinic of Tartu University Hospital, Tartu, Estonia.
| | - Kärt Uppin
- Psychiatry Clinic of Tartu University Hospital, Tartu, Estonia.
| | - Eduard Maron
- Psychiatry Clinic of Tartu University Hospital, Tartu, Estonia; North Estonia Medical Centre, Department of Psychiatry, Tallinn, Estonia; Centre for Mental Health, Imperial College London, UK.
| | - Eero Vasar
- Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia; Centre of Excellence for Genomics and Translational Medicine, University of Tartu, Tartu, Estonia.
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