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Skampardoni I, Nasrallah IM, Abdulkadir A, Wen J, Melhem R, Mamourian E, Erus G, Doshi J, Singh A, Yang Z, Cui Y, Hwang G, Ren Z, Pomponio R, Srinivasan D, Govindarajan ST, Parmpi P, Wittfeld K, Grabe HJ, Bülow R, Frenzel S, Tosun D, Bilgel M, An Y, Marcus DS, LaMontagne P, Heckbert SR, Austin TR, Launer LJ, Sotiras A, Espeland MA, Masters CL, Maruff P, Fripp J, Johnson SC, Morris JC, Albert MS, Bryan RN, Yaffe K, Völzke H, Ferrucci L, Benzinger TL, Ezzati A, Shinohara RT, Fan Y, Resnick SM, Habes M, Wolk D, Shou H, Nikita K, Davatzikos C. Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals. JAMA Psychiatry 2024; 81:456-467. [PMID: 38353984 PMCID: PMC10867779 DOI: 10.1001/jamapsychiatry.2023.5599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 11/29/2023] [Indexed: 02/17/2024]
Abstract
Importance Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. Objective To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Design, Setting, and Participants Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Exposures Individuals WODCI at baseline scan. Main Outcomes and Measures Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid β (Aβ), and future cognitive decline were assessed. Results In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aβ positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). Conclusions and Relevance The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.
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Affiliation(s)
- Ioanna Skampardoni
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Ilya M. Nasrallah
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Department of Radiology, University of Pennsylvania, Philadelphia
| | - Ahmed Abdulkadir
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Junhao Wen
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles
| | - Randa Melhem
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Elizabeth Mamourian
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Guray Erus
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Jimit Doshi
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Ashish Singh
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Zhijian Yang
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Yuhan Cui
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Gyujoon Hwang
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Zheng Ren
- Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles
| | - Raymond Pomponio
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Dhivya Srinivasan
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | | | - Paraskevi Parmpi
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
- German Centre for Neurodegenerative Diseases, Site Greifswald, Greifswald, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
- German Centre for Neurodegenerative Diseases, Site Greifswald, Greifswald, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Daniel S. Marcus
- Department of Radiology, Washington University School of Medicine, St Louis, Missouri
| | - Pamela LaMontagne
- Department of Radiology, Washington University School of Medicine, St Louis, Missouri
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit, University of Washington, Seattle
- Department of Epidemiology, University of Washington, Seattle
| | - Thomas R. Austin
- Cardiovascular Health Research Unit, University of Washington, Seattle
- Department of Epidemiology, University of Washington, Seattle
| | - Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, Maryland
| | - Aristeidis Sotiras
- Department of Radiology and Institute of Informatics, Washington University in St Louis, St Louis, Missouri
| | - Mark A. Espeland
- Sticht Centre for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Colin L. Masters
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison
| | - John C. Morris
- Knight Alzheimer Disease Research Centre, Washington University in St Louis, St Louis, Missouri
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - R. Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia
| | - Kristine Yaffe
- Departments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California San Francisco, San Francisco
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Tammie L.S. Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Ali Ezzati
- Department of Neurology, University of California, Irvine
| | - Russell T. Shinohara
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia
| | - Yong Fan
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Mohamad Habes
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio
| | - David Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia
| | - Haochang Shou
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia
| | - Konstantina Nikita
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Christos Davatzikos
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
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Grosu C, Klauser P, Dwir D, Khadimallah I, Alemán-Gómez Y, Laaboub N, Piras M, Fournier M, Preisig M, Conus P, Draganski B, Eap CB. Associations between antipsychotics-induced weight gain and brain networks of impulsivity. Transl Psychiatry 2024; 14:162. [PMID: 38531873 DOI: 10.1038/s41398-024-02881-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 03/13/2024] [Accepted: 03/15/2024] [Indexed: 03/28/2024] Open
Abstract
Given the unpredictable rapid onset and ubiquitous consequences of weight gain induced by antipsychotics, there is a pressing need to get insights into the underlying processes at the brain system level that will allow stratification of "at risk" patients. The pathophysiological hypothesis at hand is focused on brain networks governing impulsivity that are modulated by neuro-inflammatory processes. To this aim, we investigated brain anatomy and functional connectivity in patients with early psychosis (median age: 23 years, IQR = 21-27) using anthropometric data and magnetic resonance imaging acquired one month to one year after initiation of AP medication. Our analyses included 19 patients with high and rapid weight gain (i.e., ≥5% from baseline weight after one month) and 23 patients with low weight gain (i.e., <5% from baseline weight after one month). We replicated our analyses in young (26 years, IQR = 22-33, N = 102) and middle-aged (56 years, IQR = 51-62, N = 875) healthy individuals from the general population. In early psychosis patients, higher weight gain was associated with poor impulse control score (β = 1.35; P = 0.03). Here, the observed brain differences comprised nodes of impulsivity networks - reduced frontal lobe grey matter volume (Pcorrected = 0.007) and higher striatal volume (Pcorrected = 0.048) paralleled by disruption of fronto-striatal functional connectivity (R = -0.32; P = 0.04). Weight gain was associated with the inflammatory biomarker plasminogen activator inhibitor-1 (β = 4.9, P = 0.002). There was no significant association between increased BMI or weight gain and brain anatomy characteristics in both cohorts of young and middle-aged healthy individuals. Our findings support the notion of weight gain in treated psychotic patients associated with poor impulse control, impulsivity-related brain networks and chronic inflammation.
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Affiliation(s)
- Claire Grosu
- Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland.
| | - Paul Klauser
- Service of Child and Adolescent Psychiatry, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Daniella Dwir
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Ines Khadimallah
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Yasser Alemán-Gómez
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
- Connectomics Lab, Department of Radiology, Lausanne University Hospital, Lausanne, Switzerland
| | - Nermine Laaboub
- Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Marianna Piras
- Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Margot Fournier
- Center for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Martin Preisig
- Psychiatric Epidemiology and Psychopathology Research Center, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland
| | - Philippe Conus
- Service of General Psychiatry, Department of Psychiatry, Lausanne University Hospital, Prilly, Switzerland
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neuroscience - Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Neurology Department, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Chin B Eap
- Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Prilly, Switzerland.
- School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland.
- Center for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva and University of Lausanne, Lausanne, Switzerland.
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3
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Corbin N, Oliveira R, Raynaud Q, Di Domenicantonio G, Draganski B, Kherif F, Callaghan MF, Lutti A. Statistical analyses of motion-corrupted MRI relaxometry data computed from multiple scans. J Neurosci Methods 2023; 398:109950. [PMID: 37598941 DOI: 10.1016/j.jneumeth.2023.109950] [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: 04/12/2023] [Revised: 05/30/2023] [Accepted: 08/12/2023] [Indexed: 08/22/2023]
Abstract
BACKGROUND Consistent noise variance across data points (i.e. homoscedasticity) is required to ensure the validity of statistical analyses of MRI data conducted using linear regression methods. However, head motion leads to degradation of image quality, introducing noise heteroscedasticity into ordinary-least square analyses. NEW METHOD The recently introduced QUIQI method restores noise homoscedasticity by means of weighted least square analyses in which the weights, specific for each dataset of an analysis, are computed from an index of motion-induced image quality degradation. QUIQI was first demonstrated in the context of brain maps of the MRI parameter R2 * , which were computed from a single set of images with variable echo time. Here, we extend this framework to quantitative maps of the MRI parameters R1, R2 * , and MTsat, computed from multiple sets of images. RESULTS QUIQI restores homoscedasticity in analyses of quantitative MRI data computed from multiple scans. QUIQI allows for optimization of the noise model by using metrics quantifying heteroscedasticity and free energy. COMPARISON WITH EXISTING METHODS QUIQI restores homoscedasticity more effectively than insertion of an image quality index in the analysis design and yields higher sensitivity than simply removing the datasets most corrupted by head motion from the analysis. CONCLUSION QUIQI provides an optimal approach to group-wise analyses of a range of quantitative MRI parameter maps that is robust to inherent homoscedasticity.
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Affiliation(s)
- Nadège Corbin
- Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/University Bordeaux, Bordeaux, France; Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Rita Oliveira
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Quentin Raynaud
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Giulia Di Domenicantonio
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Neurology Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
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4
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García-García I, Donica O, Cohen AA, Gonseth Nusslé S, Heini A, Nusslé S, Pichard C, Rietschel E, Tanackovic G, Folli S, Draganski B. Maintaining brain health across the lifespan. Neurosci Biobehav Rev 2023; 153:105365. [PMID: 37604360 DOI: 10.1016/j.neubiorev.2023.105365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 07/24/2023] [Accepted: 08/17/2023] [Indexed: 08/23/2023]
Abstract
Across the lifespan, the human body and brain endure the impact of a plethora of exogenous and endogenous factors that determine the health outcome in old age. The overwhelming inter-individual variance spans between progressive frailty with loss of autonomy to largely preserved physical, cognitive, and social functions. Understanding the mechanisms underlying the diverse aging trajectories can inform future strategies to maintain a healthy body and brain. Here we provide a comprehensive overview of the current literature on lifetime factors governing brain health. We present the growing body of evidence that unhealthy alimentary regime, sedentary behaviour, sleep pathologies, cardio-vascular risk factors, and chronic inflammation exert their harmful effects in a cumulative and gradual manner, and that timely and efficient intervention could promote healthy and successful aging. We discuss the main effects and interactions between these risk factors and the resulting brain health outcomes to follow with a description of current strategies aiming to eliminate, treat, or counteract the risk factors. We conclude that the detailed insights about modifiable risk factors could inform personalized multi-domain strategies for brain health maintenance on the background of increased longevity.
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Affiliation(s)
- Isabel García-García
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Centre for Research in Neurosciences, Lausanne University Hospital, University of Lausanne, Switzerland; Clinique la Prairie, Montreux, Switzerland
| | | | - Armand Aaron Cohen
- Department of Geriatrics and Rehabilitation, Hadassah University Medical Center Mount Scopus, Jerusalem, Israel
| | | | | | | | - Claude Pichard
- Nutrition Unit, University Hospital of Geneva, Geneva, Switzerland
| | | | | | | | - Bogdan Draganski
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Centre for Research in Neurosciences, Lausanne University Hospital, University of Lausanne, Switzerland; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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5
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Trofimova O, Latypova A, DiDomenicantonio G, Lutti A, de Lange AMG, Kliegel M, Stringhini S, Marques-Vidal P, Vaucher J, Vollenweider P, Strippoli MPF, Preisig M, Kherif F, Draganski B. Topography of associations between cardiovascular risk factors and myelin loss in the ageing human brain. Commun Biol 2023; 6:392. [PMID: 37037939 PMCID: PMC10086032 DOI: 10.1038/s42003-023-04741-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 03/21/2023] [Indexed: 04/12/2023] Open
Abstract
Our knowledge of the mechanisms underlying the vulnerability of the brain's white matter microstructure to cardiovascular risk factors (CVRFs) is still limited. We used a quantitative magnetic resonance imaging (MRI) protocol in a single centre setting to investigate the cross-sectional association between CVRFs and brain tissue properties of white matter tracts in a large community-dwelling cohort (n = 1104, age range 46-87 years). Arterial hypertension was associated with lower myelin and axonal density MRI indices, paralleled by higher extracellular water content. Obesity showed similar associations, though with myelin difference only in male participants. Associations between CVRFs and white matter microstructure were observed predominantly in limbic and prefrontal tracts. Additional genetic, lifestyle and psychiatric factors did not modulate these results, but moderate-to-vigorous physical activity was linked to higher myelin content independently of CVRFs. Our findings complement previously described CVRF-related changes in brain water diffusion properties pointing towards myelin loss and neuroinflammation rather than neurodegeneration.
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Affiliation(s)
- Olga Trofimova
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Adeliya Latypova
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Giulia DiDomenicantonio
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ann-Marie G de Lange
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Matthias Kliegel
- Department of Psychology, University of Geneva, Geneva, Switzerland
| | - Silvia Stringhini
- Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland
- Institute of Social and Preventive Medicine, Lausanne University Hospital, Lausanne, Switzerland
- Unit of Population Epidemiology, Division of Primary Care Medicine, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Julien Vaucher
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Marie-Pierre F Strippoli
- Center for Research in Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Martin Preisig
- Center for Research in Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Neurology Department, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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6
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Marchi NA, Pizzarotti B, Solelhac G, Berger M, Haba‐Rubio J, Preisig M, Vollenweider P, Marques‐Vidal P, Lutti A, Kherif F, Heinzer R, Draganski B. Abnormal brain iron accumulation in obstructive sleep apnea: A quantitative MRI study in the HypnoLaus cohort. J Sleep Res 2022; 31:e13698. [PMID: 35830960 PMCID: PMC9787990 DOI: 10.1111/jsr.13698] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/23/2022] [Accepted: 06/28/2022] [Indexed: 12/30/2022]
Abstract
Obstructive sleep apnea syndrome (OSA) may be a risk factor for Alzheimer's disease. One of the hallmarks of Alzheimer's disease is disturbed iron homeostasis leading to abnormal iron deposition in brain tissue. To date, there is no empirical evidence to support the hypothesis of altered brain iron homeostasis in patients with obstructive sleep apnea as well. Data were analysed from 773 participants in the HypnoLaus study (mean age 55.9 ± 10.3 years) who underwent polysomnography and brain MRI. Cross-sectional associations were tested between OSA parameters and the MRI effective transverse relaxation rate (R2*) - indicative of iron content - in 68 grey matter regions, after adjustment for confounders. The group with severe OSA (apnea-hypopnea index ≥30/h) had higher iron levels in the left superior frontal gyrus (F3,760 = 4.79, p = 0.003), left orbital gyri (F3,760 = 5.13, p = 0.002), right and left middle temporal gyrus (F3,760 = 4.41, p = 0.004 and F3,760 = 13.08, p < 0.001, respectively), left angular gyrus (F3,760 = 6.29, p = 0.001), left supramarginal gyrus (F3,760 = 4.98, p = 0.003), and right cuneus (F3,760 = 7.09, p < 0.001). The parameters of nocturnal hypoxaemia were all consistently associated with higher iron levels. Measures of sleep fragmentation had less consistent associations with iron content. This study provides the first evidence of increased brain iron levels in obstructive sleep apnea. The observed iron changes could reflect underlying neuropathological processes that appear to be driven primarily by hypoxaemic mechanisms.
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Affiliation(s)
- Nicola Andrea Marchi
- Centre for Investigation and Research on Sleep, Department of MedicineLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
- Laboratory for Research in Neuroimaging, Department of Clinical NeurosciencesLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
| | - Beatrice Pizzarotti
- Laboratory for Research in Neuroimaging, Department of Clinical NeurosciencesLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
| | - Geoffroy Solelhac
- Centre for Investigation and Research on Sleep, Department of MedicineLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
| | - Mathieu Berger
- Centre for Investigation and Research on Sleep, Department of MedicineLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
| | - José Haba‐Rubio
- Centre for Investigation and Research on Sleep, Department of MedicineLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
| | - Martin Preisig
- Centre for Research in Psychiatric Epidemiology and Psychopathology, Department of PsychiatryLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
| | - Peter Vollenweider
- Service of Internal Medicine, Department of MedicineLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
| | - Pedro Marques‐Vidal
- Service of Internal Medicine, Department of MedicineLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging, Department of Clinical NeurosciencesLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging, Department of Clinical NeurosciencesLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
| | - Raphael Heinzer
- Centre for Investigation and Research on Sleep, Department of MedicineLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging, Department of Clinical NeurosciencesLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
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7
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Subramaniapillai S, Suri S, Barth C, Maximov II, Voldsbekk I, van der Meer D, Gurholt TP, Beck D, Draganski B, Andreassen OA, Ebmeier KP, Westlye LT, de Lange AG. Sex- and age-specific associations between cardiometabolic risk and white matter brain age in the UK Biobank cohort. Hum Brain Mapp 2022; 43:3759-3774. [PMID: 35460147 PMCID: PMC9294301 DOI: 10.1002/hbm.25882] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 03/24/2022] [Accepted: 04/05/2022] [Indexed: 12/13/2022] Open
Abstract
Cardiometabolic risk (CMR) factors are associated with accelerated brain aging and increased risk for sex-dimorphic illnesses such as Alzheimer's disease (AD). Yet, it is unknown how CMRs interact with sex and apolipoprotein E-ϵ4 (APOE4), a known genetic risk factor for AD, to influence brain age across different life stages. Using age prediction based on multi-shell diffusion-weighted imaging data in 21,308 UK Biobank participants, we investigated whether associations between white matter Brain Age Gap (BAG) and body mass index (BMI), waist-to-hip ratio (WHR), body fat percentage (BF%), and APOE4 status varied (i) between males and females, (ii) according to age at menopause in females, and (iii) across different age groups in males and females. We report sex differences in associations between BAG and all three CMRs, with stronger positive associations among males compared to females. Independent of APOE4 status, higher BAG (older brain age relative to chronological age) was associated with greater BMI, WHR, and BF% in males, whereas in females, higher BAG was associated with greater WHR, but not BMI and BF%. These divergent associations were most prominent within the oldest group of females (66-81 years), where greater BF% was linked to lower BAG. Earlier menopause transition was associated with higher BAG, but no interactions were found with CMRs. In conclusion, the findings point to sex- and age-specific associations between CMRs and brain age. Incorporating sex as a factor of interest in studies addressing CMR may promote sex-specific precision medicine, consequently improving health care for both males and females.
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Affiliation(s)
- Sivaniya Subramaniapillai
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
- Department of Psychology, Faculty of ScienceMcGill UniversityMontrealQuebecCanada
- Department of PsychologyUniversity of OsloOsloNorway
| | - Sana Suri
- Department of PsychiatryUniversity of OxfordOxfordUK
- Wellcome Centre for Integrative NeuroimagingUniversity of OxfordOxfordUK
| | - Claudia Barth
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital and University of OsloOsloNorway
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
| | - Ivan I. Maximov
- Department of PsychologyUniversity of OsloOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital and University of OsloOsloNorway
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
| | - Irene Voldsbekk
- Department of PsychologyUniversity of OsloOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital and University of OsloOsloNorway
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital and University of OsloOsloNorway
- School of Mental Health and Neuroscience, Faculty of Health Medicine and Life SciencesMaastricht UniversityMaastrichtThe Netherlands
| | - Tiril P. Gurholt
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital and University of OsloOsloNorway
| | - Dani Beck
- Department of PsychologyUniversity of OsloOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital and University of OsloOsloNorway
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
| | - Bogdan Draganski
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
- Department of NeurologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital and University of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
| | | | - Lars T. Westlye
- Department of PsychologyUniversity of OsloOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and AddictionOslo University Hospital and University of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
| | - Ann‐Marie G. de Lange
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
- Department of PsychologyUniversity of OsloOsloNorway
- Department of PsychiatryUniversity of OxfordOxfordUK
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8
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Grosu C, Trofimova O, Gholam-Rezaee M, Strippoli MPF, Kherif F, Lutti A, Preisig M, Draganski B, Eap CB. CYP2C19 expression modulates affective functioning and hippocampal subiculum volume-a large single-center community-dwelling cohort study. Transl Psychiatry 2022; 12:316. [PMID: 35931695 PMCID: PMC9356029 DOI: 10.1038/s41398-022-02091-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 07/19/2022] [Accepted: 07/21/2022] [Indexed: 11/09/2022] Open
Abstract
Given controversial findings of reduced depressive symptom severity and increased hippocampus volume in CYP2C19 poor metabolizers, we sought to provide empirical evidence from a large-scale single-center longitudinal cohort in the community-dwelling adult population-Colaus|PsyCoLaus in Lausanne, Switzerland (n = 4152). We looked for CYP2C19 genotype-related behavioral and brain anatomy patterns using a comprehensive set of psychometry, water diffusion- and relaxometry-based magnetic resonance imaging (MRI) data (BrainLaus, n = 1187). Our statistical models tested for differential associations between poor metabolizer and other metabolizer status with imaging-derived indices of brain volume and tissue properties that explain individuals' current and lifetime mood characteristics. The observed association between CYP2C19 genotype and lifetime affective status showing higher functioning scores in poor metabolizers, was mainly driven by female participants (ß = 3.9, p = 0.010). There was no difference in total hippocampus volume between poor metabolizer and other metabolizer, though there was higher subiculum volume in the right hippocampus of poor metabolizers (ß = 0.03, pFDRcorrected = 0.036). Our study supports the notion of association between mood phenotype and CYP2C19 genotype, however, finds no evidence for concomitant hippocampus volume differences, with the exception of the right subiculum.
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Affiliation(s)
- Claire Grosu
- grid.9851.50000 0001 2165 4204Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, University of Lausanne, Prilly, Switzerland
| | - Olga Trofimova
- grid.9851.50000 0001 2165 4204Department of Clinical Neurosciences, Laboratory for Research in Neuroimaging LREN, Centre for Research in Neuroscience, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Mehdi Gholam-Rezaee
- grid.9851.50000 0001 2165 4204Center for Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital, University of Lausanne, Prilly, Switzerland
| | - Marie-Pierre F. Strippoli
- grid.9851.50000 0001 2165 4204Center for Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital, University of Lausanne, Prilly, Switzerland
| | - Ferath Kherif
- grid.9851.50000 0001 2165 4204Department of Clinical Neurosciences, Laboratory for Research in Neuroimaging LREN, Centre for Research in Neuroscience, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Antoine Lutti
- grid.9851.50000 0001 2165 4204Department of Clinical Neurosciences, Laboratory for Research in Neuroimaging LREN, Centre for Research in Neuroscience, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Martin Preisig
- grid.9851.50000 0001 2165 4204Center for Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, Lausanne University Hospital, University of Lausanne, Prilly, Switzerland
| | - Bogdan Draganski
- Department of Clinical Neurosciences, Laboratory for Research in Neuroimaging LREN, Centre for Research in Neuroscience, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland. .,Neurology Department, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Chin B. Eap
- grid.9851.50000 0001 2165 4204Unit of Pharmacogenetics and Clinical Psychopharmacology, Centre for Psychiatric Neuroscience, Department of Psychiatry, Lausanne University Hospital, University of Lausanne, Prilly, Switzerland ,grid.8591.50000 0001 2322 4988School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland ,grid.9851.50000 0001 2165 4204Center for Research and Innovation in Clinical Pharmaceutical Sciences, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland ,grid.9851.50000 0001 2165 4204Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, University of Lausanne, Lausanne, Switzerland
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9
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Loued-Khenissi L, Trofimova O, Vollenweider P, Marques-Vidal P, Preisig M, Lutti A, Kliegel M, Sandi C, Kherif F, Stringhini S, Draganski B. Signatures of life course socioeconomic conditions in brain anatomy. Hum Brain Mapp 2022; 43:2582-2606. [PMID: 35195323 PMCID: PMC9057097 DOI: 10.1002/hbm.25807] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 01/19/2022] [Accepted: 01/31/2022] [Indexed: 11/11/2022] Open
Abstract
Socioeconomic status (SES) plays a significant role in health and disease. At the same time, early-life conditions affect neural function and structure, suggesting the brain may be a conduit for the biological embedding of SES. Here, we investigate the brain anatomy signatures of SES in a large-scale population cohort aged 45-85 years. We assess both gray matter morphometry and tissue properties indicative of myelin content. Higher life course SES is associated with increased volume in several brain regions, including postcentral and temporal gyri, cuneus, and cerebellum. We observe more widespread volume differences and higher myelin content in the sensorimotor network but lower myelin content in the temporal lobe associated with childhood SES. Crucially, childhood SES differences persisted in adult brains even after controlling for adult SES, highlighting the unique contribution of early-life conditions to brain anatomy, independent of later changes in SES. These findings inform on the biological underpinnings of social inequality, particularly as they pertain to early-life conditions.
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Affiliation(s)
- Leyla Loued-Khenissi
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne.,Theory of Pain Laboratory, University of Geneva, Geneva
| | - Olga Trofimova
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne
| | - Peter Vollenweider
- Department of medicine, Internal medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Pedro Marques-Vidal
- Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Martin Preisig
- Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne
| | - Matthias Kliegel
- Laboratoire du Vieillissement Cognitif, Université de Genève, Geneva, Switzerland
| | - Carmen Sandi
- Laboratory of Behavioral Genetics, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland
| | - Ferhat Kherif
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne
| | - Silvia Stringhini
- University Centre for General Medicine and Public Health (UNISANTE), Lausanne University, Lausanne, Switzerland.,Unit of Population Epidemiology, Primary Care Division, Geneva University Hospitals, Geneva, Switzerland
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne.,Neurology Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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10
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Lutti A, Corbin N, Ashburner J, Ziegler G, Draganski B, Phillips C, Kherif F, Callaghan MF, Di Domenicantonio G. Restoring statistical validity in group analyses of motion-corrupted MRI data. Hum Brain Mapp 2022; 43:1973-1983. [PMID: 35112434 PMCID: PMC8933245 DOI: 10.1002/hbm.25767] [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] [Received: 08/06/2021] [Revised: 12/09/2021] [Accepted: 12/14/2021] [Indexed: 01/07/2023] Open
Abstract
Motion during the acquisition of magnetic resonance imaging (MRI) data degrades image quality, hindering our capacity to characterise disease in patient populations. Quality control procedures allow the exclusion of the most affected images from analysis. However, the criterion for exclusion is difficult to determine objectively and exclusion can lead to a suboptimal compromise between image quality and sample size. We provide an alternative, data‐driven solution that assigns weights to each image, computed from an index of image quality using restricted maximum likelihood. We illustrate this method through the analysis of quantitative MRI data. The proposed method restores the validity of statistical tests, and performs near optimally in all brain regions, despite local effects of head motion. This method is amenable to the analysis of a broad type of MRI data and can accommodate any measure of image quality.
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Affiliation(s)
- Antoine Lutti
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Nadège Corbin
- Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/University Bordeaux, Bordeaux, France.,Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - John Ashburner
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Gabriel Ziegler
- Institute for Cognitive Neurology and Dementia Research, University of Magdeburg, Germany
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,Neurology Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Christophe Phillips
- GIGA Cyclotron Research Centre - in vivo imaging, GIGA Institute, University of Liège, Liège, Belgium
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Giulia Di Domenicantonio
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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