51
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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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52
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Calvert GHM, Carson RG. Neural mechanisms mediating cross education: With additional considerations for the ageing brain. Neurosci Biobehav Rev 2021; 132:260-288. [PMID: 34801578 DOI: 10.1016/j.neubiorev.2021.11.025] [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: 09/07/2021] [Revised: 11/03/2021] [Accepted: 11/16/2021] [Indexed: 12/14/2022]
Abstract
CALVERT, G.H.M., and CARSON, R.G. Neural mechanisms mediating cross education: With additional considerations for the ageing brain. NEUROSCI BIOBEHAV REV 21(1) XXX-XXX, 2021. - Cross education (CE) is the process whereby a regimen of unilateral limb training engenders bilateral improvements in motor function. The contralateral gains thus derived may impart therapeutic benefits for patients with unilateral deficits arising from orthopaedic injury or stroke. Despite this prospective therapeutic utility, there is little consensus concerning its mechanistic basis. The precise means through which the neuroanatomical structures and cellular processes that mediate CE may be influenced by age-related neurodegeneration are also almost entirely unknown. Notwithstanding the increased incidence of unilateral impairment in later life, age-related variations in the expression of CE have been examined only infrequently. In this narrative review, we consider several mechanisms which may mediate the expression of CE with specific reference to the ageing CNS. We focus on the adaptive potential of cellular processes that are subserved by a specific set of neuroanatomical pathways including: the corticospinal tract, corticoreticulospinal projections, transcallosal fibres, and thalamocortical radiations. This analysis may inform the development of interventions that exploit the therapeutic utility of CE training in older persons.
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Affiliation(s)
- Glenn H M Calvert
- Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Richard G Carson
- Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, Dublin, Ireland; School of Psychology, Queen's University Belfast, Belfast, Northern Ireland, UK; School of Human Movement and Nutrition Sciences, The University of Queensland, Brisbane, Australia.
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53
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Modenato C, Martin-Brevet S, Moreau CA, Rodriguez-Herreros B, Kumar K, Draganski B, Sønderby IE, Jacquemont S. Lessons Learned From Neuroimaging Studies of Copy Number Variants: A Systematic Review. Biol Psychiatry 2021; 90:596-610. [PMID: 34509290 DOI: 10.1016/j.biopsych.2021.05.028] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 05/28/2021] [Accepted: 05/30/2021] [Indexed: 01/06/2023]
Abstract
Pathogenic copy number variants (CNVs) and aneuploidies alter gene dosage and are associated with neurodevelopmental psychiatric disorders such as autism spectrum disorder and schizophrenia. Brain mechanisms mediating genetic risk for neurodevelopmental psychiatric disorders remain largely unknown, but there is a rapid increase in morphometry studies of CNVs using T1-weighted structural magnetic resonance imaging. Studies have been conducted one mutation at a time, leaving the field with a complex catalog of brain alterations linked to different genomic loci. Our aim was to provide a systematic review of neuroimaging phenotypes across CNVs associated with developmental psychiatric disorders including autism and schizophrenia. We included 76 structural magnetic resonance imaging studies on 20 CNVs at the 15q11.2, 22q11.2, 1q21.1 distal, 16p11.2 distal and proximal, 7q11.23, 15q11-q13, and 22q13.33 (SHANK3) genomic loci as well as aneuploidies of chromosomes X, Y, and 21. Moderate to large effect sizes on global and regional brain morphometry are observed across all genomic loci, which is in line with levels of symptom severity reported for these variants. This is in stark contrast with the much milder neuroimaging effects observed in idiopathic psychiatric disorders. Data also suggest that CNVs have independent effects on global versus regional measures as well as on cortical surface versus thickness. Findings highlight a broad diversity of regional morphometry patterns across genomic loci. This heterogeneity of brain patterns provides insight into the weak effects reported in magnetic resonance imaging studies of cognitive dimension and psychiatric conditions. Neuroimaging studies across many more variants will be required to understand links between gene function and brain morphometry.
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Affiliation(s)
- Claudia Modenato
- Laboratory for Research in Neuroimaging, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Sandra Martin-Brevet
- Laboratory for Research in Neuroimaging, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Clara A Moreau
- Sainte-Justine Hospital Research Center, Montreal, Quebec, Canada; Human Genetics and Cognitive Functions, Centre National de la Recherche Scientifique UMR 3571, Department of Neuroscience, Université de Paris, Institut Pasteur, Paris, France
| | - Borja Rodriguez-Herreros
- Service des Troubles du Spectre de l'Autisme et Apparentés, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Kuldeep Kumar
- Sainte-Justine Hospital Research Center, Montreal, Quebec, Canada; Department of Pediatrics, University of Montreal, Montreal, Quebec, Canada
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland; Neurology Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ida E Sønderby
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway; Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, Oslo University Hospital, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Sébastien Jacquemont
- Sainte-Justine Hospital Research Center, Montreal, Quebec, Canada; Department of Pediatrics, University of Montreal, Montreal, Quebec, Canada.
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54
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Zareba MR, Fafrowicz M, Marek T, Beldzik E, Oginska H, Domagalik A. Late chronotype is linked to greater cortical thickness in the left fusiform and entorhinal gyri. BIOL RHYTHM RES 2021. [DOI: 10.1080/09291016.2021.1990501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Michal Rafal Zareba
- Institute of Zoology and Biomedical Research, Faculty of Biology, Jagiellonian University, Kraków, Poland
- Brain Imaging Core Facility, Malopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland
| | - Magdalena Fafrowicz
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland
| | - Tadeusz Marek
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland
| | - Ewa Beldzik
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland
| | - Halszka Oginska
- Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland
| | - Aleksandra Domagalik
- Brain Imaging Core Facility, Malopolska Centre of Biotechnology, Jagiellonian University, Kraków, Poland
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55
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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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56
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Deary IJ. Two Cheers for the Cognitive Irregulars: Intelligence's Contributions to Ageing Well and Staying Alive. J Intell 2021; 9:jintelligence9030041. [PMID: 34449683 PMCID: PMC8395851 DOI: 10.3390/jintelligence9030041] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/10/2021] [Accepted: 08/12/2021] [Indexed: 12/16/2022] Open
Abstract
Here, intelligence is taken to mean scores from psychometric tests of cognitive functions. This essay describes how cognitive tests offer assessments of brain functioning—an otherwise difficult-to-assess organ—that have proved enduringly useful in the field of health and medicine. The two “consequential world problems” (the phrase used by the inviters of this essay) addressed in this article are (i) the ageing of modern societies (and the resulting increase in the numbers of people with ageing-related cognitive decrements and dementias) and (ii) health inequalities, including mortality. Cognitive tests have an ubiquitous place in both of these topics, i.e., the important fields of cognitive ageing and cognitive epidemiology, respectively. The cognitive tests that have sprouted in these fields are often brief and not mainstream, large psychometric test batteries; I refer to them as ‘irregulars’. These two problems are not separate, because results found with mental/cognitive/intelligence tests have produced a growing understanding that intelligence and health have a reciprocal, life-long relationship. Intelligence tests contribute to the applied research that is trying to help people to stay sharp, stay healthy, and stay alive.
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Affiliation(s)
- Ian J Deary
- Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
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57
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Yeung HW, Shen X, Stolicyn A, de Nooij L, Harris MA, Romaniuk L, Buchanan CR, Waiter GD, Sandu AL, McNeil CJ, Murray A, Steele JD, Campbell A, Porteous D, Lawrie SM, McIntosh AM, Cox SR, Smith KM, Whalley HC. Spectral clustering based on structural magnetic resonance imaging and its relationship with major depressive disorder and cognitive ability. Eur J Neurosci 2021; 54:6281-6303. [PMID: 34390586 DOI: 10.1111/ejn.15423] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 08/09/2021] [Indexed: 11/29/2022]
Abstract
There is increasing interest in using data-driven unsupervised methods to identify structural underpinnings of common mental illnesses, including major depressive disorder (MDD) and associated traits such as cognition. However, studies are often limited to severe clinical cases with small sample sizes and most do not include replication. Here, we examine two relatively large samples with structural magnetic resonance imaging (MRI), measures of lifetime MDD and cognitive variables: Generation Scotland (GS subsample, N = 980) and UK Biobank (UKB, N = 8,900), for discovery and replication, using an exploratory approach. Regional measures of FreeSurfer derived cortical thickness (CT), cortical surface area (CSA), cortical volume (CV) and subcortical volume (subCV) were input into a clustering process, controlling for common covariates. The main analysis steps involved constructing participant K-nearest neighbour graphs and graph partitioning with Markov stability to determine optimal clustering of participants. Resultant clusters were (1) checked whether they were replicated in an independent cohort and (2) tested for associations with depression status and cognitive measures. Participants separated into two clusters based on structural brain measurements in GS subsample, with large Cohen's d effect sizes between clusters in higher order cortical regions, commonly associated with executive function and decision making. Clustering was replicated in the UKB sample, with high correlations of cluster effect sizes for CT, CSA, CV and subCV between cohorts across regions. The identified clusters were not significantly different with respect to MDD case-control status in either cohort (GS subsample: pFDR = .2239-.6585; UKB: pFDR = .2003-.7690). Significant differences in general cognitive ability were, however, found between the clusters for both datasets, for CSA, CV and subCV (GS subsample: d = 0.2529-.3490, pFDR < .005; UKB: d = 0.0868-0.1070, pFDR < .005). Our results suggest that there are replicable natural groupings of participants based on cortical and subcortical brain measures, which may be related to differences in cognitive performance, but not to the MDD case-control status.
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Affiliation(s)
- Hon Wah Yeung
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Xueyi Shen
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Aleks Stolicyn
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Laura de Nooij
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Mathew A Harris
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Liana Romaniuk
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Colin R Buchanan
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Gordon D Waiter
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Anca-Larisa Sandu
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Christopher J McNeil
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Alison Murray
- Aberdeen Biomedical Imaging Centre, Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - J Douglas Steele
- School of Medicine, University of Dundee, Dundee, UK.,Department of Neurology, NHS Tayside, Ninewells Hospital and Medical School, Dundee, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - David Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK.,Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | | | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK.,Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Lothian Birth Cohorts group, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Keith M Smith
- Usher Institute, University of Edinburgh, Edinburgh, UK.,Health Data Research UK, London, UK
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58
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Wheater E, Shenkin SD, Muñoz Maniega S, Valdés Hernández M, Wardlaw JM, Deary IJ, Bastin ME, Boardman JP, Cox SR. Birth weight is associated with brain tissue volumes seven decades later but not with MRI markers of brain ageing. NEUROIMAGE-CLINICAL 2021; 31:102776. [PMID: 34371238 PMCID: PMC8358699 DOI: 10.1016/j.nicl.2021.102776] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/22/2021] [Accepted: 07/25/2021] [Indexed: 12/03/2022]
Abstract
Larger birth weight is associated with larger brain tissue volumes at age 73. Birth weight is not associated with age-associated brain features. Effect of birth weight on brain volumes is independent of overall body size. Early life growth is likely to confer brain tissue reserve in later life.
Birth weight, an indicator of fetal growth, is associated with cognitive outcomes in early life (which are predictive of cognitive ability in later life) and risk of metabolic and cardiovascular disease across the life course. Brain health in older age, indexed by MRI features, is associated with cognitive performance, but little is known about how variation in normal birth weight impacts on brain structure in later life. In a community dwelling cohort of participants in their early seventies we tested the hypothesis that birth weight is associated with the following MRI features: total brain (TB), grey matter (GM) and normal appearing white matter (NAWM) volumes; whiter matter hyperintensity (WMH) volume; a general factor of fractional anisotropy (gFA) and peak width skeletonised mean diffusivity (PSMD) across the white matter skeleton. We also investigated the associations of birth weight with cortical surface area, volume and thickness. Birth weight was positively associated with TB, GM and NAWM volumes in later life (β ≥ 0.194), and with regional cortical surface area but not gFA, PSMD, WMH volume, or cortical volume or thickness. These positive relationships appear to be explained by larger intracranial volume, rather than by age-related tissue atrophy, and are independent of body height and weight in adulthood. This suggests that larger birth weight is linked to more brain tissue reserve in older life, rather than age-related brain structural features, such as tissue atrophy or WMH volume.
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Affiliation(s)
- Emily Wheater
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom
| | - Susan D Shenkin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Geriatric Medicine, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Susana Muñoz Maniega
- Geriatric Medicine, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom; Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom; Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE), Edinburgh, United Kingdom
| | - Maria Valdés Hernández
- Geriatric Medicine, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom; Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom; Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE), Edinburgh, United Kingdom
| | - Joanna M Wardlaw
- Geriatric Medicine, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom; Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom; Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE), Edinburgh, United Kingdom; UK Dementia Research Institute Centre at the University of Edinburgh, United Kingdom
| | - Ian J Deary
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom; Department Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E Bastin
- Geriatric Medicine, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom; Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom; Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE), Edinburgh, United Kingdom
| | - James P Boardman
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Simon R Cox
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom; Scottish Imaging Network, A Platform for Scientific Excellence Collaboration (SINAPSE), Edinburgh, United Kingdom; Department Psychology, University of Edinburgh, Edinburgh, United Kingdom.
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59
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Williams CM, Peyre H, Toro R, Ramus F. Neuroanatomical norms in the UK Biobank: The impact of allometric scaling, sex, and age. Hum Brain Mapp 2021; 42:4623-4642. [PMID: 34268815 PMCID: PMC8410561 DOI: 10.1002/hbm.25572] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 06/03/2021] [Accepted: 06/11/2021] [Indexed: 12/18/2022] Open
Abstract
Few neuroimaging studies are sufficiently large to adequately describe population‐wide variations. This study's primary aim was to generate neuroanatomical norms and individual markers that consider age, sex, and brain size, from 629 cerebral measures in the UK Biobank (N = 40,028). The secondary aim was to examine the effects and interactions of sex, age, and brain allometry—the nonlinear scaling relationship between a region and brain size (e.g., total brain volume)—across cerebral measures. Allometry was a common property of brain volumes, thicknesses, and surface areas (83%) and was largely stable across age and sex. Sex differences occurred in 67% of cerebral measures (median |β| = .13): 37% of regions were larger in males and 30% in females. Brain measures (49%) generally decreased with age, although aging effects varied across regions and sexes. While models with an allometric or linear covariate adjustment for brain size yielded similar significant effects, omitting brain allometry influenced reported sex differences in variance. Finally, we contribute to the reproducibility of research on sex differences in the brain by replicating previous studies examining cerebral sex differences. This large‐scale study advances our understanding of age, sex, and brain allometry's impact on brain structure and provides data for future UK Biobank studies to identify the cerebral regions that covary with specific phenotypes, independently of sex, age, and brain size.
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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, Paris, France
| | - Hugo Peyre
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, Paris, France.,INSERM UMR 1141, Paris Diderot University, Paris, France.,Department of Child and Adolescent Psychiatry, Robert Debré Hospital, APHP, Paris, France
| | - Roberto Toro
- Human Genetics and Cognitive Functions, Institut Pasteur, UMR 3571 CNRS, Paris, France.,Center for Research and Interdisciplinarity (CRI), INSERM U1284, Paris, France.,Université de Paris, Paris, France
| | - Franck Ramus
- Laboratoire de Sciences Cognitives et Psycholinguistique, Département d'Études Cognitives, École Normale Supérieure, EHESS, CNRS, PSL University, Paris, France
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60
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Du J, Koch FC, Xia A, Jiang J, Crawford JD, Lam BCP, Thalamuthu A, Lee T, Kochan N, Fawns-Ritchie C, Brodaty H, Xu Q, Sachdev PS, Wen W. Difference in distribution functions: A new diffusion weighted imaging metric for estimating white matter integrity. Neuroimage 2021; 240:118381. [PMID: 34252528 DOI: 10.1016/j.neuroimage.2021.118381] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/27/2021] [Accepted: 07/08/2021] [Indexed: 11/19/2022] Open
Abstract
Diffusion weighted imaging (DWI) is a widely recognized neuroimaging technique to evaluate the microstructure of brain white matter. The objective of this study is to establish an improved automated DWI marker for estimating white matter integrity and investigating ageing related cognitive decline. The concept of Wasserstein distance was introduced to help establish a new measure: difference in distribution functions (DDF), which captures the difference of reshaping one's mean diffusivity (MD) distribution to a reference MD distribution. This new DWI measure was developed using a population-based cohort (n=19,369) from the UK Biobank. Validation was conducted using the data drawn from two independent cohorts: the Sydney Memory and Ageing Study, a community-dwelling sample (n=402), and the Renji Cerebral Small Vessel Disease Cohort Study (RCCS), which consisted of cerebral small vessel disease (CSVD) patients (n=171) and cognitively normal controls (NC) (n=43). DDF was associated with age across all three samples and better explained the variance of changes than other established DWI measures, such as fractional anisotropy, mean diffusivity and peak width of skeletonized mean diffusivity (PSMD). Significant correlations between DDF and cognition were found in the UK Biobank cohort and the MAS cohort. Binary logistic analysis and receiver operator characteristic curve analysis of RCCS demonstrated that DDF had higher sensitivity in distinguishing CSVD patients from NC than the other DWI measures. To demonstrate the flexibility of DDF, we calculated regional DDF which also showed significant correlation with age and cognition. DDF can be used as a marker for monitoring the white matter microstructural changes and ageing related cognitive decline in the elderly.
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Affiliation(s)
- Jing Du
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia.
| | - Forrest C Koch
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - Aihua Xia
- School of Mathematics and Statistics, University of Melbourne, Melbourne, Victoria 3010, Australia
| | - Jiyang Jiang
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - John D Crawford
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - Ben C P Lam
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - Teresa Lee
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia; Neuropsychiatric Institute (NPI), Euroa Centre, Prince of Wales Hospital, Randwick, New South Wales 2031, Australia
| | - Nicole Kochan
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - Chloe Fawns-Ritchie
- Centre for Cognitive Ageing and Cognitive Epidemiology (CCACE), Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Henry Brodaty
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia; Dementia Centre for Research Collaboration, School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia
| | - Qun Xu
- Department of Health Manage Centre, RenJi Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China; Department of Neurology, RenJi Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
| | - Perminder S Sachdev
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia; Neuropsychiatric Institute (NPI), Euroa Centre, Prince of Wales Hospital, Randwick, New South Wales 2031, Australia
| | - Wei Wen
- Centre for Healthy Brain Aging (CHeBA), School of Psychiatry, UNSW Sydney, New South Wales 2052, Australia; Neuropsychiatric Institute (NPI), Euroa Centre, Prince of Wales Hospital, Randwick, New South Wales 2031, Australia
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Cognitive impairment after focal brain lesions is better predicted by damage to structural than functional network hubs. Proc Natl Acad Sci U S A 2021; 118:2018784118. [PMID: 33941692 DOI: 10.1073/pnas.2018784118] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Hubs are highly connected brain regions important for coordinating processing in brain networks. It is unclear, however, which measures of network "hubness" are most useful in identifying brain regions critical to human cognition. We tested how closely two measures of hubness-edge density and participation coefficient, derived from white and gray matter, respectively-were associated with general cognitive impairment after brain damage in two large cohorts of patients with focal brain lesions (N = 402 and 102, respectively) using cognitive tests spanning multiple cognitive domains. Lesions disrupting white matter regions with high edge density were associated with cognitive impairment, whereas lesions damaging gray matter regions with high participation coefficient had a weaker, less consistent association with cognitive outcomes. Similar results were observed with six other gray matter hubness measures. This suggests that damage to densely connected white matter regions is more cognitively impairing than similar damage to gray matter hubs, helping to explain interindividual differences in cognitive outcomes after brain damage.
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Is there a “g-neuron”? Establishing a systematic link between general intelligence (g) and the von Economo neuron. INTELLIGENCE 2021. [DOI: 10.1016/j.intell.2021.101540] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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63
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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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Jockwitz C, Mérillat S, Liem F, Oschwald J, Amunts K, Jäncke L, Caspers S. Generalizing Longitudinal Age Effects on Brain Structure - A Two-Study Comparison Approach. Front Hum Neurosci 2021; 15:635687. [PMID: 33935669 PMCID: PMC8085300 DOI: 10.3389/fnhum.2021.635687] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 03/03/2021] [Indexed: 11/13/2022] Open
Abstract
Cross-sectional studies indicate that normal aging is accompanied by decreases in brain structure. Longitudinal studies, however, are relatively rare and inconsistent regarding their outcomes. Particularly the heterogeneity of methods, sample characteristics and the high inter-individual variability in older adults prevent the deduction of general trends. Therefore, the current study aimed to compare longitudinal age-related changes in brain structure (measured through cortical thickness) in two large independent samples of healthy older adults (n = 161 each); the Longitudinal Healthy Aging Brain (LHAB) database project at the University of Zurich, Switzerland, and 1000BRAINS at the Research Center Juelich, Germany. Annual percentage changes in the two samples revealed stable to slight decreases in cortical thickness over time. After correction for major covariates, i.e., baseline age, sex, education, and image quality, sample differences were only marginally present. Results suggest that general trends across time might be generalizable over independent samples, assuming the same methodology is used, and similar sample characteristics are present.
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Affiliation(s)
- Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.,Institute for Anatomy I, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Susan Mérillat
- University Research Priority Program "Dynamics of Healthy Aging", University of Zurich, Zurich, Switzerland
| | - Franziskus Liem
- University Research Priority Program "Dynamics of Healthy Aging", University of Zurich, Zurich, Switzerland
| | - Jessica Oschwald
- University Research Priority Program "Dynamics of Healthy Aging", University of Zurich, Zurich, Switzerland
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.,C. and O. Vogt Institute for Brain Research, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Lutz Jäncke
- University Research Priority Program "Dynamics of Healthy Aging", University of Zurich, Zurich, Switzerland.,Division of Neuropsychology, University of Zurich, Zurich, Switzerland
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.,Institute for Anatomy I, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
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Madole JW, Ritchie SJ, Cox SR, Buchanan CR, Hernández MV, Maniega SM, Wardlaw JM, Harris MA, Bastin ME, Deary IJ, Tucker-Drob EM. Aging-Sensitive Networks Within the Human Structural Connectome Are Implicated in Late-Life Cognitive Declines. Biol Psychiatry 2021; 89:795-806. [PMID: 32828527 PMCID: PMC7736316 DOI: 10.1016/j.biopsych.2020.06.010] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 05/20/2020] [Accepted: 06/06/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Aging-related cognitive decline is a primary risk factor for Alzheimer's disease and related dementias. More precise identification of the neurobiological bases of cognitive decline in aging populations may provide critical insights into the precursors of late-life dementias. METHODS Using structural and diffusion brain magnetic resonance imaging data from the UK Biobank (n = 8185; age range, 45-78 years), we examined aging of regional gray matter volumes (nodes) and white matter structural connectivity (edges) within 9 well-characterized networks of interest in the human brain connectome. In the independent Lothian Birth Cohort 1936 (n = 534; all 73 years of age), we tested whether aging-sensitive connectome elements are enriched for key domains of cognitive function before and after controlling for early-life cognitive ability. RESULTS In the UK Biobank, age differences in individual connectome elements corresponded closely with principal component loadings reflecting connectome-wide integrity (|rnodes| = .420; |redges| = .583), suggesting that connectome aging occurs on broad dimensions of variation in brain architecture. In the Lothian Birth Cohort 1936, composite indices of node integrity were predictive of all domains of cognitive function, whereas composite indices of edge integrity were associated specifically with processing speed. Elements within the central executive network were disproportionately predictive of late-life cognitive function relative to the network's small size. Associations with processing speed and visuospatial ability remained after controlling for childhood cognitive ability. CONCLUSIONS These results implicate global dimensions of variation in the human structural connectome in aging-related cognitive decline. The central executive network may demarcate a constellation of elements that are centrally important to age-related cognitive impairments.
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Affiliation(s)
- James W Madole
- Department of Psychology, University of Texas at Austin, Austin, Texas.
| | - Stuart J Ritchie
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, United Kingdom
| | - Simon R Cox
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom; Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom; Scottish Imaging Network: A Platform for Scientific Excellence Collaboration, Edinburgh, United Kingdom
| | - Colin R Buchanan
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom; Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom; Scottish Imaging Network: A Platform for Scientific Excellence Collaboration, Edinburgh, United Kingdom
| | - Maria Valdés Hernández
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Scottish Imaging Network: A Platform for Scientific Excellence Collaboration, Edinburgh, United Kingdom
| | - Susana Muñoz Maniega
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Scottish Imaging Network: A Platform for Scientific Excellence Collaboration, Edinburgh, United Kingdom
| | - Joanna M Wardlaw
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Scottish Imaging Network: A Platform for Scientific Excellence Collaboration, Edinburgh, United Kingdom
| | - Mathew A Harris
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E Bastin
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Scottish Imaging Network: A Platform for Scientific Excellence Collaboration, Edinburgh, United Kingdom
| | - Ian J Deary
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, United Kingdom; Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Elliot M Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, Texas; Population Research Center, University of Texas at Austin, Austin, Texas
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Huguet J, Falcon C, Fusté D, Girona S, Vicente D, Molinuevo JL, Gispert JD, Operto G. Management and Quality Control of Large Neuroimaging Datasets: Developments From the Barcelonaβeta Brain Research Center. Front Neurosci 2021; 15:633438. [PMID: 33935631 PMCID: PMC8081968 DOI: 10.3389/fnins.2021.633438] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 03/02/2021] [Indexed: 12/02/2022] Open
Abstract
Recent decades have witnessed an increasing number of large to very large imaging studies, prominently in the field of neurodegenerative diseases. The datasets collected during these studies form essential resources for the research aiming at new biomarkers. Collecting, hosting, managing, processing, or reviewing those datasets is typically achieved through a local neuroinformatics infrastructure. In particular for organizations with their own imaging equipment, setting up such a system is still a hard task, and relying on cloud-based solutions, albeit promising, is not always possible. This paper proposes a practical model guided by core principles including user involvement, lightweight footprint, modularity, reusability, and facilitated data sharing. This model is based on the experience from an 8-year-old research center managing cohort research programs on Alzheimer’s disease. Such a model gave rise to an ecosystem of tools aiming at improved quality control through seamless automatic processes combined with a variety of code libraries, command line tools, graphical user interfaces, and instant messaging applets. The present ecosystem was shaped around XNAT and is composed of independently reusable modules that are freely available on GitLab/GitHub. This paradigm is scalable to the general community of researchers working with large neuroimaging datasets.
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Affiliation(s)
- Jordi Huguet
- Barcelonabeta Brain Research Center, Barcelona, Spain
| | - Carles Falcon
- Barcelonabeta Brain Research Center, Barcelona, Spain
| | - David Fusté
- Barcelonabeta Brain Research Center, Barcelona, Spain
| | - Sergi Girona
- Barcelona Supercomputing Center, Barcelona, Spain
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Wortinger LA, Jørgensen KN, Barth C, Nerland S, Smelror RE, Vaskinn A, Ueland T, Andreassen OA, Agartz I. Significant association between intracranial volume and verbal intellectual abilities in patients with schizophrenia and a history of birth asphyxia. Psychol Med 2021; 52:1-10. [PMID: 33750510 PMCID: PMC9772907 DOI: 10.1017/s0033291721000489] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 01/20/2021] [Accepted: 02/04/2021] [Indexed: 12/30/2022]
Abstract
BACKGROUND The etiology of schizophrenia (SZ) is proposed to include an interplay between a genetic risk for disease development and the biological environment of pregnancy and birth, where early adversities may contribute to the poorer developmental outcome. We investigated whether a history of birth asphyxia (ASP) moderates the relationship between intracranial volume (ICV) and intelligence in SZ, bipolar disorder (BD) and healthy controls (HC). METHODS Two hundred seventy-nine adult patients (18-42 years) on the SZ and BD spectrums and 216 HC were evaluated for ASP based on information from the Medical Birth Registry of Norway. Participants underwent structural magnetic resonance imaging (MRI) to estimate ICV and intelligence quotient (IQ) assessment using the Wechsler Abbreviated Scale of Intelligence (WASI). Multiple linear regressions were used for analyses. RESULTS We found a significant three-way interaction (ICV × ASP × diagnosis) on the outcome variable, IQ, indicating that the correlation between ICV and IQ was stronger in patients with SZ who experienced ASP compared to SZ patients without ASP. This moderation by ASP was not found in BD or HC groups. In patients with SZ, the interaction between ICV and a history of the ASP was specifically related to the verbal subcomponent of IQ as measured by WASI. CONCLUSIONS The significant positive association between ICV and IQ in patients with SZ who had experienced ASP might indicate abnormal neurodevelopment. Our findings give support for ICV together with verbal intellectual abilities as clinically relevant markers that can be added to prediction tools to enhance evaluations of SZ risk.
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Affiliation(s)
- Laura Anne Wortinger
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kjetil Nordbø Jørgensen
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Claudia Barth
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Stener Nerland
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Runar Elle Smelror
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Anja Vaskinn
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, NORMENT, Oslo University Hospital, Oslo, Norway
| | - Torill Ueland
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, NORMENT, Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Ole A. Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, NORMENT, Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet, Stockholm, Sweden
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Feilong M, Guntupalli JS, Haxby JV. The neural basis of intelligence in fine-grained cortical topographies. eLife 2021; 10:e64058. [PMID: 33683205 PMCID: PMC7993992 DOI: 10.7554/elife.64058] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 03/05/2021] [Indexed: 02/01/2023] Open
Abstract
Intelligent thought is the product of efficient neural information processing, which is embedded in fine-grained, topographically organized population responses and supported by fine-grained patterns of connectivity among cortical fields. Previous work on the neural basis of intelligence, however, has focused on coarse-grained features of brain anatomy and function because cortical topographies are highly idiosyncratic at a finer scale, obscuring individual differences in fine-grained connectivity patterns. We used a computational algorithm, hyperalignment, to resolve these topographic idiosyncrasies and found that predictions of general intelligence based on fine-grained (vertex-by-vertex) connectivity patterns were markedly stronger than predictions based on coarse-grained (region-by-region) patterns. Intelligence was best predicted by fine-grained connectivity in the default and frontoparietal cortical systems, both of which are associated with self-generated thought. Previous work overlooked fine-grained architecture because existing methods could not resolve idiosyncratic topographies, preventing investigation where the keys to the neural basis of intelligence are more likely to be found.
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Affiliation(s)
- Ma Feilong
- Center for Cognitive Neuroscience, Dartmouth CollegeHanover, NHUnited States
| | | | - James V Haxby
- Center for Cognitive Neuroscience, Dartmouth CollegeHanover, NHUnited States
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O'Connell KS, Sønderby IE, Frei O, van der Meer D, Athanasiu L, Smeland OB, Alnæs D, Kaufmann T, Westlye LT, Steen VM, Andreassen OA, Hughes T, Djurovic S. Association between complement component 4A expression, cognitive performance and brain imaging measures in UK Biobank. Psychol Med 2021; 52:1-11. [PMID: 33653435 PMCID: PMC9772918 DOI: 10.1017/s0033291721000179] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 01/06/2021] [Accepted: 01/19/2021] [Indexed: 12/30/2022]
Abstract
Abstract. BACKGROUND Altered expression of the complement component C4A gene is a known risk factor for schizophrenia. Further, predicted brain C4A expression has also been associated with memory function highlighting that altered C4A expression in the brain may be relevant for cognitive and behavioral traits. METHODS We obtained genetic information and performance measures on seven cognitive tasks for up to 329 773 individuals from the UK Biobank, as well as brain imaging data for a subset of 33 003 participants. Direct genotypes for variants (n = 3213) within the major histocompatibility complex region were used to impute C4 structural variation, from which predicted expression of the C4A and C4B genes in human brain tissue were predicted. We investigated if predicted brain C4A or C4B expression were associated with cognitive performance and brain imaging measures using linear regression analyses. RESULTS We identified significant negative associations between predicted C4A expression and performance on select cognitive tests, and significant associations with MRI-based cortical thickness and surface area in select regions. Finally, we observed significant inconsistent partial mediation of the effects of predicted C4A expression on cognitive performance, by specific brain structure measures. CONCLUSIONS These results demonstrate that the C4 risk locus is associated with the central endophenotypes of cognitive performance and brain morphology, even when considered independently of other genetic risk factors and in individuals without mental or neurological disorders.
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Affiliation(s)
- Kevin S. O'Connell
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ida E. Sønderby
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Dennis van der Meer
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Lavinia Athanasiu
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Olav B. Smeland
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Dag Alnæs
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Tobias Kaufmann
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Lars T. Westlye
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Vidar M. Steen
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Dr Einar Martens' Research Group for Biological Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Ole A. Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Timothy Hughes
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
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Verhelst H, Dhollander T, Gerrits R, Vingerhoets G. Fibre-specific laterality of white matter in left and right language dominant people. Neuroimage 2021; 230:117812. [PMID: 33524578 DOI: 10.1016/j.neuroimage.2021.117812] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 12/23/2020] [Accepted: 01/20/2021] [Indexed: 12/13/2022] Open
Abstract
Language is the most commonly described lateralised cognitive function, relying more on the left hemisphere compared to the right hemisphere in over 90% of the population. Most research examining the structure-function relationship of language lateralisation only included people showing a left language hemisphere dominance. In this work, we applied a state-of-the-art "fixel-based" analysis approach, allowing statistical analysis of white matter micro- and macrostructure on a fibre-specific level in a sample of participants with left and right language dominance (LLD and RLD). Both groups showed a similar extensive pattern of white matter lateralisation including a comparable leftwards lateralisation of the arcuate fasciculus, regardless of their functional language lateralisation. These results suggest that lateralisation of language functioning and the arcuate fasciculus are driven by independent biases. Finally, a significant group difference of lateralisation was detected in the forceps minor, with a leftwards lateralisation in LLD and rightwards lateralisation for the RLD group.
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Affiliation(s)
- Helena Verhelst
- Department of Experimental Psychology, Ghent University, Belgium.
| | - Thijs Dhollander
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
| | - Robin Gerrits
- Department of Experimental Psychology, Ghent University, Belgium
| | - Guy Vingerhoets
- Department of Experimental Psychology, Ghent University, Belgium
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de la Fuente J, Davies G, Grotzinger AD, Tucker-Drob EM, Deary IJ. A general dimension of genetic sharing across diverse cognitive traits inferred from molecular data. Nat Hum Behav 2021; 5:49-58. [PMID: 32895543 PMCID: PMC9346507 DOI: 10.1038/s41562-020-00936-2] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 07/27/2020] [Indexed: 01/28/2023]
Abstract
It has been known since 1904 that, in humans, diverse cognitive traits are positively intercorrelated. This forms the basis for the general factor of intelligence (g). Here, we directly test whether there is a partial genetic basis for individual differences in g using data from seven different cognitive tests (n = 11,263-331,679) and genome-wide autosomal single-nucleotide polymorphisms. A genetic g factor accounts for an average of 58.4% (s.e. = 4.8%) of the genetic variance in the cognitive traits considered, with the proportion varying widely across traits (range, 9-95%). We distil genetic loci that are broadly relevant for many cognitive traits (g) from loci associated specifically with individual cognitive traits. These results contribute to elucidating the aetiology of a long-known yet poorly understood phenomenon, revealing a fundamental dimension of genetic sharing across diverse cognitive traits.
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Affiliation(s)
- Javier de la Fuente
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Gail Davies
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | | | - Elliot M Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, TX, USA.
- Population Research Center, University of Texas at Austin, Austin, TX, USA.
| | - Ian J Deary
- Lothian Birth Cohorts, University of Edinburgh, Edinburgh, UK.
- Department of Psychology, University of Edinburgh, Edinburgh, UK.
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Morgunova A, Pokhvisneva I, Nolvi S, Entringer S, Wadhwa P, Gilmore J, Styner M, Buss C, Sassi RB, Hall GBC, O'Donnell KJ, Meaney MJ, Silveira PP, Flores CA. DCC gene network in the prefrontal cortex is associated with total brain volume in childhood. J Psychiatry Neurosci 2021; 46:E154-E163. [PMID: 33206040 PMCID: PMC7955849 DOI: 10.1503/jpn.200081] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Genetic variation in the guidance cue DCC gene is linked to psychopathologies involving dysfunction in the prefrontal cortex. We created an expression-based polygenic risk score (ePRS) based on the DCC coexpression gene network in the prefrontal cortex, hypothesizing that it would be associated with individual differences in total brain volume. METHODS We filtered single nucleotide polymorphisms (SNPs) from genes coexpressed with DCC in the prefrontal cortex obtained from an adult postmortem donors database (BrainEAC) for genes enriched in children 1.5 to 11 years old (BrainSpan). The SNPs were weighted by their effect size in predicting gene expression in the prefrontal cortex, multiplied by their allele number based on an individual's genotype data, and then summarized into an ePRS. We evaluated associations between the DCC ePRS and total brain volume in children in 2 community-based cohorts: the Maternal Adversity, Vulnerability and Neurodevelopment (MAVAN) and University of California, Irvine (UCI) projects. For comparison, we calculated a conventional PRS based on a genome-wide association study of total brain volume. RESULTS Higher ePRS was associated with higher total brain volume in children 8 to 10 years old (β = 0.212, p = 0.043; n = 88). The conventional PRS at several different thresholds did not predict total brain volume in this cohort. A replication analysis in an independent cohort of newborns from the UCI study showed an association between the ePRS and newborn total brain volume (β = 0.101, p = 0.048; n = 80). The genes included in the ePRS demonstrated high levels of coexpression throughout the lifespan and are primarily involved in regulating cellular function. LIMITATIONS The relatively small sample size and age differences between the main and replication cohorts were limitations. CONCLUSION Our findings suggest that the DCC coexpression network in the prefrontal cortex is critically involved in whole brain development during the first decade of life. Genes comprising the ePRS are involved in gene translation control and cell adhesion, and their expression in the prefrontal cortex at different stages of life provides a snapshot of their dynamic recruitment.
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Affiliation(s)
- Alice Morgunova
- From the Integrated Program in Neuroscience (IPN), McGill University, Montréal, Que., Canada (Morgunova); the Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Que., Canada (O'Donnell, Meaney, Silveira, Flores); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que., Canada (Flores); the Douglas Research Centre, Montréal, Que., Canada (Morgunova, Flores, Silveira); the Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University, Montréal, Que., Canada (Pokhvisneva, O'Donnell, Meaney, Silveira); the Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ont., Canada (O'Donnell, Meaney); the Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR; Meaney); the Department of Medical Psychology Charité Universitätsmedizin, Berlin, Germany (Nolvi, Buss); the FinnBrain Birth Cohort Study, Department of Clinical Medicine, University of Turku, Turku, Finland (Nolvi); the Development, Health and Disease Research Program, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Institute of Medical Psychology, Charité-Universitätsmedizin Berlin, Berlin, Germany (Entringer); the Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Obstetrics and Gynecology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Epidemiology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Gilmore, Styner); the Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Styner); the Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ont., Canada (Sassi); and the Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ont., Canada (Hall)
| | - Irina Pokhvisneva
- From the Integrated Program in Neuroscience (IPN), McGill University, Montréal, Que., Canada (Morgunova); the Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Que., Canada (O'Donnell, Meaney, Silveira, Flores); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que., Canada (Flores); the Douglas Research Centre, Montréal, Que., Canada (Morgunova, Flores, Silveira); the Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University, Montréal, Que., Canada (Pokhvisneva, O'Donnell, Meaney, Silveira); the Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ont., Canada (O'Donnell, Meaney); the Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR; Meaney); the Department of Medical Psychology Charité Universitätsmedizin, Berlin, Germany (Nolvi, Buss); the FinnBrain Birth Cohort Study, Department of Clinical Medicine, University of Turku, Turku, Finland (Nolvi); the Development, Health and Disease Research Program, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Institute of Medical Psychology, Charité-Universitätsmedizin Berlin, Berlin, Germany (Entringer); the Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Obstetrics and Gynecology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Epidemiology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Gilmore, Styner); the Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Styner); the Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ont., Canada (Sassi); and the Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ont., Canada (Hall)
| | - Saara Nolvi
- From the Integrated Program in Neuroscience (IPN), McGill University, Montréal, Que., Canada (Morgunova); the Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Que., Canada (O'Donnell, Meaney, Silveira, Flores); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que., Canada (Flores); the Douglas Research Centre, Montréal, Que., Canada (Morgunova, Flores, Silveira); the Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University, Montréal, Que., Canada (Pokhvisneva, O'Donnell, Meaney, Silveira); the Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ont., Canada (O'Donnell, Meaney); the Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR; Meaney); the Department of Medical Psychology Charité Universitätsmedizin, Berlin, Germany (Nolvi, Buss); the FinnBrain Birth Cohort Study, Department of Clinical Medicine, University of Turku, Turku, Finland (Nolvi); the Development, Health and Disease Research Program, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Institute of Medical Psychology, Charité-Universitätsmedizin Berlin, Berlin, Germany (Entringer); the Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Obstetrics and Gynecology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Epidemiology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Gilmore, Styner); the Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Styner); the Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ont., Canada (Sassi); and the Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ont., Canada (Hall)
| | - Sonja Entringer
- From the Integrated Program in Neuroscience (IPN), McGill University, Montréal, Que., Canada (Morgunova); the Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Que., Canada (O'Donnell, Meaney, Silveira, Flores); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que., Canada (Flores); the Douglas Research Centre, Montréal, Que., Canada (Morgunova, Flores, Silveira); the Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University, Montréal, Que., Canada (Pokhvisneva, O'Donnell, Meaney, Silveira); the Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ont., Canada (O'Donnell, Meaney); the Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR; Meaney); the Department of Medical Psychology Charité Universitätsmedizin, Berlin, Germany (Nolvi, Buss); the FinnBrain Birth Cohort Study, Department of Clinical Medicine, University of Turku, Turku, Finland (Nolvi); the Development, Health and Disease Research Program, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Institute of Medical Psychology, Charité-Universitätsmedizin Berlin, Berlin, Germany (Entringer); the Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Obstetrics and Gynecology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Epidemiology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Gilmore, Styner); the Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Styner); the Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ont., Canada (Sassi); and the Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ont., Canada (Hall)
| | - Pathik Wadhwa
- From the Integrated Program in Neuroscience (IPN), McGill University, Montréal, Que., Canada (Morgunova); the Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Que., Canada (O'Donnell, Meaney, Silveira, Flores); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que., Canada (Flores); the Douglas Research Centre, Montréal, Que., Canada (Morgunova, Flores, Silveira); the Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University, Montréal, Que., Canada (Pokhvisneva, O'Donnell, Meaney, Silveira); the Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ont., Canada (O'Donnell, Meaney); the Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR; Meaney); the Department of Medical Psychology Charité Universitätsmedizin, Berlin, Germany (Nolvi, Buss); the FinnBrain Birth Cohort Study, Department of Clinical Medicine, University of Turku, Turku, Finland (Nolvi); the Development, Health and Disease Research Program, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Institute of Medical Psychology, Charité-Universitätsmedizin Berlin, Berlin, Germany (Entringer); the Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Obstetrics and Gynecology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Epidemiology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Gilmore, Styner); the Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Styner); the Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ont., Canada (Sassi); and the Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ont., Canada (Hall)
| | - John Gilmore
- From the Integrated Program in Neuroscience (IPN), McGill University, Montréal, Que., Canada (Morgunova); the Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Que., Canada (O'Donnell, Meaney, Silveira, Flores); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que., Canada (Flores); the Douglas Research Centre, Montréal, Que., Canada (Morgunova, Flores, Silveira); the Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University, Montréal, Que., Canada (Pokhvisneva, O'Donnell, Meaney, Silveira); the Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ont., Canada (O'Donnell, Meaney); the Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR; Meaney); the Department of Medical Psychology Charité Universitätsmedizin, Berlin, Germany (Nolvi, Buss); the FinnBrain Birth Cohort Study, Department of Clinical Medicine, University of Turku, Turku, Finland (Nolvi); the Development, Health and Disease Research Program, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Institute of Medical Psychology, Charité-Universitätsmedizin Berlin, Berlin, Germany (Entringer); the Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Obstetrics and Gynecology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Epidemiology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Gilmore, Styner); the Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Styner); the Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ont., Canada (Sassi); and the Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ont., Canada (Hall)
| | - Martin Styner
- From the Integrated Program in Neuroscience (IPN), McGill University, Montréal, Que., Canada (Morgunova); the Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Que., Canada (O'Donnell, Meaney, Silveira, Flores); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que., Canada (Flores); the Douglas Research Centre, Montréal, Que., Canada (Morgunova, Flores, Silveira); the Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University, Montréal, Que., Canada (Pokhvisneva, O'Donnell, Meaney, Silveira); the Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ont., Canada (O'Donnell, Meaney); the Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR; Meaney); the Department of Medical Psychology Charité Universitätsmedizin, Berlin, Germany (Nolvi, Buss); the FinnBrain Birth Cohort Study, Department of Clinical Medicine, University of Turku, Turku, Finland (Nolvi); the Development, Health and Disease Research Program, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Institute of Medical Psychology, Charité-Universitätsmedizin Berlin, Berlin, Germany (Entringer); the Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Obstetrics and Gynecology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Epidemiology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Gilmore, Styner); the Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Styner); the Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ont., Canada (Sassi); and the Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ont., Canada (Hall)
| | - Claudia Buss
- From the Integrated Program in Neuroscience (IPN), McGill University, Montréal, Que., Canada (Morgunova); the Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Que., Canada (O'Donnell, Meaney, Silveira, Flores); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que., Canada (Flores); the Douglas Research Centre, Montréal, Que., Canada (Morgunova, Flores, Silveira); the Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University, Montréal, Que., Canada (Pokhvisneva, O'Donnell, Meaney, Silveira); the Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ont., Canada (O'Donnell, Meaney); the Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR; Meaney); the Department of Medical Psychology Charité Universitätsmedizin, Berlin, Germany (Nolvi, Buss); the FinnBrain Birth Cohort Study, Department of Clinical Medicine, University of Turku, Turku, Finland (Nolvi); the Development, Health and Disease Research Program, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Institute of Medical Psychology, Charité-Universitätsmedizin Berlin, Berlin, Germany (Entringer); the Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Obstetrics and Gynecology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Epidemiology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Gilmore, Styner); the Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Styner); the Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ont., Canada (Sassi); and the Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ont., Canada (Hall)
| | - Roberto Britto Sassi
- From the Integrated Program in Neuroscience (IPN), McGill University, Montréal, Que., Canada (Morgunova); the Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Que., Canada (O'Donnell, Meaney, Silveira, Flores); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que., Canada (Flores); the Douglas Research Centre, Montréal, Que., Canada (Morgunova, Flores, Silveira); the Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University, Montréal, Que., Canada (Pokhvisneva, O'Donnell, Meaney, Silveira); the Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ont., Canada (O'Donnell, Meaney); the Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR; Meaney); the Department of Medical Psychology Charité Universitätsmedizin, Berlin, Germany (Nolvi, Buss); the FinnBrain Birth Cohort Study, Department of Clinical Medicine, University of Turku, Turku, Finland (Nolvi); the Development, Health and Disease Research Program, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Institute of Medical Psychology, Charité-Universitätsmedizin Berlin, Berlin, Germany (Entringer); the Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Obstetrics and Gynecology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Epidemiology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Gilmore, Styner); the Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Styner); the Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ont., Canada (Sassi); and the Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ont., Canada (Hall)
| | - Geoffrey B C Hall
- From the Integrated Program in Neuroscience (IPN), McGill University, Montréal, Que., Canada (Morgunova); the Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Que., Canada (O'Donnell, Meaney, Silveira, Flores); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que., Canada (Flores); the Douglas Research Centre, Montréal, Que., Canada (Morgunova, Flores, Silveira); the Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University, Montréal, Que., Canada (Pokhvisneva, O'Donnell, Meaney, Silveira); the Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ont., Canada (O'Donnell, Meaney); the Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR; Meaney); the Department of Medical Psychology Charité Universitätsmedizin, Berlin, Germany (Nolvi, Buss); the FinnBrain Birth Cohort Study, Department of Clinical Medicine, University of Turku, Turku, Finland (Nolvi); the Development, Health and Disease Research Program, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Institute of Medical Psychology, Charité-Universitätsmedizin Berlin, Berlin, Germany (Entringer); the Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Obstetrics and Gynecology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Epidemiology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Gilmore, Styner); the Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Styner); the Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ont., Canada (Sassi); and the Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ont., Canada (Hall)
| | - Kieran J O'Donnell
- From the Integrated Program in Neuroscience (IPN), McGill University, Montréal, Que., Canada (Morgunova); the Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Que., Canada (O'Donnell, Meaney, Silveira, Flores); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que., Canada (Flores); the Douglas Research Centre, Montréal, Que., Canada (Morgunova, Flores, Silveira); the Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University, Montréal, Que., Canada (Pokhvisneva, O'Donnell, Meaney, Silveira); the Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ont., Canada (O'Donnell, Meaney); the Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR; Meaney); the Department of Medical Psychology Charité Universitätsmedizin, Berlin, Germany (Nolvi, Buss); the FinnBrain Birth Cohort Study, Department of Clinical Medicine, University of Turku, Turku, Finland (Nolvi); the Development, Health and Disease Research Program, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Institute of Medical Psychology, Charité-Universitätsmedizin Berlin, Berlin, Germany (Entringer); the Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Obstetrics and Gynecology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Epidemiology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Gilmore, Styner); the Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Styner); the Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ont., Canada (Sassi); and the Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ont., Canada (Hall)
| | - Michael J Meaney
- From the Integrated Program in Neuroscience (IPN), McGill University, Montréal, Que., Canada (Morgunova); the Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Que., Canada (O'Donnell, Meaney, Silveira, Flores); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que., Canada (Flores); the Douglas Research Centre, Montréal, Que., Canada (Morgunova, Flores, Silveira); the Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University, Montréal, Que., Canada (Pokhvisneva, O'Donnell, Meaney, Silveira); the Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ont., Canada (O'Donnell, Meaney); the Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR; Meaney); the Department of Medical Psychology Charité Universitätsmedizin, Berlin, Germany (Nolvi, Buss); the FinnBrain Birth Cohort Study, Department of Clinical Medicine, University of Turku, Turku, Finland (Nolvi); the Development, Health and Disease Research Program, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Institute of Medical Psychology, Charité-Universitätsmedizin Berlin, Berlin, Germany (Entringer); the Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Obstetrics and Gynecology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Epidemiology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Gilmore, Styner); the Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Styner); the Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ont., Canada (Sassi); and the Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ont., Canada (Hall)
| | - Patricia P Silveira
- From the Integrated Program in Neuroscience (IPN), McGill University, Montréal, Que., Canada (Morgunova); the Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Que., Canada (O'Donnell, Meaney, Silveira, Flores); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que., Canada (Flores); the Douglas Research Centre, Montréal, Que., Canada (Morgunova, Flores, Silveira); the Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University, Montréal, Que., Canada (Pokhvisneva, O'Donnell, Meaney, Silveira); the Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ont., Canada (O'Donnell, Meaney); the Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR; Meaney); the Department of Medical Psychology Charité Universitätsmedizin, Berlin, Germany (Nolvi, Buss); the FinnBrain Birth Cohort Study, Department of Clinical Medicine, University of Turku, Turku, Finland (Nolvi); the Development, Health and Disease Research Program, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Institute of Medical Psychology, Charité-Universitätsmedizin Berlin, Berlin, Germany (Entringer); the Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Obstetrics and Gynecology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Epidemiology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Gilmore, Styner); the Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Styner); the Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ont., Canada (Sassi); and the Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ont., Canada (Hall)
| | - Cecilia A Flores
- From the Integrated Program in Neuroscience (IPN), McGill University, Montréal, Que., Canada (Morgunova); the Department of Psychiatry, Faculty of Medicine, McGill University, Montréal, Que., Canada (O'Donnell, Meaney, Silveira, Flores); the Department of Neurology and Neurosurgery, McGill University, Montréal, Que., Canada (Flores); the Douglas Research Centre, Montréal, Que., Canada (Morgunova, Flores, Silveira); the Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University, Montréal, Que., Canada (Pokhvisneva, O'Donnell, Meaney, Silveira); the Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ont., Canada (O'Donnell, Meaney); the Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR; Meaney); the Department of Medical Psychology Charité Universitätsmedizin, Berlin, Germany (Nolvi, Buss); the FinnBrain Birth Cohort Study, Department of Clinical Medicine, University of Turku, Turku, Finland (Nolvi); the Development, Health and Disease Research Program, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, USA (Entringer, Wadhwa); the Institute of Medical Psychology, Charité-Universitätsmedizin Berlin, Berlin, Germany (Entringer); the Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Obstetrics and Gynecology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Epidemiology, School of Medicine, University of California, Irvine, CA, USA (Wadhwa); the Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Gilmore, Styner); the Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA (Styner); the Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ont., Canada (Sassi); and the Department of Psychology, Neuroscience and Behaviour, McMaster University, Hamilton, Ont., Canada (Hall)
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Blesa M, Galdi P, Cox SR, Sullivan G, Stoye DQ, Lamb GJ, Quigley AJ, Thrippleton MJ, Escudero J, Bastin ME, Smith KM, Boardman JP. Hierarchical Complexity of the Macro-Scale Neonatal Brain. Cereb Cortex 2020; 31:2071-2084. [PMID: 33280008 PMCID: PMC7945030 DOI: 10.1093/cercor/bhaa345] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
The human adult structural connectome has a rich nodal hierarchy, with highly diverse connectivity patterns aligned to the diverse range of functional specializations in the brain. The emergence of this hierarchical complexity in human development is unknown. Here, we substantiate the hierarchical tiers and hierarchical complexity of brain networks in the newborn period, assess correspondences with hierarchical complexity in adulthood, and investigate the effect of preterm birth, a leading cause of atypical brain development and later neurocognitive impairment, on hierarchical complexity. We report that neonatal and adult structural connectomes are both composed of distinct hierarchical tiers and that hierarchical complexity is greater in term born neonates than in preterms. This is due to diversity of connectivity patterns of regions within the intermediate tiers, which consist of regions that underlie sensorimotor processing and its integration with cognitive information. For neonates and adults, the highest tier (hub regions) is ordered, rather than complex, with more homogeneous connectivity patterns in structural hubs. This suggests that the brain develops first a more rigid structure in hub regions allowing for the development of greater and more diverse functional specialization in lower level regions, while connectivity underpinning this diversity is dysmature in infants born preterm.
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Affiliation(s)
- Manuel Blesa
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Paola Galdi
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Simon R Cox
- Lothian Birth Cohorts Group, Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, UK
| | - Gemma Sullivan
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - David Q Stoye
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Gillian J Lamb
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Alan J Quigley
- Department of Radiology, Royal Hospital for Sick Children, Edinburgh EH9 1LF, UK
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK.,Edinburgh Imaging, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Javier Escudero
- School of Engineering, Institute for Digital Communications, University of Edinburgh, Edinburgh EH9 3FG, UK
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
| | - Keith M Smith
- Usher Institute, University of Edinburgh, Edinburgh EH16 4UX, UK.,Health Data Research UK, London NW1 2BE, UK
| | - James P Boardman
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, UK.,Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, UK
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74
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Hyatt CS, Hallowell ES, Owens MM, Weiss BM, Sweet LH, Miller JD. An fMRI investigation of the relations between Extraversion, internalizing psychopathology, and neural activation following reward receipt in the Human Connectome Project sample. PERSONALITY NEUROSCIENCE 2020; 3:e13. [PMID: 33354651 PMCID: PMC7737192 DOI: 10.1017/pen.2020.11] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 05/21/2020] [Accepted: 05/25/2020] [Indexed: 12/22/2022]
Abstract
Quantitative models of psychopathology (i.e., HiTOP) propose that personality and psychopathology are intertwined, such that the various processes that characterize personality traits may be useful in describing and predicting manifestations of psychopathology. In the current study, we used data from the Human Connectome Project (N = 1050) to investigate neural activation following receipt of a reward during an fMRI task as one shared mechanism that may be related to the personality trait Extraversion (specifically its sub-component Agentic Extraversion) and internalizing psychopathology. We also conducted exploratory analyses on the links between neural activation following reward receipt and the other Five-Factor Model personality traits, as well as separate analyses by gender. No significant relations (p < .005) were observed between any personality trait or index of psychopathology and neural activation following reward receipt, and most effect sizes were null to very small in nature (i.e., r < |.05|). We conclude by discussing the appropriate interpretation of these null findings, and provide suggestions for future research that spans psychological and neurobiological levels of analysis.
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Affiliation(s)
| | | | - Max M. Owens
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - Brandon M. Weiss
- Department of Psychology, University of Georgia, Athens, GA, USA
| | | | - Joshua D. Miller
- Department of Psychology, University of Georgia, Athens, GA, USA
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75
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Assem M, Blank IA, Mineroff Z, Ademoğlu A, Fedorenko E. Activity in the fronto-parietal multiple-demand network is robustly associated with individual differences in working memory and fluid intelligence. Cortex 2020; 131:1-16. [PMID: 32777623 PMCID: PMC7530021 DOI: 10.1016/j.cortex.2020.06.013] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 05/11/2020] [Accepted: 06/09/2020] [Indexed: 01/04/2023]
Abstract
Numerous brain lesion and fMRI studies have linked individual differences in executive abilities and fluid intelligence to brain regions of the fronto-parietal "multiple-demand" (MD) network. Yet, fMRI studies have yielded conflicting evidence as to whether better executive abilities are associated with stronger or weaker MD activations and whether this relationship is restricted to the MD network. Here, in a large-sample (n = 216) fMRI investigation, we found that stronger activity in MD regions - functionally defined in individual participants - was robustly associated with more accurate and faster responses on a spatial working memory task performed in the scanner, as well as fluid intelligence measured independently (n = 114). In line with some prior claims about a relationship between language and fluid intelligence, we also found a weak association between activity in the brain regions of the left fronto-temporal language network during an independent passive reading task, and performance on the working memory task. However, controlling for the level of MD activity abolished this relationship, whereas the MD activity-behavior association remained highly reliable after controlling for the level of activity in the language network. Finally, we demonstrate how unreliable MD activity measures, coupled with small sample sizes, could falsely lead to the opposite, negative, association that has been reported in some prior studies. Taken together, these results demonstrate that a core component of individual differences variance in executive abilities and fluid intelligence is selectively and robustly positively associated with the level of activity in the MD network, a result that aligns well with lesion studies.
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Affiliation(s)
- Moataz Assem
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Idan A Blank
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Psychology, UCLA, Los Angeles, CA, USA
| | - Zachary Mineroff
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ahmet Ademoğlu
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
| | - Evelina Fedorenko
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Boston, USA.
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76
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Sripada C, Angstadt M, Rutherford S, Taxali A, Shedden K. Toward a "treadmill test" for cognition: Improved prediction of general cognitive ability from the task activated brain. Hum Brain Mapp 2020; 41:3186-3197. [PMID: 32364670 PMCID: PMC7375130 DOI: 10.1002/hbm.25007] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 03/06/2020] [Accepted: 04/03/2020] [Indexed: 02/02/2023] Open
Abstract
General cognitive ability (GCA) refers to a trait-like ability that contributes to performance across diverse cognitive tasks. Identifying brain-based markers of GCA has been a longstanding goal of cognitive and clinical neuroscience. Recently, predictive modeling methods have emerged that build whole-brain, distributed neural signatures for phenotypes of interest. In this study, we employ a predictive modeling approach to predict GCA based on fMRI task activation patterns during the N-back working memory task as well as six other tasks in the Human Connectome Project dataset (n = 967), encompassing 15 task contrasts in total. We found tasks are a highly effective basis for prediction of GCA: The 2-back versus 0-back contrast achieved a 0.50 correlation with GCA scores in 10-fold cross-validation, and 13 out of 15 task contrasts afforded statistically significant prediction of GCA. Additionally, we found that task contrasts that produce greater frontoparietal activation and default mode network deactivation-a brain activation pattern associated with executive processing and higher cognitive demand-are more effective in the prediction of GCA. These results suggest a picture analogous to treadmill testing for cardiac function: Placing the brain in a more cognitively demanding task state significantly improves brain-based prediction of GCA.
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Affiliation(s)
- Chandra Sripada
- Department of PsychiatryUniversity of MichiganAnn ArborMichiganUSA
| | - Mike Angstadt
- Department of PsychiatryUniversity of MichiganAnn ArborMichiganUSA
| | - Saige Rutherford
- Department of PsychiatryUniversity of MichiganAnn ArborMichiganUSA
| | - Aman Taxali
- Department of PsychiatryUniversity of MichiganAnn ArborMichiganUSA
| | - Kerby Shedden
- Department of StatisticsUniversity of MichiganAnn ArborMichiganUSA
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77
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Elliott ML. MRI-based biomarkers of accelerated aging and dementia risk in midlife: how close are we? Ageing Res Rev 2020; 61:101075. [PMID: 32325150 DOI: 10.1016/j.arr.2020.101075] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 03/10/2020] [Accepted: 04/15/2020] [Indexed: 01/18/2023]
Abstract
The global population is aging, leading to an increasing burden of age-related neurodegenerative disease. Efforts to intervene against age-related dementias in older adults have generally proven ineffective. These failures suggest that a lifetime of brain aging may be difficult to reverse once widespread deterioration has occurred. To test interventions in younger populations, biomarkers of brain aging are needed that index subtle signs of accelerated brain deterioration that are part of the putative pathway to dementia. Here I review potential MRI-based biomarkers that could connect midlife brain aging to later life dementia. I survey the literature with three questions in mind, 1) Does the biomarker index age-related changes across the lifespan? 2) Does the biomarker index cognitive ability and cognitive decline? 3) Is the biomarker sensitive to known risk factors for dementia? I find that while there is preliminary support for some midlife MRI-based biomarkers for accelerated aging, the longitudinal research that would best answer these questions is still in its infancy and needs to be further developed. I conclude with suggestions for future research.
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Affiliation(s)
- Maxwell L Elliott
- Department of Psychology and Neuroscience, Duke University, 2020 West Main Street, Suite 030, Durham, NC, 27701, USA.
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78
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Bae E, Hur JW, Kim J, Kwon JS, Lee J, Lee SH, Lim CY. Multi-group analysis using generalized additive kernel canonical correlation analysis. Sci Rep 2020; 10:12624. [PMID: 32724222 PMCID: PMC7387565 DOI: 10.1038/s41598-020-69575-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 07/13/2020] [Indexed: 11/09/2022] Open
Abstract
Multivariate analysis has been widely used and one of the popular multivariate analysis methods is canonical correlation analysis (CCA). CCA finds the linear combination in each group that maximizes the Pearson correlation. CCA has been extended to a kernel CCA for nonlinear relationships and generalized CCA that can consider more than two groups. We propose an extension of CCA that allows multi-group and nonlinear relationships in an additive fashion for a better interpretation, which we termed as Generalized Additive Kernel Canonical Correlation Analysis (GAKCCA). In addition to exploring multi-group relationship with nonlinear extension, GAKCCA can reveal contribution of variables in each group; which enables in-depth structural analysis. A simulation study shows that GAKCCA can distinguish a relationship between groups and whether they are correlated or not. We applied GAKCCA to real data on neurodevelopmental status, psychosocial factors, clinical problems as well as neurophysiological measures of individuals. As a result, it is shown that the neurophysiological domain has a statistically significant relationship with the neurodevelopmental domain and clinical domain, respectively, which was not revealed in the ordinary CCA.
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Affiliation(s)
- Eunseong Bae
- Department of Statistics, University of California, Davis, CA, USA
| | - Ji-Won Hur
- Department of Psychology, Korea University, Seoul, Korea
| | - Jinyoung Kim
- Department of Brain & Cognitive Sciences, Seoul National University, Seoul, Korea
| | - Jun Soo Kwon
- Department of Brain & Cognitive Sciences, Seoul National University, Seoul, Korea
- Department of Psychiatry, Seoul National University, Seoul, Korea
| | - Jongho Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Sang-Hun Lee
- Department of Brain & Cognitive Sciences, Seoul National University, Seoul, Korea
| | - Chae Young Lim
- Department of Statistics, Seoul National University, Seoul, Korea.
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79
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Warling A, Liu S, Wilson K, Whitman E, Lalonde FM, Clasen LS, Blumenthal JD, Raznahan A. Sex chromosome aneuploidy alters the relationship between neuroanatomy and cognition. AMERICAN JOURNAL OF MEDICAL GENETICS PART C-SEMINARS IN MEDICAL GENETICS 2020; 184:493-505. [PMID: 32515138 DOI: 10.1002/ajmg.c.31795] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 04/29/2020] [Indexed: 01/18/2023]
Abstract
Sex chromosome aneuploidy (SCA) increases the risk for cognitive deficits, and confers changes in regional cortical thickness (CT) and surface area (SA). Neuroanatomical correlates of inter-individual variation in cognitive ability have been described in health, but are not well-characterized in SCA. Here, we modeled relationships between general cognitive ability (estimated using full-scale IQ [FSIQ] from Wechsler scales) and regional estimates of SA and CT (from structural MRI scans) in both aneuploid (28 XXX, 55 XXY, 22 XYY, 19 XXYY) and typically-developing euploid (79 XX, 85 XY) individuals. Results indicated widespread decoupling of normative anatomical-cognitive relationships in SCA: we found five regions where SCA significantly altered SA-FSIQ relationships, and five regions where SCA significantly altered CT-FSIQ relationships. The majority of areas were characterized by the presence of positive anatomy-IQ relationships in health, but no or slightly negative anatomy-IQ relationships in SCA. Disrupted anatomical-cognitive relationships generalized from the full cohort to karyotypically defined subcohorts (i.e., XX-XXX; XY-XYY; XY-XXY), demonstrating continuity across multiple supernumerary SCA conditions. As the first direct evidence of altered regional neuroanatomical-cognitive relationships in supernumerary SCA, our findings shed light on potential genetic and structural correlates of the cognitive phenotype in SCA, and may have implications for other neurogenetic disorders.
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Affiliation(s)
- Allysa Warling
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Siyuan Liu
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Kathleen Wilson
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Ethan Whitman
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland, USA
| | - François M Lalonde
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Liv S Clasen
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Jonathan D Blumenthal
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Armin Raznahan
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, Maryland, USA
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80
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Mitchell BL, Cuéllar-Partida G, Grasby KL, Campos AI, Strike LT, Hwang LD, Okbay A, Thompson PM, Medland SE, Martin NG, Wright MJ, Rentería ME. Educational attainment polygenic scores are associated with cortical total surface area and regions important for language and memory. Neuroimage 2020; 212:116691. [PMID: 32126298 DOI: 10.1016/j.neuroimage.2020.116691] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 02/06/2020] [Accepted: 02/26/2020] [Indexed: 02/01/2023] Open
Abstract
It is well established that higher cognitive ability is associated with larger brain size. However, individual variation in intelligence exists despite brain size and recent studies have shown that a simple unifactorial view of the neurobiology underpinning cognitive ability is probably unrealistic. Educational attainment (EA) is often used as a proxy for cognitive ability since it is easily measured, resulting in large sample sizes and, consequently, sufficient statistical power to detect small associations. This study investigates the association between three global (total surface area (TSA), intra-cranial volume (ICV) and average cortical thickness) and 34 regional cortical measures with educational attainment using a polygenic scoring (PGS) approach. Analyses were conducted on two independent target samples of young twin adults with neuroimaging data, from Australia (N = 1097) and the USA (N = 723), and found that higher EA-PGS were significantly associated with larger global brain size measures, ICV and TSA (R2 = 0.006 and 0.016 respectively, p < 0.001) but not average thickness. At the regional level, we identified seven cortical regions-in the frontal and temporal lobes-that showed variation in surface area and average cortical thickness over-and-above the global effect. These regions have been robustly implicated in language, memory, visual recognition and cognitive processing. Additionally, we demonstrate that these identified brain regions partly mediate the association between EA-PGS and cognitive test performance. Altogether, these findings advance our understanding of the neurobiology that underpins educational attainment and cognitive ability, providing focus points for future research.
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Affiliation(s)
- Brittany L Mitchell
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia.
| | - Gabriel Cuéllar-Partida
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Katrina L Grasby
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Adrian I Campos
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Lachlan T Strike
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Liang-Dar Hwang
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Aysu Okbay
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sarah E Medland
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Nicholas G Martin
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | - Margaret J Wright
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia; Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD, Australia
| | - Miguel E Rentería
- Department of Genetics & Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; School of Biomedical Sciences, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
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81
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Harris SE, Cox SR, Bell S, Marioni RE, Prins BP, Pattie A, Corley J, Muñoz Maniega S, Valdés Hernández M, Morris Z, John S, Bronson PG, Tucker-Drob EM, Starr JM, Bastin ME, Wardlaw JM, Butterworth AS, Deary IJ. Neurology-related protein biomarkers are associated with cognitive ability and brain volume in older age. Nat Commun 2020; 11:800. [PMID: 32041957 PMCID: PMC7010796 DOI: 10.1038/s41467-019-14161-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 12/19/2019] [Indexed: 12/31/2022] Open
Abstract
Identifying biological correlates of late life cognitive function is important if we are to ascertain biomarkers for, and develop treatments to help reduce, age-related cognitive decline. Here, we investigated the associations between plasma levels of 90 neurology-related proteins (Olink® Proteomics) and general fluid cognitive ability in the Lothian Birth Cohort 1936 (LBC1936, N = 798), Lothian Birth Cohort 1921 (LBC1921, N = 165), and the INTERVAL BioResource (N = 4451). In the LBC1936, 22 of the proteins were significantly associated with general fluid cognitive ability (β between -0.11 and -0.17). MRI-assessed total brain volume partially mediated the association between 10 of these proteins and general fluid cognitive ability. In an age-matched subsample of INTERVAL, effect sizes for the 22 proteins, although smaller, were all in the same direction as in LBC1936. Plasma levels of a number of neurology-related proteins are associated with general fluid cognitive ability in later life, mediated by brain volume in some cases.
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Affiliation(s)
- Sarah E Harris
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK. .,Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.
| | - Simon R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.,Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, 300 Bath St, Glasgow, UK
| | - Steven Bell
- UK Medical Research Council/British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK.,The National Institute for Health Research Blood and Transplant Unit in Donor Health and Genomics at the University of Cambridge, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK.,Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge Neurology Unit, Cambridge Biomedical Campus, Cambridge, CB20QQ, UK
| | - Riccardo E Marioni
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.,Centre for Genomic and Experimental Medicine, MRC Institute of Genetics and Molecular Medicine, The University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK
| | - Bram P Prins
- UK Medical Research Council/British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK
| | - Alison Pattie
- Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Janie Corley
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.,Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Susana Muñoz Maniega
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.,Brain Research Imaging Centre, Neuroimaging Sciences, The University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, UK.,UK Dementia Research Institute at the University of Edinburgh, Edinburgh BioQuarter, Edinburgh, UK
| | - Maria Valdés Hernández
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.,Brain Research Imaging Centre, Neuroimaging Sciences, The University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, UK.,UK Dementia Research Institute at the University of Edinburgh, Edinburgh BioQuarter, Edinburgh, UK
| | - Zoe Morris
- Brain Research Imaging Centre, Neuroimaging Sciences, The University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, UK
| | - Sally John
- Translational Biology, Biogen, Cambridge, MA, 02142, USA
| | | | - Elliot M Tucker-Drob
- Department of Psychology, University of Texas, 108 E Dean Keeton St, Austin, TX, USA
| | - John M Starr
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.,Alzheimer Scotland Dementia Research Centre, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, 300 Bath St, Glasgow, UK.,Brain Research Imaging Centre, Neuroimaging Sciences, The University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, UK
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.,Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, 300 Bath St, Glasgow, UK.,Brain Research Imaging Centre, Neuroimaging Sciences, The University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh, UK.,UK Dementia Research Institute at the University of Edinburgh, Edinburgh BioQuarter, Edinburgh, UK
| | - Adam S Butterworth
- UK Medical Research Council/British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK.,The National Institute for Health Research Blood and Transplant Unit in Donor Health and Genomics at the University of Cambridge, University of Cambridge, Strangeways Research Laboratory, Wort's Causeway, Cambridge, CB1 8RN, UK
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK.,Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
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