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van Drunen L, Dobbelaar S, Crone EA, Wierenga LM. Genetic and environmental influences on structural brain development from childhood to adolescence: A longitudinal twin study on cortical thickness, surface area, and subcortical volume. Dev Cogn Neurosci 2024; 68:101407. [PMID: 38870602 PMCID: PMC11225697 DOI: 10.1016/j.dcn.2024.101407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 06/10/2024] [Accepted: 06/10/2024] [Indexed: 06/15/2024] Open
Abstract
The human brain undergoes structural development from childhood to adolescence, with specific regions in the sensorimotor, social, and affective networks continuing to grow into adulthood. While genetic and environmental factors contribute to individual differences in these brain trajectories, the extent remains understudied. Our longitudinal study, utilizing up to three biennial MRI scans (n=485), aimed to assess the genetic and environmental effects on brain structure (age 7) and development (ages 7-14) in these regions. Heritability estimates varied across brain regions, with all regions showing genetic influence (ranging from 18 % to 59 %) with additional shared environmental factors affecting the primary motor cortex (30 %), somatosensory cortex (35 %), DLPFC (5 %), TPJ (17 %), STS (17 %), precuneus (10 %), hippocampus (22 %), amygdala (5 %), and nucleus accumbens (10 %). Surface area was more genetically driven (38 %) than cortical thickness (14 %). Longitudinal brain changes were primarily driven by genetics (ranging from 1 % to 29 %), though shared environment factors (additionally) influenced the somatosensory cortex (11 %), DLPFC (7 %), cerebellum (28 %), TPJ (16 %), STS (20 %), and hippocampus (17 %). These findings highlight the importance of further investigating brain-behavior associations and the influence of enriched and deprived environments from childhood to adolescence. Ultimately, our study can provide insights for interventions aimed at supporting children's development.
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Affiliation(s)
- L van Drunen
- Leiden Consortium of Individual Development (L-CID), the Netherlands; Erasmus University Rotterdam, Social and Behavioral Sciences, the Netherlands; Leiden Institute for Brain and Cognition (LIBC), the Netherlands; Institute of Psychology, Leiden University, the Netherlands.
| | - S Dobbelaar
- Leiden Consortium of Individual Development (L-CID), the Netherlands; Erasmus University Rotterdam, Social and Behavioral Sciences, the Netherlands; Leiden Institute for Brain and Cognition (LIBC), the Netherlands; Institute of Psychology, Leiden University, the Netherlands
| | - E A Crone
- Leiden Consortium of Individual Development (L-CID), the Netherlands; Erasmus University Rotterdam, Social and Behavioral Sciences, the Netherlands; Leiden Institute for Brain and Cognition (LIBC), the Netherlands
| | - L M Wierenga
- Leiden Consortium of Individual Development (L-CID), the Netherlands; Leiden Institute for Brain and Cognition (LIBC), the Netherlands; Institute of Psychology, Leiden University, the Netherlands
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Ding Q, Li X, Rakesh D, Peng S, Xu J, Chen J, Jiang N, Luo Y, Li X, Qin S, Whittle S. The Influence of Maternal and Paternal Parenting on Adolescent Brain Structure. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00171-X. [PMID: 38960280 DOI: 10.1016/j.bpsc.2024.06.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 06/17/2024] [Accepted: 06/19/2024] [Indexed: 07/05/2024]
Abstract
BACKGROUND Adolescents raised in families with different maternal and paternal parenting combinations exhibit variations in neurocognition and psychopathology; however, whether neural differences exist remains unexplored. This study used a longitudinal twin sample to delineate how different parenting combinations influence adolescent brain structure and to elucidate the genetic contribution. METHODS A cohort of 216 twins participated in parenting assessments during early adolescence and underwent magnetic resonance imaging scanning during middle adolescence. We utilized latent profile analysis to distinguish between various maternal and paternal parenting profiles and subsequently investigated their influences on brain anatomy. Biometric analysis was applied to assess genetic influences on brain structure, and associations with internalizing symptoms were explored. RESULTS In early adolescence, 4 parenting profiles emerged, which were characterized by levels of harshness and hostility in one or both parents. Compared with adolescents in "catparent" families (low harshness/hostility in both parents), those raised in "tigermom" families (harsh/hostile mother only) exhibited a smaller nucleus accumbens volume and larger temporal cortex surface area; those in "tigerdad" families demonstrated larger thalamus volumes; and those in "tigerparent" families displayed smaller volumes in the midanterior corpus callosum. Genetic risk factors contributed significantly to the observed brain structural heterogeneity and internalizing symptoms. However, the influences of parenting profiles and brain structure on internalizing symptoms were not significant. CONCLUSIONS The findings underscore distinct brain structural features linked to maternal and paternal parenting combinations, particularly in terms of subcortical volume and cortical surface area. This study suggests an interdependent role of maternal and paternal parenting in shaping adolescent neurodevelopment.
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Affiliation(s)
- Qingwen Ding
- Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xinying Li
- Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
| | - Divyangana Rakesh
- Neuroimaging Department, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, United Kingdom
| | - Siya Peng
- IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Jiahua Xu
- Psychiatry Research Center, Beijing Huilongguan Hospital, Peking University Huilonguan Clinical Medical School, Beijing, China
| | - Jie Chen
- Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Nengzhi Jiang
- School of Psychology, Shandong Second Medical University, Weifang, Shandong, China
| | - Yu Luo
- Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xuebing Li
- Chinese Academy of Sciences Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Shaozheng Qin
- IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Sarah Whittle
- Centre for Youth Mental Health, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia; Orygen, Parkville, Victoria, Australia
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Pine JG, Agrawal A, Bogdan R, Kandala S, Cooper S, Barch DM. Shared and unique heritability of hippocampal subregion volumes in children and adults. Neuroimage 2024; 285:120471. [PMID: 38007188 DOI: 10.1016/j.neuroimage.2023.120471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 11/16/2023] [Accepted: 11/20/2023] [Indexed: 11/27/2023] Open
Abstract
Behavioral genetic analyses have not demonstrated robust, unique, genetic correlates of hippocampal subregion volume. Genetic differentiation of hippocampal longitudinal axis subregion volume has not yet been investigated in population-based samples, although this has been demonstrated in rodent and post-mortem human tissue work. The following study is the first population-based investigation of genetic factors that contribute to gray matter volume along the hippocampal longitudinal axis. Twin-based biometric analyses demonstrated that longitudinal axis subregions are associated with significant, unique, genetic variance, and that longitudinal axis subregions are also associated with significant shared, hippocampus-general, genetic factors. Our study's findings suggest that genetic differences in hippocampal longitudinal axis structure can be detected in individual differences in gray matter volume in population-level research designs.
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Affiliation(s)
- Jacob G Pine
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, United States of America.
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Ryan Bogdan
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, United States of America
| | - Sridhar Kandala
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Shelly Cooper
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, United States of America
| | - Deanna M Barch
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, United States of America; Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
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Watts R, Rader L, Grant J, Filippi CG. Genetic and Environmental Contributions to Subcortical Gray Matter Microstructure and Volume in the Developing Brain. Behav Genet 2023; 53:208-218. [PMID: 37129746 PMCID: PMC10154259 DOI: 10.1007/s10519-023-10142-1] [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] [Accepted: 04/12/2023] [Indexed: 05/03/2023]
Abstract
Using baseline (ages 9-10) and two-year follow-up (ages 11-12) data from monozygotic and dizygotic twins enrolled in the longitudinal Adolescent Brain Cognitive DevelopmentSM Study, we investigated the genetic and environmental contributions to microstructure and volume of nine subcortical gray matter regions. Microstructure was assessed using diffusion MRI data analyzed using restriction spectrum imaging (RSI) and diffusion tensor imaging (DTI) models. The highest heritability estimates (estimate [95% confidence interval]) for microstructure were found using the RSI model in the pallidum (baseline: 0.859 [0.818, 0.889], follow-up: 0.835 [0.787, 0.871]), putamen (baseline: 0.859 [0.819, 0.889], follow-up: 0.874 [0.838, 0.902]), and thalamus (baseline: 0.855 [0.814, 0.887], follow-up: 0.819 [0.769, 0.857]). For volumes the corresponding regions were the caudate (baseline: 0.831 [0.688, 0.992], follow-up: 0.848 [0.701, 1.011]) and putamen (baseline: 0.906 [0.875, 0.914], follow-up: 0.906 [0.885, 0.923]). The subcortical regions displayed high genetic stability (rA = 0.743-1.000) across time and exhibited unique environmental correlations (rE = 0.194-0.610). Individual differences in both gray matter microstructure and volumes can be largely explained by additive genetic effects in this sample.
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Affiliation(s)
- Richard Watts
- Department of Psychology, Yale University, 2 Hillhouse Avenue, New Haven, CT, 06520, USA.
| | - Lydia Rader
- Institute for Behavioral Genetics, Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Justin Grant
- Department of Radiology, Tufts University School of Medicine, Boston, MA, USA
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Ma Q, Wang H, Rolls ET, Xiang S, Li J, Li Y, Zhou Q, Cheng W, Li F. Lower gestational age is associated with lower cortical volume and cognitive and educational performance in adolescence. BMC Med 2022; 20:424. [PMID: 36329481 PMCID: PMC9635194 DOI: 10.1186/s12916-022-02627-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Gestational age (GA) is associated with later cognition and behavior. However, it is unclear how specific cognitive domains and brain structural development varies with the stepwise change of gestational duration. METHODS This large-scale longitudinal cohort study analyzed 11,878 early adolescents' brain volume maps at 9-10 years (baseline) and 5685 at 11-12 years (a 2-year follow-up) from the Adolescent Brain Cognitive Development (ABCD) study. According to gestational age, adolescents were divided into five categorical groups: ≤ 33 weeks, 34-35 weeks, 36 weeks, 37-39 weeks, and ≥ 40 weeks. The NIH Toolbox was used to estimate neurocognitive performance, including crystallized and fluid intelligence, which was measured for 11,878 adolescents at baseline with crystallized intelligence and relevant subscales obtained at 2-year follow-up (with participant numbers ranging from 6185 to 6310 depending on the cognitive domain). An additional large population-based cohort of 618,070 middle adolescents at ninth-grade (15-16 years) from the Danish national register was utilized to validate the association between gestational age and academic achievements. A linear mixed model was used to examine the group differences between gestational age and neurocognitive performance, school achievements, and grey matter volume. A mediation analysis was performed to examine whether brain structural volumes mediated the association between GA and neurocognition, followed with a longitudinal analysis to track the changes. RESULTS Significant group differences were found in all neurocognitive scores, school achievements, and twenty-five cortical regional volumes (P < 0.05, Bonferroni corrected). Specifically, lower gestational ages were associated with graded lower cognition and school achievements and with smaller brain volumes of the fronto-parieto-temporal, fusiform, cingulate, insula, postcentral, hippocampal, thalamic, and pallidal regions. These lower brain volumes mediated the association between gestational age and cognitive function (P = 1 × 10-8, β = 0.017, 95% CI: 0.007-0.028). Longitudinal analysis showed that compared to full term adolescents, preterm adolescents still had smaller brain volumes and crystallized intelligence scores at 11-12 years. CONCLUSIONS These results emphasize the relationships between gestational age at birth and adolescents' lower brain volume, and lower cognitive and educational performance, measured many years later when 9-10 and 11-12 years old. The study indicates the importance of early screening and close follow-up for neurocognitive and behavioral development for children and adolescents born with gestational ages that are even a little lower than full term.
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Affiliation(s)
- Qing Ma
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, 200433, China
| | - Hui Wang
- Department of Developmental and Behavioral Pediatric & Child Primary Care/MOE-Shanghai Key Laboratory of Children's Environmental Health, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200082, China
| | - Edmund T Rolls
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, Conventry, UK.,Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Shitong Xiang
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, 200433, China
| | - Jiong Li
- Department of Clinical Medicine, Aarhus University, Aarhus, 8000, Denmark
| | - Yuzhu Li
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, 200433, China
| | - Qiongjie Zhou
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, 200011, China.,Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai, 200011, China
| | - Wei Cheng
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China. .,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, 200433, China. .,Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, 321004, China. .,Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Shanghai, 200032, China.
| | - Fei Li
- Department of Developmental and Behavioral Pediatric & Child Primary Care/MOE-Shanghai Key Laboratory of Children's Environmental Health, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200082, China.
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Li CS, Chen Y, Ide JS. Gray matter volumetric correlates of attention deficit and hyperactivity traits in emerging adolescents. Sci Rep 2022; 12:11367. [PMID: 35790754 PMCID: PMC9256746 DOI: 10.1038/s41598-022-15124-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 06/20/2022] [Indexed: 11/08/2022] Open
Abstract
Previous research has demonstrated reduction in cortical and subcortical, including basal ganglia (BG), gray matter volumes (GMV) in individuals with attention deficit hyperactivity disorder (ADHD), a neurodevelopmental condition that is more prevalent in males than in females. However, the volumetric deficits vary across studies. Whether volumetric reductions are more significant in males than females; to what extent these neural markers are heritable and relate to cognitive dysfunction in ADHD remain unclear. To address these questions, we followed published routines and performed voxel-based morphometry analysis of a data set (n = 11,502; 5,464 girls, 9-10 years) curated from the Adolescent Brain Cognition Development project, a population-based study of typically developing children. Of the sample, 634 and 2,826 were identified as monozygotic twins and dizygotic twins/siblings, respectively. In linear regressions, a cluster in the hypothalamus showed larger GMV, and bilateral caudate and putamen, lateral orbitofrontal and occipital cortex showed smaller GMVs, in correlation with higher ADHD scores in girls and boys combined. When examined separately, boys relative to girls showed more widespread (including BG) and stronger associations between GMV deficits and ADHD scores. ADHD traits and the volumetric correlates demonstrated heritability estimates (a2) between 0.59 and 0.79, replicating prior findings of the genetic basis of ADHD. Further, ADHD traits and the volumetric correlates (except for the hypothalamus) were each negatively and positively correlated with N-back performance. Together, these findings confirm volumetric deficits in children with more prominent ADHD traits. Highly heritable in both girls and boys and potentially more significant in boys than in girls, the structural deficits underlie diminished capacity in working memory and potentially other cognitive deficits in ADHD.
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Affiliation(s)
- Clara S Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA
- Smith College, Northampton, MA, 06492, USA
| | - Yu Chen
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA.
| | - Jaime S Ide
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA.
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Teeuw J, Klein M, Mota NR, Brouwer RM, van ‘t Ent D, Al-Hassaan Z, Franke B, Boomsma DI, Hulshoff Pol HE. Multivariate Genetic Structure of Externalizing Behavior and Structural Brain Development in a Longitudinal Adolescent Twin Sample. Int J Mol Sci 2022; 23:ijms23063176. [PMID: 35328598 PMCID: PMC8949114 DOI: 10.3390/ijms23063176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/10/2022] [Accepted: 03/10/2022] [Indexed: 12/10/2022] Open
Abstract
Externalizing behavior in its more extreme form is often considered a problem to the individual, their families, teachers, and society as a whole. Several brain structures have been linked to externalizing behavior and such associations may arise if the (co)development of externalizing behavior and brain structures share the same genetic and/or environmental factor(s). We assessed externalizing behavior with the Child Behavior Checklist and Youth Self Report, and the brain volumes and white matter integrity (fractional anisotropy [FA] and mean diffusivity [MD]) with magnetic resonance imaging in the BrainSCALE cohort, which consisted of twins and their older siblings from 112 families measured longitudinally at ages 10, 13, and 18 years for the twins. Genetic covariance modeling based on the classical twin design, extended to also include siblings of twins, showed that genes influence externalizing behavior and changes therein (h2 up to 88%). More pronounced externalizing behavior was associated with higher FA (observed correlation rph up to +0.20) and lower MD (rph up to −0.20), with sizeable genetic correlations (FA ra up to +0.42; MD ra up to −0.33). The cortical gray matter (CGM; rph up to −0.20) and cerebral white matter (CWM; rph up to +0.20) volume were phenotypically but not genetically associated with externalizing behavior. These results suggest a potential mediating role for global brain structures in the display of externalizing behavior during adolescence that are both partially explained by the influence of the same genetic factor.
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Affiliation(s)
- Jalmar Teeuw
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands; (R.M.B.); (Z.A.-H.); (H.E.H.P.)
- Correspondence: ; Tel.: +31-(088)-75-53-387
| | - Marieke Klein
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA;
- Department of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands; (N.R.M.); (B.F.)
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 XZ Nijmegen, The Netherlands
| | - Nina Roth Mota
- Department of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands; (N.R.M.); (B.F.)
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 XZ Nijmegen, The Netherlands
| | - Rachel M. Brouwer
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands; (R.M.B.); (Z.A.-H.); (H.E.H.P.)
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Dennis van ‘t Ent
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands; (D.v.‘t.E.); (D.I.B.)
| | - Zyneb Al-Hassaan
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands; (R.M.B.); (Z.A.-H.); (H.E.H.P.)
| | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands; (N.R.M.); (B.F.)
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 XZ Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Dorret I. Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands; (D.v.‘t.E.); (D.I.B.)
- Amsterdam Public Health (APH) Research Institute, 1081 BT Amsterdam, The Netherlands
| | - Hilleke E. Hulshoff Pol
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands; (R.M.B.); (Z.A.-H.); (H.E.H.P.)
- Department of Psychology, Utrecht University, 3584 CS Utrecht, The Netherlands
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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: 9] [Impact Index Per Article: 4.5] [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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9
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Keresztes A, Raffington L, Bender AR, Bögl K, Heim C, Shing YL. Longitudinal Developmental Trajectories Do Not Follow Cross-Sectional Age Associations in Hippocampal Subfield and Memory Development. Dev Cogn Neurosci 2022; 54:101085. [PMID: 35278767 PMCID: PMC8917271 DOI: 10.1016/j.dcn.2022.101085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 02/10/2022] [Accepted: 02/11/2022] [Indexed: 11/03/2022] Open
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Dispositional Negative Emotionality in Childhood and Adolescence Predicts Structural Variation in the Amygdala and Caudal Anterior Cingulate During Early Adulthood: Theoretically and Empirically Based Tests. Res Child Adolesc Psychopathol 2021; 49:1275-1288. [PMID: 33871795 DOI: 10.1007/s10802-021-00811-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/15/2021] [Indexed: 10/21/2022]
Abstract
Substantial evidence implicates the amygdala and related structures in the processing of negative emotions. Furthermore, neuroimaging evidence suggests that variations in amygdala volumes are related to trait-like individual differences in neuroticism/negative emotionality, although many questions remain about the nature of such associations. We conducted planned tests of the directional prediction that dispositional negative emotionality measured at 10-17 years using parent and youth ratings on the Child and Adolescent Dispositions Scale (CADS) would predict larger volumes of the amygdala in adulthood and conducted exploratory tests of associations with other regions implicated in emotion processing. Participants were 433 twins strategically selected for neuroimaging during wave 2 from wave 1 of the Tennessee Twins Study (TTS) by oversampling on internalizing and/or externalizing psychopathology risk. Controlling for age, sex, race-ethnicity, handedness, scanner, and total brain volume, youth-rated negative emotionality positively predicted bilateral amygdala volumes after correction for multiple testing. Each unit difference of one standard deviation (SD) in negative emotionality was associated with a .12 SD unit difference in larger volumes of both amygdalae. Parent-rated negative emotionality predicted greater thickness of the left caudal/dorsal anterior cingulate cortex (β = 0.28). Associations of brain structure with negative emotionality were not moderated by sex. These results are striking because dispositions assessed at 10-17 years of age were predictive of grey matter volumes measured 12-13 years later in adulthood. Future longitudinal studies should examine the timing of amygdala/cingulate associations with dispositional negative emotionality to determine when these associations emerge during development.
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Brain Volume Abnormalities in Youth at High Risk for Depression: Adolescent Brain and Cognitive Development Study. J Am Acad Child Adolesc Psychiatry 2020; 59:1178-1188. [PMID: 31634568 PMCID: PMC7165045 DOI: 10.1016/j.jaac.2019.09.032] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Revised: 09/16/2019] [Accepted: 10/10/2019] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Children of parents with depression are two to three times more likely to develop major depressive disorder than children without parental history; however, subcortical brain volume abnormalities characterizing major depressive disorder risk remain unclear. The Adolescent Brain and Cognitive Development (ABCD) Study provides an opportunity to identify subcortical differences associated with parental depressive history. METHOD Structural magnetic resonance data were acquired from 9- and 10-year-old children (N = 11,876; release 1.1, n = 4,521; release 2.0.1, n = 7,355). Approximately one-third of the children had a parental depressive history, providing sufficient power to test differences in subcortical brain volume between low- and high-risk youths. Children from release 1.1 were examined as a discovery sample, and we sought to replicate effects in release 2.0.1. Secondary analyses tested group differences in the prevalence of depressive disorders and clarified whether subcortical brain differences were present in youths with a lifetime depressive disorder history. RESULTS Parental depressive history was related to smaller right putamen volume in the discovery (release 1.1; d = -0.10) and replication (release 2.0.1; d = -0.10) samples. However, in release 1.1, this effect was driven by maternal depressive history (d = -0.14), whereas in release 2.0.1, paternal depressive history showed a stronger relationship with putamen volume (d = -0.09). Furthermore, high-risk children exhibited a near twofold greater occurrence of depressive disorders relative to low-risk youths (maternal history odds ratio =1.99; paternal history odds ratio = 1.45), but youths with a lifetime depressive history did not exhibit significant subcortical abnormalities. CONCLUSION A parental depressive history was associated with smaller putamen volume, which may affect reward learning processes that confer increased risk for major depressive disorder.
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Bas-Hoogendam JM, van Steenbergen H, van der Wee NJA, Westenberg PM. Amygdala hyperreactivity to faces conditioned with a social-evaluative meaning- a multiplex, multigenerational fMRI study on social anxiety endophenotypes. NEUROIMAGE-CLINICAL 2020; 26:102247. [PMID: 32247196 PMCID: PMC7125356 DOI: 10.1016/j.nicl.2020.102247] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 03/12/2020] [Accepted: 03/14/2020] [Indexed: 12/31/2022]
Abstract
Social anxiety disorder (SAD) runs in families, but the neurobiological pathways underlying the genetic susceptibility towards SAD are largely unknown. Here, we employed an endophenotype approach, and tested the hypothesis that amygdala hyperreactivity to faces conditioned with a social-evaluative meaning is a candidate SAD endophenotype. We used data from the multiplex, multigenerational Leiden Family Lab study on Social Anxiety Disorder (eight families, n = 105) and investigated amygdala activation during a social-evaluative conditioning paradigm with high ecological validity in the context of SAD. Three neutral faces were repeatedly presented in combination with socially negative, positive or neutral sentences. We focused on two endophenotype criteria: co-segregation of the candidate endophenotype with the disorder within families, and heritability. Analyses of the fMRI data were restricted to the amygdala as a region of interest, and association analyses revealed that bilateral amygdala hyperreactivity in response to the conditioned faces co-segregated with social anxiety (SA; continuous measure) within the families; we found, however, no relationship between SA and brain activation in response to more specific fMRI contrasts. Furthermore, brain activation in a small subset of voxels within these amygdala clusters was at least moderately heritable. Taken together, these findings show that amygdala engagement in response to conditioned faces with a social-evaluative meaning qualifies as a neurobiological candidate endophenotype of social anxiety. Thereby, these data shed light on the genetic vulnerability to develop SAD.
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Affiliation(s)
- Janna Marie Bas-Hoogendam
- Institute of Psychology, Leiden University, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands; Department of Psychiatry, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden, The Netherlands.
| | - Henk van Steenbergen
- Institute of Psychology, Leiden University, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden, The Netherlands.
| | - Nic J A van der Wee
- Department of Psychiatry, Leiden University Medical Center, Albinusdreef 2, 2333 ZA Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden, The Netherlands.
| | - P Michiel Westenberg
- Institute of Psychology, Leiden University, Wassenaarseweg 52, 2333 AK Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden, The Netherlands.
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13
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Maggioni E, Squarcina L, Dusi N, Diwadkar VA, Brambilla P. Twin MRI studies on genetic and environmental determinants of brain morphology and function in the early lifespan. Neurosci Biobehav Rev 2020; 109:139-149. [PMID: 31911159 DOI: 10.1016/j.neubiorev.2020.01.003] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 12/19/2019] [Accepted: 01/02/2020] [Indexed: 02/04/2023]
Abstract
Neurodevelopment represents a period of increased opportunity and vulnerability, during which a complex confluence of genetic and environmental factors influences brain growth trajectories, cognitive and mental health outcomes. Recently, magnetic resonance imaging (MRI) studies on twins have increased our knowledge of the extent to which genes, the environment and their interactions shape inter-individual brain variability. The present review draws from highly salient MRI studies in young twin samples to provide a robust assessment of the heritability of structural and functional brain changes during development. The available studies suggest that (as with many other traits), global brain morphology and network organization are highly heritable from early childhood to young adulthood. Conversely, genetic correlations among brain regions exhibit heterogeneous trajectories, and this heterogeneity reflects the progressive, experience-related increase in brain network complexity. Studies also support the key role of environment in mediating brain network differentiation via changes of genetic expression and hormonal levels. Thus, rest- and task-related functional brain circuits seem to result from a contextual and dynamic expression of heritability.
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Affiliation(s)
- Eleonora Maggioni
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, via F. Sforza 28, Milano, Italy
| | - Letizia Squarcina
- Child Psychopathology Unit, Scientific Institute, IRCCS Eugenio Medea, via Don Luigi Monza 20, Bosisio Parini, LC, Italy
| | - Nicola Dusi
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, via F. Sforza 28, Milano, Italy
| | - Vaibhav A Diwadkar
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University, 42 W Warren Ave, Detroit, MI, United States
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, via F. Sforza 28, Milano, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
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14
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Zheng D, Chen J, Wang X, Zhou Y. Genetic contribution to the phenotypic correlation between trait impulsivity and resting-state functional connectivity of the amygdala and its subregions. Neuroimage 2019; 201:115997. [PMID: 31284029 DOI: 10.1016/j.neuroimage.2019.07.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 06/23/2019] [Accepted: 07/04/2019] [Indexed: 11/30/2022] Open
Abstract
Trait impulsivity, a predisposition to respond to stimuli without regard for the potentially negative consequences, contributes to many maladaptive behaviors. Studies have shown that both genetic factors and interregional functional interactions underlie trait impulsivity. However, whether common genes contribute to both trait impulsivity and its neural basis is still unknown. This study investigated the phenotypic correlations between trait impulsivity and the resting-state functional connectivity (rsFC) of the amygdala as well as its subregions and the genetic contribution to the phenotypic correlations. By recruiting a sample of 292 twins in late adolescence and young adulthood, we found that trait impulsivity was positively correlated with the rsFC between the left full amygdala and the right dorsolateral prefrontal cortex (DLPFC). Further analyses on the subregions of the amygdala showed that trait impulsivity was positively correlated with the rsFCs between the left basolateral (BL) amygdala and both the right DLPFC and the right inferior frontal gyrus and with the rsFCs between the right superficial (SF) amygdala and both the dorsal anterior cingulate cortex and right anterior insula. Bivariate genetic modelling analyses found genetic overlaps between trait impulsivity and the rsFC of the left full amygdala or the left BL amygdala with the right DLPFC. The proportions of phenotypic associations accounted for by overlapping genes were 82% and 60%, respectively. These results provide evidence for the genetic overlap between trait impulsivity and the intrinsic brain functional connectivity centered at the amygdala and especially at its BL subregion.
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Affiliation(s)
- Dang Zheng
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, PR China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, PR China; Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, PR China
| | - Jie Chen
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, PR China; CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, 100101, PR China
| | - Xiaoming Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, PR China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, PR China
| | - Yuan Zhou
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, 100101, PR China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, PR China; Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, PR China; The National Clinical Research Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, 100088, China.
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15
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Kennedy JT, Astafiev SV, Golosheykin S, Korucuoglu O, Anokhin AP. Shared genetic influences on adolescent body mass index and brain structure: A voxel-based morphometry study in twins. Neuroimage 2019; 199:261-272. [PMID: 31163268 DOI: 10.1016/j.neuroimage.2019.05.053] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2019] [Revised: 05/17/2019] [Accepted: 05/19/2019] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Previous research has demonstrated significant relationships between obesity and brain structure. Both phenotypes are heritable, but it is not known whether they are influenced by common genetic factors. We investigated the genetic etiology of the relationship between individual variability in brain morphology and BMIz using structural MRI in adolescent twins. METHOD The sample (n = 258) consisted of 54 monozygotic and 75 dizygotic twin pairs (mean(SD) age = 13.61(0.505), BMIz = 0.608(1.013). Brain structure (volume and density of gray and white matter) was assessed using VBM. Significant voxelwise heritability of brain structure was established using the Accelerated Permutation inference for ACE models (APACE) program, with structural heritability varying from 15 to 97%, depending on region. Bivariate heritability analyses were carried out comparing additive genetic and unique environment models with and without shared genetics on BMIz and the voxels showing significant heritability in the APACE analyses. RESULTS BMIz was positively related to gray matter volume in the brainstem and thalamus and negatively related to gray matter volume in the bilateral uncus and medial orbitofrontal cortex, gray matter density in the cerebellum, prefrontal lobe, temporal lobe, and limbic system, and white matter density in the brainstem. Bivariate heritability analyses showed that BMIz and brain structure share ∼1/3 of their genes and that ∼95% of the phenotypic correlation between BMIz and brain structure is due to shared additive genetic influences. These regions included areas related to decision-making, motivation, liking vs. wanting, taste, interoception, reward processing/learning, caloric evaluation, and inhibition. CONCLUSION These results suggested genetic factors are responsible for the relationship between BMIz and heritable BMIz related brain structure in areas related to eating behavior.
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Affiliation(s)
- James T Kennedy
- Department of Psychiatry, Washington University School of Medicine, United States.
| | - Serguei V Astafiev
- Department of Psychiatry, Washington University School of Medicine, United States
| | - Semyon Golosheykin
- Department of Psychiatry, Washington University School of Medicine, United States
| | - Ozlem Korucuoglu
- Department of Psychiatry, Washington University School of Medicine, United States
| | - Andrey P Anokhin
- Department of Psychiatry, Washington University School of Medicine, United States
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Catharine VL, Helena V, Ellen D, Guy V, Karel D, Karen C. Exploration of gray matter correlates of cognitive training benefit in adolescents with chronic traumatic brain injury. Neuroimage Clin 2019; 23:101827. [PMID: 31005776 PMCID: PMC6477162 DOI: 10.1016/j.nicl.2019.101827] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 03/19/2019] [Accepted: 04/13/2019] [Indexed: 12/23/2022]
Abstract
Sustaining a traumatic brain injury (TBI) during adolescence has a profound effect on brain development and can result in persistent executive functioning deficits in daily life. Cognitive recovery from pediatric-TBI relies on the potential of neuroplasticity, which can be fostered by restorative training-programs. However the structural mechanisms underlying cognitive recovery in the immature brain are poorly understood. This study investigated gray matter plasticity following 2 months of cognitive training in young patients with TBI. Sixteen adolescents in the chronic stage of moderate-severe-TBI (9 male, mean age = 15y8m ± 1y7m) were enrolled in a cognitive computerized training program for 8 weeks (5 times/week, 40 min/session). Pre-and post-intervention, and 6 months after completion of the training, participants underwent a comprehensive neurocognitive test-battery and anatomical Magnetic Resonance Imaging scans. We selected 9 cortical-subcortical Regions-Of-Interest associated with Executive Functioning (EF-ROIs) and 3 control regions from the Desikan-Killiany atlas. Baseline analyses showed significant decreased gray matter density in the superior frontal gyri p = 0.033, superior parietal gyri p = 0.015 and thalamus p = 0.006 in adolescents with TBI compared to age and gender matched controls. Linear mixed model analyses of longitudinal volumetric data of the EF-ROI revealed no strong evidence of training-related changes in the group with TBI. However, compared to the change over time in the control regions between post-intervention and 6 months follow-up, the change in the EF-ROIs showed a significant difference. Exploratory analyses revealed a negative correlation between the change on the Digit Symbol Substitution test and the change in volume of the putamen (r = -0.596, p = 0.015). This preliminary study contributes to the insights of training-related plasticity mechanisms after pediatric-TBI.
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Affiliation(s)
- Vander Linden Catharine
- Ghent University Hospital, Child Rehabilitation Centre K7, Corneel Heymanslaan 10, 9000 Ghent, Belgium.
| | - Verhelst Helena
- Ghent University, Department of Experimental Psychology, Faculty of Psychology and Educational Sciences, Henri Dunantlaan 2, 9000 Ghent, Belgium.
| | - Deschepper Ellen
- Ghent University, Biostatistics Unit, Department of Public Health, Corneel Heymanslaan 10, 9000 Ghent, Belgium.
| | - Vingerhoets Guy
- Ghent University, Department of Experimental Psychology, Faculty of Psychology and Educational Sciences, Henri Dunantlaan 2, 9000 Ghent, Belgium.
| | - Deblaere Karel
- Ghent University Hospital, Department of Neuroradiology, Corneel Heymanslaan 10, 9000 Ghent, Belgium.
| | - Caeyenberghs Karen
- Australian Catholic University, Mary McKillop Institute for Health Research Level 5, 215 Spring Street, Melbourne, VIC 3000, Australia.
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17
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Luo Q, Chen Q, Wang W, Desrivières S, Quinlan EB, Jia T, Macare C, Robert GH, Cui J, Guedj M, Palaniyappan L, Kherif F, Banaschewski T, Bokde ALW, Büchel C, Flor H, Frouin V, Garavan H, Gowland P, Heinz A, Ittermann B, Martinot JL, Artiges E, Paillère-Martinot ML, Nees F, Orfanos DP, Poustka L, Fröhner JH, Smolka MN, Walter H, Whelan R, Callicott JH, Mattay VS, Pausova Z, Dartigues JF, Tzourio C, Crivello F, Berman KF, Li F, Paus T, Weinberger DR, Murray RM, Schumann G, Feng J. Association of a Schizophrenia-Risk Nonsynonymous Variant With Putamen Volume in Adolescents: A Voxelwise and Genome-Wide Association Study. JAMA Psychiatry 2019; 76:435-445. [PMID: 30649180 PMCID: PMC6450291 DOI: 10.1001/jamapsychiatry.2018.4126] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Accepted: 10/16/2018] [Indexed: 02/03/2023]
Abstract
Importance Deviation from normal adolescent brain development precedes manifestations of many major psychiatric symptoms. Such altered developmental trajectories in adolescents may be linked to genetic risk for psychopathology. Objective To identify genetic variants associated with adolescent brain structure and explore psychopathologic relevance of such associations. Design, Setting, and Participants Voxelwise genome-wide association study in a cohort of healthy adolescents aged 14 years and validation of the findings using 4 independent samples across the life span with allele-specific expression analysis of top hits. Group comparison of the identified gene-brain association among patients with schizophrenia, unaffected siblings, and healthy control individuals. This was a population-based, multicenter study combined with a clinical sample that included participants from the IMAGEN cohort, Saguenay Youth Study, Three-City Study, and Lieber Institute for Brain Development sample cohorts and UK biobank who were assessed for both brain imaging and genetic sequencing. Clinical samples included patients with schizophrenia and unaffected siblings of patients from the Lieber Institute for Brain Development study. Data were analyzed between October 2015 and April 2018. Main Outcomes and Measures Gray matter volume was assessed by neuroimaging and genetic variants were genotyped by Illumina BeadChip. Results The discovery sample included 1721 adolescents (873 girls [50.7%]), with a mean (SD) age of 14.44 (0.41) years. The replication samples consisted of 8690 healthy adults (4497 women [51.8%]) from 4 independent studies across the life span. A nonsynonymous genetic variant (minor T allele of rs13107325 in SLC39A8, a gene implicated in schizophrenia) was associated with greater gray matter volume of the putamen (variance explained of 4.21% in the left hemisphere; 8.66; 95% CI, 6.59-10.81; P = 5.35 × 10-18; and 4.44% in the right hemisphere; t = 8.90; 95% CI, 6.75-11.19; P = 6.80 × 10-19) and also with a lower gene expression of SLC39A8 specifically in the putamen (t127 = -3.87; P = 1.70 × 10-4). The identified association was validated in samples across the life span but was significantly weakened in both patients with schizophrenia (z = -3.05; P = .002; n = 157) and unaffected siblings (z = -2.08; P = .04; n = 149). Conclusions and Relevance Our results show that a missense mutation in gene SLC39A8 is associated with larger gray matter volume in the putamen and that this association is significantly weakened in schizophrenia. These results may suggest a role for aberrant ion transport in the etiology of psychosis and provide a target for preemptive developmental interventions aimed at restoring the functional effect of this mutation.
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Affiliation(s)
- Qiang Luo
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China
- School of Life Sciences and State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, China
- Centre for Population Neuroscience and Precision Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, Social Genetic and Developmental Psychiatry Centre, London, England
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland
| | - Wenjia Wang
- Pharnext, Issy-les-Moulineaux, Ile de France, France
- Institut National de la Santé et de la Recherche Médicale Unit 897, University of Bordeaux, Bordeaux, Aquitaine, France
| | - Sylvane Desrivières
- Centre for Population Neuroscience and Precision Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, Social Genetic and Developmental Psychiatry Centre, London, England
| | - Erin Burke Quinlan
- Centre for Population Neuroscience and Precision Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, Social Genetic and Developmental Psychiatry Centre, London, England
| | - Tianye Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Centre for Population Neuroscience and Precision Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, Social Genetic and Developmental Psychiatry Centre, London, England
| | - Christine Macare
- Centre for Population Neuroscience and Precision Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, Social Genetic and Developmental Psychiatry Centre, London, England
| | - Gabriel H. Robert
- EA 4712 “Behavior and Basal Ganglia,” Rennes University 1, Rennes, France
| | - Jing Cui
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Mickaël Guedj
- Pharnext, Issy-les-Moulineaux, Ile de France, France
| | - Lena Palaniyappan
- Departments of Psychiatry and Medical Biophysics, Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Lausanne, Switzerland
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
| | - Arun L. W. Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | | | - Herta Flor
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany
| | - Vincent Frouin
- NeuroSpin, Commissariat à L'énergie Atomique, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, England
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité, Universitätsmedizin Berlin, Germany
| | - Bernd Ittermann
- Physikalisch-Technische Bundesanstalt Braunschweig and Berlin, Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale Unit 1000, Neuroimaging and Psychiatry, University Paris Sud–Paris Saclay, University Paris Descartes, Paris, France
- Service Hospitalier Frédéric Joliot, Orsay, France
- Maison de Solenn, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale Unit 1000, Neuroimaging and Psychiatry, University Paris Sud–Paris Saclay, University Paris Descartes, Paris, France
- Service Hospitalier Frédéric Joliot, Orsay, France
- GH Nord Essonne Psychiatry Department, Orsay, France
| | - Marie-Laure Paillère-Martinot
- Institut National de la Santé et de la Recherche Médicale Unit 1000, Neuroimaging and Psychiatry, University Paris Sud–Paris Saclay, University Paris Descartes, Paris, France
- Assistance Publique–Hôpitaux de Paris, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | | | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Göttingen, Germany
- Clinic for Child and Adolescent Psychiatry, Medical University of Vienna, Währinger Gürtel, Vienna, Austria
| | - Juliane H. Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N. Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité, Universitätsmedizin Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Joseph H. Callicott
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Venkata S. Mattay
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland
- Departments of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Departments of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Zdenka Pausova
- The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
| | - Jean-François Dartigues
- Institut National de la Santé et de la Recherche Médicale Unit 1219, Université de Bordeaux, Bordeaux, France
| | - Christophe Tzourio
- Institut National de la Santé et de la Recherche Médicale Unit 1219, Université de Bordeaux, Bordeaux, France
| | - Fabrice Crivello
- University de Bordeaux, Institut des Maladies Neurodégénératives, Bordeaux, France
- Centre National de la Recherche Scientifique, Institut des Maladies Neurodégénératives, Bordeaux, France
- Commissariat à L'énergie Atomiquecea, Institut des Maladies Neurodégénératives-Equipe 5, Bordeaux, France
| | - Karen F. Berman
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Fei Li
- Developmental and Behavioral Pediatric Department and Child Primary Care Department, MOE-Shanghai Key Lab for Children's Environmental Health, Xinhua Hospital Affiliated To Shang Jiaotong University School of Medicine, Shanghai, China
| | - Tomáš Paus
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Departments of Psychology and Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Daniel R. Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland
- Departments of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- McKusick Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Robin M. Murray
- Centre for Population Neuroscience and Precision Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, Social Genetic and Developmental Psychiatry Centre, London, England
| | - Gunter Schumann
- Centre for Population Neuroscience and Precision Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, Social Genetic and Developmental Psychiatry Centre, London, England
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, China
- School of Life Sciences and State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, England
- Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
- Shanghai Center for Mathematical Sciences, Shanghai, China
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18
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Bernier A, Dégeilh F, Leblanc É, Daneault V, Bailey HN, Beauchamp MH. Mother-Infant Interaction and Child Brain Morphology: A Multidimensional Approach to Maternal Sensitivity. INFANCY 2018; 24:120-138. [DOI: 10.1111/infa.12270] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 09/12/2018] [Accepted: 09/17/2018] [Indexed: 12/29/2022]
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19
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Tamnes CK, Bos MGN, van de Kamp FC, Peters S, Crone EA. Longitudinal development of hippocampal subregions from childhood to adulthood. Dev Cogn Neurosci 2018; 30:212-222. [PMID: 29597156 PMCID: PMC5945606 DOI: 10.1016/j.dcn.2018.03.009] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Revised: 03/20/2018] [Accepted: 03/20/2018] [Indexed: 11/19/2022] Open
Abstract
Detailed descriptions of the development of the hippocampus promise to shed light on the neural foundation of development of memory and other cognitive functions, as well as the emergence of major mental disorders. Hippocampus is a heterogeneous structure with a well characterized internal complexity, but development of its distinct subregions in humans has remained poorly described. We analyzed magnetic resonance imaging (MRI) data from a large longitudinal sample (270 participants, 678 scans) using an automated segmentation tool and mixed models to delineate the development of hippocampal subregion volumes from childhood to adulthood. We also examined sex differences in subregion volumes and their development, and associations between hippocampal subregions and general cognitive ability. Nonlinear developmental trajectories with early volume increases were observed for subiculum, cornu ammonis (CA) 1, molecular layer (ML) and fimbria. In contrast, parasubiculum, presubiculum, CA2/3, CA4 and the granule cell layer of the dentate gyrus (GC-DG) showed linear volume decreases. No sex differences were found in hippocampal subregion development. Finally, general cognitive ability was positively associated with CA2/3 and CA4 volumes, as well as with ML development. In conclusion, hippocampal subregions appear to develop in diversified ways across adolescence, and specific subregions may link to general cognitive level.
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Affiliation(s)
| | - Marieke G N Bos
- Institute of Psychology, Leiden University, Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | | | - Sabine Peters
- Institute of Psychology, Leiden University, Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Eveline A Crone
- Institute of Psychology, Leiden University, Leiden, The Netherlands; Leiden Institute for Brain and Cognition, Leiden, The Netherlands
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20
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Morphometry and Development: Changes in Brain Structure from Birth to Adult Age. NEUROMETHODS 2018. [DOI: 10.1007/978-1-4939-7647-8_10] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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21
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Vijayakumar N, Mills KL, Alexander-Bloch A, Tamnes CK, Whittle S. Structural brain development: A review of methodological approaches and best practices. Dev Cogn Neurosci 2017; 33:129-148. [PMID: 29221915 PMCID: PMC5963981 DOI: 10.1016/j.dcn.2017.11.008] [Citation(s) in RCA: 83] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Revised: 06/05/2017] [Accepted: 11/16/2017] [Indexed: 11/26/2022] Open
Abstract
Continued advances in neuroimaging technologies and statistical modelling capabilities have improved our knowledge of structural brain development in children and adolescents. While this has provided an increasingly nuanced understanding of brain development, the field is still plagued by inconsistent findings. This review highlights the methodological diversity in existing longitudinal magnetic resonance imaging (MRI) studies on structural brain development during childhood and adolescence, and addresses how such variation might contribute to inconsistencies in the literature. We discuss the impact of method choices at multiple decision points across the research process, from study design and sample selection, to image processing and statistical analysis. We also highlight the extent to which different methodological considerations have been empirically examined, drawing attention to specific areas that would benefit from future investigation. Where appropriate, we recommend certain best practices that would be beneficial for the field to adopt, including greater completeness and transparency in reporting methods, in order to ultimately develop an accurate and detailed understanding of normative child and adolescent brain development.
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Affiliation(s)
| | | | | | | | - Sarah Whittle
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Australia; Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia
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22
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The utility of twins in developmental cognitive neuroscience research: How twins strengthen the ABCD research design. Dev Cogn Neurosci 2017; 32:30-42. [PMID: 29107609 PMCID: PMC5847422 DOI: 10.1016/j.dcn.2017.09.001] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 08/31/2017] [Accepted: 09/05/2017] [Indexed: 02/01/2023] Open
Abstract
The ABCD twin study will elucidate the genetic and environmental contributions to a wide range of mental and physical health outcomes in children, including substance use, brain and behavioral development, and their interrelationship. Comparisons within and between monozygotic and dizygotic twin pairs, further powered by multiple assessments, provide information about genetic and environmental contributions to developmental associations, and enable stronger tests of causal hypotheses, than do comparisons involving unrelated children. Thus a sub-study of 800 pairs of same-sex twins was embedded within the overall Adolescent Brain and Cognitive Development (ABCD) design. The ABCD Twin Hub comprises four leading centers for twin research in Minnesota, Colorado, Virginia, and Missouri. Each site is enrolling 200 twin pairs, as well as singletons. The twins are recruited from registries of all twin births in each State during 2006-2008. Singletons at each site are recruited following the same school-based procedures as the rest of the ABCD study. This paper describes the background and rationale for the ABCD twin study, the ascertainment of twin pairs and implementation strategy at each site, and the details of the proposed analytic strategies to quantify genetic and environmental influences and test hypotheses critical to the aims of the ABCD study.
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23
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Brouwer RM, Panizzon MS, Glahn DC, Hibar DP, Hua X, Jahanshad N, Abramovic L, de Zubicaray GI, Franz CE, Hansell NK, Hickie IB, Koenis MMG, Martin NG, Mather KA, McMahon KL, Schnack HG, Strike LT, Swagerman SC, Thalamuthu A, Wen W, Gilmore JH, Gogtay N, Kahn RS, Sachdev PS, Wright MJ, Boomsma DI, Kremen WS, Thompson PM, Hulshoff Pol HE. Genetic influences on individual differences in longitudinal changes in global and subcortical brain volumes: Results of the ENIGMA plasticity working group. Hum Brain Mapp 2017; 38:4444-4458. [PMID: 28580697 DOI: 10.1002/hbm.23672] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 05/16/2017] [Accepted: 05/17/2017] [Indexed: 12/12/2022] Open
Abstract
Structural brain changes that occur during development and ageing are related to mental health and general cognitive functioning. Individuals differ in the extent to which their brain volumes change over time, but whether these differences can be attributed to differences in their genotypes has not been widely studied. Here we estimate heritability (h2 ) of changes in global and subcortical brain volumes in five longitudinal twin cohorts from across the world and in different stages of the lifespan (N = 861). Heritability estimates of brain changes were significant and ranged from 16% (caudate) to 42% (cerebellar gray matter) for all global and most subcortical volumes (with the exception of thalamus and pallidum). Heritability estimates of change rates were generally higher in adults than in children suggesting an increasing influence of genetic factors explaining individual differences in brain structural changes with age. In children, environmental influences in part explained individual differences in developmental changes in brain structure. Multivariate genetic modeling showed that genetic influences of change rates and baseline volume significantly overlapped for many structures. The genetic influences explaining individual differences in the change rate for cerebellum, cerebellar gray matter and lateral ventricles were independent of the genetic influences explaining differences in their baseline volumes. These results imply the existence of genetic variants that are specific for brain plasticity, rather than brain volume itself. Identifying these genes may increase our understanding of brain development and ageing and possibly have implications for diseases that are characterized by deviant developmental trajectories of brain structure. Hum Brain Mapp 38:4444-4458, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Rachel M Brouwer
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Matthew S Panizzon
- Department of Psychiatry, University of California, San Diego, California
| | - David C Glahn
- Department of Psychiatry, Yale University of Medicine, New Haven, Connecticut
| | - Derrek P Hibar
- Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, California
| | - Xue Hua
- Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, California
| | - Neda Jahanshad
- Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, California
| | - Lucija Abramovic
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Greig I de Zubicaray
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, Australia
| | - Carol E Franz
- Department of Psychiatry, University of California, San Diego, California
| | - Narelle K Hansell
- Queensland Brain Institute, University of Queensland, St. Lucia, QLD, Australia
| | - Ian B Hickie
- Clinical Research Unit, Brain & Mind Research Institute, University of Sydney, NSW, Australia
| | - Marinka M G Koenis
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Karen A Mather
- Centre for Healthy Brain Ageing, Psychiatry, University of New South Wales, Sydney, Australia
| | - Katie L McMahon
- Centre for Advanced Imaging, University of Queensland, St. Lucia, QLD, Australia
| | - Hugo G Schnack
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lachlan T Strike
- Queensland Brain Institute, University of Queensland, St. Lucia, QLD, Australia
| | - Suzanne C Swagerman
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing, Psychiatry, University of New South Wales, Sydney, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing, Psychiatry, University of New South Wales, Sydney, Australia
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - Nitin Gogtay
- National Institute of Mental Health, Bethesda, Maryland
| | - René S Kahn
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, Psychiatry, University of New South Wales, Sydney, Australia
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, St. Lucia, QLD, Australia.,Centre for Advanced Imaging, University of Queensland, St. Lucia, QLD, Australia
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - William S Kremen
- Department of Psychiatry, University of California, San Diego, California
| | - Paul M Thompson
- Imaging Genetics Center, Keck School of Medicine of USC, Marina del Rey, California
| | - Hilleke E Hulshoff Pol
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
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24
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Brain morphology in school-aged children with prenatal opioid exposure: A structural MRI study. Early Hum Dev 2017; 106-107:33-39. [PMID: 28187337 DOI: 10.1016/j.earlhumdev.2017.01.009] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Revised: 01/27/2017] [Accepted: 01/30/2017] [Indexed: 12/21/2022]
Abstract
BACKGROUND Both animal and human studies have suggested that prenatal opioid exposure may be detrimental to the developing fetal brain. However, results are somewhat conflicting. Structural brain changes in children with prenatal opioid exposure have been reported in a few studies, and such changes may contribute to neuropsychological impairments observed in exposed children. AIM To investigate the association between prenatal opioid exposure and brain morphology in school-aged children. STUDY DESIGN A cross-sectional magnetic resonance imaging (MRI) study of prenatally opioid-exposed children and matched controls. SUBJECTS A hospital-based sample (n=16) of children aged 10-14years with prenatal exposure to opioids and 1:1 sex- and age-matched unexposed controls. OUTCOME MEASURES Automated brain volume measures obtained from T1-weighted MRI scans using FreeSurfer. RESULTS Volumes of the basal ganglia, thalamus, and cerebellar white matter were reduced in the opioid-exposed group, whereas there were no statistically significant differences in global brain measures (total brain, cerebral cortex, and cerebral white matter volumes). CONCLUSIONS In line with the limited findings reported in the literature to date, our study showed an association between prenatal opioid exposure and reduced regional brain volumes. Adverse effects of opioids on the developing fetal brain may explain this association. However, further research is needed to explore the causal nature and functional consequences of these findings.
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25
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Greenspan KS, Arakelian CR, van Erp TGM. Heritability of Hippocampal Formation Sub-region Volumes. ACTA ACUST UNITED AC 2016; 7. [PMID: 28503392 DOI: 10.21767/2171-6625.1000159] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND Hippocampal formation (HF) volume and episodic memory performance are substantially heritable, but HF subregion heritability estimates and their possible shared genetic variance with episodic memory performance remain to be determined. METHODS AND FINDINGS This study provides heritability estimates for hippocampal subregions (e.g, Cornu Amonis, Subiculum, Parasubiculum, Molecular and Granule Cell Layers of the Dentate Gryus) and Total HF volumes obtained using FreeSurfer 6.0. In addition, this study assesses the heritability of object sequence and verbal episodic memory performance, and the amount of shared genetic variance between HF subregions and Total HF volume and episodic memory performance. HF volumes were obtained from high-resolution brain scans from a sample of 499 siblings (mean age±SD=30.0±3.1, 203 men), including 51 monozygotic and 46 dizygotic twin pairs and 305 non-twin siblings, collected by the Human Connectome Project (www.humanconnectome.org). Heritability estimates for HF subregions ranged from 0.42-0.87 and shared genetic variance of HF subregions with hippocampal volume was substantial (mean=0.79, range=0.50-0.98). HF subregion volumes residualized for Total HF and percent HF subregion volumes were also found to be substantially heritable (range=0.04-0.86 and 0.07-0.84, respectively). Verbal (h2=0.47) but not object sequence episodic memory was found to be significantly heritable; though the amount of shared genetic variance between HF subregions and verbal episodic memory was low (mean=0.10, range=0.01-0.20). CONCLUSIONS These findings suggest that HF subregion volumes are heritable and can be used as quantitative phenotypes in genetic association studies. The low shared genetic variance between HF subregions and verbal episodic memory suggests that quantitative trait analyses may not benefit from including both HF volume and episodic memory as bivariate traits in healthy individuals. The extent to which HF subregion volumes share genetic variance with neuropsychiatric disorders, and as such add value to our ability to identify genetic risk loci for these disorders, remains to be determined.
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Affiliation(s)
- Kiefer S Greenspan
- Department of Psychiatry and Human Behavior, University of California, Irvine, SOM.,Department of Psychiatry, University of Pittsburgh, SOM
| | - Claire R Arakelian
- Department of Psychiatry and Human Behavior, University of California, Irvine, SOM
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, University of California, Irvine, SOM
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26
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Distinct Genetic Influences on Cortical and Subcortical Brain Structures. Sci Rep 2016; 6:32760. [PMID: 27595976 PMCID: PMC5011703 DOI: 10.1038/srep32760] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Accepted: 08/09/2016] [Indexed: 12/13/2022] Open
Abstract
This study examined the heritability of brain grey matter structures in a subsample of older adult twins (93 MZ and 68 DZ twin pairs; mean age 70 years) from the Older Australian Twins Study. The heritability estimates of subcortical regions ranged from 0.41 (amygdala) to 0.73 (hippocampus), and of cortical regions, from 0.55 (parietal lobe) to 0.78 (frontal lobe). Corresponding structures in the two hemispheres were influenced by the same genetic factors and high genetic correlations were observed between the two hemispheric regions. There were three genetically correlated clusters, comprising (i) the cortical lobes (frontal, temporal, parietal and occipital lobes); (ii) the basal ganglia (caudate, putamen and pallidum) with weak genetic correlations with cortical lobes, and (iii) the amygdala, hippocampus, thalamus and nucleus accumbens grouped together, which genetically correlated with both basal ganglia and cortical lobes, albeit relatively weakly. Our study demonstrates a complex but patterned and clustered genetic architecture of the human brain, with divergent genetic determinants of cortical and subcortical structures, in particular the basal ganglia.
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27
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Sato W, Kochiyama T, Kubota Y, Uono S, Sawada R, Yoshimura S, Toichi M. The association between perceived social support and amygdala structure. Neuropsychologia 2016; 85:237-44. [PMID: 27039164 DOI: 10.1016/j.neuropsychologia.2016.03.036] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2015] [Revised: 03/10/2016] [Accepted: 03/29/2016] [Indexed: 01/17/2023]
Abstract
The subjective perception of social support plays a crucial role in human well-being. However, its structural neural substrates remain unknown. We hypothesized that the amygdala, specifically its laterobasal and superficial subregions, which have been suggested to serve social functions, could be associated with the level of perceived social support. To test this hypothesis, we assessed perceived social support using the Multidimensional Scale of Perceived Social Support. In addition, we measured the volume and shape of the amygdala using structural magnetic resonance imaging in 49 healthy participants. Global amygdala volume in the left hemisphere was positively associated with the perceived social support score after adjusting for total cerebral volume, sex, age, intelligence, and five-factor personality domains. The local shape of the laterobasal and superficial subregions of the left amygdala showed the same association with perceived social support. These data suggest that the social subregions of the left amygdala are associated with the implementation of perceived social support.
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Affiliation(s)
- Wataru Sato
- Department of Neurodevelopmental Psychiatry, Habilitation and Rehabilitation, Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo, Kyoto 606-8507, Japan.
| | - Takanori Kochiyama
- Brain Activity Imaging Center, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan
| | - Yasutaka Kubota
- Health and Medical Services Center, Shiga University, 1-1-1, Baba, Hikone, Shiga 522-8522, Japan
| | - Shota Uono
- Department of Neurodevelopmental Psychiatry, Habilitation and Rehabilitation, Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo, Kyoto 606-8507, Japan
| | - Reiko Sawada
- Department of Neurodevelopmental Psychiatry, Habilitation and Rehabilitation, Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo, Kyoto 606-8507, Japan
| | - Sayaka Yoshimura
- Department of Neurodevelopmental Psychiatry, Habilitation and Rehabilitation, Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo, Kyoto 606-8507, Japan
| | - Motomi Toichi
- Faculty of Human Health Science, Graduate School of Medicine, Kyoto University, 53 Shogoin-Kawaharacho, Sakyo-ku, Kyoto 606-8507, Japan; The Organization for Promoting Neurodevelopmental Disorder Research, 40 Shogoin-Sannocho, Sakyo, Kyoto 606-8392, Japan
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28
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Whelan CD, Hibar DP, van Velzen LS, Zannas AS, Carrillo-Roa T, McMahon K, Prasad G, Kelly S, Faskowitz J, deZubiracay G, Iglesias JE, van Erp TGM, Frodl T, Martin NG, Wright MJ, Jahanshad N, Schmaal L, Sämann PG, Thompson PM. Heritability and reliability of automatically segmented human hippocampal formation subregions. Neuroimage 2016; 128:125-137. [PMID: 26747746 PMCID: PMC4883013 DOI: 10.1016/j.neuroimage.2015.12.039] [Citation(s) in RCA: 92] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Revised: 11/28/2015] [Accepted: 12/23/2015] [Indexed: 12/01/2022] Open
Abstract
The human hippocampal formation can be divided into a set of cytoarchitecturally and functionally distinct subregions, involved in different aspects of memory formation. Neuroanatomical disruptions within these subregions are associated with several debilitating brain disorders including Alzheimer's disease, major depression, schizophrenia, and bipolar disorder. Multi-center brain imaging consortia, such as the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium, are interested in studying disease effects on these subregions, and in the genetic factors that affect them. For large-scale studies, automated extraction and subsequent genomic association studies of these hippocampal subregion measures may provide additional insight. Here, we evaluated the test-retest reliability and transplatform reliability (1.5T versus 3T) of the subregion segmentation module in the FreeSurfer software package using three independent cohorts of healthy adults, one young (Queensland Twins Imaging Study, N=39), another elderly (Alzheimer's Disease Neuroimaging Initiative, ADNI-2, N=163) and another mixed cohort of healthy and depressed participants (Max Planck Institute, MPIP, N=598). We also investigated agreement between the most recent version of this algorithm (v6.0) and an older version (v5.3), again using the ADNI-2 and MPIP cohorts in addition to a sample from the Netherlands Study for Depression and Anxiety (NESDA) (N=221). Finally, we estimated the heritability (h(2)) of the segmented subregion volumes using the full sample of young, healthy QTIM twins (N=728). Test-retest reliability was high for all twelve subregions in the 3T ADNI-2 sample (intraclass correlation coefficient (ICC)=0.70-0.97) and moderate-to-high in the 4T QTIM sample (ICC=0.5-0.89). Transplatform reliability was strong for eleven of the twelve subregions (ICC=0.66-0.96); however, the hippocampal fissure was not consistently reconstructed across 1.5T and 3T field strengths (ICC=0.47-0.57). Between-version agreement was moderate for the hippocampal tail, subiculum and presubiculum (ICC=0.78-0.84; Dice Similarity Coefficient (DSC)=0.55-0.70), and poor for all other subregions (ICC=0.34-0.81; DSC=0.28-0.51). All hippocampal subregion volumes were highly heritable (h(2)=0.67-0.91). Our findings indicate that eleven of the twelve human hippocampal subregions segmented using FreeSurfer version 6.0 may serve as reliable and informative quantitative phenotypes for future multi-site imaging genetics initiatives such as those of the ENIGMA consortium.
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Affiliation(s)
- Christopher D Whelan
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA
| | - Derrek P Hibar
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA
| | - Laura S van Velzen
- Department of Psychiatry and Neuroscience Campus Amsterdam, VU University Medical Center and GGZ inGeest, Amsterdam, The Netherlands
| | - Anthony S Zannas
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany; Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA
| | - Tania Carrillo-Roa
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Katie McMahon
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia
| | - Gautam Prasad
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA
| | - Sinéad Kelly
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA
| | - Joshua Faskowitz
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA
| | - Greig deZubiracay
- Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Juan E Iglesias
- Basque Center on Cognition, Brain and Language, Donostia, Gipuzkoa, Spain
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, University of California, Irvine, USA
| | - Thomas Frodl
- Department of Psychiatry, Otto-von Guericke-University of Magdeburg, Germany
| | - Nicholas G Martin
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - Neda Jahanshad
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA
| | - Lianne Schmaal
- Department of Psychiatry and Neuroscience Campus Amsterdam, VU University Medical Center and GGZ inGeest, Amsterdam, The Netherlands
| | - Philipp G Sämann
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Paul M Thompson
- Imaging Genetics Center, University of Southern California, Marina del Rey, CA, USA.
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29
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Walhovd KB, Westerhausen R, de Lange AMG, Bråthen ACS, Grydeland H, Engvig A, Fjell AM. Premises of plasticity - And the loneliness of the medial temporal lobe. Neuroimage 2015; 131:48-54. [PMID: 26505299 DOI: 10.1016/j.neuroimage.2015.10.060] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Revised: 09/17/2015] [Accepted: 10/21/2015] [Indexed: 11/26/2022] Open
Abstract
In this perspective paper, we examine possible premises of plasticity in the neural substrates underlying cognitive change. We take the special role of the medial temporal lobe as an anchoring point, but also investigate characteristics throughout the cortex. Specifically, we examine the dimensions of evolutionary expansion, heritability, variability of morphometric change, and inter-individual variance in myelination with respect to the plastic potential of different brain regions. We argue that areas showing less evolutionary expansion, lower heritability, greater variability of cortical thickness change through the lifespan, and greater inter-individual differences in intracortical myelin content have a great extent of plasticity. While different regions of the brain show these features to varying extent, analyses converge on the medial temporal lobe including the hippocampi as the target of all these premises. We discuss implications for effects of training on brain structures, and conditions under which plasticity may be evoked.
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Affiliation(s)
- Kristine B Walhovd
- Research Group for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373, Norway; Department of Physical medicine and rehabilitation, Unit of neuropsychology, Oslo University Hospital, 0424, Norway.
| | - René Westerhausen
- Research Group for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373, Norway
| | - Ann-Marie Glasø de Lange
- Research Group for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373, Norway
| | - Anne Cecilie Sjøli Bråthen
- Research Group for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373, Norway
| | - Håkon Grydeland
- Research Group for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373, Norway
| | - Andreas Engvig
- Department of Medicine, Diakonhjemmet Hospital, Oslo, Norway
| | - Anders M Fjell
- Research Group for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, 0373, Norway
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