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Shen J, Zhang Y, Zhu Z, Cheng Y, Cai B, Zhao Y, Zhao H. Joint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation. Neuroimage 2024; 297:120739. [PMID: 39009250 DOI: 10.1016/j.neuroimage.2024.120739] [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: 01/23/2024] [Revised: 07/07/2024] [Accepted: 07/12/2024] [Indexed: 07/17/2024] Open
Abstract
Heritability and genetic covariance/correlation quantify the marginal and shared genetic effects across traits. They offer insights on the genetic architecture of complex traits and diseases. To explore how genetic variations contribute to brain function variations, we estimated heritability and genetic correlation across cortical thickness, surface area, and volume of 33 anatomically predefined regions in left and right hemispheres, using summary statistics of genome-wide association analyses of 31,968 participants in the UK Biobank. To characterize the relationships between these regions of interest, we constructed a genetic network for these regions using recursive two-way cut-offs in similarity matrices defined by genetic correlations. The inferred genetic network matches the brain lobe mapping more closely than the network inferred from phenotypic similarities. We further studied the associations between the genetic network for brain regions and 30 complex traits through a novel composite-linkage disequilibrium score regression method. We identified seven significant pairs, which offer insights on the genetic basis for regions of interest mediated by cortical measures.
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Affiliation(s)
- Jiangnan Shen
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Yiliang Zhang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Zhaohan Zhu
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Youshu Cheng
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Biao Cai
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA; Department of Management Sciences, City University of Hong Kong, Hong Kong S.A.R, China
| | - Yize Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA; Department of Genetics, Yale University School of Medicine, New Haven, CT, USA; Program of Computational Biology and Biomedical Informatics, Yale University, New Haven, CT, USA.
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Poortman SR, Setiaman N, Barendse MEA, Schnack HG, Hillegers MHJ, van Haren NEM. Non-linear development of brain morphometry in child and adolescent offspring of individuals with bipolar disorder or schizophrenia. Eur Neuropsychopharmacol 2024; 87:56-66. [PMID: 39084058 DOI: 10.1016/j.euroneuro.2024.06.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 06/19/2024] [Accepted: 06/29/2024] [Indexed: 08/02/2024]
Abstract
Offspring of parents with severe mental illness (e.g., bipolar disorder or schizophrenia) are at increased risk of developing psychopathology. Structural brain alterations have been found in child and adolescent offspring of patients with bipolar disorder and schizophrenia, but the developmental trajectories of brain anatomy in this high-familial-risk population are still unclear. 300 T1-weighted scans were obtained of 187 offspring of at least one parent diagnosed with bipolar disorder (n=80) or schizophrenia (n=53) and offspring of parents without severe mental illness (n=54). The age range was 8 to 23 years old; 113 offspring underwent two scans. Global brain measures and regional cortical thickness and surface area were computed. A generalized additive mixed model was used to capture non-linear age trajectories. Offspring of parents with schizophrenia had smaller total brain volume than offspring of parents with bipolar disorder (d=-0.20, p=0.004) and control offspring (d=-0.22, p=0.005) and lower mean cortical thickness than control offspring (d=-0.23, p<0.001). Offspring of parents with schizophrenia showed differential age trajectories of mean cortical thickness and cerebral white matter volume compared with control offspring (both p's=0.003). Regionally, offspring of parents with schizophrenia had a significantly different trajectory of cortical thickness in the middle temporal gyrus versus control offspring (p<0.001) and bipolar disorder offspring (p=0.001), which was no longer significant after correcting for mean cortical thickness. These findings suggest that particularly familial high risk of schizophrenia is related to reductions and deviating developmental trajectories of global brain structure measures, which were not driven by specific regions.
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Affiliation(s)
- Simon R Poortman
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children's Hospital, Rotterdam, the Netherlands.
| | - Nikita Setiaman
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children's Hospital, Rotterdam, the Netherlands
| | - Marjolein E A Barendse
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children's Hospital, Rotterdam, the Netherlands
| | - Hugo G Schnack
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children's Hospital, Rotterdam, the Netherlands; Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
| | - Manon H J Hillegers
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children's Hospital, Rotterdam, the Netherlands; Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
| | - Neeltje E M van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Sophia Children's Hospital, Rotterdam, the Netherlands; Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
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3
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Peng L, Cai H, Tang Y, Zhou F, Liu Y, Xu Z, Chen Q, Chen X. Causal associations between chronic heart failure and the cerebral cortex: results from Mendelian randomization study and integrated bioinformatics analysis. Front Cardiovasc Med 2024; 11:1396311. [PMID: 39027007 PMCID: PMC11254706 DOI: 10.3389/fcvm.2024.1396311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 06/11/2024] [Indexed: 07/20/2024] Open
Abstract
Background Chronic heart failure (CHF) patients exhibit alterations in cerebral cortical structure and cognitive function. However, the mechanisms by which CHF affects cortical structure and functional regions remain unknown. This study aims to investigate potential causal relationship between CHF and cerebral cortical structure through Mendelian randomization (MR). Methods The research utilized genome-wide association studies (GWAS) to explore the causal association between CHF and cerebral cortical structure. The results were primarily analyzed using the inverse-variance weighted (IVW). The reliability of the data was verified through horizontal pleiotropy and heterogeneity analysis by MR-Egger intercept test and Cochran's Q-test, respectively. Replication analysis was conducted in the Integrative Epidemiology Unit (IEU) OpenGWAS project for further validation. In addition, we collected mediator genes that mediate causality to reveal potential mechanisms. Integrated bioinformatics analysis was conducted using the Open Target Genetics platform, the STRING database, and Cytoscape software. Results The IVW results did not reveal any significant causal association between genetically predicted CHF and the overall structure of the cerebral cortex or the surface area (SA) of the 34 functional regions of the cerebral cortex (P > 0.05). However, the results revealed that CHF increased the thickness (TH) of pars opercularis (IVW: β = 0.015, 95% CI: 0.005-0.025, P = 3.16E-03). Replication analysis supported the causal association between CHF and pars opercularis TH (IVW: β = 0.02, 95% CI: 0.010-0.033, P = 1.84E-04). We examined the degree centrality values of the top 10 mediator genes, namely CDKN1A, CELSR2, NME5, SURF4, PSMA5, TSC1, RPL7A, SURF6, PRDX3, and FTO. Conclusion Genetic evidence indicates a positive correlation between CHF and pars opercularis TH.
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Affiliation(s)
- Liqi Peng
- The First Clinical College of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Huzhi Cai
- International Medical Department, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, China
| | - Yanping Tang
- College of Integrative Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Fang Zhou
- Health Management Department, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, China
| | - Yuemei Liu
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, China
| | - Zelin Xu
- Preventive Treatment Center, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, China
| | - Qingyang Chen
- Intensive Care Unit, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, China
| | - Xinyu Chen
- Preventive Treatment Center, The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, China
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Yu J. Age-related decline in thickness and surface area in the cortical surface and hippocampus: lifespan trajectories and decade-by-decade analyses. GeroScience 2024:10.1007/s11357-024-01220-1. [PMID: 38831181 DOI: 10.1007/s11357-024-01220-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 05/21/2024] [Indexed: 06/05/2024] Open
Abstract
Previous studies on age-related changes in cortical and hippocampal morphology were not designed or able to reveal the complex spatial patterns of changes across the lifespan. To this end, the current study examined these changes in a decade-by-decade manner by comparing consecutive age decades at the vertex-wise level. Additionally, the lifespan trajectories of cortical/hippocampal mean thickness and total surface area were modeled and plotted out to provide an overview of their age-related changes. Using two lifespan datasets (Ntotal = 1378; 18 ≤ age ≤ 100), vertex-wise thickness and surface area measurements were extracted from the cortical and unfolded hippocampal surfaces and analyzed using whole-brain/hippocampus vertex-wise analyses. Lifespan trajectories of cortical/hippocampal mean thickness and total surface area were modeled with generalized additive models for location, scale, and shape. These models revealed fairly linear declines in both cortical measures and inverted U-shaped trajectories for both hippocampal measures. Across the different age decades, the sizes and locations of cortical thinning clusters were highly variable across the age decades. No significant clusters of cortical surface area changes were observed across the age decades. Significant clusters of hippocampal surface area and thickness reduction were not observed until the 70s. Generally, the agreement between datasets on the hippocampal findings was much higher than those of the cortical surface. These findings revealed important nuances in the age-related changes of cortical and hippocampal morphology and cautioned against using lifespan trajectories to infer decade-by-decade changes in the cortical surface and the hippocampus.
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Affiliation(s)
- Junhong Yu
- Psychology, School of Social Sciences, Nanyang Technological University, 48 Nanyang Avenue, Singapore, 639798, Singapore.
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5
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Strike LT, Kerestes R, McMahon KL, de Zubicaray GI, Harding IH, Medland SE. Heritability of cerebellar subregion volumes in adolescent and young adult twins. Hum Brain Mapp 2024; 45:e26717. [PMID: 38798116 PMCID: PMC11128777 DOI: 10.1002/hbm.26717] [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: 01/02/2024] [Revised: 04/23/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024] Open
Abstract
Twin studies have found gross cerebellar volume to be highly heritable. However, whether fine-grained regional volumes within the cerebellum are similarly heritable is still being determined. Anatomical MRI scans from two independent datasets (QTIM: Queensland Twin IMaging, N = 798, mean age 22.1 years; QTAB: Queensland Twin Adolescent Brain, N = 396, mean age 11.3 years) were combined with an optimised and automated cerebellum parcellation algorithm to segment and measure 28 cerebellar regions. We show that the heritability of regional volumetric measures varies widely across the cerebellum (h 2 $$ {h}^2 $$ 47%-91%). Additionally, the good to excellent test-retest reliability for a subsample of QTIM participants suggests that non-genetic variance in cerebellar volumes is due primarily to unique environmental influences rather than measurement error. We also show a consistent pattern of strong associations between the volumes of homologous left and right hemisphere regions. Associations were predominantly driven by genetic effects shared between lobules, with only sparse contributions from environmental effects. These findings are consistent with similar studies of the cerebrum and provide a first approximation of the upper bound of heritability detectable by genome-wide association studies.
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Affiliation(s)
- Lachlan T. Strike
- Psychiatric Genetics, QIMR Berghofer Medical Research InstituteBrisbaneAustralia
- School of Psychology and Counselling, Faculty of HealthQueensland University of TechnologyKelvin GroveQueenslandAustralia
- School of Biomedical Sciences, Faculty of MedicineUniversity of QueenslandBrisbaneAustralia
| | - Rebecca Kerestes
- Department of Neuroscience, Central Clinical SchoolMonash UniversityMelbourneAustralia
| | - Katie L. McMahon
- School of Clinical Sciences, Centre for Biomedical TechnologiesQueensland University of TechnologyBrisbaneQueenslandAustralia
| | - Greig I. de Zubicaray
- School of Psychology and Counselling, Faculty of HealthQueensland University of TechnologyKelvin GroveQueenslandAustralia
| | - Ian H. Harding
- Department of Neuroscience, Central Clinical SchoolMonash UniversityMelbourneAustralia
- Cerebellum and Neurodegeneration, QIMR Berghofer Medical Research InstituteBrisbaneAustralia
| | - Sarah E. Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research InstituteBrisbaneAustralia
- School of Psychology and Counselling, Faculty of HealthQueensland University of TechnologyKelvin GroveQueenslandAustralia
- School of PsychologyUniversity of QueenslandBrisbaneAustralia
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6
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Tubiolo PN, Williams JC, Van Snellenberg JX. A tale of two n-backs: Diverging associations of dorsolateral prefrontal cortex activation with n-back task performance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.23.595597. [PMID: 38826388 PMCID: PMC11142179 DOI: 10.1101/2024.05.23.595597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Background In studying the neural correlates of working memory (WM) ability via functional magnetic resonance imaging (fMRI) in health and disease, it is relatively uncommon for investigators to report associations between brain activation and measures of task performance. Additionally, how the choice of WM task impacts observed activation-performance relationships is poorly understood. We sought to illustrate the impact of WM task on brain-behavior correlations using two large, publicly available datasets. Methods We conducted between-participants analyses of task-based fMRI data from two publicly available datasets: the Human Connectome Project (HCP; n = 866) and the Queensland Twin Imaging (QTIM) Study (n = 459). Participants performed two distinct variations of the n-back WM task with different stimuli, timings, and response paradigms. Associations between brain activation ([2-back - 0-back] contrast) and task performance (2-back % correct) were investigated separately in each dataset, as well as across datasets, within the dorsolateral prefrontal cortex (dlPFC), medial prefrontal cortex, and whole cortex. Results Global patterns of activation to task were similar in both datasets. However, opposite associations between activation and task performance were observed in bilateral pre-supplementary motor area and left middle frontal gyrus. Within the dlPFC, HCP participants exhibited a significantly greater activation-performance relationship in bilateral middle frontal gyrus relative to QTIM Study participants. Conclusions The observation of diverging activation-performance relationships between two large datasets performing variations of the n-back task serves as a critical reminder for investigators to exercise caution when selecting WM tasks and interpreting neural activation in response to a WM task.
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Affiliation(s)
- Philip N Tubiolo
- Department of Biomedical Engineering, Stony Brook University
- Department of Psychiatry and Behavioral Health, Renaissance School of Medicine at Stony Brook University
| | - John C Williams
- Department of Biomedical Engineering, Stony Brook University
- Department of Psychiatry and Behavioral Health, Renaissance School of Medicine at Stony Brook University
| | - Jared X Van Snellenberg
- Department of Biomedical Engineering, Stony Brook University
- Department of Psychiatry and Behavioral Health, Renaissance School of Medicine at Stony Brook University
- Department of Psychology, Stony Brook University
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McGhee CA, Honari H, Siqueiros-Sanchez M, Serur Y, van Staalduinen EK, Stevenson D, Bruno JL, Raman MM, Green T. Influences of RASopathies on Neuroanatomical Variation in Children. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00103-4. [PMID: 38621478 DOI: 10.1016/j.bpsc.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 03/09/2024] [Accepted: 04/04/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND RASopathies are a group of disorders characterized by pathogenic mutations in the Ras/mitogen-activated protein kinase (Ras/MAPK) signaling pathway. Distinct pathogenic variants in genes encoding proteins in the Ras/MAPK pathway cause Noonan syndrome (NS) and neurofibromatosis type 1 (NF1), which are associated with increased risk for autism spectrum disorder and attention-deficit/hyperactivity disorder. METHODS This study examined the effect of RASopathies (NS and NF1) on human neuroanatomy, specifically on surface area (SA), cortical thickness (CT), and subcortical volumes. Using vertex-based analysis for cortical measures and Desikan region of interest parcellation for subcortical volumes, we compared structural T1-weighted images of children with RASopathies (n = 91, mean age = 8.81 years, SD = 2.12) to those of sex- and age-matched typically developing children (n = 74, mean age = 9.07 years, SD = 1.77). RESULTS Compared with typically developing children, RASopathies had convergent effects on SA and CT, exhibiting increased SA in the precentral gyrus, decreased SA in occipital regions, and thinner CT in the precentral gyrus. RASopathies exhibited divergent effects on subcortical volumes, with syndrome-specific influences from NS and NF1. Overall, children with NS showed decreased volumes in striatal and thalamic structures, and children with NF1 displayed increased volumes in the hippocampus, amygdala, and thalamus. CONCLUSIONS Our study reveals the converging and diverging neuroanatomical effects of RASopathies on human neurodevelopment. The convergence of cortical effects on SA and CT indicates a shared influence of Ras/MAPK hyperactivation on the human brain. Therefore, considering these measures as objective outcome indicators for targeted treatments is imperative.
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Affiliation(s)
- Chloe Alexa McGhee
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, California.
| | - Hamed Honari
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, California
| | | | - Yaffa Serur
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, California
| | - Eric K van Staalduinen
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, California
| | - David Stevenson
- Division of Medical Genetics, Stanford University, Stanford, California
| | - Jennifer L Bruno
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, California
| | - Mira Michelle Raman
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, California
| | - Tamar Green
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, California
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8
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Wang G, Jiang N, Ma Y, Suo D, Liu T, Funahashi S, Yan T. Using a deep generation network reveals neuroanatomical specificity in hemispheres. PATTERNS (NEW YORK, N.Y.) 2024; 5:100930. [PMID: 38645770 PMCID: PMC11026975 DOI: 10.1016/j.patter.2024.100930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/08/2024] [Accepted: 01/15/2024] [Indexed: 04/23/2024]
Abstract
Asymmetry is an important property of brain organization, but its nature is still poorly understood. Capturing the neuroanatomical components specific to each hemisphere facilitates the understanding of the establishment of brain asymmetry. Since deep generative networks (DGNs) have powerful inference and recovery capabilities, we use one hemisphere to predict the opposite hemisphere by training the DGNs, which automatically fit the built-in dependencies between the left and right hemispheres. After training, the reconstructed images approximate the homologous components in the hemisphere. We use the difference between the actual and reconstructed hemispheres to measure hemisphere-specific components due to asymmetric expression of environmental and genetic factors. The results show that our model is biologically plausible and that our proposed metric of hemispheric specialization is reliable, representing a wide range of individual variation. Together, this work provides promising tools for exploring brain asymmetry and new insights into self-supervised DGNs for representing the brain.
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Affiliation(s)
- Gongshu Wang
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Ning Jiang
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yunxiao Ma
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Dingjie Suo
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Tiantian Liu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Shintaro Funahashi
- Advanced Research Institute for Multidisciplinary Science, Beijing Institute of Technology, Beijing 100081, China
- Department of Cognitive and Behavioral Sciences, Graduate School of Human and Environmental Science, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan
- Kokoro Research Center, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan
| | - Tianyi Yan
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
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Norbom LB, Rokicki J, Eilertsen EM, Wiker T, Hanson J, Dahl A, Alnæs D, Fernández‐Cabello S, Beck D, Agartz I, Andreassen OA, Westlye LT, Tamnes CK. Parental education and income are linked to offspring cortical brain structure and psychopathology at 9-11 years. JCPP ADVANCES 2024; 4:e12220. [PMID: 38486948 PMCID: PMC10933599 DOI: 10.1002/jcv2.12220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 12/21/2023] [Indexed: 03/17/2024] Open
Abstract
Background A child's socioeconomic environment can shape central aspects of their life, including vulnerability to mental disorders. Negative environmental influences in youth may interfere with the extensive and dynamic brain development occurring at this time. Indeed, there are numerous yet diverging reports of associations between parental socioeconomic status (SES) and child cortical brain morphometry. Most of these studies have used single metric- or unimodal analyses of standard cortical morphometry that downplay the probable scenario where numerous biological pathways in sum account for SES-related cortical differences in youth. Methods To comprehensively capture such variability, using data from 9758 children aged 8.9-11.1 years from the ABCD Study®, we employed linked independent component analysis (LICA) and fused vertex-wise cortical thickness, surface area, curvature and grey-/white-matter contrast (GWC). LICA revealed 70 uni- and multimodal components. We then assessed the linear relationships between parental education, parental income and each of the cortical components, controlling for age, sex, genetic ancestry, and family relatedness. We also assessed whether cortical structure moderated the negative relationships between parental SES and child general psychopathology. Results Parental education and income were both associated with larger surface area and higher GWC globally, in addition to local increases in surface area and to a lesser extent bidirectional GWC and cortical thickness patterns. The negative relation between parental income and child psychopathology were attenuated in children with a multimodal pattern of larger frontal- and smaller occipital surface area, and lower medial occipital thickness and GWC. Conclusion Structural brain MRI is sensitive to SES diversity in childhood, with GWC emerging as a particularly relevant marker together with surface area. In low-income families, having a more developed cortex across MRI metrics, appears beneficial for mental health.
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Affiliation(s)
- Linn B. Norbom
- PROMENTA Research CenterDepartment of PsychologyUniversity of OsloOsloNorway
- NORMENTInstitute of Clinical MedicineUniversity of OsloOsloNorway
| | - Jaroslav Rokicki
- NORMENTInstitute of Clinical MedicineUniversity of OsloOsloNorway
- Centre of Research and Education in Forensic PsychiatryOslo University HospitalOsloNorway
| | - Espen M. Eilertsen
- PROMENTA Research CenterDepartment of PsychologyUniversity of OsloOsloNorway
| | - Thea Wiker
- PROMENTA Research CenterDepartment of PsychologyUniversity of OsloOsloNorway
- NORMENTInstitute of Clinical MedicineUniversity of OsloOsloNorway
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
| | - Jamie Hanson
- Learning Research and Development Center University of PittsburghPennsylvaniaPittsburghUSA
- Department of PsychologyUniversity of PittsburghPennsylvaniaPittsburghUSA
| | - Andreas Dahl
- NORMENTInstitute of Clinical MedicineUniversity of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Dag Alnæs
- NORMENTInstitute of Clinical MedicineUniversity of OsloOsloNorway
- Department of PsychologyPedagogy and LawKristiania University CollegeOsloNorway
| | | | - Dani Beck
- PROMENTA Research CenterDepartment of PsychologyUniversity of OsloOsloNorway
- NORMENTInstitute of Clinical MedicineUniversity of OsloOsloNorway
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
| | - Ingrid Agartz
- NORMENTInstitute of Clinical MedicineUniversity of OsloOsloNorway
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
- K.G Jebsen Center for Neurodevelopmental DisordersUniversity of OsloOsloNorway
- Centre for Psychiatry ResearchDepartment of Clinical NeuroscienceKarolinska Institutet & Stockholm Health Care ServicesStockholmSweden
| | - Ole A. Andreassen
- K.G Jebsen Center for Neurodevelopmental DisordersUniversity of OsloOsloNorway
- NORMENTDivision of Mental Health and AddictionOslo University Hospital & Institute of Clinical MedicineUniversity of OsloOsloNorway
| | - Lars T. Westlye
- Department of PsychologyUniversity of OsloOsloNorway
- K.G Jebsen Center for Neurodevelopmental DisordersUniversity of OsloOsloNorway
- NORMENTDivision of Mental Health and AddictionOslo University Hospital & Institute of Clinical MedicineUniversity of OsloOsloNorway
| | - Christian K. Tamnes
- PROMENTA Research CenterDepartment of PsychologyUniversity of OsloOsloNorway
- NORMENTInstitute of Clinical MedicineUniversity of OsloOsloNorway
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
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10
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Dishner KA, McRae-Posani B, Bhowmik A, Jochelson MS, Holodny A, Pinker K, Eskreis-Winkler S, Stember JN. A Survey of Publicly Available MRI Datasets for Potential Use in Artificial Intelligence Research. J Magn Reson Imaging 2024; 59:450-480. [PMID: 37888298 PMCID: PMC10873125 DOI: 10.1002/jmri.29101] [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: 07/31/2023] [Revised: 10/15/2023] [Accepted: 10/16/2023] [Indexed: 10/28/2023] Open
Abstract
Artificial intelligence (AI) has the potential to bring transformative improvements to the field of radiology; yet, there are barriers to widespread clinical adoption. One of the most important barriers has been access to large, well-annotated, widely representative medical image datasets, which can be used to accurately train AI programs. Creating such datasets requires time and expertise and runs into constraints around data security and interoperability, patient privacy, and appropriate data use. Recognizing these challenges, several institutions have started curating and providing publicly available, high-quality datasets that can be accessed by researchers to advance AI models. The purpose of this work was to review the publicly available MRI datasets that can be used for AI research in radiology. Despite being an emerging field, a simple internet search for open MRI datasets presents an overwhelming number of results. Therefore, we decided to create a survey of the major publicly accessible MRI datasets in different subfields of radiology (brain, body, and musculoskeletal), and list the most important features of value to the AI researcher. To complete this review, we searched for publicly available MRI datasets and assessed them based on several parameters (number of subjects, demographics, area of interest, technical features, and annotations). We reviewed 110 datasets across sub-fields with 1,686,245 subjects in 12 different areas of interest ranging from spine to cardiac. This review is meant to serve as a reference for researchers to help spur advancements in the field of AI for radiology. LEVEL OF EVIDENCE: Level 4 TECHNICAL EFFICACY: Stage 6.
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Affiliation(s)
- Katharine A. Dishner
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- SUNY Downstate College of Medicine, Brooklyn, NY 11203
| | - Bala McRae-Posani
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Weill Cornell Medicine, New York, NY 10065
| | - Arka Bhowmik
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Maxine S. Jochelson
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | - Andrei Holodny
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065
- Department of Neuroscience, Weill Cornell Graduate School of the Medical Sciences, New York, NY 10065
| | - Katja Pinker
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
| | | | - Joseph N. Stember
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065
- Department of Radiology, Weill Cornell Medicine, New York, NY 10065
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11
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Wang J, He Y. Toward individualized connectomes of brain morphology. Trends Neurosci 2024; 47:106-119. [PMID: 38142204 DOI: 10.1016/j.tins.2023.11.011] [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: 09/14/2023] [Revised: 11/16/2023] [Accepted: 11/30/2023] [Indexed: 12/25/2023]
Abstract
The morphological brain connectome (MBC) delineates the coordinated patterns of local morphological features (such as cortical thickness) across brain regions. While classically constructed using population-based approaches, there is a growing trend toward individualized modeling. Currently, the methods for individualized MBCs are varied, posing challenges for method selection and cross-study comparisons. Here, we summarize how individualized MBCs are modeled through low-order methods (correlation-, divergence-, distance-, and deviation-based methods) describing relations in brain morphology, as well as high-order methods capturing similarities in these low-order relations. We discuss the merits and limitations of different methods, examining them in the context of robustness, reproducibility, and reliability. We highlight the importance of elucidating the cellular and molecular mechanisms underlying the individualized connectome, and establishing normative benchmarks to assess individual variation in development, aging, and neuropsychiatric disorders.
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Affiliation(s)
- Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; Center for Studies of Psychological Application, South China Normal University, Guangzhou 510631, China.
| | - Yong He
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.
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12
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Dahl A, Eilertsen EM, Rodriguez-Cabello SF, Norbom LB, Tandberg AD, Leonardsen E, Lee SH, Ystrom E, Tamnes CK, Alnæs D, Westlye LT. Genetic and brain similarity independently predict childhood anthropometrics and neighborhood socioeconomic conditions. Dev Cogn Neurosci 2024; 65:101339. [PMID: 38184855 PMCID: PMC10818201 DOI: 10.1016/j.dcn.2023.101339] [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: 08/24/2023] [Revised: 12/22/2023] [Accepted: 12/31/2023] [Indexed: 01/09/2024] Open
Abstract
Linking the developing brain with individual differences in clinical and demographic traits is challenging due to the substantial interindividual heterogeneity of brain anatomy and organization. Here we employ an integrative approach that parses individual differences in both cortical thickness and common genetic variants, and assess their effects on a wide set of childhood traits. The approach uses a linear mixed model framework to obtain the unique effects of each type of similarity, as well as their covariance. We employ this approach in a sample of 7760 unrelated children in the ABCD cohort baseline sample (mean age 9.9, 46.8% female). In general, associations between cortical thickness similarity and traits were limited to anthropometrics such as height, weight, and birth weight, as well as a marker of neighborhood socioeconomic conditions. Common genetic variants explained significant proportions of variance across nearly all included outcomes, although estimates were somewhat lower than previous reports. No significant covariance of the effects of genetic and cortical thickness similarity was found. The present findings highlight the connection between anthropometrics as well as neighborhood socioeconomic conditions and the developing brain, which appear to be independent from individual differences in common genetic variants in this population-based sample.
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Affiliation(s)
- Andreas Dahl
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Espen M Eilertsen
- Research Center for Developmental Processes and Gradients in Mental Health (PROMENTA), Department of Psychology, University of Oslo, Oslo, Norway
| | - Sara F Rodriguez-Cabello
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Linn B Norbom
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Research Center for Developmental Processes and Gradients in Mental Health (PROMENTA), Department of Psychology, University of Oslo, Oslo, Norway
| | - Anneli D Tandberg
- Department of Psychology, University of Oslo, Oslo, Norway; Research Center for Developmental Processes and Gradients in Mental Health (PROMENTA), Department of Psychology, University of Oslo, Oslo, Norway
| | - Esten Leonardsen
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sang Hong Lee
- Australian Centre for Precision Health, UniSA Allied Health & Human Performance, University of South Australia, Adelaide, Australia; South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, Australia
| | - Eivind Ystrom
- Research Center for Developmental Processes and Gradients in Mental Health (PROMENTA), Department of Psychology, University of Oslo, Oslo, Norway; Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - Christian K Tamnes
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Research Center for Developmental Processes and Gradients in Mental Health (PROMENTA), Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Norway
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13
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Yee Y, Ellegood J, French L, Lerch JP. Organization of thalamocortical structural covariance and a corresponding 3D atlas of the mouse thalamus. Neuroimage 2024; 285:120453. [PMID: 37979895 DOI: 10.1016/j.neuroimage.2023.120453] [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: 03/14/2022] [Revised: 10/29/2023] [Accepted: 11/08/2023] [Indexed: 11/20/2023] Open
Abstract
For information from sensory organs to be processed by the brain, it is usually passed to appropriate areas of the cerebral cortex. Almost all of this information passes through the thalamus, a relay structure that reciprocally connects to the vast majority of the cortex. The thalamus facilitates this information transfer through a set of thalamocortical connections that vary in cellular structure, molecular profiles, innervation patterns, and firing rates. Additionally, corticothalamic connections allow for intracortical information transfer through the thalamus. These efferent and afferent connections between the thalamus and cortex have been the focus of many studies, and the importance of cortical connectivity in defining thalamus anatomy is demonstrated by multiple studies that parcellate the thalamus based on cortical connectivity profiles. Here, we examine correlated morphological variation between the thalamus and cortex, or thalamocortical structural covariance. For each voxel in the thalamus as a seed, we construct a cortical structural covariance map that represents correlated cortical volume variation, and examine whether high structural covariance is observed in cortical areas that are functionally relevant to the seed. Then, using these cortical structural covariance maps as features, we subdivide the thalamus into six non-overlapping regions (clusters of voxels), and assess whether cortical structural covariance is associated with cortical connectivity that specifically originates from these regions. We show that cortical structural covariance is high in areas of the cortex that are functionally related to the seed voxel, cortical structural covariance varies along cortical depth, and sharp transitions in cortical structural covariance profiles are observed when varying seed locations in the thalamus. Subdividing the thalamus based on structural covariance, we additionally demonstrate that the six thalamic clusters of voxels stratify cortical structural covariance along the dorsal-ventral, medial-lateral, and anterior-posterior axes. These cluster-associated structural covariance patterns are prominently detected in cortical regions innervated by fibers projecting out of their related thalamic subdivisions. Together, these results advance our understanding of how the thalamus and the cortex couple in their volumes. Our results indicate that these volume correlations reflect functional organization and structural connectivity, and further provides a novel segmentation of the mouse thalamus that can be used to examine thalamic structural variation and thalamocortical structural covariation in disease models.
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Affiliation(s)
- Yohan Yee
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada.
| | - Jacob Ellegood
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Canada
| | - Leon French
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Jason P Lerch
- Department of Medical Biophysics, University of Toronto, Toronto, Canada; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
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14
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Bedford SA, Lai MC, Lombardo MV, Chakrabarti B, Ruigrok A, Suckling J, Anagnostou E, Lerch JP, Taylor M, Nicolson R, Stelios G, Crosbie J, Schachar R, Kelley E, Jones J, Arnold PD, Courchesne E, Pierce K, Eyler LT, Campbell K, Barnes CC, Seidlitz J, Alexander-Bloch AF, Bullmore ET, Baron-Cohen S, Bethlehem RA. Brain-charting autism and attention deficit hyperactivity disorder reveals distinct and overlapping neurobiology. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.06.23299587. [PMID: 38106166 PMCID: PMC10723556 DOI: 10.1101/2023.12.06.23299587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Background Autism and attention deficit hyperactivity disorder (ADHD) are heterogeneous neurodevelopmental conditions with complex underlying neurobiology. Despite overlapping presentation and sex-biased prevalence, autism and ADHD are rarely studied together, and sex differences are often overlooked. Normative modelling provides a unified framework for studying age-specific and sex-specific divergences in neurodivergent brain development. Methods Here we use normative modelling and a large, multi-site neuroimaging dataset to characterise cortical anatomy associated with autism and ADHD, benchmarked against models of typical brain development based on a sample of over 75,000 individuals. We also examined sex and age differences, relationship with autistic traits, and explored the co-occurrence of autism and ADHD (autism+ADHD). Results We observed robust neuroanatomical signatures of both autism and ADHD. Overall, autistic individuals showed greater cortical thickness and volume localised to the superior temporal cortex, whereas individuals with ADHD showed more global effects of cortical thickness increases but lower cortical volume and surface area across much of the cortex. The autism+ADHD group displayed a unique pattern of widespread increases in cortical thickness, and certain decreases in surface area. We also found evidence that sex modulates the neuroanatomy of autism but not ADHD, and an age-by-diagnosis interaction for ADHD only. Conclusions These results indicate distinct cortical differences in autism and ADHD that are differentially impacted by age, sex, and potentially unique patterns related to their co-occurrence.
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Affiliation(s)
- Saashi A. Bedford
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Meng-Chuan Lai
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Department of Psychiatry, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei 100229, Taiwan
| | - Michael V. Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Bhismadev Chakrabarti
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Centre for Autism, School of Psychology and Clinical Language Sciences, University of Reading, Reading RG6 6ES, UK
| | - Amber Ruigrok
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Evdokia Anagnostou
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Department of Pediatrics, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Jason P. Lerch
- Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Margot Taylor
- Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Rob Nicolson
- Department of Psychiatry, University of Western Ontario, London, Ontario, Canada
| | | | - Jennifer Crosbie
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Genetics & Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Russell Schachar
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- Program in Neurosciences and Mental Health, Research Institute, Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Genetics & Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Elizabeth Kelley
- Department of Psychology, Queen’s University, Kingston, ON K7L 3N6 Canada
- Centre for Neuroscience Studies, Queen’s University, Kingston, ON K7L 3N6 Canada
- Department of Psychiatry, Queen’s University, Kingston, ON K7L 3N6 Canada
| | - Jessica Jones
- Department of Psychology, Queen’s University, Kingston, ON K7L 3N6 Canada
- Centre for Neuroscience Studies, Queen’s University, Kingston, ON K7L 3N6 Canada
- Department of Psychiatry, Queen’s University, Kingston, ON K7L 3N6 Canada
| | - Paul D. Arnold
- The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Departments of Psychiatry and Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Eric Courchesne
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Karen Pierce
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Lisa T. Eyler
- Department of Psychiatry, University of California San Diego, La Jolla, California, USA
| | - Kathleen Campbell
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Cynthia Carter Barnes
- Department of Neurosciences, University of California San Diego, La Jolla, California, USA
| | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
| | - Aaron F. Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
| | - Edward T. Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Cambridge Lifetime Autism Spectrum Service (CLASS), Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Richard A.I. Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
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15
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Yao X, Klugah-Brown B, Yang H, Biswal B. Structural and functional network analysis of twins using fMRI data. Cereb Cortex 2023; 33:11060-11069. [PMID: 37771046 DOI: 10.1093/cercor/bhad345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 09/30/2023] Open
Abstract
Similarities between twins have been widely demonstrated, underscoring the remarkable influence of genetics across numerous traits. In this study, we explore the genetic underpinnings of the human brain by examining MRI data from the Queensland Twin Imaging study. Specifically, this study seeks to compare brain structure and function between twins and unrelated subjects, with an emphasis on describing the effects of genetic factors. To achieve these goals, we employed the source-based morphometry method to extract intrinsic components and elucidate recognizable patterns. Our results show that twins exhibit a higher degree of similarity in gray and white matter density compared with unrelated individuals. In addition, four distinct states of brain activity were identified using coactivation patterns analysis. Furthermore, twins demonstrated a greater degree of similarity in the temporal and spatial features of each state compared with unrelated subjects. Taken together, these results support the hypothesis that twins show greater similarity in both brain structure and dynamic functional brain activity. Further exploration of these methods may advance our understanding of the complex interplay between genes, environment, and brain networks.
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Affiliation(s)
- Xing Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Benjamin Klugah-Brown
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Hang Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA
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16
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Huang Y, Zhang T, Zhang S, Zhang W, Yang L, Zhu D, Liu T, Jiang X, Han J, Guo L. Genetic Influence on Gyral Peaks. Neuroimage 2023; 280:120344. [PMID: 37619794 DOI: 10.1016/j.neuroimage.2023.120344] [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: 05/10/2023] [Accepted: 08/21/2023] [Indexed: 08/26/2023] Open
Abstract
Genetic mechanisms have been hypothesized to be a major determinant in the formation of cortical folding. Although there is an increasing number of studies examining the heritability of cortical folding, most of them focus on sulcal pits rather than gyral peaks. Gyral peaks, which reflect the highest local foci on gyri and are consistent across individuals, remain unstudied in terms of heritability. To address this knowledge gap, we used high-resolution data from the Human Connectome Project (HCP) to perform classical twin analysis and estimate the heritability of gyral peaks across various brain regions. Our results showed that the heritability of gyral peaks was heterogeneous across different cortical regions, but relatively symmetric between hemispheres. We also found that pits and peaks are different in a variety of anatomic and functional measures. Further, we explored the relationship between the levels of heritability and the formation of cortical folding by utilizing the evolutionary timeline of gyrification. Our findings indicate that the heritability estimates of both gyral peaks and sulcal pits decrease linearly with the evolution timeline of gyrification. This suggests that the cortical folds which formed earlier during gyrification are subject to stronger genetic influences than the later ones. Moreover, the pits and peaks coupled by their time of appearance are also positively correlated in respect of their heritability estimates. These results fill the knowledge gap regarding genetic influences on gyral peaks and significantly advance our understanding of how genetic factors shape the formation of cortical folding. The comparison between peaks and pits suggests that peaks are not a simple morphological mirror of pits but could help complete the understanding of folding patterns.
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Affiliation(s)
- Ying Huang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China; School of Information and Technology, Northwest University, Xi'an 710127, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China.
| | - Songyao Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Weihan Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Li Yang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Dajiang Zhu
- Computer Science & Engineering, University of Texas at Arlington, TX 76010, USA
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30602, USA
| | - Xi Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
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17
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Guardiola-Ripoll M, Almodóvar-Payá C, Arias-Magnasco A, Latorre-Guardia M, Papiol S, Canales-Rodríguez EJ, García-León MÁ, Fuentes-Claramonte P, Salavert J, Tristany J, Torres L, Rodríguez-Cano E, Salvador R, Pomarol-Clotet E, Fatjó-Vilas M. Human-specific evolutionary markers linked to foetal neurodevelopment modulate brain surface area in schizophrenia. Commun Biol 2023; 6:1040. [PMID: 37833414 PMCID: PMC10576001 DOI: 10.1038/s42003-023-05356-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 09/13/2023] [Indexed: 10/15/2023] Open
Abstract
Schizophrenia may represent a trade-off in the evolution of human-specific ontogenetic mechanisms that guide neurodevelopment. Human Accelerated Regions (HARs) are evolutionary markers functioning as neurodevelopmental transcription enhancers that have been associated with brain configuration, neural information processing, and schizophrenia risk. Here, we have investigated the influence of HARs' polygenic load on neuroanatomical measures through a case-control approach (128 patients with schizophrenia and 115 controls). To this end, we have calculated the global schizophrenia Polygenic Risk Score (Global PRSSZ) and that specific to HARs (HARs PRSSZ). We have also estimated the polygenic burden restricted to the HARs linked to transcriptional regulatory elements active in the foetal brain (FB-HARs PRSSZ) and the adult brain (AB-HARs PRSSZ). We have explored the main effects of the PRSs and the PRSs x diagnosis interactions on brain regional cortical thickness (CT) and surface area (SA). The results indicate that a higher FB-HARs PRSSZ is associated with patients' lower SA in the lateral orbitofrontal cortex, the superior temporal cortex, the pars triangularis and the paracentral lobule. While noHARs-derived PRSs show an effect on the risk, our neuroanatomical findings suggest that the human-specific transcriptional regulation during the prenatal period underlies SA variability, highlighting the role of these evolutionary markers in the schizophrenia genomic architecture.
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Affiliation(s)
- Maria Guardiola-Ripoll
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain.
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain.
| | - Carmen Almodóvar-Payá
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain
| | | | | | - Sergi Papiol
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
- Max Planck Institute of Psychiatry, Munich, Germany
| | - Erick J Canales-Rodríguez
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - María Ángeles García-León
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain
| | - Paola Fuentes-Claramonte
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain
| | - Josep Salavert
- Hospital Sant Rafael, Germanes Hospitalàries, Barcelona, Spain
| | - Josep Tristany
- Hospital Sagrat Cor, Germanes Hospitalàries, Martorell, Spain
| | - Llanos Torres
- Hospital Mare de Déu de la Mercè, Germanes Hospitalàries, Barcelona, Spain
| | - Elena Rodríguez-Cano
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain
- Hospital Benito Menni, Germanes Hospitalàries, Sant Boi de Llobregat, Spain
| | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain
| | - Mar Fatjó-Vilas
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain.
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain.
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Universitat de Barcelona, Barcelona, Spain.
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18
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Huang Y, Wu Z, Li T, Wang X, Wang Y, Xing L, Zhu H, Lin W, Wang L, Guo L, Gilmore JH, Li G. Mapping Genetic Topography of Cortical Thickness and Surface Area in Neonatal Brains. J Neurosci 2023; 43:6010-6020. [PMID: 37369585 PMCID: PMC10451118 DOI: 10.1523/jneurosci.1841-22.2023] [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: 09/26/2022] [Revised: 06/05/2023] [Accepted: 06/10/2023] [Indexed: 06/29/2023] Open
Abstract
Adult twin neuroimaging studies have revealed that cortical thickness (CT) and surface area (SA) are differentially influenced by genetic information, leading to their spatially distinct genetic patterning and topography. However, the postnatal origins of the genetic topography of CT and SA remain unclear, given the dramatic cortical development from neonates to adults. To fill this critical gap, this study unprecedentedly explored how genetic information differentially regulates the spatial topography of CT and SA in the neonatal brain by leveraging brain magnetic resonance (MR) images from 202 twin neonates with minimal influence by the complicated postnatal environmental factors. We capitalized on infant-dedicated computational tools and a data-driven spectral clustering method to parcellate the cerebral cortex into a set of distinct regions purely according to the genetic correlation of cortical vertices in terms of CT and SA, respectively, and accordingly created the first genetically informed cortical parcellation maps of neonatal brains. Both genetic parcellation maps exhibit bilaterally symmetric and hierarchical patterns, but distinct spatial layouts. For CT, regions with closer genetic relationships demonstrate an anterior-posterior (A-P) division, while for SA, regions with greater genetic proximity are typically within the same lobe. Certain genetically informed regions exhibit strong similarities between neonates and adults, with the most striking similarities in the medial surface in terms of SA, despite their overall substantial differences in genetic parcellation maps. These results greatly advance our understanding of the development of genetic influences on the spatial patterning of cortical morphology.SIGNIFICANCE STATEMENT Genetic influences on cortical thickness (CT) and surface area (SA) are complex and could evolve throughout the lifespan. However, studies revealing distinct genetic topography of CT and SA have been limited to adults. Using brain structural magnetic resonance (MR) images of twins, we unprecedentedly discovered the distinct genetically-informed parcellation maps of CT and SA in neonatal brains, respectively. Each genetic parcellation map comprises a distinct spatial layout of cortical regions, where vertices within the same region share high genetic correlation. These genetic parcellation maps of CT and SA of neonates largely differ from those of adults, despite their highly remarkable similarities in the medial cortex of SA. These discoveries provide important insights into the genetic organization of the early cerebral cortex development.
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Affiliation(s)
- Ying Huang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Tengfei Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516
| | - Ya Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Lei Xing
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi 710129, China
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
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19
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Brown SSG, Westwater ML, Seidlitz J, Ziauddeen H, Fletcher PC. Hypothalamic volume is associated with body mass index. Neuroimage Clin 2023; 39:103478. [PMID: 37558541 PMCID: PMC10509524 DOI: 10.1016/j.nicl.2023.103478] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 06/19/2023] [Accepted: 07/18/2023] [Indexed: 08/11/2023]
Abstract
The hypothalamus is an important neuroendocrine hub for the control of appetite and satiety. In animal studies it has been established that hypothalamic lesioning or stimulation causes alteration to feeding behaviour and consequently body mass, and exposure to high calorie diets induces hypothalamic inflammation. These findings suggest that alterations in hypothalamic structure and function are both a cause and a consequence of changes to food intake. However, there is limited in vivo human data relating the hypothalamus to obesity or eating disorders, in part due to technical problems relating to its small size. Here, we used a novel automated segmentation algorithm to exploratorily investigate the relationship between hypothalamic volume, normalised to intracranial volume, and body mass index (BMI). The analysis was applied across four independent datasets comprising of young adults (total n = 1,351 participants) spanning a range of BMIs (13.3 - 47.8 kg/m2). We compared underweight (including individuals with anorexia nervosa), healthy weight, overweight and obese individuals in a series of complementary analyses. We report that overall hypothalamic volume is significantly larger in overweight and obese groups of young adults. This was also observed for a number of hypothalamic sub-regions. In the largest dataset (the HCP-Young Adult dataset (n = 1111)) there was a significant relationship between hypothalamic volume and BMI. We suggest that our findings of a positive relationship between hypothalamic volume and BMI is potentially consistent with hypothalamic inflammation as seen in animal models in response to high fat diet, although more research is needed to establish a causal relationship. Overall, we present novel, in vivo findings that link elevated BMI to altered hypothalamic structure. This has important implications for study of the neural mechanisms of obesity in humans.
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Affiliation(s)
- Stephanie S G Brown
- Department of Psychiatry, University of Cambridge, Addenbrookes Hospital, Cambridge CB2 0QQ, United Kingdom.
| | - Margaret L Westwater
- Department of Psychiatry, University of Cambridge, Addenbrookes Hospital, Cambridge CB2 0QQ, United Kingdom; Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford OX3 7JX, United Kingdom
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA; Lifespan Brain Institute of Children's Hospital of Philadelphia and University of Pennsylvania, Philadelphia, PA, USA
| | - Hisham Ziauddeen
- Department of Psychiatry, University of Cambridge, Addenbrookes Hospital, Cambridge CB2 0QQ, United Kingdom
| | - Paul C Fletcher
- Department of Psychiatry, University of Cambridge, Addenbrookes Hospital, Cambridge CB2 0QQ, United Kingdom; Wellcome Trust MRC Institute of Metabolic Science, University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0QQ, United Kingdom; Cambridgeshire and Peterborough NHS Trust, United Kingdom
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20
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Yang F, Liu R, He S, Ruan S, He B, Li J, Pan L. Being a morning man has causal effects on the cerebral cortex: a Mendelian randomization study. Front Neurosci 2023; 17:1222551. [PMID: 37547136 PMCID: PMC10400340 DOI: 10.3389/fnins.2023.1222551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Accepted: 07/05/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction Numerous studies have suggested a connection between circadian rhythm and neurological disorders with cognitive and consciousness impairments in humans, yet little evidence stands for a causal relationship between circadian rhythm and the brain cortex. Methods The top 10,000 morningness-related single-nucleotide polymorphisms of the Genome-wide association study (GWAS) summary statistics were used to filter the instrumental variables. GWAS summary statistics from the ENIGMA Consortium were used to assess the causal relationship between morningness and variates like cortical thickness (TH) or surficial area (SA) on the brain cortex. The inverse-variance weighted (IVW) and weighted median (WM) were used as the major estimates whereas MR-Egger, MR Pleiotropy RESidual Sum and Outlier, leave-one-out analysis, and funnel-plot were used for heterogeneity and pleiotropy detecting. Results Regionally, morningness decreased SA of the rostral middle frontal gyrus with genomic control (IVW: β = -24.916 mm, 95% CI: -47.342 mm to -2.490 mm, p = 0.029. WM: β = -33.208 mm, 95% CI: -61.933 mm to -4.483 mm, p = 0.023. MR Egger: β < 0) and without genomic control (IVW: β = -24.581 mm, 95% CI: -47.552 mm to -1.609 mm, p = 0.036. WM: β = -32.310 mm, 95% CI: -60.717 mm to -3.902 mm, p = 0.026. MR Egger: β < 0) on a nominal significance, with no heterogeneity or no outliers. Conclusions and implications Circadian rhythm causally affects the rostral middle frontal gyrus; this sheds new light on the potential use of MRI in disease diagnosis, revealing the significance of circadian rhythm on the progression of disease, and might also suggest a fresh therapeutic approach for disorders related to the rostral middle frontal gyrus-related.
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Affiliation(s)
- Fan Yang
- Department of Anesthesiology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Province, China
- Department of Anesthesiology, Central Hospital of Shaoyang, Shaoyang, Hunan Province, China
- Guangxi Key Laboratory for Basic Science and Prevention of Perioperative Organ Disfunction, Nanning, Guangxi Province, China
- Guangxi Clinical Research Center for Anesthesiology, Nanning, Guangxi Province, China
- Guangxi Engineering Research Center for Tissue and Organ Injury and Repair Medicine, Nanning, Guangxi Province, China
| | - Ru Liu
- Department of Anesthesiology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Province, China
| | - Sheng He
- Department of Anesthesiology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Province, China
- Department of Anesthesiology, The First Affiliated Hospital of Southern China University, Hengyang, China
| | - Sijie Ruan
- Department of Anesthesiology, Central Hospital of Shaoyang, Shaoyang, Hunan Province, China
| | - Binghua He
- Department of Anesthesiology, Central Hospital of Shaoyang, Shaoyang, Hunan Province, China
| | - Junda Li
- Department of Anesthesiology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Province, China
| | - Linghui Pan
- Department of Anesthesiology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Province, China
- Guangxi Key Laboratory for Basic Science and Prevention of Perioperative Organ Disfunction, Nanning, Guangxi Province, China
- Guangxi Clinical Research Center for Anesthesiology, Nanning, Guangxi Province, China
- Guangxi Engineering Research Center for Tissue and Organ Injury and Repair Medicine, Nanning, Guangxi Province, China
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21
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Slapø NB, Jørgensen KN, Elvsåshagen T, Nerland S, Roelfs D, Valstad M, Timpe CMF, Richard G, Beck D, Sæther LS, Frogner Werner MC, Lagerberg TV, Andreassen OA, Melle I, Agartz I, Westlye LT, Moberget T, Jönsson EG. Relationship between function and structure in the visual cortex in healthy individuals and in patients with severe mental disorders. Psychiatry Res Neuroimaging 2023; 332:111633. [PMID: 37028226 DOI: 10.1016/j.pscychresns.2023.111633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/12/2023] [Accepted: 03/17/2023] [Indexed: 04/09/2023]
Abstract
Patients with schizophrenia spectrum disorders (SCZspect) and bipolar disorders (BD) show impaired function in the primary visual cortex (V1), indicated by altered visual evoked potential (VEP). While the neural substrate for altered VEP in these patients remains elusive, altered V1 structure may play a role. One previous study found a positive relationship between the amplitude of the P100 component of the VEP and V1 surface area, but not V1 thickness, in a small sample of healthy individuals. Here, we aimed to replicate these findings in a larger healthy control (HC) sample (n = 307) and to examine the same relationship in patients with SCZspect (n = 30) or BD (n = 45). We also compared the mean P100 amplitude, V1 surface area and V1 thickness between controls and patients and found no significant group differences. In HC only, we found a significant positive P100-V1 surface area association, while there were no significant P100-V1 thickness relationships in HC, SCZspect or BD. Together, our results confirm previous findings of a positive P100-V1 surface area association in HC, whereas larger patient samples are needed to further clarify the function-structure relationship in V1 in SCZspect and BD.
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Affiliation(s)
- Nora Berz Slapø
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway.
| | - Kjetil Nordbø Jørgensen
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatry, Telemark Hospital, Skien, Norway
| | - Torbjørn Elvsåshagen
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Neurology, Oslo University Hospital, Norway
| | - Stener Nerland
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Daniel Roelfs
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway
| | - Mathias Valstad
- Department of Mental Disorders, Norwegian Institute of Public Health, Norway
| | - Clara M F Timpe
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway
| | | | - Dani Beck
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | | | | | - Trine Vik Lagerberg
- NORMENT, Division of Mental Health and Addiction, Oslo University hospital, Norway
| | - Ole A Andreassen
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University hospital, Norway
| | - Ingrid Melle
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway
| | - Ingrid Agartz
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University hospital, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Sciences, Stockholm Region, Stockholm, Sweden
| | - Lars T Westlye
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway
| | - Torgeir Moberget
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Behavioral Sciences, Faculty of Health Sciences, Oslo Metropolitan University, OsloMet, Oslo, Norway
| | - Erik G Jönsson
- NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Sciences, Stockholm Region, Stockholm, Sweden
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22
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de Matos K, Cury C, Chougar L, Strike LT, Rolland T, Riche M, Hemforth L, Martin A, Banaschewski T, Bokde ALW, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Paillère Martinot ML, Artiges E, Nees F, Papadopoulos Orfanos D, Lemaitre H, Paus T, Poustka L, Hohmann S, Millenet S, Fröhner JH, Smolka MN, Vaidya N, Walter H, Whelan R, Schumann G, Frouin V, Bach Cuadra M, Colliot O, Couvy-Duchesne B. Temporo-basal sulcal connections: a manual annotation protocol and an investigation of sexual dimorphism and heritability. Brain Struct Funct 2023; 228:1459-1478. [PMID: 37358662 DOI: 10.1007/s00429-023-02663-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 06/07/2023] [Indexed: 06/27/2023]
Abstract
The temporo-basal region of the human brain is composed of the collateral, the occipito-temporal, and the rhinal sulci. We manually rated (using a novel protocol) the connections between rhinal/collateral (RS-CS), collateral/occipito-temporal (CS-OTS) and rhinal/occipito-temporal (RS-OTS) sulci, using the MRI of nearly 3400 individuals including around 1000 twins. We reported both the associations between sulcal polymorphisms as well with a wide range of demographics (e.g. age, sex, handedness). Finally, we also estimated the heritability, and the genetic correlation between sulcal connections. We reported the frequency of the sulcal connections in the general population, which were hemisphere dependent. We found a sexual dimorphism of the connections, especially marked in the right hemisphere, with a CS-OTS connection more frequent in females (approximately 35-40% versus 20-25% in males) and an RS-CS connection more common in males (approximately 40-45% versus 25-30% in females). We confirmed associations between sulcal connections and characteristics of incomplete hippocampal inversion (IHI). We estimated the broad sense heritability to be 0.28-0.45 for RS-CS and CS-OTS connections, with hints of dominant contribution for the RS-CS connection. The connections appeared to share some of their genetic causing factors as indicated by strong genetic correlations. Heritability appeared much smaller for the (rarer) RS-OTS connection.
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Affiliation(s)
- Kevin de Matos
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013, Paris, France
- CIBM Center for Biomedical Imaging, Vaud, Switzerland
- Radiology Department, Lausanne University and University Hospital, Lausanne, Switzerland
| | - Claire Cury
- CNRS, Inria, Inserm, IRISA UMR 6074, Empenn ERL U-1228, University of Rennes, 35000, Rennes, France
| | - Lydia Chougar
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013, Paris, France
- Service de neuroradiologie, AP-HP, Pitié-Salpêtrière, Paris, France
| | - Lachlan T Strike
- Queensland Brain Institute, The University of Queensland, St Lucia, QLD, 4072, Australia
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Thibault Rolland
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013, Paris, France
| | - Maximilien Riche
- Department of Neurosurgery, AP-HP, La Pitié-Salpêtrière Hospital, Sorbonne University, 75013, Paris, France
| | - Lisa Hemforth
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013, Paris, France
| | - Alexandre Martin
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013, Paris, France
- Inria Sophia Antipolis, Morpheme Project, Paris, France
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Square J5, 68159, Mannheim, Germany
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Sylvane Desrivières
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, London, UK
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131, Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, 91191, Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, VT, 05405, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, Berlin Institute of Health, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Brunswick, Berlin, Germany
| | - Jean-Luc Martinot
- INSERM U 1299 "Trajectoires développementales & psychiatrie", CNRS, Institut National de la Santé et de la Recherche Médicale, Ecole Normale Supérieure Paris-Saclay, Centre Borelli, University Paris-Saclay, Gif-sur-Yvette, France
| | - Marie-Laure Paillère Martinot
- INSERM U 1299 "Trajectoires développementales & psychiatrie", CNRS, AP-HP, Institut National de la Santé et de la Recherche Médicale, Ecole Normale Supérieure Paris-Saclay, Centre Borelli, University Paris-Saclay, Gif-sur-Yvette, France
- Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
| | - Eric Artiges
- INSERM U 1299 "Trajectoires développementales & psychiatrie", CNRS, Institut National de la Santé et de la Recherche Médicale, Ecole Normale Supérieure Paris-Saclay, Centre Borelli, University Paris-Saclay, Gif-sur-Yvette, France
- Psychiatry Department, EPS Barthélémy Durand, Etampes, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Square J5, 68159, Mannheim, Germany
- Institute of Cognitive and Clinical Neuroscience, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Square J5, Mannheim, Germany
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
| | | | - Herve Lemaitre
- NeuroSpin, CEA, Université Paris-Saclay, 91191, Gif-sur-Yvette, France
- UMR 5293, CNRS, CEA, Institut des Maladies Neurodégénératives, Université de Bordeaux, 33076, Bordeaux, France
| | - Tomáš Paus
- Departments of Psychiatry and Neuroscience, Université de Montréal and Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
- Departments of Psychiatry and Psychology, University of Toronto, Toronto, ON, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Square J5, 68159, Mannheim, Germany
| | - Sabina Millenet
- Department of Child and Adolescent Psychiatry and Psychotherapy, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Square J5, 68159, Mannheim, Germany
| | - 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
| | - Nilakshi Vaidya
- Department of Psychiatry and Neuroscience, Centre for Population Neuroscience and Stratified Medicine (PONS), Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, Berlin Institute of Health, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Gunter Schumann
- Department of Psychiatry and Neuroscience, Centre for Population Neuroscience and Stratified Medicine (PONS), Charité Universitätsmedizin Berlin, Berlin, Germany
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-Inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Vincent Frouin
- NeuroSpin, CEA, Université Paris-Saclay, 91191, Gif-sur-Yvette, France
| | - Meritxell Bach Cuadra
- CIBM Center for Biomedical Imaging, Vaud, Switzerland
- Radiology Department, Lausanne University and University Hospital, Lausanne, Switzerland
| | - Olivier Colliot
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013, Paris, France
| | - Baptiste Couvy-Duchesne
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, 75013, Paris, France.
- Institute for Molecular Biosciences, The University of Queensland, St Lucia, QLD, 4062, Australia.
- ARAMIS Team, Pitié-Salpêtrière Hospital, Institut du Cerveau, 75013, Paris, France.
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23
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Kennedy KG, Islam AH, Karthikeyan S, Metcalfe AWS, McCrindle BW, MacIntosh BJ, Black S, Goldstein BI. Differential association of endothelial function with brain structure in youth with versus without bipolar disorder. J Psychosom Res 2023; 167:111180. [PMID: 36764023 DOI: 10.1016/j.jpsychores.2023.111180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/22/2023] [Accepted: 01/29/2023] [Indexed: 02/04/2023]
Abstract
BACKGROUND Mood symptoms and disorders are associated with impaired endothelial function, a marker of early atherosclerosis. Given the increased vascular burden and neurostructural differences among individuals with mood disorders, we investigated the endothelial function and brain structure interface in relation to youth bipolar disorder (BD). METHODS This cross-sectional case-controlled study included 115 youth, ages 13-20 years (n = 66 BD; n = 49 controls [CG]). Cortical thickness and volume for regions of interest (ROI; insular cortex, ventrolateral prefrontal cortex [vlPFC], temporal lobe) were acquired from FreeSurfer processed T1-weighted MRI images. Endothelial function was assessed using pulse amplitude tonometry, yielding a reactive hyperemia index (RHI). ROI and vertex-wise analyses controlling for age, sex, obesity, and intracranial volume investigated for RHI-neurostructural associations, and RHI-by-diagnosis interactions. RESULTS In ROI analyses, higher RHI (i.e., better endothelial function) was associated with lower thickness in the insular cortex (β = -0.19, pFDR = 0.03), vlPFC (β = -0.30, pFDR = 0.003), and temporal lobe (β = -0.22, pFDR = 0.01); and lower temporal lobe volume (β = -0.16, pFDR = 0.01) in the overall sample. In vertex-wise analyses, higher RHI was associated with lower cortical thickness and volume in the insular cortex, prefrontal cortex (e.g., vlPFC), and temporal lobe. Additionally, higher RHI was associated with lower vlPFC and temporal lobe volume to a greater extent in youth with BD vs. CG. CONCLUSIONS Better endothelial function was associated with lower regional brain thickness and volume, contrasting the hypothesized associations. Additionally, we found evidence that this pattern was exaggerated in youth with BD. Future studies examining the direction of the observed associations and underlying mechanisms are warranted.
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Affiliation(s)
- Kody G Kennedy
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada; Department of Pharmacology, University of Toronto, Toronto, ON, Canada.
| | - Alvi H Islam
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada; Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Sudhir Karthikeyan
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada.
| | - Arron W S Metcalfe
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada
| | - Brian W McCrindle
- Faculty of Medicine, University of Toronto, Toronto, Canada; Hospital for Sick Children, Toronto, Canada; Labatt Family Heart Centre, Hospital for Sick Children, Toronto, Ontario, Canada; Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada.
| | - Bradley J MacIntosh
- Hurvitz Brain Sciences, Sunnybrook Research Institute, Toronto, Canada; Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada; Computational Radiology & Artificial Intelligence (CRAI) Unit, Dept of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
| | - Sandra Black
- Faculty of Medicine, University of Toronto, Toronto, Canada; Hurvitz Brain Sciences, Sunnybrook Research Institute, Toronto, Canada; Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, Toronto, Canada.
| | - Benjamin I Goldstein
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Canada; Department of Pharmacology, University of Toronto, Toronto, ON, Canada; Faculty of Medicine, University of Toronto, Toronto, Canada.
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24
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Smirni D, Smirni P, Lavanco G, Caci B. Premorbid Personality Traits as Risk Factors for Behavioral Addictions: A Systematic Review of a Vulnerability Hypothesis. CHILDREN (BASEL, SWITZERLAND) 2023; 10:467. [PMID: 36980025 PMCID: PMC10047899 DOI: 10.3390/children10030467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/24/2023] [Accepted: 02/25/2023] [Indexed: 03/03/2023]
Abstract
The debate on personality structure and behavioral addictions is an outstanding issue. According to some authors, behavioral addictions could arise from a premorbid personality, while for others, it could result from a pathological use of technological tools. The current study aims to investigate whether, in the latest literature, personality traits have been identified as predictors of behavioral addictions. A literature search was conducted under the PRISMA methodology, considering the most relevant studies of the five-factor model from the past 10 years. Overall, most studies on addiction, personality traits, and personality genetics proved that behavioral addiction may be an epiphenomenon of a pre-existing personality structure, and that it more easily occurs in vulnerable subjects with emotional instability, negative affects, and unsatisfactory relationships with themselves, others, and events. Such neurotic personality structure was common to any addictive behavior, and was the main risk factor for both substance and behavioral addictions. Therefore, in clinical and educational contexts, it becomes crucial to primarily focus on the vulnerability factors, at-risk personality traits, and protective and moderating traits such as extroversion, agreeableness, conscientiousness, and openness to experience; meanwhile, treatment of behavioral addictions is frequently focused on overt pathological behaviors.
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Affiliation(s)
- Daniela Smirni
- Department of Psychology, Educational Science and Human Movement, University of Palermo, 90128 Palermo, Italy
| | - Pietro Smirni
- Department of Educational Sciences, University of Catania, 95124 Catania, Italy
| | - Gioacchino Lavanco
- Department of Psychology, Educational Science and Human Movement, University of Palermo, 90128 Palermo, Italy
| | - Barbara Caci
- Department of Psychology, Educational Science and Human Movement, University of Palermo, 90128 Palermo, Italy
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25
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Grotzinger AD, Mallard TT, Liu Z, Seidlitz J, Ge T, Smoller JW. Multivariate genomic architecture of cortical thickness and surface area at multiple levels of analysis. Nat Commun 2023; 14:946. [PMID: 36806290 PMCID: PMC9941500 DOI: 10.1038/s41467-023-36605-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 02/08/2023] [Indexed: 02/22/2023] Open
Abstract
Recent work in imaging genetics suggests high levels of genetic overlap within cortical regions for cortical thickness (CT) and surface area (SA). We model this multivariate system of genetic relationships by applying Genomic Structural Equation Modeling (Genomic SEM) and parsimoniously define five genomic brain factors underlying both CT and SA along with a general factor capturing genetic overlap across all brain regions. We validate these factors by demonstrating the generalizability of the model to a semi-independent sample and show that the factors align with biologically and functionally relevant parcellations of the cortex. We apply Stratified Genomic SEM to identify specific categories of genes (e.g., neuronal cell types) that are disproportionately associated with pleiotropy across specific subclusters of brain regions, as indexed by the genomic factors. Finally, we examine genetic associations with psychiatric and cognitive correlates, finding that broad aspects of cognitive function are associated with a general factor for SA and that psychiatric associations are null. These analyses provide key insights into the multivariate genomic architecture of two critical features of the cerebral cortex.
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Affiliation(s)
- Andrew D Grotzinger
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA.
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA.
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
| | - Zhaowen Liu
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
| | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
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26
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Maes HHM, Lapato DM, Schmitt JE, Luciana M, Banich MT, Bjork JM, Hewitt JK, Madden PA, Heath AC, Barch DM, Thompson WK, Iacono WG, Neale MC. Genetic and Environmental Variation in Continuous Phenotypes in the ABCD Study®. Behav Genet 2023; 53:1-24. [PMID: 36357558 PMCID: PMC9823057 DOI: 10.1007/s10519-022-10123-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 10/11/2022] [Indexed: 11/12/2022]
Abstract
Twin studies yield valuable insights into the sources of variation, covariation and causation in human traits. The ABCD Study® (abcdstudy.org) was designed to take advantage of four universities known for their twin research, neuroimaging, population-based sampling, and expertise in genetic epidemiology so that representative twin studies could be performed. In this paper we use the twin data to: (i) provide initial estimates of heritability for the wide range of phenotypes assessed in the ABCD Study using a consistent direct variance estimation approach, assuring that both data and methodology are sound; and (ii) provide an online resource for researchers that can serve as a reference point for future behavior genetic studies of this publicly available dataset. Data were analyzed from 772 pairs of twins aged 9-10 years at study inception, with zygosity determined using genotypic data, recruited and assessed at four twin hub sites. The online tool provides twin correlations and both standardized and unstandardized estimates of additive genetic, and environmental variation for 14,500 continuously distributed phenotypic features, including: structural and functional neuroimaging, neurocognition, personality, psychopathology, substance use propensity, physical, and environmental trait variables. The estimates were obtained using an unconstrained variance approach, so they can be incorporated directly into meta-analyses without upwardly biasing aggregate estimates. The results indicated broad consistency with prior literature where available and provided novel estimates for phenotypes without prior twin studies or those assessed at different ages. Effects of site, self-identified race/ethnicity, age and sex were statistically controlled. Results from genetic modeling of all 53,172 continuous variables, including 38,672 functional MRI variables, will be accessible via the user-friendly open-access web interface we have established, and will be updated as new data are released from the ABCD Study. This paper provides an overview of the initial results from the twin study embedded within the ABCD Study, an introduction to the primary research domains in the ABCD study and twin methodology, and an evaluation of the initial findings with a focus on data quality and suitability for future behavior genetic studies using the ABCD dataset. The broad introductory material is provided in recognition of the multidisciplinary appeal of the ABCD Study. While this paper focuses on univariate analyses, we emphasize the opportunities for multivariate, developmental and causal analyses, as well as those evaluating heterogeneity by key moderators such as sex, demographic factors and genetic background.
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Affiliation(s)
- Hermine H. M. Maes
- grid.224260.00000 0004 0458 8737Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980033, Richmond, VA 23298-0033 USA ,grid.224260.00000 0004 0458 8737Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA USA ,grid.224260.00000 0004 0458 8737Massey Cancer Center, Virginia Commonwealth University, Richmond, VA USA
| | - Dana M. Lapato
- grid.224260.00000 0004 0458 8737Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980033, Richmond, VA 23298-0033 USA
| | - J. Eric Schmitt
- grid.25879.310000 0004 1936 8972Departments of Radiology and Psychiatry, University of Pennsylvania, Philadelphia, PA USA
| | - Monica Luciana
- grid.17635.360000000419368657Department of Psychology, University of Minnesota, Minneapolis, USA
| | - Marie T. Banich
- grid.266190.a0000000096214564Department of Psychology and Neuroscience, University of Colorado, Boulder, USA ,grid.266190.a0000000096214564Institute of Cognitive Science, University of Colorado, Boulder, USA
| | - James M. Bjork
- grid.224260.00000 0004 0458 8737Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA USA
| | - John K. Hewitt
- grid.266190.a0000000096214564Institute of Cognitive Science, University of Colorado, Boulder, USA ,grid.266190.a0000000096214564Institute for Behavioral Genetics, University of Colorado, Boulder, USA
| | - Pamela A. Madden
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University in St Louis, St Louis, MO USA
| | - Andrew C. Heath
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University in St Louis, St Louis, MO USA
| | - Deanna M. Barch
- grid.4367.60000 0001 2355 7002Department of Psychiatry, Washington University in St Louis, St Louis, MO USA
| | - Wes K. Thompson
- grid.266100.30000 0001 2107 4242Division of Biostatistics and Department of Radiology, Population Neuroscience and Genetics Lab, University of California at San Diego, La Jolla, CA USA
| | - William G. Iacono
- grid.17635.360000000419368657Department of Psychology, University of Minnesota, Minneapolis, USA
| | - Michael C. Neale
- grid.224260.00000 0004 0458 8737Department of Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, PO Box 980033, Richmond, VA 23298-0033 USA ,grid.224260.00000 0004 0458 8737Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA USA
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27
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Whittle S, Pozzi E, Rakesh D, Kim JM, Yap MBH, Schwartz OS, Youssef G, Allen NB, Vijayakumar N. Harsh and Inconsistent Parental Discipline Is Associated With Altered Cortical Development in Children. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:989-997. [PMID: 35158076 DOI: 10.1016/j.bpsc.2022.02.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 02/01/2022] [Accepted: 02/03/2022] [Indexed: 01/01/2023]
Abstract
BACKGROUND A growing body of evidence suggests that parenting behaviors may affect child mental health via altering brain development. There is a scarcity of research, however, that has investigated associations between parenting behavior and brain structure using longitudinal magnetic resonance imaging. This study aimed to investigate associations between parenting behaviors and structural brain development across the transition from childhood to adolescence. METHODS Participants were 246 children who provided 436 magnetic resonance imaging datasets covering the age range from 8 to 13 years. Parents (94% mothers) completed self-report measures of parenting behavior, and both children and parents reported on child mental health. Factor analysis was used to identify dimensions of parental behavior. Linear mixed-effects models investigated associations between parenting behaviors and age-related change in cortical thickness and surface area and subcortical volume. Mediation models tested whether brain changes mediated associations between parenting behaviors and changes in internalizing/externalizing symptoms. RESULTS Hypothesized associations between parenting and amygdala, hippocampal, and frontal trajectories were not supported. Rather, higher levels of parent harsh/inconsistent discipline were associated with decreases in surface area in medial parietal and temporal pole regions and reduced cortical thinning in medial parietal regions. Some effects were present in female but not male children. There were no associations between these neurodevelopmental alterations and symptoms. CONCLUSIONS This study provides insight into the links between parenting behavior and child neurodevelopment. Given the functions of implicated regions, findings may suggest that parental harsh/inconsistent discipline affects the development of neural circuits subserving sensorimotor and social functioning in children.
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Affiliation(s)
- Sarah Whittle
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia.
| | - Elena Pozzi
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia
| | - Divyangana Rakesh
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia
| | - Julia Minji Kim
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia
| | - Marie B H Yap
- School of Psychological Science, Turner Institute for Brain and Mental Health, Monash University, Melbourne, Victoria, Australia; Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Orli S Schwartz
- Department of Psychiatry, University of Melbourne, Parkville, Victoria, Australia; Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, Parkville, Victoria, Australia
| | - George Youssef
- Department of Psychology, Deakin University, Burwood, Victoria, Australia
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28
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Associations between brain imaging and polygenic scores of mental health and educational attainment in children aged 9-11. Neuroimage 2022; 263:119611. [PMID: 36070838 DOI: 10.1016/j.neuroimage.2022.119611] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 08/03/2022] [Accepted: 09/03/2022] [Indexed: 12/25/2022] Open
Abstract
Psychiatric disorders are highly heritable and polygenic, and many have their peak onset in late childhood and adolescence, a period of tremendous changes. Although the neurodevelopmental antecedents of mental illness are widely acknowledged, research in youth population cohorts is still scarce, preventing our progress towards the early characterization of these disorders. We included 7,124 children (9-11 years old) from the Adolescent Brain and Cognitive Development Study to map the associations of structural and diffusion brain imaging with common genetic variants and polygenic scores for psychiatric disorders and educational attainment. We used principal component analysis to derive imaging components, and calculated their heritability. We then assessed the relationship of imaging components with genetic and clinical psychiatric risk with univariate models and Canonical correlation analysis (CCA). Most imaging components had moderate heritability. Univariate models showed limited evidence and small associations of polygenic scores with brain structure at this age. CCA revealed two significant modes of covariation. The first mode linked higher polygenic scores for educational attainment with less externalizing problems and larger surface area. The second mode related higher polygenic scores for schizophrenia, bipolar disorder, and autism spectrum disorder to higher global cortical thickness, smaller white matter volumes of the fornix and cingulum, larger medial occipital surface area and smaller surface area of lateral and medial temporal regions. While cross-validation suggested limited generalizability, our results highlight the potential of multivariate models to better understand the transdiagnostic and distributed relationships between mental health and brain structure in late childhood.
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29
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Friedman NP, Banich MT, Keller MC. Twin studies to GWAS: there and back again. Trends Cogn Sci 2021; 25:855-869. [PMID: 34312064 PMCID: PMC8446317 DOI: 10.1016/j.tics.2021.06.007] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 06/25/2021] [Accepted: 06/28/2021] [Indexed: 01/01/2023]
Abstract
The field of human behavioral genetics has come full circle. It began by using twin/family studies to estimate the relative importance of genetic and environmental influences. As large-scale genotyping became cost-effective, genome-wide association studies (GWASs) yielded insights about the nature of genetic influences and new methods that use GWAS data to estimate heritability and genetic correlations invigorated the field. Yet these newer GWAS methods have not replaced twin/family studies. In this review, we discuss the strengths and weaknesses of the two approaches with respect to characterizing genetic and environmental influences, measurement of behavioral phenotypes, and evaluation of causal models, with a particular focus on cognitive neuroscience. This discussion highlights how twin/family studies and GWAS complement and mutually reinforce one another.
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Affiliation(s)
- Naomi P Friedman
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA; Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO 80309, USA.
| | - Marie T Banich
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA; Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Matthew C Keller
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USA; Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO 80309, USA
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30
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Ferschmann L, Bos MGN, Herting MM, Mills KL, Tamnes CK. Contextualizing adolescent structural brain development: Environmental determinants and mental health outcomes. Curr Opin Psychol 2021; 44:170-176. [PMID: 34688028 DOI: 10.1016/j.copsyc.2021.09.014] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/15/2021] [Accepted: 09/20/2021] [Indexed: 01/04/2023]
Abstract
The spatiotemporal group-level patterns of brain macrostructural development are relatively well-documented. Current research emphasizes individual variability in brain development, including its causes and consequences. Although genetic factors and prenatal and perinatal events play critical roles, calls are now made to also study brain development in transactional interplay with the different aspects of an individual's physical and social environment. Such focus is highly relevant for research on adolescence, a period involving a multitude of contextual changes paralleled by continued refinement of complex cognitive and affective neural systems. Here, we discuss associations between selected aspects of an individual's physical and social environment and adolescent brain structural development and possible links to mental health. We also touch on methodological considerations for future research.
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Affiliation(s)
- Lia Ferschmann
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway.
| | - Marieke G N Bos
- Institute of Psychology, Leiden University, the Netherlands; Leiden Institute for Brain and Cognition, Leiden University, the Netherlands
| | - Megan M Herting
- Department of Population and Public Health Sciences, University of Southern California, USA
| | - Kathryn L Mills
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Department of Psychology, University of Oregon, USA
| | - Christian K Tamnes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
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31
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Rakesh D, Whittle S. Socioeconomic status and the developing brain - A systematic review of neuroimaging findings in youth. Neurosci Biobehav Rev 2021; 130:379-407. [PMID: 34474050 DOI: 10.1016/j.neubiorev.2021.08.027] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 08/13/2021] [Accepted: 08/25/2021] [Indexed: 02/06/2023]
Abstract
A growing literature has shown associations between socioeconomic disadvantage and neural properties (such as brain structure and function). In this review, we aimed to synthesize findings on the neural correlates of socioeconomic status (SES) in youth samples across neuroimaging modalities. We also aimed to disentangle the effects of different SES measures (e.g., parent income and education) in our synthesis. We found relatively consistent patterns of positive associations between SES and both volume and cortical surface area of frontal regions, and amygdala, hippocampal, and striatal volume (with most consistent results for composite SES indices). Despite limited longitudinal work, results suggest that SES is associated with developmental trajectories of gray matter structure. Higher SES was also found to be associated with increased fractional anisotropy of some white matter tracts, although there were more null than positive findings. Finally, methodological heterogeneity in brain function and connectivity studies prevented us from making strong inferences. Based on our findings, we make recommendations for future research, discuss the role of mitigating factors, and implications for policy.
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Affiliation(s)
- Divyangana Rakesh
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, VIC, Australia.
| | - Sarah Whittle
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, VIC, Australia
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32
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Koike S, Uematsu A, Sasabayashi D, Maikusa N, Takahashi T, Ohi K, Nakajima S, Noda Y, Hirano Y. Recent Advances and Future Directions in Brain MR Imaging Studies in Schizophrenia: Toward Elucidating Brain Pathology and Developing Clinical Tools. Magn Reson Med Sci 2021; 21:539-552. [PMID: 34408115 DOI: 10.2463/mrms.rev.2021-0050] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Schizophrenia is a common severe psychiatric disorder that affects approximately 1% of general population through the life course. Historically, in Kraepelin's time, schizophrenia was a disease unit conceptualized as dementia praecox; however, since then, the disease concept has changed. Recent MRI studies had shown that the neuropathology of the brain in this disorder was characterized by mild progression before and after the onset of the disease, and that the brain alterations were relatively smaller than assumed. Although genetic factors contribute to the brain alterations in schizophrenia, which are thought to be trait differences, other changes include factors that are common in psychiatric diseases. Furthermore, it has been shown that the brain differences specific to schizophrenia were relatively small compared to other changes, such as those caused by brain development, aging, and gender. In addition, compared to the disease and participant factors, machine and imaging protocol differences could affect MRI signals, which should be addressed in multi-site studies. Recent advances in MRI modalities, such as multi-shell diffusion-weighted imaging, magnetic resonance spectroscopy, and multimodal brain imaging analysis, may be candidates to sharpen the characterization of schizophrenia-specific factors and provide new insights. The Brain/MINDS Beyond Human Brain MRI (BMB-HBM) project has been launched considering the differences and noises irrespective of the disease pathologies and includes the future perspectives of MRI studies for various psychiatric and neurological disorders. The sites use restricted MRI machines and harmonized multi-modal protocols, standardized image preprocessing, and traveling subject harmonization. Data sharing to the public will be planned in FY 2024. In the future, we believe that combining a high-quality human MRI dataset with genetic data, randomized controlled trials, and MRI for non-human primates and animal models will enable us to understand schizophrenia, elucidate its neural bases and therapeutic targets, and provide tools for clinical application at bedside.
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Affiliation(s)
- Shinsuke Koike
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo.,University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM).,University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB).,The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), The University of Tokyo
| | - Akiko Uematsu
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences.,Research Center for Idling Brain Science (RCIBS), University of Toyama
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo
| | - Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences.,Research Center for Idling Brain Science (RCIBS), University of Toyama
| | - Kazutaka Ohi
- Department of Psychiatry and Psychotherapy, Gifu University Graduate School of Medicine
| | | | - Yoshihiro Noda
- Department of Neuropsychiatry, Keio University School of Medicine
| | - Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University.,Institute of Industrial Science, The University of Tokyo
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33
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Naqvi S, Sleyp Y, Hoskens H, Indencleef K, Spence JP, Bruffaerts R, Radwan A, Eller RJ, Richmond S, Shriver MD, Shaffer JR, Weinberg SM, Walsh S, Thompson J, Pritchard JK, Sunaert S, Peeters H, Wysocka J, Claes P. Shared heritability of human face and brain shape. Nat Genet 2021; 53:830-839. [PMID: 33821002 PMCID: PMC8232039 DOI: 10.1038/s41588-021-00827-w] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Accepted: 02/16/2021] [Indexed: 02/08/2023]
Abstract
Evidence from model organisms and clinical genetics suggests coordination between the developing brain and face, but the role of this link in common genetic variation remains unknown. We performed a multivariate genome-wide association study of cortical surface morphology in 19,644 individuals of European ancestry, identifying 472 genomic loci influencing brain shape, of which 76 are also linked to face shape. Shared loci include transcription factors involved in craniofacial development, as well as members of signaling pathways implicated in brain-face cross-talk. Brain shape heritability is equivalently enriched near regulatory regions active in either forebrain organoids or facial progenitors. However, we do not detect significant overlap between shared brain-face genome-wide association study signals and variants affecting behavioral-cognitive traits. These results suggest that early in embryogenesis, the face and brain mutually shape each other through both structural effects and paracrine signaling, but this interplay may not impact later brain development associated with cognitive function.
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Affiliation(s)
- Sahin Naqvi
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA.
- Departments of Genetics and Biology, Stanford University School of Medicine, Stanford, CA, USA.
| | - Yoeri Sleyp
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Hanne Hoskens
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
| | - Karlijne Indencleef
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Jeffrey P Spence
- Departments of Genetics and Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Rose Bruffaerts
- Department of Neurosciences, KU Leuven, Leuven, Belgium, Hasselt University, Hasselt, Belgium
- Neurology Department, University Hospitals Leuven, Leuven, Belgium, Hasselt University, Hasselt, Belgium
- Biomedical Research Institute Hasselt University Hasselt Belgium, Hasselt University, Hasselt, Belgium
| | - Ahmed Radwan
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
- Department of Imaging and Pathology, Translational MRI, KU Leuven, Leuven, Belgium
| | - Ryan J Eller
- Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Stephen Richmond
- Applied Clinical Research and Public Health, School of Dentistry, Cardiff University, Cardiff, UK
| | - Mark D Shriver
- Department of Anthropology, Pennsylvania State University, State College, PA, USA
| | - John R Shaffer
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Seth M Weinberg
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Anthropology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Susan Walsh
- Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - James Thompson
- Department of Psychology, George Mason University, Fairfax, VA, USA
| | - Jonathan K Pritchard
- Departments of Genetics and Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Stefan Sunaert
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
- Department of Imaging and Pathology, Translational MRI, KU Leuven, Leuven, Belgium
| | - Hilde Peeters
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Joanna Wysocka
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA.
| | - Peter Claes
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium.
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
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Isaiah A, Ernst T, Cloak CC, Clark DB, Chang L. Associations between frontal lobe structure, parent-reported obstructive sleep disordered breathing and childhood behavior in the ABCD dataset. Nat Commun 2021; 12:2205. [PMID: 33850154 PMCID: PMC8044120 DOI: 10.1038/s41467-021-22534-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 03/16/2021] [Indexed: 02/03/2023] Open
Abstract
Parents frequently report behavioral problems among children who snore. Our understanding of the relationship between symptoms of obstructive sleep disordered breathing (oSDB) and childhood behavioral problems associated with brain structural alterations is limited. Here, we examine the associations between oSDB symptoms, behavioral measures such as inattention, and brain morphometry in the Adolescent Brain Cognitive Development (ABCD) study comprising 10,140 preadolescents. We observe that parent-reported symptoms of oSDB are associated with composite and domain-specific problem behaviors measured by parent responses to the Child Behavior Checklist. Alterations of brain structure demonstrating the strongest negative associations with oSDB symptoms are within the frontal lobe. The relationships between oSDB symptoms and behavioral measures are mediated by significantly smaller volumes of multiple frontal lobe regions. These results provide population-level evidence for an association between regional structural alterations in cortical gray matter and problem behaviors reported in children with oSDB.
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Grants
- U01 DA041174 NIDA NIH HHS
- U01 DA041048 NIDA NIH HHS
- U01 DA041156 NIDA NIH HHS
- U01 DA041093 NIDA NIH HHS
- U01 DA041106 NIDA NIH HHS
- U01 DA050989 NIDA NIH HHS
- U01 DA041089 NIDA NIH HHS
- U01 DA050988 NIDA NIH HHS
- U01 DA041028 NIDA NIH HHS
- U01 DA041134 NIDA NIH HHS
- U24 DA041147 NIDA NIH HHS
- U01 DA050987 NIDA NIH HHS
- P50 DA046346 NIDA NIH HHS
- U01 DA041022 NIDA NIH HHS
- U01 DA041025 NIDA NIH HHS
- U01 DA051037 NIDA NIH HHS
- U01 DA051016 NIDA NIH HHS
- U01 DA041148 NIDA NIH HHS
- U24 DA041123 NIDA NIH HHS
- U01 DA041120 NIDA NIH HHS
- U01 DA051039 NIDA NIH HHS
- U01 DA051018 NIDA NIH HHS
- U01 DA051038 NIDA NIH HHS
- U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- The ABCD Study is supported by the National Institutes of Health (NIH) and additional federal partners under award numbers U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, U24DA041147, U01DA041093, and U01DA041025. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/Consortium_Members.pdf.
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Affiliation(s)
- Amal Isaiah
- Department of Otorhinolaryngology-Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, MD, USA.
- Department of Pediatrics, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Thomas Ernst
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Christine C Cloak
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Duncan B Clark
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Linda Chang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD, USA
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Schmitt S, Meller T, Stein F, Brosch K, Ringwald K, Pfarr JK, Bordin C, Peusch N, Steinsträter O, Grotegerd D, Dohm K, Meinert S, Förster K, Redlich R, Opel N, Hahn T, Jansen A, Forstner AJ, Streit F, Witt SH, Rietschel M, Müller-Myhsok B, Nöthen MM, Dannlowski U, Krug A, Kircher T, Nenadić I. Effects of polygenic risk for major mental disorders and cross-disorder on cortical complexity. Psychol Med 2021; 52:1-12. [PMID: 33827729 PMCID: PMC9811276 DOI: 10.1017/s0033291721001082] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 02/25/2021] [Accepted: 03/05/2021] [Indexed: 01/12/2023]
Abstract
BACKGROUND MRI-derived cortical folding measures are an indicator of largely genetically driven early developmental processes. However, the effects of genetic risk for major mental disorders on early brain development are not well understood. METHODS We extracted cortical complexity values from structural MRI data of 580 healthy participants using the CAT12 toolbox. Polygenic risk scores (PRS) for schizophrenia, bipolar disorder, major depression, and cross-disorder (incorporating cumulative genetic risk for depression, schizophrenia, bipolar disorder, autism spectrum disorder, and attention-deficit hyperactivity disorder) were computed and used in separate general linear models with cortical complexity as the regressand. In brain regions that showed a significant association between polygenic risk for mental disorders and cortical complexity, volume of interest (VOI)/region of interest (ROI) analyses were conducted to investigate additional changes in their volume and cortical thickness. RESULTS The PRS for depression was associated with cortical complexity in the right orbitofrontal cortex (right hemisphere: p = 0.006). A subsequent VOI/ROI analysis showed no association between polygenic risk for depression and either grey matter volume or cortical thickness. We found no associations between cortical complexity and polygenic risk for either schizophrenia, bipolar disorder or psychiatric cross-disorder when correcting for multiple testing. CONCLUSIONS Changes in cortical complexity associated with polygenic risk for depression might facilitate well-established volume changes in orbitofrontal cortices in depression. Despite the absence of psychopathology, changed cortical complexity that parallels polygenic risk for depression might also change reward systems, which are also structurally affected in patients with depressive syndrome.
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Affiliation(s)
- Simon Schmitt
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Philipps-Universität Marburg and Justus Liebig Universität Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany
- Marburg University Hospital – UKGM, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Philipps-Universität Marburg and Justus Liebig Universität Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Philipps-Universität Marburg and Justus Liebig Universität Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Philipps-Universität Marburg and Justus Liebig Universität Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany
- Marburg University Hospital – UKGM, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Kai Ringwald
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Philipps-Universität Marburg and Justus Liebig Universität Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany
| | - Julia-Katharina Pfarr
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Philipps-Universität Marburg and Justus Liebig Universität Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany
| | - Clemens Bordin
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Nina Peusch
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Olaf Steinsträter
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Dominik Grotegerd
- Department of Psychiatry, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Katharina Dohm
- Department of Psychiatry, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Susanne Meinert
- Department of Psychiatry, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Katharina Förster
- Department of Psychiatry, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Ronny Redlich
- Department of Psychiatry, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
- Department of Psychology, University of Halle, Halle, Germany
| | - Nils Opel
- Department of Psychiatry, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Tim Hahn
- Department of Psychiatry, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Philipps-Universität Marburg and Justus Liebig Universität Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany
- Faculty of Medicine, Core-Facility BrainImaging, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039, Germany
| | - Andreas J. Forstner
- Centre for Human Genetics, Philipps-Universität Marburg, Baldingerstr., 35033 Marburg, Germany
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
| | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, 68159 Mannheim, Germany
| | - Stephanie H. Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, 68159 Mannheim, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, J5, 68159 Mannheim, Germany
| | - Bertram Müller-Myhsok
- Munich Cluster for Systems Neurology (SyNergy), Feodor-Lynen-Str. 17, 81377 Munich, Germany
- Institute of Translational Medicine, University of Liverpool, Crown Street, Liverpool L69 3BX, UK
- Max-Planck-Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Markus M. Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Venusberg-Campus 1, 53127 Bonn, Germany
| | - Udo Dannlowski
- Department of Psychiatry, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Campus 1, Building A9, 48149 Münster, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Philipps-Universität Marburg and Justus Liebig Universität Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Philipps-Universität Marburg and Justus Liebig Universität Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany
- Marburg University Hospital – UKGM, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Philipps-Universität Marburg and Justus Liebig Universität Giessen, Hans-Meerwein-Str. 6, 35032 Marburg, Germany
- Marburg University Hospital – UKGM, Rudolf-Bultmann-Str. 8, 35039 Marburg, Germany
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Li Y, Wang N, Wang H, Lv Y, Zou Q, Wang J. Surface-based single-subject morphological brain networks: Effects of morphological index, brain parcellation and similarity measure, sample size-varying stability and test-retest reliability. Neuroimage 2021; 235:118018. [PMID: 33794358 DOI: 10.1016/j.neuroimage.2021.118018] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 12/04/2020] [Accepted: 03/22/2021] [Indexed: 12/14/2022] Open
Abstract
Morphological brain networks, in particular those at the individual level, have become an important approach for studying the human brain connectome; however, relevant methodology is far from being well-established in their formation, description and reproducibility. Here, we extended our previous study by constructing and characterizing single-subject morphological similarity networks from brain volume to surface space and systematically evaluated their reproducibility with respect to effects of different choices of morphological index, brain parcellation atlas and similarity measure, sample size-varying stability and test-retest reliability. Using the Human Connectome Project dataset, we found that surface-based single-subject morphological similarity networks shared common small-world organization, high parallel efficiency, modular architecture and bilaterally distributed hubs regardless of different analytical strategies. Nevertheless, quantitative values of all interregional similarities, global network measures and nodal centralities were significantly affected by choices of morphological index, brain parcellation atlas and similarity measure. Moreover, the morphological similarity networks varied along with the number of participants and approached stability until the sample size exceeded ~70. Using an independent test-retest dataset, we found fair to good, even excellent, reliability for most interregional similarities and network measures, which were also modulated by different analytical strategies, in particular choices of morphological index. Specifically, fractal dimension and sulcal depth outperformed gyrification index and cortical thickness, higher-resolution atlases outperformed lower-resolution atlases, and Jensen-Shannon divergence-based similarity outperformed Kullback-Leibler divergence-based similarity. Altogether, our findings propose surface-based single-subject morphological similarity networks as a reliable method to characterize the human brain connectome and provide methodological recommendations and guidance for future research.
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Affiliation(s)
- Yinzhi Li
- Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
| | - Ningkai Wang
- Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
| | - Hao Wang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Yating Lv
- Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Qihong Zou
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China; Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education.
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37
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Isherwood SJS, Bazin PL, Alkemade A, Forstmann BU. Quantity and quality: Normative open-access neuroimaging databases. PLoS One 2021; 16:e0248341. [PMID: 33705468 PMCID: PMC7951909 DOI: 10.1371/journal.pone.0248341] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 02/24/2021] [Indexed: 11/19/2022] Open
Abstract
The focus of this article is to compare twenty normative and open-access neuroimaging databases based on quantitative measures of image quality, namely, signal-to-noise (SNR) and contrast-to-noise ratios (CNR). We further the analysis through discussing to what extent these databases can be used for the visualization of deeper regions of the brain, such as the subcortex, as well as provide an overview of the types of inferences that can be drawn. A quantitative comparison of contrasts including T1-weighted (T1w) and T2-weighted (T2w) images are summarized, providing evidence for the benefit of ultra-high field MRI. Our analysis suggests a decline in SNR in the caudate nuclei with increasing age, in T1w, T2w, qT1 and qT2* contrasts, potentially indicative of complex structural age-dependent changes. A similar decline was found in the corpus callosum of the T1w, qT1 and qT2* contrasts, though this relationship is not as extensive as within the caudate nuclei. These declines were accompanied by a declining CNR over age in all image contrasts. A positive correlation was found between scan time and the estimated SNR as well as a negative correlation between scan time and spatial resolution. Image quality as well as the number and types of contrasts acquired by these databases are important factors to take into account when selecting structural data for reuse. This article highlights the opportunities and pitfalls associated with sampling existing databases, and provides a quantitative backing for their usage.
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Affiliation(s)
- Scott Jie Shen Isherwood
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands
| | - Pierre-Louis Bazin
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Anneke Alkemade
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands
| | - Birte Uta Forstmann
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands
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38
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Hammill C, Lerch JP, Taylor MJ, Ameis SH, Chakravarty MM, Szatmari P, Anagnostou E, Lai MC. Quantitative and Qualitative Sex Modulations in the Brain Anatomy of Autism. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:898-909. [PMID: 33713843 DOI: 10.1016/j.bpsc.2021.03.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 02/27/2021] [Accepted: 03/01/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND Sex-based neurobiological heterogeneity in autism is poorly understood. Research is disproportionately biased to males, leading to an unwarranted presumption that autism neurobiology is the same across sexes. Previous neuroimaging studies using amalgamated multicenter datasets to increase autistic female samples are characterized by large statistical noise. METHODS We used a better-powered dataset of 1183 scans of 839 individuals-299 (467 scans) autistic males, 74 (102 scans) autistic females, 240 (334 scans) control males, and 226 (280 scans) control females-to test two whole-brain models of overall/global sex modulations on autism neuroanatomy, by summary measures computed across the brain: the local magnitude model, in which the same brain regions/circuitries are involved across sexes but effect sizes are larger in females, indicating quantitative sex modulation; and spatial dissimilarity model, in which the neuroanatomy differs spatially between sexes, indicating qualitative sex modulation. The male and female autism groups were matched on age, IQ, and autism symptoms. Autism brain features were defined by comparisons with same-sex control individuals. RESULTS Across five metrics (cortical thickness, surface area, volume, mean absolute curvature, and subcortical volume), we found no evidence supporting the local magnitude model. We found indicators supporting the spatial dissimilarity model on cortical mean absolute curvature and subcortical volume, but not on other metrics. CONCLUSIONS The overall/global autism neuroanatomy in females and males does not simply differ quantitatively in the same brain regions/circuitries. They may differ qualitatively in spatial involvement in cortical curvature and subcortical volume. The neuroanatomy of autism may be partly sex specific. Sex stratification to inform autism preclinical/clinical research is needed to identify sex-informed neurodevelopmental targets.
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Affiliation(s)
| | - Jason P Lerch
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
| | - Margot J Taylor
- Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada
| | - Stephanie H Ameis
- Department of Psychiatry, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada; Margaret and Wallace McCain Centre for Child, Youth and Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Peter Szatmari
- Department of Psychiatry, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada; Margaret and Wallace McCain Centre for Child, Youth and Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Evdokia Anagnostou
- Holland Bloorview Kids Rehabilitation Hospital and Department of Paediatrics, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Meng-Chuan Lai
- Department of Psychiatry, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada; Margaret and Wallace McCain Centre for Child, Youth and Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom; Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan.
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Human Connectome Project: heritability of brain volumes in young healthy adults. Exp Brain Res 2021; 239:1273-1286. [PMID: 33611617 DOI: 10.1007/s00221-021-06057-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Accepted: 02/04/2021] [Indexed: 01/17/2023]
Abstract
Here we report on the heritability and Intraclass Correlation Coefficients (ICCs) of brain volumes in 1,103 young healthy adults with mean age 29.2 years. Among them are: 153 monozygotic (MZ) twin pairs and 86 dizygotic (DZ) twin pairs, 133 non-twin siblings of MZ twins, 76 non-twin siblings of DZ twins, 335 siblings, and 81 unrelated individuals. ICCs were calculated between pairs of the following genetic groups: (1) MZ twins; (2) DZ twins; (3) MZ twins-their singleton siblings; (4) DZ twins-their singleton siblings; (5) siblings (SB); and (6) unrelated individuals (NR). We studied 4 brain groups: global, lobar, subcortical, and cortical brain regions. For each of 4 brain groups we found the same order of ICCs ranging from the highest values for MZ twins, statistically significantly smaller for the DZ twins and 3 sibling groups, and practically zero for NR. The DZ twins and 3 sibling groups were not different. No hemispheric difference was found in any genetic group. Among brain groups, the highest heritability was for the global regions, followed by lobar and subcortical groups. Only the cortical brain group heritability was statistically lower than other brain groups. We found less genetic control on the left hemisphere than on the right but no significant difference between hemispheres, and no hemispheric lateralization of heritability for any of the brain groups. These findings document substantial and systematic heritability of global and regional brain volumes.
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40
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Antoniades M, Haas SS, Modabbernia A, Bykowsky O, Frangou S, Borgwardt S, Schmidt A. Personalized Estimates of Brain Structural Variability in Individuals With Early Psychosis. Schizophr Bull 2021; 47:1029-1038. [PMID: 33547470 PMCID: PMC8266574 DOI: 10.1093/schbul/sbab005] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Early psychosis in first-episode psychosis (FEP) and clinical high-risk (CHR) individuals has been associated with alterations in mean regional measures of brain morphology. Examination of variability in brain morphology could assist in quantifying the degree of brain structural heterogeneity in clinical relative to healthy control (HC) samples. METHODS Structural magnetic resonance imaging data were obtained from CHR (n = 71), FEP (n = 72), and HC individuals (n = 55). Regional brain variability in cortical thickness (CT), surface area (SA), and subcortical volume (SV) was assessed with the coefficient of variation (CV). Furthermore, the person-based similarity index (PBSI) was employed to quantify the similarity of CT, SA, and SV profile of each individual to others within the same diagnostic group. Normative modeling of the PBSI-CT, PBSI-SA, and PBSI-SV was used to identify CHR and FEP individuals whose scores deviated markedly from those of the healthy individuals. RESULTS There was no effect of diagnosis on the CV for any regional measure (P > .38). CHR and FEP individuals differed significantly from the HC group in terms of PBSI-CT (P < .0001), PBSI-SA (P < .0001), and PBSI-SV (P = .01). In the clinical groups, normative modeling identified 32 (22%) individuals with deviant PBSI-CT, 12 (8.4%) with deviant PBSI-SA, and 21 (15%) with deviant PBSI-SV; differences of small effect size indicated that individuals with deviant PBSI scores had lower IQ and higher psychopathology. CONCLUSIONS Examination of brain structural variability in early psychosis indicated heterogeneity at the level of individual profiles and encourages further large-scale examination to identify individuals that deviate markedly from normative reference data.
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Affiliation(s)
- Mathilde Antoniades
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Oleg Bykowsky
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
- Centre for Brain Health, Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Stefan Borgwardt
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
- Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - André Schmidt
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
- To whom correspondence should be addressed; Wilhelm Klein-Strasse 27, 4002 Basel, Switzerland; tel: +41 0(61) 325 59 29, fax: +41 (0)61 325 55 82, e-mail:
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41
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Lv Y, Wei W, Han X, Song Y, Han Y, Zhou C, Zhou D, Zhang F, Wu X, Liu J, Zhao L, Zhang C, Wang N, Wang J. Multiparametric and multilevel characterization of morphological alterations in patients with transient ischemic attack. Hum Brain Mapp 2021; 42:2045-2060. [PMID: 33463862 PMCID: PMC8046078 DOI: 10.1002/hbm.25344] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 11/25/2020] [Accepted: 01/07/2021] [Indexed: 11/07/2022] Open
Abstract
Transient ischemic attack (TIA), an important risk factor for stroke, is associated with widespread disruptions of functional brain architecture. However, TIA-related structural alterations are not well established. By analyzing structural MRI data from 50 TIA patients versus 40 healthy controls (HCs), here we systematically investigated TIA-related morphological alterations in multiple cortical surface-based indices (cortical thickness [CT], fractal dimension [FD], gyrification index [GI], and sulcal depth [SD]) at multiple levels (local topography, interregional connectivity and whole-brain network topology). For the observed alterations, their associations with clinical risk factors and abilities as diagnostic and prognostic biomarkers were further examined. We found that compared with the HCs, the TIA patients showed widespread morphological alterations and the alterations depended on choices of morphological index and analytical level. Specifically, the patients exhibited: (a) regional CT decreases in the transverse temporal gyrus and lateral sulcus; (b) impaired FD- and GI-based connectivity mainly involving visual, somatomotor and ventral attention networks and interhemispheric connections; and (c) altered GI-based whole-brain network efficiency and decreased FD-based nodal centrality in the middle frontal gyrus. Moreover, the impaired morphological connectivity showed high sensitivities and specificities for distinguishing the patients from HCs. Altogether, these findings demonstrate the emergence of morphological index-dependent and analytical level-specific alterations in TIA, which provide novel insights into neurobiological mechanisms underlying TIA and may serve as potential biomarkers to help diagnosis of the disease. Meanwhile, our findings highlight the necessity of using multiparametric and multilevel approaches for a complete mapping of cerebral morphology in health and disease.
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Affiliation(s)
- Yating Lv
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Zhejiang, Hangzhou, China.,Institute of Psychological Science, Hangzhou Normal University, Zhejiang, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Zhejiang, Hangzhou, China.,Department of Neurology, Anshan Changda Hospital, Anshan, Liaoning, China
| | - Wei Wei
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Zhejiang, Hangzhou, China.,Institute of Psychological Science, Hangzhou Normal University, Zhejiang, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Zhejiang, Hangzhou, China.,Department of Neurology, Anshan Changda Hospital, Anshan, Liaoning, China
| | - Xiujie Han
- Department of Neurology, Anshan Changda Hospital, Anshan, Liaoning, China
| | - Yulin Song
- Department of Neurology, Anshan Changda Hospital, Anshan, Liaoning, China
| | - Yu Han
- Department of Neurology, The First Affiliated Hospital, Dalian Medical University, Dalian, Liaoning, China
| | - Chengshu Zhou
- Department of Neurology, Anshan Changda Hospital, Anshan, Liaoning, China
| | - Dan Zhou
- Department of Neurology, Anshan Changda Hospital, Anshan, Liaoning, China
| | - Fuding Zhang
- Department of Neurology, Anshan Changda Hospital, Anshan, Liaoning, China
| | - Xiaoyan Wu
- Department of Image, Anshan Changda Hospital, Anshan, Liaoning, China
| | - Jinling Liu
- Department of Ultrasonics, Anshan Changda Hospital, Anshan, Liaoning, China
| | - Lijuan Zhao
- Department of Neurology, Anshan Changda Hospital, Anshan, Liaoning, China
| | - Cairong Zhang
- Department of Neurology, Anshan Changda Hospital, Anshan, Liaoning, China
| | - Ningkai Wang
- Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China.,Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China.,Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China
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42
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Woertz EN, Wilk MA, Duwell EJ, Mathis JR, Carroll J, DeYoe EA. The relationship between retinal cone density and cortical magnification in human albinism. J Vis 2020; 20:10. [PMID: 32543650 PMCID: PMC7416892 DOI: 10.1167/jov.20.6.10] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
The human fovea lies at the center of the retina and supports high-acuity vision. In normal visual system development, the highest acuity is correlated with both a high density of cone photoreceptors in the fovea and a magnified retinotopic representation of the fovea in the visual cortex. Both cone density and the cortical area dedicated to each degree of visual space—the latter describing cortical magnification (CM)—steadily decrease with increasing eccentricity from the fovea. In albinism, peak cone density at the fovea and visual acuity are decreased, but seem to be within normal limits in the periphery, thus providing a model to explore the correlation between retinal structure, cortical structure, and behavior. Here, we used adaptive optics scanning light ophthalmoscopy to assess retinal cone density and functional magnetic resonance imaging to measure CM in the primary visual cortex of normal controls and individuals with albinism. We find that retinotopic organization is more varied among individuals with albinism than previously appreciated. Additionally, CM outside the fovea is similar to that in controls, but also more variable. CM in albinism and controls exceeds that which might be predicted based on cone density alone, but is more accurately predicted by retinal ganglion cell density. This finding suggests that decreased foveal cone density in albinism may be partially counteracted by nonuniform connectivity between cones and their downstream signaling partners. Together, these results emphasize that central as well as retinal factors must be included to provide a complete picture of aberrant structure and function in albinism.
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43
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Kruggel F, Solodkin A. Heritability of Structural Patterning in the Human Cerebral Cortex. Neuroimage 2020; 221:117169. [PMID: 32693166 DOI: 10.1016/j.neuroimage.2020.117169] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 06/29/2020] [Accepted: 07/11/2020] [Indexed: 01/11/2023] Open
Abstract
Genetic influences that govern the spatial patterning of the human cortex and its structural variability are still incompletely known. We analyzed structural MR images in twins, siblings, and pairs of unrelated subjects. A comprehensive set of methods was employed to quantify properties of cortical features at different spatial scales. Measures were used to assess the influence of genetic similarity on structural patterning. Results indicated that: (1) Genetic effects significantly influence all structural features assessed here at all spatial resolutions, albeit at different strengths. (2) While strong genetic effects were found at the whole-brain and hemisphere level, effects were weaker at the regional and vertex level, depending on the measure under study. (3) Besides cortical thickness, sulcal (geodesic) depth was found to be under strong genetic control. The local pattern indicated that two axes along (a) the anterior-posterior direction (insula to parieto-occipital sulcus), and (b) superior-inferior direction (central sulcus to callosal sulcus) presumably determine the segregation of four quadrants in each hemisphere early in development. (4) While strong structural asymmetries were found at the regional level, genetic influences on laterality were relatively minor.
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Affiliation(s)
- Frithjof Kruggel
- Department of Biomedical Engineering, University of California, Irvine, USA.
| | - Ana Solodkin
- School of Behavioral and Brain Sciences, University of Texas, Dallas, USA
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44
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de Zwarte SMC, Brouwer RM, Agartz I, Alda M, Alonso‐Lana S, Bearden CE, Bertolino A, Bonvino A, Bramon E, Buimer EEL, Cahn W, Canales‐Rodríguez EJ, Cannon DM, Cannon TD, Caseras X, Castro‐Fornieles J, Chen Q, Chung Y, De la Serna E, del Mar Bonnin C, Demro C, Di Giorgio A, Doucet GE, Eker MC, Erk S, Fatjó‐Vilas M, Fears SC, Foley SF, Frangou S, Fullerton JM, Glahn DC, Goghari VM, Goikolea JM, Goldman AL, Gonul AS, Gruber O, Hajek T, Hawkins EL, Heinz A, Hidiroglu Ongun C, Hillegers MHJ, Houenou J, Hulshoff Pol HE, Hultman CM, Ingvar M, Johansson V, Jönsson EG, Kane F, Kempton MJ, Koenis MMG, Kopecek M, Krämer B, Lawrie SM, Lenroot RK, Marcelis M, Mattay VS, McDonald C, Meyer‐Lindenberg A, Michielse S, Mitchell PB, Moreno D, Murray RM, Mwangi B, Nabulsi L, Newport J, Olman CA, van Os J, Overs BJ, Ozerdem A, Pergola G, Picchioni MM, Piguet C, Pomarol‐Clotet E, Radua J, Ramsay IS, Richter A, Roberts G, Salvador R, Saricicek Aydogan A, Sarró S, Schofield PR, Simsek EM, Simsek F, Soares JC, Sponheim SR, Sugranyes G, Toulopoulou T, Tronchin G, Vieta E, Walter H, Weinberger DR, Whalley HC, Wu M, Yalin N, Andreassen OA, Ching CRK, Thomopoulos SI, van Erp TGM, Jahanshad N, Thompson PM, Kahn RS, van Haren NEM. Intelligence, educational attainment, and brain structure in those at familial high-risk for schizophrenia or bipolar disorder. Hum Brain Mapp 2020; 43:414-430. [PMID: 33027543 PMCID: PMC8675411 DOI: 10.1002/hbm.25206] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 08/28/2020] [Accepted: 09/03/2020] [Indexed: 12/25/2022] Open
Abstract
First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d = -0.42, p = 3 × 10-5 ), with weak evidence of IQ reductions among BD-FDRs (d = -0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment.
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Affiliation(s)
- Sonja M. C. de Zwarte
- Department of PsychiatryUniversity Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht UniversityUtrechtNetherlands
| | - Rachel M. Brouwer
- Department of PsychiatryUniversity Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht UniversityUtrechtNetherlands
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of OsloOsloNorway,Centre for Psychiatry Research, Department of Clinical NeuroscienceKarolinska Institutet & Stockholm Health Care Services, Stockholm RegionStockholmSweden,Department of PsychiatryDiakonhjemmet HospitalOsloNorway
| | - Martin Alda
- Department of PsychiatryDalhousie UniversityHalifaxNova ScotiaCanada,National Institute of Mental HealthKlecanyCzech Republic
| | - Silvia Alonso‐Lana
- FIDMAG Germanes Hospitalàries Research FoundationBarcelonaSpain,CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental)MadridSpain
| | - Carrie E. Bearden
- Semel Institute for Neuroscience and Human Behavior, University of CaliforniaCaliforniaLos AngelesUSA,Department of PsychologyUniversity of CaliforniaCaliforniaLos AngelesUSA
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense OrgansUniversity of Bari 'Aldo Moro'BariItaly
| | - Aurora Bonvino
- Department of Basic Medical Science, Neuroscience and Sense OrgansUniversity of Bari 'Aldo Moro'BariItaly
| | - Elvira Bramon
- Division of Psychiatry, Neuroscience in Mental Health Research DepartmentUniversity College LondonLondonUK
| | - Elizabeth E. L. Buimer
- Department of PsychiatryUniversity Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht UniversityUtrechtNetherlands
| | - Wiepke Cahn
- Department of PsychiatryUniversity Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht UniversityUtrechtNetherlands
| | - Erick J. Canales‐Rodríguez
- FIDMAG Germanes Hospitalàries Research FoundationBarcelonaSpain,CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental)MadridSpain
| | - Dara M. Cannon
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland GalwayGalwayIreland
| | - Tyrone D. Cannon
- Department of PsychologyYale UniversityNew HavenConnecticutUSA,Department of PsychiatryYale University School of MedicineNew HavenConnecticutUSA
| | - Xavier Caseras
- MRC Centre for Neuropsychiatric Genetics and GenomicsCardiff UniversityCardiffUK
| | - Josefina Castro‐Fornieles
- CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental)MadridSpain,Department of Child and Adolescent Psychiatry and Psychology, 2017SGR881Institute of Neuroscience, Hospital Clínic of BarcelonaBarcelonaSpain,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain,University of BarcelonaBarcelonaSpain
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical CampusBaltimoreMarylandUSA
| | - Yoonho Chung
- Department of PsychologyYale UniversityNew HavenConnecticutUSA
| | - Elena De la Serna
- CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental)MadridSpain,Department of Child and Adolescent Psychiatry and Psychology, 2017SGR881Institute of Neuroscience, Hospital Clínic of BarcelonaBarcelonaSpain,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain,University of BarcelonaBarcelonaSpain
| | - Caterina del Mar Bonnin
- CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental)MadridSpain,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain,Bipolar and Depressive Disorders UnitHospital Clinic, University of BarcelonaBarcelonaSpain
| | - Caroline Demro
- Department of Psychiatry and Behavioral SciencesUniversity of MinnesotaMinneapolisMinnesotaUSA
| | | | - Gaelle E. Doucet
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA,Boys Town National Research HospitalOmahaNEUSA
| | - Mehmet Cagdas Eker
- SoCAT LAB, Department of PsychiatrySchool of Medicine, Ege UniversityIzmirTurkey
| | - Susanne Erk
- Research Division of Mind and Brain, Department of Psychiatry and PsychotherapyCharité Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt‐Universität zu Berlin, and Berlin Institute of HealthBerlinGermany
| | - Mar Fatjó‐Vilas
- FIDMAG Germanes Hospitalàries Research FoundationBarcelonaSpain,CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental)MadridSpain
| | - Scott C. Fears
- Department of Psychiatry and Biobehavioral ScienceUniversity of CaliforniaLos AngelesCaliforniaUSA,Center for Neurobehavioral GeneticsUniversity of CaliforniaLos AngelesCaliforniaUSA
| | - Sonya F. Foley
- Cardiff University Brain Research Imaging Centre, Cardiff UniversityCardiffUK
| | - Sophia Frangou
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Janice M. Fullerton
- Neuroscience Research AustraliaSydneyAustralia,School of Medical Sciences, University of New South WalesSydneyAustralia
| | - David C. Glahn
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford HospitalHartfordConnecticutUSA,Tommy Fuss Center for Neuropsychiatric Disease ResearchBoston Children's HospitalBostonMassachusettsUSA,Harvard Medical SchoolBostonMassachusettsUSA
| | - Vina M. Goghari
- Department of Psychology and Graduate Department of Psychological Clinical ScienceUniversity of TorontoTorontoOntarioCanada
| | - Jose M. Goikolea
- CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental)MadridSpain,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain,Bipolar and Depressive Disorders UnitHospital Clinic, University of BarcelonaBarcelonaSpain
| | - Aaron L. Goldman
- Lieber Institute for Brain Development, Johns Hopkins Medical CampusBaltimoreMarylandUSA
| | - Ali Saffet Gonul
- SoCAT LAB, Department of PsychiatrySchool of Medicine, Ege UniversityIzmirTurkey,Department of Psychiatry and Behavioral SciencesMercer University School of MedicineMaconGeorgiaUSA
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General PsychiatryUniversity of HeidelbergHeidelbergGermany
| | - Tomas Hajek
- Department of PsychiatryDalhousie UniversityHalifaxNova ScotiaCanada,National Institute of Mental HealthKlecanyCzech Republic
| | - Emma L. Hawkins
- Division of PsychiatryRoyal Edinburgh Hospital, University of EdinburghEdinburghUK
| | - Andreas Heinz
- SoCAT LAB, Department of PsychiatrySchool of Medicine, Ege UniversityIzmirTurkey
| | | | - Manon H. J. Hillegers
- Department of PsychiatryUniversity Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht UniversityUtrechtNetherlands,Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical Center‐Sophia Children's HospitalRotterdamNetherlands
| | - Josselin Houenou
- APHP, Mondor University HospitalsCréteilFrance,INSERM U955 Team 15 "Translational Psychiatry"CréteilFrance,NeuroSpin neuroimaging platform, Psychiatry Team, UNIACT Lab, CEA SaclayGif‐Sur‐YvetteFrance
| | - Hilleke E. Hulshoff Pol
- Department of PsychiatryUniversity Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht UniversityUtrechtNetherlands
| | - Christina M. Hultman
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
| | - Martin Ingvar
- Section for Neuroscience, Department of Clinical NeuroscienceKarolinska InstitutetStockholmSweden,Department of NeuroradiologyKarolinska University HospitalStockholmSweden
| | - Viktoria Johansson
- Centre for Psychiatry Research, Department of Clinical NeuroscienceKarolinska Institutet & Stockholm Health Care Services, Stockholm RegionStockholmSweden,Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
| | - Erik G. Jönsson
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of OsloOsloNorway,Centre for Psychiatry Research, Department of Clinical NeuroscienceKarolinska Institutet & Stockholm Health Care Services, Stockholm RegionStockholmSweden
| | - Fergus Kane
- Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College LondonLondonUK
| | - Matthew J. Kempton
- Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College LondonLondonUK
| | - Marinka M. G. Koenis
- Department of PsychiatryYale University School of MedicineNew HavenConnecticutUSA,Olin Neuropsychiatry Research Center, Institute of Living, Hartford HospitalHartfordConnecticutUSA
| | - Miloslav Kopecek
- National Institute of Mental HealthKlecanyCzech Republic,Department of Psychiatry, Third Faculty of MedicineCharles UniversityPragueCzech Republic
| | - Bernd Krämer
- Section for Experimental Psychopathology and Neuroimaging, Department of General PsychiatryUniversity of HeidelbergHeidelbergGermany
| | - Stephen M. Lawrie
- Division of PsychiatryRoyal Edinburgh Hospital, University of EdinburghEdinburghUK
| | - Rhoshel K. Lenroot
- Neuroscience Research AustraliaSydneyAustralia,School of Psychiatry, University of New South WalesSydneyAustralia,Department of Psychiatry and Behavioral SciencesUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Machteld Marcelis
- Department of Psychiatry and NeuropsychologySchool for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht UniversityMaastrichtNetherlands
| | - Venkata S. Mattay
- Lieber Institute for Brain Development, Johns Hopkins Medical CampusBaltimoreMarylandUSA,Departments of Neurology and RadiologyJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Colm McDonald
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland GalwayGalwayIreland
| | - Andreas Meyer‐Lindenberg
- Department of Psychiatry and PsychotherapyCentral Institute of Mental Health, Medical Faculty Mannheim, University of HeidelbergMannheimGermany
| | - Stijn Michielse
- Department of Psychiatry and NeuropsychologySchool for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht UniversityMaastrichtNetherlands
| | | | - Dolores Moreno
- CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental)MadridSpain,Child and Adolescent Psychiatry DepartmentHospital General Universitario Gregorio Marañón (IiSGM), School of Medicine, Universidad ComplutenseMadridSpain
| | - Robin M. Murray
- Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College LondonLondonUK
| | - Benson Mwangi
- Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Leila Nabulsi
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland GalwayGalwayIreland
| | - Jason Newport
- Department of PsychiatryDalhousie UniversityHalifaxNova ScotiaCanada
| | - Cheryl A. Olman
- Department of Psychology and Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Jim van Os
- Department of PsychiatryUniversity Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht UniversityUtrechtNetherlands,Department of Psychiatry and NeuropsychologySchool for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht UniversityMaastrichtNetherlands
| | | | - Aysegul Ozerdem
- Department of Psychiatry, Faculty of MedicineDokuz Eylül UniversityIzmirTurkey,Department of NeurosciencesHealth Sciences Institute, Dokuz Eylül UniversityIzmirTurkey,Department of Psychiatry and PsychologyMayo ClinicRochesterMinnesotaUSA
| | - Giulio Pergola
- Department of Basic Medical Science, Neuroscience and Sense OrgansUniversity of Bari 'Aldo Moro'BariItaly
| | - Marco M. Picchioni
- Department of Forensic and Neurodevelopmental ScienceInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Camille Piguet
- INSERM U955 Team 15 "Translational Psychiatry"CréteilFrance,NeuroSpin neuroimaging platform, Psychiatry Team, UNIACT Lab, CEA SaclayGif‐Sur‐YvetteFrance,Department of Psychiatry, Faculty of MedicineUniversity of GenevaGenevaSwitzerland,School of Medicine, Universitat Internacional de CatalunyaBarcelonaSpain
| | - Edith Pomarol‐Clotet
- FIDMAG Germanes Hospitalàries Research FoundationBarcelonaSpain,CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental)MadridSpain
| | - Joaquim Radua
- Centre for Psychiatry Research, Department of Clinical NeuroscienceKarolinska Institutet & Stockholm Health Care Services, Stockholm RegionStockholmSweden,CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental)MadridSpain,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain,Early Psychosis: Interventions and Clinical‐detection (EPIC) lab, Department of Psychosis StudiesInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUK
| | - Ian S. Ramsay
- Department of Psychiatry and Behavioral SciencesUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Anja Richter
- Section for Experimental Psychopathology and Neuroimaging, Department of General PsychiatryUniversity of HeidelbergHeidelbergGermany
| | - Gloria Roberts
- School of Psychiatry, University of New South WalesSydneyAustralia
| | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research FoundationBarcelonaSpain,CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental)MadridSpain
| | - Aybala Saricicek Aydogan
- Department of NeurosciencesHealth Sciences Institute, Dokuz Eylül UniversityIzmirTurkey,Department of Psychiatry, Faculty of MedicineIzmir Katip Çelebi UniversityIzmirTurkey
| | - Salvador Sarró
- FIDMAG Germanes Hospitalàries Research FoundationBarcelonaSpain,CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental)MadridSpain
| | - Peter R. Schofield
- School of Medical Sciences, University of New South WalesSydneyAustralia,Olin Neuropsychiatry Research Center, Institute of Living, Hartford HospitalHartfordConnecticutUSA
| | | | - Fatma Simsek
- SoCAT LAB, Department of PsychiatrySchool of Medicine, Ege UniversityIzmirTurkey,Institute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK,Cigli State HospitalDepartment of PsychiatryIzmirTurkey
| | - Jair C. Soares
- Department of Psychiatry and Behavioral SciencesThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Scott R. Sponheim
- Department of Psychiatry and Behavioral SciencesUniversity of MinnesotaMinneapolisMinnesotaUSA,Minneapolis VA Health Care SystemMinneapolisMinnesotaUSA
| | - Gisela Sugranyes
- CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental)MadridSpain,Department of Child and Adolescent Psychiatry and Psychology, 2017SGR881Institute of Neuroscience, Hospital Clínic of BarcelonaBarcelonaSpain,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain,University of BarcelonaBarcelonaSpain
| | - Timothea Toulopoulou
- Department of PsychologyBilkent UniversityAnkaraTurkey,Department of Basic and Clinical NeuroscienceInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Giulia Tronchin
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland GalwayGalwayIreland
| | - Eduard Vieta
- CIBERSAM (Centro de Investigación Biomédica en Red de Salud Mental)MadridSpain,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain,Bipolar and Depressive Disorders UnitHospital Clinic, University of BarcelonaBarcelonaSpain
| | - Henrik Walter
- Research Division of Mind and Brain, Department of Psychiatry and PsychotherapyCharité Universitätsmedizin Berlin, Freie Universität Berlin, Humboldt‐Universität zu Berlin, and Berlin Institute of HealthBerlinGermany
| | - Daniel R. Weinberger
- Bipolar and Depressive Disorders UnitHospital Clinic, University of BarcelonaBarcelonaSpain
| | - Heather C. Whalley
- Department of Psychology, Faculty of ArtsDokuz Eylül UniversityİzmirTurkey
| | - Mon‐Ju Wu
- Department of Psychology and Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Nefize Yalin
- Centre for Affective Disorders, Department of Psychological MedicineInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of OsloOsloNorway,Division of Mental Health and AddictionOslo University HospitalOsloNorway
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Theo G. M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA,Center for the Neurobiology of Learning and MemoryUniversity of California IrvineIrvineCaliforniaUSA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - René S. Kahn
- Department of PsychiatryUniversity Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht UniversityUtrechtNetherlands,Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Neeltje E. M. van Haren
- Department of PsychiatryUniversity Medical Center Utrecht Brain Center, University Medical Center Utrecht, Utrecht UniversityUtrechtNetherlands,Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical Center‐Sophia Children's HospitalRotterdamNetherlands
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45
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Valk SL, Hoffstaedter F, Camilleri JA, Kochunov P, Yeo BT, Eickhoff SB. Personality and local brain structure: Their shared genetic basis and reproducibility. Neuroimage 2020; 220:117067. [DOI: 10.1016/j.neuroimage.2020.117067] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 06/12/2020] [Accepted: 06/15/2020] [Indexed: 12/16/2022] Open
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46
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Schmitt JE, Raznahan A, Liu S, Neale MC. The Heritability of Cortical Folding: Evidence from the Human Connectome Project. Cereb Cortex 2020; 31:702-715. [PMID: 32959043 DOI: 10.1093/cercor/bhaa254] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 08/09/2020] [Accepted: 08/10/2020] [Indexed: 12/13/2022] Open
Abstract
The mechanisms underlying cortical folding are incompletely understood. Prior studies have suggested that individual differences in sulcal depth are genetically mediated, with deeper and ontologically older sulci more heritable than others. In this study, we examine FreeSurfer-derived estimates of average convexity and mean curvature as proxy measures of cortical folding patterns using a large (N = 1096) genetically informative young adult subsample of the Human Connectome Project. Both measures were significantly heritable near major sulci and primary fissures, where approximately half of individual differences could be attributed to genetic factors. Genetic influences near higher order gyri and sulci were substantially lower and largely nonsignificant. Spatial permutation analysis found that heritability patterns were significantly anticorrelated to maps of evolutionary and neurodevelopmental expansion. We also found strong phenotypic correlations between average convexity, curvature, and several common surface metrics (cortical thickness, surface area, and cortical myelination). However, quantitative genetic models suggest that correlations between these metrics are largely driven by nongenetic factors. These findings not only further our understanding of the neurobiology of gyrification, but have pragmatic implications for the interpretation of heritability maps based on automated surface-based measurements.
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Affiliation(s)
- J Eric Schmitt
- Departments of Radiology and Psychiatry, Division of Neuroradiology, Brain Behavior Laboratory, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Armin Raznahan
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Siyuan Liu
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Michael C Neale
- Departments of Psychiatry and Human and Molecular Genetics, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298-980126, USA
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47
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Pizzagalli F, Auzias G, Yang Q, Mathias SR, Faskowitz J, Boyd JD, Amini A, Rivière D, McMahon KL, de Zubicaray GI, Martin NG, Mangin JF, Glahn DC, Blangero J, Wright MJ, Thompson PM, Kochunov P, Jahanshad N. The reliability and heritability of cortical folds and their genetic correlations across hemispheres. Commun Biol 2020; 3:510. [PMID: 32934300 PMCID: PMC7493906 DOI: 10.1038/s42003-020-01163-1] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 07/24/2020] [Indexed: 12/22/2022] Open
Abstract
Cortical folds help drive the parcellation of the human cortex into functionally specific regions. Variations in the length, depth, width, and surface area of these sulcal landmarks have been associated with disease, and may be genetically mediated. Before estimating the heritability of sulcal variation, the extent to which these metrics can be reliably extracted from in-vivo MRI must be established. Using four independent test-retest datasets, we found high reliability across the brain (intraclass correlation interquartile range: 0.65-0.85). Heritability estimates were derived for three family-based cohorts using variance components analysis and pooled (total N > 3000); the overall sulcal heritability pattern was correlated to that derived for a large population cohort (N > 9000) calculated using genomic complex trait analysis. Overall, sulcal width was the most heritable metric, and earlier forming sulci showed higher heritability. The inter-hemispheric genetic correlations were high, yet select sulci showed incomplete pleiotropy, suggesting hemisphere-specific genetic influences.
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Grants
- P41 EB015922 NIBIB NIH HHS
- R01 EB015611 NIBIB NIH HHS
- P01 AG026276 NIA NIH HHS
- R21 NS064534 NINDS NIH HHS
- R01 MH078111 NIMH NIH HHS
- R01 HD050735 NICHD NIH HHS
- R01 NS056307 NINDS NIH HHS
- R01 MH121246 NIMH NIH HHS
- P50 MH071616 NIMH NIH HHS
- R03 EB012461 NIBIB NIH HHS
- R01 AG059874 NIA NIH HHS
- U24 RR021382 NCRR NIH HHS
- P30 AG066444 NIA NIH HHS
- P01 AG003991 NIA NIH HHS
- P50 AG005681 NIA NIH HHS
- U54 EB020403 NIBIB NIH HHS
- R01 MH117601 NIMH NIH HHS
- U54 MH091657 NIMH NIH HHS
- R01 AG021910 NIA NIH HHS
- R01 MH078143 NIMH NIH HHS
- P41 RR015241 NCRR NIH HHS
- S10 OD023696 NIH HHS
- R01 MH083824 NIMH NIH HHS
- This research was funded in part by NIH ENIGMA Center grant U54 EB020403, supported by the Big Data to Knowledge (BD2K) Centers of Excellence program funded by a cross-NIH initiative. Additional grant support was provided by: R01 AG059874, R01 MH117601, R01 MH121246, and P41 EB015922. QTIM was supported by NIH R01 HD050735, and the NHMRC 486682, Australia; GOBS: Financial support for this study was provided by the National Institute of Mental Health grants MH078143 (PI: DC Glahn), MH078111 (PI: J Blangero), and MH083824 (PI: DC Glahn & J Blangero); HCP data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University; UK Biobank: This research was conducted using the UK Biobank Resource under Application Number ‘11559’; BrainVISA’s Morphologist software development received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Grant Agreement No 720270 & 785907 (Human Brain ProjectSGA1 & SGA2), and by the FRM DIC20161236445. OASIS: Cross-Sectional: Principal Investigators: D. Marcus, R. Buckner, J. Csernansky J. Morris; P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382. KKI was supported by NIH grants NCRR P41 RR015241 (Peter C.M. van Zijl), 1R01NS056307 (Jerry Prince), 1R21NS064534-01A109 (Bennett A. Landman/Jerry L. Prince), 1R03EB012461-01 (Bennett A. Landman). Neda Jahanshad and Paul Thompson are MPIs of a research project grant from Biogen, Inc. (PO 969323).
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Affiliation(s)
- Fabrizio Pizzagalli
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA.
| | - Guillaume Auzias
- Institut de Neurosciences de la Timone, UMR7289, Aix-Marseille Université & CNRS, Marseille, France
| | - Qifan Yang
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Samuel R Mathias
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Joshua Faskowitz
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Joshua D Boyd
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Armand Amini
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Denis Rivière
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Gif-sur-Yvette, France
- CATI, Multicenter Neuroimaging Platform, Paris, France
| | - Katie L McMahon
- School of Clinical Sciences and Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD 4000, Australia
| | - Greig I de Zubicaray
- Faculty of Health, Queensland University of Technology (QUT), Brisbane, QLD, 4000, Australia
| | | | - Jean-François Mangin
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Gif-sur-Yvette, France
- CATI, Multicenter Neuroimaging Platform, Paris, France
| | - David C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - John Blangero
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, 4072, Australia
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, 4072, Australia
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA.
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48
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Forde NJ, Jeyachandra J, Joseph M, Jacobs GR, Dickie E, Satterthwaite TD, Shinohara RT, Ameis SH, Voineskos AN. Sex Differences in Variability of Brain Structure Across the Lifespan. Cereb Cortex 2020; 30:5420-5430. [PMID: 32483605 PMCID: PMC7566684 DOI: 10.1093/cercor/bhaa123] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Revised: 03/16/2020] [Accepted: 04/19/2020] [Indexed: 12/13/2022] Open
Abstract
Several brain disorders exhibit sex differences in onset, presentation, and prevalence. Increased understanding of the neurobiology of sex-based differences in variability across the lifespan can provide insight into both disease vulnerability and resilience. In n = 3069 participants, from 8 to 95 years of age, we found widespread greater variability in males compared with females in cortical surface area and global and subcortical volumes for discrete brain regions. In contrast, variance in cortical thickness was similar for males and females. These findings were supported by multivariate analysis accounting for structural covariance, and present and stable across the lifespan. Additionally, we examined variability among brain regions by sex. We found significant age-by-sex interactions across neuroimaging metrics, whereby in very early life males had reduced among-region variability compared with females, while in very late life this was reversed. Overall, our findings of greater regional variability, but less among-region variability in males in early life may aid our understanding of sex-based risk for neurodevelopmental disorders. In contrast, our findings in late life may provide a potential sex-based risk mechanism for dementia.
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Affiliation(s)
- Natalie J Forde
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, M5T 1R8, Toronto, Canada
| | - Jerrold Jeyachandra
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, M5T 1R8, Toronto, Canada
| | - Michael Joseph
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, M5T 1R8, Toronto, Canada
| | - Grace R Jacobs
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, M5T 1R8, Toronto, Canada
- Institute of Medical Science, Faculty of Medicine, University of Toronto, M5S 1A8, Toronto, Canada
| | - Erin Dickie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, M5T 1R8, Toronto, Canada
| | - Theodore D Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn-CHOP Lifespan Brain Institute, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19103, USA
| | - Stephanie H Ameis
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, M5T 1R8, Toronto, Canada
- Department of Psychiatry, Faculty of Medicine, University of Toronto, M5T 1R8, Toronto, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, M5T 1R8, Toronto, Canada
- Department of Psychiatry, Faculty of Medicine, University of Toronto, M5T 1R8, Toronto, Canada
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49
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Han Y, Adolphs R. Estimating the heritability of psychological measures in the Human Connectome Project dataset. PLoS One 2020; 15:e0235860. [PMID: 32645058 PMCID: PMC7347217 DOI: 10.1371/journal.pone.0235860] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 06/24/2020] [Indexed: 12/03/2022] Open
Abstract
The Human Connectome Project (HCP) is a large structural and functional MRI dataset with a rich array of behavioral and genotypic measures, as well as a biologically verified family structure. This makes it a valuable resource for investigating questions about individual differences, including questions about heritability. While its MRI data have been analyzed extensively in this regard, to our knowledge a comprehensive estimation of the heritability of the behavioral dataset has never been conducted. Using a set of behavioral measures of personality, emotion and cognition, we show that it is possible to re-identify the same individual across two testing times (fingerprinting), and to identify identical twins significantly above chance. Standard heritability estimates of 37 behavioral measures were derived from twin correlations, and machine-learning models (univariate linear model, Ridge classifier and Random Forest model) were trained to classify monozygotic twins and dizygotic twins. Correlations between the standard heritability metric and each set of model weights ranged from 0.36 to 0.7, and questionnaire-based and task-based measures did not differ significantly in their heritability. We further explored the heritability of a smaller number of latent factors extracted from the 37 measures and repeated the heritability estimation; in this case, the correlations between the standard heritability and each set of model weights were lower, ranging from 0.05 to 0.43. One specific discrepancy arose for the general intelligence factor, which all models assigned high importance, but the standard heritability calculation did not. We present a thorough investigation of the heritabilities of the behavioral measures in the HCP as a resource for other investigators, and illustrate the utility of machine-learning methods for qualitative characterization of the differential heritability across diverse measures.
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Affiliation(s)
- Yanting Han
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States of America
- * E-mail:
| | - Ralph Adolphs
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, United States of America
- Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, United States of America
- Chen Neuroscience Institute, California Institute of Technology, Pasadena, CA, United States of America
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50
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Tahmasian M, Samea F, Khazaie H, Zarei M, Kharabian Masouleh S, Hoffstaedter F, Camilleri J, Kochunov P, Yeo BTT, Eickhoff SB, Valk SL. The interrelation of sleep and mental and physical health is anchored in grey-matter neuroanatomy and under genetic control. Commun Biol 2020; 3:171. [PMID: 32273564 PMCID: PMC7145855 DOI: 10.1038/s42003-020-0892-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 03/16/2020] [Indexed: 12/15/2022] Open
Abstract
Humans need about seven to nine hours of sleep per night. Sleep habits are heritable, associated with brain function and structure, and intrinsically related to well-being, mental, and physical health. However, the biological basis of the interplay of sleep and health is incompletely understood. Here we show, by combining neuroimaging and behavioral genetic approaches in two independent large-scale datasets (HCP (n = 1106), age range: 22-37, eNKI (n = 783), age range: 12-85), that sleep, mental, and physical health have a shared neurobiological basis in grey matter anatomy; and that these relationships are driven by shared genetic factors. Though local associations between sleep and cortical thickness were inconsistent across samples, we identified two robust latent components, highlighting the multivariate interdigitation of sleep, intelligence, BMI, depression, and macroscale cortical structure. Our observations provide a system-level perspective on the interrelation of sleep, mental, and physical conditions, anchored in grey-matter neuroanatomy.
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Affiliation(s)
- Masoud Tahmasian
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Fateme Samea
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Habibolah Khazaie
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Mojtaba Zarei
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Shahrzad Kharabian Masouleh
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Julia Camilleri
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Peter Kochunov
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore, 119077, Singapore
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02114, USA
- Centre for Sleep and Cognition, National University of Singapore, Singapore, 119077, Singapore
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, 119077, Singapore
| | - Simon Bodo Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Sofie Louise Valk
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425, Jülich, Germany.
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany.
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