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Hendriks J, Mutsaerts HJ, Joules R, Peña-Nogales Ó, Rodrigues PR, Wolz R, Burchell GL, Barkhof F, Schrantee A. A systematic review of (semi-)automatic quality control of T1-weighted MRI scans. Neuroradiology 2024; 66:31-42. [PMID: 38047983 PMCID: PMC10761394 DOI: 10.1007/s00234-023-03256-0] [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: 09/28/2023] [Accepted: 11/16/2023] [Indexed: 12/05/2023]
Abstract
PURPOSE Artifacts in magnetic resonance imaging (MRI) scans degrade image quality and thus negatively affect the outcome measures of clinical and research scanning. Considering the time-consuming and subjective nature of visual quality control (QC), multiple (semi-)automatic QC algorithms have been developed. This systematic review presents an overview of the available (semi-)automatic QC algorithms and software packages designed for raw, structural T1-weighted (T1w) MRI datasets. The objective of this review was to identify the differences among these algorithms in terms of their features of interest, performance, and benchmarks. METHODS We queried PubMed, EMBASE (Ovid), and Web of Science databases on the fifth of January 2023, and cross-checked reference lists of retrieved papers. Bias assessment was performed using PROBAST (Prediction model Risk Of Bias ASsessment Tool). RESULTS A total of 18 distinct algorithms were identified, demonstrating significant variations in methods, features, datasets, and benchmarks. The algorithms were categorized into rule-based, classical machine learning-based, and deep learning-based approaches. Numerous unique features were defined, which can be roughly divided into features capturing entropy, contrast, and normative measures. CONCLUSION Due to dataset-specific optimization, it is challenging to draw broad conclusions about comparative performance. Additionally, large variations exist in the used datasets and benchmarks, further hindering direct algorithm comparison. The findings emphasize the need for standardization and comparative studies for advancing QC in MR imaging. Efforts should focus on identifying a dataset-independent measure as well as algorithm-independent methods for assessing the relative performance of different approaches.
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Affiliation(s)
- Janine Hendriks
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, PK -1, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands.
| | - Henk-Jan Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, PK -1, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
| | | | | | | | - Robin Wolz
- IXICO Plc, London, EC1A 9PN, UK
- Imperial College London, London, SW7 2BX, UK
| | - George L Burchell
- Medical Library, Vrije Universiteit Amsterdam, Amsterdam, 1081 HV, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location VUmc, PK -1, De Boelelaan 1117, Amsterdam, 1081 HV, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, WC1N 3BG, UK
| | - Anouk Schrantee
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Location AMC, Amsterdam, 1105 AZ, The Netherlands
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2
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Ghassabian A, van den Dries M, Trasande L, Lamballais S, Spaan S, Martinez-Moral MP, Kannan K, Jaddoe VWV, Engel SM, Pronk A, White T, Tiemeier H, Guxens M. Prenatal exposure to common plasticizers: a longitudinal study on phthalates, brain volumetric measures, and IQ in youth. Mol Psychiatry 2023; 28:4814-4822. [PMID: 37644173 PMCID: PMC11062447 DOI: 10.1038/s41380-023-02225-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 08/01/2023] [Accepted: 08/08/2023] [Indexed: 08/31/2023]
Abstract
Exposure to phthalates, used as plasticizers and solvents in consumer products, is ubiquitous. Despite growing concerns regarding their neurotoxicity, brain differences associated with gestational exposure to phthalates are understudied. We included 775 mother-child pairs from Generation R, a population-based pediatric neuroimaging study with prenatal recruitment, who had data on maternal gestational phthalate levels and T1-weighted magnetic resonance imaging in children at age 10 years. Maternal urinary concentrations of phthalate metabolites were measured at early, mid-, and late pregnancy. Child IQ was assessed at age 14 years. We investigated the extent to which prenatal exposure to phthalates is associated with brain volumetric measures and whether brain structural measures mediate the association of prenatal phthalate exposure with IQ. We found that higher maternal concentrations of monoethyl phthalate (mEP, averaged across pregnancy) were associated with smaller total gray matter volumes in offspring at age 10 years (β per log10 increase in creatinine adjusted mEP = -10.7, 95%CI: -18.12, -3.28). Total gray matter volumes partially mediated the association between higher maternal mEP and lower child IQ (β for mediated path =-0.31, 95%CI: -0.62, 0.01, p = 0.05, proportion mediated = 18%). An association of higher monoisobutyl phthalate (mIBP) and smaller cerebral white matter volumes was present only in girls, with cerebral white matter volumes mediating the association between higher maternal mIBP and lower IQ in girls. Our findings suggest the global impact of prenatal phthalate exposure on brain volumetric measures that extends into adolescence and underlies less optimal cognitive development.
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Affiliation(s)
- Akhgar Ghassabian
- Department of Pediatrics, New York University School of Medicine, New York, NY, USA
- Department of Population Health, New York University School of Medicine, New York, NY, USA
| | - Michiel van den Dries
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands
- The Generation R Study Group, Erasmus University Medical Center, Rotterdam, the Netherlands
- ISGlobal, Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health, Instituto de Salud Carlos III, 28029, Madrid, Spain
| | - Leonardo Trasande
- Department of Pediatrics, New York University School of Medicine, New York, NY, USA
- Department of Population Health, New York University School of Medicine, New York, NY, USA
- New York University College of Global Public Health, New York City, NY, USA
- New York University Wagner School of Public Service, New York City, NY, USA
| | - Sander Lamballais
- Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Suzanne Spaan
- Department of Risk Analysis for Products in Development, TNO, Utrecht, the Netherlands
| | | | | | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Pediatrics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Stephanie M Engel
- Department of Epidemiology, Gilling School of Global Public Health, University of North Carolina Chapel Hill, Chapel Hill, NC, USA
| | - Anjoeka Pronk
- Department of Risk Analysis for Products in Development, TNO, Utrecht, the Netherlands
| | - Tonya White
- Section on Social and Cognitive Developmental Neuroscience, National Institute of Mental Health Bethesda, Bethesda, MD, USA
| | - Henning Tiemeier
- The Generation R Study Group, Erasmus University Medical Center, Rotterdam, the Netherlands.
- Department of Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Boston, MA, USA.
| | - Mònica Guxens
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands
- ISGlobal, Barcelona, Spain
- Pompeu Fabra University, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health, Instituto de Salud Carlos III, 28029, Madrid, Spain
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Thijssen LCP, de Rooij M, Barentsz JO, Huisman HJ. Radiomics based automated quality assessment for T2W prostate MR images. Eur J Radiol 2023; 165:110928. [PMID: 37354769 DOI: 10.1016/j.ejrad.2023.110928] [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: 04/14/2023] [Revised: 05/30/2023] [Accepted: 06/12/2023] [Indexed: 06/26/2023]
Abstract
PURPOSE The guidelines for prostate cancer recommend the use of MRI in the prostate cancer pathway. Due to the variability in prostate MR image quality, the reliability of this technique in the detection of prostate cancer is highly variable in clinical practice. This leads to the need for an objective and automated assessment of image quality to ensure an adequate acquisition and hereby to improve the reliability of MRI. The aim of this study is to investigate the feasibility of Blind/referenceless image spatial quality evaluator (Brisque) and radiomics in automated image quality assessment of T2-weighted (T2W) images. METHOD Anonymized axial T2W images from 140 patients were scored for quality using a five-point Likert scale (low, suboptimal, acceptable, good, very good quality) in consensus by two readers. Images were dichotomized into clinically acceptable (very good, good and acceptable quality images) and clinically unacceptable (low and suboptimal quality images) in order to train and verify the model. Radiomics and Brisque features were extracted from a central cuboid volume including the prostate. A reduced feature set was used to fit a Linear Discriminant Analysis (LDA) model to predict image quality. Two hundred times repeated 5-fold cross-validation was used to train the model and test performance by assessing the classification accuracy, the discrimination accuracy as receiver operating curve - area under curve (ROC-AUC), and by generating confusion matrices. RESULTS Thirty-four images were classified as clinically unacceptable and 106 were classified as clinically acceptable. The accuracy of the independent test set (mean ± standard deviation) was 85.4 ± 5.5%. The ROC-AUC was 0.856 (0.851 - 0.861) (mean; 95% confidence interval). CONCLUSIONS Radiomics AI can automatically detect a significant portion of T2W images of suboptimal image quality. This can help improve image quality at the time of acquisition, thus reducing repeat scans and improving diagnostic accuracy.
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Xerxa Y, White T, Busa S, Trasande L, Hillegers MHJ, Jaddoe VW, Castellanos FX, Ghassabian A. Gender Diversity and Brain Morphology Among Adolescents. JAMA Netw Open 2023; 6:e2313139. [PMID: 37171820 PMCID: PMC10182431 DOI: 10.1001/jamanetworkopen.2023.13139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/13/2023] Open
Abstract
Importance Gender-diverse youths have higher rates of mental health problems compared with the general population, as shown in both clinical and nonclinical populations. Brain correlates of gender diversity, however, have been reported only among youths with gender dysphoria or in transgender individuals. Objective To examine brain morphologic correlates of gender diversity among adolescents from a general pediatric population who were assigned male or female at birth, separately. Design, Setting, and Participants This cross-sectional study was embedded in Generation R, a multiethnic population-based study conducted in Rotterdam, the Netherlands. Adolescents who were born between April 1, 2002, and January 31, 2006, and had information on self-reported or parent-reported gender diversity and structural neuroimaging at ages 13 to 15 years were included. Data analysis was performed from April 1 to July 31, 2022. Exposures Gender-diverse experiences among adolescents were measured with selected items from the Achenbach System of Empirically Based Assessment forms and the Gender Identity/Gender Dysphoria Questionnaire for Adolescents and Adults, as reported by adolescents and/or their parents. Main Outcomes and Measures High-resolution structural neuroimaging data were collected using a 3-T magnetic resonance imaging scanner (at a single site). We used linear regression models to examine differences in global brain volumetric measures between adolescents who reported gender diversity and those who did not. Results This study included 2165 participants, with a mean (SD) age of 13.8 (0.6) years at scanning. A total of 1159 participants (53.5%) were assigned female at birth and 1006 (46.5%) were assigned male at birth. With regard to maternal country of origin, 1217 mothers (57.6%) were from the Netherlands and 896 (42.4%) were from outside the Netherlands. Adolescents who reported gender diversity did not differ in global brain volumetric measures from adolescents who did not report gender diversity. In whole-brain, vertexwise analyses among adolescents assigned male at birth, thicker cortices in the left inferior temporal gyrus were observed among youths who reported gender diversity compared with those who did not. No associations were observed between gender diversity and surface area in vertexwise analyses. Conclusions and Relevance The findings of this cross-sectional study suggest that global brain volumetric measures did not differ between adolescents who reported gender diversity and those who did not. However, these findings further suggest that gender diversity in the general population correlates with specific brain morphologic features in the inferior temporal gyrus among youths who are assigned male at birth. Replication of these findings is necessary to elucidate the potential neurobiological basis of gender diversity in the general population. Future longitudinal studies should also investigate the directionality of these associations.
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Affiliation(s)
- Yllza Xerxa
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
- Generation R Study Group, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands
- Section on Social and Cognitive Developmental Neuroscience, National Institute of Mental Health, Bethesda, Maryland
| | - Samantha Busa
- Department of Child and Adolescent Psychiatry, New York University Grossman School of Medicine, New York, New York
| | - Leonardo Trasande
- Department of Pediatrics, New York University Grossman School of Medicine, New York, New York
- Department of Population Health, New York University Grossman School of Medicine, New York, New York
- Department of Environmental Medicine, New York University Grossman School of Medicine, New York, New York
| | - Manon H J Hillegers
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Vincent W Jaddoe
- Generation R Study Group, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
- Department of Pediatrics, Sophia Children's Hospital, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Francisco Xavier Castellanos
- Department of Child and Adolescent Psychiatry, New York University Grossman School of Medicine, New York, New York
| | - Akhgar Ghassabian
- Department of Pediatrics, New York University Grossman School of Medicine, New York, New York
- Department of Population Health, New York University Grossman School of Medicine, New York, New York
- Department of Environmental Medicine, New York University Grossman School of Medicine, New York, New York
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5
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Elyounssi S, Kunitoki K, Clauss JA, Laurent E, Kane K, Hughes DE, Hopkinson CE, Bazer O, Sussman RF, Doyle AE, Lee H, Tervo-Clemmens B, Eryilmaz H, Gollub RL, Barch DM, Satterthwaite TD, Dowling KF, Roffman JL. Uncovering and mitigating bias in large, automated MRI analyses of brain development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.28.530498. [PMID: 36909456 PMCID: PMC10002762 DOI: 10.1101/2023.02.28.530498] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Large, population-based MRI studies of adolescents promise transformational insights into neurodevelopment and mental illness risk 1,2. However, MRI studies of youth are especially susceptible to motion and other artifacts 3,4. These artifacts may go undetected by automated quality control (QC) methods that are preferred in high-throughput imaging studies, 5 and can potentially introduce non-random noise into clinical association analyses. Here we demonstrate bias in structural MRI analyses of children due to inclusion of lower quality images, as identified through rigorous visual quality control of 11,263 T1 MRI scans obtained at age 9-10 through the Adolescent Brain Cognitive Development (ABCD) Study6. Compared to the best-rated images (44.9% of the sample), lower-quality images generally associated with decreased cortical thickness and increased cortical surface area measures (Cohen's d 0.14-2.84). Variable image quality led to counterintuitive patterns in analyses that associated structural MRI and clinical measures, as inclusion of lower-quality scans altered apparent effect sizes in ways that increased risk for both false positives and negatives. Quality-related biases were partially mitigated by controlling for surface hole number, an automated index of topological complexity that differentiated lower-quality scans with good specificity at Baseline (0.81-0.93) and in 1,000 Year 2 scans (0.88-1.00). However, even among the highest-rated images, subtle topological errors occurred during image preprocessing, and their correction through manual edits significantly and reproducibly changed thickness measurements across much of the cortex (d 0.15-0.92). These findings demonstrate that inadequate QC of youth structural MRI scans can undermine advantages of large sample size to detect meaningful associations.
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Affiliation(s)
- Safia Elyounssi
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Keiko Kunitoki
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Jacqueline A. Clauss
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Eline Laurent
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Kristina Kane
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Dylan E. Hughes
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Departments of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles
| | - Casey E. Hopkinson
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Oren Bazer
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Rachel Freed Sussman
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Alysa E. Doyle
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Center for Genomic Medicine, Massachusetts General Hospital
| | - Hang Lee
- Biostatistics Center, Massachusetts General Hospital and Harvard Medical School
| | | | - Hamdi Eryilmaz
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Randy L. Gollub
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Deanna M. Barch
- Department of Psychological and Brain Sciences, Washington University in St. Louis
| | - Theodore D. Satterthwaite
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine
- Penn Lifespan and Neuroimaging Center, University of Pennsylvania Perelman School of Medicine
- Penn-CHOP Lifespan Brain Institute
| | - Kevin F. Dowling
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Department of Psychiatry, University of Pittsburgh
| | - Joshua L. Roffman
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
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Arinze JT, Vinke EJ, Verhamme KMC, de Ridder MAJ, Stricker B, Ikram MK, Brusselle G, Vernooij MW. Chronic Cough-Related Differences in Brain Morphometry in Adults: A Population-Based Study. Chest 2023:S0012-3692(23)00187-3. [PMID: 36781103 DOI: 10.1016/j.chest.2023.02.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 02/13/2023] Open
Abstract
BACKGROUND Individuals with cough hypersensitivity have increased central neural responses to tussive stimuli, which may result in maladaptive morphometric changes in the central cough processing systems. RESEARCH QUESTION Are the volumes of the brain regions implicated in cough hypersensitivity different in adults with chronic cough compared with adults without chronic cough? STUDY DESIGN AND METHODS Between 2009 and 2014, participants in the Rotterdam Study, a population-based cohort, underwent brain MRI and were interviewed for chronic cough, which was defined as daily coughing for at least 3 months. Regional brain volumes were quantified with the use of parcellation software. Based on literature review, we identified and studied seven brain regions that previously had been associated with altered functional brain activity in chronic cough. The relationship between chronic cough and regional brain volumes was investigated with the use of multivariable regression models. RESULTS Chronic cough was prevalent in 9.6% (No. = 349) of the 3,620 study participants (mean age, 68.5 ± 9.0 years; 54.6% women). Participants with chronic cough had significantly smaller anterior cingulate cortex volume than participants without chronic cough (mean difference, -126.16 mm3; 95% CI, -245.67 to -6.66; P = .039). Except for anterior cingulate cortex, there were no significant difference in the volume of other brain regions based on chronic cough status. The volume difference in the anterior cingulate cortex was more pronounced in the left hemisphere (mean difference, -88.11 mm3; 95% CI, -165.16 to -11.06; P = .025) and in men (mean difference, -242.58 mm3; 95% CI, -428.60 to -56.55; P = .011). INTERPRETATION Individuals with chronic cough have a smaller volume of the anterior cingulate cortex, which is a brain region involved in cough suppression. CLINICAL TRIAL REGISTRATION The Netherlands National Trial Registry (NTR; www.trialregister.nl) and the World Health Organization's International Clinical Trials Registry Platform (ICTRP; www.who.int/ictrp/network/primary/en/) under the joint catalogue number NTR6831.
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Affiliation(s)
- Johnmary T Arinze
- Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium; Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Elisabeth J Vinke
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Katia M C Verhamme
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Maria A J de Ridder
- Department of Medical Informatics, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Bruno Stricker
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - M K Ikram
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Guy Brusselle
- Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium; Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Respiratory Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.
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Provins C, MacNicol E, Seeley SH, Hagmann P, Esteban O. Quality control in functional MRI studies with MRIQC and fMRIPrep. FRONTIERS IN NEUROIMAGING 2023; 1:1073734. [PMID: 37555175 PMCID: PMC10406249 DOI: 10.3389/fnimg.2022.1073734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 12/19/2022] [Indexed: 08/10/2023]
Abstract
The implementation of adequate quality assessment (QA) and quality control (QC) protocols within the magnetic resonance imaging (MRI) research workflow is resource- and time-consuming and even more so is their execution. As a result, QA/QC practices highly vary across laboratories and "MRI schools", ranging from highly specialized knowledge spots to environments where QA/QC is considered overly onerous and costly despite evidence showing that below-standard data increase the false positive and false negative rates of the final results. Here, we demonstrate a protocol based on the visual assessment of images one-by-one with reports generated by MRIQC and fMRIPrep, for the QC of data in functional (blood-oxygen dependent-level; BOLD) MRI analyses. We particularize the proposed, open-ended scope of application to whole-brain voxel-wise analyses of BOLD to correspondingly enumerate and define the exclusion criteria applied at the QC checkpoints. We apply our protocol on a composite dataset (n = 181 subjects) drawn from open fMRI studies, resulting in the exclusion of 97% of the data (176 subjects). This high exclusion rate was expected because subjects were selected to showcase artifacts. We describe the artifacts and defects more commonly found in the dataset that justified exclusion. We moreover release all the materials we generated in this assessment and document all the QC decisions with the expectation of contributing to the standardization of these procedures and engaging in the discussion of QA/QC by the community.
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Affiliation(s)
- Céline Provins
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Eilidh MacNicol
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Saren H. Seeley
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Oscar Esteban
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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8
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Poels EMP, Kamperman AM, Bijma HH, Honig A, van Kamp IL, Kushner SA, Hoogendijk WJG, Bergink V, White T. Brain development after intrauterine exposure to lithium: A magnetic resonance imaging study in school-age children. Bipolar Disord 2023; 25:181-190. [PMID: 36633504 DOI: 10.1111/bdi.13297] [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] [Indexed: 01/13/2023]
Abstract
OBJECTIVE Lithium is often continued during pregnancy to reduce the risk of perinatal mood episodes for women with bipolar disorder. However, little is known about the effect of intrauterine lithium exposure on brain development. The aim of this study was to investigate brain structure in children after intrauterine exposure to lithium. METHODS Participants were offspring, aged 8-14 years, of women with a diagnosis of bipolar spectrum disorder. In total, 63 children participated in the study: 30 with and 33 without intrauterine exposure to lithium. Global brain volume outcomes and white matter integrity were assessed using structural MRI and diffusion tensor imaging, respectively. Primary outcomes were total brain, cortical and subcortical gray matter, cortical white matter, lateral ventricles, cerebellum, hippocampus and amygdala volumes, cortical thickness, cortical surface area and global fractional anisotropy, and mean diffusivity. To assess how our data compared to the general population, global brain volumes were compared to data from the Generation R study (N = 3243). RESULTS In our primary analyses, we found no statistically significant associations between intrauterine exposure to lithium and structural brain measures. There was a non-significant trend toward reduced subcortical gray matter volume. Compared to the general population, lithium-exposed children showed reduced subcortical gray and cortical white matter volumes. CONCLUSION We found no differences in brain structure between lithium-exposed and non-lithium-exposed children aged 8-14 years following correction for multiple testing. While a rare population to study, future and likely multi-site studies with larger datasets are required to validate and extend these initial findings.
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Affiliation(s)
- Eline M P Poels
- Department of Psychiatry, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Astrid M Kamperman
- Department of Psychiatry, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Hilmar H Bijma
- Department of Obstetrics and Gynaecology, Division Obstetrics and Fetal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Adriaan Honig
- Department of Psychiatry, OLVG, Amsterdam, The Netherlands.,Department of Psychiatry, Amsterdam UMC, Location VUmc, Amsterdam, The Netherlands
| | - Inge L van Kamp
- Department of Obstetrics, Leiden University Medical Center, Leiden, The Netherlands
| | - Steven A Kushner
- Department of Psychiatry, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Witte J G Hoogendijk
- Department of Psychiatry, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Veerle Bergink
- Department of Psychiatry, Erasmus University Medical Center, Rotterdam, The Netherlands.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus MC, Sophia Children's Hospital, Rotterdam, The Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
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D’Onofrio P, Norman LJ, Sudre G, White T, Shaw P. The Anatomy of Friendship: Neuroanatomic Homophily of the Social Brain among Classroom Friends. Cereb Cortex 2022; 32:3031-3041. [PMID: 35848863 PMCID: PMC9290566 DOI: 10.1093/cercor/bhab398] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 09/28/2021] [Accepted: 09/30/2021] [Indexed: 01/01/2023] Open
Abstract
Homophily refers to the tendency to like similar others. Here, we ask if homophily extends to brain structure. Specifically: do children who like one another have more similar brain structures? We hypothesized that neuroanatomic similarity tied to friendship is most likely to pertain to brain regions that support social cognition. To test this hypothesis, we analyzed friendship network data from 1186 children in 49 classrooms. Within each classroom, we identified "friendship distance"-mutual friends, friends-of-friends, and more distantly connected or unconnected children. In total, 125 children (mean age = 7.57 years, 65 females) also had good quality neuroanatomic magnetic resonance imaging scans from which we extracted properties of the "social brain." We found that similarity of the social brain varied by friendship distance: mutual friends showed greater similarity in social brain networks compared with friends-of-friends (β = 0.65, t = 2.03, P = 0.045) and even more remotely connected peers (β = 0.77, t = 2.83, P = 0.006); friends-of-friends did not differ from more distantly connected peers (β = -0.13, t = -0.53, P = 0.6). We report that mutual friends have similar "social brain" networks, adding a neuroanatomic dimension to the adage that "birds of a feather flock together."
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Affiliation(s)
- Patrick D’Onofrio
- Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, NHGRI/NIH, Bethesda, MD 20892, USA
| | - Luke J Norman
- Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, NHGRI/NIH, Bethesda, MD 20892, USA
| | - Gustavo Sudre
- Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, NHGRI/NIH, Bethesda, MD 20892, USA
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus MC, Sophia Children’s Hospital Kamer, Rotterdam, 3000 CB, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, 3015 GD, The Netherlands
| | - Philip Shaw
- Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, NHGRI/NIH, Bethesda, MD 20892, USA
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10
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Durkut M, Blok E, Suleri A, White T. The longitudinal bidirectional relationship between autistic traits and brain morphology from childhood to adolescence: a population-based cohort study. Mol Autism 2022; 13:31. [PMID: 35790991 PMCID: PMC9258195 DOI: 10.1186/s13229-022-00504-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 05/24/2022] [Indexed: 11/10/2022] Open
Abstract
Objective Autistic traits are associated with alterations in brain morphology. However, the anatomic location of these differences and their developmental trajectories are unclear. The primary objective of this longitudinal study was to explore the bidirectional relationship between autistic traits and brain morphology from childhood to adolescence. Method Participants were drawn from a population-based cohort. Cross-sectional and longitudinal analyses included 1950 (mean age 13.5) and 304 participants (mean ages 6.2 and 13.5), respectively. Autistic traits were measured with the Social Responsiveness Scale. Global brain measures and surface-based measures of gyrification, cortical thickness and surface area were obtained from T1-weighted MRI scans. Cross-sectional associations were assessed using linear regression analyses. Cross-lagged panel models were used to determine the longitudinal bidirectional relationship between autistic traits and brain morphology. Results Cross-sectionally, higher levels of autistic traits in adolescents are associated with lower gyrification in the pars opercularis, insula and superior temporal cortex; smaller surface area in the middle temporal and postcentral cortex; larger cortical thickness in the superior frontal cortex; and smaller cerebellum cortex volume. Longitudinally, both autistic traits and brain measures were quite stable, with neither brain measures predicting changes in autistic traits, nor vice-versa. Limitations Autistic traits were assessed at only two time points, and thus we could not distinguish within- versus between-person effects. Furthermore, two different MRI scanners were used between baseline and follow-up for imaging data acquisition. Conclusions Our findings point to early changes in brain morphology in children with autistic symptoms that remain quite stable over time. The observed relationship did not change substantially after excluding children with high levels of autistic traits, bolstering the evidence for the extension of the neurobiology of autistic traits to the general population. Supplementary Information The online version contains supplementary material available at 10.1186/s13229-022-00504-7.
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11
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Pulli EP, Silver E, Kumpulainen V, Copeland A, Merisaari H, Saunavaara J, Parkkola R, Lähdesmäki T, Saukko E, Nolvi S, Kataja EL, Korja R, Karlsson L, Karlsson H, Tuulari JJ. Feasibility of FreeSurfer Processing for T1-Weighted Brain Images of 5-Year-Olds: Semiautomated Protocol of FinnBrain Neuroimaging Lab. Front Neurosci 2022; 16:874062. [PMID: 35585923 PMCID: PMC9108497 DOI: 10.3389/fnins.2022.874062] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 04/12/2022] [Indexed: 02/03/2023] Open
Abstract
Pediatric neuroimaging is a quickly developing field that still faces important methodological challenges. Pediatric images usually have more motion artifact than adult images. The artifact can cause visible errors in brain segmentation, and one way to address it is to manually edit the segmented images. Variability in editing and quality control protocols may complicate comparisons between studies. In this article, we describe in detail the semiautomated segmentation and quality control protocol of structural brain images that was used in FinnBrain Birth Cohort Study and relies on the well-established FreeSurfer v6.0 and ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium tools. The participants were typically developing 5-year-olds [n = 134, 5.34 (SD 0.06) years, 62 girls]. Following a dichotomous quality rating scale for inclusion and exclusion of images, we explored the quality on a region of interest level to exclude all regions with major segmentation errors. The effects of manual edits on cortical thickness values were relatively minor: less than 2% in all regions. Supplementary Material cover registration and additional edit options in FreeSurfer and comparison to the computational anatomy toolbox (CAT12). Overall, we conclude that despite minor imperfections FreeSurfer can be reliably used to segment cortical metrics from T1-weighted images of 5-year-old children with appropriate quality assessment in place. However, custom templates may be needed to optimize the results for the subcortical areas. Through visual assessment on a level of individual regions of interest, our semiautomated segmentation protocol is hopefully helpful for investigators working with similar data sets, and for ensuring high quality pediatric neuroimaging data.
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Affiliation(s)
- Elmo P. Pulli
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
- *Correspondence: Elmo P. Pulli, ; orcid.org/0000-0003-3871-8563
| | - Eero Silver
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
| | - Venla Kumpulainen
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
| | - Anni Copeland
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Harri Merisaari
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Radiology, University of Turku, Turku, Finland
| | - Jani Saunavaara
- Department of Medical Physics, Turku University Hospital, Turku, Finland
| | - Riitta Parkkola
- Department of Radiology, University of Turku, Turku, Finland
- Department of Radiology, Turku University Hospital, Turku, Finland
| | - Tuire Lähdesmäki
- Department of Pediatrics and Adolescent Medicine, Turku University Hospital, University of Turku, Turku, Finland
| | - Ekaterina Saukko
- Department of Radiology, Turku University Hospital, Turku, Finland
| | - Saara Nolvi
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Turku Institute for Advanced Studies, University of Turku, Turku, Finland
- Department of Psychology, University of Turku, Turku, Finland
| | - Eeva-Leena Kataja
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Riikka Korja
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychology, University of Turku, Turku, Finland
| | - Linnea Karlsson
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
| | - Hasse Karlsson
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
| | - Jetro J. Tuulari
- Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
- Turku Collegium for Science, Medicine and Technology, University of Turku, Turku, Finland
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
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12
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Blok E, Geenjaar EPT, Geenjaar EAW, Calhoun VD, White T. Neurodevelopmental Trajectories in Children With Internalizing, Externalizing and Emotion Dysregulation Symptoms. Front Psychiatry 2022; 13:846201. [PMID: 35370828 PMCID: PMC8974911 DOI: 10.3389/fpsyt.2022.846201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 02/03/2022] [Indexed: 11/17/2022] Open
Abstract
INTRODUCTION Childhood and adolescence are crucial periods for brain and behavioral development. However, it is not yet clear how and when deviations from typical brain development are related to broad domains of psychopathology. METHODS Using three waves of neuroimaging data within the population-based Generation R Study sample, spanning a total age range of 6-16 years, we applied normative modeling to establish typical development curves for (sub-)cortical volume in 37 brain regions, and cortical thickness in 32 brain regions. Z-scores representing deviations from typical development were extracted and related to internalizing, externalizing and dysregulation profile (DP) symptoms. RESULTS Normative modeling showed regional differences in developmental trajectories. Psychopathology symptoms were related to negative deviations from typical development for cortical volume in widespread regions of the cortex and subcortex, and to positive deviations from typical development for cortical thickness in the orbitofrontal, frontal pole, pericalcarine and posterior cingulate regions of the cortex. DISCUSSION Taken together, this study charts developmental curves across the cerebrum for (sub-)cortical volume and cortical thickness. Our findings show that psychopathology symptoms, are associated with widespread differences in brain development, in which those with DP symptoms are most heavily affected.
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Affiliation(s)
- Elisabet Blok
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC Sophia Childrens Hospital, University Medical Center Rotterdam, Rotterdam, Netherlands.,The Generation R Study Group, Erasmus MC, University Medical Centre Rotterdam, Rotterdam, Netherlands
| | - Eloy P T Geenjaar
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States.,Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, United States
| | - Eloïse A W Geenjaar
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC Sophia Childrens Hospital, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Vince D Calhoun
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States.,Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, United States
| | - Tonya White
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC Sophia Childrens Hospital, University Medical Center Rotterdam, Rotterdam, Netherlands.,Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Centre Rotterdam, Rotterdam, Netherlands
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13
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White T, Blok E, Calhoun VD. Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed. Hum Brain Mapp 2022; 43:278-291. [PMID: 32621651 PMCID: PMC8675413 DOI: 10.1002/hbm.25120] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/12/2020] [Accepted: 06/22/2020] [Indexed: 12/19/2022] Open
Abstract
Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples. However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who "give" their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community.
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Affiliation(s)
- Tonya White
- Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical CenterRotterdamThe Netherlands
- Department of RadiologyErasmus University Medical CenterRotterdamThe Netherlands
| | - Elisabet Blok
- Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical CenterRotterdamThe Netherlands
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
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14
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El Marroun H, Klapwijk ET, Koevoets M, Brouwer RM, Peters S, Van't Ent D, Boomsma DI, Muetzel RL, Crone EA, Hulshoff Pol HE, Franken IHA. Alcohol use and brain morphology in adolescence: A longitudinal study in three different cohorts. Eur J Neurosci 2021; 54:6012-6026. [PMID: 34390509 PMCID: PMC9291789 DOI: 10.1111/ejn.15411] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 07/28/2021] [Accepted: 07/28/2021] [Indexed: 11/30/2022]
Abstract
Alcohol consumption is commonly initiated during adolescence, but the effects on human brain development remain unknown. In this multisite study, we investigated the longitudinal associations of adolescent alcohol use and brain morphology. Three longitudinal cohorts in the Netherlands (BrainScale n = 200, BrainTime n = 239 and a subsample of the Generation R study n = 318) of typically developing participants aged between 8 and 29 years were included. Adolescent alcohol use was self‐reported. Longitudinal neuroimaging data were collected for at least two time points. Processing pipelines and statistical analyses were harmonized across cohorts. Main outcomes were global and regional brain volumes, which were a priori selected. Linear mixed effect models were used to test main effects of alcohol use and interaction effects of alcohol use with age in each cohort separately. Alcohol use was associated with adolescent's brain morphology showing accelerated decrease in grey matter volumes, in particular in the frontal and cingulate cortex volumes, and decelerated increase in white matter volumes. No dose–response association was observed. The findings were most prominent and consistent in the older cohorts (BrainScale and BrainTime). In summary, this longitudinal study demonstrated differences in neurodevelopmental trajectories of grey and white matter volume in adolescents who consume alcohol compared with non‐users. These findings highlight the importance to further understand underlying neurobiological mechanisms when adolescents initiate alcohol consumption. Therefore, further studies need to determine to what extent this reflects the causal nature of this association, as this longitudinal observational study does not allow for causal inference.
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Affiliation(s)
- Hanan El Marroun
- Department of Child and Adolescent Psychiatry, University Medical Center Rotterdam, Erasmus MC, Rotterdam, The Netherlands.,Department of Pediatrics, University Medical Center Rotterdam, Erasmus MC, Rotterdam, The Netherlands.,Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Eduard T Klapwijk
- Department of Developmental and Educational Psychology, Leiden University, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Martijn Koevoets
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Rachel M Brouwer
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Sabine Peters
- Department of Developmental and Educational Psychology, Leiden University, Leiden, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Dennis Van't Ent
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Ryan L Muetzel
- Department of Child and Adolescent Psychiatry, University Medical Center Rotterdam, Erasmus MC, Rotterdam, The Netherlands
| | - Eveline A Crone
- Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, The Netherlands.,Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - Hilleke E Hulshoff Pol
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Ingmar H A Franken
- Department of Child and Adolescent Psychiatry, University Medical Center Rotterdam, Erasmus MC, Rotterdam, The Netherlands.,Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, The Netherlands
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15
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Gilmore AD, Buser NJ, Hanson JL. Variations in structural MRI quality significantly impact commonly used measures of brain anatomy. Brain Inform 2021; 8:7. [PMID: 33860392 PMCID: PMC8050166 DOI: 10.1186/s40708-021-00128-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 03/29/2021] [Indexed: 11/10/2022] Open
Abstract
Subject motion can introduce noise into neuroimaging data and result in biased estimations of brain structure. In-scanner motion can compromise data quality in a number of ways and varies widely across developmental and clinical populations. However, quantification of structural image quality is often limited to proxy or indirect measures gathered from functional scans; this may be missing true differences related to these potential artifacts. In this study, we take advantage of novel informatic tools, the CAT12 toolbox, to more directly measure image quality from T1-weighted images to understand if these measures of image quality: (1) relate to rigorous quality-control checks visually completed by human raters; (2) are associated with sociodemographic variables of interest; (3) influence regional estimates of cortical surface area, cortical thickness, and subcortical volumes from the commonly used Freesurfer tool suite. We leverage public-access data that includes a community-based sample of children and adolescents, spanning a large age-range (N = 388; ages 5-21). Interestingly, even after visually inspecting our data, we find image quality significantly impacts derived cortical surface area, cortical thickness, and subcortical volumes from multiple regions across the brain (~ 23.4% of all areas investigated). We believe these results are important for research groups completing structural MRI studies using Freesurfer or other morphometric tools. As such, future studies should consider using measures of image quality to minimize the influence of this potential confound in group comparisons or studies focused on individual differences.
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Affiliation(s)
- Alysha D Gilmore
- Learning Research & Development Center, University of Pittsburgh, 3939 O'Hara Street, Pittsburgh, PA, 15260, USA
| | - Nicholas J Buser
- Learning Research & Development Center, University of Pittsburgh, 3939 O'Hara Street, Pittsburgh, PA, 15260, USA
| | - Jamie L Hanson
- Learning Research & Development Center, University of Pittsburgh, 3939 O'Hara Street, Pittsburgh, PA, 15260, USA.
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16
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Klapwijk ET, van den Bos W, Tamnes CK, Raschle NM, Mills KL. Opportunities for increased reproducibility and replicability of developmental neuroimaging. Dev Cogn Neurosci 2021; 47:100902. [PMID: 33383554 PMCID: PMC7779745 DOI: 10.1016/j.dcn.2020.100902] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 11/19/2020] [Accepted: 12/08/2020] [Indexed: 01/08/2023] Open
Abstract
Many workflows and tools that aim to increase the reproducibility and replicability of research findings have been suggested. In this review, we discuss the opportunities that these efforts offer for the field of developmental cognitive neuroscience, in particular developmental neuroimaging. We focus on issues broadly related to statistical power and to flexibility and transparency in data analyses. Critical considerations relating to statistical power include challenges in recruitment and testing of young populations, how to increase the value of studies with small samples, and the opportunities and challenges related to working with large-scale datasets. Developmental studies involve challenges such as choices about age groupings, lifespan modelling, analyses of longitudinal changes, and data that can be processed and analyzed in a multitude of ways. Flexibility in data acquisition, analyses and description may thereby greatly impact results. We discuss methods for improving transparency in developmental neuroimaging, and how preregistration can improve methodological rigor. While outlining challenges and issues that may arise before, during, and after data collection, solutions and resources are highlighted aiding to overcome some of these. Since the number of useful tools and techniques is ever-growing, we highlight the fact that many practices can be implemented stepwise.
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Affiliation(s)
- Eduard T Klapwijk
- Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, the Netherlands; Institute of Psychology, Leiden University, Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden, the Netherlands.
| | - Wouter van den Bos
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands; Max Planck Institute for Human Development, Center for Adaptive Rationality, Berlin, Germany
| | - Christian K Tamnes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatry, Diakonhjemmet Hospital, Oslo, Norway
| | - Nora M Raschle
- Jacobs Center for Productive Youth Development at the University of Zurich, Zurich, Switzerland
| | - Kathryn L Mills
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; Department of Psychology, University of Oregon, Eugene, OR, USA
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17
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Brain structure prior to non-central nervous system cancer diagnosis: A population-based cohort study. NEUROIMAGE-CLINICAL 2021; 28:102466. [PMID: 33395962 PMCID: PMC7578754 DOI: 10.1016/j.nicl.2020.102466] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 09/29/2020] [Accepted: 10/06/2020] [Indexed: 11/21/2022]
Abstract
In a population-based setting we studied brain structure before cancer diagnosis. Brain structure was not altered before non-CNS cancer diagnosis. The effect of cancer on the brain before clinical manifestation is not supported.
Purpose Many studies have shown that patients with non-central nervous system (CNS) cancer can have brain abnormalities, such as reduced gray matter volume and cerebral microbleeds. These abnormalities can sometimes be present even before start of treatment, suggesting a potential detrimental effect of non-CNS cancer itself on the brain. In these previous studies, psychological factors associated with a cancer diagnosis and selection bias may have influenced results. To overcome these limitations, we investigated brain structure with magnetic resonance imaging (MRI) prior to cancer diagnosis. Patients and methods Between 2005 and 2014, 4,622 participants from the prospective population-based Rotterdam Study who were free of cancer, dementia, and stroke, underwent brain MRI and were subsequently followed for incident cancer until January 1st, 2015. We investigated the association between brain MRI measurements, including cerebral small vessel disease, volumes of global brain tissue, lobes, and subcortical structures, and global white matter microstructure, and the risk of non-CNS cancer using Cox proportional hazards models. Age was used as time scale. Models were corrected for e.g. sex, intracranial volume, educational level, body mass index, hypertension, diabetes mellitus, smoking status, alcohol use, and depression sum-score. Results During a median (interquartile range) follow-up of 7.0 years (4.9–8.1), 353 participants were diagnosed with non-CNS cancer. Results indicated that persons who develop cancer do not have more brain abnormalities before clinical manifestation of the disease than persons who remain free of cancer. The largest effect estimates were found for the relation between presence of lacunar infarcts and the risk of cancer (hazard ratio [HR] 95% confidence interval [CI] = 1.39 [0.97–1.98]) and for total brain volume (HR [95%CI] per standard deviation increase in total brain volume = 0.76 [0.55–1.04]). Conclusion We did not observe associations between small vessel disease, brain tissue volumes, and global white matter microstructure, and subsequent cancer risk in an unselected population. These findings deviate from previous studies indicating brain abnormalities among patients shortly after cancer diagnosis.
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18
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Design and overview of the Origins of Alzheimer's Disease Across the Life course (ORACLE) study. Eur J Epidemiol 2020; 36:117-127. [PMID: 33324997 PMCID: PMC7847463 DOI: 10.1007/s10654-020-00696-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 11/07/2020] [Indexed: 12/15/2022]
Abstract
Brain development and deterioration across the lifespan are integral to the etiology of late-life neurodegenerative disease. Factors that influence the health of the adult brain remain to be elucidated and include risk factors, protective factors, and factors related to cognitive and brain reserve.
To address this knowledge gap we designed a life-course study on brain health, which received funding through the EU ERC Programme under the name Origins of Alzheimer’s Disease Across the Life course (ORACLE) Study. The ORACLE Study is embedded within Generation R, a prospective population-based cohort study of children and their parents, and links this with the Rotterdam Study, a population-based study in middle-aged and elderly persons. The studies are based in Rotterdam, the Netherlands. Generation R focuses on child health from fetal life until adolescence with repeated in-person examinations, but has also included data collection on the children’s parents. The ORACLE Study aims to extend the parental data collection in nearly 2000 parents with extensive measures on brain health, including neuroimaging, cognitive testing and motor testing. Additionally, questionnaires on migraine, depressive symptoms, sleep, and neurological family history were completed. These data allow for the investigation of longitudinal influences on adult brain health as well as intergenerational designs involving children and parents. As a secondary focus, the sampling is enriched by mothers (n = 356) that suffered from hypertensive disorders during pregnancy in order to study brain health in this high-risk population. This article provides an overview of the rationale and the design of the ORACLE Study.
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19
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El Marroun H, Zou R, Leeuwenburg MF, Steegers EAP, Reiss IKM, Muetzel RL, Kushner SA, Tiemeier H. Association of Gestational Age at Birth With Brain Morphometry. JAMA Pediatr 2020; 174:1149-1158. [PMID: 32955580 PMCID: PMC7506610 DOI: 10.1001/jamapediatrics.2020.2991] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
IMPORTANCE Preterm and postterm births are associated with adverse neuropsychiatric outcomes. However, it remains unclear whether variation of gestational age within the 37- to 42-week range of term deliveries is associated with neurodevelopment. OBJECTIVE To investigate the association of gestational age at birth (GAB) with structural brain morphometry in children aged 10 years. DESIGN, SETTING, AND PARTICIPANTS This population-based cohort study included pregnant women living in Rotterdam, the Netherlands, with an expected delivery date between April 1, 2002, and January 31, 2006. The study evaluated 3079 singleton children with GAB ranging from 26.3 to 43.3 weeks and structural neuroimaging at 10 years of age from the Generation R Study, a longitudinal, population-based prospective birth cohort from early pregnancy onward in Rotterdam. Data analysis was performed from March 1, 2019, to February 28, 2020, and at the time of the revision based on reviewer suggestions. EXPOSURES The GAB was calculated based on ultrasonographic assessment of crown-rump length (<12 weeks 5 days) or biparietal diameter (≥12 weeks 5 days) in dedicated research centers. MAIN OUTCOMES AND MEASURES Brain structure, including global and regional brain volumes and surface-based cortical measures (thickness, surface area, and gyrification), was quantified by magnetic resonance imaging. RESULTS In the 3079 children (1546 [50.2%] female) evaluated at 10 years of age, GAB was linearly associated with global and regional brain volumes. Longer gestational duration was associated with larger brain volumes; for example, every 1-week-longer gestational duration corresponded to an additional 4.5 cm3/wk (95% CI, 2.7-6.3 cm3/wk) larger total brain volume. These associations persisted when the sample was restricted to children born at term (GAB of 37-42 weeks: 4.8 cm3/wk; 95% CI, 1.8-7.7 cm3/wk). No evidence of nonlinear associations between GA and brain morphometry was observed. CONCLUSIONS AND RELEVANCE In this cohort study, gestational duration was linearly associated with brain morphometry during childhood, including within the window of term delivery. These findings may have marked clinical importance, particularly given the prevalence of elective cesarean deliveries.
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Affiliation(s)
- Hanan El Marroun
- Department of Child and Adolescent Psychiatry, University Medical Center Rotterdam, Erasmus Medical Center, Rotterdam, the Netherlands,Department of Pediatrics, University Medical Center Rotterdam, Erasmus Medical Center, Rotterdam, the Netherlands,Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioral Sciences, Erasmus University, Rotterdam, the Netherlands
| | - Runyu Zou
- Department of Child and Adolescent Psychiatry, University Medical Center Rotterdam, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Michelle F. Leeuwenburg
- Department of Child and Adolescent Psychiatry, University Medical Center Rotterdam, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Eric A. P. Steegers
- Department of Obstetrics and Gynaecology, University Medical Center Rotterdam, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Irwin K. M. Reiss
- Department of Pediatrics, Division of Neonatology, University Medical Center Rotterdam, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Ryan L. Muetzel
- Department of Child and Adolescent Psychiatry, University Medical Center Rotterdam, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Steven A. Kushner
- Department of Psychiatry, University Medical Center Rotterdam, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry, University Medical Center Rotterdam, Erasmus Medical Center, Rotterdam, the Netherlands,Department of Social and Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston, Massachusetts
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Is it time to switch your T1W sequence? Assessing the impact of prospective motion correction on the reliability and quality of structural imaging. Neuroimage 2020; 226:117585. [PMID: 33248256 PMCID: PMC7898192 DOI: 10.1016/j.neuroimage.2020.117585] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 10/29/2020] [Accepted: 11/18/2020] [Indexed: 12/11/2022] Open
Abstract
New large neuroimaging studies, such as the Adolescent Brain Cognitive Development study (ABCD) and Human Connectome Project (HCP) Development studies are adopting a new T1-weighted imaging sequence with prospective motion correction (PMC) in favor of the more traditional 3-Dimensional Magnetization-Prepared Rapid Gradient-Echo Imaging (MPRAGE) sequence. Here, we used a developmental dataset (ages 5-21, N = 348) from the Healthy Brain Network (HBN) Initiative to directly compare two widely used MRI structural sequences: one based on the Human Connectome Project (MPRAGE) and another based on the ABCD study (MPRAGE+PMC). We aimed to determine if the morphometric measurements obtained from both protocols are equivalent or if one sequence has a clear advantage over the other. The sequences were also compared through quality control measurements. Inter- and intra-sequence reliability were assessed with another set of participants (N = 71) from HBN that performed two MPRAGE and two MPRAGE+PMC sequences within the same imaging session, with one MPRAGE (MPRAGE1) and MPRAGE+PMC (MPRAGE+PMC1) pair at the beginning of the session and another pair (MPRAGE2 and MPRAGE+PMC2) at the end of the session. Intraclass correlation coefficients (ICC) scores for morphometric measurements such as volume and cortical thickness showed that intra-sequence reliability is the highest with the two MPRAGE+PMC sequences and lowest with the two MPRAGE sequences. Regarding inter-sequence reliability, ICC scores were higher for the MPRAGE1 - MPRAGE+PMC1 pair at the beginning of the session than the MPRAGE1 - MPRAGE2 pair, possibly due to the higher motion artifacts in the MPRAGE2 run. Results also indicated that the MPRAGE+PMC sequence is robust, but not impervious, to high head motion. For quality control metrics, the traditional MPRAGE yielded better results than MPRAGE+PMC in 5 of the 8 measurements. In conclusion, morphometric measurements evaluated here showed high inter-sequence reliability between the MPRAGE and MPRAGE+PMC sequences, especially in images with low head motion. We suggest that studies targeting hyperkinetic populations use the MPRAGE+PMC sequence, given its robustness to head motion and higher reliability scores. However, neuroimaging researchers studying non-hyperkinetic participants can choose either MPRAGE or MPRAGE+PMC sequences, but should carefully consider the apparent tradeoff between relatively increased reliability, but reduced quality control metrics when using the MPRAGE+PMC sequence.
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Zou R, El Marroun H, Cecil C, Jaddoe VWV, Hillegers M, Tiemeier H, White T. Maternal folate levels during pregnancy and offspring brain development in late childhood. Clin Nutr 2020; 40:3391-3400. [PMID: 33279309 DOI: 10.1016/j.clnu.2020.11.025] [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: 05/22/2020] [Revised: 11/13/2020] [Accepted: 11/15/2020] [Indexed: 01/13/2023]
Abstract
BACKGROUND Cumulative evidence shows that low maternal folate levels during pregnancy are associated with offspring neuropsychiatric disorders even in the absence of neural tube defects. However, the relationship between prenatal exposure to folate and brain development in late childhood has been rarely investigated. METHODS In 2095 children from a prospective population-based cohort in Rotterdam, the Netherlands, we examined the association of maternal folate levels during pregnancy with downstream brain development in offspring. Maternal folate concentrations were measured from venous blood in early gestation. Child structural neuroimaging data were measured at age 9-11 years. In addition, measures of child head circumference using fetal ultrasound in the third trimester and total brain volume using magnetic resonance imaging at age 6-8 years were used for analyses with repeated assessments of brain development. RESULTS Maternal folate deficiency (i.e., <7 nmol/L) during pregnancy was associated with smaller total brain volume (B = -18.7 cm3, 95% CI -37.2 to -0.2) and smaller cerebral white matter (B = -7.2 cm3, 95% CI -11.8 to -2.6) in children aged 9-11 years. No differences in cortical thickness or surface area were observed. Analysis of the repeated brain assessments showed that children exposed to deficient folate concentrations in utero had persistently smaller brains compared to controls from the third trimester to childhood (β = -0.4, 95% CI -0.6 to -0.1). CONCLUSIONS Low maternal folate levels during pregnancy are associated with altered offspring brain development in childhood, suggesting the importance of essential folate concentrations in early pregnancy.
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Affiliation(s)
- Runyu Zou
- Department of Child and Adolescent Psychiatry, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands; The Generation R Study Group, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Hanan El Marroun
- Department of Child and Adolescent Psychiatry, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Pediatrics, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioral Sciences, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Charlotte Cecil
- Department of Child and Adolescent Psychiatry, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus MC University Medical Center Rotterdam, the Netherlands; Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 ZC, Leiden, the Netherlands
| | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Pediatrics, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Manon Hillegers
- Department of Child and Adolescent Psychiatry, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Social and Behavioral Sciences, T.H. Chan School of Public Health, Harvard University, Boston, MA, United States
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands.
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Hodge SM, Haselgrove C, Honor L, Kennedy DN, Frazier JA. An assessment of the autism neuroimaging literature for the prospects of re-executability. F1000Res 2020; 9:1031. [PMID: 33796274 PMCID: PMC7968525 DOI: 10.12688/f1000research.25306.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/22/2021] [Indexed: 11/20/2022] Open
Abstract
Background: The degree of reproducibility of the neuroimaging literature in psychiatric application areas has been called into question and the issues that relate to this reproducibility are extremely complex. Some of these complexities have to do with the underlying biology of the disorders that we study and others arise due to the technology we apply to the analysis of the data we collect. Ultimately, the observations we make get communicated to the rest of the community through publications in the scientific literature. Methods: We sought to perform a 're-executability survey' to evaluate the recent neuroimaging literature with an eye toward seeing if the technical aspects of our publication practices are helping or hindering the overall quest for a more reproducible understanding of brain development and aging. The topic areas examined include availability of the data, the precision of the imaging method description and the reporting of the statistical analytic approach, and the availability of the complete results. We applied the survey to 50 publications in the autism neuroimaging literature that were published between September 16, 2017 to October 1, 2018. Results: The results of the survey indicate that for the literature examined, data that is not already part of a public repository is rarely available, software tools are usually named but versions and operating system are not, it is expected that reasonably skilled analysts could approximately perform the analyses described, and the complete results of the studies are rarely available. Conclusions: We have identified that there is ample room for improvement in research publication practices. We hope exposing these issues in the retrospective literature can provide guidance and motivation for improving this aspect of our reporting practices in the future.
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Affiliation(s)
- Steven M. Hodge
- Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
| | - Christian Haselgrove
- Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
| | - Leah Honor
- Lamar Soutter Library, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
| | - David N. Kennedy
- Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
| | - Jean A. Frazier
- Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
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Hodge SM, Haselgrove C, Honor L, Kennedy DN, Frazier JA. An assessment of the autism neuroimaging literature for the prospects of re-executability. F1000Res 2020; 9:1031. [PMID: 33796274 PMCID: PMC7968525 DOI: 10.12688/f1000research.25306.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/04/2020] [Indexed: 05/04/2024] Open
Abstract
Background: The degree of reproducibility of the neuroimaging literature in psychiatric application areas has been called into question and the issues that relate to this reproducibility are extremely complex. Some of these complexities have to do with the underlying biology of the disorders that we study and others arise due to the technology we apply to the analysis of the data we collect. Ultimately, the observations we make get communicated to the rest of the community through publications in the scientific literature. Methods: We sought to perform a 're-executability survey' to evaluate the recent neuroimaging literature with an eye toward seeing if our publication practices are helping or hindering the overall quest for a more reproducible understanding of brain development and aging. The topic areas examined include availability of the data, the precision of the imaging method description and the reporting of the statistical analytic approach, and the availability of the complete results. We applied the survey to 50 publications in the autism neuroimaging literature that were published between September 16, 2017 to October 1, 2018. Results: The results of the survey indicate that for the literature examined, data that is not already part of a public repository is rarely available, software tools are usually named but versions and operating system are not, it is expected that reasonably skilled analysts could approximately perform the analyses described, and the complete results of the studies are rarely available. Conclusions: We have identified that there is ample room for improvement in research publication practices. We hope exposing these issues in the retrospective literature can provide guidance and motivation for improving this aspect of our reporting practices in the future.
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Affiliation(s)
- Steven M. Hodge
- Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
| | - Christian Haselgrove
- Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
| | - Leah Honor
- Lamar Soutter Library, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
| | - David N. Kennedy
- Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
| | - Jean A. Frazier
- Eunice Kennedy Shriver Center, Department of Psychiatry, University of Massachusetts Medical School, Worcester, Massachusetts, 01655, USA
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24
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Liu S, Thung KH, Lin W, Yap PT, Shen D. Real-Time Quality Assessment of Pediatric MRI via Semi-Supervised Deep Nonlocal Residual Neural Networks. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2020; 29:10.1109/TIP.2020.2992079. [PMID: 32396089 PMCID: PMC7648726 DOI: 10.1109/tip.2020.2992079] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In this paper, we introduce an image quality assessment (IQA) method for pediatric T1- and T2-weighted MR images. IQA is first performed slice-wise using a nonlocal residual neural network (NR-Net) and then volume-wise by agglomerating the slice QA results using random forest. Our method requires only a small amount of quality-annotated images for training and is designed to be robust to annotation noise that might occur due to rater errors and the inevitable mix of good and bad slices in an image volume. Using a small set of quality-assessed images, we pre-train NR-Net to annotate each image slice with an initial quality rating (i.e., pass, questionable, fail), which we then refine by semi-supervised learning and iterative self-training. Experimental results demonstrate that our method, trained using only samples of modest size, exhibit great generalizability, capable of real-time (milliseconds per volume) large-scale IQA with nearperfect accuracy.
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25
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Beelen C, Phan TV, Wouters J, Ghesquière P, Vandermosten M. Investigating the Added Value of FreeSurfer's Manual Editing Procedure for the Study of the Reading Network in a Pediatric Population. Front Hum Neurosci 2020; 14:143. [PMID: 32390814 PMCID: PMC7194167 DOI: 10.3389/fnhum.2020.00143] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 03/30/2020] [Indexed: 01/08/2023] Open
Abstract
Insights into brain anatomy are important for the early detection of neurodevelopmental disorders, such as dyslexia. FreeSurfer is one of the most frequently applied automatized software tools to study brain morphology. However, quality control of the outcomes provided by FreeSurfer is often ignored and could lead to wrong statistical inferences. Additional manual editing of the data may be a solution, although not without a cost in time and resources. Past research in adults on comparing the automatized method of FreeSurfer with and without additional manual editing indicated that although editing may lead to significant differences in morphological measures between the methods in some regions, it does not substantially change the sensitivity to detect clinical differences. Given that automated approaches are more likely to fail in pediatric-and inherently more noisy-data, we investigated in the current study whether FreeSurfer can be applied fully automatically or additional manual edits of T1-images are needed in a pediatric sample. Specifically, cortical thickness and surface area measures with and without additional manual edits were compared in six regions of interest (ROIs) of the reading network in 5-to-6-year-old children with and without dyslexia. Results revealed that additional editing leads to statistical differences in the morphological measures, but that these differences are consistent across subjects and that the sensitivity to reveal statistical differences in the morphological measures between children with and without dyslexia is not affected, even though conclusions of marginally significant findings can differ depending on the method used. Thereby, our results indicate that additional manual editing of reading-related regions in FreeSurfer has limited gain for pediatric samples.
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Affiliation(s)
- Caroline Beelen
- Parenting and Special Education Research Unit, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
| | | | - Jan Wouters
- Research Group ExpORL, Department of Neuroscience, KU Leuven, Leuven, Belgium
| | - Pol Ghesquière
- Parenting and Special Education Research Unit, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium
| | - Maaike Vandermosten
- Research Group ExpORL, Department of Neuroscience, KU Leuven, Leuven, Belgium
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26
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Bedford SA, Park MTM, Devenyi GA, Tullo S, Germann J, Patel R, Anagnostou E, Baron-Cohen S, Bullmore ET, Chura LR, Craig MC, Ecker C, Floris DL, Holt RJ, Lenroot R, Lerch JP, Lombardo MV, Murphy DGM, Raznahan A, Ruigrok ANV, Smith E, Spencer MD, Suckling J, Taylor MJ, Thurm A, Lai MC, Chakravarty MM. Large-scale analyses of the relationship between sex, age and intelligence quotient heterogeneity and cortical morphometry in autism spectrum disorder. Mol Psychiatry 2020; 25:614-628. [PMID: 31028290 DOI: 10.1038/s41380-019-0420-6] [Citation(s) in RCA: 97] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 03/25/2019] [Accepted: 03/26/2019] [Indexed: 01/29/2023]
Abstract
Significant heterogeneity across aetiologies, neurobiology and clinical phenotypes have been observed in individuals with autism spectrum disorder (ASD). Neuroimaging-based neuroanatomical studies of ASD have often reported inconsistent findings which may, in part, be attributable to an insufficient understanding of the relationship between factors influencing clinical heterogeneity and their relationship to brain anatomy. To this end, we performed a large-scale examination of cortical morphometry in ASD, with a specific focus on the impact of three potential sources of heterogeneity: sex, age and full-scale intelligence (FIQ). To examine these potentially subtle relationships, we amassed a large multi-site dataset that was carefully quality controlled (yielding a final sample of 1327 from the initial dataset of 3145 magnetic resonance images; 491 individuals with ASD). Using a meta-analytic technique to account for inter-site differences, we identified greater cortical thickness in individuals with ASD relative to controls, in regions previously implicated in ASD, including the superior temporal gyrus and inferior frontal sulcus. Greater cortical thickness was observed in sex specific regions; further, cortical thickness differences were observed to be greater in younger individuals and in those with lower FIQ, and to be related to overall clinical severity. This work serves as an important step towards parsing factors that influence neuroanatomical heterogeneity in ASD and is a potential step towards establishing individual-specific biomarkers.
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Affiliation(s)
- Saashi A Bedford
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, QC, Canada.
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada.
| | - Min Tae M Park
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, QC, Canada
- Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Gabriel A Devenyi
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Stephanie Tullo
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, QC, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Jurgen Germann
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, QC, Canada
| | - Raihaan Patel
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, QC, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | | | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Edward T Bullmore
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Lindsay R Chura
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Michael C Craig
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- National Autism Unit, Bethlem Royal Hospital, London, UK
| | - Christine Ecker
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Goethe University, Frankfurt am Main, Germany
| | - Dorothea L Floris
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Hassenfeld Children's Hospital at NYU Langone Department of Child and Adolescent Psychiatry, Child Study Center, New York City, NY, USA
| | - Rosemary J Holt
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Rhoshel Lenroot
- Department of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - Jason P Lerch
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Michael V Lombardo
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cyprus, Nicosia, Cyprus
| | - Declan G M Murphy
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Armin Raznahan
- Developmental Neurogenomics Unit, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - Amber N V Ruigrok
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Elizabeth Smith
- Section on Behavioral Pediatrics, National Institute of Mental Health, Bethesda, MD, USA
| | - Michael D Spencer
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Margot J Taylor
- Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada
| | - Audrey Thurm
- Section on Behavioral Pediatrics, National Institute of Mental Health, Bethesda, MD, USA
| | - Meng-Chuan Lai
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Program in Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, QC, Canada.
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada.
- Department of Psychiatry, McGill University, Montreal, QC, Canada.
- Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada.
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27
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Benhajali Y, Badhwar A, Spiers H, Urchs S, Armoza J, Ong T, Pérusse D, Bellec P. A Standardized Protocol for Efficient and Reliable Quality Control of Brain Registration in Functional MRI Studies. Front Neuroinform 2020; 14:7. [PMID: 32180712 PMCID: PMC7059806 DOI: 10.3389/fninf.2020.00007] [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: 07/18/2019] [Accepted: 02/12/2020] [Indexed: 01/16/2023] Open
Abstract
Automatic alignment of brain anatomy in a standard space is a key step when processing magnetic resonance imaging for group analyses. Such brain registration is prone to failure, and the results are therefore typically reviewed visually to ensure quality. There is however no standard, validated protocol available to perform this visual quality control (QC). We propose here a standardized QC protocol for brain registration, with minimal training overhead and no required knowledge of brain anatomy. We validated the reliability of three-level QC ratings (OK, Maybe, Fail) across different raters. Nine experts each rated N = 100 validation images, and reached moderate to good agreement (kappa from 0.4 to 0.68, average of 0.54 ± 0.08), with the highest agreement for "Fail" images (Dice from 0.67 to 0.93, average of 0.8 ± 0.06). We then recruited volunteers through the Zooniverse crowdsourcing platform, and extracted a consensus panel rating for both the Zooniverse raters (N = 41) and the expert raters. The agreement between expert and Zooniverse panels was high (kappa = 0.76). Overall, our protocol achieved a good reliability when performing a two level assessment (Fail vs. OK/Maybe) by an individual rater, or aggregating multiple three-level ratings (OK, Maybe, Fail) from a panel of experts (3 minimum) or non-experts (15 minimum). Our brain registration QC protocol will help standardize QC practices across laboratories, improve the consistency of reporting of QC in publications, and will open the way for QC assessment of large datasets which could be used to train automated QC systems.
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Affiliation(s)
- Yassine Benhajali
- Département d’Anthropologie, Université de Montréal, Montreal, QC, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montreal, QC, Canada
| | - AmanPreet Badhwar
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montreal, QC, Canada
| | - Helen Spiers
- The Zooniverse, Department of Physics, University of Oxford, Oxford, United Kingdom
- Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, United Kingdom
| | - Sebastian Urchs
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montreal, QC, Canada
- Integrated Program in Neuroscience, Montreal Neurological Institute, Montreal, QC, Canada
| | - Jonathan Armoza
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montreal, QC, Canada
- English Department, New York University, New York, NY, United States
| | - Thomas Ong
- Jewish General Hospital, Department of Radiology, McGill University, Montreal, QC, Canada
| | - Daniel Pérusse
- Département d’Anthropologie, Université de Montréal, Montreal, QC, Canada
| | - Pierre Bellec
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montreal, QC, Canada
- Département de Psychologie, Université de Montréal, Montreal, QC, Canada
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Cortical gyrification in relation to age and cognition in older adults. Neuroimage 2020; 212:116637. [PMID: 32081782 DOI: 10.1016/j.neuroimage.2020.116637] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 01/31/2020] [Accepted: 02/12/2020] [Indexed: 12/21/2022] Open
Abstract
Gyrification of the cerebral cortex changes with aging and relates to development of cognitive function during early life and midlife. Little is known about how gyrification relates to age and cognitive function later in life. We investigated this in 4397 individuals (mean age: 63.5 years, range: 45.7 to 97.9) from the Rotterdam Study, a population-based cohort. Global and local gyrification were assessed from T1-weighted images. A measure for global cognition, the g-factor, was calculated from five cognitive tests. Older age was associated with lower gyrification (mean difference per year = -0.0021; 95% confidence interval = -0.0025; -0.0017). Non-linear terms did not improve the models. Age related to lower gyrification in the parietal, frontal, temporal and occipital regions, and higher gyrification in the medial prefrontal cortex. Higher levels of the g-factor were associated with higher global gyrification (mean difference per g-factor unit = 0.0044; 95% confidence interval = 0.0015; 0.0073). Age and the g-factor did not interact in relation to gyrification (p > 0.05). The g-factor bilaterally associated with gyrification in three distinct clusters. The first cluster encompassed the superior temporal gyrus, the insular cortex and the postcentral gyrus, the second cluster the lingual gyrus and the precuneus, and the third cluster the orbitofrontal cortex. These clusters largely remained statistically significant after correction for cortical surface area. Overall, the results support the notion that gyrification varies with aging and cognition during and after midlife, and suggest that gyrification is a potential marker for age-related brain and cognitive decline beyond midlife.
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Hoogman M, Muetzel R, Guimaraes JP, Shumskaya E, Mennes M, Zwiers MP, Jahanshad N, Sudre G, Mostert J, Wolfers T, Earl EA, Vila JCS, Vives-Gilabert Y, Khadka S, Novotny SE, Hartman CA, Heslenfeld DJ, Schweren LJ, Ambrosino S, Oranje B, de Zeeuw P, Chaim-Avancini TM, Rosa PGP, Zanetti MV, Malpas CB, Kohls G, von Polier GG, Seitz J, Biederman J, Doyle AE, Dale AM, van Erp TG, Epstein JN, Jernigan TL, Baur-Streubel R, Ziegler GC, Zierhut KC, Schrantee A, Høvik MF, Lundervold AJ, Kelly C, McCarthy H, Skokauskas N, O'Gorman Tuura RL, Calvo A, Lera-Miguel S, Nicolau R, Chantiluke KC, Christakou A, Vance A, Cercignani M, Gabel MC, Asherson P, Baumeister S, Brandeis D, Hohmann S, Bramati IE, Tovar-Moll F, Fallgatter AJ, Kardatzki B, Schwarz L, Anikin A, Baranov A, Gogberashvili T, Kapilushniy D, Solovieva A, El Marroun H, White T, Karkashadze G, Namazova-Baranova L, Ethofer T, Mattos P, Banaschewski T, Coghill D, Plessen KJ, Kuntsi J, Mehta MA, Paloyelis Y, Harrison NA, Bellgrove MA, Silk TJ, Cubillo AI, Rubia K, Lazaro L, Brem S, Walitza S, Frodl T, Zentis M, Castellanos FX, Yoncheva YN, Haavik J, Reneman L, Conzelmann A, Lesch KP, Pauli P, Reif A, Tamm L, Konrad K, Weiss EO, Busatto GF, Louza MR, Durston S, Hoekstra PJ, Oosterlaan J, Stevens MC, Ramos-Quiroga JA, Vilarroya O, Fair DA, Nigg JT, Thompson PM, Buitelaar JK, Faraone SV, Shaw P, Tiemeier H, Bralten J, Franke B. Brain Imaging of the Cortex in ADHD: A Coordinated Analysis of Large-Scale Clinical and Population-Based Samples. Am J Psychiatry 2019; 176:531-542. [PMID: 31014101 PMCID: PMC6879185 DOI: 10.1176/appi.ajp.2019.18091033] [Citation(s) in RCA: 205] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
OBJECTIVE Neuroimaging studies show structural alterations of various brain regions in children and adults with attention deficit hyperactivity disorder (ADHD), although nonreplications are frequent. The authors sought to identify cortical characteristics related to ADHD using large-scale studies. METHODS Cortical thickness and surface area (based on the Desikan-Killiany atlas) were compared between case subjects with ADHD (N=2,246) and control subjects (N=1,934) for children, adolescents, and adults separately in ENIGMA-ADHD, a consortium of 36 centers. To assess familial effects on cortical measures, case subjects, unaffected siblings, and control subjects in the NeuroIMAGE study (N=506) were compared. Associations of the attention scale from the Child Behavior Checklist with cortical measures were determined in a pediatric population sample (Generation-R, N=2,707). RESULTS In the ENIGMA-ADHD sample, lower surface area values were found in children with ADHD, mainly in frontal, cingulate, and temporal regions; the largest significant effect was for total surface area (Cohen's d=-0.21). Fusiform gyrus and temporal pole cortical thickness was also lower in children with ADHD. Neither surface area nor thickness differences were found in the adolescent or adult groups. Familial effects were seen for surface area in several regions. In an overlapping set of regions, surface area, but not thickness, was associated with attention problems in the Generation-R sample. CONCLUSIONS Subtle differences in cortical surface area are widespread in children but not adolescents and adults with ADHD, confirming involvement of the frontal cortex and highlighting regions deserving further attention. Notably, the alterations behave like endophenotypes in families and are linked to ADHD symptoms in the population, extending evidence that ADHD behaves as a continuous trait in the population. Future longitudinal studies should clarify individual lifespan trajectories that lead to nonsignificant findings in adolescent and adult groups despite the presence of an ADHD diagnosis.
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Affiliation(s)
- Martine Hoogman
- Department of Human Genetics, Radboud university medical center, Nijmegen, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Ryan Muetzel
- Department of Child and Adolescent Psychiatry, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Joao P. Guimaraes
- Department of Human Genetics, Radboud university medical center, Nijmegen, Netherlands
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, Nijmegen, The Netherlands
| | - Elena Shumskaya
- Department of Human Genetics, Radboud university medical center, Nijmegen, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Maarten Mennes
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Marcel P. Zwiers
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, Nijmegen, The Netherlands
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Gustavo Sudre
- National Human Genome Research Institute, Bethesda, MD, USA
| | - Jeanette Mostert
- Department of Human Genetics, Radboud university medical center, Nijmegen, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Thomas Wolfers
- Department of Human Genetics, Radboud university medical center, Nijmegen, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Eric A. Earl
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland OR, USA
| | | | | | - Sabin Khadka
- Olin Neuropsychiatry Research Center, Hartford Hospital, Hartford, CT, USA
| | | | - Catharina A. Hartman
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), Groningen, The Netherlands
| | - Dirk J. Heslenfeld
- Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lizanne J.S. Schweren
- University of Groningen, University Medical Center Groningen, Department of Child and Adolescent Psychiatry, Groningen, The Netherlands
| | - Sara Ambrosino
- NICHE Lab, Department of Psychiatry, UMC Utrecht Brain Center, Utrecht, The Netherlands
| | - Bob Oranje
- NICHE Lab, Department of Psychiatry, UMC Utrecht Brain Center, Utrecht, The Netherlands
| | - Patrick de Zeeuw
- NICHE Lab, Department of Psychiatry, UMC Utrecht Brain Center, Utrecht, The Netherlands
| | - Tiffany M. Chaim-Avancini
- Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Sao Paulo, Brazil
- Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Sao Paulo, Brazil
| | - Pedro G. P. Rosa
- Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Sao Paulo, Brazil
- Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Sao Paulo, Brazil
| | - Marcus V. Zanetti
- Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Sao Paulo, Brazil
- Center for Interdisciplinary Research on Applied Neurosciences (NAPNA), University of São Paulo, Sao Paulo, Brazil
| | - Charles B. Malpas
- Developmental Imaging Group, Murdoch Children's Research Institute, Melbourne, Australia
- Clinical Outcomes Research Unit (CORe), Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Melbourne, Australia
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia
| | - Gregor Kohls
- Child Neuropsychology Section, University Hospital RWTH Aachen, Aachen, Germany
| | - Georg G. von Polier
- Child and Adolescent Psychiatry, University Hospital RWTH Aachen, Aachen, Germany
| | - Jochen Seitz
- Child and Adolescent Psychiatry, University Hospital RWTH Aachen, Aachen, Germany
| | - Joseph Biederman
- Clinical and Research Programs in Pediatric Psychopharmacology and Adult ADHD, Department of Psychiatry, Massachusetts General Hospital, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, USA
| | - Alysa E. Doyle
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, USA
| | - Anders M. Dale
- Departments of Neurosciences, Radiology, and Psychiatry, UC San Diego, USA
- Center for Multimodal Imaging and Genetics (CMIG), UC San Diego, CA, USA
| | - Theo G.M. van Erp
- Clinical and Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
| | - Jeffrey N. Epstein
- Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | | | | | - Georg C. Ziegler
- Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany
| | | | - Anouk Schrantee
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam; the Netherlands
| | - Marie F. Høvik
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Astri J. Lundervold
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
- K.G. Jebsen Centre for Neuropsychiatric Disorders, Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Clare Kelly
- School of Psychology and Department of Psychiatry at the School of Medicine, Trinity College Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Ireland
- Department of Child and Adolescent Psychiatry, NYU Langone Medical Center, New York, NY, USA
| | - Hazel McCarthy
- Department of Psychiatry, Trinity College Dublin, Ireland
- Centre of Advanced Medical Imaging, St James's Hospital, Dublin, Ireland
| | - Norbert Skokauskas
- Department of Psychiatry, Trinity College Dublin, Ireland
- Institute of Mental Health, Norwegian University of Science and Technology, Norway
| | - Ruth L. O'Gorman Tuura
- Center for MR Research, University Children's Hospital, Zurich, Switzerland
- Zurich Center for Integrative Human Physiology (ZIHP), Zurich, Switserland
| | - Anna Calvo
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Sara Lera-Miguel
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neurosciencies, Hospital Clínic, Barcelona, Spain
| | - Rosa Nicolau
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neurosciencies, Hospital Clínic, Barcelona, Spain
| | - Kaylita C. Chantiluke
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Anastasia Christakou
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- School of Psychology and Clinical Language Sciences, Centre for Integrative Neuroscience and Neurodynamics, University of Reading, Reading, UK
| | - Alasdair Vance
- Department of Paediatrics, University of Melbourne, Australia
| | - Mara Cercignani
- Department of Neuroscience, Brighton and Sussex Medical School, Falmer, Brighton, UK
| | - Matt C. Gabel
- Department of Neuroscience, Brighton and Sussex Medical School, Falmer, Brighton, UK
| | - Philip Asherson
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Sarah Baumeister
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Medical Faculty Mannheim / Heidelberg University, Mannheim, Germany
| | - Daniel Brandeis
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Medical Faculty Mannheim / Heidelberg University, Mannheim, Germany
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Medical Faculty Mannheim / Heidelberg University, Mannheim, Germany
| | | | - Fernanda Tovar-Moll
- D'Or Institute for Research and Education, Rio de Janeiro, Brazil
- Morphological Sciences Program, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Andreas J. Fallgatter
- Department of Psychiatry and Psychotherapy, University Hospital of Tuebingen, Tuebingen, Germany
- LEAD Graduate School, University of Tuebingen, Germany
| | - Bernd Kardatzki
- Department of Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen, Germany
| | - Lena Schwarz
- Department of Psychiatry and Psychotherapy, University Hospital of Tuebingen, Tuebingen, Germany
| | - Anatoly Anikin
- National Medical Research Center for Children's Health, Department of magnetic resonance imaging and densitometry, Moscow, Russia
| | - Alexandr Baranov
- National Medical Research Center for Children's Health, Moscow, Russia
| | - Tinatin Gogberashvili
- National Medical Research Center for Children's Health, Laboratory of Neurology and Cognitive Health, Moscow, Russia
| | - Dmitry Kapilushniy
- National Medical Research Center for Children's Health, Department of Information Technologies, Moscow, Russia
| | | | - Hanan El Marroun
- Department of Child and Adolescent Psychiatry, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Paediatrics, Erasmus MC - Sophia, Rotterdam, the Netherlands
- Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Radiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Georgii Karkashadze
- National Medical Research Center for Children's Health, Laboratory of Neurology and Cognitive Health, Moscow, Russia
| | | | - Thomas Ethofer
- Department of Psychiatry and Psychotherapy, University Hospital of Tuebingen, Tuebingen, Germany
- Department of Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen, Germany
| | - Paulo Mattos
- D'Or Institute for Research and Education, Rio de Janeiro, Brazil
- Federal University of Rio de Janeiro, Brazil
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Medical Faculty Mannheim / Heidelberg University, Mannheim, Germany
| | - David Coghill
- Department of Paediatrics, University of Melbourne, Australia
- Departments of Psychiatry, University of Melbourne, Melbourne, Australia
- Murdoch Children's Research Institute, Melbourne, Australia
- Division of Neuroscience, University of Dundee, Dundee, UK
| | - Kerstin J. Plessen
- Child and Adolescent Mental Health Centre, Capital Region Copenhagen, Denmark
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, University Hospital Lausanne, Switzerland
| | - Jonna Kuntsi
- Social, Genetic and Developmental Psychiatry Centre; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Mitul A. Mehta
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Yannis Paloyelis
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Neil A. Harrison
- Department of Neuroscience, Brighton and Sussex Medical School, Falmer, Brighton, UK
- Sussex Partnership NHS Foundation Trust, Swandean, East Sussex, UK
| | - Mark A. Bellgrove
- Monash Institute for Cognitive and Clinical Neurosciences (MICCN) and School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Tim J. Silk
- Department of Paediatrics, University of Melbourne, Australia
- Murdoch Children's Research Institute, Melbourne, Australia
- Deakin University, School of Psychology, Geelong, Australia
| | - Ana I. Cubillo
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Katya Rubia
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Luisa Lazaro
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neurosciencies, Hospital Clínic, Barcelona, Spain
- Department of Medicine, University of Barcelona, Spain
| | - Silvia Brem
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
- University of Zurich and ETH Zurich, Neuroscience Center Zurich, Zurich, Switzerland
| | - Susanne Walitza
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Thomas Frodl
- Department of Psychiatry, Trinity College Dublin, Ireland
- Department of Psychiatry and Psychotherapy, Otto von Guericke University Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Germany
| | | | - Francisco X. Castellanos
- Department of Child and Adolescent Psychiatry, NYU Langone Medical Center, New York, NY, USA
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Yuliya N. Yoncheva
- Department of Child and Adolescent Psychiatry, NYU Langone Medical Center, New York, NY, USA
| | - Jan Haavik
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
- K.G. Jebsen Centre for Neuropsychiatric Disorders, Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Liesbeth Reneman
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam; the Netherlands
- Brain Imaging Center, Amsterdam University Medical Centers, Amsterdam; the Netherlands
| | - Annette Conzelmann
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Tübingen, Germany
- Department of Biological Psychology, Clinical Psychology and Psychotherapy, Würzburg, Germany
| | - Klaus-Peter Lesch
- Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany
- Laboratory of Psychiatric Neurobiology, Institute of Molecular Medicine, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Department of Neuroscience, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, The Netherlands
| | - Paul Pauli
- Department of Biological Psychology, Clinical Psychology and Psychotherapy, Würzburg, Germany
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
| | - Leanne Tamm
- Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Kerstin Konrad
- Child Neuropsychology Section, University Hospital RWTH Aachen, Aachen, Germany
- JARA Institute Molecular Neuroscience and Neuroimaging (INM-11), Institute for Neuroscience and Medicine, Research Center Jülich, Germany
| | - Eileen Oberwelland Weiss
- Translational Neuroscience, Child and Adolescent Psychiatry, University Hospital RWTH Aachen, Aachen, Germany
- Cognitive Neuroscience (INM-3), Institute for Neuroscience and Medicine, Research Center Jülich, Germany
| | - Geraldo F. Busatto
- Laboratory of Psychiatric Neuroimaging (LIM-21), Department and Institute of Psychiatry, Faculty of Medicine, University of São Paulo, Sao Paulo, Brazil
- Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Mario R. Louza
- Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Sarah Durston
- NICHE Lab, Department of Psychiatry, UMC Utrecht Brain Center, Utrecht, The Netherlands
| | - Pieter J. Hoekstra
- University of Groningen, University Medical Center Groningen, Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation (ICPE), Groningen, The Netherlands
| | - Jaap Oosterlaan
- Clinical Neuropsychology Section, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Emma Children’s Hospital Amsterdam Medical Center, Amsterdam, The Netherlands
- Department of Pediatrics, VU Medical Center, Amsterdam, The Netherlands
| | - Michael C. Stevens
- Olin Neuropsychiatry Research Center, Hartford Hospital, Hartford, CT, USA
- Department of Psychiatry, Yale University School of Medicine, USA
| | - J. Antoni Ramos-Quiroga
- Department of Psychiatry and Forensic Medicine, Universitat Autonoma de Barcelona, Spain
- Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Catalonia, Spain
- Biomedical Network Research Centre on Mental Health (CIBERSAM), Barcelona, Catalonia, Spain
| | - Oscar Vilarroya
- Department of Psychiatry and Forensic Medicine, Universitat Autonoma de Barcelona, Spain
- Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
| | - Damien A. Fair
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland OR, USA
- Department of Psychiatry, Oregon Health & Science University, Portland OR, USA
| | - Joel T. Nigg
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland OR, USA
- Department of Psychiatry, Oregon Health & Science University, Portland OR, USA
| | - Paul M. Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Jan K. Buitelaar
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
- Karakter Child and Adolescent Psychiatry University Center, Nijmegen, The Netherlands
| | | | - Philip Shaw
- National Human Genome Research Institute, Bethesda, MD, USA
- National Institute of Mental Health, Bethesda, MD, USA
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
- Department of Social and Behavioral Science, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Janita Bralten
- Department of Human Genetics, Radboud university medical center, Nijmegen, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Barbara Franke
- Department of Human Genetics, Radboud university medical center, Nijmegen, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
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Alemany S, Jansen PR, Muetzel RL, Marques N, El Marroun H, Jaddoe VWV, Polderman TJC, Tiemeier H, Posthuma D, White T. Common Polygenic Variations for Psychiatric Disorders and Cognition in Relation to Brain Morphology in the General Pediatric Population. J Am Acad Child Adolesc Psychiatry 2019; 58:600-607. [PMID: 30768412 DOI: 10.1016/j.jaac.2018.09.443] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 08/30/2018] [Accepted: 01/02/2019] [Indexed: 10/27/2022]
Abstract
OBJECTIVE This study examined the relation between polygenic scores (PGSs) for 5 major psychiatric disorders and 2 cognitive traits with brain magnetic resonance imaging morphologic measurements in a large population-based sample of children. In addition, this study tested for differences in brain morphology-mediated associations between PGSs for psychiatric disorders and PGSs for related behavioral phenotypes. METHOD Participants included 1,139 children from the Generation R Study assessed at 10 years of age with genotype and neuroimaging data available. PGSs were calculated for schizophrenia, bipolar disorder, major depression disorder, attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, intelligence, and educational attainment using results from the most recent genome-wide association studies. Image processing was performed using FreeSurfer to extract cortical and subcortical brain volumes. RESULTS Greater genetic susceptibility for ADHD was associated with smaller caudate volume (strongest prior = 0.01: β = -0.07, p = .006). In boys, mediation analysis estimates showed that 11% of the association between the PGS for ADHD and the PGS attention problems was mediated by differences in caudate volume (n = 535), whereas mediation was not significant in girls or the entire sample. PGSs for educational attainment and intelligence showed positive associations with total brain volume (strongest prior = 0.5: β = 0.14, p = 7.12 × 10-8; and β = 0.12, p = 6.87 × 10-7, respectively). CONCLUSION The present findings indicate that the neurobiological manifestation of polygenic susceptibility for ADHD, educational attainment, and intelligence involve early morphologic differences in caudate and total brain volumes in childhood. Furthermore, the genetic risk for ADHD might influence attention problems through the caudate nucleus in boys.
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Affiliation(s)
- Silvia Alemany
- Barcelona Institute for Global Health and the Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
| | - Philip R Jansen
- Erasmus University Medical Center, Rotterdam, the Netherlands; Generation R Study Group, Erasmus University Medical Center; Amsterdam Neuroscience, VU University, Amsterdam, the Netherlands; Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University Amsterdam, the Netherlands
| | - Ryan L Muetzel
- Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Natália Marques
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, Portugal
| | - Hanan El Marroun
- Erasmus University Medical Center, Rotterdam, the Netherlands; Generation R Study Group, Erasmus University Medical Center
| | - Vincent W V Jaddoe
- Erasmus University Medical Center, Rotterdam, the Netherlands; Generation R Study Group, Erasmus University Medical Center
| | - Tinca J C Polderman
- Amsterdam Neuroscience, VU University, Amsterdam, the Netherlands; Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University Amsterdam, the Netherlands
| | - Henning Tiemeier
- Erasmus University Medical Center, Rotterdam, the Netherlands; Harvard TH Chan School of Public Health, Boston, MA
| | - Danielle Posthuma
- Amsterdam Neuroscience, VU University, Amsterdam, the Netherlands; Center for Neurogenomics and Cognitive Research (CNCR), Amsterdam Neuroscience, VU University Amsterdam, the Netherlands; VU University Medical Center (VUMC), Amsterdam
| | - Tonya White
- Erasmus University Medical Center, Rotterdam, the Netherlands
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Esteban O, Blair RW, Nielson DM, Varada JC, Marrett S, Thomas AG, Poldrack RA, Gorgolewski KJ. Crowdsourced MRI quality metrics and expert quality annotations for training of humans and machines. Sci Data 2019; 6:30. [PMID: 30975998 PMCID: PMC6472378 DOI: 10.1038/s41597-019-0035-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Accepted: 03/12/2019] [Indexed: 12/29/2022] Open
Abstract
The neuroimaging community is steering towards increasingly large sample sizes, which are highly heterogeneous because they can only be acquired by multi-site consortia. The visual assessment of every imaging scan is a necessary quality control step, yet arduous and time-consuming. A sizeable body of evidence shows that images of low quality are a source of variability that may be comparable to the effect size under study. We present the MRIQC Web-API, an open crowdsourced database that collects image quality metrics extracted from MR images and corresponding manual assessments by experts. The database is rapidly growing, and currently contains over 100,000 records of image quality metrics of functional and anatomical MRIs of the human brain, and over 200 expert ratings. The resource is designed for researchers to share image quality metrics and annotations that can readily be reused in training human experts and machine learning algorithms. The ultimate goal of the database is to allow the development of fully automated quality control tools that outperform expert ratings in identifying subpar images.
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Affiliation(s)
- Oscar Esteban
- Deptartment of Psychology, Stanford University, Stanford, CA, USA.
| | - Ross W Blair
- Deptartment of Psychology, Stanford University, Stanford, CA, USA
| | - Dylan M Nielson
- Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Jan C Varada
- Functional MRI Facility, National Institute of Mental Health, Bethesda, MD, USA
| | - Sean Marrett
- Functional MRI Facility, National Institute of Mental Health, Bethesda, MD, USA
| | - Adam G Thomas
- Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
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32
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Dremmen MHG, Bouhuis RH, Blanken LME, Muetzel RL, Vernooij MW, Marroun HE, Jaddoe VWV, Verhulst FC, Tiemeier H, White T. Cavum Septum Pellucidum in the General Pediatric Population and Its Relation to Surrounding Brain Structure Volumes, Cognitive Function, and Emotional or Behavioral Problems. AJNR Am J Neuroradiol 2019; 40:340-346. [PMID: 30679220 DOI: 10.3174/ajnr.a5939] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 12/01/2018] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE The cavum septum pellucidum, a cavity filled with CSF, is localized between the 2 lateral ventricles of the brain. The cavum is present in all neonates, but it typically closes within 5 months after birth. In some cases, this closure does not occur and a persistent or enlarged cavum septum pellucidum has been linked, in some studies, to psychiatric disorders. However, the clinical relevance in the general population is unknown. In this study, we examined the relationship between the cavum septum pellucidum and volumes of brain structures, cognitive function, and emotional and behavioral problems in children. MATERIALS AND METHODS This study was embedded in the Generation R Study, a prospective cohort in Rotterdam, the Netherlands. MR imaging studies of 1070 children, 6-10 years of age, were systematically evaluated for the presence and length of a persistent cavum septum pellucidum. An enlarged cavum septum pellucidum was defined as a cavum length of ≥6 mm. Groups without, with persistent, and with enlarged cavum septi pellucidi were compared for brain structure volumes, nonverbal intelligence, and emotional and behavioral problems. RESULTS The prevalence of cavum septi pellucidi in our sample was 4.6%. Children with an enlarged cavum septum pellucidum had a larger corpus callosum, greater thalamic and total white matter-to-total brain volume ratio, and smaller lateral ventricle volumes. We did not find a relationship between cavum septi pellucidi and cognitive function or emotional and behavioral problems. CONCLUSIONS The cavum septum pellucidum is a normal structural brain variation without clinical implications in this population-based sample of school-aged children.
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Affiliation(s)
- M H G Dremmen
- From the Departments of Radiology (M.H.G.D., R.H.B., M.W.V., T.W.)
| | - R H Bouhuis
- From the Departments of Radiology (M.H.G.D., R.H.B., M.W.V., T.W.)
| | - L M E Blanken
- Department of Child and Adolescent Psychiatry (L.M.E.B., R.L.M., H.E.M., F.C.V., H.T., T.W.)
- Generation R Study Group (L.M.E.B., R.L.M., H.E.M.), Erasmus Medical Center-Sophia, Rotterdam, the Netherlands
| | - R L Muetzel
- Epidemiology (R.L.M., M.W.V., V.W.V.J.)
- Department of Child and Adolescent Psychiatry (L.M.E.B., R.L.M., H.E.M., F.C.V., H.T., T.W.)
- Generation R Study Group (L.M.E.B., R.L.M., H.E.M.), Erasmus Medical Center-Sophia, Rotterdam, the Netherlands
| | - M W Vernooij
- From the Departments of Radiology (M.H.G.D., R.H.B., M.W.V., T.W.)
- Epidemiology (R.L.M., M.W.V., V.W.V.J.)
| | - H E Marroun
- Department of Child and Adolescent Psychiatry (L.M.E.B., R.L.M., H.E.M., F.C.V., H.T., T.W.)
- Generation R Study Group (L.M.E.B., R.L.M., H.E.M.), Erasmus Medical Center-Sophia, Rotterdam, the Netherlands
| | - V W V Jaddoe
- Epidemiology (R.L.M., M.W.V., V.W.V.J.)
- Pediatrics (V.W.V.J.), Erasmus Medical Center, Rotterdam, the Netherlands
| | - F C Verhulst
- Department of Child and Adolescent Psychiatry (L.M.E.B., R.L.M., H.E.M., F.C.V., H.T., T.W.)
- Department of Clinical Medicine (F.C.V.), University of Copenhagen, Copenhagen, Denmark
| | - H Tiemeier
- Department of Child and Adolescent Psychiatry (L.M.E.B., R.L.M., H.E.M., F.C.V., H.T., T.W.)
- Harvard School of Public Health (H.T.), Boston, Massachusetts
| | - T White
- From the Departments of Radiology (M.H.G.D., R.H.B., M.W.V., T.W.)
- Department of Child and Adolescent Psychiatry (L.M.E.B., R.L.M., H.E.M., F.C.V., H.T., T.W.)
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33
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Qoala-T: A supervised-learning tool for quality control of FreeSurfer segmented MRI data. Neuroimage 2019; 189:116-129. [PMID: 30633965 DOI: 10.1016/j.neuroimage.2019.01.014] [Citation(s) in RCA: 100] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Revised: 01/04/2019] [Accepted: 01/06/2019] [Indexed: 01/22/2023] Open
Abstract
Performing quality control to detect image artifacts and data-processing errors is crucial in structural magnetic resonance imaging, especially in developmental studies. Currently, many studies rely on visual inspection by trained raters for quality control. The subjectivity of these manual procedures lessens comparability between studies, and with growing study sizes quality control is increasingly time consuming. In addition, both inter-rater as well as intra-rater variability of manual quality control is high and may lead to inclusion of poor quality scans and exclusion of scans of usable quality. In the current study we present the Qoala-T tool, which is an easy and free to use supervised-learning model to reduce rater bias and misclassification in manual quality control procedures using FreeSurfer-processed scans. First, we manually rated quality of N = 784 FreeSurfer-processed T1-weighted scans acquired in three different waves in a longitudinal study. Different supervised-learning models were then compared to predict manual quality ratings using FreeSurfer segmented output data. Results show that the Qoala-T tool using random forests is able to predict scan quality with both high sensitivity and specificity (mean area under the curve (AUC) = 0.98). In addition, the Qoala-T tool was also able to adequately predict the quality of two novel unseen datasets (total N = 872). Finally, analyses of age effects showed that younger participants were more likely to have lower scan quality, underlining that scan quality might confound findings attributed to age effects. These outcomes indicate that this procedure could further help to reduce variability related to manual quality control, thereby benefiting the comparability of data quality between studies.
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34
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Peltonen JI, Mäkelä T, Salli E. MRI quality assurance based on 3D FLAIR brain images. MAGMA (NEW YORK, N.Y.) 2018; 31:689-699. [PMID: 30120616 DOI: 10.1007/s10334-018-0699-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 08/06/2018] [Accepted: 08/08/2018] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Quality assurance (QA) of magnetic resonance imaging (MRI) often relies on imaging phantoms with suitable structures and uniform regions. However, the connection between phantom measurements and actual clinical image quality is ambiguous. Thus, it is desirable to measure objective image quality directly from clinical images. MATERIALS AND METHODS In this work, four measurements suitable for clinical image QA were presented: image resolution, contrast-to-noise ratio, quality index and bias index. The methods were applied to a large cohort of clinical 3D FLAIR volumes over a test period of 9.5 months. The results were compared with phantom QA. Additionally, the effect of patient movement on the presented measures was studied. RESULTS A connection between the presented clinical QA methods and scanner performance was observed: the values reacted to MRI equipment breakdowns that occurred during the study period. No apparent correlation with phantom QA results was found. The patient movement was found to have a significant effect on the resolution and contrast-to-noise ratio values. DISCUSSION QA based on clinical images provides a direct method for following MRI scanner performance. The methods could be used to detect problems, and potentially reduce scanner downtime. Furthermore, with the presented methodologies comparisons could be made between different sequences and imaging settings. In the future, an online QA system could recognize insufficient image quality and suggest an immediate re-scan.
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Affiliation(s)
- Juha I Peltonen
- HUS Medical Imaging Center, Radiology, Helsinki University Hospital, University of Helsinki, P.O. Box 340, FI-00029, Helsinki, Finland. .,Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, P.O. Box 12200, FI-00076, Espoo, Finland.
| | - Teemu Mäkelä
- HUS Medical Imaging Center, Radiology, Helsinki University Hospital, University of Helsinki, P.O. Box 340, FI-00029, Helsinki, Finland.,Department of Physics, University of Helsinki, P.O. Box 64, FI-00014, Helsinki, Finland
| | - Eero Salli
- HUS Medical Imaging Center, Radiology, Helsinki University Hospital, University of Helsinki, P.O. Box 340, FI-00029, Helsinki, Finland
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35
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Sudre G, Mangalmurti A, Shaw P. Growing out of attention deficit hyperactivity disorder: Insights from the 'remitted' brain. Neurosci Biobehav Rev 2018; 94:198-209. [PMID: 30194962 DOI: 10.1016/j.neubiorev.2018.08.010] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 08/14/2018] [Accepted: 08/16/2018] [Indexed: 12/14/2022]
Abstract
We consider developmental and cognitive models to explain why some children 'grow out' of attention deficit hyperactivity disorder (ADHD) by adulthood. The first model views remission as a convergence towards more typical brain function and structure. In support, some studies find that adult remitters are indistinguishable from those who were never affected in the neural substrates of 'top-down' mechanisms of cognitive control, some 'bottom-up' processes of vigilance/response preparation, prefrontal cortical morphology and intrinsic functional connectivity. A second model postulates that remission is driven by the recruitment of new brain systems that compensate for ADHD symptoms. It draws support from demonstrations of atypical, but possibly beneficial, patterns of connectivity within the cognitive control network in adult remitters. The final model holds that some childhood ADHD anomalies show lifelong persistence, regardless of adult outcome, supported by shared reports of anomalies in remitters and persisters in posterior cerebral and striato-thalamic regions. The models are compatible: different processes driving remission might occur in different brain regions. These models provide a framework for future studies which might inform novel treatments to 'accelerate' remission.
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Affiliation(s)
- Gustavo Sudre
- Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, National Human Genome Research Institute, and The National Institute of Mental Health, NIH, United States
| | - Aman Mangalmurti
- Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, National Human Genome Research Institute, and The National Institute of Mental Health, NIH, United States
| | - Philip Shaw
- Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, National Human Genome Research Institute, and The National Institute of Mental Health, NIH, United States.
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36
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White T, Jansen PR, Muetzel RL, Sudre G, El Marroun H, Tiemeier H, Qiu A, Shaw P, Michael AM, Verhulst FC. Automated quality assessment of structural magnetic resonance images in children: Comparison with visual inspection and surface-based reconstruction. Hum Brain Mapp 2017; 39:1218-1231. [PMID: 29206318 DOI: 10.1002/hbm.23911] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 11/22/2017] [Accepted: 11/29/2017] [Indexed: 01/26/2023] Open
Abstract
Motion-related artifacts are one of the major challenges associated with pediatric neuroimaging. Recent studies have shown a relationship between visual quality ratings of T1 images and cortical reconstruction measures. Automated algorithms offer more precision in quantifying movement-related artifacts compared to visual inspection. Thus, the goal of this study was to test three different automated quality assessment algorithms for structural MRI scans. The three algorithms included a Fourier-, integral-, and a gradient-based approach which were run on raw T1 -weighted imaging data collected from four different scanners. The four cohorts included a total of 6,662 MRI scans from two waves of the Generation R Study, the NIH NHGRI Study, and the GUSTO Study. Using receiver operating characteristics with visually inspected quality ratings of the T1 images, the area under the curve (AUC) for the gradient algorithm, which performed better than either the integral or Fourier approaches, was 0.95, 0.88, and 0.82 for the Generation R, NHGRI, and GUSTO studies, respectively. For scans of poor initial quality, repeating the scan often resulted in a better quality second image. Finally, we found that even minor differences in automated quality measurements were associated with FreeSurfer derived measures of cortical thickness and surface area, even in scans that were rated as good quality. Our findings suggest that the inclusion of automated quality assessment measures can augment visual inspection and may find use as a covariate in analyses or to identify thresholds to exclude poor quality data.
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Affiliation(s)
- Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Centre, Rotterdam, Netherlands.,Department of Radiology, Erasmus University Medical Centre, Rotterdam, Netherlands
| | - Philip R Jansen
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Centre, Rotterdam, Netherlands.,Department of Radiology, Erasmus University Medical Centre, Rotterdam, Netherlands.,The Generation R Study Group, Erasmus University Medical Centre, Rotterdam, Netherlands
| | - Ryan L Muetzel
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Centre, Rotterdam, Netherlands.,The Generation R Study Group, Erasmus University Medical Centre, Rotterdam, Netherlands
| | - Gustavo Sudre
- The Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, National Human Genome Research Institute, NIH, Bethesda, Maryland
| | - Hanan El Marroun
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Centre, Rotterdam, Netherlands.,The Generation R Study Group, Erasmus University Medical Centre, Rotterdam, Netherlands.,Department of Pediatrics, Erasmus University Medical Centre, Rotterdam, Netherlands
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Centre, Rotterdam, Netherlands.,Department of Pediatrics, Erasmus University Medical Centre, Rotterdam, Netherlands.,Department of Epidemiology, Erasmus University Medical Centre, Rotterdam, Netherlands
| | - Anqi Qiu
- Department of Biomedical Engineering and Clinical Imaging Research Center, National University of Singapore, Singapore, Singapore.,Singapore Institute for Clinical Sciences, Singapore, Singapore
| | - Philip Shaw
- The Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, National Human Genome Research Institute, NIH, Bethesda, Maryland
| | - Andrew M Michael
- Autism and Developmental Medicine Institute, Geisinger Health System, Lewisburg, Pennsylvania, 17837
| | - Frank C Verhulst
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Centre, Rotterdam, Netherlands.,Department of Clinical Medicine at the Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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