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Mak E, Reid RI, Przybelski SA, Lesnick TG, Schwarz CG, Senjem ML, Raghavan S, Vemuri P, Jack CR, Min HK, Jain MK, Miyagawa T, Forsberg LK, Fields JA, Savica R, Graff-Radford J, Jones DT, Botha H, St Louis EK, Knopman DS, Ramanan VK, Dickson DW, Graff-Radford NR, Ferman TJ, Petersen RC, Lowe VJ, Boeve BF, O'Brien JT, Kantarci K. Influences of amyloid-β and tau on white matter neurite alterations in dementia with Lewy bodies. NPJ Parkinsons Dis 2024; 10:76. [PMID: 38570511 PMCID: PMC10991290 DOI: 10.1038/s41531-024-00684-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 03/13/2024] [Indexed: 04/05/2024] Open
Abstract
Dementia with Lewy bodies (DLB) is a neurodegenerative condition often co-occurring with Alzheimer's disease (AD) pathology. Characterizing white matter tissue microstructure using Neurite Orientation Dispersion and Density Imaging (NODDI) may help elucidate the biological underpinnings of white matter injury in individuals with DLB. In this study, diffusion tensor imaging (DTI) and NODDI metrics were compared in 45 patients within the dementia with Lewy bodies spectrum (mild cognitive impairment with Lewy bodies (n = 13) and probable dementia with Lewy bodies (n = 32)) against 45 matched controls using conditional logistic models. We evaluated the associations of tau and amyloid-β with DTI and NODDI parameters and examined the correlations of AD-related white matter injury with Clinical Dementia Rating (CDR). Structural equation models (SEM) explored relationships among age, APOE ε4, amyloid-β, tau, and white matter injury. The DLB spectrum group exhibited widespread white matter abnormalities, including reduced fractional anisotropy, increased mean diffusivity, and decreased neurite density index. Tau was significantly associated with limbic and temporal white matter injury, which was, in turn, associated with worse CDR. SEM revealed that amyloid-β exerted indirect effects on white matter injury through tau. We observed widespread disruptions in white matter tracts in DLB that were not attributed to AD pathologies, likely due to α-synuclein-related injury. However, a fraction of the white matter injury could be attributed to AD pathology. Our findings underscore the impact of AD pathology on white matter integrity in DLB and highlight the utility of NODDI in elucidating the biological basis of white matter injury in DLB.
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Affiliation(s)
- Elijah Mak
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Robert I Reid
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | - Scott A Przybelski
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Timothy G Lesnick
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Hoon Ki Min
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Manoj K Jain
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| | - Toji Miyagawa
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | - Julie A Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Rodolfo Savica
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Erik K St Louis
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
- Center for Sleep Medicine, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | - Dennis W Dickson
- Department of Psychiatry and Psychology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Tanis J Ferman
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
| | - Ronald C Petersen
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - John T O'Brien
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.
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Fernandez L, Corben LA, Bilal H, Delatycki MB, Egan GF, Harding IH. Free-Water Imaging in Friedreich Ataxia Using Multi-Compartment Models. Mov Disord 2024; 39:370-379. [PMID: 37927246 DOI: 10.1002/mds.29648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 09/14/2023] [Accepted: 10/11/2023] [Indexed: 11/07/2023] Open
Abstract
BACKGROUND The neurological phenotype of Friedreich ataxia (FRDA) is characterized by neurodegeneration and neuroinflammation in the cerebellum and brainstem. Novel neuroimaging approaches quantifying brain free-water using diffusion magnetic resonance imaging (dMRI) are potentially more sensitive to these processes than standard imaging markers. OBJECTIVES To quantify the extent of free-water and microstructural change in FRDA-relevant brain regions using neurite orientation dispersion and density imaging (NODDI), and bitensor diffusion tensor imaging (btDTI). METHOD Multi-shell dMRI was acquired from 14 individuals with FRDA and 14 controls. Free-water measures from NODDI (FISO) and btDTI (FW) were compared between groups in the cerebellar cortex, dentate nuclei, cerebellar peduncles, and brainstem. The relative sensitivity of the free-water measures to group differences was compared to microstructural measures of NODDI intracellular volume, free-water corrected fractional anisotropy, and conventional uncorrected fractional anisotropy. RESULTS In individuals with FRDA, FW was elevated in the cerebellar cortex, peduncles (excluding middle), dentate, and brainstem (P < 0.005). FISO was elevated primarily in the cerebellar lobules (P < 0.001). On average, FW effect sizes were larger than all other markers (mean ηρ 2 = 0.43), although microstructural measures also had very large effects in the superior and inferior cerebellar peduncles and brainstem (ηρ 2 > 0.37). Across all regions and metrics, effect sizes were largest in the superior cerebellar peduncles (ηρ 2 > 0.46). CONCLUSIONS Multi-compartment diffusion measures of free-water and neurite integrity distinguish FRDA from controls with large effects. Free-water magnitude in the brainstem and cerebellum provided the greatest distinction between groups. This study supports further applications of multi-compartment diffusion modeling, and investigations of free-water as a measure of disease expression and progression in FRDA. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Lara Fernandez
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Louise A Corben
- Bruce Lefroy Centre for Genetic Health Research, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
- Turner Institute for Brain and Mental Health & School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Hiba Bilal
- Turner Institute for Brain and Mental Health & School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Martin B Delatycki
- Bruce Lefroy Centre for Genetic Health Research, Murdoch Children's Research Institute, Parkville, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
- Victorian Clinical Genetics Service, Melbourne, Victoria, Australia
| | - Gary F Egan
- Turner Institute for Brain and Mental Health & School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Ian H Harding
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
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Ananthavarathan P, Sahi N, Chard DT. An update on the role of magnetic resonance imaging in predicting and monitoring multiple sclerosis progression. Expert Rev Neurother 2024; 24:201-216. [PMID: 38235594 DOI: 10.1080/14737175.2024.2304116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/08/2024] [Indexed: 01/19/2024]
Abstract
INTRODUCTION While magnetic resonance imaging (MRI) is established in diagnosing and monitoring disease activity in multiple sclerosis (MS), its utility in predicting and monitoring disease progression is less clear. AREAS COVERED The authors consider changing concepts in the phenotypic classification of MS, including progression independent of relapses; pathological processes underpinning progression; advances in MRI measures to assess them; how well MRI features explain and predict clinical outcomes, including models that assess disease effects on neural networks, and the potential role for machine learning. EXPERT OPINION Relapsing-remitting and progressive MS have evolved from being viewed as mutually exclusive to having considerable overlap. Progression is likely the consequence of several pathological elements, each important in building more holistic prognostic models beyond conventional phenotypes. MRI is well placed to assess pathogenic processes underpinning progression, but we need to bridge the gap between MRI measures and clinical outcomes. Mapping pathological effects on specific neural networks may help and machine learning methods may be able to optimize predictive markers while identifying new, or previously overlooked, clinically relevant features. The ever-increasing ability to measure features on MRI raises the dilemma of what to measure and when, and the challenge of translating research methods into clinically useable tools.
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Affiliation(s)
- Piriyankan Ananthavarathan
- Department of Neuroinflammation, University College London Queen Square Multiple Sclerosis Centre, London, UK
| | - Nitin Sahi
- Department of Neuroinflammation, University College London Queen Square Multiple Sclerosis Centre, London, UK
| | - Declan T Chard
- Clinical Research Associate & Consultant Neurologist, Institute of Neurology - Queen Square Multiple Sclerosis Centre, London, UK
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Seyedmirzaei H, Nabizadeh F, Aarabi MH, Pini L. Neurite Orientation Dispersion and Density Imaging in Multiple Sclerosis: A Systematic Review. J Magn Reson Imaging 2023; 58:1011-1029. [PMID: 37042392 DOI: 10.1002/jmri.28727] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 03/27/2023] [Accepted: 03/28/2023] [Indexed: 04/13/2023] Open
Abstract
Diffusion-weighted imaging has been applied to investigate alterations in multiple sclerosis (MS). In the last years, advanced diffusion models were used to identify subtle changes and early lesions in MS. Among these models, neurite orientation dispersion and density imaging (NODDI) is an emerging approach, quantifying specific neurite morphology in both grey (GM) and white matter (WM) tissue and increasing the specificity of diffusion imaging. In this systematic review, we summarized the NODDI findings in MS. A search was conducted on PubMed, Scopus, and Embase, which yielded a total number of 24 eligible studies. Compared to healthy tissue, these studies identified consistent alterations in NODDI metrics involving WM (neurite density index), and GM lesions (neurite density index), or normal-appearing WM tissue (isotropic volume fraction and neurite density index). Despite some limitations, we pointed out the potential of NODDI in MS to unravel microstructural alterations. These results might pave the way to a deeper understanding of the pathophysiological mechanism of MS. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
| | | | | | - Lorenzo Pini
- Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
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Martinie O, Karan P, Traverse E, Mercier C, Descoteaux M, Robert MT. The Challenge of Diffusion Magnetic Resonance Imaging in Cerebral Palsy: A Proposed Method to Identify White Matter Pathways. Brain Sci 2023; 13:1386. [PMID: 37891755 PMCID: PMC10605121 DOI: 10.3390/brainsci13101386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 09/19/2023] [Accepted: 09/26/2023] [Indexed: 10/29/2023] Open
Abstract
Cerebral palsy (CP), a neuromotor disorder characterized by prenatal brain lesions, leads to white matter alterations and sensorimotor deficits. However, the CP-related diffusion neuroimaging literature lacks rigorous and consensual methodology for preprocessing and analyzing data due to methodological challenges caused by the lesion extent. Advanced methods are available to reconstruct diffusion signals and can update current advances in CP. Our study demonstrates the feasibility of analyzing diffusion CP data using a standardized and open-source pipeline. Eight children with CP (8-12 years old) underwent a single diffusion magnetic resonance imaging (MRI) session on a 3T scanner (Achieva 3.0T (TX), Philips Healthcare Medical Systems, Best, The Netherlands). Exclusion criteria were contraindication to MRI and claustrophobia. Anatomical and diffusion images were acquired. Data were corrected and analyzed using Tractoflow 2.3.0 version, an open-source and robust tool. The tracts were extracted with customized procedures based on existing atlases and freely accessed standardized libraries (ANTs, Scilpy). DTI, CSD, and NODDI metrics were computed for each tract. Despite lesion heterogeneity and size, we successfully reconstructed major pathways, except for a participant with a larger lesion. Our results highlight the feasibility of identifying and quantifying subtle white matter pathways. Ultimately, this will increase our understanding of the clinical symptoms to provide precision medicine and optimize rehabilitation.
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Affiliation(s)
- Ophélie Martinie
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Québec, QC G1M 2S8, Canada; (O.M.); (E.T.); (C.M.)
- Department of Rehabilitation, Université Laval, Québec, QC G1V 0A6, Canada
| | - Philippe Karan
- Department of Computer Sciences, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada; (P.K.); (M.D.)
| | - Elodie Traverse
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Québec, QC G1M 2S8, Canada; (O.M.); (E.T.); (C.M.)
- Department of Rehabilitation, Université Laval, Québec, QC G1V 0A6, Canada
| | - Catherine Mercier
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Québec, QC G1M 2S8, Canada; (O.M.); (E.T.); (C.M.)
- Department of Rehabilitation, Université Laval, Québec, QC G1V 0A6, Canada
| | - Maxime Descoteaux
- Department of Computer Sciences, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada; (P.K.); (M.D.)
| | - Maxime T. Robert
- Centre for Interdisciplinary Research in Rehabilitation and Social Integration, Québec, QC G1M 2S8, Canada; (O.M.); (E.T.); (C.M.)
- Department of Rehabilitation, Université Laval, Québec, QC G1V 0A6, Canada
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Caranova M, Soares JF, Batista S, Castelo-Branco M, Duarte JV. A systematic review of microstructural abnormalities in multiple sclerosis detected with NODDI and DTI models of diffusion-weighted magnetic resonance imaging. Magn Reson Imaging 2023; 104:61-71. [PMID: 37775062 DOI: 10.1016/j.mri.2023.09.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/31/2023] [Accepted: 09/25/2023] [Indexed: 10/01/2023]
Abstract
Multiple sclerosis (MS), namely the phenotype of the relapsing-remitting form, is the most common white matter disease and is mostly characterized by demyelination and inflammation, which lead to neurodegeneration and cognitive decline. Its diagnosis and monitoring are performed through conventional structural MRI, in which T2-hyperintense lesions can be identified, but this technique lacks sensitivity and specificity, mainly in detecting damage to normal appearing tissues. Models of diffusion-weighted MRI such as diffusion-tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) allow to uncover microstructural abnormalities that occur in MS, mainly in normal appearing tissues such as the normal appearing white matter (NAWM), which allows to overcome limitations of conventional MRI. DTI is the standard method used for modelling this kind of data, but it has limitations, which can be tackled by using more complex diffusion models, such as NODDI, which provides additional information on morphological properties of tissues. Although there are several studies in MS using both diffusion models, there is no formal assessment that summarizes the findings of both methods in lesioned and normal appearing tissues, and whether one is more advantageous than the other. Hence, this systematic review aims to identify what microstructural abnormalities are seen in lesions and/or NAWM in relapsing-remitting MS while using two different approaches to modelling diffusion data, namely DTI and NODDI, and if one of them is more appropriate than the other or if they are complementary to each other. The search was performed using PubMed, which was last searched on November 2022, and aimed at finding studies that either utilized both DTI and NODDI in the same dataset, or only one of the methods. Eleven articles were included in this review, which included cohorts with a relatively low sample size (total number of patients = 254, total number of healthy controls = 240), and patients with a moderate disease duration, all with relapsing-remitting MS. Overall, studies found decreased fractional anisotropy (FA), neurite density index (NDI) and orientation dispersion index (ODI), and increased mean, axial and radial diffusivities (MD, AD and RD, respectively) in lesions, when compared to contralateral NAWM and healthy controls' white matter. Compared to healthy controls' white matter, NAWM showed lower FA and NDI and higher MD, AD, RD, and ODI. Results from the included articles confirm that there is active demyelination and inflammation in both lesions and NAWM, as well as loss in neurites, and that structural damage is not confined to focal lesions, which is in concordance with histological findings and results from other imaging techniques. Furthermore, NODDI is suggested to have higher sensitivity and specificity, as seen by inspecting imaging results, compared to DTI, while still being clinically feasible. The use of biomarkers derived from such advanced diffusion models in clinical practice could imply a better understanding of treatment efficacy and disease progression, without relying on the manifestation of clinical symptoms, such as relapses.
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Affiliation(s)
- Maria Caranova
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal.
| | - Júlia F Soares
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Sónia Batista
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal; Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Miguel Castelo-Branco
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal; Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - João Valente Duarte
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute for Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal; Faculty of Medicine, University of Coimbra, Coimbra, Portugal
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Lapucci C, Tazza F, Rebella S, Boffa G, Sbragia E, Bruschi N, Mancuso E, Mavilio N, Signori A, Roccatagliata L, Cellerino M, Schiavi S, Inglese M. Central vein sign and diffusion MRI differentiate microstructural features within white matter lesions of multiple sclerosis patients with comorbidities. Front Neurol 2023; 14:1084661. [PMID: 36970546 PMCID: PMC10030505 DOI: 10.3389/fneur.2023.1084661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 01/30/2023] [Indexed: 03/29/2023] Open
Abstract
Introduction The Central Vein Sign (CVS) has been suggested as a potential biomarker to improve diagnostic specificity in multiple sclerosis (MS). Nevertheless, the impact of comorbidities on CVS performance has been poorly investigated so far. Despite the similar features shared by MS, migraine and Small Vessel Disease (SVD) at T2-weighted conventional MRI sequences, ex-vivo studies demonstrated their heterogeneous histopathological substrates. If in MS, inflammation, primitive demyelination and axonal loss coexist, in SVD demyelination is secondary to ischemic microangiopathy, while the contemporary presence of inflammatory and ischemic processes has been suggested in migraine. The aims of this study were to investigate the impact of comorbidities (risk factors for SVD and migraine) on the global and subregional assessment of the CVS in a large cohort of MS patients and to apply the Spherical Mean Technique (SMT) diffusion model to evaluate whether perivenular and non-perivenular lesions show distinctive microstructural features. Methods 120 MS patients stratified into 4 Age Groups performed 3T brain MRI. WM lesions were classified in "perivenular" and "non-perivenular" by visual inspection of FLAIR* images; mean values of SMT metrics, indirect estimators of inflammation, demyelination and fiber disruption (EXTRAMD: extraneurite mean diffusivity, EXTRATRANS: extraneurite transverse diffusivity and INTRA: intraneurite signal fraction, respectively) were extracted. Results Of the 5303 lesions selected for the CVS assessment, 68.7% were perivenular. Significant differences were found between perivenular and non-perivenular lesion volume in the whole brain (p < 0.001) and between perivenular and non-perivenular lesion volume and number in all the four subregions (p < 0.001 for all). The percentage of perivenular lesions decreased from youngest to oldest patients (79.7%-57.7%), with the deep/subcortical WM of oldest patients as the only subregion where the number of non-perivenular was higher than the number of perivenular lesions. Older age and migraine were independent predictors of a higher percentage of non-perivenular lesions (p < 0.001 and p = 0.013 respectively). Whole brain perivenular lesions showed higher inflammation, demyelination and fiber disruption than non perivenular lesions (p = 0.001, p = 0.001 and p = 0.02 for EXTRAMD, EXTRATRANS and INTRA respectively). Similar findings were found in the deep/subcortical WM (p = 0.001 for all). Compared to non-perivenular lesions, (i) perivenular lesions located in periventricular areas showed a more severe fiber disruption (p = 0.001), (ii) perivenular lesions located in juxtacortical and infratentorial regions exhibited a higher degree of inflammation (p = 0.01 and p = 0.05 respectively) and (iii) perivenular lesions located in infratentorial areas showed a higher degree of demyelination (p = 0.04). Discussion Age and migraine have a relevant impact in reducing the percentage of perivenular lesions, particularly in the deep/subcortical WM. SMT may differentiate perivenular lesions, characterized by higher inflammation, demyelination and fiber disruption, from non perivenular lesions, where these pathological processes seemed to be less pronounced. The development of new non-perivenular lesions, especially in the deep/subcortical WM of older patients, should be considered a "red flag" for a different -other than MS- pathophysiology.
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Affiliation(s)
- Caterina Lapucci
- HNSR, IRRCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Francesco Tazza
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | | | - Giacomo Boffa
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Elvira Sbragia
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Nicolò Bruschi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Elisabetta Mancuso
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Nicola Mavilio
- Department of Neuroradiology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Alessio Signori
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Luca Roccatagliata
- Department of Neuroradiology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Maria Cellerino
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Simona Schiavi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Matilde Inglese
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino IRCCS, Genoa, Italy
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York EN, Meijboom R, Thrippleton MJ, Bastin ME, Kampaite A, White N, Chandran S, Waldman AD. Longitudinal microstructural MRI markers of demyelination and neurodegeneration in early relapsing-remitting multiple sclerosis: Magnetisation transfer, water diffusion and g-ratio. Neuroimage Clin 2022; 36:103228. [PMID: 36265199 PMCID: PMC9668599 DOI: 10.1016/j.nicl.2022.103228] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 10/07/2022] [Accepted: 10/08/2022] [Indexed: 11/11/2022]
Abstract
INTRODUCTION Quantitative microstructural MRI, such as myelin-sensitive magnetisation transfer ratio (MTR) or saturation (MTsat), axon-sensitive water diffusion Neurite Orientation Dispersion and Density Imaging (NODDI), and the aggregate g-ratio, may provide more specific markers of white matter integrity than conventional MRI for early patient stratification in relapsing-remitting multiple sclerosis (RRMS). The aim of this study was to determine the sensitivity of such markers to longitudinal pathological change within cerebral white matter lesions (WML) and normal-appearing white matter (NAWM) in recently diagnosed RRMS. METHODS Seventy-nine people with recently diagnosed RRMS, from the FutureMS longitudinal cohort, were recruited to an extended MRI protocol at baseline and one year later. Twelve healthy volunteers received the same MRI protocol, repeated within two weeks. Ethics approval and written informed consent were obtained. 3T MRI included magnetisation transfer, and multi-shell diffusion-weighted imaging. NAWM and whole brain were segmented from 3D T1-weighted MPRAGE, and WML from T2-weighted FLAIR. MTR, MTsat, NODDI isotropic (ISOVF) and intracellular (ICVF) volume fractions, and g-ratio (calculated from MTsat and NODDI data) were measured within WML and NAWM. Brain parenchymal fraction (BPF) was also calculated. Longitudinal change in BPF and microstructural metrics was assessed with paired t-tests (α = 0.05) and linear mixed models, adjusted for confounding factors with False Discovery Rate (FDR) correction for multiple comparisons. Longitudinal changes were compared with test-retest Bland-Altman limits of agreement from healthy control white matter. The influence of longitudinal change on g-ratio was explored through post-hoc analysis in silico by computing g-ratio with realistic simulated MTsat and NODDI values. RESULTS In NAWM, g-ratio and ICVF increased, and MTsat decreased over one year (adjusted mean difference = 0.007, 0.005, and -0.057 respectively, all FDR-corrected p < 0.05). There was no significant change in MTR, ISOVF, or BPF. In WML, MTsat, NODDI ICVF and ISOVF increased over time (adjusted mean difference = 0.083, 0.024 and 0.016, respectively, all FDR-corrected p < 0.05). Group-level longitudinal changes exceeded test-retest limits of agreement for NODDI ISOVF and ICVF in WML only. In silico analysis showed g-ratio may increase due to a decrease in MTsat or ISOVF, or an increase in ICVF. DISCUSSION G-ratio and MTsat changes in NAWM over one year may indicate subtle myelin loss in early RRMS, which were not apparent with BPF or NAWM MTR. Increases in NAWM and WML NODDI ICVF were not anticipated, and raise the possibility of axonal swelling or morphological change. Increases in WML MTsat may reflect myelin repair. Changes in NODDI ISOVF are more likely to reflect alterations in water content. Competing MTsat and ICVF changes may account for the absence of g-ratio change in WML. Longitudinal changes in microstructural measures are significant at a group level, however detection in individual patients in early RRMS is limited by technique reproducibility. CONCLUSION MTsat and g-ratio are more sensitive than MTR to early pathological changes in RRMS, but complex dependence of g-ratio on NODDI parameters limit the interpretation of aggregate measures in isolation. Improvements in technique reproducibility and validation of MRI biophysical models across a range of pathological tissue states are needed.
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Affiliation(s)
- Elizabeth N York
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom; Anne Rowling Regenerative Neurology Clinic, Edinburgh, United Kingdom.
| | - Rozanna Meijboom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Agniete Kampaite
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Nicole White
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Siddharthan Chandran
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Anne Rowling Regenerative Neurology Clinic, Edinburgh, United Kingdom; UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Adam D Waldman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom; Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom.
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9
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Radhakrishnan H, Shabestari SK, Blurton-Jones M, Obenaus A, Stark CEL. Using Advanced Diffusion-Weighted Imaging to Predict Cell Counts in Gray Matter: Potential and Pitfalls. Front Neurosci 2022; 16:881713. [PMID: 35720733 PMCID: PMC9204138 DOI: 10.3389/fnins.2022.881713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 05/18/2022] [Indexed: 11/22/2022] Open
Abstract
Recent advances in diffusion imaging have given it the potential to non-invasively detect explicit neurobiological properties, beyond what was previously possible with conventional structural imaging. However, there is very little known about what cytoarchitectural properties these metrics, especially those derived from newer multi-shell models like Neurite Orientation Dispersion and Density Imaging (NODDI) correspond to. While these diffusion metrics do not promise any inherent cell type specificity, different brain cells have varying morphologies, which could influence the diffusion signal in distinct ways. This relationship is currently not well-characterized. Understanding the possible cytoarchitectural signatures of diffusion measures could allow them to estimate important neurobiological properties like cell counts, potentially resulting in a powerful clinical diagnostic tool. Here, using advanced diffusion imaging (NODDI) in the mouse brain, we demonstrate that different regions have unique relationships between cell counts and diffusion metrics. We take advantage of this exclusivity to introduce a framework to predict cell counts of different types of cells from the diffusion metrics alone, in a region-specific manner. We also outline the challenges of reliably developing such a model and discuss the precautions the field must take when trying to tie together medical imaging modalities and histology.
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Affiliation(s)
- Hamsanandini Radhakrishnan
- Mathematical, Computational and Systems Biology, University of California, Irvine, Irvine, CA, United States
| | - Sepideh Kiani Shabestari
- Department of Neurobiology and Behavior, School of Biological Sciences, University of California, Irvine, Irvine, CA, United States
| | - Mathew Blurton-Jones
- Department of Neurobiology and Behavior, School of Biological Sciences, University of California, Irvine, Irvine, CA, United States
| | - Andre Obenaus
- Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, United States
| | - Craig E. L. Stark
- Mathematical, Computational and Systems Biology, University of California, Irvine, Irvine, CA, United States
- Department of Neurobiology and Behavior, School of Biological Sciences, University of California, Irvine, Irvine, CA, United States
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10
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Hori M, Maekawa T, Kamiya K, Hagiwara A, Goto M, Takemura MY, Fujita S, Andica C, Kamagata K, Cohen-Adad J, Aoki S. Advanced Diffusion MR Imaging for Multiple Sclerosis in the Brain and Spinal Cord. Magn Reson Med Sci 2022; 21:58-70. [PMID: 35173096 PMCID: PMC9199983 DOI: 10.2463/mrms.rev.2021-0091] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Diffusion tensor imaging (DTI) has been established its usefulness in evaluating normal-appearing white matter (NAWM) and other lesions that are difficult to evaluate with routine clinical MRI in the evaluation of the brain and spinal cord lesions in multiple sclerosis (MS), a demyelinating disease. With the recent advances in the software and hardware of MRI systems, increasingly complex and sophisticated MRI and analysis methods, such as q-space imaging, diffusional kurtosis imaging, neurite orientation dispersion and density imaging, white matter tract integrity, and multiple diffusion encoding, referred to as advanced diffusion MRI, have been proposed. These are capable of capturing in vivo microstructural changes in the brain and spinal cord in normal and pathological states in greater detail than DTI. This paper reviews the current status of recent advanced diffusion MRI for assessing MS in vivo as part of an issue celebrating two decades of magnetic resonance in medical sciences (MRMS), an official journal of the Japanese Society of Magnetic Resonance in Medicine.
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Affiliation(s)
- Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center.,Department of Radiology, Juntendo University School of Medicine
| | - Tomoko Maekawa
- Department of Radiology, Juntendo University School of Medicine
| | - Kouhei Kamiya
- Department of Radiology, Toho University Omori Medical Center.,Department of Radiology, Juntendo University School of Medicine
| | | | - Masami Goto
- Department of Radiological Technology, Faculty of Health Science, Juntendo University
| | | | - Shohei Fujita
- Department of Radiology, Juntendo University School of Medicine
| | | | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine
| | | | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine
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11
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Gyori NG, Palombo M, Clark CA, Zhang H, Alexander DC. Training data distribution significantly impacts the estimation of tissue microstructure with machine learning. Magn Reson Med 2021; 87:932-947. [PMID: 34545955 DOI: 10.1002/mrm.29014] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 08/30/2021] [Accepted: 08/30/2021] [Indexed: 12/18/2022]
Abstract
PURPOSE Supervised machine learning (ML) provides a compelling alternative to traditional model fitting for parameter mapping in quantitative MRI. The aim of this work is to demonstrate and quantify the effect of different training data distributions on the accuracy and precision of parameter estimates when supervised ML is used for fitting. METHODS We fit a two- and three-compartment biophysical model to diffusion measurements from in-vivo human brain, as well as simulated diffusion data, using both traditional model fitting and supervised ML. For supervised ML, we train several artificial neural networks, as well as random forest regressors, on different distributions of ground truth parameters. We compare the accuracy and precision of parameter estimates obtained from the different estimation approaches using synthetic test data. RESULTS When the distribution of parameter combinations in the training set matches those observed in healthy human data sets, we observe high precision, but inaccurate estimates for atypical parameter combinations. In contrast, when training data is sampled uniformly from the entire plausible parameter space, estimates tend to be more accurate for atypical parameter combinations but may have lower precision for typical parameter combinations. CONCLUSION This work highlights that estimation of model parameters using supervised ML depends strongly on the training-set distribution. We show that high precision obtained using ML may mask strong bias, and visual assessment of the parameter maps is not sufficient for evaluating the quality of the estimates.
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Affiliation(s)
- Noemi G Gyori
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.,Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Marco Palombo
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Christopher A Clark
- Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Hui Zhang
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
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12
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Abstract
Quantitative diffusion imaging techniques enable the characterization of tissue microstructural properties of the human brain "in vivo", and are widely used in neuroscientific and clinical contexts. In this review, we present the basic physical principles behind diffusion imaging and provide an overview of the current diffusion techniques, including standard and advanced techniques as well as their main clinical applications. Standard diffusion tensor imaging (DTI) offers sensitivity to changes in microstructure due to diseases and enables the characterization of single fiber distributions within a voxel as well as diffusion anisotropy. Nonetheless, its inability to represent complex intravoxel fiber topologies and the limited biological specificity of its metrics motivated the development of several advanced diffusion MRI techniques. For example, high-angular resolution diffusion imaging (HARDI) techniques enabled the characterization of fiber crossing areas and other complex fiber topologies in a single voxel and supported the development of higher-order signal representations aiming to decompose the diffusion MRI signal into distinct microstructure compartments. Biophysical models, often known by their acronym (e.g., CHARMED, WMTI, NODDI, DBSI, DIAMOND) contributed to capture the diffusion properties from each of such tissue compartments, enabling the computation of voxel-wise maps of axonal density and/or morphology that hold promise as clinically viable biomarkers in several neurological and neuroscientific applications; for example, to quantify tissue alterations due to disease or healthy processes. Current challenges and limitations of state-of-the-art models are discussed, including validation efforts. Finally, novel diffusion encoding approaches (e.g., b-tensor or double diffusion encoding) may increase the biological specificity of diffusion metrics towards intra-voxel diffusion heterogeneity in clinical settings, holding promise in neurological applications.
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Affiliation(s)
- Eloy Martinez-Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona. Barcelona. Spain.
| | - Francesco Grussu
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain; Queen Square MS Center, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Ferran Prados
- Queen Square MS Center, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK; Center for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK; E-health Center, Universitat Oberta de Catalunya. Barcelona. Spain
| | - Elisabeth Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona. Barcelona. Spain
| | - Sara Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona. Barcelona. Spain
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