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Vandewouw MM, Norris-Brilliant A, Rahman A, Assimopoulos S, Morton SU, Kushki A, Cunningham S, King E, Goldmuntz E, Miller TA, Thomas NH, Adams HR, Cleveland J, Cnota JF, Ellen Grant P, Goldberg CS, Huang H, Li JS, McQuillen P, Porter GA, Roberts AE, Russell MW, Seidman CE, Tivarus ME, Chung WK, Hagler DJ, Newburger JW, Panigrahy A, Lerch JP, Gelb BD, Anagnostou E. Identifying novel data-driven subgroups in congenital heart disease using multi-modal measures of brain structure. Neuroimage 2024; 297:120721. [PMID: 38968977 DOI: 10.1016/j.neuroimage.2024.120721] [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: 04/05/2024] [Revised: 06/18/2024] [Accepted: 07/03/2024] [Indexed: 07/07/2024] Open
Abstract
Individuals with congenital heart disease (CHD) have an increased risk of neurodevelopmental impairments. Given the hypothesized complexity linking genomics, atypical brain structure, cardiac diagnoses and their management, and neurodevelopmental outcomes, unsupervised methods may provide unique insight into neurodevelopmental variability in CHD. Using data from the Pediatric Cardiac Genomics Consortium Brain and Genes study, we identified data-driven subgroups of individuals with CHD from measures of brain structure. Using structural magnetic resonance imaging (MRI; N = 93; cortical thickness, cortical volume, and subcortical volume), we identified subgroups that differed primarily on cardiac anatomic lesion and language ability. In contrast, using diffusion MRI (N = 88; white matter connectivity strength), we identified subgroups that were characterized by differences in associations with rare genetic variants and visual-motor function. This work provides insight into the differential impacts of cardiac lesions and genomic variation on brain growth and architecture in patients with CHD, with potentially distinct effects on neurodevelopmental outcomes.
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Affiliation(s)
- Marlee M Vandewouw
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
| | | | - Anum Rahman
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, ON, Canada; Translational Medicine, The Hospital for Sick Children, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Stephania Assimopoulos
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Sarah U Morton
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA; Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Azadeh Kushki
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Sean Cunningham
- Department of Pediatrics, Division of General Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Eileen King
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA; Division of Biostatistics and Epidemiology, Cincinnati Children's Hospital Medical Centre, Cincinnati, OH, USA
| | - Elizabeth Goldmuntz
- Division of Cardiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Thomas A Miller
- Department of Pediatrics, Maine Medical Center, Portland, ME, USA
| | - Nina H Thomas
- Department of Child and Adolescent Psychiatry and Behavioral Sciences and Center for Human Phenomic Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Heather R Adams
- Departments of Neurology and Pediatrics, University of Rochester Medical Center, Rochester, NY, USA
| | - John Cleveland
- Departments of Surgery and Pediatrics, Keck School of Medicine, University of Southern California, LA, USA
| | - James F Cnota
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA; Heart Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - P Ellen Grant
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA; Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Department of Radiology, Boston Children's Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Caren S Goldberg
- Department of Pediatrics, C.S. Mott Children's Hospital, University of Michigan, Ann Arbor, MI, USA
| | - Hao Huang
- Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jennifer S Li
- Department of Pediatrics, Duke University Medical Center, Durham, NC, USA
| | - Patrick McQuillen
- Departments of Pediatrics and Neurology, University of California San Francisco, San Francisco, CA, USA
| | - George A Porter
- Departments of Neurology and Pediatrics, University of Rochester Medical Center, Rochester, NY, USA
| | - Amy E Roberts
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Department of Cardiology, Boston Children's Hospital, Boston, MA USA; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA
| | - Mark W Russell
- Department of Pediatrics, C.S. Mott Children's Hospital, University of Michigan, Ann Arbor, MI, USA
| | - Christine E Seidman
- Department of Genetics, Harvard Medical School, Boston, MA, USA; Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA; Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Madalina E Tivarus
- Department of Imaging Sciences and Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, USA
| | - Wendy K Chung
- Departments of Pediatrics and Medicine, Columbia University, New York, NY, USA
| | - Donald J Hagler
- Center for Multimodal Imaging and Genetics, University of California San Diego, USA; Department of Radiology, School of Medicine, University of California San Diego, USA; Departments of Cognitive Science and Neuroscience, University of California San Diego, USA
| | - Jane W Newburger
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Department of Cardiology, Boston Children's Hospital, Boston, MA USA
| | - Ashok Panigrahy
- Department of Pediatric Radiology, Children's Hospital of Pittsburgh, University of Pittsburgh Medical Center, Pittsburgh, PA USA
| | - Jason P Lerch
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Program in Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Bruce D Gelb
- Mindich Child Health and Development Institute and Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Evdokia Anagnostou
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada; Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
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Lopez DA, Cardenas-Iniguez C, Subramaniam P, Adise S, Bottenhorn KL, Badilla P, Mukwekwerere E, Tally L, Ahanmisi O, Bedichek IL, Matera SD, Perez-Tamayo GM, Sissons N, Winters O, Harkness A, Nakiyingi E, Encizo J, Xiang Z, Wilson IG, Smith AN, Hill AR, Adames AK, Robertson E, Boughter JR, Lopez-Flores A, Skoler ER, Dorholt L, Nagel BJ, Huber RS. Transparency and reproducibility in the Adolescent Brain Cognitive Development (ABCD) study. Dev Cogn Neurosci 2024; 68:101408. [PMID: 38924835 PMCID: PMC11254940 DOI: 10.1016/j.dcn.2024.101408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 05/27/2024] [Accepted: 06/11/2024] [Indexed: 06/28/2024] Open
Abstract
BACKGROUND Transparency can build trust in the scientific process, but scientific findings can be undermined by poor and obscure data use and reporting practices. The purpose of this work is to report how data from the Adolescent Brain Cognitive Development (ABCD) Study has been used to date, and to provide practical recommendations on how to improve the transparency and reproducibility of findings. METHODS Articles published from 2017 to 2023 that used ABCD Study data were reviewed using more than 30 data extraction items to gather information on data use practices. Total frequencies were reported for each extraction item, along with computation of a Level of Completeness (LOC) score that represented overall endorsement of extraction items. Univariate linear regression models were used to examine the correlation between LOC scores and individual extraction items. Post hoc analysis included examination of whether LOC scores were correlated with the logged 2-year journal impact factor. RESULTS There were 549 full-length articles included in the main analysis. Analytic scripts were shared in 30 % of full-length articles. The number of participants excluded due to missing data was reported in 60 % of articles, and information on missing data for individual variables (e.g., household income) was provided in 38 % of articles. A table describing the analytic sample was included in 83 % of articles. A race and/or ethnicity variable was included in 78 % of reviewed articles, while its inclusion was justified in only 41 % of these articles. LOC scores were highly correlated with extraction items related to examination of missing data. A bottom 10 % of LOC score was significantly correlated with a lower logged journal impact factor when compared to the top 10 % of LOC scores (β=-0.77, 95 % -1.02, -0.51; p-value < 0.0001). CONCLUSION These findings highlight opportunities for improvement in future papers using ABCD Study data to readily adapt analytic practices for better transparency and reproducibility efforts. A list of recommendations is provided to facilitate adherence in future research.
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Affiliation(s)
- Daniel A Lopez
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, United States; Center for Mental Health Innovation, Oregon Health & Science University, Portland, OR, United States
| | - Carlos Cardenas-Iniguez
- Department of Population and Public Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States
| | - Punitha Subramaniam
- Department of Psychiatry, University of Utah, Salt Lake City, UT, United States
| | - Shana Adise
- Division of Endocrinology, Diabetes and Metabolism, Children's Hospital of Los Angeles, Los Angeles, CA, United States
| | - Katherine L Bottenhorn
- Department of Population and Public Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States
| | - Paola Badilla
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO, United States
| | - Ellen Mukwekwerere
- Department of Psychology, Psychiatry, and Radiology, Washington University in St. Louis, St. Louis, MO, United States
| | - Laila Tally
- Center for Children and Families and Department of Psychology, Florida International University, Miami, FL, United States
| | - Omoengheme Ahanmisi
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland, Baltimore, MD, United States
| | - Isabelle L Bedichek
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, United States
| | - Serena D Matera
- The Frederick J. and Marion A. Schindler Cognitive Neurophysiology Laboratory Department of Neuroscience and The Ernest J. Del Monte Institute for Neuroscience University of Rochester School of Medicine and Dentistry, Rochester, NY, United States
| | | | - Nicholas Sissons
- Departments of Psychiatry and Radiology, University of Vermont, Burlington, VT, United States
| | - Owen Winters
- Department of Psychiatry, Medical University of South Carolina, Charleston, SC, United States
| | - Anya Harkness
- Center for Health Sciences, SRI International, Menlo Park, CS, United States
| | - Elizabeth Nakiyingi
- Department of Psychology, Psychiatry, and Radiology, Washington University in St. Louis, St. Louis, MO, United States
| | - Jennell Encizo
- Department of Psychology, Psychiatry, and Radiology, Washington University in St. Louis, St. Louis, MO, United States
| | - Zhuoran Xiang
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Isabelle G Wilson
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
| | - Allison N Smith
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
| | - Anthony R Hill
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, United States
| | - Amanda K Adames
- Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
| | - Elizabeth Robertson
- Department of Psychiatry, Medical University of South Carolina, Charleston, SC, United States
| | - Joseph R Boughter
- Department of Psychology, Psychiatry, and Radiology, Washington University in St. Louis, St. Louis, MO, United States
| | - Arturo Lopez-Flores
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, United States
| | - Emma R Skoler
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO, United States
| | - Lyndsey Dorholt
- Department of Psychology, University of Minnesota, Minneapolis, MN, United States
| | - Bonnie J Nagel
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, United States; Center for Mental Health Innovation, Oregon Health & Science University, Portland, OR, United States
| | - Rebekah S Huber
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, United States; Department of Psychiatry, University of Utah, Salt Lake City, UT, United States; Center for Mental Health Innovation, Oregon Health & Science University, Portland, OR, United States.
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Rosenblatt M, Tejavibulya L, Sun H, Camp CC, Khaitova M, Adkinson BD, Jiang R, Westwater ML, Noble S, Scheinost D. Power and reproducibility in the external validation of brain-phenotype predictions. Nat Hum Behav 2024:10.1038/s41562-024-01931-7. [PMID: 39085406 DOI: 10.1038/s41562-024-01931-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 06/18/2024] [Indexed: 08/02/2024]
Abstract
Brain-phenotype predictive models seek to identify reproducible and generalizable brain-phenotype associations. External validation, or the evaluation of a model in external datasets, is the gold standard in evaluating the generalizability of models in neuroimaging. Unlike typical studies, external validation involves two sample sizes: the training and the external sample sizes. Thus, traditional power calculations may not be appropriate. Here we ran over 900 million resampling-based simulations in functional and structural connectivity data to investigate the relationship between training sample size, external sample size, phenotype effect size, theoretical power and simulated power. Our analysis included a wide range of datasets: the Healthy Brain Network, the Adolescent Brain Cognitive Development Study, the Human Connectome Project (Development and Young Adult), the Philadelphia Neurodevelopmental Cohort, the Queensland Twin Adolescent Brain Project, and the Chinese Human Connectome Project; and phenotypes: age, body mass index, matrix reasoning, working memory, attention problems, anxiety/depression symptoms and relational processing. High effect size predictions achieved adequate power with training and external sample sizes of a few hundred individuals, whereas low and medium effect size predictions required hundreds to thousands of training and external samples. In addition, most previous external validation studies used sample sizes prone to low power, and theoretical power curves should be adjusted for the training sample size. Furthermore, model performance in internal validation often informed subsequent external validation performance (Pearson's r difference <0.2), particularly for well-harmonized datasets. These results could help decide how to power future external validation studies.
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Affiliation(s)
- Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Huili Sun
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Chris C Camp
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Milana Khaitova
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Brendan D Adkinson
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Margaret L Westwater
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Stephanie Noble
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Psychology, Northeastern University, Boston, MA, USA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
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4
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Tansey R, Graff K, Rai S, Merrikh D, Godfrey KJ, Vanderwal T, Bray S. Development of human visual cortical function: A scoping review of task- and naturalistic-fMRI studies through the interactive specialization and maturational frameworks. Neurosci Biobehav Rev 2024; 162:105729. [PMID: 38763178 DOI: 10.1016/j.neubiorev.2024.105729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 05/12/2024] [Accepted: 05/14/2024] [Indexed: 05/21/2024]
Abstract
Overarching theories such as the interactive specialization and maturational frameworks have been proposed to describe human functional brain development. However, these frameworks have not yet been systematically examined across the fMRI literature. Visual processing is one of the most well-studied fields in neuroimaging, and research in this area has recently expanded to include naturalistic paradigms that facilitate study in younger age ranges, allowing for an in-depth critical appraisal of these frameworks across childhood. To this end, we conducted a scoping review of 94 developmental visual fMRI studies, including both traditional experimental task and naturalistic studies, across multiple sub-domains (early visual processing, category-specific higher order processing, naturalistic visual processing). We found that across domains, many studies reported progressive development, but few studies describe regressive or emergent changes necessary to fit the maturational or interactive specialization frameworks. Our findings suggest a need for the expansion of developmental frameworks and clearer reporting of both progressive and regressive changes, along with well-powered, longitudinal studies.
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Affiliation(s)
- Ryann Tansey
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
| | - Kirk Graff
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Shefali Rai
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Daria Merrikh
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Kate J Godfrey
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Tamara Vanderwal
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada; BC Children's Hospital Research Institute, Vancouver, BC, Canada
| | - Signe Bray
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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5
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Weinstein SM, Tu D, Hu F, Pan R, Zhang R, Vandekar SN, Baller EB, Gur RC, Gur RE, Alexander-Bloch AF, Satterthwaite TD, Park JY. Mapping Individual Differences in Intermodal Coupling in Neurodevelopment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.26.600817. [PMID: 38979274 PMCID: PMC11230458 DOI: 10.1101/2024.06.26.600817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Within-individual coupling between measures of brain structure and function evolves in development and may underlie differential risk for neuropsychiatric disorders. Despite increasing interest in the development of structure-function relationships, rigorous methods to quantify and test individual differences in coupling remain nascent. In this article, we explore and address gaps in approaches for testing and spatially localizing individual differences in intermodal coupling. We propose a new method, called CIDeR, which is designed to simultaneously perform hypothesis testing in a way that limits false positive results and improve detection of true positive results. Through a comparison across different approaches to testing individual differences in intermodal coupling, we delineate subtle differences in the hypotheses they test, which may ultimately lead researchers to arrive at different results. Finally, we illustrate the utility of CIDeR in two applications to brain development using data from the Philadelphia Neurodevelopmental Cohort.
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Affiliation(s)
- Sarah M. Weinstein
- Department of Epidemiology and Biostatistics, Temple University College of Public Health, Philadelphia, PA, USA
| | - Danni Tu
- Regeneron Pharmaceuticals, Tarrytown, NY, USA
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Fengling Hu
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruyi Pan
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
- Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Rongqian Zhang
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Simon N. Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Erica B. Baller
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, PA, USA
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute (LiBI), Philadelphia, PA, USA
| | - Raquel E. Gur
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute (LiBI), Philadelphia, PA, USA
| | - Aaron F. Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute (LiBI), Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, PA, USA
- Penn-CHOP Lifespan Brain Institute (LiBI), Philadelphia, PA, USA
| | - Jun Young Park
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
- Department of Psychology, University of Toronto, Toronto, ON, Canada
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Kang K, Seidlitz J, Bethlehem RA, Xiong J, Jones MT, Mehta K, Keller AS, Tao R, Randolph A, Larsen B, Tervo-Clemmens B, Feczko E, Miranda Dominguez O, Nelson S, Schildcrout J, Fair D, Satterthwaite TD, Alexander-Bloch A, Vandekar S. Study design features increase replicability in cross-sectional and longitudinal brain-wide association studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.29.542742. [PMID: 37398345 PMCID: PMC10312450 DOI: 10.1101/2023.05.29.542742] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Brain-wide association studies (BWAS) are a fundamental tool in discovering brain-behavior associations. Several recent studies showed that thousands of study participants are required for good replicability of BWAS because the standardized effect sizes (ESs) are much smaller than the reported standardized ESs in smaller studies. Here, we perform analyses and meta-analyses of a robust effect size index using 63 longitudinal and cross-sectional magnetic resonance imaging studies from the Lifespan Brain Chart Consortium (77,695 total scans) to demonstrate that optimizing study design is critical for increasing standardized ESs and replicability in BWAS. A meta-analysis of brain volume associations with age indicates that BWAS with larger variability in covariate have larger reported standardized ES. In addition, the longitudinal studies we examined reported systematically larger standardized ES than cross-sectional studies. Analyzing age effects on global and regional brain measures from the United Kingdom Biobank and the Alzheimer's Disease Neuroimaging Initiative, we show that modifying longitudinal study design through sampling schemes improves the standardized ESs and replicability. Sampling schemes that improve standardized ESs and replicability include increasing between-subject age variability in the sample and adding a single additional longitudinal measurement per subject. To ensure that our results are generalizable, we further evaluate these longitudinal sampling schemes on cognitive, psychopathology, and demographic associations with structural and functional brain outcome measures in the Adolescent Brain and Cognitive Development dataset. We demonstrate that commonly used longitudinal models can, counterintuitively, reduce standardized ESs and replicability. The benefit of conducting longitudinal studies depends on the strengths of the between- versus within-subject associations of the brain and non-brain measures. Explicitly modeling between- versus within-subject effects avoids averaging the effects and allows optimizing the standardized ESs for each separately. Together, these results provide guidance for study designs that improve the replicability of BWAS.
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Affiliation(s)
- Kaidi Kang
- Department of Biostatistics, Vanderbilt University Medical Center
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, The Children’s Hospital of Philadelphia
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
| | | | - Jiangmei Xiong
- Department of Biostatistics, Vanderbilt University Medical Center
| | - Megan T. Jones
- Department of Biostatistics, Vanderbilt University Medical Center
| | - Kahini Mehta
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania
| | - Arielle S. Keller
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center
| | - Anita Randolph
- Department of Pediatrics, University of Minnesota Medical School
| | - Bart Larsen
- Department of Pediatrics, University of Minnesota Medical School
| | - Brenden Tervo-Clemmens
- Department of Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School
| | - Eric Feczko
- Department of Pediatrics, University of Minnesota Medical School
| | | | - Steve Nelson
- Department of Pediatrics, University of Minnesota Medical School
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Damien Fair
- Department of Pediatrics, University of Minnesota Medical School
| | - Theodore D. Satterthwaite
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania
| | - Aaron Alexander-Bloch
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, The Children’s Hospital of Philadelphia
- Department of Psychiatry, University of Pennsylvania
- Lifespan Brain Institute of The Children’s Hospital of Philadelphia and Penn Medicine
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center
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7
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Boyes A, Levenstein JM, McLoughlin LT, Driver C, Mills L, Lagopoulos J, Hermens DF. A short-interval longitudinal study of associations between psychological distress and hippocampal grey matter in early adolescence. Brain Imaging Behav 2024; 18:519-528. [PMID: 38216837 PMCID: PMC11222233 DOI: 10.1007/s11682-023-00847-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/23/2023] [Indexed: 01/14/2024]
Abstract
This study of Australian adolescents (N = 88, 12-13-years-old) investigated the relationship between hippocampal grey matter volume (GMV) and self-reported psychological distress (K10) at four timepoints, across 12 months. Participants were divided into two groups; those who had K10 scores between 10 and 15 for all four timepoints were categorised as "low distress" (i.e., control group; n = 38), while participants who had K10 scores of 16 or higher at least once over the year were categorised as "moderate-high distress" (n = 50). Associations were tested by GEE fitting of GMV and K10 measures at the same time point, and in the preceding and subsequent timepoints. Analyses revealed smaller preceding left GMV and larger preceding right GMV were associated with higher subsequent K10 scores in the "moderate-high distress" group. This was not observed in the control group. In contrast, the control group showed significant co-occurring associations (i.e., at the same TP) between GMV and K10 scores. The "moderate-high distress" group experienced greater variability in distress. These results suggest that GMV development in early adolescence is differently associated with psychological distress for those who experience "moderate-high distress" at some point over the year, compared to controls. These findings offer a novel way to utilise short-interval, multiple time-point longitudinal data to explore changes in volume and experience of psychological distress in early adolescents. The results suggest hippocampal volume in early adolescence may be linked to fluctuations in psychological distress.
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Affiliation(s)
- Amanda Boyes
- Thompson Institute, UniSC, 12 Innovation Parkway, Birtinya, QLD, 4575, Australia.
| | - Jacob M Levenstein
- Thompson Institute, UniSC, 12 Innovation Parkway, Birtinya, QLD, 4575, Australia
| | - Larisa T McLoughlin
- Thompson Institute, UniSC, 12 Innovation Parkway, Birtinya, QLD, 4575, Australia
| | - Christina Driver
- Thompson Institute, UniSC, 12 Innovation Parkway, Birtinya, QLD, 4575, Australia
| | - Lia Mills
- Thompson Institute, UniSC, 12 Innovation Parkway, Birtinya, QLD, 4575, Australia
| | - Jim Lagopoulos
- Thompson Institute, UniSC, 12 Innovation Parkway, Birtinya, QLD, 4575, Australia
| | - Daniel F Hermens
- Thompson Institute, UniSC, 12 Innovation Parkway, Birtinya, QLD, 4575, Australia
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8
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Lopez DA, Cardenas-Iniguez C, Subramaniam P, Adise S, Bottenhorn KL, Badilla P, Mukwekwerere E, Tally L, Ahanmisi O, Bedichek IL, Matera SD, Perez-Tamayo GM, Sissons N, Winters O, Harkness A, Nakiyingi E, Encizo J, Xiang Z, Wilson IG, Smith AN, Hill AR, Adames AK, Robertson E, Boughter JR, Lopez-Flores A, Skoler ER, Dorholt L, Nagel BJ, Huber RS. Transparency and Reproducibility in the Adolescent Brain Cognitive Development (ABCD) Study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.30.24308222. [PMID: 38854118 PMCID: PMC11160844 DOI: 10.1101/2024.05.30.24308222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Background Transparency can build trust in the scientific process, but scientific findings can be undermined by poor and obscure data use and reporting practices. The purpose of this work is to report how data from the Adolescent Brain Cognitive Development (ABCD) Study has been used to date, and to provide practical recommendations on how to improve the transparency and reproducibility of findings. Methods Articles published from 2017 to 2023 that used ABCD Study data were reviewed using more than 30 data extraction items to gather information on data use practices. Total frequencies were reported for each extraction item, along with computation of a Level of Completeness (LOC) score that represented overall endorsement of extraction items. Univariate linear regression models were used to examine the correlation between LOC scores and individual extraction items. Post hoc analysis included examination of whether LOC scores were correlated with the logged 2-year journal impact factor. Results There were 549 full-length articles included in the main analysis. Analytic scripts were shared in 30% of full-length articles. The number of participants excluded due to missing data was reported in 60% of articles, and information on missing data for individual variables (e.g., household income) was provided in 38% of articles. A table describing the analytic sample was included in 83% of articles. A race and/or ethnicity variable was included in 78% of reviewed articles, while its inclusion was justified in only 41% of these articles. LOC scores were highly correlated with extraction items related to examination of missing data. A bottom 10% of LOC score was significantly correlated with a lower logged journal impact factor when compared to the top 10% of LOC scores (β=-0.77, 95% -1.02, -0.51; p-value < 0.0001). Conclusion These findings highlight opportunities for improvement in future papers using ABCD Study data to readily adapt analytic practices for better transparency and reproducibility efforts. A list of recommendations is provided to facilitate adherence in future research.
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Affiliation(s)
- Daniel A. Lopez
- Department of Psychiatry, Oregon Health & Science University, Portland, Oregon
- Center for Mental Health Innovation, Oregon Health & Science University, Portland, Oregon
| | - Carlos Cardenas-Iniguez
- Department of Population and Public Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, California
| | | | - Shana Adise
- Division of Endocrinology, Diabetes and Metabolism, Children’s Hospital of Los Angeles, Los Angeles, California
| | - Katherine L. Bottenhorn
- Department of Population and Public Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, California
| | - Paola Badilla
- Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado
| | - Ellen Mukwekwerere
- Department of Psychology, Psychiatry, and Radiology, Washington University in St. Louis, St. Louis, Missouri
| | - Laila Tally
- Center for Children and Families and Department of Psychology, Florida International University, Miami, Florida
| | - Omoengheme Ahanmisi
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland, Baltimore, Maryland
| | - Isabelle L. Bedichek
- Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, Virginia
| | - Serena D. Matera
- The Frederick J. and Marion A. Schindler Cognitive Neurophysiology Laboratory Department of Neuroscience and The Ernest J. Del Monte Institute for Neuroscience University of Rochester School of Medicine and Dentistry, Rochester, New York
| | | | - Nicholas Sissons
- Departments of Psychiatry and Radiology, University of Vermont, Burlington, Vermont
| | - Owen Winters
- Department of Psychiatry, Medical University of South Carolina, Charleston, South Carolina
| | - Anya Harkness
- Center for Health Sciences, SRI International, Menlo Park, California
| | - Elizabeth Nakiyingi
- Department of Psychology, Psychiatry, and Radiology, Washington University in St. Louis, St. Louis, Missouri
| | - Jennell Encizo
- Department of Psychology, Psychiatry, and Radiology, Washington University in St. Louis, St. Louis, Missouri
| | - Zhuoran Xiang
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Isabelle G. Wilson
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin
| | - Allison N. Smith
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan
| | - Anthony R. Hill
- Department of Psychiatry, Oregon Health & Science University, Portland, Oregon
| | - Amanda K. Adames
- Department of Psychiatry, University of California, San Diego, San Diego, California
| | - Elizabeth Robertson
- Department of Psychiatry, Medical University of South Carolina, Charleston, South Carolina
| | - Joseph R. Boughter
- Department of Psychology, Psychiatry, and Radiology, Washington University in St. Louis, St. Louis, Missouri
| | - Arturo Lopez-Flores
- Department of Psychiatry, Oregon Health & Science University, Portland, Oregon
| | - Emma R. Skoler
- Institute for Behavioral Genetics, University of Colorado, Boulder, Colorado
| | - Lyndsey Dorholt
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota
| | - Bonnie J. Nagel
- Department of Psychiatry, Oregon Health & Science University, Portland, Oregon
- Center for Mental Health Innovation, Oregon Health & Science University, Portland, Oregon
| | - Rebekah S. Huber
- Department of Psychiatry, Oregon Health & Science University, Portland, Oregon
- Department of Psychiatry, University of Utah, Salt Lake City, Utah
- Center for Mental Health Innovation, Oregon Health & Science University, Portland, Oregon
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9
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Parsons S, McCormick EM. Limitations of two time point data for understanding individual differences in longitudinal modeling - What can difference reveal about change? Dev Cogn Neurosci 2024; 66:101353. [PMID: 38335910 PMCID: PMC10864828 DOI: 10.1016/j.dcn.2024.101353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 01/13/2024] [Accepted: 02/01/2024] [Indexed: 02/12/2024] Open
Abstract
Emerging neuroimaging studies investigating changes in the brain aim to collect sufficient data points to examine trajectories of change across key developmental periods. Yet, current studies are often constrained by the number of time points available now. We demonstrate that these constraints should be taken seriously and that studies with two time points should focus on particular questions (e.g., group-level or intervention effects), while complex questions of individual differences and investigations into causes and consequences of those differences should be deferred until additional time points can be incorporated into models of change. We generated underlying longitudinal data and fit models with 2, 3, 4, and 5 time points across 1000 samples. While fixed effects could be recovered on average even with few time points, recovery of individual differences was particularly poor for the two time point model, correlating at r = 0.41 with the true individual parameters - meaning these scores share only 16.8% of variance As expected, models with more time points recovered the growth parameter more accurately; yet parameter recovery for the three time point model was still low, correlating around r = 0.57. We argue that preliminary analyses on early subsets of time points in longitudinal analyses should focus on these average or group-level effects and that individual difference questions should be addressed in samples that maximize the number of time points available. We conclude with recommendations for researchers using early time point models, including ideas for preregistration, careful interpretation of 2 time point results, and treating longitudinal analyses as dynamic, where early findings are updated as additional information becomes available.
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Affiliation(s)
- Sam Parsons
- Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Ethan M McCormick
- Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands; Methodology & Statistics Department, Institute of Psychology, Leiden University, Leiden, The Netherlands.
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10
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Bonte M, Brem S. Unraveling individual differences in learning potential: A dynamic framework for the case of reading development. Dev Cogn Neurosci 2024; 66:101362. [PMID: 38447471 PMCID: PMC10925938 DOI: 10.1016/j.dcn.2024.101362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 02/02/2024] [Accepted: 03/01/2024] [Indexed: 03/08/2024] Open
Abstract
Children show an enormous capacity to learn during development, but with large individual differences in the time course and trajectory of learning and the achieved skill level. Recent progress in developmental sciences has shown the contribution of a multitude of factors including genetic variation, brain plasticity, socio-cultural context and learning experiences to individual development. These factors interact in a complex manner, producing children's idiosyncratic and heterogeneous learning paths. Despite an increasing recognition of these intricate dynamics, current research on the development of culturally acquired skills such as reading still has a typical focus on snapshots of children's performance at discrete points in time. Here we argue that this 'static' approach is often insufficient and limits advancements in the prediction and mechanistic understanding of individual differences in learning capacity. We present a dynamic framework which highlights the importance of capturing short-term trajectories during learning across multiple stages and processes as a proxy for long-term development on the example of reading. This framework will help explain relevant variability in children's learning paths and outcomes and fosters new perspectives and approaches to study how children develop and learn.
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Affiliation(s)
- Milene Bonte
- Department of Cognitive Neuroscience and Maastricht Brain Imaging Center, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands.
| | - Silvia Brem
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, University of Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich and ETH Zurich, Switzerland; URPP Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zurich, Zurich, Switzerland
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11
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Adkinson BD, Rosenblatt M, Dadashkarimi J, Tejavibulya L, Jiang R, Noble S, Scheinost D. Brain-phenotype predictions can survive across diverse real-world data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576916. [PMID: 38328100 PMCID: PMC10849571 DOI: 10.1101/2024.01.23.576916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Recent work suggests that machine learning models predicting psychiatric treatment outcomes based on clinical data may fail when applied to unharmonized samples. Neuroimaging predictive models offer the opportunity to incorporate neurobiological information, which may be more robust to dataset shifts. Yet, among the minority of neuroimaging studies that undertake any form of external validation, there is a notable lack of attention to generalization across dataset-specific idiosyncrasies. Research settings, by design, remove the between-site variations that real-world and, eventually, clinical applications demand. Here, we rigorously test the ability of a range of predictive models to generalize across three diverse, unharmonized samples: the Philadelphia Neurodevelopmental Cohort (n=1291), the Healthy Brain Network (n=1110), and the Human Connectome Project in Development (n=428). These datasets have high inter-dataset heterogeneity, encompassing substantial variations in age distribution, sex, racial and ethnic minority representation, recruitment geography, clinical symptom burdens, fMRI tasks, sequences, and behavioral measures. We demonstrate that reproducible and generalizable brain-behavior associations can be realized across diverse dataset features with sample sizes in the hundreds. Results indicate the potential of functional connectivity-based predictive models to be robust despite substantial inter-dataset variability. Notably, for the HCPD and HBN datasets, the best predictions were not from training and testing in the same dataset (i.e., cross-validation) but across datasets. This result suggests that training on diverse data may improve prediction in specific cases. Overall, this work provides a critical foundation for future work evaluating the generalizability of neuroimaging predictive models in real-world scenarios and clinical settings.
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Affiliation(s)
- Brendan D Adkinson
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Javid Dadashkarimi
- Department of Radiology, Athinoula. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA, 02129, USA
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Bioengineering, Northeastern University, Boston, MA, 02120, USA
- Department of Psychology, Northeastern University, Boston, MA, 02115, USA
| | - Dustin Scheinost
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA
- Wu Tsai Institute, Yale University, New Haven, CT, 06510, USA
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12
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Petersen IT, Apfelbaum KS, McMurray B. Adapting Open Science and Pre-registration to Longitudinal Research. INFANT AND CHILD DEVELOPMENT 2024; 33:e2315. [PMID: 38425545 PMCID: PMC10904029 DOI: 10.1002/icd.2315] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 03/16/2022] [Indexed: 03/02/2024]
Abstract
Open science practices, such as pre-registration and data sharing, increase transparency and may improve the replicability of developmental science. However, developmental science has lagged behind other fields in implementing open science practices. This lag may arise from unique challenges and considerations of longitudinal research. In this paper, preliminary guidelines are provided for adapting open science practices to longitudinal research to facilitate researchers' use of these practices. The guidelines propose a serial and modular approach to registration that includes an initial pre-registration of the methods and focal hypotheses of the longitudinal study, along with subsequent pre- or co-registered questions, hypotheses, and analysis plans associated with specific papers. Researchers are encouraged to share their research materials and relevant data with associated papers, and to report sufficient information for replicability. In addition, there should be careful consideration about requirements regarding the timing of data sharing, to avoid disincentivizing longitudinal research.
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Affiliation(s)
- Isaac T Petersen
- Department of Psychological and Brain Sciences, University of Iowa
| | | | - Bob McMurray
- Department of Psychological and Brain Sciences, Department of Communication Sciences and Disorders and Department of Linguistics, University of Iowa
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13
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Alex AM, Aguate F, Botteron K, Buss C, Chong YS, Dager SR, Donald KA, Entringer S, Fair DA, Fortier MV, Gaab N, Gilmore JH, Girault JB, Graham AM, Groenewold NA, Hazlett H, Lin W, Meaney MJ, Piven J, Qiu A, Rasmussen JM, Roos A, Schultz RT, Skeide MA, Stein DJ, Styner M, Thompson PM, Turesky TK, Wadhwa PD, Zar HJ, Zöllei L, de Los Campos G, Knickmeyer RC. A global multicohort study to map subcortical brain development and cognition in infancy and early childhood. Nat Neurosci 2024; 27:176-186. [PMID: 37996530 PMCID: PMC10774128 DOI: 10.1038/s41593-023-01501-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 10/16/2023] [Indexed: 11/25/2023]
Abstract
The human brain grows quickly during infancy and early childhood, but factors influencing brain maturation in this period remain poorly understood. To address this gap, we harmonized data from eight diverse cohorts, creating one of the largest pediatric neuroimaging datasets to date focused on birth to 6 years of age. We mapped the developmental trajectory of intracranial and subcortical volumes in ∼2,000 children and studied how sociodemographic factors and adverse birth outcomes influence brain structure and cognition. The amygdala was the first subcortical volume to mature, whereas the thalamus exhibited protracted development. Males had larger brain volumes than females, and children born preterm or with low birthweight showed catch-up growth with age. Socioeconomic factors exerted region- and time-specific effects. Regarding cognition, males scored lower than females; preterm birth affected all developmental areas tested, and socioeconomic factors affected visual reception and receptive language. Brain-cognition correlations revealed region-specific associations.
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Affiliation(s)
- Ann M Alex
- Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI, USA
| | - Fernando Aguate
- Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI, USA
- Departments of Epidemiology & Biostatistics, Michigan State University, East Lansing, MI, USA
| | - Kelly Botteron
- Mallinickrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Claudia Buss
- Department of Medical Psychology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Pediatrics, University of California Irvine, Irvine, CA, USA
- Development, Health and Disease Research Program, University of California Irvine, Irvine, CA, USA
| | - Yap-Seng Chong
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore
| | - Stephen R Dager
- Department of Radiology, University of Washington Medical Center, Seattle, WA, USA
| | - Kirsten A Donald
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Sonja Entringer
- Department of Medical Psychology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Pediatrics, University of California Irvine, Irvine, CA, USA
- Development, Health and Disease Research Program, University of California Irvine, Irvine, CA, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Marielle V Fortier
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Diagnostic & Interventional Imaging, KK Women's and Children's Hospital, Singapore, Singapore
| | - Nadine Gaab
- Harvard Graduate School of Education, Harvard University, Cambridge, MA, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jessica B Girault
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Carboro, NC, USA
| | - Alice M Graham
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Nynke A Groenewold
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa
- South African Medical Research Council (SA-MRC) Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
- Department of Psychiatry, University of Cape Town, Cape Town, South Africa
- Department of Paediatrics and Child Health, University of Cape Town, Faculty of Health Sciences, Cape Town, South Africa
| | - Heather Hazlett
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Carboro, NC, USA
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Weili Lin
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michael J Meaney
- Department of Radiology, University of Washington Medical Center, Seattle, WA, USA
| | - Joseph Piven
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Carboro, NC, USA
- Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA
| | - Anqi Qiu
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
- NUS (Suzhou) Research Institute, National University of Singapore, Suzhou, China
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
- Institute of Data Science, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Health Technology and Informatics, Hong Kong Polytechnic University, Hung Hom, China
| | - Jerod M Rasmussen
- Department of Pediatrics, University of California Irvine, Irvine, CA, USA
- Development, Health and Disease Research Program, University of California Irvine, Irvine, CA, USA
| | - Annerine Roos
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - Robert T Schultz
- Center for Autism Research, Children's Hospital of Philadelphia and the University of Pennsylvania, Philadelphia, PA, USA
| | - Michael A Skeide
- Research Group Learning in Early Childhood, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Dan J Stein
- Department of Psychiatry, University of Cape Town, Cape Town, South Africa
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - Martin Styner
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Carboro, NC, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of University of Southern California, Marina del Rey, CA, USA
| | - Ted K Turesky
- Harvard Graduate School of Education, Harvard University, Cambridge, MA, USA
| | - Pathik D Wadhwa
- Department of Pediatrics, University of California Irvine, Irvine, CA, USA
- Development, Health and Disease Research Program, University of California Irvine, Irvine, CA, USA
- Departments of Psychiatry and Human Behavior, Obstetrics & Gynecology, Epidemiology, University of California, Irvine, Irvine, CA, USA
| | - Heather J Zar
- South African Medical Research Council (SA-MRC) Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
- Department of Paediatrics and Child Health, University of Cape Town, Faculty of Health Sciences, Cape Town, South Africa
| | - Lilla Zöllei
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Gustavo de Los Campos
- Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI, USA
- Departments of Epidemiology & Biostatistics, Michigan State University, East Lansing, MI, USA
- Department of Statistics & Probability, Michigan State University, East Lansing, MI, USA
| | - Rebecca C Knickmeyer
- Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI, USA.
- Department of Pediatrics and Human Development, Michigan State University, East Lansing, MI, USA.
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14
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Hatzenbuehler ML, McLaughlin KA, Weissman DG, Cikara M. A research agenda for understanding how social inequality is linked to brain structure and function. Nat Hum Behav 2024; 8:20-31. [PMID: 38172629 PMCID: PMC11112523 DOI: 10.1038/s41562-023-01774-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 11/01/2023] [Indexed: 01/05/2024]
Abstract
Consistent evidence documents powerful effects of social inequality on health, well-being and academic achievement. Yet research on whether social inequality may also be linked to brain structure and function has, until recently, been rare. Here we describe three methodological approaches that can be used to study this question-single site, single study; multi-site, single study; and spatial meta-analysis. We review empirical work that, using these approaches, has observed associations between neural outcomes and structural measures of social inequality-including structural stigma, community-level prejudice, gender inequality, neighbourhood disadvantage and the generosity of the social safety net for low-income families. We evaluate the relative strengths and limitations of these approaches, discuss ethical considerations and outline directions for future research. In doing so, we advocate for a paradigm shift in cognitive neuroscience that explicitly incorporates upstream structural and contextual factors, which we argue holds promise for uncovering the neural correlates of social inequality.
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Affiliation(s)
| | | | - David G Weissman
- Department of Psychology, Harvard University, Cambridge, MA, USA
| | - Mina Cikara
- Department of Psychology, Harvard University, Cambridge, MA, USA
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15
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Dimanova P, Borbás R, Raschle NM. From mother to child: How intergenerational transfer is reflected in similarity of corticolimbic brain structure and mental health. Dev Cogn Neurosci 2023; 64:101324. [PMID: 37979300 PMCID: PMC10692656 DOI: 10.1016/j.dcn.2023.101324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 10/31/2023] [Accepted: 11/10/2023] [Indexed: 11/20/2023] Open
Abstract
BACKGROUND Intergenerational transfer effects include traits transmission from parent to child. While behaviorally well documented, studies on intergenerational transfer effects for brain structure or functioning are scarce, especially those examining relations of behavioral and neurobiological endophenotypes. This study aims to investigate behavioral and neural intergenerational transfer effects associated with the corticolimbic circuitry, relevant for socioemotional functioning and mental well-being. METHODS T1-neuroimaging and behavioral data was obtained from 72 participants (39 mother-child dyads/ 39 children; 7-13 years; 16 girls/ 33 mothers; 26-52 years). Gray matter volume (GMV) was extracted from corticolimbic regions (subcortical: amygdala, hippocampus, nucleus accumbens; neocortical: anterior cingulate, medial orbitofrontal areas). Mother-child similarity was quantified by correlation coefficients and comparisons to random adult-child pairs. RESULTS We identified significant corticolimbic mother-child similarity (r = 0.663) stronger for subcortical over neocortical regions. Mother-child similarity in mental well-being was significant (r = 0.409) and the degree of dyadic similarity in mental well-being was predicted by similarity in neocortical, but not subcortical GMV. CONCLUSION Intergenerational neuroimaging reveals significant mother-child transfer for corticolimbic GMV, most strongly for subcortical regions. However, variations in neocortical similarity predicted similarity in mother-child well-being. Ultimately, such techniques may enhance our knowledge of behavioral and neural familial transfer effects relevant for health and disease.
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Affiliation(s)
- Plamina Dimanova
- Jacobs Center for Productive Youth Development, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University and ETH Zurich, Zurich, Switzerland.
| | - Réka Borbás
- Jacobs Center for Productive Youth Development, University of Zurich, Zurich, Switzerland
| | - Nora Maria Raschle
- Jacobs Center for Productive Youth Development, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University and ETH Zurich, Zurich, Switzerland.
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16
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Zhang R, Blair RJR, Blair KS, Dobbertin M, Elowsky J, Bashford-Largo J, Dominguez AJ, Hatch M, Bajaj S. Reduced grey matter volume in adolescents with conduct disorder: a region-of-interest analysis using multivariate generalized linear modeling. DISCOVER MENTAL HEALTH 2023; 3:25. [PMID: 37975932 PMCID: PMC10656392 DOI: 10.1007/s44192-023-00052-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 11/03/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND Conduct disorder (CD) involves a group of behavioral and emotional problems that usually begins during childhood or adolescence. Structural brain alterations have been observed in CD, including the amygdala, insula, ventrolateral and medial prefrontal cortex, anterior cingulate cortex, and fusiform gyrus. The current study developed a multivariate generalized linear model (GLM) to differentiate adolescents with CD from typically developing (TD) adolescents in terms of grey matter volume (GMV). METHODS The whole-brain structural MRI data were collected from 96 adolescents with CD (mean age = [Formula: see text] years; mean IQ = [Formula: see text]; 63 males) and 90 TD individuals (mean age = [Formula: see text] years; mean IQ = [Formula: see text]; 59 males) matched on age, IQ, and sex. Region-wise GMV was extracted following whole-brain parcellation into 68 cortical and 14 subcortical regions for each participant. A multivariate GLM was developed to predict the GMV of the pre-hypothesized regions-of-interest (ROIs) based on CD diagnosis, with intracranial volume, age, sex, and IQ serving as the covariate. RESULTS A diagnosis of CD was a significant predictor for GMV in the right pars orbitalis, right insula, right superior temporal gyrus, left fusiform gyrus, and left amygdala (F(1, 180) = 5.460-10.317, p < 0.05, partial eta squared = 0.029-0.054). The CD participants had smaller GMV in these regions than the TD participants (MCD-MTD = [- 614.898] mm3-[- 53.461] mm3). CONCLUSIONS Altered GMV within specific regions may serve as a biomarker for the development of CD in adolescents. Clinical work can potentially target these biomarkers to treat adolescents with CD.
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Affiliation(s)
- Ru Zhang
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.
| | - R James R Blair
- Child and Adolescent Mental Health Centre, Mental Health Services, Capital Region of Denmark, Copenhagen, Denmark
| | - Karina S Blair
- Center for Neurobehavioral Research, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Matthew Dobbertin
- Inpatient Psychiatric Care Unit, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Jaimie Elowsky
- Department of Psychology, University of Nebraska-Lincoln, Lincoln, NE, USA
| | | | - Ahria J Dominguez
- Clinical Health, Emotion, and Neuroscience (CHEN) Laboratory, Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center (UNMC), Omaha, NE, USA
| | - Melissa Hatch
- Mind and Brain Health Labs (MBHL), Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center (UNMC), Omaha, NE, USA
| | - Sahil Bajaj
- Department of Cancer Systems Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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17
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Zhang R, Blair RJR, Blair KS, Dobbertin M, Elowsky J, Bashford-Largo J, Dominguez AJ, Hatch M, Bajaj S. Reduced Grey Matter Volume in Adolescents with Conduct Disorder: A Region-of-Interest Analysis Using Multivariate Generalized Linear Modeling. RESEARCH SQUARE 2023:rs.3.rs-3425545. [PMID: 37961148 PMCID: PMC10635381 DOI: 10.21203/rs.3.rs-3425545/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Background Conduct disorder (CD) involves a group of behavioral and emotional problems that usually begins during childhood or adolescence. Structural brain alterations have been observed in CD, including the amygdala, insula, ventrolateral and medial prefrontal cortex, anterior cingulate cortex, and fusiform gyrus. The current study developed a multivariate generalized linear model (GLM) to differentiate adolescents with CD from typically developing (TD) adolescents in terms of grey matter volume (GMV). Methods The whole-brain structural MRI data were collected from 96 adolescents with CD (mean age = years; mean IQ = ; 63 males) and 90 TD individuals (mean age = years; mean IQ = ; 59 males) matched on age, IQ, and sex. Region-wise GMV was extracted following whole-brain parcellation into 68 cortical and 14 subcortical regions for each participant. A multivariate GLM was developed to predict the GMV of the pre-hypothesized regions-of-interest (ROIs) based on CD diagnosis, with intracranial volume, age, sex, and IQ serving as the covariate. Results A diagnosis of CD was a significant predictor for GMV in the right pars orbitalis, right insula, right superior temporal gyrus, left fusiform gyrus, and left amygdala (F(1, 180) = 5.460 - 10.317, p < 0.05, partial eta squared = 0.029 - 0.054). The CD participants had smaller GMV in these regions than the TD participants (MCD - MTD = [-614.898] mm3 - [-53.461] mm3). Conclusions Altered GMV within specific regions may serve as a biomarker for the development of CD in adolescents. Clinical work can potentially target these biomarkers to treat adolescents with CD.
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Affiliation(s)
- Ru Zhang
- University of Southern California
| | | | | | | | | | | | | | | | - Sahil Bajaj
- The University of Texas MD Anderson Cancer Center
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18
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Rosenblatt M, Tejavibulya L, Camp CC, Jiang R, Westwater ML, Noble S, Scheinost D. Power and reproducibility in the external validation of brain-phenotype predictions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.25.563971. [PMID: 37961654 PMCID: PMC10634903 DOI: 10.1101/2023.10.25.563971] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Identifying reproducible and generalizable brain-phenotype associations is a central goal of neuroimaging. Consistent with this goal, prediction frameworks evaluate brain-phenotype models in unseen data. Most prediction studies train and evaluate a model in the same dataset. However, external validation, or the evaluation of a model in an external dataset, provides a better assessment of robustness and generalizability. Despite the promise of external validation and calls for its usage, the statistical power of such studies has yet to be investigated. In this work, we ran over 60 million simulations across several datasets, phenotypes, and sample sizes to better understand how the sizes of the training and external datasets affect statistical power. We found that prior external validation studies used sample sizes prone to low power, which may lead to false negatives and effect size inflation. Furthermore, increases in the external sample size led to increased simulated power directly following theoretical power curves, whereas changes in the training dataset size offset the simulated power curves. Finally, we compared the performance of a model within a dataset to the external performance. The within-dataset performance was typically within r=0.2 of the cross-dataset performance, which could help decide how to power future external validation studies. Overall, our results illustrate the importance of considering the sample sizes of both the training and external datasets when performing external validation.
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Affiliation(s)
| | - Link Tejavibulya
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT
| | - Chris C. Camp
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT
| | - Rongtao Jiang
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Margaret L. Westwater
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
| | - Stephanie Noble
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
- Department of Bioengineering, Northeastern University, Boston, MA
- Department of Psychology, Northeastern University, Boston, MA
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT
- Child Study Center, Yale School of Medicine, New Haven, CT
- Department of Statistics & Data Science, Yale University, New Haven, CT
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19
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Canada KL, Saifullah S, Gardner JC, Sutton BP, Fabiani M, Gratton G, Raz N, Daugherty AM. Development and validation of a quality control procedure for automatic segmentation of hippocampal subfields. Hippocampus 2023; 33:1048-1057. [PMID: 37246462 PMCID: PMC10524242 DOI: 10.1002/hipo.23552] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 05/03/2023] [Accepted: 05/13/2023] [Indexed: 05/30/2023]
Abstract
Automatic segmentation methods for in vivo magnetic resonance imaging are increasing in popularity because of their high efficiency and reproducibility. However, automatic methods can be perfectly reliable and consistently wrong, and the validity of automatic segmentation methods cannot be taken for granted. Quality control (QC) by trained and reliable human raters is necessary to ensure the validity of automatic measurements. Yet QC practices for applied neuroimaging research are underdeveloped. We report a detailed QC and correction procedure to accompany our validated atlas for hippocampal subfield segmentation. We document a two-step QC procedure for identifying segmentation errors, along with a taxonomy of errors and an error severity rating scale. This detailed procedure has high between-rater reliability for error identification and manual correction. The latter introduces at maximum 3% error variance in volume measurement. All procedures were cross-validated on an independent sample collected at a second site with different imaging parameters. The analysis of error frequency revealed no evidence of bias. An independent rater with a third sample replicated procedures with high within-rater reliability for error identification and correction. We provide recommendations for implementing the described method along with hypothesis testing strategies. In sum, we present a detailed QC procedure that is optimized for efficiency while prioritizing measurement validity and suits any automatic atlas.
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Affiliation(s)
| | | | - Jennie C. Gardner
- Department of Psychology, University of Illinois at
Urbana-Champaign, Urbana, IL
- Beckman Institute for Advanced Science and Technology,
University of Illinois at Urbana-Champaign, Champaign, IL
| | - Bradley P. Sutton
- Beckman Institute for Advanced Science and Technology,
University of Illinois at Urbana-Champaign, Champaign, IL
| | - Monica Fabiani
- Department of Psychology, University of Illinois at
Urbana-Champaign, Urbana, IL
- Beckman Institute for Advanced Science and Technology,
University of Illinois at Urbana-Champaign, Champaign, IL
| | - Gabriele Gratton
- Department of Psychology, University of Illinois at
Urbana-Champaign, Urbana, IL
- Beckman Institute for Advanced Science and Technology,
University of Illinois at Urbana-Champaign, Champaign, IL
| | - Naftali Raz
- Department of Psychology, Stony Brook University, Stony
Brook, NY
- Max Planck Institute for Human Development, Berlin,
Germany
| | - Ana M. Daugherty
- Institute of Gerontology, Wayne State University, Detroit,
MI
- Department of Psychology, Wayne State University, Detroit,
MI
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20
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Nielsen AN, Graham AM, Sylvester CM. Baby Brains at Work: How Task-Based Functional Magnetic Resonance Imaging Can Illuminate the Early Emergence of Psychiatric Risk. Biol Psychiatry 2023; 93:880-892. [PMID: 36935330 PMCID: PMC10149573 DOI: 10.1016/j.biopsych.2023.01.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 12/19/2022] [Accepted: 01/12/2023] [Indexed: 01/22/2023]
Abstract
Psychiatric disorders are complex, often emerging from multiple atypical processes within specified domains over the course of development. Characterizing the development of the neural circuits supporting these domains may help break down the components of complex disorders and reveal variations in functioning associated with psychiatric risk. This review highlights the current and potential role of infant task-based functional magnetic resonance imaging (fMRI) in elucidating the developmental neurobiology of psychiatric disorders. Task-fMRI measures evoked brain activity in response to specific stimuli through changes in the blood oxygen level-dependent signal. First, we review extant studies using task fMRI from birth through the first few years of life and synthesize current evidence for when, where, and how different neural computations are performed across the infant brain. Neural circuits for sensory perception, the perception of abstract categories, and the detection of statistical regularities have been characterized with task fMRI in infants, providing developmental context for identifying and interpreting variation in the functioning of neural circuits related to psychiatric risk. Next, we discuss studies that specifically examine variation in the functioning of these neural circuits during infancy in relation to risk for psychiatric disorders. These studies reveal when maturation of specific neural circuits diverges, the influence of environmental risk factors, and the potential utility for task fMRI to facilitate early treatment or prevention of later psychiatric problems. Finally, we provide considerations for future infant task-fMRI studies with the potential to advance understanding of both functioning of neural circuits during infancy and subsequent risk for psychiatric disorders.
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Affiliation(s)
- Ashley N Nielsen
- Department of Neurology, Washington University School of Medicine, St. Louis, Missouri.
| | - Alice M Graham
- Department of Psychiatry, Oregon Health and Sciences University, Portland, Oregon
| | - Chad M Sylvester
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
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21
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Iqbal S, N. Qureshi A, Li J, Mahmood T. On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:3173-3233. [PMID: 37260910 PMCID: PMC10071480 DOI: 10.1007/s11831-023-09899-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 02/19/2023] [Indexed: 06/02/2023]
Abstract
Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.
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Affiliation(s)
- Saeed Iqbal
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Punjab 54000 Pakistan
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 Beijing China
| | - Adnan N. Qureshi
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore, Punjab 54000 Pakistan
| | - Jianqiang Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124 Beijing China
- Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing, 100124 Beijing China
| | - Tariq Mahmood
- Artificial Intelligence and Data Analytics (AIDA) Lab, College of Computer & Information Sciences (CCIS), Prince Sultan University, Riyadh, 11586 Kingdom of Saudi Arabia
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22
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Pellowski JA, Wedderburn CJ, Groenewold NA, Roos A, Subramoney S, Hoffman N, Fouche JP, Joshi SH, Woods RP, Narr KL, Zar HJ, Donald KA, Stein DJ. Maternal perinatal depression and child brain structure at 2-3 years in a South African birth cohort study. Transl Psychiatry 2023; 13:96. [PMID: 36941258 PMCID: PMC10027817 DOI: 10.1038/s41398-023-02395-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/24/2023] [Accepted: 03/03/2023] [Indexed: 03/23/2023] Open
Abstract
Maternal perinatal depression is associated with risk of adverse child developmental outcomes and differences in offspring brain structure. Evidence from low- and middle-income countries is lacking as is an investigation of antenatal, postnatal, and persistent depression in the same sample. In a South African birth cohort, we investigated the effect of antenatal and postpartum maternal depressive symptoms on offspring brain structure at 2-3 years of age. Magnetic resonance imaging was performed, extracting cortical thickness and surface areas in frontal cortex regions of interest and subcortical volumes using FreeSurfer software. Maternal depressive symptoms were measured using the Edinburgh Postpartum Depression Scale and the Beck Depression Inventory II antenatally and at 6-10 weeks, 6 months, 12 months, and 18 months postpartum and analyzed dichotomously and continuously. Linear regressions were used controlling for child age, sex, intracranial volume, maternal education, age, smoking, alcohol use and HIV. 146 children were included with 38 (37%) exposed to depressive symptoms antenatally and 44 (35%) exposed postnatally. Of these, 16 (13%) were exposed to both. Postpartum, but not antenatal, depressive symptoms were associated with smaller amygdala volumes in children (B = -74.73, p = 0.01). Persistent maternal depressive symptoms across pregnancy and postpartum were also independently associated with smaller amygdala volumes (B = -78.61, p = 0.047). Differences in amygdala volumes among children exposed to postnatal as well as persistent maternal depressive symptomatology underscore the importance of identifying women at-risk for depression during the entire perinatal period.
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Affiliation(s)
- Jennifer A Pellowski
- Department of Behavioral and Social Sciences and International Health Institute, Brown University School of Public Health, Providence, RI, USA.
- Division of Epidemiology and Biostatistics, University of Cape Town School of Public Health and Family Medicine, Cape Town, SA, South Africa.
| | - Catherine J Wedderburn
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, Cape Town, South Africa
- Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, England
- The Neuroscience Institute, University of Cape Town, SA, Cape Town, South Africa
| | - Nynke A Groenewold
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, Cape Town, South Africa
- The Neuroscience Institute, University of Cape Town, SA, Cape Town, South Africa
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, SA, South Africa
| | - Annerine Roos
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, Cape Town, South Africa
- The Neuroscience Institute, University of Cape Town, SA, Cape Town, South Africa
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, SA, South Africa
- South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, SA, Cape Town, South Africa
| | - Sivenesi Subramoney
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, Cape Town, South Africa
| | - Nadia Hoffman
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, SA, South Africa
- South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, SA, Cape Town, South Africa
| | - Jean-Paul Fouche
- The Neuroscience Institute, University of Cape Town, SA, Cape Town, South Africa
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, SA, South Africa
- South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, SA, Cape Town, South Africa
| | - Shantanu H Joshi
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Roger P Woods
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Katherine L Narr
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Heather J Zar
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, Cape Town, South Africa
- South African Medical Research Council (SAMRC) Unit on Child and Adolescent Health, University of Cape Town, Cape Town, SA, South Africa
| | - Kirsten A Donald
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, Cape Town, South Africa
- The Neuroscience Institute, University of Cape Town, SA, Cape Town, South Africa
| | - Dan J Stein
- The Neuroscience Institute, University of Cape Town, SA, Cape Town, South Africa
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, SA, South Africa
- South African Medical Research Council (SAMRC) Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, SA, Cape Town, South Africa
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23
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Kampa M, Sebastian A, Tüscher O, Stark R, Klucken T. Refocus on stopping! Replication of reduced right amygdala reactivity to negative, visual primes during inhibition of motor responses. NEUROIMAGE: REPORTS 2023. [DOI: 10.1016/j.ynirp.2022.100151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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24
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Vandewouw MM, Brian J, Crosbie J, Schachar RJ, Iaboni A, Georgiades S, Nicolson R, Kelley E, Ayub M, Jones J, Taylor MJ, Lerch JP, Anagnostou E, Kushki A. Identifying Replicable Subgroups in Neurodevelopmental Conditions Using Resting-State Functional Magnetic Resonance Imaging Data. JAMA Netw Open 2023; 6:e232066. [PMID: 36912839 PMCID: PMC10011941 DOI: 10.1001/jamanetworkopen.2023.2066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/14/2023] Open
Abstract
IMPORTANCE Neurodevelopmental conditions, such as autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD), have highly heterogeneous and overlapping phenotypes and neurobiology. Data-driven approaches are beginning to identify homogeneous transdiagnostic subgroups of children; however, findings have yet to be replicated in independently collected data sets, a necessity for translation into clinical settings. OBJECTIVE To identify subgroups of children with and without neurodevelopmental conditions with shared functional brain characteristics using data from 2 large, independent data sets. DESIGN, SETTING, AND PARTICIPANTS This case-control study used data from the Province of Ontario Neurodevelopmental (POND) network (study recruitment began June 2012 and is ongoing; data were extracted April 2021) and the Healthy Brain Network (HBN; study recruitment began May 2015 and is ongoing; data were extracted November 2020). POND and HBN data are collected from institutions across Ontario and New York, respectively. Participants who had diagnoses of ASD, ADHD, and OCD or were typically developing (TD); were aged between 5 and 19 years; and successfully completed the resting-state and anatomical neuroimaging protocol were included in the current study. MAIN OUTCOMES AND MEASURES The analyses consisted of a data-driven clustering procedure on measures derived from each participant's resting-state functional connectome, performed independently on each data set. Differences between each pair of leaves in the resulting clustering decision trees in the demographic and clinical characteristics were tested. RESULTS Overall, 551 children and adolescents were included from each data set. POND included 164 participants with ADHD; 217 with ASD; 60 with OCD; and 110 with TD (median [IQR] age, 11.87 [9.51-14.76] years; 393 [71.2%] male participants; 20 [3.6%] Black, 28 [5.1%] Latino, and 299 [54.2%] White participants) and HBN included 374 participants with ADHD; 66 with ASD; 11 with OCD; and 100 with TD (median [IQR] age, 11.50 [9.22-14.20] years; 390 [70.8%] male participants; 82 [14.9%] Black, 57 [10.3%] Hispanic, and 257 [46.6%] White participants). In both data sets, subgroups with similar biology that differed significantly in intelligence as well as hyperactivity and impulsivity problems were identified, yet these groups showed no consistent alignment with current diagnostic categories. For example, there was a significant difference in Strengths and Weaknesses ADHD Symptoms and Normal Behavior Hyperactivity/Impulsivity subscale (SWAN-HI) between 2 subgroups in the POND data (C and D), with subgroup D having increased hyperactivity and impulsivity traits compared with subgroup C (median [IQR], 2.50 [0.00-7.00] vs 1.00 [0.00-5.00]; U = 1.19 × 104; P = .01; η2 = 0.02). A significant difference in SWAN-HI scores between subgroups g and d in the HBN data was also observed (median [IQR], 1.00 [0.00-4.00] vs 0.00 [0.00-2.00]; corrected P = .02). There were no differences in the proportion of each diagnosis between the subgroups in either data set. CONCLUSIONS AND RELEVANCE The findings of this study suggest that homogeneity in the neurobiology of neurodevelopmental conditions transcends diagnostic boundaries and is instead associated with behavioral characteristics. This work takes an important step toward translating neurobiological subgroups into clinical settings by being the first to replicate our findings in independently collected data sets.
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Affiliation(s)
- Marlee M. Vandewouw
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Jessica Brian
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Jennifer Crosbie
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Russell J. Schachar
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Alana Iaboni
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
| | - Stelios Georgiades
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
| | - Robert Nicolson
- Department of Psychiatry, Western University, London, Ontario, Canada
| | - Elizabeth Kelley
- Department of Psychology, Queen’s University, Kingston, Ontario, Canada
- Centre for Neuroscience Studies, Queen’s University, Kingston, Ontario, Canada
- Department of Psychiatry, Queen’s University, Kingston, Ontario, Canada
| | - Muhammad Ayub
- Department of Psychiatry, Queen’s University, Kingston, Ontario, Canada
| | - Jessica Jones
- Department of Psychology, Queen’s University, Kingston, Ontario, Canada
- Centre for Neuroscience Studies, Queen’s University, Kingston, Ontario, Canada
- Department of Psychiatry, Queen’s University, Kingston, Ontario, Canada
| | - Margot J. Taylor
- Program in Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - Jason P. Lerch
- Program in Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Evdokia Anagnostou
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Program in Neurosciences & Mental Health, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Azadeh Kushki
- Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
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25
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Carmichael O. The Role of fMRI in Drug Development: An Update. ADVANCES IN NEUROBIOLOGY 2023; 30:299-333. [PMID: 36928856 DOI: 10.1007/978-3-031-21054-9_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Functional magnetic resonance imaging (fMRI) of the brain is a technology that holds great potential for increasing the efficiency of drug development for the central nervous system (CNS). In preclinical studies and both early- and late-phase human trials, fMRI has the potential to improve cross-species translation of drug effects, help to de-risk compounds early in development, and contribute to the portfolio of evidence for a compound's efficacy and mechanism of action. However, to date, the utilization of fMRI in the CNS drug development process has been limited. The purpose of this chapter is to explore this mismatch between potential and utilization. This chapter provides introductory material related to fMRI and drug development, describes what is required of fMRI measurements for them to be useful in a drug development setting, lists current capabilities of fMRI in this setting and challenges faced in its utilization, and ends with directions for future development of capabilities in this arena. This chapter is the 5-year update of material from a previously published workshop summary (Carmichael et al., Drug DiscovToday 23(2):333-348, 2018).
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Affiliation(s)
- Owen Carmichael
- Pennington Biomedical Research Center, Baton Rouge, LA, USA.
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26
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Yuan Q, Li H, Du B, Dang Q, Chang Q, Zhang Z, Zhang M, Ding G, Lu C, Guo T. The cerebellum and cognition: further evidence for its role in language control. Cereb Cortex 2022; 33:35-49. [PMID: 35226917 DOI: 10.1093/cercor/bhac051] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 12/27/2021] [Accepted: 01/26/2022] [Indexed: 11/14/2022] Open
Abstract
The cognitive function of the human cerebellum could be characterized as enigmatic. However, researchers have attempted to detail the comprehensive role of the cerebellum in several cognitive processes in recent years. Here, using functional magnetic resonance imaging (fMRI) and transcranial direct current stimulation (tDCS), we revealed different functions of bilateral cerebellar lobules in bilingual language production. Specifically, brain activation showed the bilateral posterolateral cerebellum was associated with bilingual language control, and an effective connectivity analysis built brain networks for the interaction between the cerebellum and the cerebral cortex. Furthermore, anodal tDCS over the right cerebellum significantly optimizes language control performance in bilinguals. Together, these results reveal a precise asymmetrical functional distribution of the cerebellum in bilingual language production, suggesting that the right cerebellum is more involved in language control. In contrast, its left counterpart undertakes a computational role in cognitive control function by connecting with more prefrontal, parietal, subcortical brain areas.
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Affiliation(s)
- Qiming Yuan
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Hehui Li
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen 518061, China
| | - Boqi Du
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Qinpu Dang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Qianwen Chang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Zhaoqi Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Man Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Guosheng Ding
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
| | - Chunming Lu
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
| | - Taomei Guo
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
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27
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Klein SD, Collins PF, Luciana M. Developmental trajectories of delay discounting from childhood to young adulthood: longitudinal associations and test-retest reliability. Cogn Psychol 2022; 139:101518. [PMID: 36183669 PMCID: PMC10888509 DOI: 10.1016/j.cogpsych.2022.101518] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 09/05/2022] [Accepted: 09/12/2022] [Indexed: 01/27/2023]
Abstract
Delay discounting (DD) indexes an individual's preference for smaller immediate rewards over larger delayed rewards, and is considered a form of cognitive impulsivity. Cross-sectional studies have demonstrated that DD peaks in adolescence; longitudinal studies are needed to validate this putative developmental trend, and to determine whether DD assesses a temporary state, or reflects a more stable behavioral trait. In this study, 140 individuals aged 9-23 completed a delay discounting (DD) task and cognitive battery at baseline and every-two years thereafter, yielding five assessments over approximately 10 years. Models fit with the inverse effect of age best approximated the longitudinal trajectory of two DD measures, hyperbolic discounting (log[k]) and area under the indifference-point curve (AUC). Discounting of future rewards increased rapidly from childhood to adolescence and appeared to plateau in late adolescence for both models of DD. Participants with greater verbal intelligence and working memory displayed reduced DD across the duration of the study, suggesting a functional interrelationship between these domains and DD from early adolescence to adulthood. Furthermore, AUC demonstrated good to excellent reliability across assessment points that was superior to log(k), with both measures demonstrating acceptable stability once participants reached late adolescence. The developmental trajectories of DD we observed from childhood through young adulthood suggest that DD may index cognitive control more than reward sensitivity, and that despite modest developmental changes with maturation, AUC may be conceptualized as a trait variable related to cognitive control vs impulsivity.
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Affiliation(s)
- Samuel D Klein
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA.
| | - Paul F Collins
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Monica Luciana
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
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28
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Mediating effect of pubertal stages on the family environment and neurodevelopment: An open-data replication and multiverse analysis of an ABCD Study ®. NEUROIMAGE. REPORTS 2022; 2:100133. [PMID: 36561641 PMCID: PMC9770593 DOI: 10.1016/j.ynirp.2022.100133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Increasing evidence demonstrates that environmental factors meaningfully impact the development of the brain (Hyde et al., 2020; McEwen and Akil, 2020). Recent work from the Adolescent Brain Cognitive Development (ABCD) Study® suggests that puberty may indirectly account for some association between the family environment and brain structure and function (Thijssen et al., 2020). However, a limited number of large studies have evaluated what, how, and why environmental factors impact neurodevelopment. When these topics are investigated, there is typically inconsistent operationalization of variables between studies which may be measuring different aspects of the environment and thus different associations in the analytic models. Multiverse analyses (Steegen et al., 2016) are an efficacious technique for investigating the effect of different operationalizations of the same construct on underlying interpretations. While one of the assets of Thijssen et al. (2020) was its large sample from the ABCD data, the authors used an early release that contained 38% of the full ABCD sample. Then, the analyses used several 'researcher degrees of freedom' (Gelman and Loken, 2014) to operationalize key independent, mediating and dependent variables, including but not limited to, the use of a latent factor of preadolescents' environment comprised of different subfactors, such as parental monitoring and child-reported family conflict. While latent factors can improve reliability of constructs, the nuances of each subfactor and measure that comprise the environment may be lost, making the latent factors difficult to interpret in the context of individual differences. This study extends the work of Thijssen et al. (2020) by evaluating the extent to which the analytic choices in their study affected their conclusions. In Aim 1, using the same variables and models, we replicate findings from the original study using the full sample in Release 3.0. Then, in Aim 2, using a multiverse analysis we extend findings by considering nine alternative operationalizations of family environment, three of puberty, and five of brain measures (total of 135 models) to evaluate the impact on conclusions from Aim 1. In these results, 90% of the directions of effects and 60% of the p-values (e.g. p > .05 and p < .05) across effects were comparable between the two studies. However, raters agreed that only 60% of the effects had replicated. Across the multiverse analyses, there was a degree of variability in beta estimates across the environmental variables, and lack of consensus between parent reported and child reported pubertal development for the indirect effects. This study demonstrates the challenge in defining which effects replicate, the nuance across environmental variables in the ABCD data, and the lack of consensus across parent and child reported puberty scales in youth.
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29
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Beard SJ, Yoon L, Venticinque JS, Shepherd NE, Guyer AE. The brain in social context: A systematic review of substance use and social processing from adolescence to young adulthood. Dev Cogn Neurosci 2022; 57:101147. [PMID: 36030675 PMCID: PMC9434028 DOI: 10.1016/j.dcn.2022.101147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/08/2022] [Accepted: 08/10/2022] [Indexed: 11/19/2022] Open
Abstract
Substance use escalates between adolescence and young adulthood, and most experimentation occurs among peers. To understand underlying mechanisms, research has focused on neural response during relevant psychological processes. Functional magnetic resonance imaging (fMRI) research provides a wealth of information about brain activity when processing monetary rewards; however, most studies have used tasks devoid of social stimuli. Given that adolescent neurodevelopment is sculpted by the push-and-pull of peers and emotions, identifying neural substrates is important for intervention. We systematically reviewed 28 fMRI studies examining substance use and neural responses to stimuli including social reward, emotional faces, social influence, and social stressors. We found substance use was positively associated with social-reward activity (e.g., in the ventral striatum), and negatively with social-stress activity (e.g., in the amygdala). For emotion, findings were mixed with more use linked to heightened response (e.g., in amygdala), but also with decreased response (e.g., in insula). For social influence, evidence supported both positive (e.g., cannabis and nucleus accumbens during conformity) and negative (e.g., polydrug and ventromedial PFC during peers' choices) relations between activity and use. Based on the literature, we offer recommendations for future research on the neural processing of social information to better identify risks for substance use.
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Affiliation(s)
- Sarah J Beard
- Center for Mind and Brain, University of California, Davis, 267 Cousteau Pl, Davis, CA 95618, USA; Department of Human Ecology, University of California, Davis, 301 Shields Ave, Davis, CA 95616, USA.
| | - Leehyun Yoon
- Center for Mind and Brain, University of California, Davis, 267 Cousteau Pl, Davis, CA 95618, USA.
| | - Joseph S Venticinque
- Center for Mind and Brain, University of California, Davis, 267 Cousteau Pl, Davis, CA 95618, USA; Department of Human Ecology, University of California, Davis, 301 Shields Ave, Davis, CA 95616, USA.
| | - Nathan E Shepherd
- Center for Mind and Brain, University of California, Davis, 267 Cousteau Pl, Davis, CA 95618, USA.
| | - Amanda E Guyer
- Center for Mind and Brain, University of California, Davis, 267 Cousteau Pl, Davis, CA 95618, USA; Department of Human Ecology, University of California, Davis, 301 Shields Ave, Davis, CA 95616, USA.
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30
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Makin ADJ, Tyson-Carr J, Rampone G, Derpsch Y, Wright D, Bertamini M. Lessons from a catalogue of 6674 brain recordings. eLife 2022; 11:66388. [PMID: 35703370 PMCID: PMC9200404 DOI: 10.7554/elife.66388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 04/14/2022] [Indexed: 11/26/2022] Open
Abstract
It is now possible for scientists to publicly catalogue all the data they have ever collected on one phenomenon. For a decade, we have been measuring a brain response to visual symmetry called the sustained posterior negativity (SPN). Here we report how we have made a total of 6674 individual SPNs from 2215 participants publicly available, along with data extraction and visualization tools (https://osf.io/2sncj/). We also report how re-analysis of the SPN catalogue has shed light on aspects of the scientific process, such as statistical power and publication bias, and revealed new scientific insights.
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Affiliation(s)
- Alexis D J Makin
- Department of Psychological Sciences, University of Liverpool, Liverpool, United Kingdom
| | - John Tyson-Carr
- Department of Psychological Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Giulia Rampone
- Department of Psychological Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Yiovanna Derpsch
- Department of Psychological Sciences, University of Liverpool, Liverpool, United Kingdom.,School of Psychology, University of East Anglia, Norwich, United Kingdom
| | - Damien Wright
- Patrick Wild Centre, Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, United Kingdom
| | - Marco Bertamini
- Department of Psychological Sciences, University of Liverpool, Liverpool, United Kingdom.,Dipartimento di Psicologia Generale, Università di Padova, Padova, Italy
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31
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Saragosa-Harris NM, Chaku N, MacSweeney N, Guazzelli Williamson V, Scheuplein M, Feola B, Cardenas-Iniguez C, Demir-Lira E, McNeilly EA, Huffman LG, Whitmore L, Michalska KJ, Damme KS, Rakesh D, Mills KL. A practical guide for researchers and reviewers using the ABCD Study and other large longitudinal datasets. Dev Cogn Neurosci 2022; 55:101115. [PMID: 35636343 PMCID: PMC9156875 DOI: 10.1016/j.dcn.2022.101115] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 04/07/2022] [Accepted: 05/17/2022] [Indexed: 12/14/2022] Open
Abstract
As the largest longitudinal study of adolescent brain development and behavior to date, the Adolescent Brain Cognitive Development (ABCD) Study® has provided immense opportunities for researchers across disciplines since its first data release in 2018. The size and scope of the study also present a number of hurdles, which range from becoming familiar with the study design and data structure to employing rigorous and reproducible analyses. The current paper is intended as a guide for researchers and reviewers working with ABCD data, highlighting the features of the data (and the strengths and limitations therein) as well as relevant analytical and methodological considerations. Additionally, we explore justice, equity, diversity, and inclusion efforts as they pertain to the ABCD Study and other large-scale datasets. In doing so, we hope to increase both accessibility of the ABCD Study and transparency within the field of developmental cognitive neuroscience.
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Affiliation(s)
| | - Natasha Chaku
- Department of Psychology, University of Michigan, Ann Arbor, USA.
| | - Niamh MacSweeney
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, UK.
| | | | | | - Brandee Feola
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Carlos Cardenas-Iniguez
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Ece Demir-Lira
- Department of Psychological and Brain Sciences, University of Iowa, IA, USA
| | | | | | | | - Kalina J Michalska
- Department of Psychology, University of California Riverside, Riverside, CA, USA
| | - Katherine Sf Damme
- Institute of Developmental Science, Northwestern University, Chicago, IL, USA
| | - Divyangana Rakesh
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Victoria, Australia
| | - Kathryn L Mills
- Department of Psychology, University of Oregon, USA; PROMENTA Research Center, Department of Psychology, University of Oslo, Norway
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32
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Resting-state functional connectivity of social brain regions predicts motivated dishonesty. Neuroimage 2022; 256:119253. [PMID: 35490914 DOI: 10.1016/j.neuroimage.2022.119253] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 04/11/2022] [Accepted: 04/16/2022] [Indexed: 11/23/2022] Open
Abstract
Motivated dishonesty is a typical social behavior varying from person to person. Resting-state fMRI (rsfMRI) is capable of identifying unique patterns from functional connectivity (FC) between brain regions. Recent work has built a link between brain networks in resting state to dishonesty in Western participants. To determine and reproduce the relevant neural patterns and build an interpretable model to predict dishonesty, we analyzed two conceptually similar datasets containing rsfMRI data with different dishonesty tasks. Both tasks implemented the information-passing paradigm, in which monetary rewards were employed to induce dishonesty. We applied connectome-based predictive modeling (CPM) to build a model among FC within and between four social brain networks (reward, self-referential, moral, and cognitive control). The CPM analysis indicated that FCs of social brain networks are predictive of dishonesty rate, especially FCs within reward network, and between self-referential and cognitive control networks. Our study offers an conceptual replication with integrated model to predict dishonesty with rsfMRI, and the results suggest that frequent motivated dishonest decisions may require the higher engagement of social brain regions.
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33
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Kraus D, Horowitz‐Kraus T. Functional MRI research involving healthy children: Ethics, safety and recommended procedures. Acta Paediatr 2022; 111:741-749. [PMID: 34986521 DOI: 10.1111/apa.16247] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/26/2021] [Accepted: 01/04/2022] [Indexed: 12/11/2022]
Abstract
AIM This specific review aims to expose clinicians, researchers and administrators in hospitals to the importance, procedures and safety of fMRI studies to promote the increased utilisation of such studies in different geographical places worldwide. The child's brain is developing rapidly, both structurally and functionally. These functional changes can only be detected using functional scans generated from an MRI machine and referred to as a functional MRI (fMRI). This method may be used clinically in complex medical and surgical conditions (e.g., epilepsy surgery), but these days are often used for research purposes. However, due to ethical and logistical considerations, fMRI in the paediatric population is not widely and equally used in different geographical places. CONCLUSIONS The benefits of using this method to define the functional changes occurring in the developing brain are discussed in this review, along with desensitisation methods recommended when working with this vulnerable population in research and even in a clinical setting.
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Affiliation(s)
- Dror Kraus
- Pediatric Neurology Institute Schneider Children's Medical Center of Israel Tel Aviv University Petach‐Tiqua Israel
| | - Tzipi Horowitz‐Kraus
- Educational Neuroimaging Group Faculty of Education in Science and Technology Faculty of Biomedical Engineering Haifa Israel
- Kennedy Krieger Institute Baltimore Maryland USA
- Department of Psychiatry and Behavioral Sciences Johns Hopkins University School of Medicine Baltimore Maryland USA
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34
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Cohen AL. Using causal methods to map symptoms to brain circuits in neurodevelopment disorders: moving from identifying correlates to developing treatments. J Neurodev Disord 2022; 14:19. [PMID: 35279095 PMCID: PMC8918299 DOI: 10.1186/s11689-022-09433-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 03/03/2022] [Indexed: 11/20/2022] Open
Abstract
A wide variety of model systems and experimental techniques can provide insight into the structure and function of the human brain in typical development and in neurodevelopmental disorders. Unfortunately, this work, whether based on manipulation of animal models or observational and correlational methods in humans, has a high attrition rate in translating scientific discovery into practicable treatments and therapies for neurodevelopmental disorders.With new computational and neuromodulatory approaches to interrogating brain networks, opportunities exist for "bedside-to bedside-translation" with a potentially shorter path to therapeutic options. Specifically, methods like lesion network mapping can identify brain networks involved in the generation of complex symptomatology, both from acute onset lesion-related symptoms and from focal developmental anomalies. Traditional neuroimaging can examine the generalizability of these findings to idiopathic populations, while non-invasive neuromodulation techniques such as transcranial magnetic stimulation provide the ability to do targeted activation or inhibition of these specific brain regions and networks. In parallel, real-time functional MRI neurofeedback also allow for endogenous neuromodulation of specific targets that may be out of reach for transcranial exogenous methods.Discovery of novel neuroanatomical circuits for transdiagnostic symptoms and neuroimaging-based endophenotypes may now be feasible for neurodevelopmental disorders using data from cohorts with focal brain anomalies. These novel circuits, after validation in large-scale highly characterized research cohorts and tested prospectively using noninvasive neuromodulation and neurofeedback techniques, may represent a new pathway for symptom-based targeted therapy.
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Affiliation(s)
- Alexander Li Cohen
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA. .,Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. .,Laboratory for Brain Network Imaging and Modulation, Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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35
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Dufford AJ, Hahn CA, Peterson H, Gini S, Mehta S, Alfano A, Scheinost D. (Un)common space in infant neuroimaging studies: A systematic review of infant templates. Hum Brain Mapp 2022; 43:3007-3016. [PMID: 35261126 PMCID: PMC9120551 DOI: 10.1002/hbm.25816] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 01/24/2022] [Accepted: 02/13/2022] [Indexed: 11/08/2022] Open
Abstract
In neuroimaging, spatial normalization is an important step that maps an individual's brain onto a template brain permitting downstream statistical analyses. Yet, in infant neuroimaging, there remain several technical challenges that have prevented the establishment of a standardized template for spatial normalization. Thus, many different approaches are used in the literature. To quantify the popularity and variability of these approaches in infant neuroimaging studies, we performed a systematic review of infant magnetic resonance imaging (MRI) studies from 2000 to 2020. Here, we present results from 834 studies meeting inclusion criteria. Studies were classified into (a) processing data in single subject space, (b) using an off the shelf, or "off the shelf," template, (c) creating a study specific template, or (d) using a hybrid of these methods. We found that across the studies in the systematic review, single subject space was the most used (no common space). This was the most used common space for diffusion-weighted imaging and structural MRI studies while functional MRI studies preferred off the shelf atlases. We found a pattern such that more recently published studies are more commonly using off the shelf atlases. When considering special populations, preterm studies most used single subject space while, when no special populations were being analyzed, an off the shelf template was most common. The most used off the shelf templates were the UNC Infant Atlases (24%). Using a systematic review of infant neuroimaging studies, we highlight a lack of an established "standard" template brain in these studies.
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Affiliation(s)
- Alexander J. Dufford
- Department of Radiology and Biomedical ImagingYale School of MedicineNew HavenConnecticutUSA
| | - C. Alice Hahn
- Department of Radiology and Biomedical ImagingYale School of MedicineNew HavenConnecticutUSA
| | - Hannah Peterson
- Department of Radiology and Biomedical ImagingYale School of MedicineNew HavenConnecticutUSA
| | - Silvia Gini
- Department of Radiology and Biomedical ImagingYale School of MedicineNew HavenConnecticutUSA
| | - Saloni Mehta
- Department of Radiology and Biomedical ImagingYale School of MedicineNew HavenConnecticutUSA
| | - Alexis Alfano
- Department of PsychologyQuinnipiac UniversityHamdenConnecticutUSA
| | - Dustin Scheinost
- Department of Radiology and Biomedical ImagingYale School of MedicineNew HavenConnecticutUSA,Department of Statistics and Data ScienceYale UniversityNew HavenConnecticutUSA,Interdepartmental Neuroscience ProgramYale UniversityNew HavenConnecticutUSA,Department of Biomedical EngineeringYale UniversityNew HavenConnecticutUSA,Child Study CenterYale School of MedicineNew HavenConnecticutUSA
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36
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Lee KS, Xiao J, Luo J, Leibenluft E, Liew Z, Tseng WL. Characterizing the Neural Correlates of Response Inhibition and Error Processing in Children With Symptoms of Irritability and/or Attention-Deficit/Hyperactivity Disorder in the ABCD Study®. Front Psychiatry 2022; 13:803891. [PMID: 35308882 PMCID: PMC8931695 DOI: 10.3389/fpsyt.2022.803891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 01/28/2022] [Indexed: 11/13/2022] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD), characterized by symptoms of inattention and/or hyperactivity and impulsivity, is a neurodevelopmental disorder associated with executive dysfunctions, including response inhibition and error processing. Research has documented a common co-occurrence between ADHD and pediatric irritability. The latter is more characterized by affective symptoms, specifically frequent temper outbursts and low frustration tolerance relative to typically developing peers. Shared and non-shared neural correlates of youths with varied profiles of ADHD and irritability symptoms during childhood remain largely unknown. This study first classified a large sample of youths in the Adolescent Brain Cognitive Development (ABCD) study at baseline into distinct phenotypic groups based on ADHD and irritability symptoms (N = 11,748), and then examined shared and non-shared neural correlates of response inhibition and error processing during the Stop Signal Task in a subset of sample with quality neuroimaging data (N = 5,948). Latent class analysis (LCA) revealed four phenotypic groups, i.e., high ADHD with co-occurring irritability symptoms (n = 787, 6.7%), moderate ADHD with low irritability symptoms (n = 901, 7.7%), high irritability with no ADHD symptoms (n = 279, 2.4%), and typically developing peers with low ADHD and low irritability symptoms (n = 9,781, 83.3%). Latent variable modeling revealed group differences in the neural coactivation network supporting response inhibition in the fronto-parietal regions, but limited differences in error processing across frontal and posterior regions. These neural differences were marked by decreased coactivation in the irritability only group relative to youths with ADHD and co-occurring irritability symptoms and typically developing peers during response inhibition. Together, this study provided initial evidence for differential neural mechanisms of response inhibition associated with ADHD, irritability, and their co-occurrence. Precision medicine attending to individual differences in ADHD and irritability symptoms and the underlying mechanisms are warranted when treating affected children and families.
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Affiliation(s)
- Ka Shu Lee
- Department of Experimental Psychology, Medical Sciences Division, University of Oxford, Oxford, United Kingdom
- Yale Child Study Center, Yale School of Medicine, New Haven, CT, United States
| | - Jingyuan Xiao
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, United States
- Yale Center for Perinatal, Pediatric and Environmental Epidemiology, Yale School of Public Health, New Haven, CT, United States
| | - Jiajun Luo
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, United States
- Yale Center for Perinatal, Pediatric and Environmental Epidemiology, Yale School of Public Health, New Haven, CT, United States
- Institute for Population and Precision Health, The University of Chicago, Chicago, IL, United States
| | - Ellen Leibenluft
- Section on Mood Dysregulation and Neuroscience, Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, United States
| | - Zeyan Liew
- Yale Center for Perinatal, Pediatric and Environmental Epidemiology, Yale School of Public Health, New Haven, CT, United States
| | - Wan-Ling Tseng
- Yale Child Study Center, Yale School of Medicine, New Haven, CT, United States
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Eke DO, Bernard A, Bjaalie JG, Chavarriaga R, Hanakawa T, Hannan AJ, Hill SL, Martone ME, McMahon A, Ruebel O, Crook S, Thiels E, Pestilli F. International data governance for neuroscience. Neuron 2022; 110:600-612. [PMID: 34914921 PMCID: PMC8857067 DOI: 10.1016/j.neuron.2021.11.017] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 06/16/2021] [Accepted: 11/15/2021] [Indexed: 12/19/2022]
Abstract
As neuroscience projects increase in scale and cross international borders, different ethical principles, national and international laws, regulations, and policies for data sharing must be considered. These concerns are part of what is collectively called data governance. Whereas neuroscience data transcend borders, data governance is typically constrained within geopolitical boundaries. An international data governance framework and accompanying infrastructure can assist investigators, institutions, data repositories, and funders with navigating disparate policies. Here, we propose principles and operational considerations for how data governance in neuroscience can be navigated at an international scale and highlight gaps, challenges, and opportunities in a global brain data ecosystem. We consider how to approach data governance in a way that balances data protection requirements and the need for open science, so as to promote international collaboration through federated constructs such as the International Brain Initiative (IBI).
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Affiliation(s)
- Damian O Eke
- Centre for Computing and Social Responsibility, De Montfort University, Leicester, UK; Human Brain Project
| | | | | | - Ricardo Chavarriaga
- Center for Artificial Intelligence, School of Engineering, Zurich University of Applied Sciences, Zurich, Switzerland
| | | | - Anthony J Hannan
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Australia
| | - Sean L Hill
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | | | | | - Oliver Ruebel
- Scientific Data Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Sharon Crook
- School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ, USA
| | - Edda Thiels
- National Science Foundation, Alexandria, VA, USA
| | - Franco Pestilli
- Department of Psychology, Center for Perceptual Systems, Center for Theoretical and Computational Neuroscience, and Institute for Neuroscience, University of Texas, Austin, TX, USA.
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Calderoni S. Sex/gender differences in children with autism spectrum disorder: A brief overview on epidemiology, symptom profile, and neuroanatomy. J Neurosci Res 2022; 101:739-750. [PMID: 35043482 DOI: 10.1002/jnr.25000] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 11/01/2021] [Accepted: 12/03/2021] [Indexed: 12/11/2022]
Abstract
Autism spectrum disorders (ASD) are a heterogeneous group of neurodevelopmental conditions whose shared core features are impairments in social interaction and communication as well as restricted patterns of behavior, interests, and activities. The significant and consistent male preponderance in ASD prevalence has historically affected the scientific knowledge of autism in females as regards, inter alia, the clinical presentation, the genetic architecture, and the structural brain underpinnings. Indeed, females with ASD are under-investigated as samples recruited for clinical research typically reflect the strong male bias of the disorder. In the last years, the study of the various aspects of sex/gender (s/g) differences in ASD is gaining increased clinical and research interest resulting in a growing number of investigations on this topic. Here, I review and discuss evidence emerged from epidemiological, clinical, and neuroimaging studies in the last decade focusing on s/g differences in children with ASD. These studies are the prerequisites for the development of assessment and treatment practices which take into consideration s/g differences in ASD. Ultimately, a better understanding of s/g differences aims at improving healthcare for both ASD males and females.
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Affiliation(s)
- Sara Calderoni
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Pisa, Italy.,Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
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Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain. Neuroinformatics 2022; 20:109-137. [PMID: 33974213 PMCID: PMC8111663 DOI: 10.1007/s12021-021-09519-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/07/2021] [Indexed: 02/06/2023]
Abstract
We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.
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Abreu-Mendoza RA, Pincus M, Chamorro Y, Jolles D, Matute E, Rosenberg-Lee M. Parietal and hippocampal hyper-connectivity is associated with low math achievement in adolescence - A preliminary study. Dev Sci 2021; 25:e13187. [PMID: 34761855 DOI: 10.1111/desc.13187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 08/18/2021] [Accepted: 10/22/2021] [Indexed: 11/27/2022]
Abstract
Mathematical cognition requires coordinated activity across multiple brain regions, leading to the emergence of resting-state functional connectivity as a method for studying the neural basis of differences in mathematical achievement. Hyper-connectivity of the intraparietal sulcus (IPS), a key locus of mathematical and numerical processing, has been associated with poor mathematical skills in childhood, whereas greater connectivity has been related to better performance in adulthood. No studies to date have considered its role in adolescence. Further, hippocampal connectivity can predict mathematical learning, yet no studies have considered its contributions to contemporaneous measures of math achievement. Here, we used seed-based resting-state fMRI analyses to examine IPS and hippocampal intrinsic functional connectivity relations to math achievement in a group of 31 adolescents (mean age = 16.42 years, range 15-17), whose math performance spanned the 1% to 99% percentile. After controlling for IQ, IPS connectivity was negatively related to math achievement, akin to findings in children. However, the specific temporo-occipital regions were more akin to the posterior loci implicated in adults. Hippocampal connectivity with frontal regions was also negatively correlated with concurrent math measures, which contrasts with results from learning studies. Finally, hyper-connectivity was not a global feature of low math performance, as math performance did not modulate connectivity of Heschl's gyrus, a control seed not involved in math cognition. Our results provide preliminary evidence that adolescence is a transitional stage in which patterns found in childhood and adulthood can be observed; most notably, hyper-connectivity continues to be related to low math ability into this period.
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Affiliation(s)
| | - Melanie Pincus
- Department of Psychology, Rutgers University-Newark, Newark, New Jersey, USA
| | - Yaira Chamorro
- Instituto de Neurociencias, CUCBA, Universidad de Guadalajara, Guadalajara, Jalisco, México
| | - Dietsje Jolles
- Department of Education and Child Studies, Leiden University, Leiden, The Netherlands
| | - Esmeralda Matute
- Instituto de Neurociencias, CUCBA, Universidad de Guadalajara, Guadalajara, Jalisco, México
| | - Miriam Rosenberg-Lee
- Department of Psychology, Rutgers University-Newark, Newark, New Jersey, USA.,Center for Molecular and Behavioral Neuroscience, Rutgers University-Newark, Newark, New Jersey, USA
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Goddings AL, Roalf D, Lebel C, Tamnes CK. Development of white matter microstructure and executive functions during childhood and adolescence: a review of diffusion MRI studies. Dev Cogn Neurosci 2021; 51:101008. [PMID: 34492631 PMCID: PMC8424510 DOI: 10.1016/j.dcn.2021.101008] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 07/26/2021] [Accepted: 08/24/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) provides indirect measures of white matter microstructure that can be used to make inferences about structural connectivity within the brain. Over the last decade, a growing literature of cross-sectional and longitudinal studies have documented relationships between dMRI indices and cognitive development. In this review, we provide a brief overview of dMRI methods and how they can be used to study white matter and connectivity and review the extant literature examining the links between dMRI indices and executive functions during development. We explore the links between white matter microstructure and specific executive functions: inhibition, working memory and cognitive shifting, as well as performance on complex executive function tasks. Concordance in findings across studies are highlighted, and potential explanations for discrepancies between results, together with challenges with using dMRI in child and adolescent populations, are discussed. Finally, we explore future directions that are necessary to better understand the links between child and adolescent development of structural connectivity of the brain and executive functions.
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Affiliation(s)
- Anne-Lise Goddings
- UCL Great Ormond Street Institute of Child Health, University College London, UK.
| | - David Roalf
- Department of Psychiatry, University of Pennsylvania, USA; Lifespan Brain Institute, Children's Hospital of Philadelphia and the University of Pennsylvania, USA
| | - Catherine Lebel
- Department of Radiology, University of Calgary, Alberta, Canada
| | - Christian K Tamnes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Norway; NORMENT, Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
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Ilyka D, Johnson MH, Lloyd-Fox S. Infant social interactions and brain development: A systematic review. Neurosci Biobehav Rev 2021; 130:448-469. [PMID: 34506843 PMCID: PMC8522805 DOI: 10.1016/j.neubiorev.2021.09.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 08/31/2021] [Accepted: 09/01/2021] [Indexed: 01/21/2023]
Abstract
Associations between caregiver-infant behaviours during social interactions and brain development outcomes were investigated. Caregivers' and infants' behaviours in interactions related to children’s structural, functional and connectivity measures. Concurrent associations between behavioural and brain measures were apparent as early as three months postnatally. Long-term associations between behaviours in early interactions and brain development outcomes were observed decades later. Individual differences in early interactions and associated brain development is an important avenue for further research.
From birth, interactions with others are an integral part of a person’s daily life. In infancy, social exchanges are thought to be critical for optimal brain development. This systematic review explores this association by drawing together infant studies that relate adult-infant behaviours – coded from their social interactions - to children’s brain measures collected during a neuroimaging session in infancy, childhood, adolescence or adulthood. In total, we identified 55 studies that explored associations between infants’ social interactions and neural measures. These studies show that several aspects of caregiver-infant behaviours are associated with, or predict, a variety of neural responses in infants, children and adolescents. The presence of both concurrent and long-term associations - some of which are first observed just a few months postnatally and extend into adulthood - open an important research avenue and motivate further longitudinal studies.
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Affiliation(s)
- Dianna Ilyka
- Department of Psychology, University of Cambridge, United Kingdom.
| | - Mark H Johnson
- Department of Psychology, University of Cambridge, United Kingdom
| | - Sarah Lloyd-Fox
- Department of Psychology, University of Cambridge, United Kingdom
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van Atteveldt N, Vandermosten M, Weeda W, Bonte M. How to capture developmental brain dynamics: gaps and solutions. NPJ SCIENCE OF LEARNING 2021; 6:10. [PMID: 33941785 PMCID: PMC8093270 DOI: 10.1038/s41539-021-00088-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 03/25/2021] [Indexed: 05/03/2023]
Abstract
Capturing developmental and learning-induced brain dynamics is extremely challenging as changes occur interactively across multiple levels and emerging functions. Different levels include the (social) environment, cognitive and behavioral levels, structural and functional brain changes, and genetics, while functions include domains such as math, reading, and executive function. Here, we report the insights that emerged from the workshop “Capturing Developmental Brain Dynamics”, organized to bring together multidisciplinary approaches to integrate data on development and learning across different levels, functions, and time points. During the workshop, current main gaps in our knowledge and tools were identified including the need for: (1) common frameworks, (2) longitudinal, large-scale, multisite studies using representative participant samples, (3) understanding interindividual variability, (4) explicit distinction of understanding versus predicting, and (5) reproducible research. After illustrating interactions across levels and functions during development, we discuss the identified gaps and provide solutions to advance the capturing of developmental brain dynamics.
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Affiliation(s)
- Nienke van Atteveldt
- Dept. of Clinical Developmental Psychology & Institute Learn!, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
| | - Maaike Vandermosten
- Dept. of Neuroscience, and Leuven Brain Institute, Experimental ORL, KU Leuven, Leuven, Belgium
| | - Wouter Weeda
- Dept. of Methodology & Statistics, Leiden University, Leiden, The Netherlands
| | - Milene Bonte
- Dept. of Cognitive Neuroscience, and Maastricht Brain Imaging Center, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
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Advancing our understanding of cognitive development and motor vehicle crash risk: A multiverse representation analysis. Cortex 2021; 138:90-100. [PMID: 33677330 DOI: 10.1016/j.cortex.2021.01.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 01/28/2021] [Accepted: 01/30/2021] [Indexed: 12/27/2022]
Abstract
Neurobiological and cognitive maturational models are the dominant theoretical account of adolescents' risk-taking behavior. Both the protracted development of working memory (WM) through adolescence, as well as individual differences in WM capacity have been theorized to be related to risk-taking behavior, including reckless driving. In a cohort study of 84 adolescent drivers Walshe et al. (2019) found adolescents who crashed had an attenuated trajectory of WM growth compared to adolescent drivers who never reported being in a crash, but observed no difference in WM capacity at baseline. The objectives of this report were to attempt to replicate these associations and to evaluate their robustness using a hybrid multiverse - specification curve analysis approach, henceforth called multiverse representation analysis (MRA). The authors of the original report provided their data: 84 adolescent drivers with annual evaluations of WM and other risk factors from 2005 to 2013, and of driving experiences in 2015. The original analysis was implemented as described in the original report. An MRA approach was used to evaluate the robustness of the association between developmental trajectories of WM and adolescents' risk-taking (indexed by motor vehicle crash involvement) to different reasonable methodological choices. We enumerated 6 reasonable choice points in data processing-analysis configurations: (1) model type: latent growth or multi-level regression, (2) treatment of WM data; (3) which waves are included; (4) covariate treatment; (5) how time is coded; and (6) link function/estimation method: weighted least squares means and variance estimation (WLSMV) with a linear link versus logistic regression with maximum likelihood estimation. This multiverse consists of 96 latent growth models and 18 multi-level regression models.
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