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Gao C, Yang Q, Kim ME, Khairi NM, Cai LY, Newlin NR, Kanakaraj P, Remedios LW, Krishnan AR, Yu X, Yao T, Zhang P, Schilling KG, Moyer D, Archer DB, Resnick SM, Landman BA. Characterizing patterns of diffusion tensor imaging variance in aging brains. J Med Imaging (Bellingham) 2024; 11:044007. [PMID: 39185477 PMCID: PMC11344569 DOI: 10.1117/1.jmi.11.4.044007] [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: 03/11/2024] [Revised: 07/26/2024] [Accepted: 07/30/2024] [Indexed: 08/27/2024] Open
Abstract
Purpose As large analyses merge data across sites, a deeper understanding of variance in statistical assessment across the sources of data becomes critical for valid analyses. Diffusion tensor imaging (DTI) exhibits spatially varying and correlated noise, so care must be taken with distributional assumptions. Here, we characterize the role of physiology, subject compliance, and the interaction of the subject with the scanner in the understanding of DTI variability, as modeled in the spatial variance of derived metrics in homogeneous regions. Approach We analyze DTI data from 1035 subjects in the Baltimore Longitudinal Study of Aging, with ages ranging from 22.4 to 103 years old. For each subject, up to 12 longitudinal sessions were conducted. We assess the variance of DTI scalars within regions of interest (ROIs) defined by four segmentation methods and investigate the relationships between the variance and covariates, including baseline age, time from the baseline (referred to as "interval"), motion, sex, and whether it is the first scan or the second scan in the session. Results Covariate effects are heterogeneous and bilaterally symmetric across ROIs. Inter-session interval is positively related ( p ≪ 0.001 ) to FA variance in the cuneus and occipital gyrus, but negatively ( p ≪ 0.001 ) in the caudate nucleus. Males show significantly ( p ≪ 0.001 ) higher FA variance in the right putamen, thalamus, body of the corpus callosum, and cingulate gyrus. In 62 out of 176 ROIs defined by the Eve type-1 atlas, an increase in motion is associated ( p < 0.05 ) with a decrease in FA variance. Head motion increases during the rescan of DTI ( Δ μ = 0.045 mm per volume). Conclusions The effects of each covariate on DTI variance and their relationships across ROIs are complex. Ultimately, we encourage researchers to include estimates of variance when sharing data and consider models of heteroscedasticity in analysis. This work provides a foundation for study planning to account for regional variations in metric variance.
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Affiliation(s)
- Chenyu Gao
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
| | - Qi Yang
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Michael E. Kim
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Nazirah Mohd Khairi
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
| | - Leon Y. Cai
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Nancy R. Newlin
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Praitayini Kanakaraj
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Lucas W. Remedios
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Aravind R. Krishnan
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
| | - Xin Yu
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Tianyuan Yao
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Panpan Zhang
- Vanderbilt University Medical Center, Department of Biostatistics, Nashville, Tennessee, United States
| | - Kurt G. Schilling
- Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, Tennessee, United States
- Vanderbilt University, Vanderbilt University Institute of Imaging Science, Nashville, Tennessee, United States
| | - Daniel Moyer
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Derek B. Archer
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, Tennessee, United States
| | - Susan M. Resnick
- National Institute on Aging, Laboratory of Behavioral Neuroscience, Baltimore, Maryland, United States
| | - Bennett A. Landman
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, Tennessee, United States
- Vanderbilt University, Vanderbilt University Institute of Imaging Science, Nashville, Tennessee, United States
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2
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Schrempft S, Trofimova O, Künzi M, Ramponi C, Lutti A, Kherif F, Latypova A, Vollenweider P, Marques-Vidal P, Preisig M, Kliegel M, Stringhini S, Draganski B. The Neurobiology of Life Course Socioeconomic Conditions and Associated Cognitive Performance in Middle to Late Adulthood. J Neurosci 2024; 44:e1231232024. [PMID: 38499361 PMCID: PMC11044112 DOI: 10.1523/jneurosci.1231-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 01/18/2024] [Accepted: 01/23/2024] [Indexed: 03/20/2024] Open
Abstract
Despite major advances, our understanding of the neurobiology of life course socioeconomic conditions is still scarce. This study aimed to provide insight into the pathways linking socioeconomic exposures-household income, last known occupational position, and life course socioeconomic trajectories-with brain microstructure and cognitive performance in middle to late adulthood. We assessed socioeconomic conditions alongside quantitative relaxometry and diffusion-weighted magnetic resonance imaging indicators of brain tissue microstructure and cognitive performance in a sample of community-dwelling men and women (N = 751, aged 50-91 years). We adjusted the applied regression analyses and structural equation models for the linear and nonlinear effects of age, sex, education, cardiovascular risk factors, and the presence of depression, anxiety, and substance use disorders. Individuals from lower-income households showed signs of advanced brain white matter (WM) aging with greater mean diffusivity (MD), lower neurite density, lower myelination, and lower iron content. The association between household income and MD was mediated by neurite density (B = 0.084, p = 0.003) and myelination (B = 0.019, p = 0.009); MD partially mediated the association between household income and cognitive performance (B = 0.017, p < 0.05). Household income moderated the relation between WM microstructure and cognitive performance, such that greater MD, lower myelination, or lower neurite density was only associated with poorer cognitive performance among individuals from lower-income households. Individuals from higher-income households showed preserved cognitive performance even with greater MD, lower myelination, or lower neurite density. These findings provide novel mechanistic insights into the associations between socioeconomic conditions, brain anatomy, and cognitive performance in middle to late adulthood.
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Affiliation(s)
- Stephanie Schrempft
- Unit of Population Epidemiology, Division of Primary Care, Geneva University Hospitals, Geneva CH-1205, Switzerland
| | - Olga Trofimova
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne CH-1015, Switzerland
- Swiss Institute of Bioinformatics, Lausanne CH-1015, Switzerland
| | - Morgane Künzi
- Swiss National Centre of Competences in Research, "LIVES - Overcoming Vulnerability: Life Course Perspectives," University of Lausanne and University of Geneva, Lausanne CH-1015 and Carouge CH-1227, Switzerland
- Department of Psychology, University of Geneva, Geneva CH-1205, Switzerland
- Center for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva, Carouge CH-1227, Switzerland
| | - Cristina Ramponi
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
| | - Adeliya Latypova
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
| | - Martin Preisig
- Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne CH-1008, Switzerland
| | - Matthias Kliegel
- Swiss National Centre of Competences in Research, "LIVES - Overcoming Vulnerability: Life Course Perspectives," University of Lausanne and University of Geneva, Lausanne CH-1015 and Carouge CH-1227, Switzerland
- Department of Psychology, University of Geneva, Geneva CH-1205, Switzerland
- Center for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva, Carouge CH-1227, Switzerland
| | - Silvia Stringhini
- Unit of Population Epidemiology, Division of Primary Care, Geneva University Hospitals, Geneva CH-1205, Switzerland
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva CH-1211, Switzerland
- University Centre for General Medicine and Public Health, University of Lausanne, Lausanne CH-1005, Switzerland
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
- Neurology Department, Max-Planck-Institute for Human Cognitive and Brain Sciences, D-04303 Leipzig, Germany
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3
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Raynaud Q, Di Domenicantonio G, Yerly J, Dardano T, van Heeswijk RB, Lutti A. A characterization of cardiac-induced noise in R 2 * maps of the brain. Magn Reson Med 2024; 91:237-251. [PMID: 37708206 DOI: 10.1002/mrm.29853] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/11/2023] [Accepted: 08/15/2023] [Indexed: 09/16/2023]
Abstract
PURPOSE Cardiac pulsation increases the noise level in brain maps of the transverse relaxation rate R2 *. Cardiac-induced noise is challenging to mitigate during the acquisition of R2 * mapping data because its characteristics are unknown. In this work, we aim to characterize cardiac-induced noise in brain maps of the MRI parameter R2 *. METHODS We designed a sampling strategy to acquire multi-echo 3D data in 12 intervals of the cardiac cycle, monitored with a fingertip pulse-oximeter. We measured the amplitude of cardiac-induced noise in this data and assessed the effect of cardiac pulsation on R2 * maps computed across echoes. The area of k-space that contains most of the cardiac-induced noise in R2 * maps was then identified. Based on these characteristics, we introduced a tentative sampling strategy that aims to mitigate cardiac-induced noise in R2 * maps of the brain. RESULTS In inferior brain regions, cardiac pulsation accounts for R2 * variations of up to 3 s-1 across the cardiac cycle (i.e., ∼35% of the overall variability). Cardiac-induced fluctuations occur throughout the cardiac cycle, with a reduced intensity during the first quarter of the cycle. A total of 50% to 60% of the overall cardiac-induced noise is localized near the k-space center (k < 0.074 mm-1 ). The tentative cardiac noise mitigation strategy reduced the variability of R2 * maps across repetitions by 11% in the brainstem and 6% across the whole brain. CONCLUSION We provide a characterization of cardiac-induced noise in brain R2 * maps that can be used as a basis for the design of mitigation strategies during data acquisition.
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Affiliation(s)
- Quentin Raynaud
- Laboratory for Research in Neuroimaging, Department for Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Giulia Di Domenicantonio
- Laboratory for Research in Neuroimaging, Department for Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Jérôme Yerly
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | - Thomas Dardano
- Laboratory for Research in Neuroimaging, Department for Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ruud B van Heeswijk
- Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging, Department for Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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4
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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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5
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Fukunaga K, Fujiwara Y, Enzaki M, Komi M, Hirai T, Azuma M. [Usefulness of Voxel-Based Quantification (VBQ) Smoothing in Relaxation Time Mapping]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2023; 79:913-922. [PMID: 37544734 DOI: 10.6009/jjrt.2023-1378] [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] [Indexed: 08/08/2023]
Abstract
PURPOSE Voxel-based quantification (VBQ) smoothing is a technique used to smooth quantitative parametric maps in the Montreal Neurological Institute standard space. Although VBQ smoothing could suppress changes in quantitative values at tissue boundaries, its effectiveness on relaxation time (T1 and T2 values and proton density PD) maps has not been investigated. The purpose of this study was to clarify the usefulness of VBQ smoothing in relaxation time mapping. METHOD T1 and T2 values and PD maps of the brains of 20 healthy participants were obtained using a two-dimensional multi-dynamic multi-echo sequence. VBQ and Gaussian smoothing were applied to the relaxation time maps by varying the kernel size by 1 mm from 1 to 6 mm. Changes in relaxation time before and after VBQ and Gaussian smoothing for the putamen, caudate nucleus, substantia nigra, and corpus callosum on the relaxation time maps were evaluated. RESULT The changes in relaxation time after VBQ smoothing application were smaller than those in that after Gaussian smoothing application. Although the differences in the relaxation time for all tissues before and after VBQ and Gaussian smoothing applications increased with increasing kernel size for all relaxation times for both methods, the changes in the relaxation time for VBQ smoothing were smaller than those in that for Gaussian smoothing. CONCLUSION VBQ smoothing can suppress the change in the relaxation time on the boundary of the tissue and is thus a useful smoothing technique in relaxation time mapping.
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Affiliation(s)
- Kota Fukunaga
- Graduate School of Health Sciences, Kumamoto University
| | - Yasuhiro Fujiwara
- Department of Medical Image Sciences, Faculty of Life Sciences, Kumamoto University
| | | | | | - Toshinori Hirai
- Department of Diagnostic Radiology, Faculty of Medicine, Kumamoto University
| | - Minako Azuma
- Department of Radiology, Faculty of Medicine, University of Miyazaki
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6
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Bernal J, Schreiber S, Menze I, Ostendorf A, Pfister M, Geisendörfer J, Nemali A, Maass A, Yakupov R, Peters O, Preis L, Schneider L, Herrera AL, Priller J, Spruth EJ, Altenstein S, Schneider A, Fliessbach K, Wiltfang J, Schott BH, Rostamzadeh A, Glanz W, Buerger K, Janowitz D, Ewers M, Perneczky R, Rauchmann BS, Teipel S, Kilimann I, Laske C, Munk MH, Spottke A, Roy N, Dobisch L, Dechent P, Scheffler K, Hetzer S, Wolfsgruber S, Kleineidam L, Schmid M, Berger M, Jessen F, Wirth M, Düzel E, Ziegler G. Arterial hypertension and β-amyloid accumulation have spatially overlapping effects on posterior white matter hyperintensity volume: a cross-sectional study. Alzheimers Res Ther 2023; 15:97. [PMID: 37226207 DOI: 10.1186/s13195-023-01243-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 05/09/2023] [Indexed: 05/26/2023]
Abstract
BACKGROUND White matter hyperintensities (WMH) in subjects across the Alzheimer's disease (AD) spectrum with minimal vascular pathology suggests that amyloid pathology-not just arterial hypertension-impacts WMH, which in turn adversely influences cognition. Here we seek to determine the effect of both hypertension and Aβ positivity on WMH, and their impact on cognition. METHODS We analysed data from subjects with a low vascular profile and normal cognition (NC), subjective cognitive decline (SCD), and amnestic mild cognitive impairment (MCI) enrolled in the ongoing observational multicentre DZNE Longitudinal Cognitive Impairment and Dementia Study (n = 375, median age 70.0 [IQR 66.0, 74.4] years; 178 female; NC/SCD/MCI 127/162/86). All subjects underwent a rich neuropsychological assessment. We focused on baseline memory and executive function-derived from multiple neuropsychological tests using confirmatory factor analysis-, baseline preclinical Alzheimer's cognitive composite 5 (PACC5) scores, and changes in PACC5 scores over the course of three years (ΔPACC5). RESULTS Subjects with hypertension or Aβ positivity presented the largest WMH volumes (pFDR < 0.05), with spatial overlap in the frontal (hypertension: 0.42 ± 0.17; Aβ: 0.46 ± 0.18), occipital (hypertension: 0.50 ± 0.16; Aβ: 0.50 ± 0.16), parietal lobes (hypertension: 0.57 ± 0.18; Aβ: 0.56 ± 0.20), corona radiata (hypertension: 0.45 ± 0.17; Aβ: 0.40 ± 0.13), optic radiation (hypertension: 0.39 ± 0.18; Aβ: 0.74 ± 0.19), and splenium of the corpus callosum (hypertension: 0.36 ± 0.12; Aβ: 0.28 ± 0.12). Elevated global and regional WMH volumes coincided with worse cognitive performance at baseline and over 3 years (pFDR < 0.05). Aβ positivity was negatively associated with cognitive performance (direct effect-memory: - 0.33 ± 0.08, pFDR < 0.001; executive: - 0.21 ± 0.08, pFDR < 0.001; PACC5: - 0.29 ± 0.09, pFDR = 0.006; ΔPACC5: - 0.34 ± 0.04, pFDR < 0.05). Splenial WMH mediated the relationship between hypertension and cognitive performance (indirect-only effect-memory: - 0.05 ± 0.02, pFDR = 0.029; executive: - 0.04 ± 0.02, pFDR = 0.067; PACC5: - 0.05 ± 0.02, pFDR = 0.030; ΔPACC5: - 0.09 ± 0.03, pFDR = 0.043) and WMH in the optic radiation partially mediated that between Aβ positivity and memory (indirect effect-memory: - 0.05 ± 0.02, pFDR = 0.029). CONCLUSIONS Posterior white matter is susceptible to hypertension and Aβ accumulation. Posterior WMH mediate the association between these pathologies and cognitive dysfunction, making them a promising target to tackle the downstream damage related to the potentially interacting and potentiating effects of the two pathologies. TRIAL REGISTRATION German Clinical Trials Register (DRKS00007966, 04/05/2015).
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Affiliation(s)
- Jose Bernal
- Institute of Cognitive Neurology and Dementia Research, Otto-Von-Guericke University Magdeburg, Magdeburg, Germany.
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany.
| | - Stefanie Schreiber
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
- Department of Neurology, Medical Faculty, University Hospital Magdeburg, Magdeburg, Germany
| | - Inga Menze
- Institute of Cognitive Neurology and Dementia Research, Otto-Von-Guericke University Magdeburg, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Anna Ostendorf
- Institute of Cognitive Neurology and Dementia Research, Otto-Von-Guericke University Magdeburg, Magdeburg, Germany
| | - Malte Pfister
- Department of Neurology, Medical Faculty, University Hospital Magdeburg, Magdeburg, Germany
| | - Jonas Geisendörfer
- Department of Neurology, Medical Faculty, University Hospital Magdeburg, Magdeburg, Germany
| | - Aditya Nemali
- Institute of Cognitive Neurology and Dementia Research, Otto-Von-Guericke University Magdeburg, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin-Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Lukas Preis
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin-Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Luisa Schneider
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin-Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Ana Lucia Herrera
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin-Institute of Psychiatry and Psychotherapy, Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
- School of Medicine, Department of Psychiatry and Psychotherapy, Technical University of Munich, Munich, Germany
- University of Edinburgh and UK DRI, Edinburgh, UK
| | - Eike Jakob Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Slawek Altenstein
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Clinic for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Clinic for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany
| | - Jens Wiltfang
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Goettingen, Germany
- Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Björn H Schott
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Goettingen, Germany
| | - Ayda Rostamzadeh
- Department of Psychiatry, University of Cologne, Cologne, Germany
| | - Wenzel Glanz
- Institute of Cognitive Neurology and Dementia Research, Otto-Von-Guericke University Magdeburg, Magdeburg, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Robert Perneczky
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy) Munich, Munich, Germany
- Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, UK
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Stefan Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Clinic for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany
| | - Nina Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Peter Dechent
- MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University Goettingen, Göttingen, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Stefan Hetzer
- Berlin Center for Advanced Neuroimaging, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Steffen Wolfsgruber
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Clinic for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany
| | - Luca Kleineidam
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Clinic for Neurodegenerative Diseases and Geriatric Psychiatry, University of Bonn, Bonn, Germany
| | - Matthias Schmid
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Institute for Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany
| | - Moritz Berger
- Institute for Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Bonn, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Psychiatry, University of Cologne, Cologne, Germany
- Excellence Cluster On Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Miranka Wirth
- German Center for Neurodegenerative Diseases (DZNE), Tatzberg 41, Dresden, 01307, Germany.
| | - Emrah Düzel
- Institute of Cognitive Neurology and Dementia Research, Otto-Von-Guericke University Magdeburg, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
- Institute of Cognitive Neuroscience, University College London, London, UK
| | - Gabriel Ziegler
- Institute of Cognitive Neurology and Dementia Research, Otto-Von-Guericke University Magdeburg, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Leipziger Str. 44, 39120, Magdeburg, Germany
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7
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Santos AN, Kherif F, Melie-Garcia L, Lutti A, Chiappini A, Rauschenbach L, Dinger TF, Riess C, El Rahal A, Darkwah Oppong M, Sure U, Dammann P, Draganski B. Parkinson's disease may disrupt overlapping subthalamic nucleus and pallidal motor networks. Neuroimage Clin 2023; 38:103432. [PMID: 37210889 PMCID: PMC10213095 DOI: 10.1016/j.nicl.2023.103432] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 04/13/2023] [Accepted: 05/07/2023] [Indexed: 05/23/2023]
Abstract
There is an ongoing debate about differential clinical outcome and associated adverse effects of deep brain stimulation (DBS) in Parkinson's disease (PD) targeting the subthalamic nucleus (STN) or the globus pallidus pars interna (GPi). Given that functional connectivity profiles suggest beneficial DBS effects within a common network, the empirical evidence about the underlying anatomical circuitry is still scarce. Therefore, we investigate the STN and GPi-associated structural covariance brain patterns in PD patients and healthy controls. We estimate GPi's and STN's whole-brain structural covariance from magnetic resonance imaging (MRI) in a normative mid- to old-age community-dwelling cohort (n = 1184) across maps of grey matter volume, magnetization transfer (MT) saturation, longitudinal relaxation rate (R1), effective transversal relaxation rate (R2*) and effective proton density (PD*). We compare these with the structural covariance estimates in patients with idiopathic PD (n = 32) followed by validation using a reduced size controls' cohort (n = 32). In the normative data set, we observed overlapping spatially distributed cortical and subcortical covariance patterns across maps confined to basal ganglia, thalamus, motor, and premotor cortical areas. Only the subcortical and midline motor cortical areas were confirmed in the reduced size cohort. These findings contrasted with the absence of structural covariance with cortical areas in the PD cohort. We interpret with caution the differential covariance maps of overlapping STN and GPi networks in patients with PD and healthy controls as correlates of motor network disruption. Our study provides face validity to the proposed extension of the currently existing structural covariance methods based on morphometry features to multiparameter MRI sensitive to brain tissue microstructure.
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Affiliation(s)
- Alejandro N Santos
- Laboratory of Research in Neuroimaging (LREN) -Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Department of Neurosurgery, University Hospital Essen, Essen, Germany; Center for Translational Neuroscience and Behavioral Science (C-TNBS), University of Duisburg, Essen, Germany
| | - Ferath Kherif
- Laboratory of Research in Neuroimaging (LREN) -Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Lester Melie-Garcia
- Laboratory of Research in Neuroimaging (LREN) -Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Antoine Lutti
- Laboratory of Research in Neuroimaging (LREN) -Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Alessio Chiappini
- Department of Neurosurgery, University Hospital Basel, Basel, Switzerland
| | - Laurèl Rauschenbach
- Department of Neurosurgery, University Hospital Essen, Essen, Germany; Center for Translational Neuroscience and Behavioral Science (C-TNBS), University of Duisburg, Essen, Germany
| | - Thiemo F Dinger
- Department of Neurosurgery, University Hospital Essen, Essen, Germany; Center for Translational Neuroscience and Behavioral Science (C-TNBS), University of Duisburg, Essen, Germany
| | - Christoph Riess
- Department of Neurosurgery, University Hospital Essen, Essen, Germany; Center for Translational Neuroscience and Behavioral Science (C-TNBS), University of Duisburg, Essen, Germany
| | - Amir El Rahal
- Department of Neurosurgery, University Hospital Freiburg, Freiburg im Breisgau, Germany
| | - Marvin Darkwah Oppong
- Department of Neurosurgery, University Hospital Essen, Essen, Germany; Center for Translational Neuroscience and Behavioral Science (C-TNBS), University of Duisburg, Essen, Germany
| | - Ulrich Sure
- Department of Neurosurgery, University Hospital Essen, Essen, Germany; Center for Translational Neuroscience and Behavioral Science (C-TNBS), University of Duisburg, Essen, Germany
| | - Philipp Dammann
- Department of Neurosurgery, University Hospital Essen, Essen, Germany; Center for Translational Neuroscience and Behavioral Science (C-TNBS), University of Duisburg, Essen, Germany
| | - Bogdan Draganski
- Laboratory of Research in Neuroimaging (LREN) -Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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8
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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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9
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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] [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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10
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Bernal J, Valdés-Hernández MDC, Escudero J, Duarte R, Ballerini L, Bastin ME, Deary IJ, Thrippleton MJ, Touyz RM, Wardlaw JM. Assessment of perivascular space filtering methods using a three-dimensional computational model. Magn Reson Imaging 2022; 93:33-51. [PMID: 35932975 DOI: 10.1016/j.mri.2022.07.016] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/19/2022] [Accepted: 07/30/2022] [Indexed: 10/31/2022]
Abstract
Growing interest surrounds the assessment of perivascular spaces (PVS) on magnetic resonance imaging (MRI) and their validation as a clinical biomarker of adverse brain health. Nonetheless, the limits of validity of current state-of-the-art segmentation methods are still unclear. Here, we propose an open-source three-dimensional computational framework comprising 3D digital reference objects and evaluate the performance of three PVS filtering methods under various spatiotemporal imaging considerations (including sampling, motion artefacts, and Rician noise). Specifically, we study the performance of the Frangi, Jerman and RORPO filters in enhancing PVS-like structures to facilitate segmentation. Our findings were three-fold. First, as long as voxels are isotropic, RORPO outperforms the other two filters, regardless of imaging quality. Unlike the Frangi and Jerman filters, RORPO's performance does not deteriorate as PVS volume increases. Second, the performance of all "vesselness" filters is heavily influenced by imaging quality, with sampling and motion artefacts being the most damaging for these types of analyses. Third, none of the filters can distinguish PVS from other hyperintense structures (e.g. white matter hyperintensities, stroke lesions, or lacunes) effectively, the area under precision-recall curve dropped substantially (Frangi: from 94.21 [IQR 91.60, 96.16] to 43.76 [IQR 25.19, 63.38]; Jerman: from 94.51 [IQR 91.90, 95.37] to 58.00 [IQR 35.68, 64.87]; RORPO: from 98.72 [IQR 95.37, 98.96] to 71.87 [IQR 57.21, 76.63] without and with other hyperintense structures, respectively). The use of our computational model enables comparing segmentation methods and identifying their advantages and disadvantages, thereby providing means for testing and optimising pipelines for ongoing and future studies.
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Affiliation(s)
- Jose Bernal
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK; Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany; German Centre for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Maria D C Valdés-Hernández
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK; Lothian Birth Cohorts group, Department of Psychology, The University of Edinburgh, UK.
| | - Javier Escudero
- Institute for Digital Communications, The University of Edinburgh, Edinburgh, UK
| | - Roberto Duarte
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Lucia Ballerini
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK; Lothian Birth Cohorts group, Department of Psychology, The University of Edinburgh, UK
| | - Ian J Deary
- Lothian Birth Cohorts group, Department of Psychology, The University of Edinburgh, UK
| | | | - Rhian M Touyz
- Research Institute of the McGill University Health Centre, McGill University, Montréal, Canada
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK; Lothian Birth Cohorts group, Department of Psychology, The University of Edinburgh, UK
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11
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Manfredi-Lozano M, Leysen V, Adamo M, Paiva I, Rovera R, Pignat JM, Timzoura FE, Candlish M, Eddarkaoui S, Malone SA, Silva MSB, Trova S, Imbernon M, Decoster L, Cotellessa L, Tena-Sempere M, Claret M, Paoloni-Giacobino A, Plassard D, Paccou E, Vionnet N, Acierno J, Maceski AM, Lutti A, Pfrieger F, Rasika S, Santoni F, Boehm U, Ciofi P, Buée L, Haddjeri N, Boutillier AL, Kuhle J, Messina A, Draganski B, Giacobini P, Pitteloud N, Prevot V. GnRH replacement rescues cognition in Down syndrome. Science 2022; 377:eabq4515. [PMID: 36048943 PMCID: PMC7613827 DOI: 10.1126/science.abq4515] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
At the present time, no viable treatment exists for cognitive and olfactory deficits in Down syndrome (DS). We show in a DS model (Ts65Dn mice) that these progressive nonreproductive neurological symptoms closely parallel a postpubertal decrease in hypothalamic as well as extrahypothalamic expression of a master molecule that controls reproduction-gonadotropin-releasing hormone (GnRH)-and appear related to an imbalance in a microRNA-gene network known to regulate GnRH neuron maturation together with altered hippocampal synaptic transmission. Epigenetic, cellular, chemogenetic, and pharmacological interventions that restore physiological GnRH levels abolish olfactory and cognitive defects in Ts65Dn mice, whereas pulsatile GnRH therapy improves cognition and brain connectivity in adult DS patients. GnRH thus plays a crucial role in olfaction and cognition, and pulsatile GnRH therapy holds promise to improve cognitive deficits in DS.
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Affiliation(s)
- Maria Manfredi-Lozano
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France,Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Valerie Leysen
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France,Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Michela Adamo
- Department of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, 1011 Lausanne, Switzerland,Faculty of Biology and Medicine, University of Lausanne, Lausanne 1005, Switzerland
| | - Isabel Paiva
- Laboratoire de Neurosciences Cognitives et Adaptatives (LNCA), UMR 7364, Université de Strasbourg-CNRS, Strasbourg, France
| | - Renaud Rovera
- Univ. Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron 69500, France
| | - Jean-Michel Pignat
- Department of Clinical Neurosciences, Neurorehabilitation Unit, University Hospital CHUV, Lausanne, Switzerland
| | - Fatima Ezzahra Timzoura
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France,Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Michael Candlish
- Experimental Pharmacology, Center for Molecular Signaling (PZMS), Saarland University School of Medicine, 66421, Homburg, Germany
| | - Sabiha Eddarkaoui
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France
| | - Samuel A. Malone
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France,Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Mauro S. B. Silva
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France,Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Sara Trova
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France,Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Monica Imbernon
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France,Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Laurine Decoster
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France,Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Ludovica Cotellessa
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France,Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Manuel Tena-Sempere
- Univ. Cordoba, IMIBC/HURS, CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Cordoba, Spain
| | - Marc Claret
- Neuronal Control of Metabolism Laboratory, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Spain; Centro de Investigación Biomédica en Red (CIBER) de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), 08036 Barcelona, Spain
| | - Ariane Paoloni-Giacobino
- Department of Genetic Medicine, University Hospitals of Geneva, 4 rue Gabrielle-Perret-Gentil, 1211, Genève 14, Switzerland
| | - Damien Plassard
- CNRS UMR 7104, INSERM U1258, GenomEast Platform, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Université de Strasbourg, Illkirch, France
| | - Emmanuelle Paccou
- Department of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Nathalie Vionnet
- Department of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - James Acierno
- Department of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Aleksandra Maleska Maceski
- Neurologic Clinic and Polyclinic, MS Centre and Research Centre for Clinical Neuroimmunology and Neuroscience Basel; University Hospital Basel, University of Basel, Basel 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, Switzerland
| | - Frank Pfrieger
- Centre National de la Recherche Scientifique, Université de Strasbourg, Institut des Neurosciences Cellulaires et Intégratives, 67000 Strasbourg, France
| | - S. Rasika
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France,Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Federico Santoni
- Faculty of Biology and Medicine, University of Lausanne, Lausanne 1005, Switzerland
| | - Ulrich Boehm
- Experimental Pharmacology, Center for Molecular Signaling (PZMS), Saarland University School of Medicine, 66421, Homburg, Germany
| | - Philippe Ciofi
- Univ. Bordeaux, Inserm, U1215, Neurocentre Magendie, Bordeaux, France
| | - Luc Buée
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France
| | - Nasser Haddjeri
- Univ. Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron 69500, France
| | - Anne-Laurence Boutillier
- Laboratoire de Neurosciences Cognitives et Adaptatives (LNCA), UMR 7364, Université de Strasbourg-CNRS, Strasbourg, France
| | - Jens Kuhle
- Neurologic Clinic and Polyclinic, MS Centre and Research Centre for Clinical Neuroimmunology and Neuroscience Basel; University Hospital Basel, University of Basel, Basel Switzerland
| | - Andrea Messina
- Department of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, 1011 Lausanne, Switzerland,Faculty of Biology and Medicine, University of Lausanne, Lausanne 1005, 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, Switzerland,Neurology Department, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Paolo Giacobini
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France,Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France
| | - Nelly Pitteloud
- Department of Endocrinology, Diabetology, and Metabolism, Lausanne University Hospital, 1011 Lausanne, Switzerland,Faculty of Biology and Medicine, University of Lausanne, Lausanne 1005, Switzerland,Correspondence to: and (+33 612903876)
| | - Vincent Prevot
- Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, UMR-S 1172, LabexDistAlz, Lille, France,Laboratory of Development and Plasticity of the Neuroendocrine Brain, FHU 1000 days for health, EGID, Lille, France,Correspondence to: and (+33 612903876)
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12
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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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13
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Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity. Neuroimage 2022; 262:119529. [PMID: 35926761 DOI: 10.1016/j.neuroimage.2022.119529] [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: 12/29/2021] [Revised: 07/20/2022] [Accepted: 08/01/2022] [Indexed: 11/20/2022] Open
Abstract
Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R1 and R2*, proton density PD, and magnetization transfer saturation MTsat) that are sensitive to microstructural tissue properties such as iron and myelin content. Their capability to reveal microstructural brain differences, however, is tightly bound to controlling random noise and artefacts (e.g. caused by head motion) in the signal. Here, we introduced a method to estimate the local error of PD, R1, and MTsat maps that captures both noise and artefacts on a routine basis without requiring additional data. To investigate the method's sensitivity to random noise, we calculated the model-based signal-to-noise ratio (mSNR) and showed in measurements and simulations that it correlated linearly with an experimental raw-image-based SNR map. We found that the mSNR varied with MPM protocols, magnetic field strength (3T vs. 7T) and MPM parameters: it halved from PD to R1 and decreased from PD to MTsat by a factor of 3-4. Exploring the artefact-sensitivity of the error maps, we generated robust MPM parameters using two successive acquisitions of each contrast and the acquisition-specific errors to down-weight erroneous regions. The resulting robust MPM parameters showed reduced variability at the group level as compared to their single-repeat or averaged counterparts. The error and mSNR maps may better inform power-calculations by accounting for local data quality variations across measurements. Code to compute the mSNR maps and robustly combined MPM maps is available in the open-source hMRI toolbox.
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14
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Aye N, Lehmann N, Kaufmann J, Heinze HJ, Düzel E, Taubert M, Ziegler G. Test-retest reliability of multi-parametric maps (MPM) of brain microstructure. Neuroimage 2022; 256:119249. [PMID: 35487455 DOI: 10.1016/j.neuroimage.2022.119249] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 04/22/2022] [Accepted: 04/25/2022] [Indexed: 10/18/2022] Open
Abstract
Multiparameter mapping (MPM) is a quantitative MRI protocol that is promising for studying microstructural brain changes in vivo with high specificity. Reliability values are an important prior knowledge for efficient study design and facilitating replicable findings in development, aging and neuroplasticity research. To explore longitudinal reliability of MPM we acquired the protocol in 31 healthy young subjects twice over a rescan interval of 4 weeks. We assessed the within-subject coefficient of variation (WCV), the between-subject coefficient of variation (BCV), and the intraclass correlation coefficient (ICC). Using these metrics, we investigated the reliability of (semi-) quantitative magnetization transfer saturation (MTsat), proton density (PD), transversal relaxation (R2*) and longitudinal relaxation (R1). To increase relevance for explorative studies in development and training-induced plasticity, we assess reliability both on local voxel- as well as ROI-level. Finally, we disentangle contributions and interplay of within- and between-subject variability to ICC and assess the optimal degree of spatial smoothing applied to the data. We reveal evidence that voxelwise ICC reliability of MPMs is moderate to good with median values in cortex (subcortical GM): MT: 0.789 (0.447) PD: 0.553 (0.264) R1: 0.555 (0.369) R2*: 0.624 (0.477). The Gaussian smoothing kernel of 2 to 4 mm FWHM resulted in optimal reproducibility. We discuss these findings in the context of longitudinal intervention studies and the application to research designs in neuroimaging field.
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Affiliation(s)
- Norman Aye
- Faculty of Human Sciences, Institute III, Department of Sport Science, Otto von Guericke University, Zschokkestraße 32, 39104 Magdeburg, Germany.
| | - Nico Lehmann
- Faculty of Human Sciences, Institute III, Department of Sport Science, Otto von Guericke University, Zschokkestraße 32, 39104 Magdeburg, Germany; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany
| | - Jörn Kaufmann
- Department of Neurology, Otto von Guericke University, Leipziger Straße 44, 39120 Magdeburg, Germany
| | - Hans-Jochen Heinze
- Department of Neurology, Otto von Guericke University, Leipziger Straße 44, 39120 Magdeburg, Germany; German Center for Neurodegenerative Diseases (DZNE), Leipziger Straße 44, 39120 Magdeburg, Germany; Center for Behavioral and Brain Science (CBBS), Otto von Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany; Leibniz-Institute for Neurobiology (LIN), Brenneckestraße 6, 39118 Magdeburg, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Leipziger Straße 44, 39120 Magdeburg, Germany; Center for Behavioral and Brain Science (CBBS), Otto von Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany; Institute of Cognitive Neurology and Dementia Research, Otto von Guericke University, Leipziger Str. 44, 39120 Magdeburg, Germany; Institute of Cognitive Neuroscience, University College London, Alexandra House, 17-19 Queen Square, Bloomsbury, London, WC1N 3AZ United Kingdom
| | - Marco Taubert
- Faculty of Human Sciences, Institute III, Department of Sport Science, Otto von Guericke University, Zschokkestraße 32, 39104 Magdeburg, Germany; Center for Behavioral and Brain Science (CBBS), Otto von Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany
| | - Gabriel Ziegler
- German Center for Neurodegenerative Diseases (DZNE), Leipziger Straße 44, 39120 Magdeburg, Germany; Institute of Cognitive Neurology and Dementia Research, Otto von Guericke University, Leipziger Str. 44, 39120 Magdeburg, Germany
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15
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Oliveira R, Pelentritou A, Di Domenicantonio G, De Lucia M, Lutti A. In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data. Front Neurosci 2022; 16:874023. [PMID: 35527816 PMCID: PMC9070985 DOI: 10.3389/fnins.2022.874023] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 03/24/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose We present a novel approach that allows the estimation of morphological features of axonal fibers from data acquired in vivo in humans. This approach allows the assessment of white matter microscopic properties non-invasively with improved specificity. Theory The proposed approach is based on a biophysical model of Magnetic Resonance Imaging (MRI) data and of axonal conduction velocity estimates obtained with Electroencephalography (EEG). In a white matter tract of interest, these data depend on (1) the distribution of axonal radius [P(r)] and (2) the g-ratio of the individual axons that compose this tract [g(r)]. P(r) is assumed to follow a Gamma distribution with mode and scale parameters, M and θ, and g(r) is described by a power law with parameters α and β. Methods MRI and EEG data were recorded from 14 healthy volunteers. MRI data were collected with a 3T scanner. MRI-measured g-ratio maps were computed and sampled along the visual transcallosal tract. EEG data were recorded using a 128-lead system with a visual Poffenberg paradigm. The interhemispheric transfer time and axonal conduction velocity were computed from the EEG current density at the group level. Using the MRI and EEG measures and the proposed model, we estimated morphological properties of axons in the visual transcallosal tract. Results The estimated interhemispheric transfer time was 11.72 ± 2.87 ms, leading to an average conduction velocity across subjects of 13.22 ± 1.18 m/s. Out of the 4 free parameters of the proposed model, we estimated θ – the width of the right tail of the axonal radius distribution – and β – the scaling factor of the axonal g-ratio, a measure of fiber myelination. Across subjects, the parameter θ was 0.40 ± 0.07 μm and the parameter β was 0.67 ± 0.02 μm−α. Conclusion The estimates of axonal radius and myelination are consistent with histological findings, illustrating the feasibility of this approach. The proposed method allows the measurement of the distribution of axonal radius and myelination within a white matter tract, opening new avenues for the combined study of brain structure and function, and for in vivo histological studies of the human brain.
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