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Beljaards L, Pezzotti N, Rao C, Doneva M, van Osch MJP, Staring M. AI-based motion artifact severity estimation in undersampled MRI allowing for selection of appropriate reconstruction models. Med Phys 2024; 51:3555-3565. [PMID: 38167996 DOI: 10.1002/mp.16918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 11/30/2023] [Accepted: 12/04/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Magnetic Resonance acquisition is a time consuming process, making it susceptible to patient motion during scanning. Even motion in the order of a millimeter can introduce severe blurring and ghosting artifacts, potentially necessitating re-acquisition. Magnetic Resonance Imaging (MRI) can be accelerated by acquiring only a fraction of k-space, combined with advanced reconstruction techniques leveraging coil sensitivity profiles and prior knowledge. Artificial intelligence (AI)-based reconstruction techniques have recently been popularized, but generally assume an ideal setting without intra-scan motion. PURPOSE To retrospectively detect and quantify the severity of motion artifacts in undersampled MRI data. This may prove valuable as a safety mechanism for AI-based approaches, provide useful information to the reconstruction method, or prompt for re-acquisition while the patient is still in the scanner. METHODS We developed a deep learning approach that detects and quantifies motion artifacts in undersampled brain MRI. We demonstrate that synthetically motion-corrupted data can be leveraged to train the convolutional neural network (CNN)-based motion artifact estimator, generalizing well to real-world data. Additionally, we leverage the motion artifact estimator by using it as a selector for a motion-robust reconstruction model in case a considerable amount of motion was detected, and a high data consistency model otherwise. RESULTS Training and validation were performed on 4387 and 1304 synthetically motion-corrupted images and their uncorrupted counterparts, respectively. Testing was performed on undersampled in vivo motion-corrupted data from 28 volunteers, where our model distinguished head motion from motion-free scans with 91% and 96% accuracy when trained on synthetic and on real data, respectively. It predicted a manually defined quality label ('Good', 'Medium' or 'Bad' quality) correctly in 76% and 85% of the time when trained on synthetic and real data, respectively. When used as a selector it selected the appropriate reconstruction network 93% of the time, achieving near optimal SSIM values. CONCLUSIONS The proposed method quantified motion artifact severity in undersampled MRI data with high accuracy, enabling real-time motion artifact detection that can help improve the safety and quality of AI-based reconstructions.
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Affiliation(s)
- Laurens Beljaards
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Nicola Pezzotti
- Cardiologs, Philips, Paris, France
- Faculty of Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Chinmay Rao
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | | | | | - Marius Staring
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
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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: 0] [Impact Index Per Article: 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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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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Pollak C, Kügler D, Breteler MMB, Reuter M. Quantifying MR Head Motion in the Rhineland Study - A Robust Method for Population Cohorts. Neuroimage 2023; 275:120176. [PMID: 37209757 DOI: 10.1016/j.neuroimage.2023.120176] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/22/2023] [Accepted: 05/15/2023] [Indexed: 05/22/2023] Open
Abstract
Head motion during MR acquisition reduces image quality and has been shown to bias neuromorphometric analysis. The quantification of head motion, therefore, has both neuroscientific as well as clinical applications, for example, to control for motion in statistical analyses of brain morphology, or as a variable of interest in neurological studies. The accuracy of markerless optical head tracking, however, is largely unexplored. Furthermore, no quantitative analysis of head motion in a general, mostly healthy population cohort exists thus far. In this work, we present a robust registration method for the alignment of depth camera data that sensitively estimates even small head movements of compliant participants. Our method outperforms the vendor-supplied method in three validation experiments: 1. similarity to fMRI motion traces as a low-frequency reference, 2. recovery of the independently acquired breathing signal as a high-frequency reference, and 3. correlation with image-based quality metrics in structural T1-weighted MRI. In addition to the core algorithm, we establish an analysis pipeline that computes average motion scores per time interval or per sequence for inclusion in downstream analyses. We apply the pipeline in the Rhineland Study, a large population cohort study, where we replicate age and body mass index (BMI) as motion correlates and show that head motion significantly increases over the duration of the scan session. We observe weak, yet significant interactions between this within-session increase and age, BMI, and sex. High correlations between fMRI and camera-based motion scores of proceeding sequences further suggest that fMRI motion estimates can be used as a surrogate score in the absence of better measures to control for motion in statistical analyses.
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Affiliation(s)
- Clemens Pollak
- AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - David Kügler
- AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Monique M B Breteler
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Martin Reuter
- AI in Medical Imaging, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
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5
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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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6
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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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7
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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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8
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Hossbach J, Splitthoff DN, Cauley S, Clifford B, Polak D, Lo WC, Meyer H, Maier A. Deep learning-based motion quantification from k-space for fast model-based magnetic resonance imaging motion correction. Med Phys 2022; 50:2148-2161. [PMID: 36433748 DOI: 10.1002/mp.16119] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 10/19/2022] [Accepted: 10/21/2022] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Intra-scan rigid-body motion is a costly and ubiquitous problem in clinical magnetic resonance imaging (MRI) of the head. PURPOSE State-of-the-art methods for retrospective motion correction in MRI are often computationally expensive or in the case of image-to-image deep learning (DL) based methods can be prone to undesired alterations of the image (hallucinations'). In this work we introduce a novel rigid-body motion correction method which combines the advantages of classical model-driven and data-consistency (DC) preserving approaches with a novel DL algorithm, to provide fast and robust retrospective motion correction. METHODS The proposed Motion Parameter Estimating Densenet (MoPED) retrospectively estimates subject head motion during MRI acquisitions using a DL network with DenseBlocks and multitask learning. It quantifies the 2D rigid in-plane motion parameters slice-wise for each echo train (ET) of a Cartesian T2-weighted 2D Turbo-Spin-Echo sequence. The network receives a center patch of the motion corrupted k-space as well as an additional motion-free low-resolution reference scan to provide the ground truth orientation. The supervised training utilizes motion simulations based on 28 acquisitions with subject-wise training, validation, and test data splits of 70%, 23%, and 7%. During inference, MoPED is embedded in an iterative DC-driven motion correction algorithm which alternatingly updates estimates of the motion parameters and motion-corrected low-resolution k-space data. The estimated motion parameters are then used to reconstruct the final motion corrected image. The mean absolute/squared error and the Pearson correlation coefficient were used to analyze the motion parameter estimation quality on in-silico data in a quantitative evaluation. Structural similarity (SSIM), DC error and root mean squared error (RMSE) were used as metrics of image quality improvement. Furthermore, the generalization capability of the network was analyzed on two in-vivo motion volumes with 28 slices each and on one simulated T1-weighted volume. RESULTS The motion estimation achieves a Pearson correlation of 0.968 to the simulated ground-truth of the 2433 test data slices used. In-silico results indicate that MoPED decreases the time for the optimization by a factor of around 27 compared to a conventional method and is able to reduce the RMSE of the reconstructions and average DC error by more than a factor of two compared to uncorrected images. In-vivo experiments show a decrease in computation time by a factor of around 20, a RMSE decrease from 0.055 to 0.033 and an SSIM increase from 0.795 to 0.862. Furthermore, contrast independence is demonstrated as MoPED is also able to correct T1-weighted images in simulations without retraining. Due to the model-based correction, no hallucinations were observed. CONCLUSIONS Incorporating DL in a model-based motion correction algorithm shows great benefit on the optimization and computation time. The k-space-based estimation also allows a data consistent correction and therefore avoids the risk of hallucinations of image-to-image approaches.
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Affiliation(s)
- Julian Hossbach
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.,Siemens Healthcare GmbH, Erlangen, Germany
| | | | - Stephen Cauley
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Massachusetts, Charlestown, USA
| | - Bryan Clifford
- Siemens Medical Solutions USA, Massachusetts, Boston, USA
| | - Daniel Polak
- Siemens Healthcare GmbH, Erlangen, Germany.,Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Massachusetts, Charlestown, USA
| | - Wei-Ching Lo
- Siemens Medical Solutions USA, Massachusetts, Boston, USA
| | | | - Andreas Maier
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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9
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Khedher L, Bonny JM, Marques A, Durand E, Pereira B, Chupin M, Vidal T, Chassain C, Defebvre L, Carriere N, Fraix V, Moro E, Thobois S, Metereau E, Mangone G, Vidailhet M, Corvol JC, Lehéricy S, Menjot de Champfleur N, Geny C, Spampinato U, Meissner W, Frismand S, Schmitt E, Doé de Maindreville A, Portefaix C, Remy P, Fénelon G, Luc Houeto J, Colin O, Rascol O, Peran P, Durif F. Intrasubject subcortical quantitative referencing to boost MRI sensitivity to Parkinson's disease. Neuroimage Clin 2022; 36:103231. [PMID: 36279753 PMCID: PMC9668635 DOI: 10.1016/j.nicl.2022.103231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 10/10/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022]
Abstract
Several postmortem studies have shown iron accumulation in the substantia nigra of Parkinson's disease patients. Iron concentration can be estimated via MRI-R2∗ mapping. To assess the changes in R2∗ occurring in Parkinson's disease patients compared to controls, a multicentre transversal study was carried out on a large cohort of Parkinson's disease patients (n = 163) with matched controls (n = 82). In this study, 44 patients and 11 controls were removed due to motion artefacts, 21 patient and 6 controls to preserve matching. Thus, 98 patients and 65 age and sex-matched healthy subjects were selected with enough image quality. The study was conducted on patients with early to late stage Parkinson's disease. The images were acquired at 3Tesla in 12 clinical centres. R2∗ values were measured in subcortical regions of interest (substantia nigra, red nucleus, striatum, globus pallidus externus and globus pallidus internus) contralateral (dominant side) and ipsilateral (non dominant side) to the most clinically affected hemibody. As the observed inter-subject R2∗ variability was significantly higher than the disease effect, an original strategy (intrasubject subcortical quantitative referencing, ISQR) was developed using the measurement of R2∗ in the red nucleus as an intra-subject reference. R2∗ values significantly increased in Parkinson's disease patients when compared with controls; in the substantia nigra (SN) in the dominant side (D) and in the non dominant side (ND), respectively (PSN_D and PSN_ND < 0.0001). After stratification into four subgroups according to the disease duration, no significant R2∗ difference was found in all regions of interest when comparing Parkinson's disease subgroups. By applying our ISQR strategy, R2(ISQR)∗ values significantly increased in the substantia nigra (PSN_D and PSN_ND < 0.0001) when comparing all Parkinson's disease patients to controls. R2(ISQR)∗ values in the substantia nigra significantly increased with the disease duration (PSN_D = 0.01; PSN_ND = 0.03) as well as the severity of the disease (Hoehn & Yahr scale <2 and ≥ 2, PSN_D = 0.02). Additionally, correlations between R2(ISQR)∗ and clinical features, mainly related to the severity of the disease, were found. Our results support the use of ISQR to reduce variations not directly related to Parkinson's disease, supporting the concept that ISQR strategy is useful for the evaluation of Parkinson's disease.
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Affiliation(s)
- Laila Khedher
- University Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, France,AgroResonance, INRAE, 2018. Nuclear Magnetic Resonance Facility for Agronomy, Food and Health, doi: 10.15454/1.5572398324758228E12, France,Corresponding author at: AgroResonance, INRAE, UR370 QuaPA, Saint-Genès-Champanelle F-63122, France.
| | - Jean-Marie Bonny
- AgroResonance, INRAE, 2018. Nuclear Magnetic Resonance Facility for Agronomy, Food and Health, doi: 10.15454/1.5572398324758228E12, France,AgroResonance UR370 QuaPA - INRAE, Saint-Genès-Champanelle 63122, France
| | - Ana Marques
- University Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, France,Clermont-Ferrand University Hospital, Neurology Department and NS-PARK/FCRIN Network, Clermont-Ferrand, France
| | - Elodie Durand
- University Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, France,Clermont-Ferrand University Hospital, Neurology Department and NS-PARK/FCRIN Network, Clermont-Ferrand, France
| | - Bruno Pereira
- Clermont-Ferrand University Hospital, Biostatistics Unit (DRCI), Clermont-Ferrand, France
| | - Marie Chupin
- Sorbonne Université, Institut du Cerveau - ICM, CATI, Assistance Publique Hôpitaux de Paris, Inserm, CNRS, Département de Neurologie and NS-PARK/FCRIN Network, CIC Neurosciences, Hôpital Pitié-Salpêtrière, Paris, France
| | - Tiphaine Vidal
- University Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, France,Clermont-Ferrand University Hospital, Neurology Department and NS-PARK/FCRIN Network, Clermont-Ferrand, France
| | - Carine Chassain
- University Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, France,Clermont-Ferrand University Hospital, Neurology Department and NS-PARK/FCRIN Network, Clermont-Ferrand, France
| | - Luc Defebvre
- Department of Movement Disorder and NS-PARK/FCRIN Network, Inserm 1172 University of Lille, Lille, France
| | - Nicolas Carriere
- Department of Movement Disorder and NS-PARK/FCRIN Network, Inserm 1172 University of Lille, Lille, France
| | - Valerie Fraix
- Service de Neurologie, CHU de Grenoble and NS-PARK/FCRIN Network, Université Grenoble Alpes, Grenoble Institute of Neuroscience, Grenoble, France
| | - Elena Moro
- Service de Neurologie, CHU de Grenoble and NS-PARK/FCRIN Network, Université Grenoble Alpes, Grenoble Institute of Neuroscience, Grenoble, France
| | - Stéphane Thobois
- CNRS, Institut des Sciences Cognitives Marc Jeannerod, UMR 5229 CNRS, Lyon, France,Université Claude Bernard, Lyon I, Lyon, France,Hospices Civils de Lyon, Hôpital Neurologique Pierre Wertheimer, Service de Neurologie C and NS-PARK/FCRIN Network, Lyon, France
| | - Elise Metereau
- CNRS, Institut des Sciences Cognitives Marc Jeannerod, UMR 5229 CNRS, Lyon, France,Université Claude Bernard, Lyon I, Lyon, France,Hospices Civils de Lyon, Hôpital Neurologique Pierre Wertheimer, Service de Neurologie C and NS-PARK/FCRIN Network, Lyon, France
| | - Graziella Mangone
- Sorbonne Université, Institut du Cerveau - ICM, Assistance Publique Hôpitaux de Paris, Inserm, CNRS, Département de Neurologie and NS-PARK/FCRIN Network, CIC Neurosciences, Hôpital Pitié-Salpêtrière, Paris, France
| | - Marie Vidailhet
- Sorbonne Université, Institut du Cerveau - ICM, Assistance Publique Hôpitaux de Paris, Inserm, CNRS, Département de Neurologie and NS-PARK/FCRIN Network, CIC Neurosciences, Hôpital Pitié-Salpêtrière, Paris, France
| | - Jean-Christophe Corvol
- Sorbonne Université, Institut du Cerveau - ICM, Assistance Publique Hôpitaux de Paris, Inserm, CNRS, Département de Neurologie and NS-PARK/FCRIN Network, CIC Neurosciences, Hôpital Pitié-Salpêtrière, Paris, France
| | - Stéphane Lehéricy
- Sorbonne Université, Institut du Cerveau - ICM, Assistance Publique Hôpitaux de Paris, Inserm, CNRS, Département de Neurologie and NS-PARK/FCRIN Network, CIC Neurosciences, Hôpital Pitié-Salpêtrière, Paris, France
| | - Nicolas Menjot de Champfleur
- Department of Neuroradiology, Montpellier University Hospital Center, Gui de Chauliac Hospital, Montpellier, France,I2FH, Institut d'Imagerie Fonctionnelle Humaine, Hôpital Gui de Chauliac, CHRU de Montpellier, Montpellier, France
| | - Christian Geny
- Department of Geriatrics and NS-PARK/FCRIN Network, Montpellier University Hospital, Montpellier University, Montpellier, France,EuroMov Laboratory, University of Montpellier, 700 Avenue du Pic Saint Loup, Montpellier, Montpellier 34090, France
| | - Umberto Spampinato
- Service de Neurologie - Maladies Neurodégénératives and NS-PARK/FCRIN Network, CHU Bordeaux, Bordeaux F-33000, France
| | - Wassilios Meissner
- Service de Neurologie - Maladies Neurodégénératives and NS-PARK/FCRIN Network, CHU Bordeaux, Bordeaux F-33000, France,Univ. Bordeaux, CNRS, IMN, UMR 5293, Bordeaux, Bordeaux F-33000, France,Dept. Medicine, University of Otago, Christchurch, and New Zealand Brain Research Institute, Christchurch, New Zealand
| | - Solène Frismand
- Service de Neurologie and NS-PARK/FCRIN Network, CHRU-Nancy, Nancy, France
| | - Emmanuelle Schmitt
- Service de Neurologie and NS-PARK/FCRIN Network, CHRU-Nancy, Nancy, France
| | | | - Christophe Portefaix
- Department of Radiology, Hôpital Maison blanche, Reims, France,CReSTIC Laboratory (EA 3804), University of Reims Champagne-Ardenne, Reims, France
| | - Philippe Remy
- Centre Expert Parkinson and NS-PARK/FCRIN Network, CHU Henri Mondor, AP-HP et Equipe Neuropsychologie Interventionnelle, INSERM-IMRB, Faculté de Santé, Université Paris-Est Créteil et Ecole Normale Supérieure Paris Sorbonne Université, Créteil, France
| | - Gilles Fénelon
- Centre Expert Parkinson and NS-PARK/FCRIN Network, CHU Henri Mondor, AP-HP et Equipe Neuropsychologie Interventionnelle, INSERM-IMRB, Faculté de Santé, Université Paris-Est Créteil et Ecole Normale Supérieure Paris Sorbonne Université, Créteil, France
| | - Jean Luc Houeto
- INSERM, CHU de Poitiers, Université de Poitiers, Centre d’Investigation Clinique CIC1402, Service de Neurologie and NS-PARK/FCRIN Network, Poitiers, France – CHU - Centre Expert Parkinson de Limoges, Limoges, France
| | - Olivier Colin
- INSERM, CHU de Poitiers, Université de Poitiers, Centre d’Investigation Clinique CIC1402, Service de Neurologie and NS-PARK/FCRIN Network, Poitiers, France– CH Brive la Gaillarde, France
| | - Olivier Rascol
- Centre d'Investigation Clinique CIC 1436, UMR 1214 TONIC and NS-PARK/FCRIN Network, INSERM, CHU de Toulouse et Université de Toulouse3, Toulouse, France
| | - Patrice Peran
- Centre d'Investigation Clinique CIC 1436, UMR 1214 TONIC and NS-PARK/FCRIN Network, INSERM, CHU de Toulouse et Université de Toulouse3, Toulouse, France
| | - Franck Durif
- University Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand, France,Clermont-Ferrand University Hospital, Neurology Department and NS-PARK/FCRIN Network, Clermont-Ferrand, France
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10
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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: 32] [Impact Index Per Article: 16.0] [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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11
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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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12
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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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13
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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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14
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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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15
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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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16
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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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17
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Brain tissue properties link cardio-vascular risk factors, mood and cognitive performance in the CoLaus|PsyCoLaus epidemiological cohort. Neurobiol Aging 2021; 102:50-63. [PMID: 33765431 DOI: 10.1016/j.neurobiolaging.2021.02.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 01/31/2021] [Accepted: 02/04/2021] [Indexed: 01/15/2023]
Abstract
Given the controversy about the impact of modifiable risk factors on mood and cognition in ageing, we sought to investigate the associations between cardio-vascular risk, mental health, cognitive performance and brain anatomy in mid- to old age. We analyzed a set of risk factors together with multi-parameter magnetic resonance imaging (MRI) in the CoLaus|PsyCoLaus cohort (n > 1200). Cardio-vascular risk was associated with differences in brain tissue properties - myelin, free tissue water, iron content - and regional brain volumes that we interpret in the context of micro-vascular hypoxic lesions and neurodegeneration. The interaction between clinical subtypes of major depressive disorder and cardio-vascular risk factors showed differential associations with brain structure depending on individuals' lifetime trajectory. There was a negative correlation between melancholic depression, anxiety and MRI markers of myelin and iron content in the hippocampus and anterior cingulate. Verbal memory and verbal fluency performance were positively correlated with left amygdala volumes. The concomitant analysis of brain morphometry and tissue properties allowed for a neuro-biological interpretation of the link between modifiable risk factors and brain health.
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18
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Weitnauer L, Frisch S, Melie-Garcia L, Preisig M, Schroeter ML, Sajfutdinow I, Kherif F, Draganski B. Mapping grip force to motor networks. Neuroimage 2021; 229:117735. [PMID: 33454401 DOI: 10.1016/j.neuroimage.2021.117735] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 12/30/2020] [Accepted: 01/04/2021] [Indexed: 11/18/2022] Open
Abstract
AIM There is ongoing debate about the role of cortical and subcortical brain areas in force modulation. In a whole-brain approach, we sought to investigate the anatomical basis of grip force whilst acknowledging interindividual differences in connectivity patterns. We tested if brain lesion mapping in patients with unilateral motor deficits can inform whole-brain structural connectivity analysis in healthy controls to uncover the networks underlying grip force. METHODS Using magnetic resonance imaging (MRI) and whole-brain voxel-based morphometry in chronic stroke patients (n=55) and healthy controls (n=67), we identified the brain regions in both grey and white matter significantly associated with grip force strength. The resulting statistical parametric maps (SPMs) provided seed areas for whole-brain structural covariance analysis in a large-scale community dwelling cohort (n=977) that included beyond volume estimates, parameter maps sensitive to myelin, iron and tissue water content. RESULTS The SPMs showed symmetrical bilateral clusters of correlation between upper limb motor performance, basal ganglia, posterior insula and cortico-spinal tract. The covariance analysis with the seed areas derived from the SPMs demonstrated a widespread anatomical pattern of brain volume and tissue properties, including both cortical, subcortical nodes of motor networks and sensorimotor areas projections. CONCLUSION We interpret our covariance findings as a biological signature of brain networks implicated in grip force. The data-driven definition of seed areas obtained from chronic stroke patients showed overlapping structural covariance patterns within cortico-subcortical motor networks across different tissue property estimates. This cumulative evidence lends face validity of our findings and their biological plausibility.
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Affiliation(s)
- Ladina Weitnauer
- LREN, Department of clinical neurosciences - CHUV, University Lausanne, Switzerland
| | - Stefan Frisch
- Max-Planck Institute for Human Brain and Cognitive Sciences, Leipzig, German; Department of Gerontopsychiatry, Psychosomatic Medicine, and Psychotherapy, Pfalzklinikum, Klingenmünster, Germany; Institute of Psychology, Goethe-University, Frankfurt am Main, Germany
| | - Lester Melie-Garcia
- LREN, Department of clinical neurosciences - CHUV, University Lausanne, Switzerland
| | - Martin Preisig
- Department of psychiatry - CHUV, University Lausanne, Switzerland
| | | | - Ines Sajfutdinow
- Day Clinic for Cognitive Neurology, Universitätsklinikum Leipzig, Leipzig, Germany
| | - Ferath Kherif
- LREN, Department of clinical neurosciences - CHUV, University Lausanne, Switzerland
| | - Bogdan Draganski
- LREN, Department of clinical neurosciences - CHUV, University Lausanne, Switzerland; Max-Planck Institute for Human Brain and Cognitive Sciences, Leipzig, German.
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19
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Wallace TE, Afacan O, Jaimes C, Rispoli J, Pelkola K, Dugan M, Kober T, Warfield SK. Free induction decay navigator motion metrics for prediction of diagnostic image quality in pediatric MRI. Magn Reson Med 2021; 85:3169-3181. [PMID: 33404086 PMCID: PMC7904595 DOI: 10.1002/mrm.28649] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 11/05/2020] [Accepted: 11/25/2020] [Indexed: 12/23/2022]
Abstract
Purpose To investigate the ability of free induction decay navigator (FIDnav)‐based motion monitoring to predict diagnostic utility and reduce the time and cost associated with acquiring diagnostically useful images in a pediatric patient cohort. Methods A study was carried out in 102 pediatric patients (aged 0‐18 years) at 3T using a 32‐channel head coil array. Subjects were scanned with an FID‐navigated MPRAGE sequence and images were graded by two radiologists using a five‐point scale to evaluate the impact of motion artifacts on diagnostic image quality. The correlation between image quality and four integrated FIDnav motion metrics was investigated, as well as the sensitivity and specificity of each FIDnav‐based metric to detect different levels of motion corruption in the images. Potential time and cost savings were also assessed by retrospectively applying an optimal detection threshold to FIDnav motion scores. Results A total of 12% of images were rated as non‐diagnostic, while a further 12% had compromised diagnostic value due to motion artifacts. FID‐navigated metrics exhibited a moderately strong correlation with image grade (Spearman's rho ≥ 0.56). Integrating the cross‐correlation between FIDnav signal vectors achieved the highest sensitivity and specificity for detecting non‐diagnostic images, yielding total time savings of 7% across all scans. This corresponded to a financial benefit of $2080 in this study. Conclusions Our results indicate that integrated motion metrics from FIDnavs embedded in structural MRI are a useful predictor of diagnostic image quality, which translates to substantial time and cost savings when applied to pediatric MRI examinations.
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Affiliation(s)
- Tess E Wallace
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Onur Afacan
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Camilo Jaimes
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Joanne Rispoli
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Kristina Pelkola
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, USA
| | - Monet Dugan
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, USA
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
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20
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Bazin PL, Nijsse HE, van der Zwaag W, Gallichan D, Alkemade A, Vos FM, Forstmann BU, Caan MWA. Sharpness in motion corrected quantitative imaging at 7T. Neuroimage 2020; 222:117227. [PMID: 32781231 DOI: 10.1016/j.neuroimage.2020.117227] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 07/03/2020] [Accepted: 07/31/2020] [Indexed: 12/13/2022] Open
Abstract
Sub-millimeter imaging at 7T has opened new possibilities for qualitatively and quantitatively studying brain structure as it evolves throughout the life span. However, subject motion introduces image blurring on the order of magnitude of the spatial resolution and is thus detrimental to image quality. Such motion can be corrected for, but widespread application has not yet been achieved and quantitative evaluation is lacking. This raises a need to quantitatively measure image sharpness throughout the brain. We propose a method to quantify sharpness of brain structures at sub-voxel resolution, and use it to assess to what extent limited motion is related to image sharpness. The method was evaluated in a cohort of 24 healthy volunteers with a wide and uniform age range, aiming to arrive at results that largely generalize to larger populations. Using 3D fat-excited motion navigators, quantitative R1, R2* and Quantitative Susceptibility Maps and T1-weighted images were retrospectively corrected for motion. Sharpness was quantified in all modalities for selected regions of interest (ROI) by fitting the sigmoidally shaped error function to data within locally homogeneous clusters. A strong, almost linear correlation between motion and sharpness improvement was observed, and motion correction significantly improved sharpness. Overall, the Full Width at Half Maximum reduced from 0.88 mm to 0.70 mm after motion correction, equivalent to a 2.0 times smaller voxel volume. Motion and sharpness were not found to correlate with the age of study participants. We conclude that in our data, motion correction using fat navigators is overall able to restore the measured sharpness to the imaging resolution, irrespective of the amount of motion observed during scanning.
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Affiliation(s)
- Pierre-Louis Bazin
- Integrative Model-based Cognitive Neuroscience research unit, Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands.
| | - Hannah E Nijsse
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands.
| | | | - Daniel Gallichan
- CUBRIC, School of Engineering, Cardiff University, Cardiff, United Kingdom.
| | - Anneke Alkemade
- Integrative Model-based Cognitive Neuroscience research unit, Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands.
| | - Frans M Vos
- Department of Imaging Physics, Delft University of Technology, Delft, the Netherlands.
| | - Birte U Forstmann
- Integrative Model-based Cognitive Neuroscience research unit, Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands.
| | - Matthan W A Caan
- Amsterdam UMC, University of Amsterdam, Biomedical Engineering and Physics, Amsterdam, the Netherlands.
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21
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Ziegler G, Moutoussis M, Hauser TU, Fearon P, Bullmore ET, Goodyer IM, Fonagy P, Jones PB, Lindenberger U, Dolan RJ. Childhood socio-economic disadvantage predicts reduced myelin growth across adolescence and young adulthood. Hum Brain Mapp 2020; 41:3392-3402. [PMID: 32432383 PMCID: PMC7375075 DOI: 10.1002/hbm.25024] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 03/16/2020] [Accepted: 04/17/2020] [Indexed: 12/20/2022] Open
Abstract
Socio-economic disadvantage increases exposure to life stressors. Animal research suggests early life stressors impact later neurodevelopment, including myelin developmental growth. To determine how early life disadvantage may affect myelin growth in adolescence and young adulthood, we analysed data from an accelerated longitudinal neuroimaging study measuring magnetisation transfer (MT), a myelin-sensitive marker, in 288 participants (149 female) between 14 and 25 years of age at baseline. We found that early life economic disadvantage before age 12, measured by a neighbourhood poverty index, was associated with slower myelin growth. This association was observed for magnetization transfer in cortical, subcortical and core white matter regions, and also in key subcortical nuclei. Participant IQ at baseline, alcohol use, body mass index, parental occupation and self-reported parenting quality did not account for these effects, but parental education did so partially. Specifically, positive parenting moderated the effect of socio-economic disadvantage in a protective manner. Thus, early socioeconomic disadvantage appears to alter myelin growth across adolescence. This finding has potential translational implications, including clarifying whether reducing socio-economic disadvantage during childhood, and increasing parental education and positive parenting, promote normal trajectories of brain development in economically disadvantaged contexts.
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Affiliation(s)
- Gabriel Ziegler
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.,Max Planck University College London Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Berlin, Germany.,Wellcome Centre for Human Neuroimaging, University College London, London, UK.,Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany.,DZNE Magdeburg, German Center for Neurodegenerative Diseases, Magdeburg, Germany
| | - Michael Moutoussis
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.,Max Planck University College London Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Berlin, Germany.,Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Tobias U Hauser
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.,Max Planck University College London Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Berlin, Germany.,Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Pasco Fearon
- Research Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,Research and Development Department, Cambridgeshire and Peterborough National Health Service Foundation Trust, Cambridge, UK.,Medical Research Council/Wellcome Trust Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Ian M Goodyer
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,Research and Development Department, Cambridgeshire and Peterborough National Health Service Foundation Trust, Cambridge, UK
| | - Peter Fonagy
- Research Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Peter B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,Research and Development Department, Cambridgeshire and Peterborough National Health Service Foundation Trust, Cambridge, UK
| | - Ulman Lindenberger
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.,Max Planck University College London Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Berlin, Germany.,Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Raymond J Dolan
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.,Max Planck University College London Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Berlin, Germany.,Wellcome Centre for Human Neuroimaging, University College London, London, UK
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22
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Taubert M, Roggenhofer E, Melie-Garcia L, Muller S, Lehmann N, Preisig M, Vollenweider P, Marques-Vidal P, Lutti A, Kherif F, Draganski B. Converging patterns of aging-associated brain volume loss and tissue microstructure differences. Neurobiol Aging 2020; 88:108-118. [PMID: 32035845 DOI: 10.1016/j.neurobiolaging.2020.01.006] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 01/08/2020] [Accepted: 01/13/2020] [Indexed: 11/28/2022]
Abstract
Given the worldwide increasing socioeconomic burden of aging-associated brain diseases, there is pressing need to gain in-depth knowledge about the neurobiology of brain anatomy changes across the life span. Advances in quantitative magnetic resonance imaging sensitive to brain's myelin, iron, and free water content allow for a detailed in vivo investigation of aging-related changes while reducing spurious morphometry differences. Main aim of our study is to link previous morphometry findings in aging to microstructural tissue properties in a large-scale cohort (n = 966, age range 46-86 y). Addressing previous controversies in the field, we present results obtained with different approaches to adjust local findings for global effects. Beyond the confirmation of age-related atrophy, myelin, and free water decreases, we report proportionally steeper volume, iron, and myelin decline in sensorimotor and subcortical areas paralleled by free water increase. We demonstrate aging-related white matter volume, myelin, and iron loss in frontostriatal projections. Our findings provide robust evidence for spatial overlap between volume and tissue property differences in aging that affect predominantly motor and executive networks.
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Affiliation(s)
- Marco Taubert
- Chair for Training Science, Cognition and Action, Faculty of Humanities, Otto-von-Guericke University, Magdeburg, Germany; Center for Behavioural and Brain Sciences - CBBS, Magdeburg, Germany; Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Elisabeth Roggenhofer
- Laboratory for Research in Neuroimaging LREN, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Lester Melie-Garcia
- Laboratory for Research in Neuroimaging LREN, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Sandrine Muller
- Laboratory for Research in Neuroimaging LREN, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Nico Lehmann
- Chair for Training Science, Cognition and Action, Faculty of Humanities, Otto-von-Guericke University, Magdeburg, Germany
| | - Martin Preisig
- Center for Research in Psychiatric Epidemiology and Psychopathology, Department of Psychiatry, 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
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging LREN, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging LREN, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging LREN, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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23
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Vanes LD, Moutoussis M, Ziegler G, Goodyer IM, Fonagy P, Jones PB, Bullmore ET, Dolan RJ. White matter tract myelin maturation and its association with general psychopathology in adolescence and early adulthood. Hum Brain Mapp 2019; 41:827-839. [PMID: 31661180 PMCID: PMC7268015 DOI: 10.1002/hbm.24842] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 09/30/2019] [Accepted: 10/14/2019] [Indexed: 12/12/2022] Open
Abstract
Adolescence is a time period associated with marked brain maturation that coincides with an enhanced risk for onset of psychiatric disorder. White matter tract myelination, a process that continues to unfold throughout adolescence, is reported to be abnormal in several psychiatric disorders. Here, we ask whether psychiatric vulnerability is linked to aberrant developmental myelination trajectories. We assessed a marker of myelin maturation, using magnetisation transfer (MT) imaging, in 10 major white matter tracts. We then investigated its relationship to the expression of a general psychopathology "p-factor" in a longitudinal analysis of 293 healthy participants between the ages of 14 and 24. We observed significant longitudinal MT increase across the full age spectrum in anterior thalamic radiation, hippocampal cingulum, dorsal cingulum and superior longitudinal fasciculus. MT increase in the inferior fronto-occipital fasciculus, inferior longitudinal fasciculus and uncinate fasciculus was pronounced in younger participants but levelled off during the transition into young adulthood. Crucially, longitudinal MT increase in dorsal cingulum and uncinate fasciculus decelerated as a function of mean p-factor scores over the study period. This suggests that an increased expression of psychopathology is closely linked to lower rates of myelin maturation in selective brain tracts over time. Impaired myelin growth in limbic association fibres may serve as a neural marker for emerging mental illness during the course of adolescence and early adulthood.
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Affiliation(s)
- Lucy D Vanes
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.,Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Michael Moutoussis
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.,Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Gabriel Ziegler
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | - Ian M Goodyer
- Department of Psychiatry, University of Cambridge Clinical School, Cambridge, UK
| | - Peter Fonagy
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Peter B Jones
- Department of Psychiatry, University of Cambridge Clinical School, Cambridge, UK
| | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge Clinical School, Cambridge, UK
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- Department of Psychiatry, University of Cambridge Clinical School, Cambridge, UK
| | - Raymond J Dolan
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK.,Wellcome Centre for Human Neuroimaging, University College London, London, UK
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24
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Tabelow K, Balteau E, Ashburner J, Callaghan MF, Draganski B, Helms G, Kherif F, Leutritz T, Lutti A, Phillips C, Reimer E, Ruthotto L, Seif M, Weiskopf N, Ziegler G, Mohammadi S. hMRI - A toolbox for quantitative MRI in neuroscience and clinical research. Neuroimage 2019; 194:191-210. [PMID: 30677501 PMCID: PMC6547054 DOI: 10.1016/j.neuroimage.2019.01.029] [Citation(s) in RCA: 125] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 12/21/2018] [Accepted: 01/10/2019] [Indexed: 12/20/2022] Open
Abstract
Neuroscience and clinical researchers are increasingly interested in quantitative magnetic resonance imaging (qMRI) due to its sensitivity to micro-structural properties of brain tissue such as axon, myelin, iron and water concentration. We introduce the hMRI-toolbox, an open-source, easy-to-use tool available on GitHub, for qMRI data handling and processing, presented together with a tutorial and example dataset. This toolbox allows the estimation of high-quality multi-parameter qMRI maps (longitudinal and effective transverse relaxation rates R1 and R2⋆, proton density PD and magnetisation transfer MT saturation) that can be used for quantitative parameter analysis and accurate delineation of subcortical brain structures. The qMRI maps generated by the toolbox are key input parameters for biophysical models designed to estimate tissue microstructure properties such as the MR g-ratio and to derive standard and novel MRI biomarkers. Thus, the current version of the toolbox is a first step towards in vivo histology using MRI (hMRI) and is being extended further in this direction. Embedded in the Statistical Parametric Mapping (SPM) framework, it benefits from the extensive range of established SPM tools for high-accuracy spatial registration and statistical inferences and can be readily combined with existing SPM toolboxes for estimating diffusion MRI parameter maps. From a user's perspective, the hMRI-toolbox is an efficient, robust and simple framework for investigating qMRI data in neuroscience and clinical research.
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Affiliation(s)
| | | | | | | | - Bogdan Draganski
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Switzerland; Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Gunther Helms
- Medical Radiation Physics, Department of Clinical Sciences Lund, Lund University, Lund, Sweden
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Switzerland
| | - Tobias Leutritz
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging, Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne, Switzerland
| | | | - Enrico Reimer
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | | | | | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Gabriel Ziegler
- Institute for Cognitive Neurology and Dementia Research, University of Magdeburg, Germany
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25
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Slater DA, Melie‐Garcia L, Preisig M, Kherif F, Lutti A, Draganski B. Evolution of white matter tract microstructure across the life span. Hum Brain Mapp 2019; 40:2252-2268. [PMID: 30673158 PMCID: PMC6865588 DOI: 10.1002/hbm.24522] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 12/28/2018] [Accepted: 01/02/2019] [Indexed: 01/13/2023] Open
Abstract
The human brain undergoes dramatic structural change over the life span. In a large imaging cohort of 801 individuals aged 7-84 years, we applied quantitative relaxometry and diffusion microstructure imaging in combination with diffusion tractography to investigate tissue property dynamics across the human life span. Significant nonlinear aging effects were consistently observed across tracts and tissue measures. The age at which white matter (WM) fascicles attain peak maturation varies substantially across tissue measurements and tracts. These observations of heterochronicity and spatial heterogeneity of tract maturation highlight the importance of using multiple tissue measurements to investigate each region of the WM. Our data further provide additional quantitative evidence in support of the last-in-first-out retrogenesis hypothesis of aging, demonstrating a strong correlational relationship between peak maturational timing and the extent of quadratic measurement differences across the life span for the most myelin sensitive measures. These findings present an important baseline from which to assess divergence from normative aging trends in developmental and degenerative disorders, and to further investigate the mechanisms connecting WM microstructure to cognition.
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Affiliation(s)
- David A. Slater
- Laboratory of Research in Neuroimaging (LREN) – Department of Clinical Neurosciences – CHUVUniversity of LausanneLausanneSwitzerland
| | - Lester Melie‐Garcia
- Laboratory of Research in Neuroimaging (LREN) – Department of Clinical Neurosciences – CHUVUniversity of LausanneLausanneSwitzerland
| | - Martin Preisig
- Department of Psychiatry – CHUVUniversity of LausanneLausanneSwitzerland
| | - Ferath Kherif
- Laboratory of Research in Neuroimaging (LREN) – Department of Clinical Neurosciences – CHUVUniversity of LausanneLausanneSwitzerland
| | - Antoine Lutti
- Laboratory of Research in Neuroimaging (LREN) – Department of Clinical Neurosciences – CHUVUniversity of LausanneLausanneSwitzerland
| | - Bogdan Draganski
- Laboratory of Research in Neuroimaging (LREN) – Department of Clinical Neurosciences – CHUVUniversity of LausanneLausanneSwitzerland
- Department of Clinical NeurosciencesMax‐Planck‐Institute for Human Cognitive and Brain SciencesLeipzigGermany
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26
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Denisova K. Neurobiology, not artifacts: Challenges and guidelines for imaging the high risk infant. Neuroimage 2019; 185:624-640. [DOI: 10.1016/j.neuroimage.2018.07.023] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2017] [Revised: 07/07/2018] [Accepted: 07/10/2018] [Indexed: 12/21/2022] Open
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