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Tandberg AD, Dahl A, Norbom LB, Westlye LT, Ystrom E, Tamnes CK, Eilertsen EM. Individual differences in internalizing symptoms in late childhood: A variance decomposition into cortical thickness, genetic and environmental differences. Dev Sci 2024; 27:e13537. [PMID: 38874007 DOI: 10.1111/desc.13537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 03/29/2024] [Accepted: 05/24/2024] [Indexed: 06/15/2024]
Abstract
The brain undergoes extensive development during late childhood and early adolescence. Cortical thinning is a prominent feature of this development, and some researchers have suggested that differences in cortical thickness may be related to internalizing symptoms, which typically increase during the same period. However, research has yielded inconclusive results. We utilized a new method that estimates the combined effect of individual differences in vertex-wise cortical thickness on internalizing symptoms. This approach allows for many small effects to be distributed across the cortex and avoids the necessity of correcting for multiple tests. Using a sample of 8763 children aged 8.9 to 11.1 from the ABCD study, we decomposed the total variation in caregiver-reported internalizing symptoms into differences in cortical thickness, additive genetics, and shared family environmental factors and unique environmental factors. Our results indicated that individual differences in cortical thickness accounted for less than 0.5% of the variation in internalizing symptoms. In contrast, the analysis revealed a substantial effect of additive genetics and family environmental factors on the different components of internalizing symptoms, ranging from 06% to 48% and from 0% to 34%, respectively. Overall, while this study found a minimal association between cortical thickness and internalizing symptoms, additive genetics, and familial environmental factors appear to be of importance for describing differences in internalizing symptoms in late childhood. RESEARCH HIGHLIGHTS: We utilized a new method for modelling the total contribution of vertex-wise individual differences in cortical thickness to internalizing symptoms in late childhood. The total contribution of individual differences in cortical thickness accounted for <0.5% of the variance in internalizing symptoms. Additive genetics and shared family environmental variation accounted for 17% and 34% of the variance in internalizing symptoms, respectively. Our results suggest that cortical thickness is not an important indicator for internalizing symptoms in childhood, whereas genetic and environmental differences have a substantial impact.
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Affiliation(s)
- Anneli D Tandberg
- Department of Psychology, PROMENTA Research Center, University of Oslo, Oslo, Norway
| | - Andreas Dahl
- Department of Psychology, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Center for Precision Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Linn B Norbom
- Department of Psychology, PROMENTA Research Center, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Center for Precision Psychiatry, Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Eivind Ystrom
- Department of Psychology, PROMENTA Research Center, University of Oslo, Oslo, Norway
- PsychGen Centre for Genetic Epidemiology and Mental Health, Child Health and Development, Norwegian Institute of Public Health, Oslo, Norway
| | - Christian K Tamnes
- Department of Psychology, PROMENTA Research Center, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Espen M Eilertsen
- Department of Psychology, PROMENTA Research Center, University of Oslo, Oslo, Norway
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2
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Khan YT, Tsompanidis A, Radecki MA, Dorfschmidt L, Austin T, Suckling J, Allison C, Lai MC, Bethlehem RAI, Baron-Cohen S. Sex Differences in Human Brain Structure at Birth. Biol Sex Differ 2024; 15:81. [PMID: 39420417 PMCID: PMC11488075 DOI: 10.1186/s13293-024-00657-5] [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: 08/01/2024] [Accepted: 10/04/2024] [Indexed: 10/19/2024] Open
Abstract
BACKGROUND Sex differences in human brain anatomy have been well-documented, though remain significantly underexplored during early development. The neonatal period is a critical stage for brain development and can provide key insights into the role that prenatal and early postnatal factors play in shaping sex differences in the brain. METHODS Here, we assessed on-average sex differences in global and regional brain volumes in 514 newborns aged 0-28 days (236 birth-assigned females and 278 birth-assigned males) using data from the developing Human Connectome Project. We also assessed sex-by-age interactions to investigate sex differences in early postnatal brain development. RESULTS On average, males had significantly larger intracranial and total brain volumes, even after controlling for birth weight. After controlling for total brain volume, females showed significantly greater total cortical gray matter volumes, whilst males showed greater total white matter volumes. After controlling for total brain volume in regional comparisons, females had significantly increased white matter volumes in the corpus callosum and increased gray matter volumes in the bilateral parahippocampal gyri (posterior parts), left anterior cingulate gyrus, bilateral parietal lobes, and left caudate nucleus. Males had significantly increased gray matter volumes in the right medial and inferior temporal gyrus (posterior part) and right subthalamic nucleus. Effect sizes ranged from small for regional comparisons to large for global comparisons. Significant sex-by-age interactions were noted in the left anterior cingulate gyrus and left superior temporal gyrus (posterior parts). CONCLUSIONS Our findings demonstrate that sex differences in brain structure are already present at birth and remain comparatively stable during early postnatal development, highlighting an important role of prenatal factors in shaping sex differences in the brain.
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Affiliation(s)
- Yumnah T Khan
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK.
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK.
| | - Alex Tsompanidis
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
| | - Marcin A Radecki
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Social and Affective Neuroscience Group, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Lena Dorfschmidt
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, 19139, USA
| | - Topun Austin
- Neonatal Intensive Care Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, CB2 0QQ, UK
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Peterborough Foundation NHS Trust, Cambridge, CB2 8SZ, UK
| | - Carrie Allison
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
| | - Meng-Chuan Lai
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Toronto, ON, Canada
- Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Department of Psychology, Faculty of Arts and Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | | | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, CB2 8AH, UK
- Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK
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3
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Constantinides C, Caramaschi D, Zammit S, Freeman TP, Walton E. Exploring associations between psychotic experiences and structural brain age: a population-based study in late adolescence. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.10.07.24314890. [PMID: 39417107 PMCID: PMC11482991 DOI: 10.1101/2024.10.07.24314890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/19/2024]
Abstract
Neuroimaging studies show advanced structural "brain age" in schizophrenia and related psychotic disorders, potentially reflecting aberrant brain ageing or maturation. The extent to which altered brain age is associated with subthreshold psychotic experiences (PE) in youth remains unclear. We investigated the association between PE and brain-predicted age difference (brain-PAD) in late adolescence using a population-based sample of 117 participants with PE and 115 without PE (aged 19-21 years) from the Avon Longitudinal Study of Parents and Children. Brain-PAD was estimated using a publicly available machine learning model previously trained on a combination of region-wise T1-weighted grey-matter measures. We found little evidence for an association between PEs and brain-PAD after adjusting for age and sex (Cohen's d = -0.21 [95% CI -0.47, 0.05], p = 0.11). While there was some evidence for lower brain-PAD in those with PEs relative to those without PEs after additionally adjusting for parental social class (Cohen's d = -0.31 [95% CI -0.58, -0.03], p = 0.031) or birth weight (Cohen's d = -0.29 [95% CI -0.55, -0.03], p = 0.038), adjusting for maternal education or childhood IQ did not alter the primary results. These findings do not support the notion of advanced brain age in older adolescents with PEs. However, they weakly suggest there might be a younger-looking brain in those individuals, indicative of subtle delays in structural brain maturation. Future studies with larger samples covering a wider age range and multimodal measures could further investigate brain age as a marker of psychotic experiences in youth.
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Affiliation(s)
| | - Doretta Caramaschi
- Department of Psychology, Faculty of Health and Life Sciences, University of Exeter, UK
| | - Stanley Zammit
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, UK
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Tom P Freeman
- Addiction and Mental Health Group (AIM), Department of Psychology, University of Bath, UK
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4
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Yang X, Wang Z, Li H, Qin W, Liu N, Liu Z, Wang S, Xu J, Wang J. Polygenic Score for Conscientiousness Is a Protective Factor for Reversion from Mild Cognitive Impairment to Normal Cognition. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2309889. [PMID: 38838096 PMCID: PMC11304237 DOI: 10.1002/advs.202309889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 05/21/2024] [Indexed: 06/07/2024]
Abstract
Spontaneous reversion from mild cognitive impairment (MCI) to normal cognition (NC) is little known. Based on the data of the Genetics of Personality Consortium and MCI participants from Alzheimer's Disease Neuroimaging Initiative, the authors investigate the effect of polygenic scores (PGS) for personality traits on the reversion of MCI to NC and its underlying neurobiology. PGS analysis reveals that PGS for conscientiousness (PGS-C) is a protective factor that supports the reversion from MCI to NC. Gene ontology enrichment analysis and tissue-specific enrichment analysis indicate that the protective effect of PGS-C may be attributed to affecting the glutamatergic synapses of subcortical structures, such as hippocampus, amygdala, nucleus accumbens, and caudate nucleus. The structural covariance network (SCN) analysis suggests that the left whole hippocampus and its subfields, and the left whole amygdala and its subnuclei show significantly stronger covariance with several high-cognition relevant brain regions in the MCI reverters compared to the stable MCI participants, which may help illustrate the underlying neural mechanism of the protective effect of PGS-C.
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Affiliation(s)
- Xuan Yang
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
- Department of RadiologyJining No.1 People's HospitalJiningShandong272000P. R. China
| | - Zirui Wang
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Haonan Li
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Wen Qin
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Nana Liu
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Zhixuan Liu
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Siqi Wang
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Jiayuan Xu
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
| | - Junping Wang
- Department of RadiologyTianjin Key Lab of Functional Imaging & Tianjin Institute of RadiologyTianjin Medical University General HospitalTianjin300052P. R. China
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5
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Dahl A, Eilertsen EM, Rodriguez-Cabello SF, Norbom LB, Tandberg AD, Leonardsen E, Lee SH, Ystrom E, Tamnes CK, Alnæs D, Westlye LT. Genetic and brain similarity independently predict childhood anthropometrics and neighborhood socioeconomic conditions. Dev Cogn Neurosci 2024; 65:101339. [PMID: 38184855 PMCID: PMC10818201 DOI: 10.1016/j.dcn.2023.101339] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 12/22/2023] [Accepted: 12/31/2023] [Indexed: 01/09/2024] Open
Abstract
Linking the developing brain with individual differences in clinical and demographic traits is challenging due to the substantial interindividual heterogeneity of brain anatomy and organization. Here we employ an integrative approach that parses individual differences in both cortical thickness and common genetic variants, and assess their effects on a wide set of childhood traits. The approach uses a linear mixed model framework to obtain the unique effects of each type of similarity, as well as their covariance. We employ this approach in a sample of 7760 unrelated children in the ABCD cohort baseline sample (mean age 9.9, 46.8% female). In general, associations between cortical thickness similarity and traits were limited to anthropometrics such as height, weight, and birth weight, as well as a marker of neighborhood socioeconomic conditions. Common genetic variants explained significant proportions of variance across nearly all included outcomes, although estimates were somewhat lower than previous reports. No significant covariance of the effects of genetic and cortical thickness similarity was found. The present findings highlight the connection between anthropometrics as well as neighborhood socioeconomic conditions and the developing brain, which appear to be independent from individual differences in common genetic variants in this population-based sample.
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Affiliation(s)
- Andreas Dahl
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Espen M Eilertsen
- Research Center for Developmental Processes and Gradients in Mental Health (PROMENTA), Department of Psychology, University of Oslo, Oslo, Norway
| | - Sara F Rodriguez-Cabello
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Linn B Norbom
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Research Center for Developmental Processes and Gradients in Mental Health (PROMENTA), Department of Psychology, University of Oslo, Oslo, Norway
| | - Anneli D Tandberg
- Department of Psychology, University of Oslo, Oslo, Norway; Research Center for Developmental Processes and Gradients in Mental Health (PROMENTA), Department of Psychology, University of Oslo, Oslo, Norway
| | - Esten Leonardsen
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sang Hong Lee
- Australian Centre for Precision Health, UniSA Allied Health & Human Performance, University of South Australia, Adelaide, Australia; South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, Australia
| | - Eivind Ystrom
- Research Center for Developmental Processes and Gradients in Mental Health (PROMENTA), Department of Psychology, University of Oslo, Oslo, Norway; Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - Christian K Tamnes
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Research Center for Developmental Processes and Gradients in Mental Health (PROMENTA), Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Norway
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6
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Fürtjes AE. Lessons from complex trait genetics may help us overcome the neuroimaging replication crisis. Cortex 2023; 168:76-81. [PMID: 37672842 DOI: 10.1016/j.cortex.2023.08.003] [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: 11/04/2022] [Revised: 05/18/2023] [Accepted: 08/06/2023] [Indexed: 09/08/2023]
Abstract
The research fields of Complex Trait (or Statistical) Genetics and Neuroimaging face similar challenges in identifying reliable biological correlates of common traits and diseases. This Viewpoint focuses on five major lessons that allowed population-level genetics research to overcome many of its issues of replicability and may be directly applicable to inter-individual neuroimaging research. First, the failure of candidate gene studies inspires abandoning overly simplistic studies mapping individual brain regions onto traits and diseases. Second, developments in genetics research demonstrate that robust study results can be achieved by increasing sample sizes. Third and fourth, the success of genome-wide association studies motivates the use of mass-univariate testing and sharing summary-level association data to boost large-scale collaboration and meta-analysis. Finally, applying genetics methods dealing with complex data structures to vertex-wise (or voxel-wise) neuroimaging data promises more robust discoveries without the need to develop novel neuroimaging-specific methods. Those practices - that are firmly established in genetics research - should either be further endorsed, or newly adopted by the neuroimaging community, promising to accelerate the evolution of Neuroimaging through robust discovery.
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7
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Vogel JW, Corriveau-Lecavalier N, Franzmeier N, Pereira JB, Brown JA, Maass A, Botha H, Seeley WW, Bassett DS, Jones DT, Ewers M. Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight. Nat Rev Neurosci 2023; 24:620-639. [PMID: 37620599 DOI: 10.1038/s41583-023-00731-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/26/2023] [Indexed: 08/26/2023]
Abstract
Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.
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Affiliation(s)
- Jacob W Vogel
- Department of Clinical Sciences, SciLifeLab, Lund University, Lund, Sweden.
| | - Nick Corriveau-Lecavalier
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Acadamy, University of Gothenburg, Mölndal and Gothenburg, Sweden
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Neuro Division, Department of Clinical Neurosciences, Karolinska Institute, Stockholm, Sweden
| | - Jesse A Brown
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
- Department of Pathology, University of California, San Francisco, CA, USA
| | - Dani S Bassett
- Departments of Bioengineering, Electrical and Systems Engineering, Physics and Astronomy, Neurology and Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.
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8
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Bedford SA, Ortiz-Rosa A, Schabdach JM, Costantino M, Tullo S, Piercy T, Lai MC, Lombardo MV, Di Martino A, Devenyi GA, Chakravarty MM, Alexander-Bloch AF, Seidlitz J, Baron-Cohen S, Bethlehem RA. The impact of quality control on cortical morphometry comparisons in autism. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2023; 1:1-21. [PMID: 38495338 PMCID: PMC10938341 DOI: 10.1162/imag_a_00022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 08/11/2023] [Accepted: 09/13/2023] [Indexed: 03/19/2024]
Abstract
Structural magnetic resonance imaging (MRI) quality is known to impact and bias neuroanatomical estimates and downstream analysis, including case-control comparisons, and a growing body of work has demonstrated the importance of careful quality control (QC) and evaluated the impact of image and image-processing quality. However, the growing size of typical neuroimaging datasets presents an additional challenge to QC, which is typically extremely time and labour intensive. One of the most important aspects of MRI quality is the accuracy of processed outputs, which have been shown to impact estimated neurodevelopmental trajectories. Here, we evaluate whether the quality of surface reconstructions by FreeSurfer (one of the most widely used MRI processing pipelines) interacts with clinical and demographic factors. We present a tool, FSQC, that enables quick and efficient yet thorough assessment of outputs of the FreeSurfer processing pipeline. We validate our method against other existing QC metrics, including the automated FreeSurfer Euler number, two other manual ratings of raw image quality, and two popular automated QC methods. We show strikingly similar spatial patterns in the relationship between each QC measure and cortical thickness; relationships for cortical volume and surface area are largely consistent across metrics, though with some notable differences. We next demonstrate that thresholding by QC score attenuates but does not eliminate the impact of quality on cortical estimates. Finally, we explore different ways of controlling for quality when examining differences between autistic individuals and neurotypical controls in the Autism Brain Imaging Data Exchange (ABIDE) dataset, demonstrating that inadequate control for quality can alter results of case-control comparisons.
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Affiliation(s)
- Saashi A. Bedford
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Alfredo Ortiz-Rosa
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, United States
| | - Jenna M. Schabdach
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, United States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Manuela Costantino
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada
| | - Stephanie Tullo
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Canada
| | - Tom Piercy
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | | | - Meng-Chuan Lai
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
- Department of Psychiatry and Autism Research Unit, The Hospital for Sick Children, Toronto, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Michael V. Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | | | - Gabriel A. Devenyi
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada
- Department of Psychiatry, McGill University, Montreal, Canada
| | - M. Mallar Chakravarty
- Integrated Program in Neuroscience, McGill University, Montreal, Canada
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Department of Psychiatry, McGill University, Montreal, Canada
- Department of Biomedical Engineering, McGill University, Montreal, Canada
| | - Aaron F. Alexander-Bloch
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, United States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States
| | - Jakob Seidlitz
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, United States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Cambridge Lifetime Asperger Syndrome Service (CLASS), Cambridgeshire and Peterborough, United Kingdom
| | - Richard A.I. Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
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9
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Estévez-López F, Kim HH, López-Vicente M, Legerstee JS, Hillegers MHJ, Tiemeier H, Muetzel RL. Physical symptoms and brain morphology: a population neuroimaging study in 12,286 pre-adolescents. Transl Psychiatry 2023; 13:254. [PMID: 37438345 DOI: 10.1038/s41398-023-02528-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 06/13/2023] [Accepted: 06/15/2023] [Indexed: 07/14/2023] Open
Abstract
Physical symptoms, also known as somatic symptoms, are those for which medical examinations do not reveal a sufficient underlying root cause (e.g., pain and fatigue). The extant literature of the neurobiological underpinnings of physical symptoms is largely inconsistent and primarily comprises of (clinical) case-control studies with small sample sizes. In this cross-sectional study, we studied the association between dimensionally measured physical symptoms and brain morphology in pre-adolescents from two population-based cohorts; the Generation R Study (n = 2649, 10.1 ± 0.6 years old) and ABCD Study (n = 9637, 9.9 ± 0.6 years old). Physical symptoms were evaluated using continuous scores from the somatic complaints syndrome scale from the parent-reported Child Behavior Checklist (CBCL). High-resolution structural magnetic resonance imaging (MRI) was collected using 3-Tesla MRI systems. Linear regression models were fitted for global brain metrics (cortical and subcortical grey matter and total white matter volume) and surface-based vertex-wise measures (surface area and cortical thickness). Results were meta-analysed. Symptoms of anxiety/depression were studied as a contrasting comorbidity. In the meta-analyses across cohorts, we found negative associations between physical symptoms and surface area in the (i) left hemisphere; in the lateral orbitofrontal cortex and pars triangularis and (ii) right hemisphere; in the pars triangularis, the pars orbitalis, insula, middle temporal gyrus and caudal anterior cingulate cortex. However, only a subset of regions (left lateral orbitofrontal cortex and right pars triangularis) were specifically associated with physical symptoms, while others were also related to symptoms of anxiety/depression. No significant associations were observed for cortical thickness. This study in preadolescents, the most representative and well-powered to date, showed that more physical symptoms are modestly related to less surface area of the prefrontal cortex mostly. While these effects are subtle, future prospective research is warranted to understand the longitudinal relationship of physical symptoms and brain changes over time. Particularly, to elucidate whether physical symptoms are a potential cause or consequence of distinct neurodevelopmental trajectories.
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Affiliation(s)
- Fernando Estévez-López
- Department of Social and Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
- Department of Education, Faculty of Education Sciences, SPORT Research Group (CTS-1024) and CERNEP Research Center, University of Almería, Almería, Spain.
| | - Hannah H Kim
- Department of Social and Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Mónica López-Vicente
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Jeroen S Legerstee
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Manon H J Hillegers
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Henning Tiemeier
- Department of Social and Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Ryan L Muetzel
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands
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