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Guillemin C, Vandeleene N, Charonitis M, Requier F, Delrue G, Lommers E, Maquet P, Phillips C, Collette F. Brain microstructure is linked to cognitive fatigue in early multiple sclerosis. J Neurol 2024; 271:3537-3545. [PMID: 38538776 DOI: 10.1007/s00415-024-12316-1] [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: 12/29/2023] [Revised: 03/08/2024] [Accepted: 03/08/2024] [Indexed: 05/30/2024]
Abstract
Cognitive fatigue is a major symptom of Multiple Sclerosis (MS), from the early stages of the disease. This study aims to detect if brain microstructure is altered early in the disease course and is associated with cognitive fatigue in people with MS (pwMS) compared to matched healthy controls (HC). Recently diagnosed pwMS (N = 18, age < 45 years old) with either a Relapsing-Remitting or a Clinically Isolated Syndrome course of the disease, and HC (N = 19) matched for sex, age and education were analyzed. Quantitative multiparameter maps (MTsat, PD, R1 and R2*) of pwMS and HC were calculated. Parameters were extracted within the normal appearing white matter, cortical grey matter and deep grey matter (NAWM, NACGM and NADGM, respectively). Bayesian T-test for independent samples assessed between-group differences in brain microstructure while associations between score at a cognitive fatigue scale and each parameter in each tissue class were investigated with Generalized Linear Mixed Models. Patients exhibited lower MTsat and R1 values within NAWM and NACGM, and higher R1 values in NADGM compared to HC. Cognitive fatigue was associated with PD measured in every tissue class and to MTsat in NAWM, regardless of group. Disease-specific negative correlations were found in pwMS in NAWM (R1, R2*) and NACGM (R1). These findings suggest that brain microstructure within normal appearing tissues is already altered in the very early stages of the disease. Moreover, additional microstructure alterations (e.g. diffuse and widespread demyelination or axonal degeneration) in pwMS may lead to disease-specific complaint of cognitive fatigue.
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Affiliation(s)
- Camille Guillemin
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
| | - Nora Vandeleene
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium
| | - Maëlle Charonitis
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
| | - Florence Requier
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
| | - Gaël Delrue
- Department of Neurology, CHU of Liège Sart Tilman, Liège, Belgium
| | - Emilie Lommers
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium
- Department of Neurology, CHU of Liège Sart Tilman, Liège, Belgium
| | - Pierre Maquet
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium
- Department of Neurology, CHU of Liège Sart Tilman, Liège, Belgium
| | - Christophe Phillips
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium
- GIGA In Silico Medicine, University of Liège, Liège, Belgium
| | - Fabienne Collette
- GIGA-CRC In Vivo Imaging, University of Liège, Liège, Belgium.
- Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium.
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2
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Arai T, Kamagata K, Uchida W, Andica C, Takabayashi K, Saito Y, Tuerxun R, Mahemuti Z, Morita Y, Irie R, Kirino E, Aoki S. Reduced neurite density index in the prefrontal cortex of adults with autism assessed using neurite orientation dispersion and density imaging. Front Neurol 2023; 14:1110883. [PMID: 37638188 PMCID: PMC10450631 DOI: 10.3389/fneur.2023.1110883] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 07/27/2023] [Indexed: 08/29/2023] Open
Abstract
Background Core symptoms of autism-spectrum disorder (ASD) have been associated with prefrontal cortex abnormalities. However, the mechanisms behind the observation remain incomplete, partially due to the challenges of modeling complex gray matter (GM) structures. This study aimed to identify GM microstructural alterations in adults with ASD using neurite orientation dispersion and density imaging (NODDI) and voxel-wise GM-based spatial statistics (GBSS) to reduce the partial volume effects from the white matter and cerebrospinal fluid. Materials and methods A total of 48 right-handed participants were included, of which 22 had ASD (17 men; mean age, 34.42 ± 8.27 years) and 26 were typically developing (TD) individuals (14 men; mean age, 32.57 ± 9.62 years). The metrics of NODDI (neurite density index [NDI], orientation dispersion index [ODI], and isotropic volume fraction [ISOVF]) were compared between groups using GBSS. Diffusion tensor imaging (DTI) metrics and surface-based cortical thickness were also compared. The associations between magnetic resonance imaging-based measures and ASD-related scores, including ASD-spectrum quotient, empathizing quotient, and systemizing quotient were also assessed in the region of interest (ROI) analysis. Results After controlling for age, sex, and intracranial volume, GBSS demonstrated significantly lower NDI in the ASD group than in the TD group in the left prefrontal cortex (caudal middle frontal, lateral orbitofrontal, pars orbitalis, pars triangularis, rostral middle frontal, and superior frontal region). In the ROI analysis of individuals with ASD, a significantly positive correlation was observed between the NDI in the left rostral middle frontal, superior frontal, and left frontal pole and empathizing quotient score. No significant between-group differences were observed in all DTI metrics, other NODDI (i.e., ODI and ISOVF) metrics, and cortical thickness. Conclusion GBSS analysis was used to demonstrate the ability of NODDI metrics to detect GM microstructural alterations in adults with ASD, while no changes were detected using DTI and cortical thickness evaluation. Specifically, we observed a reduced neurite density index in the left prefrontal cortices associated with reduced empathic abilities.
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Affiliation(s)
- Takashi Arai
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Koji Kamagata
- Faculty of Health Data Science, Juntendo University, Chiba, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Faculty of Health Data Science, Juntendo University, Chiba, Japan
| | - Kaito Takabayashi
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuya Saito
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Rukeye Tuerxun
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Zaimire Mahemuti
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuichi Morita
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ryusuke Irie
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Eiji Kirino
- Department of Psychiatry, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Psychiatry, Juntendo University Shizuoka Hospital, Shizuoka, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
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3
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Zhou W, Graner M, Paucek P, Beseler C, Boisen M, Bubak A, Asturias F, George W, Graner A, Ormond D, Vollmer T, Alvarez E, Yu X. Multiple sclerosis plasma IgG aggregates induce complement-dependent neuronal apoptosis. Cell Death Dis 2023; 14:254. [PMID: 37031195 PMCID: PMC10082781 DOI: 10.1038/s41419-023-05783-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 03/23/2023] [Accepted: 03/27/2023] [Indexed: 04/10/2023]
Abstract
Grey matter pathology is central to the progression of multiple sclerosis (MS). We discovered that MS plasma immunoglobulin G (IgG) antibodies, mainly IgG1, form large aggregates (>100 nm) which are retained in the flow-through after binding to Protein A. Utilizing an annexin V live-cell apoptosis detection assay, we demonstrated six times higher levels of neuronal apoptosis induced by MS plasma IgG aggregates (n = 190, from two cohorts) compared to other neurological disorders (n = 116) and healthy donors (n = 44). MS IgG aggregate-mediated, complement-dependent neuronal apoptosis was evaluated in multiple model systems including primary human neurons, primary human astrocytes, neuroblastoma SH-SY5Y cells, and newborn mouse brain slices. Immunocytochemistry revealed the co-deposition of IgG, early and late complement activation products (C1q, C3b, and membrane attack complex C5b9), as well as active caspase 3 in treated neuronal cells. Furthermore, we found that MS plasma cytotoxic antibodies are not present in Protein G flow-through, nor in the paired plasma. The neuronal apoptosis can be inhibited by IgG depletion, disruption of IgG aggregates, pan-caspase inhibitor, and is completely abolished by digestion with IgG-cleaving enzyme IdeS. Transmission electron microscopy and nanoparticle tracking analysis revealed the sizes of MS IgG aggregates are greater than 100 nm. Our data support the pathological role of MS IgG antibodies and corroborate their connection to complement activation and axonal damage, suggesting that apoptosis may be a mechanism of neurodegeneration in MS.
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Affiliation(s)
- Wenbo Zhou
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, 80045, USA
| | - Michael Graner
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, 80045, USA
| | - Petr Paucek
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, 80045, USA
| | - Cheryl Beseler
- Department of Environmental, Agricultural and Occupational Health, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Matthew Boisen
- Zalgen Labs, LLC, 12635 E. Montview Blvd., Suite 131, Aurora, Colorado, 80045, USA
| | - Andrew Bubak
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, 80045, USA
| | - Francisco Asturias
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, 80045, USA
| | - Woro George
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, 80045, USA
| | - Arin Graner
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, 80045, USA
| | - David Ormond
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, 80045, USA
| | - Timothy Vollmer
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, 80045, USA
| | - Enrique Alvarez
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, 80045, USA
| | - Xiaoli Yu
- Department of Neurosurgery, University of Colorado Anschutz Medical Campus, Aurora, Colorado, 80045, USA.
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4
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The Possible Role of Neural Cell Apoptosis in Multiple Sclerosis. Int J Mol Sci 2022; 23:ijms23147584. [PMID: 35886931 PMCID: PMC9316123 DOI: 10.3390/ijms23147584] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/04/2022] [Accepted: 07/05/2022] [Indexed: 11/17/2022] Open
Abstract
The etiology of multiple sclerosis (MS), a demyelinating disease affecting the central nervous system (CNS), remains obscure. Although apoptosis of oligodendrocytes and neurons has been observed in MS lesions, the contribution of this cell death process to disease pathogenesis remains controversial. It is usually considered that MS-associated demyelination and axonal degeneration result from neuroinflammation and an autoimmune process targeting myelin proteins. However, experimental data indicate that oligodendrocyte and/or neuronal cell death may indeed precede the development of inflammation and autoimmunity. These findings raise the question as to whether neural cell apoptosis is the key event initiating and/or driving the pathological cascade, leading to clinical functional deficits in MS. Similarly, regarding axonal damage, a key pathological feature of MS lesions, the roles of inflammation-independent and cell autonomous neuronal processes need to be further explored. While oligodendrocyte and neuronal loss in MS may not necessarily be mutually exclusive, particular attention should be given to the role of neuronal apoptosis in the development of axonal loss. If proven, MS could be viewed primarily as a neurodegenerative disease accompanied by a secondary neuroinflammatory and autoimmune process.
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5
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Andica C, Hagiwara A, Yokoyama K, Kato S, Uchida W, Nishimura Y, Fujita S, Kamagata K, Hori M, Tomizawa Y, Hattori N, Aoki S. Multimodal magnetic resonance imaging quantification of gray matter alterations in relapsing-remitting multiple sclerosis and neuromyelitis optica spectrum disorder. J Neurosci Res 2022; 100:1395-1412. [PMID: 35316545 DOI: 10.1002/jnr.25035] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 02/07/2022] [Accepted: 02/13/2022] [Indexed: 11/08/2022]
Abstract
Herein, we combined neurite orientation dispersion and density imaging (NODDI) and synthetic magnetic resonance imaging (SyMRI) to evaluate the spatial distribution and extent of gray matter (GM) microstructural alterations in patients with relapsing-remitting multiple sclerosis (RRMS) and neuromyelitis optica spectrum disorder (NMOSD). The NODDI (neurite density index [NDI], orientation dispersion index [ODI], and isotropic volume fraction [ISOVF]) and SyMRI (myelin volume fraction [MVF]) measures were compared between age- and sex-matched groups of 30 patients with RRMS (6 males and 24 females; mean age, 51.43 ± 8.02 years), 18 patients with anti-aquaporin-4 antibody-positive NMOSD (2 males and 16 females; mean age, 52.67 ± 16.07 years), and 19 healthy controls (6 males and 13 females; mean age, 51.47 ± 9.25 years) using GM-based spatial statistical analysis. Patients with RRMS showed reduced NDI and MVF and increased ODI and ISOVF, predominantly in the limbic and paralimbic regions, when compared with healthy controls, while only increases in ODI and ISOVF were observed when compared with NMOSD. Compared to NDI and MVF, the changes in ODI and ISOVF were observed more widely, including in the cerebellar cortex. These abnormalities were associated with disease progression and disability. In contrast, patients with NMOSD only showed reduced NDI mainly in the cerebellar, limbic, and paralimbic cortices when compared with healthy controls and patients with RRMS. Taken together, our study supports the notion that GM pathologies in RRMS are distinct from those of NMOSD. However, owing to the limitations of the study, the results should be cautiously interpreted.
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Affiliation(s)
- Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Radiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Kazumasa Yokoyama
- Department of Neurology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Shimpei Kato
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuma Nishimura
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Shohei Fujita
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Masaaki Hori
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - Yuji Tomizawa
- Department of Neurology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Nobutaka Hattori
- Department of Neurology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
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6
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Barile B, Marzullo A, Stamile C, Durand-Dubief F, Sappey-Marinier D. Ensemble Learning for Multiple Sclerosis Disability Estimation Using Brain Structural Connectivity. Brain Connect 2021; 12:476-488. [PMID: 34269618 DOI: 10.1089/brain.2020.1003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Multiple Sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system characterized by demyelination and neurodegeneration processes. It leads to different clinical courses and degrees of disability that need to be anticipated by the neurologist for personalized therapy. Recently, machine learning (ML) techniques have reached a high level of performance in brain disease diagnosis and/or prognosis, but the decision process of a trained ML system is typically non-transparent. Using brain structural connectivity data, a fully automatic ensemble learning model, augmented with an interpretable model, is proposed for the estimation of MS patients' disability, measured by the Expanded Disability Status Scale (EDSS). METHOD An ensemble of four boosting-based models (GBM, XGBoost, CatBoost, LightBoost) organized following a stacking generalization scheme, was developed using DTI-based structural connectivity data. In addition, an interpretable model based on conditional logistic regression was developed to explain the best performances in terms of white matter (WM) links for three classes of EDSS (Low, Medium, High). RESULTS The ensemble model reached excellent level of performance (RMSE of 0.92 ± 0.28) compared to single-based models and provided a better EDSS estimation using DTI-based structural connectivity data compared to conventional MRI measures associated with patient data (age, gender and disease duration). Used for interpretation of the estimation process, the counterfactual method showed the importance of certain brain networks, corresponding mainly to left hemisphere WM links, connecting the left superior temporal with the left posterior cingulate and the right precuneus gray matter regions, and the inter-hemispheric WM links constituting the corpus callosum. Also, a better accuracy estimation was found for the high disability class. CONCLUSION The combination of advanced ML models and sensitive techniques such as DTI-based structural connectivity demonstrated to be useful for the estimation of MS patients' disability and to point out the most important brain WM networks involved in disability.
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Affiliation(s)
- Berardino Barile
- Université Claude Bernard Lyon 1, 27098, 1CREATIS (UMR5220 & INSERM U1206), 43 Boulevard du 11 Novembre 1918, Villeurbanne, Villeurbanne, France, 69100;
| | - Aldo Marzullo
- University of Calabria, 18950, Mathematics and Computer Science, Arcavacata di Rende, Calabria, Italy;
| | | | - Francoise Durand-Dubief
- Hospices Civils de Lyon, 26900, Lyon, Auvergne-Rhône-Alpes , France.,Université Claude Bernard Lyon 1, 27098, CREATIS (UMR5220 & INSERM U1206), Villeurbanne, Auvergne-Rhône-Alpes , France;
| | - Dominique Sappey-Marinier
- Université de Lyon, 133614, Lyon, Auvergne-Rhône-Alpes , France.,Université Claude Bernard Lyon 1, 27098, CREATIS (UMR5220 & INSERM U1206), Villeurbanne, Auvergne-Rhône-Alpes , France;
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7
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Erramuzpe A, Schurr R, Yeatman JD, Gotlib IH, Sacchet MD, Travis KE, Feldman HM, Mezer AA. A Comparison of Quantitative R1 and Cortical Thickness in Identifying Age, Lifespan Dynamics, and Disease States of the Human Cortex. Cereb Cortex 2021; 31:1211-1226. [PMID: 33095854 PMCID: PMC8485079 DOI: 10.1093/cercor/bhaa288] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 08/25/2020] [Accepted: 09/03/2020] [Indexed: 07/22/2023] Open
Abstract
Brain development and aging are complex processes that unfold in multiple brain regions simultaneously. Recently, models of brain age prediction have aroused great interest, as these models can potentially help to understand neurological diseases and elucidate basic neurobiological mechanisms. We test whether quantitative magnetic resonance imaging can contribute to such age prediction models. Using R1, the longitudinal rate of relaxation, we explore lifespan dynamics in cortical gray matter. We compare R1 with cortical thickness, a well-established biomarker of brain development and aging. Using 160 healthy individuals (6-81 years old), we found that R1 and cortical thickness predicted age similarly, but the regions contributing to the prediction differed. Next, we characterized R1 development and aging dynamics. Compared with anterior regions, in posterior regions we found an earlier R1 peak but a steeper postpeak decline. We replicate these findings: firstly, we tested a subset (N = 10) of the original dataset for whom we had additional scans at a lower resolution; and second, we verified the results on an independent dataset (N = 34). Finally, we compared the age prediction models on a subset of 10 patients with multiple sclerosis. The patients are predicted older than their chronological age using R1 but not with cortical thickness.
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Affiliation(s)
| | - R Schurr
- The Hebrew University of Jerusalem, The Edmond and Lily Safra Center for Brain Sciences, Jerusalem, Israel
| | - J D Yeatman
- Graduate School of Education, Stanford University, Stanford, CA, USA
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - I H Gotlib
- Psychology, Stanford University, Stanford, CA, USA
| | - M D Sacchet
- Harvard Medical School, Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
| | - K E Travis
- Pediatrics, Stanford University, Stanford, CA, USA
| | - H M Feldman
- Development and Behavior Unit, Stanford University, Stanford, CA, USA
| | - A A Mezer
- The Hebrew University of Jerusalem, The Edmond and Lily Safra Center for Brain Sciences, Jerusalem, Israel
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8
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Ji S, Yang D, Lee J, Choi SH, Kim H, Kang KM. Synthetic MRI: Technologies and Applications in Neuroradiology. J Magn Reson Imaging 2020; 55:1013-1025. [PMID: 33188560 DOI: 10.1002/jmri.27440] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Revised: 10/29/2020] [Accepted: 10/29/2020] [Indexed: 12/14/2022] Open
Abstract
Synthetic MRI is a technique that synthesizes contrast-weighted images from multicontrast MRI data. There have been advances in synthetic MRI since the technique was introduced. Although a number of synthetic MRI methods have been developed for quantifying one or more relaxometric parameters and for generating multiple contrast-weighted images, this review focuses on several methods that quantify all three relaxometric parameters (T1 , T2 , and proton density) and produce multiple contrast-weighted images. Acquisition, quantification, and image synthesis techniques are discussed for each method. We discuss the image quality and diagnostic accuracy of synthetic MRI methods and their clinical applications in neuroradiology. Based on this analysis, we highlight areas that need to be addressed for synthetic MRI to be widely implemented in the clinic. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Sooyeon Ji
- Electrical and Computer Engineering, Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea
| | - Dongjin Yang
- Department of Radiology, Daegu Fatima Hospital, Daegu, Republic of Korea
| | - Jongho Lee
- Electrical and Computer Engineering, Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea
| | - Seung Hong Choi
- Electrical and Computer Engineering, Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea.,Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.,Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hyeonjin Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Koung Mi Kang
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
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9
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Lommers E, Guillemin C, Reuter G, Fouarge E, Delrue G, Collette F, Degueldre C, Balteau E, Maquet P, Phillips C. Voxel-Based quantitative MRI reveals spatial patterns of grey matter alteration in multiple sclerosis. Hum Brain Mapp 2020; 42:1003-1012. [PMID: 33155763 PMCID: PMC7856642 DOI: 10.1002/hbm.25274] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 10/10/2020] [Accepted: 10/22/2020] [Indexed: 12/22/2022] Open
Abstract
Despite robust postmortem evidence and potential clinical importance of gray matter (GM) pathology in multiple sclerosis (MS), assessing GM damage by conventional magnetic resonance imaging (MRI) remains challenging. This prospective cross‐sectional study aimed at characterizing the topography of GM microstructural and volumetric alteration in MS using, in addition to brain atrophy measures, three quantitative MRI (qMRI) parameters—magnetization transfer (MT) saturation, longitudinal (R1), and effective transverse (R2*) relaxation rates, derived from data acquired during a single scanning session. Our study involved 35 MS patients (14 relapsing–remitting MS; 21 primary or secondary progressive MS) and 36 age‐matched healthy controls (HC). The qMRI maps were computed and segmented in different tissue classes. Voxel‐based quantification (VBQ) and voxel‐based morphometry (VBM) statistical analyses were carried out using multiple linear regression models. In MS patients compared with HC, three configurations of GM microstructural/volumetric alterations were identified. (a) Co‐localization of GM atrophy with significant reduction of MT, R1, and/or R2*, usually observed in primary cortices. (b) Microstructural modifications without significant GM loss: hippocampus and paralimbic cortices, showing reduced MT and/or R1 values without significant atrophy. (c) Atrophy without significant change in microstructure, identified in deep GM nuclei. In conclusion, this quantitative multiparametric voxel‐based approach reveals three different spatially‐segregated combinations of GM microstructural/volumetric alterations in MS that might be associated with different neuropathology.
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Affiliation(s)
- Emilie Lommers
- GIGA - CRC in vivo imaging, University of Liège, Liège, Belgium.,Clinical Neuroimmunology Unit, Neurology Department, CHU Liège, Liège, Belgium
| | - Camille Guillemin
- GIGA - CRC in vivo imaging, University of Liège, Liège, Belgium.,Psychology and Neuroscience of Cognition Research Unit, University of Liège, Liège, Belgium
| | - Gilles Reuter
- GIGA - CRC in vivo imaging, University of Liège, Liège, Belgium.,Neurosurgery Department, CHU Liège, Liège, Belgium
| | - Eve Fouarge
- GIGA - CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Gaël Delrue
- Clinical Neuroimmunology Unit, Neurology Department, CHU Liège, Liège, Belgium
| | - Fabienne Collette
- GIGA - CRC in vivo imaging, University of Liège, Liège, Belgium.,Psychology and Neuroscience of Cognition Research Unit, University of Liège, Liège, Belgium
| | | | - Evelyne Balteau
- GIGA - CRC in vivo imaging, University of Liège, Liège, Belgium
| | - Pierre Maquet
- GIGA - CRC in vivo imaging, University of Liège, Liège, Belgium.,Clinical Neuroimmunology Unit, Neurology Department, CHU Liège, Liège, Belgium
| | - Christophe Phillips
- GIGA - CRC in vivo imaging, University of Liège, Liège, Belgium.,GIGA - in silico medicine, University of Liège, Liège, Belgium
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10
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Hagiwara A, Fujita S, Ohno Y, Aoki S. Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence. Invest Radiol 2020; 55:601-616. [PMID: 32209816 PMCID: PMC7413678 DOI: 10.1097/rli.0000000000000666] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 01/28/2020] [Indexed: 12/19/2022]
Abstract
Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.
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Affiliation(s)
- Akifumi Hagiwara
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
| | | | - Yoshiharu Ohno
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Shigeki Aoki
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
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Roles of Progesterone, Testosterone and Their Nuclear Receptors in Central Nervous System Myelination and Remyelination. Int J Mol Sci 2020; 21:ijms21093163. [PMID: 32365806 PMCID: PMC7246940 DOI: 10.3390/ijms21093163] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 04/27/2020] [Accepted: 04/28/2020] [Indexed: 12/14/2022] Open
Abstract
Progesterone and testosterone, beyond their roles as sex hormones, are neuroactive steroids, playing crucial regulatory functions within the nervous system. Among these, neuroprotection and myelin regeneration are important ones. The present review aims to discuss the stimulatory effects of progesterone and testosterone on the process of myelination and remyelination. These effects have been demonstrated in vitro (i.e., organotypic cultures) and in vivo (cuprizone- or lysolecithin-induced demyelination and experimental autoimmune encephalomyelitis (EAE)). Both steroids stimulate myelin formation and regeneration by acting through their respective intracellular receptors: progesterone receptors (PR) and androgen receptors (AR). Activation of these receptors results in multiple events involving direct transcription and translation, regulating general homeostasis, cell proliferation, differentiation, growth and myelination. It also ameliorates immune response as seen in the EAE model, resulting in a significant decrease in inflammation leading to a fast recovery. Although natural progesterone and testosterone have a therapeutic potential, their synthetic derivatives—the 19-norprogesterone (nestorone) and 7α-methyl-nortestosterone (MENT), already used as hormonal contraception or in postmenopausal hormone replacement therapies, may offer enhanced benefits for myelin repair. We summarize here a recent advancement in the field of myelin biology, to treat demyelinating disorders using the natural as well as synthetic analogs of progesterone and testosterone.
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Kapre R, Zhou J, Li X, Beckett L, Louie AY. A novel gamma GLM approach to MRI relaxometry comparisons. Magn Reson Med 2020; 84:1592-1604. [PMID: 32048764 PMCID: PMC7317199 DOI: 10.1002/mrm.28192] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 12/18/2019] [Accepted: 01/10/2020] [Indexed: 12/16/2022]
Abstract
PURPOSE To demonstrate that constant coefficient of variation (CV), but nonconstant absolute variance in MRI relaxometry (T1 , T2 , R1 , R2 ) data leads to erroneous conclusions based on standard linear models such as ordinary least squares (OLS). We propose a gamma generalized linear model identity link (GGLM-ID) framework that factors the inherent CV into parameter estimates. We first examined the effects on calculations of contrast agent relaxivity before broadening to other applications such as analysis of variance (ANOVA) and liver iron content (LIC). METHODS Eight models including OLS and GGLM-ID were initially fit to data obtained on sulfated dextran iron oxide (SDIO) nanoparticles. Both a resampling simulation on the data as well as two separate Monte Carlo simulations (with and without concentration error) were performed to determine mean square error (MSE) and type I error rate. We then evaluated the performance of OLS/GGLM-ID on R1 repeatability and LIC data sets. RESULTS OLS had an MSE of 4-5× that of GGLM-ID as well as a type I error rate of 20-30%, whereas GGLM-ID was near the nominal 5% level in the relaxivity study. Only OLS found statistically significant effects of MRI facility on relaxivity in an R1 repeatability study, but no significant differences were found in a resampling, whereas GGLM was more consistent. GGLM-ID was also superior to OLS for modeling LIC. CONCLUSIONS OLS leads to erroneous conclusions when analyzing MRI relaxometry data. GGLM-ID factors in the inherent CV of an MRI experiment, leading to more reproducible conclusions.
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Affiliation(s)
- Rohan Kapre
- Department of Biomedical Engineering, University of California, Davis, CA.,Biostatistics Graduate Group, University of California, Davis, CA
| | - Junhan Zhou
- Chemistry Graduate Group, University of California, Davis, CA
| | - Xinzhe Li
- Department of Biomedical Engineering, University of California, Davis, CA
| | - Laurel Beckett
- Biostatistics Graduate Group, University of California, Davis, CA
| | - Angelique Y Louie
- Department of Biomedical Engineering, University of California, Davis, CA.,Chemistry Graduate Group, University of California, Davis, CA
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Hagiwara A, Kamagata K, Shimoji K, Yokoyama K, Andica C, Hori M, Fujita S, Maekawa T, Irie R, Akashi T, Wada A, Suzuki M, Abe O, Hattori N, Aoki S. White Matter Abnormalities in Multiple Sclerosis Evaluated by Quantitative Synthetic MRI, Diffusion Tensor Imaging, and Neurite Orientation Dispersion and Density Imaging. AJNR Am J Neuroradiol 2019; 40:1642-1648. [PMID: 31515218 DOI: 10.3174/ajnr.a6209] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 07/28/2019] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE A number of MR-derived quantitative metrics have been suggested to assess the pathophysiology of MS, but the reports about combined analyses of these metrics are scarce. Our aim was to assess the spatial distribution of parameters for white matter myelin and axon integrity in patients with relapsing-remitting MS by multiparametric MR imaging. MATERIALS AND METHODS Twenty-four patients with relapsing-remitting MS and 24 age- and sex-matched controls were prospectively scanned by quantitative synthetic and 2-shell diffusion MR imaging. Synthetic MR imaging data were used to retrieve relaxometry parameters (R1 and R2 relaxation rates and proton density) and myelin volume fraction. Diffusion tensor metrics (fractional anisotropy and mean, axial, and radial diffusivity) and neurite orientation and dispersion index metrics (intracellular volume fraction, isotropic volume fraction, and orientation dispersion index) were retrieved from diffusion MR imaging data. These data were analyzed using Tract-Based Spatial Statistics. RESULTS Patients with MS showed significantly lower fractional anisotropy and myelin volume fraction and higher isotropic volume fraction in widespread white matter areas. Areas with different isotropic volume fractions were included within areas with lower fractional anisotropy. Myelin volume fraction showed no significant difference in some areas with significantly decreased fractional anisotropy in MS, including in the genu of the corpus callosum and bilateral anterior corona radiata, whereas myelin volume fraction was significantly decreased in some areas where fractional anisotropy showed no significant difference, including the bilateral posterior limb of the internal capsule, external capsule, sagittal striatum, fornix, and uncinate fasciculus. CONCLUSIONS We found differences in spatial distribution of abnormality in fractional anisotropy, isotropic volume fraction, and myelin volume fraction distribution in MS, which might be useful for characterizing white matter in patients with MS.
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Affiliation(s)
- A Hagiwara
- From the Departments of Radiology (A.H., K.K., K.S., C.A., M.H., S.F., T.M., R.I., T.A., A.W., M.S., S.A.)
- Department of Radiology (A.H., S.F., T.M., R.I., O.A.), Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - K Kamagata
- From the Departments of Radiology (A.H., K.K., K.S., C.A., M.H., S.F., T.M., R.I., T.A., A.W., M.S., S.A.)
| | - K Shimoji
- From the Departments of Radiology (A.H., K.K., K.S., C.A., M.H., S.F., T.M., R.I., T.A., A.W., M.S., S.A.)
- Department of Diagnostic Radiology (K.S.), Tokyo Metropolitan Geriatric Hospital, Tokyo, Japan
| | - K Yokoyama
- Neurology (K.Y., N.H.), Juntendo University School of Medicine, Tokyo, Japan
| | - C Andica
- From the Departments of Radiology (A.H., K.K., K.S., C.A., M.H., S.F., T.M., R.I., T.A., A.W., M.S., S.A.)
| | - M Hori
- From the Departments of Radiology (A.H., K.K., K.S., C.A., M.H., S.F., T.M., R.I., T.A., A.W., M.S., S.A.)
- Department of Radiology (M.H.), Toho University Omori Medical Center, Tokyo, Japan
| | - S Fujita
- From the Departments of Radiology (A.H., K.K., K.S., C.A., M.H., S.F., T.M., R.I., T.A., A.W., M.S., S.A.)
- Department of Radiology (A.H., S.F., T.M., R.I., O.A.), Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - T Maekawa
- From the Departments of Radiology (A.H., K.K., K.S., C.A., M.H., S.F., T.M., R.I., T.A., A.W., M.S., S.A.)
- Department of Radiology (A.H., S.F., T.M., R.I., O.A.), Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - R Irie
- From the Departments of Radiology (A.H., K.K., K.S., C.A., M.H., S.F., T.M., R.I., T.A., A.W., M.S., S.A.)
- Department of Radiology (A.H., S.F., T.M., R.I., O.A.), Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - T Akashi
- From the Departments of Radiology (A.H., K.K., K.S., C.A., M.H., S.F., T.M., R.I., T.A., A.W., M.S., S.A.)
| | - A Wada
- From the Departments of Radiology (A.H., K.K., K.S., C.A., M.H., S.F., T.M., R.I., T.A., A.W., M.S., S.A.)
| | - M Suzuki
- From the Departments of Radiology (A.H., K.K., K.S., C.A., M.H., S.F., T.M., R.I., T.A., A.W., M.S., S.A.)
| | - O Abe
- Department of Radiology (A.H., S.F., T.M., R.I., O.A.), Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - N Hattori
- Neurology (K.Y., N.H.), Juntendo University School of Medicine, Tokyo, Japan
| | - S Aoki
- From the Departments of Radiology (A.H., K.K., K.S., C.A., M.H., S.F., T.M., R.I., T.A., A.W., M.S., S.A.)
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