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Williams CM, Labouret G, Wolfram T, Peyre H, Ramus F. A General Cognitive Ability Factor for the UK Biobank. Behav Genet 2023; 53:85-100. [PMID: 36378351 DOI: 10.1007/s10519-022-10127-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 10/30/2022] [Indexed: 11/16/2022]
Abstract
UK Biobank participants do not have a high-quality measure of intelligence or polygenic scores (PGSs) of intelligence to simultaneously examine the genetic and neural underpinnings of intelligence. We created a standardized measure of general intelligence (g factor) relative to the UK population and estimated its quality. After running a GWAS of g on UK Biobank participants with a g factor of good quality and without neuroimaging data (N = 187,288), we derived a g PGS for UK Biobank participants with neuroimaging data. For individuals with at least one cognitive test, the g factor from eight cognitive tests (N = 501,650) explained 29% of the variance in cognitive test performance. The PGS for British individuals with neuroimaging data (N = 27,174) explained 7.6% of the variance in g. We provided high-quality g factor estimates for most UK Biobank participants and g factor PGSs for UK Biobank participants with neuroimaging data.
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Affiliation(s)
- Camille Michèle Williams
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, 75005, Paris, France. .,LSCP, Département d'Etudes Cognitives, École Normale Supérieure, 29 rue d'Ulm, 75005, Paris, France.
| | - Ghislaine Labouret
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, 75005, Paris, France
| | - Tobias Wolfram
- Faculty of Sociology, Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany
| | - Hugo Peyre
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, 75005, Paris, France.,INSERM UMR 1141, Paris Diderot University, Paris, France.,Department of Child and Adolescent Psychiatry, Robert Debré Hospital, APHP, Paris, France
| | - Franck Ramus
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, 75005, Paris, France
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2
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Schulz CA, Weinhold L, Schmid M, Nöthen MM, Nöthlings U. Analysis of associations between dietary patterns, genetic disposition, and cognitive function in data from UK Biobank. Eur J Nutr 2023; 62:511-521. [PMID: 36152054 PMCID: PMC9899759 DOI: 10.1007/s00394-022-02976-y] [Citation(s) in RCA: 1] [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: 12/06/2021] [Accepted: 07/29/2022] [Indexed: 02/07/2023]
Abstract
PURPOSE Research suggests that diet influences cognitive function and the risk for neurodegenerative disease. The present study aimed to determine whether a recently developed diet score, based on recommendations for dietary priorities for cardio metabolic health, was associated with fluid intelligence, and whether these associations were modified by individual genetic disposition. METHODS This research has been conducted using the UK Biobank Resource. Analyses were performed using self-report data on diet and the results for the verbal-numerical reasoning test of fluid intelligence of 104,895 individuals (46% male: mean age at recruitment 57.1 years (range 40-70)). For each participant, a diet score and a polygenic score (PGS) were constructed, which evaluated predefined cut-offs for the intake of fruit, vegetables, fish, processed meat, unprocessed meat, whole grain, and refined grain, and ranged from 0 (unfavorable) to 7 (favorable). To investigate whether the diet score was associated with fluid intelligence, and whether the association was modified by PGS, linear regression analyses were performed. RESULTS The average diet score was 3.9 (SD 1.4). After adjustment for selected confounders, a positive association was found between baseline fluid intelligence and PGS (P < 0.001). No association was found between baseline fluid intelligence and diet score (P = 0.601), even after stratification for PGS, or in participants with longitudinal data available (n = 9,482). CONCLUSION In this middle-aged cohort, no evidence was found for an association between the investigated diet score and either baseline or longitudinal fluid intelligence. However, as in previous reports, fluid intelligence was strongly associated with a PGS for general cognitive function.
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Affiliation(s)
| | - Leonie Weinhold
- Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Matthias Schmid
- Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
| | - Ute Nöthlings
- Institute of Nutrition and Food Sciences, Nutritional Epidemiology, University of Bonn, Bonn, Germany
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3
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Ciobanu LG, Stankov L, Ahmed M, Heathcote A, Clark SR, Aidman E. Multifactorial structure of cognitive assessment tests in the UK Biobank: A combined exploratory factor and structural equation modeling analyses. Front Psychol 2023; 14:1054707. [PMID: 36818106 PMCID: PMC9937787 DOI: 10.3389/fpsyg.2023.1054707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 01/09/2023] [Indexed: 01/27/2023] Open
Abstract
Introduction The UK Biobank cognitive assessment data has been a significant resource for researchers looking to investigate predictors and modifiers of cognitive abilities and associated health outcomes in the general population. Given the diverse nature of this data, researchers use different approaches - from the use of a single test to composing the general intelligence score, g, across the tests. We argue that both approaches are suboptimal - one being too specific and the other one too general - and suggest a novel multifactorial solution to represent cognitive abilities. Methods Using a combined Exploratory Factor (EFA) and Exploratory Structural Equation Modeling Analyses (ESEM) we developed a three-factor model to characterize an underlying structure of nine cognitive tests selected from the UK Biobank using a Cattell-Horn-Carroll framework. We first estimated a series of probable factor solutions using the maximum likelihood method of extraction. The best solution for the EFA-defined factor structure was then tested using the ESEM approach with the aim of confirming or disconfirming the decisions made. Results We determined that a three-factor model fits the UK Biobank cognitive assessment data best. Two of the three factors can be assigned to fluid reasoning (Gf) with a clear distinction between visuospatial reasoning and verbal-analytical reasoning. The third factor was identified as a processing speed (Gs) factor. Discussion This study characterizes cognitive assessment data in the UK Biobank and delivers an alternative view on its underlying structure, suggesting that the three factor model provides a more granular solution than g that can further be applied to study different facets of cognitive functioning in relation to health outcomes and to further progress examination of its biological underpinnings.
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Affiliation(s)
- Liliana G Ciobanu
- Discipline of Psychiatry, The University of Adelaide, Adelaide, SA, Australia
| | - Lazar Stankov
- School of Psychology, The University of Sydney, Sydney, NSW, Australia
| | - Muktar Ahmed
- Discipline of Psychiatry, The University of Adelaide, Adelaide, SA, Australia
| | - Andrew Heathcote
- School of Psychology, University of Newcastle, Sydney, NSW, Australia
| | - Scott Richard Clark
- Discipline of Psychiatry, The University of Adelaide, Adelaide, SA, Australia
| | - Eugene Aidman
- School of Psychology, The University of Sydney, Sydney, NSW, Australia.,School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW, Australia.,Decision Sciences Division, Defense Science and Technology Group, Adelaide, SA, Australia
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4
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Wang M, Huang S. The collective effects of genetic variants and complex traits. J Hum Genet 2022; 68:255-262. [PMID: 36513763 DOI: 10.1038/s10038-022-01105-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 12/01/2022] [Accepted: 12/02/2022] [Indexed: 12/15/2022]
Abstract
Traditional approaches in studying the genetics of complex traits have focused on identifying specific genetic variants. However, the collective effects of variants have remained largely unexplored. Here, we evaluated whether traits could be influenced by the collective effects of variants across the entire protein coding-region of the genome or the entire genome. We studied the UK Biobank exome sequencing data of 167,246 individuals as well as the genome-wide SNP array data of 408,868 individuals. We calculated for each individual four different measures of genetic variation such as heterozygosity and number of variants and two different measures of the overall deleteriousness of all variants, and performed correlations with 17 representative traits that have been studied previously. Linear regression analysis was performed with adjustment for age, sex, and genetic principal components. The results showed a high correlation among the six different measures and an inverse association of two well-correlated traits (educational attainment and height) with the total number of all variants as well as the overall deleteriousness of all variants. We have also categorized the genes based on whether they are expressed in the brain and found that the association with educational attainment only held for the brain-expressed genes. No other traits examined showed a significant correlation with the brain-expressed genes. The study demonstrates that common traits could be studied by analyzing the overall genetic variation and suggests that educational attainment is inversely related to genetic variation.
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Affiliation(s)
- Mingrui Wang
- Center for Medical Genetics, Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, 110 Xiangya Road, Changsha, Hunan, 410078, PR China
| | - Shi Huang
- Center for Medical Genetics, Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, 110 Xiangya Road, Changsha, Hunan, 410078, PR China.
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5
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Wu Y, Besson P, Azcona EA, Bandt SK, Parrish TB, Breiter HC, Katsaggelos AK. A multicohort geometric deep learning study of age dependent cortical and subcortical morphologic interactions for fluid intelligence prediction. Sci Rep 2022; 12:17760. [PMID: 36273036 PMCID: PMC9588039 DOI: 10.1038/s41598-022-22313-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 10/12/2022] [Indexed: 01/19/2023] Open
Abstract
The relationship of human brain structure to cognitive function is complex, and how this relationship differs between childhood and adulthood is poorly understood. One strong hypothesis suggests the cognitive function of Fluid Intelligence (Gf) is dependent on prefrontal cortex and parietal cortex. In this work, we developed a novel graph convolutional neural networks (gCNNs) for the analysis of localized anatomic shape and prediction of Gf. Morphologic information of the cortical ribbons and subcortical structures was extracted from T1-weighted MRIs within two independent cohorts, the Adolescent Brain Cognitive Development Study (ABCD; age: 9.93 ± 0.62 years) of children and the Human Connectome Project (HCP; age: 28.81 ± 3.70 years). Prediction combining cortical and subcortical surfaces together yielded the highest accuracy of Gf for both ABCD (R = 0.314) and HCP datasets (R = 0.454), outperforming the state-of-the-art prediction of Gf from any other brain measures in the literature. Across both datasets, the morphology of the amygdala, hippocampus, and nucleus accumbens, along with temporal, parietal and cingulate cortex consistently drove the prediction of Gf, suggesting a significant reframing of the relationship between brain morphology and Gf to include systems involved with reward/aversion processing, judgment and decision-making, motivation, and emotion.
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Affiliation(s)
- Yunan Wu
- Department of Electrical Computer Engineering, Northwestern University, Evanston, IL, USA.
| | - Pierre Besson
- grid.16753.360000 0001 2299 3507Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL USA
| | - Emanuel A. Azcona
- grid.16753.360000 0001 2299 3507Department of Electrical Computer Engineering, Northwestern University, Evanston, IL USA
| | - S. Kathleen Bandt
- grid.16753.360000 0001 2299 3507Department of Neurosurgery, Northwestern University, Feinberg School of Medicine, Chicago, IL USA
| | - Todd B. Parrish
- grid.16753.360000 0001 2299 3507Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL USA
| | - Hans C. Breiter
- grid.24827.3b0000 0001 2179 9593Departments of Computer Science and Biomedical Engineering, University of Cincinnati, Cincinnat, OH USA ,grid.32224.350000 0004 0386 9924Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General Hospital and Harvard School of Medicine, Boston, MA USA
| | - Aggelos K. Katsaggelos
- grid.16753.360000 0001 2299 3507Department of Electrical Computer Engineering, Northwestern University, Evanston, IL USA ,grid.16753.360000 0001 2299 3507Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL USA ,grid.16753.360000 0001 2299 3507Department of Computer Science, Northwestern University, Evanston, IL USA
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6
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Using large, publicly available data sets to study adolescent development: opportunities and challenges. Curr Opin Psychol 2022; 44:303-308. [PMID: 34837769 DOI: 10.1016/j.copsyc.2021.10.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 10/05/2021] [Indexed: 11/22/2022]
Abstract
Adolescence is a period of rapid change, with cognitive, mental wellbeing, environmental biological factors interacting to shape lifelong outcomes. Large, longitudinal phenotypically rich data sets available for reuse (secondary data) have revolutionized the way we study adolescence, allowing the field to examine these unfolding processes across hundreds or even thousands of individuals. Here, we outline the opportunities and challenges associated with such secondary data sets, provide an overview of particularly valuable resources available to the field, and recommend best practices to improve the rigor and transparency of analyses conducted on large, secondary data sets.
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7
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Taylor BK, Heinrichs-Graham E, Eastman JA, Frenzel MR, Wang YP, Calhoun VD, Stephen JM, Wilson TW. Longitudinal changes in the neural oscillatory dynamics underlying abstract reasoning in children and adolescents. Neuroimage 2022; 253:119094. [PMID: 35306160 PMCID: PMC9152958 DOI: 10.1016/j.neuroimage.2022.119094] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 03/09/2022] [Accepted: 03/10/2022] [Indexed: 11/18/2022] Open
Abstract
Fluid reasoning is the ability to problem solve in the absence of prior knowledge and is commonly conceptualized as “non-verbal” intelligence. Importantly, fluid reasoning abilities rapidly develop throughout childhood and adolescence. Although numerous studies have characterized the neural underpinnings of fluid reasoning in adults, there is a paucity of research detailing the developmental trajectory of this neural processing. Herein, we examine longitudinal changes in the neural oscillatory dynamics underlying fluid intelligence in a sample of typically developing youths. A total of 34 participants age 10 to 16 years-old completed an abstract reasoning task during magnetoencephalography (MEG) on two occasions set one year apart. We found robust longitudinal optimization in theta, beta, and gamma oscillatory activity across years of the study across a distributed network commonly implicated in fluid reasoning abilities. More specifically, activity tended to decrease longitudinally in additional, compensatory areas such as the right lateral prefrontal cortex and increase in areas commonly utilized in mature adult samples (e.g., left frontal and parietal cortices). Importantly, shifts in neural activity were associated with improvements in task performance from one year to the next. Overall, the data suggest a longitudinal shift in performance that is accompanied by a reconfiguration of the functional oscillatory dynamics serving fluid reasoning during this important period of development.
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Affiliation(s)
- Brittany K Taylor
- Institute for Human Neuroscience, Boys Town National Research Hospital, 378 Bucher Circle, Boys Town, NE 68010, USA; Department of Pharmacology and Neuroscience, Creighton University, Omaha, NE, USA.
| | - Elizabeth Heinrichs-Graham
- Institute for Human Neuroscience, Boys Town National Research Hospital, 378 Bucher Circle, Boys Town, NE 68010, USA; Department of Pharmacology and Neuroscience, Creighton University, Omaha, NE, USA
| | - Jacob A Eastman
- Institute for Human Neuroscience, Boys Town National Research Hospital, 378 Bucher Circle, Boys Town, NE 68010, USA
| | - Michaela R Frenzel
- Institute for Human Neuroscience, Boys Town National Research Hospital, 378 Bucher Circle, Boys Town, NE 68010, USA
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; Mind Research Network, Albuquerque, NM, USA
| | | | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, 378 Bucher Circle, Boys Town, NE 68010, USA; Department of Pharmacology and Neuroscience, Creighton University, Omaha, NE, USA
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8
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Chyzhyk D, Varoquaux G, Milham M, Thirion B. How to remove or control confounds in predictive models, with applications to brain biomarkers. Gigascience 2022; 11:giac014. [PMID: 35277962 PMCID: PMC8917515 DOI: 10.1093/gigascience/giac014] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 09/19/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND With increasing data sizes and more easily available computational methods, neurosciences rely more and more on predictive modeling with machine learning, e.g., to extract disease biomarkers. Yet, a successful prediction may capture a confounding effect correlated with the outcome instead of brain features specific to the outcome of interest. For instance, because patients tend to move more in the scanner than controls, imaging biomarkers of a disease condition may mostly reflect head motion, leading to inefficient use of resources and wrong interpretation of the biomarkers. RESULTS Here we study how to adapt statistical methods that control for confounds to predictive modeling settings. We review how to train predictors that are not driven by such spurious effects. We also show how to measure the unbiased predictive accuracy of these biomarkers, based on a confounded dataset. For this purpose, cross-validation must be modified to account for the nuisance effect. To guide understanding and practical recommendations, we apply various strategies to assess predictive models in the presence of confounds on simulated data and population brain imaging settings. Theoretical and empirical studies show that deconfounding should not be applied to the train and test data jointly: modeling the effect of confounds, on the training data only, should instead be decoupled from removing confounds. CONCLUSIONS Cross-validation that isolates nuisance effects gives an additional piece of information: confound-free prediction accuracy.
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Affiliation(s)
- Darya Chyzhyk
- Parietal project-team, INRIA Saclay-île de France, France
- CEA/Neurospin bât 145, 91191 Gif-Sur-Yvette, France
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - Gaël Varoquaux
- Parietal project-team, INRIA Saclay-île de France, France
- CEA/Neurospin bât 145, 91191 Gif-Sur-Yvette, France
| | - Michael Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA
| | - Bertrand Thirion
- Parietal project-team, INRIA Saclay-île de France, France
- CEA/Neurospin bât 145, 91191 Gif-Sur-Yvette, France
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9
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Rasmussen JM, Graham AM, Gyllenhammer LE, Entringer S, Chow DS, O’Connor TG, Fair DA, Wadhwa PD, Buss C. Neuroanatomical Correlates Underlying the Association Between Maternal Interleukin 6 Concentration During Pregnancy and Offspring Fluid Reasoning Performance in Early Childhood. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:24-33. [PMID: 33766778 PMCID: PMC8458517 DOI: 10.1016/j.bpsc.2021.03.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 03/10/2021] [Accepted: 03/11/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND Maternal inflammation during pregnancy can alter offspring brain development and influence risk for disorders commonly accompanied by deficits in cognitive functioning. We therefore examined associations between maternal interleukin 6 (IL-6) concentrations during pregnancy and offspring cognitive ability and concurrent magnetic resonance imaging-based measures of brain anatomy in early childhood. We further examined newborn brain anatomy in secondary analyses to consider whether effects are evident soon after birth and to increase capacity to differentiate effects of pre- versus postnatal exposures. METHODS IL-6 concentrations were quantified in early (12.6 ± 2.8 weeks), mid (20.4 ± 1.5 weeks), and late (30.3 ± 1.3 weeks) pregnancy. Offspring nonverbal fluid intelligence (Gf) was assessed at 5.2 ± 0.6 years using a spatial reasoning task (Wechsler Preschool and Primary Scale of Intelligence-Matrix) (n = 49). T1-weighted magnetic resonance imaging scans were acquired at birth (n = 89, postmenstrual age = 42.9 ± 2.0 weeks) and in early childhood (n = 42, scan age = 5.1 ± 1.0 years). Regional cortical volumes were examined for a joint association between maternal IL-6 and offspring Gf performance. RESULTS Average maternal IL-6 concentration during pregnancy was inversely associated with offspring Gf performance after adjusting for socioeconomic status and the quality of the caregiving and learning environment (R2 = 13%; p = .02). Early-childhood pars triangularis volume was jointly associated with maternal IL-6 and childhood Gf (pcorrected < .001). An association also was observed between maternal IL-6 and newborn pars triangularis volume (R2 = 6%; p = .02). CONCLUSIONS These findings suggest that the origins of variation in child cognitive ability can, in part, trace back to maternal conditions during the intrauterine period of life and support the role of inflammation as an important component of this putative biological pathway.
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Affiliation(s)
- Jerod M. Rasmussen
- Development, Health and Disease Research Program, University of California, Irvine, California, USA 92697.,Department of Pediatrics, University of California, Irvine, California, USA 92697.,Corresponding Authors: Claudia Buss, PhD, Institute for Medical Psychology, Charité University Medicine, Luisenstr. 57, 10117 Berlin, Germany, Tel: +49 (0)30 450 529 222, Fax: +49 (0)30 450 529 990, ; Jerod M. Rasmussen, PhD., UC Irvine Development, Health and Disease Research Program, University of California, Irvine, School of Medicine, 3117 Gillespie Neuroscience Research Facility (GNRF), 837 Health Sciences Road, Irvine, CA 92697,
| | - Alice M. Graham
- Department of Behavioral Neuroscience,Oregon Health & Science University, 3181 SW Sam Jackson Park Rd., Portland, OR, 97239, United States
| | - Lauren E. Gyllenhammer
- Development, Health and Disease Research Program, University of California, Irvine, California, USA 92697.,Department of Pediatrics, University of California, Irvine, California, USA 92697
| | - Sonja Entringer
- Development, Health and Disease Research Program, University of California, Irvine, California, USA 92697.,Department of Pediatrics, University of California, Irvine, California, USA 92697.,Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), Department of Medical Psychology, Berlin, Germany
| | - Daniel S. Chow
- Department of Radiology, University of California, Irvine, California, USA 92697
| | - Thomas G. O’Connor
- Departments of Psychiatry, Psychology, Neuroscience and Obstetrics & Gynecology, University of Rochester Medical Center, Rochester, New York, USA 14642
| | - Damien A. Fair
- Department of Behavioral Neuroscience,Oregon Health & Science University, 3181 SW Sam Jackson Park Rd., Portland, OR, 97239, United States
| | - Pathik D. Wadhwa
- Development, Health and Disease Research Program, University of California, Irvine, California, USA 92697.,Department of Pediatrics, University of California, Irvine, California, USA 92697.,Departments of Psychiatry and Human Behavior, Obstetrics & Gynecology, Epidemiology, University of California, Irvine, California, USA 92697
| | - Claudia Buss
- Development, Health and Disease Research Program, University of California, Irvine, California, USA 92697.,Department of Pediatrics, University of California, Irvine, California, USA 92697.,Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), Department of Medical Psychology, Berlin, Germany.,Corresponding Authors: Claudia Buss, PhD, Institute for Medical Psychology, Charité University Medicine, Luisenstr. 57, 10117 Berlin, Germany, Tel: +49 (0)30 450 529 222, Fax: +49 (0)30 450 529 990, ; Jerod M. Rasmussen, PhD., UC Irvine Development, Health and Disease Research Program, University of California, Irvine, School of Medicine, 3117 Gillespie Neuroscience Research Facility (GNRF), 837 Health Sciences Road, Irvine, CA 92697,
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10
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Dadi K, Varoquaux G, Houenou J, Bzdok D, Thirion B, Engemann D. Population modeling with machine learning can enhance measures of mental health. Gigascience 2021; 10:giab071. [PMID: 34651172 PMCID: PMC8559220 DOI: 10.1093/gigascience/giab071] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 07/14/2021] [Accepted: 09/22/2021] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Biological aging is revealed by physical measures, e.g., DNA probes or brain scans. In contrast, individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are typically assessed by tailored neuropsychological tests that build on expert judgement and require careful interpretation. Could machine learning on large samples from the general population be used to build proxy measures of these constructs that do not require human intervention? RESULTS Here, we built proxy measures by applying machine learning on multimodal MR images and rich sociodemographic information from the largest biomedical cohort to date: the UK Biobank. Objective model comparisons revealed that all proxies captured the target constructs and were as useful, and sometimes more useful, than the original measures for characterizing real-world health behavior (sleep, exercise, tobacco, alcohol consumption). We observed this complementarity of proxy measures and original measures at capturing multiple health-related constructs when modeling from, both, brain signals and sociodemographic data. CONCLUSION Population modeling with machine learning can derive measures of mental health from heterogeneous inputs including brain signals and questionnaire data. This may complement or even substitute for psychometric assessments in clinical populations.
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Affiliation(s)
- Kamalaker Dadi
- Inria, CEA, Neurospin, Parietal team, Université Paris
Saclay, 91120 Palaiseau, France
| | - Gaël Varoquaux
- Inria, CEA, Neurospin, Parietal team, Université Paris
Saclay, 91120 Palaiseau, France
- Montréal Neurological Institute, McGill University, Montreal,
QC, Canada
- Mila - Quebec Artificial Intelligence Institute, Montreal,
QC, Canada
| | - Josselin Houenou
- CEA, NeuroSpin, Psychiatry Team, UNIACT Lab, Université Paris
Saclay, France
- APHP, Mondor University Hospitals, Psychiatry Department,
INSERM U955 Team 15 “Translational Psychiatry,” Créteil, France
| | - Danilo Bzdok
- Inria, CEA, Neurospin, Parietal team, Université Paris
Saclay, 91120 Palaiseau, France
- Mila - Quebec Artificial Intelligence Institute, Montreal,
QC, Canada
- Department of Biomedical Engineering, Montreal Neurological Institute,
Faculty of Medicine, McGill University, Montreal, QC, Canada
| | - Bertrand Thirion
- Inria, CEA, Neurospin, Parietal team, Université Paris
Saclay, 91120 Palaiseau, France
| | - Denis Engemann
- Inria, CEA, Neurospin, Parietal team, Université Paris
Saclay, 91120 Palaiseau, France
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain
Sciences, Germany
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Silva AI, Kirov G, Kendall KM, Bracher-Smith M, Wilkinson LS, Hall J, Ulfarsson MO, Walters GB, Stefansson H, Stefansson K, Linden DE, Caseras X. Analysis of Diffusion Tensor Imaging Data From the UK Biobank Confirms Dosage Effect of 15q11.2 Copy Number Variation on White Matter and Shows Association With Cognition. Biol Psychiatry 2021; 90:307-316. [PMID: 33931204 PMCID: PMC8343146 DOI: 10.1016/j.biopsych.2021.02.969] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 02/23/2021] [Accepted: 02/23/2021] [Indexed: 01/07/2023]
Abstract
BACKGROUND Copy number variations at the 15q11.2 BP1-BP2 locus are present in 0.5%-1.0% of the population, and the deletion is associated with several neurodevelopmental disorders. Previously, we showed a reciprocal effect of 15q11.2 copy number variation on fractional anisotropy, with widespread increases in deletion carriers. We aim to expand these findings using a larger sample of participants (N = 29,166) and higher resolution imaging and by examining the implications for cognitive performance. METHODS Diffusion tensor imaging measures from participants with no neurological or psychiatric diagnoses were obtained from the UK Biobank database. We compared 15q11.2 BP1-BP2 deletion (n = 102) and duplication (n = 113) carriers to a large cohort of control individuals with no neuropsychiatric copy number variants (n = 28,951). Additionally, we assessed how changes in white matter mediated the association between carrier status and cognitive performance. RESULTS Deletion carriers showed increases in fractional anisotropy in the internal capsule and cingulum and decreases in the posterior thalamic radiation compared with both duplication carriers and control subjects (who had intermediate values). Compared with control subjects, deletion carriers had lower scores across cognitive tasks, which were partly influenced by white matter. Reduced fractional anisotropy in the posterior thalamic radiation partially contributed to worse cognitive performance in deletion carriers. CONCLUSIONS These results, together with our previous findings, provide convergent evidence for an effect of 15q11.2 BP1-BP2 on white matter microstructure, this being more pronounced in deletion carriers. Additionally, changes in white matter were found to partially mediate cognitive ability in deletion carriers, providing a link between white matter changes in 15q11.2 BP1-BP2 carriers and cognitive function.
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Affiliation(s)
- Ana I. Silva
- Neuroscience and Mental Health Research Institute, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff, United Kingdom,Cardiff University Brain Research Imaging Centre School of Psychology, Cardiff University, Cardiff, United Kingdom,School for Mental Health and Neuroscience, Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands,Address correspondence to Ana I. Silva, Ph.D.
| | - George Kirov
- Neuroscience and Mental Health Research Institute, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff, United Kingdom
| | - Kimberley M. Kendall
- Neuroscience and Mental Health Research Institute, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff, United Kingdom
| | - Mathew Bracher-Smith
- Neuroscience and Mental Health Research Institute, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff, United Kingdom
| | - Lawrence S. Wilkinson
- Neuroscience and Mental Health Research Institute, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff, United Kingdom,Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom,School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Jeremy Hall
- Neuroscience and Mental Health Research Institute, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff, United Kingdom,Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Magnus O. Ulfarsson
- deCODE genetics/Amgen, Reykjavik, Iceland,Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland
| | - G. Bragi Walters
- deCODE genetics/Amgen, Reykjavik, Iceland,Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | | | - Kari Stefansson
- deCODE genetics/Amgen, Reykjavik, Iceland,Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - David E.J. Linden
- Neuroscience and Mental Health Research Institute, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff, United Kingdom,School for Mental Health and Neuroscience, Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Xavier Caseras
- Neuroscience and Mental Health Research Institute, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff, United Kingdom.
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12
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Simpson-Kent IL, Fried EI, Akarca D, Mareva S, Bullmore ET, Kievit RA. Bridging Brain and Cognition: A Multilayer Network Analysis of Brain Structural Covariance and General Intelligence in a Developmental Sample of Struggling Learners. J Intell 2021; 9:32. [PMID: 34204009 PMCID: PMC8293355 DOI: 10.3390/jintelligence9020032] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/26/2021] [Accepted: 06/02/2021] [Indexed: 12/24/2022] Open
Abstract
Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g., specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain-behavior relationships essential for general intelligence in a large (behavioral, N = 805; cortical volume, N = 246; fractional anisotropy, N = 165) developmental (ages 5-18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade our cognitive, neural, and multilayer networks. Moreover, using community detection (Walktrap algorithm) and calculating node centrality (absolute strength and bridge strength), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role 'between' brain and behavior. We discuss implications and possible avenues for future studies.
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Affiliation(s)
- Ivan L. Simpson-Kent
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
| | - Eiko I. Fried
- Department of Clinical Psychology, Leiden University, 2300 RA Leiden, The Netherlands;
| | - Danyal Akarca
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
| | - Silvana Mareva
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
| | - Edward T. Bullmore
- Department of Psychiatry, University of Cambridge Clinical School, Cambridge, Cambridgeshire CB2 0SP, UK;
| | - the CALM Team
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
| | - Rogier A. Kievit
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, Cambridgeshire CB2 7EF, UK; (D.A.); (S.M.); (R.A.K.)
- Cognitive Neuroscience Department, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
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13
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Does interpersonal emotion regulation ability change with age? HUMAN RESOURCE MANAGEMENT REVIEW 2021. [DOI: 10.1016/j.hrmr.2021.100847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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14
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Association of SBP and BMI with cognitive and structural brain phenotypes in UK Biobank. J Hypertens 2020; 38:2482-2489. [DOI: 10.1097/hjh.0000000000002579] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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15
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Góngora D, Vega‐Hernández M, Jahanshahi M, Valdés‐Sosa PA, Bringas‐Vega ML. Crystallized and fluid intelligence are predicted by microstructure of specific white-matter tracts. Hum Brain Mapp 2020; 41:906-916. [PMID: 32026600 PMCID: PMC7267934 DOI: 10.1002/hbm.24848] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 09/19/2019] [Accepted: 10/17/2019] [Indexed: 01/10/2023] Open
Abstract
Studies of the neural basis of intelligence have focused on comparing brain imaging variables with global scales instead of the cognitive domains integrating these scales or quotients. Here, the relation between mean tract-based fractional anisotropy (mTBFA) and intelligence indices was explored. Deterministic tractography was performed using a regions of interest approach for 10 white-matter fascicles along which the mTBFA was calculated. The study sample included 83 healthy individuals from the second wave of the Cuban Human Brain Mapping Project, whose WAIS-III intelligence quotients and indices were obtained. Inspired by the "Watershed model" of intelligence, we employed a regularized hierarchical Multiple Indicator, Multiple Causes model (MIMIC), to assess the association of mTBFA with intelligence scores, as mediated by latent variables summarizing the indices. Regularized MIMIC, used due to the limited sample size, selected relevant mTBFA by means of an elastic net penalty and achieved good fits to the data. Two latent variables were necessary to describe the indices: Fluid intelligence (Perceptual Organization and Processing Speed indices) and Crystallized Intelligence (Verbal Comprehension and Working Memory indices). Regularized MIMIC revealed effects of the forceps minor tract on crystallized intelligence and of the superior longitudinal fasciculus on fluid intelligence. The model also detected the significant effect of age on both latent variables.
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Affiliation(s)
- Daylín Góngora
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
- Cuban Neuroscience CenterHavanaCuba
| | | | - Marjan Jahanshahi
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
- UCL Queen Square Institute of NeurologyLondonUK
| | - Pedro A. Valdés‐Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
- Cuban Neuroscience CenterHavanaCuba
| | - Maria L. Bringas‐Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengduChina
- Cuban Neuroscience CenterHavanaCuba
| | - CHBMP
- Cuban Neuroscience CenterHavanaCuba
- Ministry of Science, Technology and Environment of CubaHavanaCuba
- Ministry of Public Health of Republic of CubaHavanaCuba
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16
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Fuhrmann D, Simpson-Kent IL, Bathelt J, Kievit RA. A Hierarchical Watershed Model of Fluid Intelligence in Childhood and Adolescence. Cereb Cortex 2020; 30:339-352. [PMID: 31211362 PMCID: PMC7029679 DOI: 10.1093/cercor/bhz091] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 03/18/2019] [Accepted: 04/04/2019] [Indexed: 11/13/2022] Open
Abstract
Fluid intelligence is the capacity to solve novel problems in the absence of task-specific knowledge and is highly predictive of outcomes like educational attainment and psychopathology. Here, we modeled the neurocognitive architecture of fluid intelligence in two cohorts: the Centre for Attention, Leaning and Memory sample (CALM) (N = 551, aged 5-17 years) and the Enhanced Nathan Kline Institute-Rockland Sample (NKI-RS) (N = 335, aged 6-17 years). We used multivariate structural equation modeling to test a preregistered watershed model of fluid intelligence. This model predicts that white matter contributes to intermediate cognitive phenotypes, like working memory and processing speed, which, in turn, contribute to fluid intelligence. We found that this model performed well for both samples and explained large amounts of variance in fluid intelligence (R2CALM = 51.2%, R2NKI-RS = 78.3%). The relationship between cognitive abilities and white matter differed with age, showing a dip in strength around ages 7-12 years. This age effect may reflect a reorganization of the neurocognitive architecture around pre- and early puberty. Overall, these findings highlight that intelligence is part of a complex hierarchical system of partially independent effects.
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Affiliation(s)
- Delia Fuhrmann
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Ivan L Simpson-Kent
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Joe Bathelt
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Rogier A Kievit
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
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17
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Klinedinst BS, Pappas C, Le S, Yu S, Wang Q, Wang L, Allenspach-Jorn K, Mochel JP, Willette AA. Aging-related changes in fluid intelligence, muscle and adipose mass, and sex-specific immunologic mediation: A longitudinal UK Biobank study. Brain Behav Immun 2019; 82:396-405. [PMID: 31513875 PMCID: PMC7755032 DOI: 10.1016/j.bbi.2019.09.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 09/07/2019] [Accepted: 09/08/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Obesity in midlife and early late-life is associated with worse normal cognitive aging. Dual-energy X-ray absorptiometry (DEXA) suggests that visceral adipose mass (VAM) plays a predominant role, whereas non-visceral adipose mass (NVAM) and lean muscle mass (LMM) have shown conflicting relationships. It is unknown how longitudinal, cognitive changes in age-sensitive domains like fluid intelligence (FI) correspond to VAM, NVAM, and LMM in women and men. Furthermore, changes over time in blood leukocyte sub-populations may partially or fully account for sex-specific associations. METHODS Data on 4431 late middle-aged, cognitively unimpaired adults (mean = 64.5 y) was obtained from the UK Biobank prospective cohort across 22 centers. FI scores, blood leukocyte counts, and covariates (age, social class, education) were measured at three 2-year intervals over 6 years. DEXA collection overlapped with these intervals. Sex-stratified growth curves, structural equations, and Preacher-Hayes mediation were used to estimate direct and indirect effects. β-weights were standardized. RESULTS More LMM predicted gains in FI scores among women (β = 0.130, p < .001) and men (β = 0.089, p < .001). Conversely, more VAM and NVAM independently predicted FI decline equally among sexes (e.g., NVAM: women: β = -0.082, p < .001; men: β = -0.076, p < .001). Among women, FI associations were fully mediated by higher eosinophil counts via VAM (λ = 30.8%, p = .028) and lower lymphocyte counts via LMM (λ = 69.2%, p = .021). Among men, FI associations were partially mediated by lower basophils counts via LMM (λ = 4.5%, p = .042) and higher counts via VAM (λ = 50%, p = .037). CONCLUSION The proportion of LMM and VAM equally influenced male FI changes over 6 years, whereas higher LMM among women appeared to more strongly influence. FI changes. Leukocyte counts strongly mediated VAM- and LMM-related FI changes in a sex-specific manner, but not for NVAM. For clinical translation, exercise studies in older adults may benefit from assessing sex-specific values of DEXA-based tissue mass, FI, and leukocyte sub-populations to gauge potential cognitive benefits of less VAM and more LMM.
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Affiliation(s)
- Brandon S. Klinedinst
- Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, USA,Neuroscience Graduate Program, Iowa State University, Ames, IA, USA
| | - Colleen Pappas
- Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, USA
| | - Scott Le
- Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, USA,Interdisciplinary Graduate Studies Program, Iowa State University, Ames, IA, USA
| | - Shan Yu
- Department of Statistics, Iowa State University, Ames, IA, USA
| | - Qian Wang
- Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, USA,Neuroscience Graduate Program, Iowa State University, Ames, IA, USA
| | - Li Wang
- Department of Statistics, Iowa State University, Ames, IA, USA
| | | | | | - Auriel A. Willette
- Department of Food Science and Human Nutrition, Iowa State University, Ames, IA, USA,Neuroscience Graduate Program, Iowa State University, Ames, IA, USA,Interdisciplinary Graduate Studies Program, Iowa State University, Ames, IA, USA,Department of Biomedical Sciences, Iowa State University, Ames, IA, USA,Department of Neurology, University of Iowa, Iowa City, USA,Send Correspondence to: Auriel A. Willette, 1109 HNSB, 2302 Osborn Drive, Ames, IA 50011-1078, Phone: (515) 294-3110,
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18
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Cox S, Ritchie S, Fawns-Ritchie C, Tucker-Drob E, Deary I. Structural brain imaging correlates of general intelligence in UK Biobank. INTELLIGENCE 2019; 76:101376. [PMID: 31787788 PMCID: PMC6876667 DOI: 10.1016/j.intell.2019.101376] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 06/21/2019] [Indexed: 02/06/2023]
Abstract
The associations between indices of brain structure and measured intelligence are unclear. This is partly because the evidence to-date comes from mostly small and heterogeneous studies. Here, we report brain structure-intelligence associations on a large sample from the UK Biobank study. The overall N = 29,004, with N = 18,426 participants providing both brain MRI and at least one cognitive test, and a complete four-test battery with MRI data available in a minimum N = 7201, depending upon the MRI measure. Participants' age range was 44-81 years (M = 63.13, SD = 7.48). A general factor of intelligence (g) was derived from four varied cognitive tests, accounting for one third of the variance in the cognitive test scores. The association between (age- and sex- corrected) total brain volume and a latent factor of general intelligence is r = 0.276, 95% C.I. = [0.252, 0.300]. A model that incorporated multiple global measures of grey and white matter macro- and microstructure accounted for more than double the g variance in older participants compared to those in middle-age (13.6% and 5. 4%, respectively). There were no sex differences in the magnitude of associations between g and total brain volume or other global aspects of brain structure. The largest brain regional correlates of g were volumes of the insula, frontal, anterior/superior and medial temporal, posterior and paracingulate, lateral occipital cortices, thalamic volume, and the white matter microstructure of thalamic and association fibres, and of the forceps minor. Many of these regions exhibited unique contributions to intelligence, and showed highly stable out of sample prediction.
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Affiliation(s)
- S.R. Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, UK
- Department of Psychology, The University of Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - S.J. Ritchie
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
| | - C. Fawns-Ritchie
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, UK
- Department of Psychology, The University of Edinburgh, UK
| | | | - I.J. Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, UK
- Department of Psychology, The University of Edinburgh, UK
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19
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Cornelis MC, Wang Y, Holland T, Agarwal P, Weintraub S, Morris MC. Age and cognitive decline in the UK Biobank. PLoS One 2019; 14:e0213948. [PMID: 30883587 PMCID: PMC6422276 DOI: 10.1371/journal.pone.0213948] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Accepted: 03/04/2019] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVES Age-related cognitive decline is a well-known phenomenon after age 65 but little is known about earlier changes and prior studies are based on relatively small samples. We investigated the impact of age on cognitive decline in the largest population sample to date including young to old adults. METHOD Between 100,352 and 468,534 participants aged 38-73 years from UK Biobank completed at least one of seven self-administered cognitive functioning tests: prospective memory (PM), pairs matching (Pairs), fluid intelligence (FI), reaction time (RT), symbol digit substitution, trail making A and B. Up to 26,005 participants completed at least one of two follow-up assessments of PM, Pairs, FI and RT. Multivariable regression models examined the association between age (<45[reference], 45-49, 50-54, 55-59, 60-64, 65+) and cognition scores at baseline. Mixed models estimated the impact of age on cognitive decline over follow-up (~5.1 years). RESULTS FI was higher between ages 50 and 64 and lower at 65+ compared to <45 at baseline. Performance on all other baseline tests was lower with older age: with increasing age category, difference in test scores ranged from 2.5 to 7.8%(P<0.0001). Compared to <45 at baseline, RT and Pairs performance declined faster across all older age cohorts (3.0 and 1.2% change, respectively, with increasing age category, P<0.0001). Cross-sectional results yielded 8 to 12-fold higher differences in RT and Pairs with age compared to longitudinal results. CONCLUSIONS Our findings suggest that declines in cognitive abilities <65 are small. The cross-sectional differences in cognition scores for middle to older adult years may be due in part to age cohort effects.
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Affiliation(s)
- Marilyn C. Cornelis
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- * E-mail:
| | - Yamin Wang
- Rush Institute for Healthy Aging, Rush University, Chicago, Illinois, United States of America
| | - Thomas Holland
- Rush Institute for Healthy Aging, Rush University, Chicago, Illinois, United States of America
| | - Puja Agarwal
- Rush Institute for Healthy Aging, Rush University, Chicago, Illinois, United States of America
| | - Sandra Weintraub
- Mesulam Cognitive Neurology and Alzheimer’s Disease Center, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Martha Clare Morris
- Rush Institute for Healthy Aging, Rush University, Chicago, Illinois, United States of America
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