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Lewis JD, Imani V, Tohka J. Intelligence and cortical morphometry: caveats in brain-behavior associations. Brain Struct Funct 2024; 229:1417-1432. [PMID: 38795129 PMCID: PMC11176253 DOI: 10.1007/s00429-024-02792-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 03/19/2024] [Indexed: 05/27/2024]
Abstract
It is well-established that brain size is associated with intelligence. But the relationship between cortical morphometric measures and intelligence is unclear. Studies have produced conflicting results or no significant relations between intelligence and cortical morphometric measures such as cortical thickness and peri-cortical contrast. This discrepancy may be due to multicollinearity amongst the independent variables in a multivariate regression analysis, or a failure to fully account for the relationship between brain size and intelligence in some other way. Our study shows that neither cortical thickness nor peri-cortical contrast reliably improves IQ prediction accuracy beyond what is achieved with brain volume alone. We show this in multiple datasets, with child data, developmental data, and with adult data; we show this with data acquired either at multiple sites, or at a single site; we show this with data acquired with different MRI scanner manufacturers, or with all data acquired on a single scanner; and we show this with fluid intelligence, full-scale IQ, performance IQ, and verbal IQ. But our point is not really even about IQ; rather we proffer a methodological caveat and potential explanation of the discrepancies in previous results, and which applies broadly.
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Affiliation(s)
- John D Lewis
- Program in Neuroscience and Mental Health, The Hospital for Sick Children Research Institute, 555 University Avenue, Toronto, ON, M5G1X8, Canada
| | - Vandad Imani
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70210, Kuopio, Finland
| | - Jussi Tohka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Neulaniementie 2, 70210, Kuopio, Finland.
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2
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Ramli NZ, Yahaya MF, Mohd Fahami NA, Abdul Manan H, Singh M, Damanhuri HA. Brain volumetric changes in menopausal women and its association with cognitive function: a structured review. Front Aging Neurosci 2023; 15:1158001. [PMID: 37818479 PMCID: PMC10561270 DOI: 10.3389/fnagi.2023.1158001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 09/04/2023] [Indexed: 10/12/2023] Open
Abstract
The menopausal transition has been proposed to put women at risk for undesirable neurological symptoms, including cognitive decline. Previous studies suggest that alterations in the hormonal milieu modulate brain structures associated with cognitive function. This structured review provides an overview of the relevant studies that have utilized MRI to report volumetric differences in the brain following menopause, and its correlations with the evaluated cognitive functions. We performed an electronic literature search using Medline (Ovid) and Scopus to identify studies that assessed the influence of menopause on brain structure with MRI. Fourteen studies met the inclusion criteria. Brain volumetric differences have been reported most frequently in the frontal and temporal cortices as well as the hippocampus. These regions are important for higher cognitive tasks and memory. Additionally, the deficit in verbal and visuospatial memory in postmenopausal women has been associated with smaller regional brain volumes. Nevertheless, the limited number of eligible studies and cross-sectional study designs warrant further research to draw more robust conclusions.
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Affiliation(s)
- Nur Zuliani Ramli
- Department of Biochemistry, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
- Department of Biomedical Sciences, Faculty of Medicine & Health Sciences, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia
| | - Mohamad Fairuz Yahaya
- Department of Anatomy, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Nur Azlina Mohd Fahami
- Department of Pharmacology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Hanani Abdul Manan
- Functional Image Processing Laboratory, Department of Radiology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Meharvan Singh
- Department of Cell and Molecular Physiology, Stritch School of Medicine, Loyola University Chicago, Maywood, IL, United States
| | - Hanafi Ahmad Damanhuri
- Department of Biochemistry, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
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3
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Madole JW, Buchanan CR, Rhemtulla M, Ritchie SJ, Bastin ME, Deary IJ, Cox SR, Tucker-Drob EM. Strong intercorrelations among global graph-theoretic indices of structural connectivity in the human brain. Neuroimage 2023; 275:120160. [PMID: 37169117 DOI: 10.1016/j.neuroimage.2023.120160] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 04/06/2023] [Accepted: 05/08/2023] [Indexed: 05/13/2023] Open
Abstract
Graph-theoretic metrics derived from neuroimaging data have been heralded as powerful tools for uncovering neural mechanisms of psychological traits, psychiatric disorders, and neurodegenerative diseases. In N = 8,185 human structural connectomes from UK Biobank, we examined the extent to which 11 commonly-used global graph-theoretic metrics index distinct versus overlapping information with respect to interindividual differences in brain organization. Using unthresholded, FA-weighted networks we found that all metrics other than Participation Coefficient were highly intercorrelated, both with each other (mean |r| = 0.788) and with a topologically-naïve summary index of brain structure (mean edge weight; mean |r| = 0.873). In a series of sensitivity analyses, we found that overlap between metrics is influenced by the sparseness of the network and the magnitude of variation in edge weights. Simulation analyses representing a range of population network structures indicated that individual differences in global graph metrics may be intrinsically difficult to separate from mean edge weight. In particular, Closeness, Characteristic Path Length, Global Efficiency, Clustering Coefficient, and Small Worldness were nearly perfectly collinear with one another (mean |r| = 0.939) and with mean edge weight (mean |r| = 0.952) across all observed and simulated conditions. Global graph-theoretic measures are valuable for their ability to distill a high-dimensional system of neural connections into summary indices of brain organization, but they may be of more limited utility when the goal is to index separable components of interindividual variation in specific properties of the human structural connectome.
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Affiliation(s)
- James W Madole
- Department of Psychology, University of Texas at Austin, Austin, TX, USA; VA Puget Sound Health Care System, Seattle Division, Seattle, WA, USA.
| | - Colin R Buchanan
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Mijke Rhemtulla
- Department of Psychology, University of California, Davis, CA, USA
| | - Stuart J Ritchie
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
| | - Mark E Bastin
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Lothian Birth Cohorts, Department of Psychology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Elliot M Tucker-Drob
- Department of Psychology, University of Texas at Austin, Austin, TX, USA; Population Research Center and Center on Aging and Population Sciences, University of Texas at Austin, Austin, TX, USA
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4
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de Chastelaine M, Srokova S, Hou M, Kidwai A, Kafafi SS, Racenstein ML, Rugg MD. Cortical thickness, gray matter volume, and cognitive performance: a crosssectional study of the moderating effects of age on their interrelationships. Cereb Cortex 2023; 33:6474-6485. [PMID: 36627250 PMCID: PMC10183746 DOI: 10.1093/cercor/bhac518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/05/2022] [Accepted: 12/06/2022] [Indexed: 01/12/2023] Open
Abstract
In a sample comprising younger, middle-aged, and older cognitively healthy adults (N = 375), we examined associations between mean cortical thickness, gray matter volume (GMV), and performance in 4 cognitive domains-memory, speed, fluency, and crystallized intelligence. In almost all cases, the associations were moderated significantly by age, with the strongest associations in the older age group. An exception to this pattern was identified in a younger adult subgroup aged <23 years when a negative association between cognitive performance and cortical thickness was identified. Other than for speed, all associations between structural metrics and performance in specific cognitive domains were fully mediated by mean cognitive ability. Cortical thickness and GMV explained unique fractions of the variance in mean cognitive ability, speed, and fluency. In no case, however, did the amount of variance jointly explained by the 2 metrics exceed 7% of the total variance. These findings suggest that cortical thickness and GMV are distinct correlates of domain-general cognitive ability, that the strength and, for cortical thickness, the direction of these associations are moderated by age, and that these structural metrics offer only limited insights into the determinants of individual differences in cognitive performance across the adult lifespan.
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Affiliation(s)
- Marianne de Chastelaine
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, 1600, Viceroy Drive, Suite 800, Dallas, TX 75235, United States
| | - Sabina Srokova
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, 1600, Viceroy Drive, Suite 800, Dallas, TX 75235, United States
| | - Mingzhu Hou
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, 1600, Viceroy Drive, Suite 800, Dallas, TX 75235, United States
| | - Ambereen Kidwai
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, 1600, Viceroy Drive, Suite 800, Dallas, TX 75235, United States
| | - Seham S Kafafi
- Department of Psychology, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Melanie L Racenstein
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, 1600, Viceroy Drive, Suite 800, Dallas, TX 75235, United States
| | - Michael D Rugg
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, 1600, Viceroy Drive, Suite 800, Dallas, TX 75235, United States
- School of Psychology, University of East Anglia, Norwich NR4 7TJ, United Kingdom
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5
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Kim RE, Lee M, Kang DW, Wang SM, Kim D, Lim HK. Effects of education mediated by brain size on regional brain volume in adults. Psychiatry Res Neuroimaging 2023; 330:111600. [PMID: 36780773 DOI: 10.1016/j.pscychresns.2023.111600] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 01/04/2023] [Accepted: 01/17/2023] [Indexed: 01/27/2023]
Abstract
BACKGROUND AND PURPOSE Brain structure and function change with age. Both educational attainments, a proxy for cognitive reserve, and intracranial volume (ICV), a proxy for brain reserve, could contribute to resilience against degenerative change in the brain with aging. Whether the effect of educational attainment on regional brain volume in adults is mediated by ICV is yet unclear. We aimed to investigate the direct and indirect effects (mediated through ICV) of education in early life on regional brain volume in later life. MATERIALS AND METHOD We investigated the association between ICV and education level and regional brain volume in adults using magnetic resonance imaging scans of 1,731 individuals from multicenter studies. The mediation effect of ICV was analyzed to determine the association between educational attainment at an earlier age and regional brain volume in adults. RESULTS Our results showed that the effect of education on regional brain volume was significantly mediated by ICV in both men and women. The indirect (mediated via ICV) effect of education on brain volume amounted to 75% of the total effect in the hippocampus (p < 0.001) and 100% in the frontal and insular gray matter (p < 0.001). CONCLUSION Our study demonstrated that the association between educational attainment in early life and regional brain volume in later life was largely mediated by ICV. Attention should be given to the effect of educational attainment and ICV on regional brain size in adults as a measurable resilience effect in brain aging.
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Affiliation(s)
- Regina Ey Kim
- Research Institute, Neurophet Inc., Seoul, Republic of Korea; College of Medicine, Institute of Human Genomic Study, Korea University, Seoul, Republic of Korea; Department of Psychiatry, University of Iowa, Iowa, IA, United States of America
| | - Minho Lee
- Research Institute, Neurophet Inc., Seoul, Republic of Korea
| | - Dong Woo Kang
- Department of Psychiatry, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sheng-Min Wang
- Department of Psychiatry, College of Medicine, Yeouido St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea
| | - Donghyeon Kim
- Research Institute, Neurophet Inc., Seoul, Republic of Korea
| | - Hyun Kook Lim
- Department of Psychiatry, College of Medicine, Yeouido St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea.
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6
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Del Giudice M, Haltigan JD. A new look at the relations between attachment and intelligence. DEVELOPMENTAL REVIEW 2023. [DOI: 10.1016/j.dr.2022.101054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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7
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Lee JK, Cho ACB, Andrews DS, Ozonoff S, Rogers SJ, Amaral DG, Solomon M, Nordahl CW. Default mode and fronto-parietal network associations with IQ development across childhood in autism. J Neurodev Disord 2022; 14:51. [PMID: 36109700 PMCID: PMC9479280 DOI: 10.1186/s11689-022-09460-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 09/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background Intellectual disability affects approximately one third of individuals with autism spectrum disorder (autism). Yet, a major unresolved neurobiological question is what differentiates autistic individuals with and without intellectual disability. Intelligence quotients (IQs) are highly variable during childhood. We previously identified three subgroups of autistic children with different trajectories of intellectual development from early (2–3½ years) to middle childhood (9–12 years): (a) persistently high: individuals whose IQs remained in the normal range; (b) persistently low: individuals whose IQs remained in the range of intellectual disability (IQ < 70); and (c) changers: individuals whose IQs began in the range of intellectual disability but increased to the normal IQ range. The frontoparietal (FPN) and default mode (DMN) networks have established links to intellectual functioning. Here, we tested whether brain regions within the FPN and DMN differed volumetrically between these IQ trajectory groups in early childhood. Methods We conducted multivariate distance matrix regression to examine the brain regions within the FPN (11 regions x 2 hemispheres) and the DMN (12 regions x 2 hemispheres) in 48 persistently high (18 female), 108 persistently low (32 female), and 109 changers (39 female) using structural MRI acquired at baseline. FPN and DMN regions were defined using networks identified in Smith et al. (Proc Natl Acad Sci U S A 106:13040–5, 2009). IQ trajectory groups were defined by IQ measurements from up to three time points spanning early to middle childhood (mean age time 1: 3.2 years; time 2: 5.4 years; time 3: 11.3 years). Results The changers group exhibited volumetric differences in the DMN compared to both the persistently low and persistently high groups at time 1. However, the persistently high group did not differ from the persistently low group, suggesting that DMN structure may be an early predictor for change in IQ trajectory. In contrast, the persistently high group exhibited differences in the FPN compared to both the persistently low and changers groups, suggesting differences related more to concurrent IQ and the absence of intellectual disability. Conclusions Within autism, volumetric differences of brain regions within the DMN in early childhood may differentiate individuals with persistently low IQ from those with low IQ that improves through childhood. Structural differences in brain networks between these three IQ-based subgroups highlight distinct neural underpinnings of these autism sub-phenotypes. Supplementary Information The online version contains supplementary material available at 10.1186/s11689-022-09460-y.
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8
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Ivanovic D, Zamorano F, Soto-Icaza P, Rojas T, Larraín C, Silva C, Almagià A, Bustamante C, Arancibia V, Villagrán F, Valenzuela R, Barrera C, Billeke P. Brain structural parameters correlate with University Selection Test outcomes in Chilean high school graduates. Sci Rep 2022; 12:20562. [PMID: 36446926 PMCID: PMC9709063 DOI: 10.1038/s41598-022-24958-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 11/22/2022] [Indexed: 11/30/2022] Open
Abstract
How well students learn and perform in academic contexts is a focus of interest for the students, their families, and the entire educational system. Although evidence has shown that several neurobiological factors are involved in scholastic achievement (SA), specific brain measures associated with academic outcomes and whether such associations are independent of other factors remain unclear. This study attempts to identify the relationship between brain structural parameters, and the Chilean national University Selection Test (PSU) results in high school graduates within a multidimensional approach that considers socio-economic, intellectual, nutritional, and demographic variables. To this end, the brain morphology of a sample of 102 students who took the PSU test was estimated using Magnetic Resonance Imaging. Anthropometric parameters, intellectual ability (IA), and socioeconomic status (SES) were also measured. The results revealed that, independently of sex, IA, gray matter volume, right inferior frontal gyrus thickness, and SES were significantly associated with SA. These findings highlight the role of nutrition, health, and socioeconomic variables in academic success.
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Affiliation(s)
- Daniza Ivanovic
- grid.443909.30000 0004 0385 4466Laboratory of Nutrition and Neurological Sciences, Human Nutrition Area, Institute of Nutrition and Food Technology Dr. Fernando Monckeberg Barros (INTA), University of Chile, Santiago, Chile ,grid.412187.90000 0000 9631 4901Laboratorio de Neurociencia Social y Neuromodulación, Centro de Investigación en Complejidad Social (neuroCICS), Facultad de Gobierno, Universidad del Desarrollo, Santiago, Chile
| | - Francisco Zamorano
- grid.412187.90000 0000 9631 4901Unidad de Imágenes Cuantitativas Avanzadas, Departamento de Imágenes, Clínica Alemana, Universidad del Desarrollo, Santiago, Chile
| | - Patricia Soto-Icaza
- grid.412187.90000 0000 9631 4901Laboratorio de Neurociencia Social y Neuromodulación, Centro de Investigación en Complejidad Social (neuroCICS), Facultad de Gobierno, Universidad del Desarrollo, Santiago, Chile
| | - Tatiana Rojas
- grid.443909.30000 0004 0385 4466Laboratory of Nutrition and Neurological Sciences, Human Nutrition Area, Institute of Nutrition and Food Technology Dr. Fernando Monckeberg Barros (INTA), University of Chile, Santiago, Chile
| | - Cristián Larraín
- grid.412187.90000 0000 9631 4901Radiology Department, Facultad de Medicina-Clínica Alemana, Universidad del Desarrollo, Santiago, Chile
| | - Claudio Silva
- grid.412187.90000 0000 9631 4901Radiology Department, Facultad de Medicina-Clínica Alemana, Universidad del Desarrollo, Santiago, Chile
| | - Atilio Almagià
- grid.8170.e0000 0001 1537 5962Laboratory of Physical Anthropology and Human Anatomy, Institute of Biology, Faculty of Sciences, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| | - Claudia Bustamante
- grid.443909.30000 0004 0385 4466Laboratory of Nutrition and Neurological Sciences, Human Nutrition Area, Institute of Nutrition and Food Technology Dr. Fernando Monckeberg Barros (INTA), University of Chile, Santiago, Chile
| | - Violeta Arancibia
- grid.431778.e0000 0004 0482 9086Department of Global Partnership for Education (GPE) World Bank, Washington, USA
| | - Francisca Villagrán
- grid.443909.30000 0004 0385 4466Laboratory of Nutrition and Neurological Sciences, Human Nutrition Area, Institute of Nutrition and Food Technology Dr. Fernando Monckeberg Barros (INTA), University of Chile, Santiago, Chile
| | - Rodrigo Valenzuela
- grid.443909.30000 0004 0385 4466Department of Nutrition, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Cynthia Barrera
- grid.443909.30000 0004 0385 4466Department of Nutrition, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Pablo Billeke
- grid.412187.90000 0000 9631 4901Laboratorio de Neurociencia Social y Neuromodulación, Centro de Investigación en Complejidad Social (neuroCICS), Facultad de Gobierno, Universidad del Desarrollo, Santiago, Chile
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Tarumi T, Patel NR, Tomoto T, Pasha E, Khan AM, Kostroske K, Riley J, Tinajero CD, Wang C, Hynan LS, Rodrigue KM, Kennedy KM, Park DC, Zhang R. Aerobic exercise training and neurocognitive function in cognitively normal older adults: A one-year randomized controlled trial. J Intern Med 2022; 292:788-803. [PMID: 35713933 PMCID: PMC9588521 DOI: 10.1111/joim.13534] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
BACKGROUND Current evidence is inconsistent on the benefits of aerobic exercise training for preventing or attenuating age-related cognitive decline in older adults. OBJECTIVE To investigate the effects of a 1-year progressive, moderate-to-high intensity aerobic exercise intervention on cognitive function, brain volume, and cortical thickness in sedentary but otherwise healthy older adults. METHODS We randomized 73 older adults to a 1-year aerobic exercise or stretching-and-toning (active control) program. The primary outcome was a cognitive composite score calculated from eight neuropsychological tests encompassing inductive reasoning, long-term and working memory, executive function, and processing speed. Secondary outcomes were brain volume and cortical thickness assessed by MRI, and cardiorespiratory fitness measured by peak oxygen uptake (VO2 ). RESULTS One-year aerobic exercise increased peak VO2 by ∼10% (p < 0.001) while it did not change with stretching (p = 0.241). Cognitive composite scores increased in both the aerobic and stretching groups (p < 0.001 for time effect), although no group difference was observed. Total brain volume (p < 0.001) and mean cortical thickness (p = 0.001) decreased in both groups over time, while the reduction in hippocampal volume was smaller in the stretching group compared with the aerobic group (p = 0.040 for interaction). Across all participants, improvement in peak VO2 was positively correlated with increases in cognitive composite score (r = 0.282, p = 0.042) and regional cortical thickness at the inferior parietal lobe (p = 0.016). CONCLUSIONS One-year aerobic exercise and stretching interventions improved cognitive performance but did not prevent age-related brain volume loss in sedentary healthy older adults. Cardiorespiratory fitness gain was positively correlated with cognitive performance and regional cortical thickness.
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Affiliation(s)
- Takashi Tarumi
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital Dallas, Dallas, Texas, USA
- Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan
- Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Neena R. Patel
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital Dallas, Dallas, Texas, USA
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Tsubasa Tomoto
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital Dallas, Dallas, Texas, USA
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Evan Pasha
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital Dallas, Dallas, Texas, USA
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Ayaz M. Khan
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital Dallas, Dallas, Texas, USA
- Department of Diagnostic Imaging, St. Jude Children Research Hospital, Memphis, TN, USA
| | - Kayla Kostroske
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital Dallas, Dallas, Texas, USA
| | - Jonathan Riley
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital Dallas, Dallas, Texas, USA
| | - Cynthia D. Tinajero
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital Dallas, Dallas, Texas, USA
| | - Ciwen Wang
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital Dallas, Dallas, Texas, USA
| | - Linda S. Hynan
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Karen M. Rodrigue
- Center for Vital Longevity, School of Behavioral and Brain Sciences, The University of Texas at Dallas, Dallas, Texas, USA
| | - Kristen M. Kennedy
- Center for Vital Longevity, School of Behavioral and Brain Sciences, The University of Texas at Dallas, Dallas, Texas, USA
| | - Denise C. Park
- Center for Vital Longevity, School of Behavioral and Brain Sciences, The University of Texas at Dallas, Dallas, Texas, USA
| | - Rong Zhang
- Institute for Exercise and Environmental Medicine, Texas Health Presbyterian Hospital Dallas, Dallas, Texas, USA
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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Wu Y, Besson P, Azcona EA, Bandt SK, Parrish TB, Breiter HC, Katsaggelos AK. A multicohort geometric deep learning study of age dependent cortical and subcortical morphologic interactions for fluid intelligence prediction. Sci Rep 2022; 12:17760. [PMID: 36273036 PMCID: PMC9588039 DOI: 10.1038/s41598-022-22313-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 10/12/2022] [Indexed: 01/19/2023] Open
Abstract
The relationship of human brain structure to cognitive function is complex, and how this relationship differs between childhood and adulthood is poorly understood. One strong hypothesis suggests the cognitive function of Fluid Intelligence (Gf) is dependent on prefrontal cortex and parietal cortex. In this work, we developed a novel graph convolutional neural networks (gCNNs) for the analysis of localized anatomic shape and prediction of Gf. Morphologic information of the cortical ribbons and subcortical structures was extracted from T1-weighted MRIs within two independent cohorts, the Adolescent Brain Cognitive Development Study (ABCD; age: 9.93 ± 0.62 years) of children and the Human Connectome Project (HCP; age: 28.81 ± 3.70 years). Prediction combining cortical and subcortical surfaces together yielded the highest accuracy of Gf for both ABCD (R = 0.314) and HCP datasets (R = 0.454), outperforming the state-of-the-art prediction of Gf from any other brain measures in the literature. Across both datasets, the morphology of the amygdala, hippocampus, and nucleus accumbens, along with temporal, parietal and cingulate cortex consistently drove the prediction of Gf, suggesting a significant reframing of the relationship between brain morphology and Gf to include systems involved with reward/aversion processing, judgment and decision-making, motivation, and emotion.
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Affiliation(s)
- Yunan Wu
- Department of Electrical Computer Engineering, Northwestern University, Evanston, IL, USA.
| | - Pierre Besson
- grid.16753.360000 0001 2299 3507Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL USA
| | - Emanuel A. Azcona
- grid.16753.360000 0001 2299 3507Department of Electrical Computer Engineering, Northwestern University, Evanston, IL USA
| | - S. Kathleen Bandt
- grid.16753.360000 0001 2299 3507Department of Neurosurgery, Northwestern University, Feinberg School of Medicine, Chicago, IL USA
| | - Todd B. Parrish
- grid.16753.360000 0001 2299 3507Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL USA
| | - Hans C. Breiter
- grid.24827.3b0000 0001 2179 9593Departments of Computer Science and Biomedical Engineering, University of Cincinnati, Cincinnat, OH USA ,grid.32224.350000 0004 0386 9924Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General Hospital and Harvard School of Medicine, Boston, MA USA
| | - Aggelos K. Katsaggelos
- grid.16753.360000 0001 2299 3507Department of Electrical Computer Engineering, Northwestern University, Evanston, IL USA ,grid.16753.360000 0001 2299 3507Department of Radiology, Northwestern University, Feinberg School of Medicine, Chicago, IL USA ,grid.16753.360000 0001 2299 3507Department of Computer Science, Northwestern University, Evanston, IL USA
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ISHIHARA TORU, MIYAZAKI ATSUSHI, TANAKA HIROKI, MATSUDA TETSUYA. Association of Cardiovascular Risk Markers and Fitness with Task-Related Neural Activity during Animacy Perception. Med Sci Sports Exerc 2022; 54:1738-1750. [PMID: 35666157 PMCID: PMC9473717 DOI: 10.1249/mss.0000000000002963] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
PURPOSE Numerous studies have demonstrated the association between cardiovascular risk markers and fitness, and broad aspects of cognition; however, the possible association of cardiovascular risk markers and fitness with social cognition, which plays a significant role in the development and maintenance of social relationships, has largely been ignored. Herein, we investigated the relationship of cardiovascular risk markers and fitness with task-related neural activity during animacy perception. METHODS We analyzed data from the Human Connectome Project derived from 1027 adults age 22-37 yr. Canonical correlation analysis (CCA) was conducted to evaluate the association between participants' body mass index, systolic and diastolic blood pressure, submaximal endurance, gait speed, hand dexterity, and muscular strength with task-related neural activity during animacy perception. RESULTS We observed a single significant CCA mode. Body mass index and blood pressure demonstrated negative cross-loadings with task-related neural activity in the temporoparietal, superior and anterior temporal, posterior cingulate, and inferior frontal regions, whereas submaximal endurance, hand dexterity, and muscular strength demonstrated positive cross-loadings. The observed CCA variates did not seem highly heritable, as the absolute differences in CCA variates in monozygotic twins, dizygotic twins, and nontwin siblings were not statistically different. Furthermore, the cardiovascular risk markers and fitness CCA variates were positively associated with animacy perception and emotion recognition accuracy, which was mediated by the task-related neural activity. CONCLUSIONS The present findings can provide new insights into the role of markers for cardiovascular health and fitness, specifically their association with social cognition and the underlying neural basis. The intervention for cardiovascular risk and fitness could be a potentially cost-effective method of targeting social cognition.
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Affiliation(s)
- TORU ISHIHARA
- Graduate School of Human Development and Environment, Kobe University, Kobe, JAPAN
| | | | - HIROKI TANAKA
- Tamagawa University Brain Science Institute, Tokyo, JAPAN
- Japan Society for the Promotion of Science, Tokyo, JAPAN
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12
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13
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Sexual dimorphism in the relationship between brain complexity, volume and general intelligence (g): a cross-cohort study. Sci Rep 2022; 12:11025. [PMID: 35773463 PMCID: PMC9247090 DOI: 10.1038/s41598-022-15208-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 06/20/2022] [Indexed: 01/20/2023] Open
Abstract
Changes in brain morphology have been reported during development, ageing and in relation to different pathologies. Brain morphology described by the shape complexity of gyri and sulci can be captured and quantified using fractal dimension (FD). This measure of brain structural complexity, as well as brain volume, are associated with intelligence, but less is known about the sexual dimorphism of these relationships. In this paper, sex differences in the relationship between brain structural complexity and general intelligence (g) in two diverse geographic and cultural populations (UK and Indian) are investigated. 3D T1-weighted magnetic resonance imaging (MRI) data and a battery of cognitive tests were acquired from participants belonging to three different cohorts: Mysore Parthenon Cohort (MPC); Aberdeen Children of the 1950s (ACONF) and UK Biobank. We computed MRI derived structural brain complexity and g estimated from a battery of cognitive tests for each group. Brain complexity and volume were both positively corelated with intelligence, with the correlations being significant in women but not always in men. This relationship is seen across populations of differing ages and geographical locations and improves understanding of neurobiological sex-differences.
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14
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Huang Y, Zhang Y, Zhang Y, Mai X. Effects of Transcranial Direct Current Stimulation Over the Left Primary Motor Cortex on Verbal Intelligence. Front Hum Neurosci 2022; 16:888590. [PMID: 35693542 PMCID: PMC9177941 DOI: 10.3389/fnhum.2022.888590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 05/09/2022] [Indexed: 11/13/2022] Open
Abstract
Previous studies have shown that changes in gray matter density and volume in the left primary motor cortex are significantly associated with changes in individuals’ verbal intelligence quotient (VIQ), but not with their performance intelligence quotient (PIQ). In the present study, we examined the effects of transcranial direct current stimulation (tDCS) over the left primary motor cortex on performance in intelligence tests. We chose four subtests (two each for VIQ and PIQ) of the Wechsler Adult Intelligence Scale-Chinese Revised version and randomized participants into anodal, cathodal, and sham groups. We found that anodal stimulation significantly improved performance in verbal intelligence subtests compared to cathodal and sham stimulation, while performance intelligence subtest scores did not change in any stimulation condition. These findings suggest that the excitation level of the left primary motor cortex has a unique effect on verbal intelligence.
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Affiliation(s)
- Yifan Huang
- Department of Psychology, Renmin University of China, Beijing, China
| | - Yinling Zhang
- Department of Psychology, Renmin University of China, Beijing, China
| | - Yizhe Zhang
- Psychological Counseling Center, Shanghai University, Shanghai, China
| | - Xiaoqin Mai
- Department of Psychology, Renmin University of China, Beijing, China
- *Correspondence: Xiaoqin Mai,
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15
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Kweon H, Aydogan G, Dagher A, Bzdok D, Ruff CC, Nave G, Farah MJ, Koellinger PD. Human brain anatomy reflects separable genetic and environmental components of socioeconomic status. SCIENCE ADVANCES 2022; 8:eabm2923. [PMID: 35584223 PMCID: PMC9116589 DOI: 10.1126/sciadv.abm2923] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Socioeconomic status (SES) correlates with brain structure, a relation of interest given the long-observed relations of SES to cognitive abilities and health. Yet, major questions remain open, in particular, the pattern of causality that underlies this relation. In an unprecedently large study, here, we assess genetic and environmental contributions to SES differences in neuroanatomy. We first establish robust SES-gray matter relations across a number of brain regions, cortical and subcortical. These regional correlates are parsed into predominantly genetic factors and those potentially due to the environment. We show that genetic effects are stronger in some areas (prefrontal cortex, insula) than others. In areas showing less genetic effect (cerebellum, lateral temporal), environmental factors are likely to be influential. Our results imply a complex interplay of genetic and environmental factors that influence the SES-brain relation and may eventually provide insights relevant to policy.
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Affiliation(s)
- Hyeokmoon Kweon
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands
| | - Gökhan Aydogan
- Zürich Center for Neuroeconomics (ZNE), Department of Economics, University of Zurich, 8006 Zürich, Switzerland
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University, Montreal, QC H3A 2B4, Canada
| | - Danilo Bzdok
- McConnell Brain Imaging Centre, Montreal Neurological Institute (MNI), McGill University, Montreal, QC H3A 2B4, Canada
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, QC H3A 2B4, Canada
- School of Computer Science, McGill University, Montreal, QC H3A 2A7, Canada
- Mila-Quebec Artificial Intelligence Institute, Montreal, QC H2S 3H1, Canada
| | - Christian C. Ruff
- Zürich Center for Neuroeconomics (ZNE), Department of Economics, University of Zurich, 8006 Zürich, Switzerland
| | - Gideon Nave
- Marketing Department, the Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Martha J. Farah
- Center for Neuroscience & Society, University of Pennsylvania, Philadelphia, PA 19104, USA
- Corresponding author. (M.J.F.); (P.D.K.)
| | - Philipp D. Koellinger
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, Netherlands
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI 53706, USA
- Corresponding author. (M.J.F.); (P.D.K.)
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16
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Egeland J. The ups and downs of intelligence: The co-occurrence model and its associated research program. INTELLIGENCE 2022. [DOI: 10.1016/j.intell.2022.101643] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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17
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Rabinowitz JA, Campos AI, Ong JS, García-Marín LM, Alcauter S, Mitchell BL, Grasby KL, Cuéllar-Partida G, Gillespie NA, Huhn AS, Martin NG, Thompson PM, Medland SE, Maher BS, Rentería ME. Shared Genetic Etiology between Cortical Brain Morphology and Tobacco, Alcohol, and Cannabis Use. Cereb Cortex 2022; 32:796-807. [PMID: 34379727 PMCID: PMC8841600 DOI: 10.1093/cercor/bhab243] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 06/23/2021] [Accepted: 06/24/2021] [Indexed: 12/13/2022] Open
Abstract
Genome-wide association studies (GWAS) have identified genetic variants associated with brain morphology and substance use behaviors (SUB). However, the genetic overlap between brain structure and SUB has not been well characterized. We leveraged GWAS summary data of 71 brain imaging measures and alcohol, tobacco, and cannabis use to investigate their genetic overlap using linkage disequilibrium score regression. We used genomic structural equation modeling to model a "common SUB genetic factor" and investigated its genetic overlap with brain structure. Furthermore, we estimated SUB polygenic risk scores (PRS) and examined whether they predicted brain imaging traits using the Adolescent Behavior and Cognitive Development (ABCD) study. We identified 8 significant negative genetic correlations, including between (1) alcoholic drinks per week and average cortical thickness, and (2) intracranial volume with age of smoking initiation. We observed 5 positive genetic correlations, including those between (1) insula surface area and lifetime cannabis use, and (2) the common SUB genetic factor and pericalcarine surface area. SUB PRS were associated with brain structure variation in ABCD. Our findings highlight a shared genetic etiology between cortical brain morphology and SUB and suggest that genetic variants associated with SUB may be causally related to brain structure differences.
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Affiliation(s)
- Jill A Rabinowitz
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Adrian I Campos
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Jue-Sheng Ong
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
| | - Luis M García-Marín
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4072, Australia
| | - Sarael Alcauter
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro 76230, México
| | - Brittany L Mitchell
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
- School of Biomedical Science, Faculty of Health, Queensland University of Technology (QUT), Brisbane, Queensland 4059, Australia
| | - Katrina L Grasby
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
| | - Gabriel Cuéllar-Partida
- The University of Queensland Diamantina Institute, The University of Queensland, Brisbane, Queensland 4102, Australia
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Andrew S Huhn
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Baltimore, MD 21205, USA
| | - Nicholas G Martin
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA 90007, USA
| | - Sarah E Medland
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
| | - Brion S Maher
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Miguel E Rentería
- Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland 4006, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4072, Australia
- School of Biomedical Science, Faculty of Health, Queensland University of Technology (QUT), Brisbane, Queensland 4059, Australia
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18
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Fitzgerald J, Fahey L, Holleran L, Ó Broin P, Donohoe G, Morris DW. Thirteen Independent Genetic Loci Associated with Preserved Processing Speed in a Study of Cognitive Resilience in 330,097 Individuals in the UK Biobank. Genes (Basel) 2022; 13:122. [PMID: 35052462 PMCID: PMC8774848 DOI: 10.3390/genes13010122] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 12/08/2021] [Accepted: 12/15/2021] [Indexed: 02/04/2023] Open
Abstract
Cognitive resilience is the ability to withstand the negative effects of stress on cognitive functioning and is important for maintaining quality of life while aging. The UK Biobank does not have measurements of the same cognitive phenotype at distal time points. Therefore, we used education years (EY) as a proxy phenotype for past cognitive performance and current cognitive performance was based on processing speed. This represented an average time span of 40 years between past and current cognitive performance in 330,097 individuals. A confounding factor was that EY is highly polygenic and masked the genetics of resilience. To overcome this, we employed Genomics Structural Equation Modelling (GenomicSEM) to perform a genome-wide association study (GWAS)-by-subtraction using two GWAS, one GWAS of EY and resilience and a second GWAS of EY but not resilience, to generate a GWAS of Resilience. Using independent discovery and replication samples, we found 13 independent genetic loci for Resilience. Functional analyses showed enrichment in several brain regions and specific cell types. Gene-set analyses implicated the biological process "neuron differentiation", the cellular component "synaptic part" and the "WNT signalosome". Mendelian randomisation analysis showed a causative effect of white matter volume on cognitive resilience. These results may contribute to the neurobiological understanding of resilience.
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Affiliation(s)
- Joan Fitzgerald
- Cognitive Genetics and Cognitive Therapy Group, Centre for Neuroimaging & Cognitive Genomics, School of Psychology and Discipline of Biochemistry, National University of Ireland Galway, H91 TK33 Galway, Ireland; (J.F.); (L.F.); (L.H.); (G.D.)
| | - Laura Fahey
- Cognitive Genetics and Cognitive Therapy Group, Centre for Neuroimaging & Cognitive Genomics, School of Psychology and Discipline of Biochemistry, National University of Ireland Galway, H91 TK33 Galway, Ireland; (J.F.); (L.F.); (L.H.); (G.D.)
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland Galway, H91 TK33 Galway, Ireland;
| | - Laurena Holleran
- Cognitive Genetics and Cognitive Therapy Group, Centre for Neuroimaging & Cognitive Genomics, School of Psychology and Discipline of Biochemistry, National University of Ireland Galway, H91 TK33 Galway, Ireland; (J.F.); (L.F.); (L.H.); (G.D.)
| | - Pilib Ó Broin
- School of Mathematics, Statistics and Applied Mathematics, National University of Ireland Galway, H91 TK33 Galway, Ireland;
| | - Gary Donohoe
- Cognitive Genetics and Cognitive Therapy Group, Centre for Neuroimaging & Cognitive Genomics, School of Psychology and Discipline of Biochemistry, National University of Ireland Galway, H91 TK33 Galway, Ireland; (J.F.); (L.F.); (L.H.); (G.D.)
| | - Derek W. Morris
- Cognitive Genetics and Cognitive Therapy Group, Centre for Neuroimaging & Cognitive Genomics, School of Psychology and Discipline of Biochemistry, National University of Ireland Galway, H91 TK33 Galway, Ireland; (J.F.); (L.F.); (L.H.); (G.D.)
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19
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Stibel JM. Decreases in Brain Size and Encephalization in Anatomically Modern Humans. BRAIN, BEHAVIOR AND EVOLUTION 2021; 96:64-77. [PMID: 34718234 DOI: 10.1159/000519504] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 08/28/2021] [Indexed: 12/25/2022]
Abstract
Growth in human brain size and encephalization is well documented throughout much of prehistory and believed to be responsible for increasing cognitive faculties. Over the past 50,000 years, however, both body size and brain mass have decreased but little is known about the scaling relationship between the two. Here, changes to the human brain are examined using matched body remains to determine encephalization levels across an evolutionary timespan. The results find decreases to encephalization levels in modern humans as compared to earlier Holocene H. sapiens and Late Pleistocene anatomically modern Homo. When controlled for lean body mass, encephalization changes are isometric, suggesting that much of the declines in encephalization are driven by recent increases in obesity. A meta-review of genome-wide association studies finds some evidence for selective pressures acting on human cognitive ability, which may be an evolutionary consequence of the more than 5% loss in brain mass over the past 50,000 years.
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20
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Warped Bayesian linear regression for normative modelling of big data. Neuroimage 2021; 245:118715. [PMID: 34798518 DOI: 10.1016/j.neuroimage.2021.118715] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 11/04/2021] [Accepted: 11/05/2021] [Indexed: 11/20/2022] Open
Abstract
Normative modelling is becoming more popular in neuroimaging due to its ability to make predictions of deviation from a normal trajectory at the level of individual participants. It allows the user to model the distribution of several neuroimaging modalities, giving an estimation for the mean and centiles of variation. With the increase in the availability of big data in neuroimaging, there is a need to scale normative modelling to big data sets. However, the scaling of normative models has come with several challenges. So far, most normative modelling approaches used Gaussian process regression, and although suitable for smaller datasets (up to a few thousand participants) it does not scale well to the large cohorts currently available and being acquired. Furthermore, most neuroimaging modelling methods that are available assume the predictive distribution to be Gaussian in shape. However, deviations from Gaussianity can be frequently found, which may lead to incorrect inferences, particularly in the outer centiles of the distribution. In normative modelling, we use the centiles to give an estimation of the deviation of a particular participant from the 'normal' trend. Therefore, especially in normative modelling, the correct estimation of the outer centiles is of utmost importance, which is also where data are sparsest. Here, we present a novel framework based on Bayesian linear regression with likelihood warping that allows us to address these problems, that is, to correctly model non-Gaussian predictive distributions and scale normative modelling elegantly to big data cohorts. In addition, this method provides likelihood-based statistics, which are useful for model selection. To evaluate this framework, we use a range of neuroimaging-derived measures from the UK Biobank study, including image-derived phenotypes (IDPs) and whole-brain voxel-wise measures derived from diffusion tensor imaging. We show good computational scaling and improved accuracy of the warped BLR for certain IDPs and voxels if there was a deviation from normality of these parameters in their residuals. The present results indicate the advantage of a warped BLR in terms of; computational scalability and the flexibility to incorporate non-linearity and non-Gaussianity of the data, giving a wider range of neuroimaging datasets that can be correctly modelled.
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21
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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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22
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de Vlaming R, Slob EAW, Jansen PR, Dagher A, Koellinger PD, Groenen PJF, Rietveld CA. Multivariate analysis reveals shared genetic architecture of brain morphology and human behavior. Commun Biol 2021; 4:1180. [PMID: 34642422 PMCID: PMC8511103 DOI: 10.1038/s42003-021-02712-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 09/22/2021] [Indexed: 01/22/2023] Open
Abstract
Human variation in brain morphology and behavior are related and highly heritable. Yet, it is largely unknown to what extent specific features of brain morphology and behavior are genetically related. Here, we introduce a computationally efficient approach for multivariate genomic-relatedness-based restricted maximum likelihood (MGREML) to estimate the genetic correlation between a large number of phenotypes simultaneously. Using individual-level data (N = 20,190) from the UK Biobank, we provide estimates of the heritability of gray-matter volume in 74 regions of interest (ROIs) in the brain and we map genetic correlations between these ROIs and health-relevant behavioral outcomes, including intelligence. We find four genetically distinct clusters in the brain that are aligned with standard anatomical subdivision in neuroscience. Behavioral traits have distinct genetic correlations with brain morphology which suggests trait-specific relevance of ROIs. These empirical results illustrate how MGREML can be used to estimate internally consistent and high-dimensional genetic correlation matrices in large datasets.
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Affiliation(s)
- Ronald de Vlaming
- School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Eric A W Slob
- Department of Applied Economics, Erasmus School of Economics, Rotterdam, The Netherlands
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, The Netherlands
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Philip R Jansen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Clinical Genetics, VU Medical Center, Amsterdam UMC, Amsterdam, The Netherlands
| | - Alain Dagher
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Philipp D Koellinger
- School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA
| | - Patrick J F Groenen
- Econometric Institute, Erasmus School of Economics, Rotterdam, The Netherlands
| | - Cornelius A Rietveld
- Department of Applied Economics, Erasmus School of Economics, Rotterdam, The Netherlands.
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus School of Economics, Rotterdam, The Netherlands.
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23
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Navarro-Pardo E, Suay F, Murphy M. Ageing: Not only an age-related issue. Mech Ageing Dev 2021; 199:111568. [PMID: 34536447 DOI: 10.1016/j.mad.2021.111568] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 08/31/2021] [Accepted: 09/07/2021] [Indexed: 10/20/2022]
Abstract
Developments in the last century have led to an unprecedented increase in life expectancy. These changes open opportunities for humans to grow and develop in healthy and adaptive ways, adding life to years as well as years to life. There are also challenges, however - as we live longer, a greater number of people will experience chronic illness and disability, often linked to lifestyle factors. The current paper advances an argument that there are fundamental biological sex differences which, sometimes directly and sometime mediated by lifestyle factors, underpin the marked differences in morbidity and mortality that we find between the sexes. Furthermore, we argue that it is necessary to consider sex as a key factor in research on healthy ageing, allowing for the possibility that different patterns exist between males and females, and that therefore different approaches and interventions are required to optimise healthy ageing in both sexes.
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Affiliation(s)
- Esperanza Navarro-Pardo
- Department of Developmental and Educational Psychology, Universitat de València, Av. Blasco Ibañez, 21, 46008, València, Spain
| | - Ferran Suay
- Department of Biopsychology, Universitat de València, Av. Blasco Ibañez, 21, 46008, València, Spain
| | - Mike Murphy
- School of Applied Psychology, University College Cork, North Mall Campus, Cork, Ireland.
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24
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Aydogan G, Daviet R, Karlsson Linnér R, Hare TA, Kable JW, Kranzler HR, Wetherill RR, Ruff CC, Koellinger PD, Nave G. Genetic underpinnings of risky behaviour relate to altered neuroanatomy. Nat Hum Behav 2021; 5:787-794. [PMID: 33510390 PMCID: PMC10566430 DOI: 10.1038/s41562-020-01027-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 11/26/2020] [Indexed: 01/30/2023]
Abstract
Previous research points to the heritability of risk-taking behaviour. However, evidence on how genetic dispositions are translated into risky behaviour is scarce. Here, we report a genetically informed neuroimaging study of real-world risky behaviour across the domains of drinking, smoking, driving and sexual behaviour in a European sample from the UK Biobank (N = 12,675). We find negative associations between risky behaviour and grey-matter volume in distinct brain regions, including amygdala, ventral striatum, hypothalamus and dorsolateral prefrontal cortex (dlPFC). These effects are replicated in an independent sample recruited from the same population (N = 13,004). Polygenic risk scores for risky behaviour, derived from a genome-wide association study in an independent sample (N = 297,025), are inversely associated with grey-matter volume in dlPFC, putamen and hypothalamus. This relation mediates roughly 2.2% of the association between genes and behaviour. Our results highlight distinct heritable neuroanatomical features as manifestations of the genetic propensity for risk taking.
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Affiliation(s)
- Gökhan Aydogan
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland
| | - Remi Daviet
- Marketing Department, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Richard Karlsson Linnér
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Todd A Hare
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland
| | - Joseph W Kable
- Marketing Department, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Henry R Kranzler
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Veterans Integrated Service Network 4, Mental Illness Research, Education and Clinical Center, Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - Reagan R Wetherill
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Christian C Ruff
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland
| | - Philipp D Koellinger
- Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA
| | - Gideon Nave
- Marketing Department, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA.
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25
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Chen N, Zhao C, Wang M, Jones JA, Liu P, Chen X, Gong G, Liu H. Linking Cortical Morphology to Interindividual Variability in Auditory Feedback Control of Vocal Production. Cereb Cortex 2021; 31:2932-2943. [PMID: 33454738 DOI: 10.1093/cercor/bhaa401] [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: 09/03/2020] [Revised: 11/02/2020] [Accepted: 12/14/2020] [Indexed: 11/13/2022] Open
Abstract
Speakers regulate vocal motor behaviors in a compensatory manner when perceiving errors in auditory feedback. Little is known, however, about the source of interindividual variability that exists in the degree to which speakers compensate for perceived errors. The present study included 40 young adults to investigate whether individual differences in auditory integration for vocal pitch regulation, as indexed by vocal compensations for pitch perturbations in auditory feedback, can be predicted by cortical morphology as assessed by gray-matter volume, cortical thickness, and surface area in a whole-brain manner. The results showed that greater gray-matter volume in the left inferior parietal lobule and greater cortical thickness and surface area in the left superior/middle temporal gyrus, temporal pole, inferior/superior parietal lobule, and precuneus predicted larger vocal responses. Greater cortical thickness in the right inferior frontal gyrus and superior parietal lobule and surface area in the left precuneus and cuneus were significantly correlated with smaller magnitudes of vocal responses. These findings provide the first evidence that vocal compensations for feedback errors are predicted by the structural morphology of the frontal and tempo-parietal regions, and further our understanding of the neural basis that underlies interindividual variability in auditory-motor control of vocal production.
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Affiliation(s)
- Na Chen
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.,Department of Rehabilitation, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China
| | - Chenxi Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.,School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Meng Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
| | - Jeffery A Jones
- Psychology Department, Laurier Centre for Cognitive Neuroscience, Wilfrid Laurier University, Waterloo, Ontario, Canada
| | - Peng Liu
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
| | - Xi Chen
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
| | - Gaolong Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,Chinese Institute for Brain Research, Beijing 102206, China
| | - Hanjun Liu
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.,Guangdong Provincial Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
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26
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Fraza CJ, Dinga R, Beckmann CF, Marquand AF. Warped Bayesian Linear Regression for Normative Modelling of Big Data.. [PMID: 34798518 PMCID: PMC7613680 DOI: 10.1101/2021.04.05.438429] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
AbstractNormative modelling is becoming more popular in neuroimaging due to its ability to make predictions of deviation from a normal trajectory at the level of individual participants. It allows the user to model the distribution of several neuroimaging modalities, giving an estimation for the mean and centiles of variation. With the increase in the availability of big data in neuroimaging, there is a need to scale normative modelling to big data sets. However, the scaling of normative models has come with several challenges.So far, most normative modelling approaches used Gaussian process regression, and although suitable for smaller datasets (up to a few thousand participants) it does not scale well to the large cohorts currently available and being acquired. Furthermore, most neuroimaging modelling methods that are available assume the predictive distribution to be Gaussian in shape. However, deviations from Gaussianity can be frequently found, which may lead to incorrect inferences, particularly in the outer centiles of the distribution. In normative modelling, we use the centiles to give an estimation of the deviation of a particular participant from the ‘normal’ trend. Therefore, especially in normative modelling, the correct estimation of the outer centiles is of utmost importance, which is also where data are sparsest.Here, we present a novel framework based on Bayesian Linear Regression with likelihood warping that allows us to address these problems, that is, to scale normative modelling elegantly to big data cohorts and to correctly model non-Gaussian predictive distributions. In addition, this method provides also likelihood-based statistics, which are useful for model selection.To evaluate this framework, we use a range of neuroimaging-derived measures from the UK Biobank study, including image-derived phenotypes (IDPs) and whole-brain voxel-wise measures derived from diffusion tensor imaging. We show good computational scaling and improved accuracy of the warped BLR for certain IDPs and voxels if there was a deviation from normality of these parameters in their residuals.The present results indicate the advantage of a warped BLR in terms of; computational scalability and the flexibility to incorporate non-linearity and non-Gaussianity of the data, giving a wider range of neuroimaging datasets that can be correctly modelled.
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27
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Zhao B, Zou F. On polygenic risk scores for complex traits prediction. Biometrics 2021; 78:499-511. [PMID: 33786811 DOI: 10.1111/biom.13466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 03/10/2021] [Accepted: 03/15/2021] [Indexed: 12/01/2022]
Abstract
Polygenic risk scores (PRS) have gained substantial attention for complex traits prediction in genome-wide association studies (GWAS). Motivated by the polygenic model of complex traits, we study the statistical properties of PRS under the high-dimensional but sparsity free setting where the triplet ( n , p , m ) → ( ∞ , ∞ , ∞ ) with n , p , m being the sample size, the number of assayed single-nucleotide polymorphisms (SNPs), and the number of assayed causal SNPs, respectively. First, we derive asymptotic results on the out-of-sample (prediction) R-squared for PRS. These results help understand the widespread observed gap between the in-sample heritability (or partial R-squared due to the genetic features) estimate and the out-of-sample R-squared for most complex traits. Next, we investigate how features should be selected (e.g., by a p-value threshold) for constructing optimal PRS. We reveal that the optimal threshold depends largely on the genetic architecture underlying the complex trait and the sample size of the training GWAS, or the m / n ratio. For highly polygenic traits with a large m / n ratio, it is difficult to separate causal and null SNPs and stringent feature selection in principle often leads to poor PRS prediction. We numerically illustrate the theoretical results with intensive simulation studies and real data analysis on 33 complex traits with a wide range of genetic architectures in the UK Biobank database.
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Affiliation(s)
- Bingxin Zhao
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Fei Zou
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA
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28
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29
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Ma X, Tan J, Jiang L, Wang X, Cheng B, Xie P, Li Y, Wang J, Li S. Aberrant Structural and Functional Developmental Trajectories in Children With Intellectual Disability. Front Psychiatry 2021; 12:634170. [PMID: 33927652 PMCID: PMC8076543 DOI: 10.3389/fpsyt.2021.634170] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 01/11/2021] [Indexed: 12/21/2022] Open
Abstract
Intellectual disability (ID) is associated with aberrant structural and functional development of the brain, yet how the dynamical developmental changes of the structure and function of ID from childhood to around puberty remains unknown. To explore the abnormal developmental trajectories of structure and function, 40 children with ID aged 6-13 years and 30 sex-, age-, and educational level-matched healthy controls (HC) with age range from 6 to 13 were recruited. The automatic voxel-based morphometry (VBM) and resting-state functional connectivity (FC) analyses were adopted to delineate the structural and functional differences. Significantly decreased total gray matter volume (GMV) and white matter volume (WMV) in children with ID were found, and the developmental trajectories of GMV and WMV in children with ID showed an opposite direction as compared with HC. The voxel-wise VMB analysis further revealed significantly increased GMV in the dorsal medial prefrontal cortex (dmPFC), bilateral orbital part of the inferior frontal gyrus (orb_IFG.L, orb_IFG.R), right cuneus (cuneus.R), and bilateral middle frontal gyrus (MFG.L, MFG.R) in children with ID. The following seed-based whole-brain functional connectivity analyses of the brain areas with changed GMV found decreased FCs between the cuneus.R and left intraparietal sulcus (IPS.L) and between the MFG.R and anterior cingulate cortex (ACC) in children with ID. Moreover, negative correlations between GMV values in the dmPFC, orb_IFG.L, cuneus.R, and intelligence quotient (IQ) scores and positive correlations between the FCs of the cuneus.R with IPS.L and MFG.R with ACC and IQ scores were found in children with ID and HC. Our findings provide evidence for the abnormal structural and functional development in children with ID and highlight the important role of frontoparietal network in the typical development. The abnormal development of GMV and functional couplings found in this study may be the neuropathological bases of children with ID.
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Affiliation(s)
- Xuejin Ma
- Department of Radiology, The First People's Hospital of Zunyi, The Third Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Jianxia Tan
- Department of Radiology, The First People's Hospital of Zunyi, The Third Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Lin Jiang
- Department of Radiology, The First People's Hospital of Zunyi, The Third Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Xuqin Wang
- Department of Child Health, The First People's Hospital of Zunyi, The Third Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Bochao Cheng
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, China
| | - Peng Xie
- Department of Critical Care Medicine, The First People's Hospital of Zunyi, The Third Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Yuanyuan Li
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiaojian Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.,Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Shiguang Li
- Department of Radiology, The First People's Hospital of Zunyi, The Third Affiliated Hospital of Zunyi Medical University, Zunyi, China
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30
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Demange PA, Malanchini M, Mallard TT, Biroli P, Cox SR, Grotzinger AD, Tucker-Drob EM, Abdellaoui A, Arseneault L, van Bergen E, Boomsma DI, Caspi A, Corcoran DL, Domingue BW, Harris KM, Ip HF, Mitchell C, Moffitt TE, Poulton R, Prinz JA, Sugden K, Wertz J, Williams BS, de Zeeuw EL, Belsky DW, Harden KP, Nivard MG. Investigating the genetic architecture of noncognitive skills using GWAS-by-subtraction. Nat Genet 2021; 53:35-44. [PMID: 33414549 PMCID: PMC7116735 DOI: 10.1038/s41588-020-00754-2] [Citation(s) in RCA: 98] [Impact Index Per Article: 32.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 11/19/2020] [Indexed: 01/28/2023]
Abstract
Little is known about the genetic architecture of traits affecting educational attainment other than cognitive ability. We used genomic structural equation modeling and prior genome-wide association studies (GWASs) of educational attainment (n = 1,131,881) and cognitive test performance (n = 257,841) to estimate SNP associations with educational attainment variation that is independent of cognitive ability. We identified 157 genome-wide-significant loci and a polygenic architecture accounting for 57% of genetic variance in educational attainment. Noncognitive genetics were enriched in the same brain tissues and cell types as cognitive performance, but showed different associations with gray-matter brain volumes. Noncognitive genetics were further distinguished by associations with personality traits, less risky behavior and increased risk for certain psychiatric disorders. For socioeconomic success and longevity, noncognitive and cognitive-performance genetics demonstrated associations of similar magnitude. By conducting a GWAS of a phenotype that was not directly measured, we offer a view of genetic architecture of noncognitive skills influencing educational success.
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Affiliation(s)
- Perline A. Demange
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands,Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, Amsterdam, The Netherlands,Research Institute LEARN!, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Margherita Malanchini
- Department of Biological and Experimental Psychology, Queen Mary University of London, London, UK,Social, Genetic and Developmental Psychiatric Centre, Institute of Psychiatry, Psychology, & Neuroscience, King’s College London, London, UK,Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | - Travis T. Mallard
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | - Pietro Biroli
- Department of Economics, University of Zurich, Zurich, Switzerland
| | - Simon R. Cox
- Lothian Birth Cohorts group, 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
| | - Abdel Abdellaoui
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands,Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Louise Arseneault
- Social, Genetic and Developmental Psychiatric Centre, Institute of Psychiatry, Psychology, & Neuroscience, King’s College London, London, UK
| | - Elsje van Bergen
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands,Research Institute LEARN!, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Dorret I. Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Avshalom Caspi
- Social, Genetic and Developmental Psychiatric Centre, Institute of Psychiatry, Psychology, & Neuroscience, King’s College London, London, UK,Department of Psychology & Neuroscience, Duke University, Durham, NC, USA,Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA,Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - David L. Corcoran
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | | | - Kathleen Mullan Harris
- Department of Sociologyand Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hill F. Ip
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Colter Mitchell
- Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Terrie E. Moffitt
- Social, Genetic and Developmental Psychiatric Centre, Institute of Psychiatry, Psychology, & Neuroscience, King’s College London, London, UK,Department of Psychology & Neuroscience, Duke University, Durham, NC, USA,Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC, USA,Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Richie Poulton
- Department of Psychology and Dunedin Multidisciplinary Health and Development Research Unit, University of Otago, Dunedin, New Zealand
| | - Joseph A. Prinz
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
| | - Karen Sugden
- Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | - Jasmin Wertz
- Department of Psychology & Neuroscience, Duke University, Durham, NC, USA
| | | | - Eveline L. de Zeeuw
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands,Research Institute LEARN!, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Daniel W. Belsky
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA,Robert N. Butler Columbia Aging Center, Columbia University, New York, NY, USA
| | - K. Paige Harden
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | - Michel G. Nivard
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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Satary Dizaji A, Vieira BH, Khodaei MR, Ashrafi M, Parham E, Hosseinzadeh GA, Salmon CEG, Soltanianzadeh H. Linking Brain Biology to Intellectual Endowment: A Review on the Associations of Human Intelligence With Neuroimaging Data. Basic Clin Neurosci 2021; 12:1-28. [PMID: 33995924 PMCID: PMC8114859 DOI: 10.32598/bcn.12.1.574.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 05/10/2020] [Accepted: 10/28/2020] [Indexed: 11/20/2022] Open
Abstract
Human intelligence has always been a fascinating subject for scientists. Since the inception of Spearman's general intelligence in the early 1900s, there has been significant progress towards characterizing different aspects of intelligence and its relationship with structural and functional features of the brain. In recent years, the invention of sophisticated brain imaging devices using Diffusion-Weighted Imaging (DWI) and functional Magnetic Resonance Imaging (fMRI) has allowed researchers to test hypotheses about neural correlates of intelligence in humans.This review summarizes recent findings on the associations of human intelligence with neuroimaging data. To this end, first, we review the literature that has related brain morphometry to intelligence. Next, we elaborate on the applications of DWI and restingstate fMRI on the investigation of intelligence. Then, we provide a survey of literature that has used multimodal DWI-fMRI to shed light on intelligence. Finally, we discuss the state-of-the-art of individualized prediction of intelligence from neuroimaging data and point out future strategies. Future studies hold promising outcomes for machine learning-based predictive frameworks using neuroimaging features to estimate human intelligence.
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Affiliation(s)
- Aslan Satary Dizaji
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Bruno Hebling Vieira
- Inbrain Lab, Department of Physics, FFCLRP, University of São Paulo, Ribeirao Preto, Brazil
| | - Mohmmad Reza Khodaei
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Mahnaz Ashrafi
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Elahe Parham
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Gholam Ali Hosseinzadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | | | - Hamid Soltanianzadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- Radiology Image Analysis Laboratory, Henry Ford Health System, Detroit, USA
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32
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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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Lauringson V, Veldre G, Hõrak P. Adolescent Cranial Volume as a Sensitive Marker of Parental Investment: The Role of Non-material Resources? Front Psychol 2020; 11:602401. [PMID: 33384647 PMCID: PMC7769954 DOI: 10.3389/fpsyg.2020.602401] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 11/25/2020] [Indexed: 12/18/2022] Open
Abstract
Growth of different body parts in humans is sensitive to different resource constraints that are mediated by parental investment. Parental investment can involve the expenditure of material, cognitive, and emotional resources on offspring. Cranial volume, an important predictor of cognitive ability, appears understudied in this context. We asked (1) whether there are associations between growth and family structure, self-reported estimates for resource availability, and sibling number; and (2) whether these constraints relate to head and body growth in a similar manner. We assessed the associations between parental investment, height, and cranial volume in a cross-sectional study of Estonian children (born 1980-87, aged 11-17). Height correlated negatively with the number of siblings but this association became negligible in a model controlling for birthweight, parental heights, and mother's age at birth. Unlike height, cranial volume was unrelated to sibling number, but it was negatively associated with self-reported meat and general resource shortage. Cranial volume was related to family structure and paternal education. Children living with both birth-parents had larger heads than those living in families containing a step-parent. Since these family types did not differ with respect to meat or general resource shortage, our findings suggest that families including both genetic parents provide non-material benefits that stimulate predominantly cranial growth. For the studied developmental period, cranial volume appeared a more sensitive marker of growth constraints than height. The potential of using cranial volume for quantifying physical impact of non-material parental investment deserves further attention.
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Affiliation(s)
| | - Gudrun Veldre
- Department of Anatomy, Centre for Physical Anthropology, University of Tartu, Tartu, Estonia
| | - Peeter Hõrak
- Department of Zoology, University of Tartu, Tartu, Estonia
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Jansen PR, Nagel M, Watanabe K, Wei Y, Savage JE, de Leeuw CA, van den Heuvel MP, van der Sluis S, Posthuma D. Genome-wide meta-analysis of brain volume identifies genomic loci and genes shared with intelligence. Nat Commun 2020; 11:5606. [PMID: 33154357 PMCID: PMC7644755 DOI: 10.1038/s41467-020-19378-5] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 10/06/2020] [Indexed: 12/22/2022] Open
Abstract
The phenotypic correlation between human intelligence and brain volume (BV) is considerable (r ≈ 0.40), and has been shown to be due to shared genetic factors. To further examine specific genetic factors driving this correlation, we present genomic analyses of the genetic overlap between intelligence and BV using genome-wide association study (GWAS) results. First, we conduct a large BV GWAS meta-analysis (N = 47,316 individuals), followed by functional annotation and gene-mapping. We identify 18 genomic loci (14 not previously associated), implicating 343 genes (270 not previously associated) and 18 biological pathways for BV. Second, we use an existing GWAS for intelligence (N = 269,867 individuals), and estimate the genetic correlation (rg) between BV and intelligence to be 0.24. We show that the rg is partly attributable to physical overlap of GWAS hits in 5 genomic loci. We identify 92 shared genes between BV and intelligence, which are mainly involved in signaling pathways regulating cell growth. Out of these 92, we prioritize 32 that are most likely to have functional impact. These results provide information on the genetics of BV and provide biological insight into BV’s shared genetic etiology with intelligence. Brain volume and intelligence have been previously found to have shared genetic etiology, but the specific common genetic signals have not been identified. Here, the authors perform a genome-wide association study on brain volume, finding common genetic loci driving brain volume and intelligence.
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Affiliation(s)
- Philip R Jansen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Clinical Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mats Nagel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Kyoko Watanabe
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Yongbin Wei
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jeanne E Savage
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Christiaan A de Leeuw
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Child and Adolescent Psychiatry and Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, The Netherlands
| | - Sophie van der Sluis
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Child and Adolescent Psychiatry and Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, The Netherlands
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. .,Department of Child and Adolescent Psychiatry and Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, The Netherlands.
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35
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Gur RC, Butler ER, Moore TM, Rosen AFG, Ruparel K, Satterthwaite TD, Roalf DR, Gennatas ED, Bilker WB, Shinohara RT, Port A, Elliott MA, Verma R, Davatzikos C, Wolf DH, Detre JA, Gur RE. Structural and Functional Brain Parameters Related to Cognitive Performance Across Development: Replication and Extension of the Parieto-Frontal Integration Theory in a Single Sample. Cereb Cortex 2020; 31:1444-1463. [PMID: 33119049 DOI: 10.1093/cercor/bhaa282] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 07/16/2020] [Accepted: 08/24/2020] [Indexed: 02/06/2023] Open
Abstract
The parieto-frontal integration theory (PFIT) identified a fronto-parietal network of regions where individual differences in brain parameters most strongly relate to cognitive performance. PFIT was supported and extended in adult samples, but not in youths or within single-scanner well-powered multimodal studies. We performed multimodal neuroimaging in 1601 youths age 8-22 on the same 3-Tesla scanner with contemporaneous neurocognitive assessment, measuring volume, gray matter density (GMD), mean diffusivity (MD), cerebral blood flow (CBF), resting-state functional magnetic resonance imaging measures of the amplitude of low frequency fluctuations (ALFFs) and regional homogeneity (ReHo), and activation to a working memory and a social cognition task. Across age and sex groups, better performance was associated with higher volumes, greater GMD, lower MD, lower CBF, higher ALFF and ReHo, and greater activation for the working memory task in PFIT regions. However, additional cortical, striatal, limbic, and cerebellar regions showed comparable effects, hence PFIT needs expansion into an extended PFIT (ExtPFIT) network incorporating nodes that support motivation and affect. Associations of brain parameters became stronger with advancing age group from childhood to adolescence to young adulthood, effects occurring earlier in females. This ExtPFIT network is developmentally fine-tuned, optimizing abundance and integrity of neural tissue while maintaining a low resting energy state.
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Affiliation(s)
- Ruben C Gur
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.,Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.,Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Ellyn R Butler
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Tyler M Moore
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Adon F G Rosen
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Kosha Ruparel
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - David R Roalf
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Efstathios D Gennatas
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Warren B Bilker
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.,Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Allison Port
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Mark A Elliott
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.,Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Ragini Verma
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Daniel H Wolf
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Brain Behavior Laboratory and the Neurodevelopment and Psychosis Section, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.,Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.,Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
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36
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Hilger K, Winter NR, Leenings R, Sassenhagen J, Hahn T, Basten U, Fiebach CJ. Predicting intelligence from brain gray matter volume. Brain Struct Funct 2020; 225:2111-2129. [PMID: 32696074 PMCID: PMC7473979 DOI: 10.1007/s00429-020-02113-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 07/04/2020] [Indexed: 12/21/2022]
Abstract
A positive association between brain size and intelligence is firmly established, but whether region-specific anatomical differences contribute to general intelligence remains an open question. Results from voxel-based morphometry (VBM) - one of the most widely used morphometric methods - have remained inconclusive so far. Here, we applied cross-validated machine learning-based predictive modeling to test whether out-of-sample prediction of individual intelligence scores is possible on the basis of voxel-wise gray matter volume. Features were derived from structural magnetic resonance imaging data (N = 308) using (a) a purely data-driven method (principal component analysis) and (b) a domain knowledge-based approach (atlas parcellation). When using relative gray matter (corrected for total brain size), only the atlas-based approach provided significant prediction, while absolute gray matter (uncorrected) allowed for above-chance prediction with both approaches. Importantly, in all significant predictions, the absolute error was relatively high, i.e., greater than ten IQ points, and in the atlas-based models, the predicted IQ scores varied closely around the sample mean. This renders the practical value even of statistically significant prediction results questionable. Analyses based on the gray matter of functional brain networks yielded significant predictions for the fronto-parietal network and the cerebellum. However, the mean absolute errors were not reduced in contrast to the global models, suggesting that general intelligence may be related more to global than region-specific differences in gray matter volume. More generally, our study highlights the importance of predictive statistical analysis approaches for clarifying the neurobiological bases of intelligence and provides important suggestions for future research using predictive modeling.
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Affiliation(s)
- Kirsten Hilger
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany.
- Department of Psychology, Julius Maximilian University Würzburg, Würzburg, Germany.
- IDeA Center for Individual Development and Adaptive Education, Frankfurt am Main, Germany.
- Department of Psychology I, University Wuerzburg, Marcusstr. 9-11, 97070, Würzburg, Germany.
| | - Nils R Winter
- Institute of Translational Psychiatry, University Hospital Münster, Münster, Germany
| | - Ramona Leenings
- Institute of Translational Psychiatry, University Hospital Münster, Münster, Germany
| | - Jona Sassenhagen
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Tim Hahn
- Institute of Translational Psychiatry, University Hospital Münster, Münster, Germany
| | - Ulrike Basten
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Christian J Fiebach
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
- IDeA Center for Individual Development and Adaptive Education, Frankfurt am Main, Germany
- Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany
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37
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Fernandes HB, Peñaherrera-Aguirre M, Woodley of Menie MA, Figueredo AJ. Macroevolutionary patterns and selection modes for general intelligence (G) and for commonly used neuroanatomical volume measures in primates. INTELLIGENCE 2020. [DOI: 10.1016/j.intell.2020.101456] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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38
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Kaczkurkin AN, Sotiras A, Baller EB, Barzilay R, Calkins ME, Chand GB, Cui Z, Erus G, Fan Y, Gur RE, Gur RC, Moore TM, Roalf DR, Rosen AF, Ruparel K, Shinohara RT, Varol E, Wolf DH, Davatzikos C, Satterthwaite TD. Neurostructural Heterogeneity in Youths With Internalizing Symptoms. Biol Psychiatry 2020; 87:473-482. [PMID: 31690494 PMCID: PMC7007843 DOI: 10.1016/j.biopsych.2019.09.005] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Revised: 09/01/2019] [Accepted: 09/03/2019] [Indexed: 12/12/2022]
Abstract
BACKGROUND Internalizing disorders such as anxiety and depression are common psychiatric disorders that frequently begin in youth and exhibit marked heterogeneity in treatment response and clinical course. Given that symptom-based classification approaches do not align with underlying neurobiology, an alternative approach is to identify neurobiologically informed subtypes based on brain imaging data. METHODS We used a recently developed semisupervised machine learning method (HYDRA [heterogeneity through discriminative analysis]) to delineate patterns of neurobiological heterogeneity within youths with internalizing symptoms using structural data collected at 3T from a sample of 1141 youths. RESULTS Using volume and cortical thickness, cross-validation methods indicated 2 highly stable subtypes of internalizing youths (adjusted Rand index = 0.66; permutation-based false discovery rate p < .001). Subtype 1, defined by smaller brain volumes and reduced cortical thickness, was marked by impaired cognitive performance and higher levels of psychopathology than both subtype 2 and typically developing youths. Using resting-state functional magnetic resonance imaging and diffusion images not considered during clustering, we found that subtype 1 also showed reduced amplitudes of low-frequency fluctuations in frontolimbic regions at rest and reduced fractional anisotropy in several white matter tracts. In contrast, subtype 2 showed intact cognitive performance and greater volume, cortical thickness, and amplitudes during rest compared with subtype 1 and typically developing youths, despite still showing clinically significant levels of psychopathology. CONCLUSIONS We identified 2 subtypes of internalizing youths differentiated by abnormalities in brain structure, function, and white matter integrity, with one of the subtypes showing poorer functioning across multiple domains. Identification of biologically grounded internalizing subtypes may assist in targeting early interventions and assessing longitudinal prognosis.
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Affiliation(s)
- Antonia N. Kaczkurkin
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aristeidis Sotiras
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Radiology, Washington University, St. Louis, MO, 63110, USA,Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Erica B. Baller
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ran Barzilay
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Monica E. Calkins
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ganesh B. Chand
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zaixu Cui
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Guray Erus
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Philadelphia Veterans Administration Medical Center, Philadelphia, PA 19104
| | - Tyler M. Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David R. Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Adon F.G. Rosen
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kosha Ruparel
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T. Shinohara
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Erdem Varol
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Statistics, Center for Theoretical Neuroscience, Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027
| | - Daniel H. Wolf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
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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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40
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Porcu M, Wintermark M, Suri JS, Saba L. The influence of the volumetric composition of the intracranial space on neural activity in healthy subjects: a resting‐state functional magnetic resonance study. Eur J Neurosci 2019; 51:1944-1961. [DOI: 10.1111/ejn.14627] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 10/15/2019] [Accepted: 11/22/2019] [Indexed: 12/21/2022]
Affiliation(s)
- Michele Porcu
- Department of Medical Imaging AOU of Cagliari University of Cagliari Cagliari Italy
| | - Max Wintermark
- Department of Radiology Neuroradiology Division Stanford University Stanford CA USA
| | - Jasjit S. Suri
- Diagnostic and Monitoring Division AtheroPoint Roseville CA USA
| | - Luca Saba
- Department of Medical Imaging AOU of Cagliari University of Cagliari Cagliari Italy
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41
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Valge M, Meitern R, Hõrak P. Morphometric traits predict educational attainment independently of socioeconomic background. BMC Public Health 2019; 19:1696. [PMID: 31852467 PMCID: PMC6921596 DOI: 10.1186/s12889-019-8072-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 12/11/2019] [Indexed: 02/08/2023] Open
Abstract
Background Aim of this study is to describe the relationship between anthropometric traits and educational attainment among Estonian schoolchildren born between 1937 and 1962. We asked whether height, cranial volume and face width (a testosterone-dependent trait), measured in childhood predict later educational attainment independently of each other, family socioeconomic position (SEP) and sex. Associations between morphometric traits and education and their interactions with biosocial variables are of scholarly importance because higher education is nearly universally associated with low fertility in women, and often with high fertility in men. Hence, morphometric traits associated with educational attainment are targeted by natural selection and describing the exact nature of these associations is relevant for understanding the current patterns of evolution of human body size. Methods Data on morphometric measurements and family background of 11,032 Estonian schoolchildren measured between seven and 19 years of age were obtained from the study performed by Juhan Aul between 1956 and 1969. Ordinal logistic regression was used for testing the effects of morphometric traits, biosocial variables and their interaction on the cumulative probability of obtaining education beyond primary level. Results Of biosocial variables, family SEP was the most important determinant of educational attainment, followed by the sex, rural vs urban origin and the number of siblings. No significant interactions with morphometric traits were detected, i.e., within each category of SEP, rural vs urban origin and sex, taller children and those with larger heads and relatively narrower faces were more likely to proceed to secondary and/or tertiary education. The effect of height on education was independent of cranial volume, indicating that taller children did not obtain more educations because their brains were larger than those of shorter children; height per se was important. Conclusions Our main finding – that adjusting for other morphometric traits and biosocial variables, morphometric traits still robustly predicted educational attainment, is relevant for understanding the current patterns of evolution of human body size. Our findings suggest that fecundity selection acting on educational attainment could be partly responsible for the concurrent selection for smaller stature and cranial volume in women and opposite trends in men.
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Affiliation(s)
- Markus Valge
- Department of Zoology, University of Tartu, Vanemuise 46, 51014, Tartu, Estonia
| | - Richard Meitern
- Department of Zoology, University of Tartu, Vanemuise 46, 51014, Tartu, Estonia
| | - Peeter Hõrak
- Department of Zoology, University of Tartu, Vanemuise 46, 51014, Tartu, Estonia.
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42
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Zhao B, Luo T, Li T, Li Y, Zhang J, Shan Y, Wang X, Yang L, Zhou F, Zhu Z, Zhu H. Genome-wide association analysis of 19,629 individuals identifies variants influencing regional brain volumes and refines their genetic co-architecture with cognitive and mental health traits. Nat Genet 2019; 51:1637-1644. [PMID: 31676860 PMCID: PMC6858580 DOI: 10.1038/s41588-019-0516-6] [Citation(s) in RCA: 139] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2019] [Accepted: 09/23/2019] [Indexed: 12/19/2022]
Abstract
Volumetric variations of the human brain are heritable and are associated with many brain-related complex traits. Here we performed genome-wide association studies (GWAS) of 101 brain volumetric phenotypes using the UK Biobank sample including 19,629 participants. GWAS identified 365 independent genetic variants exceeding a significance threshold of 4.9 × 10-10, adjusted for testing multiple phenotypes. A gene-based association study found 157 associated genes (124 new), and functional gene mapping analysis linked 146 additional genes. Many of the discovered genetic variants and genes have previously been implicated in cognitive and mental health traits. Through genome-wide polygenic-risk-score prediction, more than 6% of the phenotypic variance (P = 3.13 × 10-24) in four other independent studies could be explained by the UK Biobank GWAS results. In conclusion, our study identifies many new genetic associations at the variant, locus and gene levels and advances our understanding of the pleiotropy and genetic co-architecture between brain volumes and other traits.
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Affiliation(s)
- Bingxin Zhao
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tianyou Luo
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jingwen Zhang
- Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Liuqing Yang
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Fan Zhou
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ziliang Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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Cox S, Ritchie S, Fawns-Ritchie C, Tucker-Drob E, Deary I. Structural brain imaging correlates of general intelligence in UK Biobank. INTELLIGENCE 2019; 76:101376. [PMID: 31787788 PMCID: PMC6876667 DOI: 10.1016/j.intell.2019.101376] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 06/21/2019] [Indexed: 02/06/2023]
Abstract
The associations between indices of brain structure and measured intelligence are unclear. This is partly because the evidence to-date comes from mostly small and heterogeneous studies. Here, we report brain structure-intelligence associations on a large sample from the UK Biobank study. The overall N = 29,004, with N = 18,426 participants providing both brain MRI and at least one cognitive test, and a complete four-test battery with MRI data available in a minimum N = 7201, depending upon the MRI measure. Participants' age range was 44-81 years (M = 63.13, SD = 7.48). A general factor of intelligence (g) was derived from four varied cognitive tests, accounting for one third of the variance in the cognitive test scores. The association between (age- and sex- corrected) total brain volume and a latent factor of general intelligence is r = 0.276, 95% C.I. = [0.252, 0.300]. A model that incorporated multiple global measures of grey and white matter macro- and microstructure accounted for more than double the g variance in older participants compared to those in middle-age (13.6% and 5. 4%, respectively). There were no sex differences in the magnitude of associations between g and total brain volume or other global aspects of brain structure. The largest brain regional correlates of g were volumes of the insula, frontal, anterior/superior and medial temporal, posterior and paracingulate, lateral occipital cortices, thalamic volume, and the white matter microstructure of thalamic and association fibres, and of the forceps minor. Many of these regions exhibited unique contributions to intelligence, and showed highly stable out of sample prediction.
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Affiliation(s)
- S.R. Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, UK
- Department of Psychology, The University of Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - S.J. Ritchie
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
| | - C. Fawns-Ritchie
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, UK
- Department of Psychology, The University of Edinburgh, UK
| | | | - I.J. Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, UK
- Department of Psychology, The University of Edinburgh, UK
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