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Uddin MN, Singh MV, Faiyaz A, Szczepankiewicz F, Nilsson M, Boodoo ZD, Sutton KR, Tivarus ME, Zhong J, Wang L, Qiu X, Weber MT, Schifitto G. Tensor-valued diffusion MRI detects brain microstructure changes in HIV infected individuals with cognitive impairment. RESEARCH SQUARE 2024:rs.3.rs-4482269. [PMID: 38946952 PMCID: PMC11213220 DOI: 10.21203/rs.3.rs-4482269/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Despite advancements, the prevalence of HIV-associated neurocognitive impairment remains at approximately 40%, attributed to factors like pre-cART (combination antiretroviral therapy) irreversible brain injury. People with HIV (PWH) treated with cART do not show significant neurocognitive changes over relatively short follow-up periods. However, quantitative neuroimaging may be able to detect ongoing subtle microstructural changes. This study aimed to investigate the sensitivity of tensor-valued diffusion encoding in detecting such changes in brain microstructural integrity in cART-treated PWH. Additionally, it explored relationships between these metrics, neurocognitive scores, and plasma levels of neurofilament light (NFL) chain and glial fibrillary acidic protein (GFAP). Using MRI at 3T, 24 PWH and 31 healthy controls underwent cross-sectional examination. The results revealed significant variations in b-tensor encoding metrics across white matter regions, with associations observed between these metrics, cognitive performance, and blood markers of neuronal and glial injury (NFL and GFAP). Moreover, a significant interaction between HIV status and imaging metrics was observed, particularly impacting total cognitive scores in both gray and white matter. These findings suggest that b-tensor encoding metrics offer heightened sensitivity in detecting subtle changes associated with axonal injury in HIV infection, underscoring their potential clinical relevance in understanding neurocognitive impairment in PWH.
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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] [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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Stellingwerff MD, Al-Saady ML, Chan KS, Dvorak A, Marques JP, Kolind S, Roosendaal SD, Wolf NI, Barkhof F, van der Knaap MS, Pouwels PJW. Applicability of multiple quantitative magnetic resonance methods in genetic brain white matter disorders. J Neuroimaging 2024; 34:61-77. [PMID: 37925602 DOI: 10.1111/jon.13167] [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: 08/09/2023] [Revised: 09/29/2023] [Accepted: 10/20/2023] [Indexed: 11/06/2023] Open
Abstract
BACKGROUND AND PURPOSE Magnetic resonance imaging (MRI) measures of tissue microstructure are important for monitoring brain white matter (WM) disorders like leukodystrophies and multiple sclerosis. They should be sensitive to underlying pathological changes. Three whole-brain isotropic quantitative methods were applied and compared within a cohort of controls and leukodystrophy patients: two novel myelin water imaging (MWI) techniques (multi-compartment relaxometry diffusion-informed MWI: MCR-DIMWI, and multi-echo T2 relaxation imaging with compressed sensing: METRICS) and neurite orientation dispersion and density imaging (NODDI). METHODS For 9 patients with different leukodystrophies (age range 0.4-62.4 years) and 15 control subjects (2.3-61.3 years), T1-weighted MRI, fluid-attenuated inversion recovery, multi-echo gradient echo with variable flip angles, METRICS, and multi-shell diffusion-weighted imaging were acquired on 3 Tesla. MCR-DIMWI, METRICS, NODDI, and quality control measures were extracted to evaluate differences between patients and controls in WM and deep gray matter (GM) regions of interest (ROIs). Pearson correlations, effect size calculations, and multi-level analyses were performed. RESULTS MCR-DIMWI and METRICS-derived myelin water fractions (MWFs) were lower and relaxation times were higher in patients than in controls. Effect sizes of MWF values and relaxation times were large for both techniques. Differences between patients and controls were more pronounced in WM ROIs than in deep GM. MCR-DIMWI-MWFs were more homogeneous within ROIs and more bilaterally symmetrical than METRICS-MWFs. The neurite density index was more sensitive in detecting differences between patients and controls than fractional anisotropy. Most measures obtained from MCR-DIMWI, METRICS, NODDI, and diffusion tensor imaging correlated strongly with each other. CONCLUSION This proof-of-concept study shows that MCR-DIMWI, METRICS, and NODDI are sensitive techniques to detect changes in tissue microstructure in WM disorders.
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Affiliation(s)
- Menno D Stellingwerff
- Department of Child Neurology, Amsterdam Leukodystrophy Center, Emma Children's Hospital, Cellular & Molecular Mechanisms, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Vrije Universiteit, Amsterdam, Netherlands
| | - Murtadha L Al-Saady
- Department of Child Neurology, Amsterdam Leukodystrophy Center, Emma Children's Hospital, Cellular & Molecular Mechanisms, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Vrije Universiteit, Amsterdam, Netherlands
| | - Kwok-Shing Chan
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Adam Dvorak
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Shannon Kolind
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
| | - Stefan D Roosendaal
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Nicole I Wolf
- Department of Child Neurology, Amsterdam Leukodystrophy Center, Emma Children's Hospital, Cellular & Molecular Mechanisms, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Vrije Universiteit, Amsterdam, Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Amsterdam, Netherlands
- Institutes of Neurology and Healthcare Engineering, University College London, London, UK
| | - Marjo S van der Knaap
- Department of Child Neurology, Amsterdam Leukodystrophy Center, Emma Children's Hospital, Cellular & Molecular Mechanisms, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Vrije Universiteit, Amsterdam, Netherlands
| | - Petra J W Pouwels
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, and Amsterdam Neuroscience, Amsterdam, Netherlands
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Zeicu C, Legouhy A, Scott CA, Oliveira JFA, Winston GP, Duncan JS, Vos SB, Thom M, Lhatoo S, Zhang H, Harper RM, Diehl B. Altered amygdala volumes and microstructure in focal epilepsy patients with tonic-clonic seizures, ictal, and post-convulsive central apnea. Epilepsia 2023; 64:3307-3318. [PMID: 37857465 PMCID: PMC10952501 DOI: 10.1111/epi.17804] [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: 06/11/2023] [Revised: 10/16/2023] [Accepted: 10/16/2023] [Indexed: 10/21/2023]
Abstract
OBJECTIVES Sudden unexpected death in epilepsy (SUDEP) is a leading cause of death for patients with epilepsy; however, the pathophysiology remains unclear. Focal-to-bilateral tonic-clonic seizures (FBTCS) are a major risk factor, and centrally-mediated respiratory depression may increase the risk further. Here, we determined the volume and microstructure of the amygdala, a key structure that can trigger apnea in people with focal epilepsy, stratified by the presence or absence of FBTCS, ictal central apnea (ICA), and post-convulsive central apnea (PCCA). METHODS Seventy-three patients with focal impaired awareness seizures without FBTC seizures (FBTCneg group) and 30 with FBTCS (FBTCpos group) recorded during video electroencephalography (VEEG) with respiratory monitoring were recruited prospectively during presurgical investigations. We acquired high-resolution T1-weighted anatomic and multi-shell diffusion images, and computed neurite orientation dispersion and density imaging (NODDI) metrics in all patients with epilepsy and 69 healthy controls. Amygdala volumetric and microstructure alterations were compared between three groups: healthy subjects, FBTCneg and FBTCpos groups. The FBTCpos group was further subdivided by the presence of ICA and PCCA, verified by VEEG. RESULTS Bilateral amygdala volumes were significantly increased in the FBTCpos cohort compared to healthy controls and the FBTCneg group. Patients with recorded PCCA had the highest increase in bilateral amygdala volume of the FBTCpos cohort. Amygdala neurite density index (NDI) values were decreased significantly in both the FBTCneg and FBTCpos groups relative to healthy controls, with values in the FBTCpos group being the lowest of the two. The presence of PCCA was associated with significantly lower NDI values vs the non-apnea FBTCpos group (p = 0.004). SIGNIFICANCE Individuals with FBTCpos and PCCA show significantly increased amygdala volumes and disrupted architecture bilaterally, with greater changes on the left side. The structural alterations reflected by NODDI and volume differences may be associated with inappropriate cardiorespiratory patterns mediated by the amygdala, particularly after FBTCS. Determination of amygdala volumetric and architectural changes may assist identification of individuals at risk.
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Affiliation(s)
- Claudia Zeicu
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Antoine Legouhy
- Centre for Medical Image Computing and Department of Computer ScienceUniversity College LondonLondonUK
| | - Catherine A. Scott
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
- Department of Clinical NeurophysiologyUniversity College London Hospitals NHS Foundation Trust National Hospital for Neurology and NeurosurgeryLondonUK
| | - Joana F. A. Oliveira
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
- Department of Clinical NeurophysiologyUniversity College London Hospitals NHS Foundation Trust National Hospital for Neurology and NeurosurgeryLondonUK
| | - Gavin P. Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
- Epilepsy Society MRI UnitChalfont St PeterUK
- Department of Medicine, Division of NeurologyQueen's UniversityKingstonOntarioCanada
| | - John S. Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Sjoerd B. Vos
- Centre for Medical Image Computing and Department of Computer ScienceUniversity College LondonLondonUK
- Neuroradiological Academic Unit, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
- Centre for Microscopy, Characterisation, and AnalysisThe University of Western AustraliaNedlandsWestern AustraliaAustralia
| | - Maria Thom
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Samden Lhatoo
- Department of NeurologyUniversity of Texas Health Sciences Center at HoustonHoustonTexasUSA
| | - Hui Zhang
- Centre for Medical Image Computing and Department of Computer ScienceUniversity College LondonLondonUK
| | - Ronald M. Harper
- Brain Research InstituteUniversity of California at Los AngelesLos AngelesCaliforniaUSA
- Department of Neurobiology, David Geffen School of MedicineUniversity of California at Los AngelesLos AngelesCaliforniaUSA
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
- Department of Clinical NeurophysiologyUniversity College London Hospitals NHS Foundation Trust National Hospital for Neurology and NeurosurgeryLondonUK
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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] [Abstract] [Key Words] [MESH Headings] [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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Cacciaguerra L, Rocca MA, Filippi M. Understanding the Pathophysiology and Magnetic Resonance Imaging of Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorders. Korean J Radiol 2023; 24:1260-1283. [PMID: 38016685 DOI: 10.3348/kjr.2023.0360] [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: 04/26/2023] [Revised: 08/09/2023] [Accepted: 08/21/2023] [Indexed: 11/30/2023] Open
Abstract
Magnetic resonance imaging (MRI) has been extensively applied in the study of multiple sclerosis (MS), substantially contributing to diagnosis, differential diagnosis, and disease monitoring. MRI studies have significantly contributed to the understanding of MS through the characterization of typical radiological features and their clinical or prognostic implications using conventional MRI pulse sequences and further with the application of advanced imaging techniques sensitive to microstructural damage. Interpretation of results has often been validated by MRI-pathology studies. However, the application of MRI techniques in the study of neuromyelitis optica spectrum disorders (NMOSD) remains an emerging field, and MRI studies have focused on radiological correlates of NMOSD and its pathophysiology to aid in diagnosis, improve monitoring, and identify relevant prognostic factors. In this review, we discuss the main contributions of MRI to the understanding of MS and NMOSD, focusing on the most novel discoveries to clarify differences in the pathophysiology of focal inflammation initiation and perpetuation, involvement of normal-appearing tissue, potential entry routes of pathogenic elements into the CNS, and existence of primary or secondary mechanisms of neurodegeneration.
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Affiliation(s)
- Laura Cacciaguerra
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Vita-Salute San Raffaele University, Milano, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Vita-Salute San Raffaele University, Milano, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milano, Italy.
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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] [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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Alghamdi AJ. The Value of Various Post-Processing Modalities of Diffusion Weighted Imaging in the Detection of Multiple Sclerosis. Brain Sci 2023; 13:brainsci13040622. [PMID: 37190587 DOI: 10.3390/brainsci13040622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 04/08/2023] Open
Abstract
Diffusion tensor imaging (DTI) showed its adequacy in evaluating the normal-appearing white matter (NAWM) and lesions in the brain that are difficult to evaluate with routine clinical magnetic resonance imaging (MRI) in multiple sclerosis (MS). Recently, MRI systems have been developed with regard to software and hardware, leading to different proposed diffusion analysis methods such as diffusion tensor imaging, q-space imaging, diffusional kurtosis imaging, neurite orientation dispersion and density imaging, and axonal diameter measurement. These methods have the ability to better detect in vivo microstructural changes in the brain than DTI. These different analysis modalities could provide supplementary inputs for MS disease characterization and help in monitoring the disease’s progression as well as treatment efficacy. This paper reviews some of the recent diffusion MRI methods used for the assessment of MS in vivo.
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Affiliation(s)
- Ahmad Joman Alghamdi
- Radiological Sciences Department, College of Applied Medical Sciences, Taif University, Taif 21944, Saudi Arabia
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Gilli F, Ceccarelli A. Magnetic resonance imaging approaches for studying mouse models of multiple sclerosis: A mini review. J Neurosci Res 2023. [DOI: 10.1002/jnr.25193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 01/30/2023] [Accepted: 03/10/2023] [Indexed: 04/03/2023]
Affiliation(s)
- Francesca Gilli
- Department of Neurology, Dartmouth Hitchcock Medical Center Geisel School of Medicine at Dartmouth Lebanon New Hampshire USA
| | - Antonia Ceccarelli
- Department of Neurology EpiCURA Centre Hospitalier Ath Belgium
- Hearthrhythmanagement, UZB Brussels Belgium
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10
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Zeicu C, Legouhy A, Scott CA, Oliveira JFA, Winston G, Duncan JS, Vos SB, Thom M, Lhatoo S, Zhang H, Harper RM, Diehl B. Altered Amygdala Volumes and Microstructure in Focal Epilepsy Patients with Tonic-Clonic Seizures, Ictal and Post-Ictal Central Apnea. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.16.23287369. [PMID: 36993530 PMCID: PMC10055587 DOI: 10.1101/2023.03.16.23287369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Objectives Sudden unexpected death in epilepsy (SUDEP) is a leading cause of death for patients with epilepsy; however, the pathophysiology remains unclear. Focal-to-bilateral tonic-clonic seizures (FBTCS) are a major risk factor, and centrally-mediated respiratory depression may increase the risk further. Here, we determined volume and microstructure of the amygdala, a key structure that can trigger apnea in people with focal epilepsy, stratified by presence or absence of FBTCS, ictal central apnea (ICA) and post-ictal central apnea (PICA). Methods 73 patients with only-focal seizures and 30 with FBTCS recorded during video EEG (VEEG) with respiratory monitoring were recruited prospectively during presurgical investigations. We acquired high-resolution T1-weighted anatomical and multi-shell diffusion images, and computed neurite orientation dispersion and density imaging (NODDI) metrics in all epilepsy patients and 69 healthy controls. Amygdala volumetric and microstructure alterations were compared between healthy subjects, and patients with only-focal seizures or FBTCS The FBTCS group was further subdivided by presence of ICA and PICA, verified by VEEG. Results Bilateral amygdala volumes were significantly increased in the FBTCS cohort compared to healthy controls and the focal cohort. Patients with recorded PICA had the highest increase in bilateral amygdala volume of the FBTCS cohort.Amygdala neurite density index (NDI) values were significantly decreased in both the focal and FBTCS groups relative to healthy controls, with values in the FBTCS group being the lowest of the two. The presence of PICA was associated with significantly lower NDI values vs the non-apnea FBTCS group (p=0.004). Significance Individuals with FBTCS and PICA show significantly increased amygdala volumes and disrupted architecture bilaterally, with greater changes on the left side. The structural alterations reflected by NODDI and volume differences may be associated with inappropriate cardiorespiratory patterns mediated by the amygdala, particularly after FBTCS. Determination of amygdala volumetric and architectural changes may assist identification of individuals at risk.
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Affiliation(s)
- Claudia Zeicu
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Antoine Legouhy
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom
| | - Catherine A. Scott
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, University College London, London, United Kingdom
- Department of Clinical Neurophysiology, University College London Hospitals NHS Foundation Trust National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Joana F. A. Oliveira
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, University College London, London, United Kingdom
- Department of Clinical Neurophysiology, University College London Hospitals NHS Foundation Trust National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Gavin Winston
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, University College London, London, United Kingdom
- Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom
- Division of Neurology, Department of Medicine, Queen’s University, Kingston, Ontario, Canada
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sjoerd B. Vos
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Centre for Microscopy, Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| | - Maria Thom
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Samden Lhatoo
- Department of Neurology, University of Texas Health Sciences Center at Houston, Houston, Texas, USA
| | - Hui Zhang
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom
| | - Ronald M. Harper
- Brain Research Institute, University of California at Los Angeles, California, USA
- Department of Neurobiology, David Geffen School of Medicine, University of California at Los Angeles, California, USA
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, Queen Square Institute of Neurology, University College London, London, United Kingdom
- Department of Clinical Neurophysiology, University College London Hospitals NHS Foundation Trust National Hospital for Neurology and Neurosurgery, London, United Kingdom
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Koo DL, Cabeen RP, Yook SH, Cen SY, Joo EY, Kim H. More extensive white matter disruptions present in untreated obstructive sleep apnea than we thought: A large sample diffusion imaging study. Hum Brain Mapp 2023; 44:3045-3056. [PMID: 36896706 PMCID: PMC10171547 DOI: 10.1002/hbm.26261] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 12/22/2022] [Accepted: 02/21/2023] [Indexed: 03/11/2023] Open
Abstract
Obstructive sleep apnea (OSA) may lead to white mater (WM) disruptions and cognitive deficits. However, no studies have investigated the full extent of the brain WM, and its associations with cognitive deficits in OSA remain unclear. We thus applied diffusion tensor imaging (DTI) tractography with multi-fiber models and used atlas-based bundle-specific approach to investigate the WM abnormalities for various tracts of the cerebral cortex, thalamus, brainstem, and cerebellum in patients with untreated OSA. We enrolled 100 OSA patients and 63 healthy controls. Fractional anisotropy (FA) and mean diffusivity (MD) values mapped on 33 regions of interest including WM tracts of cortex, thalamus, brainstem, and cerebellum were obtained from tractography-based reconstructions. We compared FA/MD values between groups and correlated FA/MD with clinical data in the OSA group after controlling for age and body mass index. OSA patients showed significantly lower FA values in multiple WM fibers including corpus callosum, inferior fronto-occipital fasciculus, middle/superior longitudinal fasciculi, thalamic radiations, and uncinate (FDR <0.05). Higher FA values were found in medial lemniscus of patients compared to controls (FDR <0.05). Lower FA values of rostrum of corpus callosum correlated with lower visual memory performance in OSA group (p < .005). Our quantitative DTI analysis demonstrated that untreated OSA could negatively impact the integrity of pathways more broadly, including brainstem structures such as medial lemniscus, in comparison to previous findings. Fiber tract abnormalities of the rostral corpus callosum were associated with impaired visual memory in untreated OSA may provide insights into the related pathomechanism.
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Affiliation(s)
- Dae Lim Koo
- Department of Neurology, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul National University College of Medicine, Seoul, South Korea
| | - Ryan P Cabeen
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Soon Hyun Yook
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Steven Yong Cen
- Department of Radiology, USC Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Eun Yeon Joo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Samsung Biomedical Research Institute, Seoul, South Korea
| | - Hosung Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
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12
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Breit S, Mazza E, Poletti S, Benedetti F. White matter integrity and pro-inflammatory cytokines as predictors of antidepressant response in MDD. J Psychiatr Res 2023; 159:22-32. [PMID: 36657311 DOI: 10.1016/j.jpsychires.2022.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 11/12/2022] [Accepted: 12/10/2022] [Indexed: 01/13/2023]
Abstract
Major depressive disorder (MDD) is a multifactorial, serious and heterogeneous mental disorder that can lead to chronic recurrent symptoms, treatment resistance and suicidal behavior. MDD often involves immune dysregulation with high peripheral levels of inflammatory cytokines that might have an influence on the clinical course and treatment response. Moreover, patients with MDD show brain volume changes as well as white matter (WM) alterations that are already existing in the early stage of illness. Mounting evidence suggests that both neuroimaging markers, such as WM integrity and blood markers, such as inflammatory cytokines might serve as predictors of treatment response in MDD. However, the relationship between peripheral inflammation, WM structure and antidepressant response is not yet clearly understood. The aim of the present review is to elucidate the association between inflammation and WM integrity and its impact on the pathophysiology and progression of MDD as well as the role of possible novel biomarkers of treatment response to improve MDD prevention and treatment strategies.
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Affiliation(s)
- Sigrid Breit
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy; Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
| | - Elena Mazza
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy; University Vita-Salute San Raffaele, Milano, Italy
| | - Sara Poletti
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy; University Vita-Salute San Raffaele, Milano, Italy
| | - Francesco Benedetti
- Psychiatry and Clinical Psychobiology, Division of Neuroscience, IRCCS Ospedale San Raffaele, Milano, Italy; University Vita-Salute San Raffaele, Milano, Italy
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13
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Fu Y, Sun Y, Wang ZB, Zhang DD, Tan L, Feng JF, Cheng W, Yu JT. Associations of Life's Simple 7 with cerebral white matter hyperintensities and microstructural integrity: UK Biobank cohort study. Eur J Neurol 2023; 30:1200-1208. [PMID: 36794682 DOI: 10.1111/ene.15750] [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: 12/21/2022] [Revised: 01/26/2023] [Accepted: 02/13/2023] [Indexed: 02/17/2023]
Abstract
BACKGROUND AND PURPOSE The American Heart Association Life's Simple 7 (LS7) metric was used to define optimal cardiovascular and brain health, but the associations with macrostructural hyperintensities and microstructural white matter damage are unclear. The objective was to determine the association of LS7 ideal cardiovascular health factors with macrostructural and microstructural integrity. METHOD A total of 37,140 participants with available LS7 and imaging data from UK Biobank were included in this study. Linear associations were implemented to examine the associations of LS7 score and subscores with white matter hyperintensity load (WMH) (WMH volume normalized by total white matter volume and logit-transformed) and diffusion imaging indices (fractional anisotropy [FA], mean diffusivity, orientation dispersion index [OD], intracellular volume fraction, isotropic volume fraction [ISOVF]). RESULTS In individuals (mean age 54.76 years; 19,697 females, 52.4%), higher LS7 score and subscores were strongly associated with lower WMH and microstructural white matter injury, including OD, ISOVF, FA. Both interaction analyses and stratified analyses of LS7 score and subscores with age and sex showed a strong association with microstructural damage markers, with remarkable age and sex differences. The association of OD was pronounced in females and populations younger than 50 years and FA, mean diffusivity and ISOVF were pronounced in males and populations older than 50 years. CONCLUSION These findings suggest that healthier LS7 profiles are associated with better profiles of both macrostructural and microstructural markers of brain health, and indicate that ideal cardiovascular health is associated with improved brain health.
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Affiliation(s)
- Yan Fu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yan Sun
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Zhi-Bo Wang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Dan-Dan Zhang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China.,Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), China.,Fudan ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.,Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
| | - Jin-Tai Yu
- Department of Neurology and Institute of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, National Center for Neurological Disorders, Shanghai, China
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14
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Preziosa P, Pagani E, Meani A, Marchesi O, Conti L, Falini A, Rocca MA, Filippi M. NODDI, diffusion tensor microstructural abnormalities and atrophy of brain white matter and gray matter contribute to cognitive impairment in multiple sclerosis. J Neurol 2023; 270:810-823. [PMID: 36201016 DOI: 10.1007/s00415-022-11415-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/29/2022] [Accepted: 09/30/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Pathologically specific MRI measures may elucidate in-vivo the heterogeneous processes contributing to cognitive impairment in multiple sclerosis (MS). PURPOSE Using diffusion tensor and neurite orientation dispersion and density imaging (NODDI), we explored the contribution of focal lesions and normal-appearing (NA) tissue microstructural abnormalities to cognitive impairment in MS. METHODS One hundred and fifty-two MS patients underwent 3 T brain MRI and a neuropsychological evaluation. Forty-eight healthy controls (HC) were also scanned. Fractional anisotropy (FA), mean diffusivity (MD), intracellular volume fraction (ICV_f) and orientation dispersion index (ODI) were assessed in cortical and white matter (WM) lesions, thalamus, NA cortex and NAWM. Predictors of cognitive impairment were identified using random forest. RESULTS Fifty-two MS patients were cognitively impaired. Compared to cognitively preserved, impaired MS patients had higher WM lesion volume (LV), lower normalized brain volume (NBV), cortical volume (NCV), thalamic volume (NTV), and WM volume (p ≤ 0.021). They also showed lower NAWM FA, higher NAWM, NA cortex and thalamic MD, lower NAWM ICV_f, lower WM lesion ODI, and higher NAWM ODI (false discovery rate-p ≤ 0.026). Cortical lesion number and microstructural abnormalities were not significantly different. The best MRI predictors of cognitive impairment (relative importance) (out-of-bag area under the curve = 0.727) were NAWM FA (100%), NTV (96.0%), NBV (84.7%), thalamic MD (43.4%), NCV (40.6%), NA cortex MD (26.0%), WM LV (23.2%) and WM lesion ODI (17.9%). CONCLUSIONS Our multiparametric MRI study including NODDI measures suggested that neuro-axonal damage and loss of microarchitecture integrity in focal WM lesions, NAWM, and GM contribute to cognitive impairment in MS.
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Affiliation(s)
- Paolo Preziosa
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Alessandro Meani
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Olga Marchesi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Lorenzo Conti
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
| | - Andrea Falini
- Neuroradiology Unit and CERMAC, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy. .,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy. .,Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy. .,Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy. .,Vita-Salute San Raffaele University, Milan, Italy.
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15
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Howard AF, Cottaar M, Drakesmith M, Fan Q, Huang SY, Jones DK, Lange FJ, Mollink J, Rudrapatna SU, Tian Q, Miller KL, Jbabdi S. Estimating axial diffusivity in the NODDI model. Neuroimage 2022; 262:119535. [PMID: 35931306 PMCID: PMC9802007 DOI: 10.1016/j.neuroimage.2022.119535] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/20/2022] [Accepted: 08/01/2022] [Indexed: 01/03/2023] Open
Abstract
To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model (d∥=1.7μm2/ms). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of ∼2-2.5μm2/ms, in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data.
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Affiliation(s)
- Amy Fd Howard
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
| | - Michiel Cottaar
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Mark Drakesmith
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States; Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China; Academy of Medical Engineering and Translational Medicine, Medical College, Tianjin University, Tianjin, China
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Frederik J Lange
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jeroen Mollink
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Suryanarayana Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Philips Innovation Campus, Bangalore, India
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States
| | - Karla L Miller
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Saad Jbabdi
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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16
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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] [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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17
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Zachariou V, Bauer CE, Pappas C, Gold BT. High cortical iron is associated with the disruption of white matter tracts supporting cognitive function in healthy older adults. Cereb Cortex 2022; 33:4815-4828. [PMID: 36182267 PMCID: PMC10110441 DOI: 10.1093/cercor/bhac382] [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: 07/11/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 01/25/2023] Open
Abstract
Aging is associated with brain iron accumulation, which has been linked to cognitive decline. However, how brain iron affects the structure and function of cognitive brain networks remains unclear. Here, we explored the possibility that iron load in gray matter is associated with disruption of white matter (WM) microstructure within a network supporting cognitive function, in a cohort of 95 cognitively normal older adults (age range: 60-86). Functional magnetic resonance imaging was used to localize a set of brain regions involved in working memory and diffusion tensor imaging based probabilistic tractography was used to identify a network of WM tracts connecting the functionally defined regions. Brain iron concentration within these regions was evaluated using quantitative susceptibility mapping and microstructural properties were assessed within the identified tracts using neurite orientation dispersion and density imaging. Results indicated that high brain iron concentration was associated with low neurite density (ND) within the task-relevant WM network. Further, regional associations were observed such that brain iron in cortical regions was linked with lower ND in neighboring but not distant WM tracts. Our results provide novel evidence suggesting that age-related increases in brain iron concentration are associated with the disruption of WM tracts supporting cognitive function in normal aging.
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Affiliation(s)
- Valentinos Zachariou
- Department of Neuroscience, University of Kentucky, Lexington, KY 40536-0298, United States.,College of Medicine, University of Kentucky, Lexington, KY 40536-0298, United States
| | - Christopher E Bauer
- Department of Neuroscience, University of Kentucky, Lexington, KY 40536-0298, United States.,College of Medicine, University of Kentucky, Lexington, KY 40536-0298, United States
| | - Colleen Pappas
- Department of Neuroscience, University of Kentucky, Lexington, KY 40536-0298, United States.,College of Medicine, University of Kentucky, Lexington, KY 40536-0298, United States
| | - Brian T Gold
- Department of Neuroscience, University of Kentucky, Lexington, KY 40536-0298, United States.,College of Medicine, University of Kentucky, Lexington, KY 40536-0298, United States.,Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40536-0298, United States.,Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, KY 40536-0298, United States
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18
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Xu T, Wu Y, Hong Y, Ahmad S, Huynh KM, Wang Z, Lin W, Chang WT, Yap PT. Rapid Diffusion Magnetic Resonance Imaging Using Slice-Interleaved Encoding. Med Image Anal 2022; 81:102548. [PMID: 35917693 PMCID: PMC9988327 DOI: 10.1016/j.media.2022.102548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 06/24/2022] [Accepted: 07/12/2022] [Indexed: 11/28/2022]
Abstract
In this paper, we present a robust reconstruction scheme for diffusion MRI (dMRI) data acquired using slice-interleaved diffusion encoding (SIDE). When combined with SIDE undersampling and simultaneous multi-slice (SMS) imaging, our reconstruction strategy is capable of significantly reducing the amount of data that needs to be acquired, enabling high-speed diffusion imaging for pediatric, elderly, and claustrophobic individuals. In contrast to the conventional approach of acquiring a full diffusion-weighted (DW) volume per diffusion wavevector, SIDE acquires in each repetition time (TR) a volume that consists of interleaved slice groups, each group corresponding to a different diffusion wavevector. This strategy allows SIDE to rapidly acquire data covering a large number of wavevectors within a short period of time. The proposed reconstruction method uses a diffusion spectrum model and multi-dimensional total variation to recover full DW images from DW volumes that are slice-undersampled due to unacquired SIDE volumes. We formulate an inverse problem that can be solved efficiently using the alternating direction method of multipliers (ADMM). Experiment results demonstrate that DW images can be reconstructed with high fidelity even when the acquisition is accelerated by 25 folds.
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Affiliation(s)
- Tiantian Xu
- Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Ye Wu
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA
| | - Yoonmi Hong
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA
| | - Sahar Ahmad
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA
| | - Khoi Minh Huynh
- Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Zhixing Wang
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA
| | - Weili Lin
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA
| | - Wei-Tang Chang
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA
| | - Pew-Thian Yap
- Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA.
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19
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Oladosu O, Liu WQ, Brown L, Pike BG, Metz LM, Zhang Y. Advanced diffusion MRI and image texture analysis detect widespread brain structural differences between relapsing-remitting and secondary progressive multiple sclerosis. Front Hum Neurosci 2022; 16:944908. [PMID: 36034111 PMCID: PMC9413838 DOI: 10.3389/fnhum.2022.944908] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 07/18/2022] [Indexed: 11/29/2022] Open
Abstract
Introduction Disease development in multiple sclerosis (MS) causes dramatic structural changes, but the exact changing patterns are unclear. Our objective is to investigate the differences in brain structure locally and spatially between relapsing-remitting MS (RRMS) and its advanced form, secondary progressive MS (SPMS), through advanced analysis of diffusion magnetic resonance imaging (MRI) and image texture. Methods A total of 20 patients with RRMS and nine patients with SPMS from two datasets underwent 3T anatomical and diffusion tensor imaging (DTI). The DTI was harmonized, augmented, and then modeled, which generated six voxel- and sub-voxel-scale measures. Texture analysis focused on T2 and FLAIR MRI, which produced two phase-based measures, namely, phase congruency and weighted mean phase. Data analysis was 3-fold, i.e., histogram analysis of whole-brain normal appearing white matter (NAWM); region of interest (ROI) analysis of NAWM and lesions within three critical white matter tracts, namely, corpus callosum, corticospinal tract, and optic radiation; and along-tract statistics. Furthermore, by calculating the z-score of core-rim pathology within lesions based on diffusion measures, we developed a novel method to define chronic active lesions and compared them between cohorts. Results Histogram features from diffusion and all but one texture measure differentiated between RRMS and SPMS. Within-tract ROI analysis detected cohort differences in both NAWM and lesions of the corpus callosum body in three measures of neurite orientation and anisotropy. Along-tract statistics detected cohort differences from multiple measures, particularly lesion extent, which increased significantly in SPMS in posterior corpus callosum and optic radiations. The number of chronic active lesions were also significantly higher (by 5-20% over z-scores 0.5 and 1.0) in SPMS than RRMS based on diffusion anisotropy, neurite content, and diameter. Conclusion Advanced diffusion MRI and texture analysis may be promising approaches for thorough understanding of brain structural changes from RRMS to SPMS, thereby providing new insight into disease development mechanisms in MS.
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Affiliation(s)
- Olayinka Oladosu
- Department of Neuroscience, Faculty of Graduate Studies, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Wei-Qiao Liu
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Lenora Brown
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Bruce G. Pike
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Luanne M. Metz
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Yunyan Zhang
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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Yoon K, Archer DB, Clarke MA, Smith SA, Oguz I, Cutter G, Xu J, Bagnato F. Transcallosal and Corticospinal White Matter Disease and Its Association With Motor Impairment in Multiple Sclerosis. Front Neurol 2022; 13:811315. [PMID: 35785345 PMCID: PMC9240189 DOI: 10.3389/fneur.2022.811315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 04/19/2022] [Indexed: 12/02/2022] Open
Abstract
Purpose In this cross-sectional, proof-of-concept study, we propose that using the more pathologically-specific neurite orientation dispersion and density imaging (NODDI) method, in conjunction with high-resolution probabilistic tractography, white matter tract templates can improve the assessment of regional axonal injury and its association with disability of people with multiple sclerosis (pwMS). Methods Parametric maps of the neurite density index, orientation dispersion index, and the apparent isotropic volume fraction (IVF) were estimated in 18 pwMS and nine matched healthy controls (HCs). Tract-specific values were measured in transcallosal (TC) fibers from the paracentral lobules and TC and corticospinal fibers from the ventral and dorsal premotor areas, presupplementary and supplementary motor areas, and primary motor cortex. The nonparametric Mann-Whitney U test assessed group differences in the NODDI-derived metrics; the Spearman's rank correlation analyses measured associations between the NODDI metrics and other clinical or radiological variables. Results IVF values of the TC fiber bundles from the paracentral, presupplementary, and supplementary motor areas were both higher in pwMS than in HCs (p ≤ 0.045) and in pwMS with motor disability compared to those without motor disability (p ≤ 0.049). IVF in several TC tracts was associated with the Expanded Disability Status Scale score (p ≤ 0.047), while regional and overall lesion burden correlated with the Timed 25-Foot Walking Test (p ≤ 0.049). Conclusion IVF alterations are present in pwMS even when the other NODDI metrics are still mostly preserved. Changes in IVF are biologically non-specific and may not necessarily drive irreversible functional loss. However, by possibly preceding downstream pathologies that are strongly associated with disability accretion, IVF changes are indicators of, otherwise, occult prelesional tissue injury.
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Affiliation(s)
- Keejin Yoon
- Neuroimaging Unit, Division of Neuroimmunology, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
- College of Arts and Sciences, Vanderbilt University, Nashville, TN, United States
| | - Derek B. Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, United States
- Vanderbilt University School of Medicine, Vanderbilt Genetics Institute, Nashville, TN, United States
| | - Margareta A. Clarke
- Neuroimaging Unit, Division of Neuroimmunology, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Seth A. Smith
- Department of Radiology and Radiological Sciences, Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Ipek Oguz
- Department of Science, Vanderbilt University, Nashville, TN, United States
| | - Gary Cutter
- Department of Biostatistics, School of Public Health, The University of Alabama at Birmingham, Birmingham, AL, United States
| | - Junzhong Xu
- Department of Radiology and Radiological Sciences, Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Francesca Bagnato
- Neuroimaging Unit, Division of Neuroimmunology, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Neurology, VA Medical Center, TN Valley Healthcare System, Nashville, TN, United States
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21
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Role of artificial intelligence in MS clinical practice. Neuroimage Clin 2022; 35:103065. [PMID: 35661470 PMCID: PMC9163993 DOI: 10.1016/j.nicl.2022.103065] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 05/04/2022] [Accepted: 05/26/2022] [Indexed: 11/24/2022]
Abstract
For medical applications, machine learning (including deep learning) are the most commonly used artificial intelligence (AI) approaches. It can improve multiple sclerosis (MS) diagnosis, prognostication and treatment monitoring. Thanks to AI, MRI and cognitive phenotypes of MS patients were identified. AI can shorten MRI protocols for MS, allowing the application of advanced techniques. It can reduce the human effort for MRI analysis, especially for lesion segmentation.
Machine learning (ML) and its subset, deep learning (DL), are branches of artificial intelligence (AI) showing promising findings in the medical field, especially when applied to imaging data. Given the substantial role of MRI in the diagnosis and management of patients with multiple sclerosis (MS), this disease is an ideal candidate for the application of AI techniques. In this narrative review, we are going to discuss the potential applications of AI for MS clinical practice, together with their limitations. Among their several advantages, ML algorithms are able to automate repetitive tasks, to analyze more data in less time and to achieve higher accuracy and reproducibility than the human counterpart. To date, these algorithms have been applied to MS diagnosis, prognosis, disease and treatment monitoring. Other fields of application have been improvement of MRI protocols as well as automated lesion and tissue segmentation. However, several challenges remain, including a better understanding of the information selected by AI algorithms, appropriate multicenter and longitudinal validations of results and practical aspects regarding hardware and software integration. Finally, one cannot overemphasize the paramount importance of human supervision, in order to optimize the use and take full advantage of the potential of AI approaches.
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22
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Callow DD, Purcell JJ, Won J, Smith JC. Neurite dispersion and density mediates the relationship between cardiorespiratory fitness and cognition in healthy younger adults. Neuropsychologia 2022; 169:108207. [PMID: 35259402 PMCID: PMC8985444 DOI: 10.1016/j.neuropsychologia.2022.108207] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 02/28/2022] [Accepted: 03/03/2022] [Indexed: 11/23/2022]
Abstract
Growing evidence suggests physical activity and cardiorespiratory fitness are associated with better cognition across the lifespan. However, the neurobiological underpinnings relating fitness and cognition remain unclear, particularly in healthy younger adults. Using a well-established and popular multi-compartment diffusion modeling approach, called Neurite Orientation and Dispersion and Density Imaging (NODDI), we investigated the relationship between physical fitness (measured via a 2-min walk test), cognition (fluid and crystallized), and gray and white matter microstructure, in a large sample (n = 816) of healthy younger adults (ages 22-35 years) from the human connectome project (HCP). Concurrent with previous literature, we found that fitness was positively associated with both fluid and crystallized cognition. Furthermore, we found that physical fitness was negatively associated with white matter orientation dispersion index (ODIWM) around the cerebellar peduncle and was negatively associated with widespread cortical and subcortical gray matter neurite density index (NDIGM). Lower ODIWM of the cerebral peduncle was associated with better fluid cognitive performance, while lower NDIGM was associated with better crystallized cognition. Finally, we found that while ODIWM partially mediated the relationship between fitness and fluid cognition, NDIGM partially mediated the relationship between fitness and crystallized cognition. This study is the first to explore the relationship between physical fitness and white and gray matter microstructure measures using NODDI. Our findings suggest that in addition to improved cognitive performance, higher physical fitness may be associated with lower white matter tract dispersion and lower neurite density in the cortical and subcortical gray matter of healthy younger adults.
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Affiliation(s)
- Daniel D. Callow
- Department of Kinesiology, University of Maryland, College Park, MD, USA
- Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA
| | | | - Junyeon Won
- Department of Kinesiology, University of Maryland, College Park, MD, USA
| | - J. Carson Smith
- Department of Kinesiology, University of Maryland, College Park, MD, USA
- Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA
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23
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Advanced diffusion-weighted imaging models better characterize white matter neurodegeneration and clinical outcomes in multiple sclerosis. J Neurol 2022; 269:4729-4741. [DOI: 10.1007/s00415-022-11104-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 10/18/2022]
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24
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Quantification of normal-appearing white matter damage in early relapse-onset multiple sclerosis through neurite orientation dispersion and density imaging. Mult Scler Relat Disord 2021; 58:103396. [DOI: 10.1016/j.msard.2021.103396] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/02/2021] [Accepted: 11/08/2021] [Indexed: 11/23/2022]
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25
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Radetz A, Mladenova K, Ciolac D, Gonzalez-Escamilla G, Fleischer V, Ellwardt E, Krämer J, Bittner S, Meuth SG, Muthuraman M, Groppa S. Linking Microstructural Integrity and Motor Cortex Excitability in Multiple Sclerosis. Front Immunol 2021; 12:748357. [PMID: 34712236 PMCID: PMC8546169 DOI: 10.3389/fimmu.2021.748357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 09/23/2021] [Indexed: 11/15/2022] Open
Abstract
Motor skills are frequently impaired in multiple sclerosis (MS) patients following grey and white matter damage with cortical excitability abnormalities. We applied advanced diffusion imaging with 3T magnetic resonance tomography for neurite orientation dispersion and density imaging (NODDI), as well as diffusion tensor imaging (DTI) in 50 MS patients and 49 age-matched healthy controls to quantify microstructural integrity of the motor system. To assess excitability, we determined resting motor thresholds using non-invasive transcranial magnetic stimulation. As measures of cognitive-motor performance, we conducted neuropsychological assessments including the Nine-Hole Peg Test, Trail Making Test part A and B (TMT-A and TMT-B) and the Symbol Digit Modalities Test (SDMT). Patients were evaluated clinically including assessments with the Expanded Disability Status Scale. A hierarchical regression model revealed that lower neurite density index (NDI) in primary motor cortex, suggestive for axonal loss in the grey matter, predicted higher motor thresholds, i.e. reduced excitability in MS patients (p = .009, adjusted r² = 0.117). Furthermore, lower NDI was indicative of decreased cognitive-motor performance (p = .007, adjusted r² = .142 for TMT-A; p = .009, adjusted r² = .129 for TMT-B; p = .006, adjusted r² = .142 for SDMT). Motor WM tracts of patients were characterized by overlapping clusters of lowered NDI (p <.05, Cohen's d = 0.367) and DTI-based fractional anisotropy (FA) (p <.05, Cohen's d = 0.300), with NDI exclusively detecting a higher amount of abnormally appearing voxels. Further, orientation dispersion index of motor tracts was increased in patients compared to controls, suggesting a decreased fiber coherence (p <.05, Cohen's d = 0.232). This study establishes a link between microstructural characteristics and excitability of neural tissue, as well as cognitive-motor performance in multiple sclerosis. We further demonstrate that the NODDI parameters neurite density index and orientation dispersion index detect a larger amount of abnormally appearing voxels in patients compared to healthy controls, as opposed to the classical DTI parameter FA. Our work outlines the potential for microstructure imaging using advanced biophysical models to forecast excitability alterations in neuroinflammation.
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Affiliation(s)
- Angela Radetz
- Neuroimaging and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Kalina Mladenova
- Neuroimaging and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Dumitru Ciolac
- Neuroimaging and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
- Laboratory of Neurobiology and Medical Genetics, Nicolae Testemițanu State University of Medicine and Pharmacy, Chişinău, Moldova
- Department of Neurology, Institute of Emergency Medicine, Chişinău, Moldova
| | - Gabriel Gonzalez-Escamilla
- Neuroimaging and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Vinzenz Fleischer
- Neuroimaging and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Erik Ellwardt
- Neuroimaging and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Julia Krämer
- Department of Neurology, Institute of Translational Neurology, University Hospital Münster, Münster, Germany
| | - Stefan Bittner
- Neuroimaging and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Sven G. Meuth
- Department of Neurology, Institute of Translational Neurology, University Hospital Münster, Münster, Germany
- Department of Neurology, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Muthuraman Muthuraman
- Neuroimaging and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Sergiu Groppa
- Neuroimaging and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
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Martinez-Heras E, Grussu F, Prados F, Solana E, Llufriu S. Diffusion-Weighted Imaging: Recent Advances and Applications. Semin Ultrasound CT MR 2021; 42:490-506. [PMID: 34537117 DOI: 10.1053/j.sult.2021.07.006] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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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Investigating Microstructural Changes in White Matter in Multiple Sclerosis: A Systematic Review and Meta-Analysis of Neurite Orientation Dispersion and Density Imaging. Brain Sci 2021; 11:brainsci11091151. [PMID: 34573172 PMCID: PMC8469792 DOI: 10.3390/brainsci11091151] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 08/24/2021] [Accepted: 08/27/2021] [Indexed: 11/17/2022] Open
Abstract
Multiple sclerosis (MS) is characterised by widespread damage of the central nervous system that includes alterations in normal-appearing white matter (NAWM) and demyelinating white matter (WM) lesions. Neurite orientation dispersion and density imaging (NODDI) has been proposed to provide a precise characterisation of WM microstructures. NODDI maps can be calculated for the Neurite Density Index (NDI) and Orientation Dispersion Index (ODI), which estimate orientation dispersion and neurite density. Although NODDI has not been widely applied in MS, this technique is promising in investigating the complexity of MS pathology, as it is more specific than diffusion tensor imaging (DTI) in capturing microstructural alterations. We conducted a meta-analysis of studies using NODDI metrics to assess brain microstructural changes and neuroaxonal pathology in WM lesions and NAWM in patients with MS. Three reviewers conducted a literature search of four electronic databases. We performed a random-effect meta-analysis and the extent of between-study heterogeneity was assessed with the I2 statistic. Funnel plots and Egger’s tests were used to assess publication bias. We identified seven studies analysing 374 participants (202 MS and 172 controls). The NDI in WM lesions and NAWM were significantly reduced compared to healthy WM and the standardised mean difference of each was −3.08 (95%CI −4.22 to (−1.95), p ≤ 0.00001, I2 = 88%) and −0.70 (95%CI −0.99 to (−0.40), p ≤ 0.00001, I2 = 35%), respectively. There was no statistically significant difference of the ODI in MS WM lesions and NAWM compared to healthy controls. This systematic review and meta-analysis confirmed that the NDI is significantly reduced in MS lesions and NAWM than in WM from healthy participants, corresponding to reduced intracellular signal fraction, which may reflect underlying damage or loss of neurites.
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Gharaylou Z, Sahraian MA, Hadjighassem M, Kohanpour M, Doosti R, Nahardani S, Moghadasi AN. Widespread Disruptions of White Matter in Familial Multiple Sclerosis: DTI and NODDI Study. Front Neurol 2021; 12:678245. [PMID: 34484098 PMCID: PMC8415561 DOI: 10.3389/fneur.2021.678245] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/14/2021] [Indexed: 11/29/2022] Open
Abstract
Diffusion tensor imaging (DTI) is a noninvasive, quantitative MRI technique that measures white matter (WM) integrity. Many brain dimensions are heritable, including white matter integrity measured with DTI. Family studies are valuable to provide insights into the interactive effects of non-environmental factors on multiple sclerosis (MS). To examine the contribution of familial factors to the diffusion signals across WM microstructure, we performed DTI and calculated neurite orientation dispersion plus density imaging (NODDI) diffusion parameters in two patient groups comprising familial and sporadic forms of multiple sclerosis and their unaffected relatives. We divided 111 subjects (49 men and 62 women: age range 19-60) into three groups conforming to their MS history. The familial MS group included 30 participants (patients; n = 16, healthy relatives; n = 14). The sporadic group included 41 participants (patients; n = 10, healthy relatives; n = 31). Forty age-matched subjects with no history of MS in their families were defined as the control group. To study white matter integrity, two methods were employed: one for calculating the mean of DTI, FA, and MD parameters on 18 tracts using Tracts Constrained by Underlying Anatomy (TRACULA) and the other for whole brain voxel-based analysis using tract-based spatial statistics (TBSS) on NDI and ODI parameters derived from NODDI and DTI parameters. Voxel-based analysis showed considerable changes in FA, MD, NDI, and ODI in the familial group when compared with the control group, reflecting widespread impairment of white matter in this group. The analysis of 18 tracts with TRACULA revealed increased MD and FA reduction in more tracts (left and right ILF, UNC, and SLFT, forceps major and minor) in familial MS patients vs. the control group. There were no significant differences between the patient groups. We found no consequential changes in healthy relatives of both patient groups in voxel-based and tract analyses. Considering the multifactorial etiology of MS, familial studies are of great importance to clarify the effects of certain predisposing factors on demyelinating brain pathology.
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Affiliation(s)
- Zeinab Gharaylou
- Multiple Sclerosis Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Sahraian
- Multiple Sclerosis Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahmoudreza Hadjighassem
- Brain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohsen Kohanpour
- Neuroimaging and Analysis Group (NIAG), Research Center for Molecular and Cellular Imaging, Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Rozita Doosti
- Multiple Sclerosis Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Shima Nahardani
- Multiple Sclerosis Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Abdorreza Naser Moghadasi
- Multiple Sclerosis Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
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Andica C, Kamagata K, Kirino E, Uchida W, Irie R, Murata S, Aoki S. Neurite orientation dispersion and density imaging reveals white matter microstructural alterations in adults with autism. Mol Autism 2021; 12:48. [PMID: 34193257 PMCID: PMC8247240 DOI: 10.1186/s13229-021-00456-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 06/24/2021] [Indexed: 11/10/2022] Open
Abstract
Background Evidences suggesting the association between behavioral anomalies in autism and white matter (WM) microstructural alterations are increasing. Diffusion tensor imaging (DTI) is widely used to infer tissue microstructure. However, due to its lack of specificity, the underlying pathology of reported differences in DTI measures in autism remains poorly understood. Herein, we applied neurite orientation dispersion and density imaging (NODDI) to quantify and define more specific causes of WM microstructural changes associated with autism in adults. Methods NODDI (neurite density index [NDI], orientation dispersion index, and isotropic volume fraction [ISOVF]) and DTI (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity, and radial diffusivity [RD]) measures were compared between autism (N = 26; 19 males and 7 females; 32.93 ± 9.24 years old) and age- and sex-matched typically developing (TD; N = 25; 17 males and 8 females; 34.43 ± 9.02 years old) groups using tract-based spatial statistics and region-of-interest analyses. Linear discriminant analysis using leave-one-out cross-validation (LDA-LOOCV) was also performed to assess the discriminative power of diffusion measures in autism and TD. Results Significantly lower NDI and higher ISOVF, suggestive of decreased neurite density and increased extracellular free-water, respectively, were demonstrated in the autism group compared with the TD group, mainly in commissural and long-range association tracts, but with distinct predominant sides. Consistent with previous reports, the autism group showed lower FA and higher MD and RD when compared with TD group. Notably, LDA-LOOCV suggests that NDI and ISOVF have relatively higher accuracy (82%) and specificity (NDI, 84%; ISOVF, 88%) compared with that of FA, MD, and RD (accuracy, 67–73%; specificity, 68–80%). Limitations The absence of histopathological confirmation limit the interpretation of our findings. Conclusions Our results suggest that NODDI measures might be useful as imaging biomarkers to diagnose autism in adults and assess its behavioral characteristics. Furthermore, NODDI allows interpretation of previous findings on changes in WM diffusion tensor metrics in individuals with autism.
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Affiliation(s)
- Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
| | - Koji Kamagata
- 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
| | - Wataru Uchida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Ryusuke Irie
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Syo Murata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Radiological Sciences, Faculty of Healthy Sciences, Komazawa University, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
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30
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Johnson D, Ricciardi A, Brownlee W, Kanber B, Prados F, Collorone S, Kaden E, Toosy A, Alexander DC, Gandini Wheeler-Kingshott CAM, Ciccarelli O, Grussu F. Comparison of Neurite Orientation Dispersion and Density Imaging and Two-Compartment Spherical Mean Technique Parameter Maps in Multiple Sclerosis. Front Neurol 2021; 12:662855. [PMID: 34194382 PMCID: PMC8236830 DOI: 10.3389/fneur.2021.662855] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 05/17/2021] [Indexed: 01/03/2023] Open
Abstract
Background: Neurite orientation dispersion and density imaging (NODDI) and the spherical mean technique (SMT) are diffusion MRI methods providing metrics with sensitivity to similar characteristics of white matter microstructure. There has been limited comparison of changes in NODDI and SMT parameters due to multiple sclerosis (MS) pathology in clinical settings. Purpose: To compare group-wise differences between healthy controls and MS patients in NODDI and SMT metrics, investigating associations with disability and correlations with diffusion tensor imaging (DTI) metrics. Methods: Sixty three relapsing-remitting MS patients were compared to 28 healthy controls. NODDI and SMT metrics corresponding to intracellular volume fraction (vin), orientation dispersion (ODI and ODE), diffusivity (D) (SMT only) and isotropic volume fraction (viso) (NODDI only) were calculated from diffusion MRI data, alongside DTI metrics (fractional anisotropy, FA; axial/mean/radial diffusivity, AD/MD/RD). Correlations between all pairs of MRI metrics were calculated in normal-appearing white matter (NAWM). Associations with expanded disability status scale (EDSS), controlling for age and gender, were evaluated. Patient-control differences were assessed voxel-by-voxel in MNI space controlling for age and gender at the 5% significance level, correcting for multiple comparisons. Spatial overlap of areas showing significant differences were compared using Dice coefficients. Results: NODDI and SMT show significant associations with EDSS (standardised beta coefficient −0.34 in NAWM and −0.37 in lesions for NODDI vin; 0.38 and −0.31 for SMT ODE and vin in lesions; p < 0.05). Significant correlations in NAWM are observed between DTI and NODDI/SMT metrics. NODDI vin and SMT vin strongly correlated (r = 0.72, p < 0.05), likewise NODDI ODI and SMT ODE (r = −0.80, p < 0.05). All DTI, NODDI and SMT metrics detect widespread differences between patients and controls in NAWM (12.57% and 11.90% of MNI brain mask for SMT and NODDI vin, Dice overlap of 0.42). Data Conclusion: SMT and NODDI detect significant differences in white matter microstructure between MS patients and controls, concurring on the direction of these changes, providing consistent descriptors of tissue microstructure that correlate with disability and show alterations beyond focal damage. Our study suggests that NODDI and SMT may play a role in monitoring MS in clinical trials and practice.
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Affiliation(s)
- Daniel Johnson
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Antonio Ricciardi
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Wallace Brownlee
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Baris Kanber
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Ferran Prados
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, United Kingdom.,e-Health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Sara Collorone
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Enrico Kaden
- Department of Computer Science, Centre for Medical Image Computing, University College London, London, United Kingdom.,Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - Ahmed Toosy
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre, London, United Kingdom
| | - Daniel C Alexander
- Department of Computer Science, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Claudia A M Gandini Wheeler-Kingshott
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Magnetic Resonance Imaging (MRI) 3T Research Centre, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Mondino Foundation, Pavia, Italy
| | - Olga Ciccarelli
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre, London, United Kingdom
| | - Francesco Grussu
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
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31
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Lucignani M, Breschi L, Espagnet MCR, Longo D, Talamanca LF, Placidi E, Napolitano A. Reliability on multiband diffusion NODDI models: A test retest study on children and adults. Neuroimage 2021; 238:118234. [PMID: 34091031 DOI: 10.1016/j.neuroimage.2021.118234] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 05/07/2021] [Accepted: 06/01/2021] [Indexed: 02/05/2023] Open
Abstract
Neurite Orientation Dispersion and Density Imaging (NODDI) and Bingham-NODDI diffusion MRI models are nowadays very well-known models in the field of diffusion MRI as they represent powerful tools for the estimation of brain microstructure. In order to efficiently translate NODDI imaging findings into the diagnostic clinical practice, a test-retest approach would be useful to assess reproducibility and reliability of NODDI biomarkers, thus providing validation on precision of different fitting toolboxes. In this context, we conducted a test-retest study with the aim to assess the effects of different factors (i.e. fitting algorithms, multiband acceleration, shell configuration, age of subject and hemispheric side) on diffusion models reliability, assessed in terms of Intra-class Correlation Coefficient (ICC) and Variation Factor (VF). To this purpose, data from pediatric and adult subjects were acquired with Simultaneous-MultiSlice (SMS) imaging method with two different acceleration factor (AF) and four b-values, subsequently combined in seven shell configurations. Data were then fitted with two different GPU-based algorithms to speed up the analysis. Results show that each factor investigated had a significant effect on reliability of several diffusion parameters. Particularly, both datasets reveal very good ICC values for higher AF, suggesting that faster acquisitions do not jeopardize the reliability and are useful to decrease motion artifacts. Although very small reliability differences appear when comparing shell configurations, more extensive diffusion parameters variability results when considering shell configuration with lower b-values, especially for simple model like NODDI. Also fitting tools have a significant effect on reliability, but their difference occurs in both datasets and AF, so it appears to be independent from either misalignment and motion artifacts, or noise and SNR. The main achievement of the present study is to show how 10 min multi-shell diffusion MRI acquisition for NODDI acquisition can have reliable results in WM. More complex models do not appear to be more prone to less data acquisition as well as noisier data thus stressing the idea of Bingham-NODDI having greater sensitivity to true subject variability.
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Affiliation(s)
- Martina Lucignani
- Medical Physics Department, Bambino Gesù Children's Hospital IRCCS, Rome, Italy
| | - Laura Breschi
- Medical Physics Department, Bambino Gesù Children's Hospital IRCCS, Rome, Italy
| | - Maria Camilla Rossi Espagnet
- Neuroradiology Unit, Bambino Gesù Children's Hospital IRCCS, Rome, Italy; Nesmos Department, Sapienza University, Rome, Italy
| | - Daniela Longo
- Neuroradiology Unit, Bambino Gesù Children's Hospital IRCCS, Rome, Italy
| | | | - Elisa Placidi
- Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Medical Physics UOC, Rome, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children's Hospital IRCCS, Rome, Italy.
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32
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Chen A, Wen S, Lakhani DA, Gao S, Yoon K, Smith SA, Dortch R, Xu J, Bagnato F. Assessing brain injury topographically using MR neurite orientation dispersion and density imaging in multiple sclerosis. J Neuroimaging 2021; 31:1003-1013. [PMID: 34033187 DOI: 10.1111/jon.12876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 04/14/2021] [Accepted: 04/29/2021] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND AND PURPOSE Axonal injury is a key player of disability in persons with multiple sclerosis (pwMS). Yet, detecting and measuring it in vivo is challenging. The neurite orientation dispersion and density imaging (NODDI) proposes a novel framework for probing axonal integrity in vivo. NODDI at 3.0 Tesla was used to quantify tissue damage in pwMS and its relationship with disease progression. METHODS Eighteen pwMS (4 clinically isolated syndrome, 11 relapsing remitting, and 3 secondary progressive MS) and nine age- and sex-matched healthy controls underwent a brain MRI, inclusive of clinical sequences and a multi-shell diffusion acquisition. Parametric maps of axial diffusivity (AD), neurite density index (ndi), apparent isotropic volume fraction (ivf), and orientation dispersion index (odi) were fitted. Anatomically matched regions of interest were used to quantify AD and NODDI-derived metrics and to assess the relations between these measures and those of disease progression. RESULTS AD, ndi, ivf, and odi significantly differed between chronic black holes (cBHs) and T2-lesions, and between the latter and normal appearing white matter (NAWM). All metrics except ivf significantly differed between NAWM located next to a cBH and that situated contra-laterally. Only NAWM odi was significantly associated with T2-lesion volume, the timed 25-foot walk test and disease duration. CONCLUSIONS NODDI is sensitive to tissue injury but its relationship with clinical progression remains limited.
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Affiliation(s)
- Amalie Chen
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, Tennessee, USA.,Neurology Residency, Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Sijin Wen
- Department of Biostatistics, West Virginia University, Morgantown, West Virginia, USA
| | - Dhairya A Lakhani
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, Tennessee, USA.,Department of Radiology, West Virginia University, Morgantown, West Virginia, USA
| | - Si Gao
- Department of Biostatistics, West Virginia University, Morgantown, West Virginia, USA
| | - Keejin Yoon
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, Tennessee, USA.,Vanderbilt University College of Arts and Science, Nashville, Tennessee, USA
| | - Seth A Smith
- Vanderbilt University Institute of Imaging Sciences, Department of Radiology and Radiological Sciences, VUMC, Nashville, Tennessee, USA
| | - Richard Dortch
- Vanderbilt University Institute of Imaging Sciences, Department of Radiology and Radiological Sciences, VUMC, Nashville, Tennessee, USA.,Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Junzhong Xu
- Vanderbilt University Institute of Imaging Sciences, Department of Radiology and Radiological Sciences, VUMC, Nashville, Tennessee, USA
| | - Francesca Bagnato
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, Tennessee, USA.,Department of Neurology, VA Hospital, TN Valley Healthcare System (TVHS) Nashville, Tennessee, USA
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33
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Oladosu O, Liu WQ, Pike BG, Koch M, Metz LM, Zhang Y. Advanced Analysis of Diffusion Tensor Imaging Along With Machine Learning Provides New Sensitive Measures of Tissue Pathology and Intra-Lesion Activity in Multiple Sclerosis. Front Neurosci 2021; 15:634063. [PMID: 34025338 PMCID: PMC8138061 DOI: 10.3389/fnins.2021.634063] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 04/15/2021] [Indexed: 12/02/2022] Open
Abstract
Tissue pathology in multiple sclerosis (MS) is highly complex, requiring multi-dimensional analysis. In this study, our goal was to test the feasibility of obtaining high angular resolution diffusion imaging (HARDI) metrics through single-shell modeling of diffusion tensor imaging (DTI) data, and investigate how advanced measures from single-shell HARDI and DTI tractography perform relative to classical DTI metrics in assessing MS pathology. We examined 52 relapsing-remitting MS patients who had 3T anatomical brain MRI and DTI. Single-shell HARDI modeling yielded 5 sub-voxel-based metrics, totalling 11 diffusion measures including 4 DTI and 2 tractography metrics. Based on machine learning of 3-dimensional regions of interest, we evaluated the importance of the measures through several tissue classification tasks. These included two within-subject comparisons: lesion versus normal appearing white matter (NAWM); and lesion core versus shell. Further, by stratifying patients as having high (above 75%ile) and low (below 25%ile) number of MS lesions, we also performed 2 classifications between subjects for lesions and NAWM respectively. Results showed that in lesion-NAWM analysis, HARDI orientation distribution function (ODF) energy, DTI fractional anisotropy (FA), and HARDI orientation dispersion index were the top three metrics, which together achieved 65.2% accuracy and 0.71 area under the receiver operating characteristic curve (AUROC). In core-shell analysis, DTI mean diffusivity (MD), radial diffusivity, and FA were the top three metrics, and MD dominated the classification, which achieved 59.3% accuracy and 0.59 AUROC alone. Between patients, FA was the leading feature in lesion comparisons, while ODF energy was the best in NAWM separation. Collectively, single-shell modeling of common diffusion data can provide robust orientation measures of lesion and NAWM pathology, and DTI metrics are most sensitive to intra-lesion abnormality. Combined analysis of both advanced and classical diffusion measures may be critical for improved understanding of MS pathology.
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Affiliation(s)
- Olayinka Oladosu
- Department of Neuroscience, Faculty of Graduate Studies, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Wei-Qiao Liu
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Bruce G Pike
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Marcus Koch
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Luanne M Metz
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Yunyan Zhang
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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34
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Collorone S, Prados F, Kanber B, Cawley NM, Tur C, Grussu F, Solanky BS, Yiannakas M, Davagnanam I, Wheeler-Kingshott CAMG, Barkhof F, Ciccarelli O, Toosy AT. Brain microstructural and metabolic alterations detected in vivo at onset of the first demyelinating event. Brain 2021; 144:1409-1421. [PMID: 33903905 PMCID: PMC8219367 DOI: 10.1093/brain/awab043] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 11/03/2020] [Accepted: 12/03/2020] [Indexed: 12/22/2022] Open
Abstract
In early multiple sclerosis, a clearer understanding of normal-brain tissue microstructural and metabolic abnormalities will provide valuable insights into its pathophysiology. We used multi-parametric quantitative MRI to detect alterations in brain tissues of patients with their first demyelinating episode. We acquired neurite orientation dispersion and density imaging [to investigate morphology of neurites (dendrites and axons)] and 23Na MRI (to estimate total sodium concentration, a reflection of underlying changes in metabolic function). In this cross-sectional study, we enrolled 42 patients diagnosed with clinically isolated syndrome or multiple sclerosis within 3 months of their first demyelinating event and 16 healthy controls. Physical and cognitive scales were assessed. At 3 T, we acquired brain and spinal cord structural scans, and neurite orientation dispersion and density imaging. Thirty-two patients and 13 healthy controls also underwent brain 23Na MRI. We measured neurite density and orientation dispersion indices and total sodium concentration in brain normal-appearing white matter, white matter lesions, and grey matter. We used linear regression models (adjusting for brain parenchymal fraction and lesion load) and Spearman correlation tests (significance level P ≤ 0.01). Patients showed higher orientation dispersion index in normal-appearing white matter, including the corpus callosum, where they also showed lower neurite density index and higher total sodium concentration, compared with healthy controls. In grey matter, compared with healthy controls, patients demonstrated: lower orientation dispersion index in frontal, parietal and temporal cortices; lower neurite density index in parietal, temporal and occipital cortices; and higher total sodium concentration in limbic and frontal cortices. Brain volumes did not differ between patients and controls. In patients, higher orientation dispersion index in corpus callosum was associated with worse performance on timed walk test (P = 0.009, B = 0.01, 99% confidence interval = 0.0001 to 0.02), independent of brain and lesion volumes. Higher total sodium concentration in left frontal middle gyrus was associated with higher disability on Expanded Disability Status Scale (rs = 0.5, P = 0.005). Increased axonal dispersion was found in normal-appearing white matter, particularly corpus callosum, where there was also axonal degeneration and total sodium accumulation. The association between increased axonal dispersion in the corpus callosum and worse walking performance implies that morphological and metabolic alterations in this structure could mechanistically contribute to disability in multiple sclerosis. As brain volumes were neither altered nor related to disability in patients, our findings suggest that these two advanced MRI techniques are more sensitive at detecting clinically relevant pathology in early multiple sclerosis.
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Affiliation(s)
- Sara Collorone
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Ferran Prados
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.,Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, UK.,Universitat Oberta de Catalunya, Barcelona, Spain
| | - Baris Kanber
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Niamh M Cawley
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Carmen Tur
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Francesco Grussu
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.,Centre for Medical Image Computing (CMIC), Department of Computer Sciences, University College London, London, UK
| | - Bhavana S Solanky
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Marios Yiannakas
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Indran Davagnanam
- Department of Brain Repair and Rehabilitation, University College London Institute of Neurology, Faculty of Brain Sciences, UCL, London, UK
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Frederik Barkhof
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Biomedical Engineering, University College London, London, UK.,Department of Brain Repair and Rehabilitation, University College London Institute of Neurology, Faculty of Brain Sciences, UCL, London, UK.,Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Vrije Universiteit, The Netherlands.,National Institute for Health Research, University College London Hospitals, Biomedical Research Centre, London, UK
| | - Olga Ciccarelli
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.,National Institute for Health Research, University College London Hospitals, Biomedical Research Centre, London, UK
| | - Ahmed T Toosy
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
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Lu PJ, Barakovic M, Weigel M, Rahmanzadeh R, Galbusera R, Schiavi S, Daducci A, La Rosa F, Bach Cuadra M, Sandkühler R, Kuhle J, Kappos L, Cattin P, Granziera C. GAMER-MRI in Multiple Sclerosis Identifies the Diffusion-Based Microstructural Measures That Are Most Sensitive to Focal Damage: A Deep-Learning-Based Analysis and Clinico-Biological Validation. Front Neurosci 2021; 15:647535. [PMID: 33889069 PMCID: PMC8055933 DOI: 10.3389/fnins.2021.647535] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 02/23/2021] [Indexed: 12/02/2022] Open
Abstract
Conventional magnetic resonance imaging (cMRI) in multiple sclerosis (MS) patients provides measures of focal brain damage and activity, which are fundamental for disease diagnosis, prognosis, and the evaluation of response to therapy. However, cMRI is insensitive to the damage to the microenvironment of the brain tissue and the heterogeneity of MS lesions. In contrast, the damaged tissue can be characterized by mathematical models on multishell diffusion imaging data, which measure different compartmental water diffusion. In this work, we obtained 12 diffusion measures from eight diffusion models, and we applied a deep-learning attention-based convolutional neural network (CNN) (GAMER-MRI) to select the most discriminating measures in the classification of MS lesions and the perilesional tissue by attention weights. Furthermore, we provided clinical and biological validation of the chosen metrics-and of their most discriminative combinations-by correlating their respective mean values in MS patients with the corresponding Expanded Disability Status Scale (EDSS) and the serum level of neurofilament light chain (sNfL), which are measures of disability and neuroaxonal damage. Our results show that the neurite density index from neurite orientation and dispersion density imaging (NODDI), the measures of the intra-axonal and isotropic compartments from microstructural Bayesian approach, and the measure of the intra-axonal compartment from the spherical mean technique NODDI were the most discriminating (respective attention weights were 0.12, 0.12, 0.15, and 0.13). In addition, the combination of the neurite density index from NODDI and the measures for the intra-axonal and isotropic compartments from the microstructural Bayesian approach exhibited a stronger correlation with EDSS and sNfL than the individual measures. This work demonstrates that the proposed method might be useful to select the microstructural measures that are most discriminative of focal tissue damage and that may also be combined to a unique contrast to achieve stronger correlations to clinical disability and neuroaxonal damage.
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Affiliation(s)
- Po-Jui Lu
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
- Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Reza Rahmanzadeh
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Riccardo Galbusera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | | | - Francesco La Rosa
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Meritxell Bach Cuadra
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Robin Sandkühler
- Center for Medical Image Analysis and Navigation, Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Jens Kuhle
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ludwig Kappos
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Philippe Cattin
- Center for Medical Image Analysis and Navigation, Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel, University Hospital Basel and University of Basel, Basel, Switzerland
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Carvalho V, Soares C, Gomes I, Carvalho A, Serrazina F, Rodrigues SG, Pinheiro J, Marques IB, Correia F, Correia AS, de Sá J, Sousa L, Vale J, Sá MJ, Sousa C, Ferreira J. RISCOP-Cognitive profile in a Portuguese cohort of radiological isolated syndrome patients: A case-control study. Mult Scler Relat Disord 2021; 50:102832. [PMID: 33596492 DOI: 10.1016/j.msard.2021.102832] [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: 12/11/2020] [Revised: 01/31/2021] [Accepted: 02/05/2021] [Indexed: 11/17/2022]
Abstract
INTRODUCTION Radiologically isolated syndrome (RIS) refers to the incidental discovery of white matter lesions suggestive of MS, on brain MRI, in asymptomatic patients. Recent studies suggest similar features of cognitive impairment between RIS and MS patients. Also, lower levels of health-related quality of life (QOL) and fatigue are reported in such patients. AIMS characterize and compare the cognitive profile of a multicentric Portuguese cohort of RIS patients with a control group. METHODS multicentric comparative study of a cohort of adult patients with RIS, and age and gender-matched controls followed in the headache outpatient clinic with prior MRI not fulfilling criteria for RIS diagnosis. We conducted interviews with participants, collected clinical data and applied the BICAMS battery and self-reported questionnaires (HADS, MFIS, MSQOL-54). RESULTS we evaluated 31 patients with RIS (median age 46 years, IQR [(Dusankova et al., 2012-52], 72% women) and 19 control individuals (median age 32 years, IQR [(O'Jile et al., 2005-48], 71% women). Prevalence of cognitive impairment did not differ between groups (16% of the RIS and 10% of the controls, p=0.579). We found no differences between groups on the BICAMS tests, although the results of the California Verbal Learning Test (CVLT-II) score presented a trend to significance, with a lower value on the RIS group (53.9 vs. 59.3, p=0.066). There were no significant differences regarding fatigue, QOL, anxiety/depression scores. CONCLUSION this is the first study on a Portuguese cohort of RIS patients assessing cognitive profile with BICAMS. A non-neglectable part of our cohort presented cognitive impairment. Our findings add to previous studies in suggesting that a more pronounced impairment of verbal memory and learning, evaluated by CVLT-II, may be present in RIS patients compared to controls. BICAMS should be assessed on future studies with larger cohorts.
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Affiliation(s)
- Vanessa Carvalho
- Department of Neurology, Hospital Pedro Hispano/Unidade Local de Saúde de Matosinhos, Matosinhos, Portugal.
| | - Carolina Soares
- Department of Neurology, Centro Hospitalar Universitário de São João, EPE, Porto, Portugal
| | - Inês Gomes
- Department of Neurology, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - Andreia Carvalho
- Department of Neurology, Centro Hospitalar de Vila Nova de Gaia/Espinho, Vila Nova de Gaia, Portugal
| | - Filipa Serrazina
- Department of Neurology, Hospital Egas Moniz, Centro Hospitalar de Lisboa Ocidental, Lisboa, Portugal
| | | | - Joaquim Pinheiro
- Department of Neurology, Centro Hospitalar de Vila Nova de Gaia/Espinho, Vila Nova de Gaia, Portugal
| | | | - Filipe Correia
- Department of Neurology, Hospital Pedro Hispano/Unidade Local de Saúde de Matosinhos, Matosinhos, Portugal
| | - Ana Sofia Correia
- Department of Neurology, Hospital Egas Moniz, Centro Hospitalar de Lisboa Ocidental, Lisboa, Portugal; CEDOC, Nova Medical School, Universidade Nova de Lisboa, Lisboa, Portugal
| | - João de Sá
- Department of Neurology, Hospital de Santa Maria, Centro Hospitalar Universitário de Lisboa Norte, Lisboa, Portugal
| | - Lívia Sousa
- Department of Neurology, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal
| | - José Vale
- Department of Neurology, Hospital Beatriz Ângelo, Loures, Portugal
| | - Maria José Sá
- Department of Neurology, Centro Hospitalar Universitário de São João, EPE, Porto, Portugal; Health Sciences Faculty, Universidade Fernando Pessoa, Porto, Portugal
| | - Cláudia Sousa
- Department of Psychology, Centro Hospitalar Universitário de São João, EPE, Porto, Portugal
| | - João Ferreira
- Department of Neurology, Hospital de Santa Maria, Centro Hospitalar Universitário de Lisboa Norte, Lisboa, Portugal
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Sacco S, Caverzasi E, Papinutto N, Cordano C, Bischof A, Gundel T, Cheng S, Asteggiano C, Kirkish G, Mallott J, Stern WA, Bastianello S, Bove RM, Gelfand JM, Goodin DS, Green AJ, Waubant E, Wilson MR, Zamvil SS, Cree BA, Hauser SL, Henry RG. Neurite Orientation Dispersion and Density Imaging for Assessing Acute Inflammation and Lesion Evolution in MS. AJNR Am J Neuroradiol 2020; 41:2219-2226. [PMID: 33154077 DOI: 10.3174/ajnr.a6862] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 07/29/2020] [Indexed: 01/22/2023]
Abstract
BACKGROUND AND PURPOSE MR imaging is essential for MS diagnosis and management, yet it has limitations in assessing axonal damage and remyelination. Gadolinium-based contrast agents add value by pinpointing acute inflammation and blood-brain barrier leakage, but with drawbacks in safety and cost. Neurite orientation dispersion and density imaging (NODDI) assesses microstructural features of neurites contributing to diffusion imaging signals. This approach may resolve the components of MS pathology, overcoming conventional MR imaging limitations. MATERIALS AND METHODS Twenty-one subjects with MS underwent serial enhanced MRIs (12.6 ± 9 months apart) including NODDI, whose key metrics are the neurite density and orientation dispersion index. Twenty-one age- and sex-matched healthy controls underwent unenhanced MR imaging with the same protocol. Fifty-eight gadolinium-enhancing and non-gadolinium-enhancing lesions were semiautomatically segmented at baseline and follow-up. Normal-appearing WM masks were generated by subtracting lesions and dirty-appearing WM from the whole WM. RESULTS The orientation dispersion index was higher in gadolinium-enhancing compared with non-gadolinium-enhancing lesions; logistic regression indicated discrimination, with an area under the curve of 0.73. At follow-up, in the 58 previously enhancing lesions, we identified 2 subgroups based on the neurite density index change across time: Type 1 lesions showed increased neurite density values, whereas type 2 lesions showed decreased values. Type 1 lesions showed greater reduction in size with time compared with type 2 lesions. CONCLUSIONS NODDI is a promising tool with the potential to detect acute MS inflammation. The observed heterogeneity among lesions may correspond to gradients in severity and clinical recovery after the acute phase.
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Affiliation(s)
- S Sacco
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California.,Institute of Radiology (S.S., C.A.), Department of Clinical Surgical Diagnostic and Pediatric Sciences
| | - E Caverzasi
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - N Papinutto
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - C Cordano
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - A Bischof
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - T Gundel
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - S Cheng
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - C Asteggiano
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California.,Institute of Radiology (S.S., C.A.), Department of Clinical Surgical Diagnostic and Pediatric Sciences
| | - G Kirkish
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - J Mallott
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - W A Stern
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - S Bastianello
- Department of Brain and Behavioral Sciences (S.B.), University of Pavia, Pavia, Italy.,Neuroradiology Department (S.B.), Istituto Di Ricovero e Cura a Carattere Scientifico Mondino Foundation, Pavia, Italy
| | - R M Bove
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - J M Gelfand
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - D S Goodin
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - A J Green
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - E Waubant
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - M R Wilson
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - S S Zamvil
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - B A Cree
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - S L Hauser
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
| | - R G Henry
- From the Department of Neurology (S.S., E.C., N.P., C.C., A.B., T.G., S.C., C.A., G.K., J.M., W.A.S., R.M.B., J.M.G., D.S.G., A.J.G., E.W., M.R.W., S.S.Z, B.A.C., S.L.H., and R.G.H.), University of California, San Francisco Weill Institute for Neurosciences, University of California, San Francisco, California
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Collorone S, Cawley N, Grussu F, Prados F, Tona F, Calvi A, Kanber B, Schneider T, Kipp L, Zhang H, Alexander DC, Thompson AJ, Toosy A, Wheeler-Kingshott CAG, Ciccarelli O. Reduced neurite density in the brain and cervical spinal cord in relapsing-remitting multiple sclerosis: A NODDI study. Mult Scler 2020; 26:1647-1657. [PMID: 31682198 DOI: 10.1177/1352458519885107] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Multiple sclerosis (MS) affects both brain and spinal cord. However, studies of the neuraxis with advanced magnetic resonance imaging (MRI) are rare because of long acquisition times. We investigated neurodegeneration in MS brain and cervical spinal cord using neurite orientation dispersion and density imaging (NODDI). OBJECTIVE The aim of this study was to investigate possible alterations, and their clinical relevance, in neurite morphology along the brain and cervical spinal cord of relapsing-remitting MS (RRMS) patients. METHODS In total, 28 RRMS patients and 20 healthy controls (HCs) underwent brain and spinal cord NODDI at 3T. Physical and cognitive disability was assessed. Individual maps of orientation dispersion index (ODI) and neurite density index (NDI) in brain and spinal cord were obtained. We examined differences in NODDI measures between groups and the relationships between NODDI metrics and clinical scores using linear regression models adjusted for age, sex and brain tissue volumes or cord cross-sectional area (CSA). RESULTS Patients showed lower NDI in the brain normal-appearing white matter (WM) and spinal cord WM than HCs. In patients, a lower NDI in the spinal cord WM was associated with higher disability. CONCLUSION Reduced neurite density occurs in the neuraxis but, especially when affecting the spinal cord, it may represent a mechanism of disability in MS.
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Affiliation(s)
- Sara Collorone
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), London, UK
| | - Niamh Cawley
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), London, UK
| | - Francesco Grussu
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), London, UK/Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, London, UK
| | - Ferran Prados
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), London, UK/Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, London, UK
| | - Francesca Tona
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), London, UK
| | - Alberto Calvi
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), London, UK/Department of Pathophysiology and Transplantation, Neurodegenerative Disease Unit, La Fondazione IRCCS Ospedale Maggiore Policlinico Mangiagalli e Regina Elena, University of Milan, Milan, Italy
| | - Baris Kanber
- Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, London, UK
| | - Torben Schneider
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), London, UK/Philips UK, Guildford, UK
| | - Lucas Kipp
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), London, UK/Stanford MS Center, Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, CA, USA
| | - Hui Zhang
- Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, London, UK
| | - Alan J Thompson
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), London, UK
| | - Ahmed Toosy
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), London, UK
| | - Claudia Am Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), London, UK/Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy/Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Olga Ciccarelli
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), London, UK/National Institute for Health Research University College London Hospitals Biomedical Research Centre, London, UK
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Jelescu IO, Palombo M, Bagnato F, Schilling KG. Challenges for biophysical modeling of microstructure. J Neurosci Methods 2020; 344:108861. [PMID: 32692999 PMCID: PMC10163379 DOI: 10.1016/j.jneumeth.2020.108861] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/10/2020] [Accepted: 07/14/2020] [Indexed: 02/07/2023]
Abstract
The biophysical modeling efforts in diffusion MRI have grown considerably over the past 25 years. In this review, we dwell on the various challenges along the journey of bringing a biophysical model from initial design to clinical implementation, identifying both hurdles that have been already overcome and outstanding issues. First, we describe the critical initial task of selecting which features of tissue microstructure can be estimated using a model and which acquisition protocol needs to be implemented to make the estimation possible. The model performance should necessarily be tested in realistic numerical simulations and in experimental data - adapting the fitting strategy accordingly, and parameter estimates should be validated against complementary techniques, when/if available. Secondly, the model performance and validity should be explored in pathological conditions, and, if appropriate, dedicated models for pathology should be developed. We build on examples from tumors, ischemia and demyelinating diseases. We then discuss the challenges associated with clinical translation and added value. Finally, we single out four major unresolved challenges that are related to: the availability of a microstructural ground truth, the validation of model parameters which cannot be accessed with complementary techniques, the development of a generalized standard model for any brain region and pathology, and the seamless communication between different parties involved in the development and application of biophysical models of diffusion.
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Optimization and numerical evaluation of multi-compartment diffusion MRI using the spherical mean technique for practical multiple sclerosis imaging. Magn Reson Imaging 2020; 74:56-63. [PMID: 32898649 DOI: 10.1016/j.mri.2020.09.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 09/01/2020] [Accepted: 09/02/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND The multi-compartment diffusion MRI using the spherical mean technique (SMT) has been suggested to enhance the pathological specificity to tissue injury in multiple sclerosis (MS) imaging, but its accuracy and precision have not been comprehensively evaluated. METHODS A Cramer-Rao Lower Bound method was used to optimize an SMT protocol for MS imaging. Finite difference computer simulations of spins in packed cylinders were then performed to evaluate the influences of five realistic pathological features in MS lesions: axon diameter, axon density, free water fraction, axonal crossing, dispersion, and undulation. RESULTS SMT derived metrics can be biased by some confounds of pathological variations, such as axon size and free water fraction. However, SMT in general provides valuable information to characterize pathological features in MS lesions with a clinically feasible protocol. CONCLUSION SMT may be used as a practical MS imaging method and should be further improved in clinical MS imaging.
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Kamiya K, Hori M, Aoki S. NODDI in clinical research. J Neurosci Methods 2020; 346:108908. [PMID: 32814118 DOI: 10.1016/j.jneumeth.2020.108908] [Citation(s) in RCA: 123] [Impact Index Per Article: 30.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 08/08/2020] [Accepted: 08/09/2020] [Indexed: 12/11/2022]
Abstract
Diffusion MRI (dMRI) has proven to be a useful imaging approach for both clinical diagnosis and research investigating the microstructures of nervous tissues, and it has helped us to better understand the neurophysiological mechanisms of many diseases. Though diffusion tensor imaging (DTI) has long been the default tool to analyze dMRI data in clinical research, acquisition with stronger diffusion weightings beyond the DTI regimen is now possible with modern clinical scanners, potentially enabling even more detailed characterization of tissue microstructures. To take advantage of such data, neurite orientation dispersion and density imaging (NODDI) has been proposed as a way to relate the dMRI signal to tissue features via biophysically inspired modeling. The number of reports demonstrating the potential clinical utility of NODDI is rapidly increasing. At the same time, the pitfalls and limitations of NODDI, and general challenges in microstructure modeling, are becoming increasingly recognized by clinicians. dMRI microstructure modeling is a rapidly evolving field with great promise, where people from different scientific backgrounds, such as physics, medicine, biology, neuroscience, and statistics, are collaborating to build novel tools that contribute to improving human healthcare. Here, we review the applications of NODDI in clinical research and discuss future perspectives for investigations toward the implementation of dMRI microstructure imaging in clinical practice.
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Affiliation(s)
- Kouhei Kamiya
- Department of Radiology, The University of Tokyo, Tokyo, Japan; Department of Radiology, Juntendo University, Tokyo, Japan; Department of Radiology, Toho University, Tokyo, Japan.
| | - Masaaki Hori
- Department of Radiology, Juntendo University, Tokyo, Japan; Department of Radiology, Toho University, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University, Tokyo, Japan
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Palacios EM, Owen JP, Yuh EL, Wang MB, Vassar MJ, Ferguson AR, Diaz-Arrastia R, Giacino JT, Okonkwo DO, Robertson CS, Stein MB, Temkin N, Jain S, McCrea M, MacDonald CL, Levin HS, Manley GT, Mukherjee P. The evolution of white matter microstructural changes after mild traumatic brain injury: A longitudinal DTI and NODDI study. SCIENCE ADVANCES 2020; 6:eaaz6892. [PMID: 32821816 PMCID: PMC7413733 DOI: 10.1126/sciadv.aaz6892] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 06/26/2020] [Indexed: 05/11/2023]
Abstract
Neuroimaging biomarkers that can detect white matter (WM) pathology after mild traumatic brain injury (mTBI) and predict long-term outcome are needed to improve care and develop therapies. We used diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) to investigate WM microstructure cross-sectionally and longitudinally after mTBI and correlate these with neuropsychological performance. Cross-sectionally, early decreases of fractional anisotropy and increases of mean diffusivity corresponded to WM regions with elevated free water fraction on NODDI. This elevated free water was more extensive in the patient subgroup reporting more early postconcussive symptoms. The longer-term longitudinal WM changes consisted of declining neurite density on NODDI, suggesting axonal degeneration from diffuse axonal injury for which NODDI is more sensitive than DTI. Therefore, NODDI is a more sensitive and specific biomarker than DTI for WM microstructural changes due to mTBI that merits further study for mTBI diagnosis, prognosis, and treatment monitoring.
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Affiliation(s)
- E. M. Palacios
- Department of Radiology & Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - J. P. Owen
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - E. L. Yuh
- Department of Radiology & Biomedical Imaging, UCSF, San Francisco, CA, USA
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
| | - M. B. Wang
- Department of Radiology & Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - M. J. Vassar
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
| | - A. R. Ferguson
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - R. Diaz-Arrastia
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - J. T. Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Charlestown, MA, USA
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA, USA
| | - D. O. Okonkwo
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - C. S. Robertson
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - M. B. Stein
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Department of Family Medicine & Public Health, University of California, San Diego, La Jolla, CA, USA
| | - N. Temkin
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - S. Jain
- Department of Family Medicine & Public Health, University of California, San Diego, La Jolla, CA, USA
| | - M. McCrea
- Departments of Neurosurgery and Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - C. L. MacDonald
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - H. S. Levin
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - G. T. Manley
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
| | - P. Mukherjee
- Department of Radiology & Biomedical Imaging, UCSF, San Francisco, CA, USA
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Corresponding author.
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Lakhani DA, Schilling KG, Xu J, Bagnato F. Advanced Multicompartment Diffusion MRI Models and Their Application in Multiple Sclerosis. AJNR Am J Neuroradiol 2020; 41:751-757. [PMID: 32354707 DOI: 10.3174/ajnr.a6484] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Accepted: 02/03/2020] [Indexed: 01/22/2023]
Abstract
Conventional MR imaging techniques are sensitive to pathologic changes of the brain and spinal cord seen in MS, but they lack specificity for underlying axonal and myelin integrity. By isolating the signal contribution from different tissue compartments, newly developed advanced multicompartment diffusion MR imaging models have the potential to detect specific tissue subtypes and associated injuries with increased pathologic specificity. These models include neurite orientation dispersion and density imaging, diffusion basis spectrum imaging, multicompartment microscopic diffusion MR imaging with the spherical mean technique, and models enabled through high-gradient diffusion MR imaging. In this review, we provide an appraisal of the current literature on the physics principles, histopathologic validation, and clinical applications of each of these techniques in both brains and spinal cords of patients with MS. We discuss limitations of each of the methods and directions that future research could take to provide additional validation of their roles as biomarkers of axonal and myelin injury in MS.
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Affiliation(s)
- D A Lakhani
- From the Neuroimaging Unit (D.A.L., F.B.), Neuroimmunology Division, Department of Neurology
- Division of Internal Medicine (D.A.L.)
- Department of Radiology (D.A.L.), West Virginia University, Morgantown, West Virginia
| | - K G Schilling
- Department of Radiology and Radiological Sciences (K.G.S., J.X.), Vanderbilt University Institute of Imaging Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - J Xu
- Department of Radiology and Radiological Sciences (K.G.S., J.X.), Vanderbilt University Institute of Imaging Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - F Bagnato
- From the Neuroimaging Unit (D.A.L., F.B.), Neuroimmunology Division, Department of Neurology
- Department of Neurology (F.B.), VA Tennessee Valley Healthcare System, Nashville, Tennessee
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Andica C, Kamagata K, Hayashi T, Hagiwara A, Uchida W, Saito Y, Kamiya K, Fujita S, Akashi T, Wada A, Abe M, Kusahara H, Hori M, Aoki S. Scan-rescan and inter-vendor reproducibility of neurite orientation dispersion and density imaging metrics. Neuroradiology 2019; 62:483-494. [PMID: 31883043 PMCID: PMC7093343 DOI: 10.1007/s00234-019-02350-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 12/15/2019] [Indexed: 02/06/2023]
Abstract
Purpose The reproducibility of neurite orientation dispersion and density imaging (NODDI) metrics in the human brain has not been explored across different magnetic resonance (MR) scanners from different vendors. This study aimed to evaluate the scan–rescan and inter-vendor reproducibility of NODDI metrics in white and gray matter of healthy subjects using two 3-T MR scanners from two vendors. Methods Ten healthy subjects (7 males; mean age 30 ± 7 years, range 23–37 years) were included in the study. Whole-brain diffusion-weighted imaging was performed with b-values of 1000 and 2000 s/mm2 using two 3-T MR scanners from two different vendors. Automatic extraction of the region of interest was performed to obtain NODDI metrics for whole and localized areas of white and gray matter. The coefficient of variation (CoV) and intraclass correlation coefficient (ICC) were calculated to assess the scan–rescan and inter-vendor reproducibilities of NODDI metrics. Results The scan–rescan and inter-vendor reproducibility of NODDI metrics (intracellular volume fraction and orientation dispersion index) were comparable with those of diffusion tensor imaging (DTI) metrics. However, the inter-vendor reproducibilities of NODDI (CoV = 2.3–14%) were lower than the scan–rescan reproducibility (CoV: scanner A = 0.8–3.8%; scanner B = 0.8–2.6%). Compared with the finding of DTI metrics, the reproducibility of NODDI metrics was lower in white matter and higher in gray matter. Conclusion The lower inter-vendor reproducibility of NODDI in some brain regions indicates that data acquired from different MRI scanners should be carefully interpreted.
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Affiliation(s)
- Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Takuya Hayashi
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Wataru Uchida
- 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
| | - Yuya Saito
- 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
| | - Kouhei Kamiya
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 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
| | - Toshiaki Akashi
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akihiko Wada
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Masahiro Abe
- Canon Medical Systems Corporation, Kanagawa, Japan
| | | | - Masaaki Hori
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.,Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
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Brain microstructural abnormalities correlate with KCC2 downregulation in refractory epilepsy. Neuroreport 2019; 30:409-414. [PMID: 30817684 DOI: 10.1097/wnr.0000000000001216] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Dysregulations in the expression level of Na-K-Cl cotransporter (NKCC1) and K-Cl cotransporter (KCC2) genes have been detected in the brain tissues of patients with refractory epilepsy. Given the importance of these proteins in the determination of Cl equilibrium potential (ECl), evaluation of the expression changes of these transporters might assist in optimizing the diagnostic approaches and therapeutic strategies. The present investigation evaluates the expression level chloride transporters in polymorphonuclear cells and their correlation with microstructural abnormalities. Thirty cases of drug-resistant epilepsy (confirmed with temporal lobe epilepsy diagnosis) fulfilled the considered inclusion criteria. Cases were divided into two groups, one with a detectable MRI lesion (19 participants; right side) and another with no MRI findings (11 participants). Whole-brain voxel-based analysis was performed on diffusion tensor imaging to measure fractional anisotropy and mean diffusivity; neurite orientation dispersion and density imaging was performed to map neurite density index and orientation dispersion index. Our results indicated that fractional anisotropy and mean diffusivity changed in temporal and extratemporal parts of the brain, whereas the changes in neurite density index and orientation dispersion index were exclusively obvious in the temporal lobe. Molecular studies revealed significantly lower levels of KCC2 expression in patients with epilepsy, a finding that remarkably correlated with microstructural changes as well. Our research showed that downregulation of KCC2 and microstructural abnormalities might contribute to the observed refractoriness in temporal lobe epilepsy.
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Chuhutin A, Hansen B, Wlodarczyk A, Owens T, Shemesh N, Jespersen SN. Diffusion Kurtosis Imaging maps neural damage in the EAE model of multiple sclerosis. Neuroimage 2019; 208:116406. [PMID: 31830588 PMCID: PMC9358435 DOI: 10.1016/j.neuroimage.2019.116406] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 11/20/2019] [Accepted: 11/25/2019] [Indexed: 01/22/2023] Open
Abstract
Diffusion kurtosis imaging (DKI) is an imaging modality that yields novel
disease biomarkers and in combination with nervous tissue modeling, provides
access to microstructural parameters. Recently, DKI and subsequent estimation of
microstructural model parameters has been used for assessment of tissue changes
in neurodegenerative diseases and associated animal models. In this study, mouse
spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of
multiple sclerosis (MS) were investigated for the first time using DKI in
combination with biophysical modeling to study the relationship between
microstructural metrics and degree of animal dysfunction. Thirteen spinal cords
were extracted from animals with varied grades of disability and scanned in a
high-field MRI scanner along with five control specimen. Diffusion weighted data
were acquired together with high resolution T2*
images. Diffusion data were fit to estimate diffusion and kurtosis tensors and
white matter modeling parameters, which were all used for subsequent statistical
analysis using a linear mixed effects model. T2*
images were used to delineate focal demyelination/inflammation. Our results
reveal a strong relationship between disability and measured microstructural
parameters in normal appearing white matter and gray matter. Relationships
between disability and mean of the kurtosis tensor, radial kurtosis, radial
diffusivity were similar to what has been found in other hypomyelinating MS
models, and in patients. However, the changes in biophysical modeling parameters
and in particular in extra-axonal axial diffusivity were clearly different from
previous studies employing other animal models of MS. In conclusion, our data
suggest that DKI and microstructural modeling can provide a unique contrast
capable of detecting EAE-specific changes correlating with clinical
disability.
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Affiliation(s)
| | | | - Agnieszka Wlodarczyk
- Department of Neurobiology Research, Institute for Molecular Medicine,University of South Denmark, Odense, Denmark
| | - Trevor Owens
- Department of Neurobiology Research, Institute for Molecular Medicine,University of South Denmark, Odense, Denmark
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Sune Nørhøj Jespersen
- CFIN, Aarhus University, Aarhus, Denmark; Department of Physics, Aarhus University, Aarhus, Denmark
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Schneider R, Genç E, Ahlborn C, Gold R, Lukas C, Bellenberg B. Temporal Dynamics of Diffusion Metrics in Early Multiple Sclerosis and Clinically Isolated Syndrome: A 2-Year Follow-Up Tract-Based Spatial Statistics Study. Front Neurol 2019; 10:1165. [PMID: 31749760 PMCID: PMC6848258 DOI: 10.3389/fneur.2019.01165] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2019] [Accepted: 10/15/2019] [Indexed: 11/13/2022] Open
Abstract
Background: Tract-based spatial statistics (TBSS) is suitable for the assessment of voxel-wise changes in fiber integrity in WM tracts in the entire brain. Longitudinal TBSS analyses of early multiple sclerosis (MS) using 3 Tesla magnetic resonance imaging (MRI) are not common. Objective: To characterize microstructural WM alterations at initial diagnosis in clinically isolated syndrome (CIS) and early MS at baseline and longitudinally over 2 years. Methods: DTI (Diffusion tensor imaging) at 3 Tesla was used to evaluate 106 therapy-naive patients with CIS or definite MS at baseline and at 1-year (N = 83) and 2-year (N = 43) follow-up compared to healthy controls (HC, N = 49). TBSS was used for voxel-wise analyses of the DTI indices of fractional anisotropy (FA) and radial, mean, and axial diffusivity (RD, MD, AD) for cross-sectional and longitudinal comparisons. Mean values of FA, RD, and cluster voxel numbers were extracted from significant clusters using an atlas-based approach. Correlations with disability (EDSS) were calculated for FA and RD changes related to affected brain regions. Results: Reductions in FA compared to HC were found at baseline in patients with CIS and RRMS and involved most supra- and infratentorial WM tracts. In the cerebellum and cerebral peduncles, these changes negatively correlated with EDSS after 2 years. FA changes in patients with CIS and RRMS evolved in the second year, particularly in the descending projection pathways and the cerebellum, and were significantly associated with EDSS. RD alterations compared to HC were undetectable in patients at baseline but were observed after 1 year and were exacerbated during the second year in all major supratentorial WM tracts, the corpus callosum, and the cerebellum. FA did not change between baseline and year 1 follow-up, but longitudinal investigation between the first and second year revealed combined dynamic FA and RD changes in the corpus callosum and corona radiata. Conclusion: TBSS of diffusion metrics at initial diagnosis and at 2-year follow-up showed microstructural WM pathology and associations between FA reduction and future disability, respectively. Combined longitudinal changes in FA and RD occurred in specific structures, where RD increases likely reflected progressing axonal degeneration. The distinct temporal dynamics of FA and RD, implying constancy during the first year, supports early therapeutic intervention for CIS and RRMS.
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Affiliation(s)
- Ruth Schneider
- Department of Neurology, St. Josef Hospital, Ruhr University Bochum, Bochum, Germany
| | - Erhan Genç
- Department of Biopsychology, Institute of Cognitive Neuroscience, Ruhr University Bochum, Bochum, Germany
| | - Christian Ahlborn
- Institute of Neuroradiology, St. Josef Hospital, Ruhr University Bochum, Bochum, Germany
| | - Ralf Gold
- Department of Neurology, St. Josef Hospital, Ruhr University Bochum, Bochum, Germany
| | - Carsten Lukas
- Institute of Neuroradiology, St. Josef Hospital, Ruhr University Bochum, Bochum, Germany
| | - Barbara Bellenberg
- Institute of Neuroradiology, St. Josef Hospital, Ruhr University Bochum, Bochum, Germany
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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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Nemanich ST, Mueller BA, Gillick BT. Neurite orientation dispersion and density imaging quantifies corticospinal tract microstructural organization in children with unilateral cerebral palsy. Hum Brain Mapp 2019; 40:4888-4900. [PMID: 31355991 DOI: 10.1002/hbm.24744] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Revised: 07/17/2019] [Accepted: 07/17/2019] [Indexed: 12/11/2022] Open
Abstract
Children with unilateral cerebral palsy (UCP) due to early brain injury exhibit disrupted connectivity of corticospinal tracts (CSTs), which can be quantified using diffusion-weighted magnetic resonance imaging (DWI). Diffusion tensor imaging (DTI) is commonly used to quantify white matter organization, however, this model lacks the biological specificity to accurately describe underlying microstructural properties. Newer approaches, such as neurite orientation dispersion and density imaging (NODDI), may provide more biologically accurate information regarding CST microstructure. In this study, we directly compared metrics of CST microstructure using NODDI and DTI models to characterize the microstructural organization of corticospinal pathways. Twenty participants with UCP participating in a neuromodulation/rehabilitation intervention underwent imaging including multi-shell DWI; 10 participants' datasets were adequately completed for neuroimaging analysis. Task fMRI-guided probabilistic tractography from motor cortex to brainstem was performed at baseline and follow-up to reconstruct the CSTs. Diffusion metrics were compared between hemispheres at baseline, and between baseline and follow-up to test for intervention effects. Correlation analyses were used to compare baseline metrics to changes in hand function following the intervention. DTI results showed that mean fractional anisotropy in lesioned and nonlesioned CSTs did not significantly differ, but mean, axial, and radial diffusivity were greater in the lesioned CST. For NODDI, intracellular volume fraction (ICVF) and orientation dispersion index (ODI) were lower in the lesioned CST. Unimanual function was strongly correlated with ICVF, but not FA. NODDI may reveal distinct properties of CST microstructure that are linked to motor function, indicating their potential in characterizing brain structure and development.
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Affiliation(s)
- Samuel T Nemanich
- Division of Physical Therapy, Department of Rehabilitation Medicine, University of Minnesota, Minneapolis, Minnesota
| | - Bryon A Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota
| | - Bernadette T Gillick
- Division of Physical Therapy, Department of Rehabilitation Medicine, University of Minnesota, Minneapolis, Minnesota
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50
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Cortese R, Collorone S, Ciccarelli O, Toosy AT. Advances in brain imaging in multiple sclerosis. Ther Adv Neurol Disord 2019; 12:1756286419859722. [PMID: 31275430 PMCID: PMC6598314 DOI: 10.1177/1756286419859722] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 05/21/2019] [Indexed: 12/31/2022] Open
Abstract
Brain imaging is increasingly used to support clinicians in diagnosing multiple sclerosis (MS) and monitoring its progression. However, the role of magnetic resonance imaging (MRI) in MS goes far beyond its clinical application. Indeed, advanced imaging techniques have helped to detect different components of MS pathogenesis in vivo, which is now considered a heterogeneous process characterized by widespread damage of the central nervous system, rather than multifocal demyelination of white matter. Recently, MRI biomarkers more sensitive to disease activity than clinical disability outcome measures, have been used to monitor response to anti-inflammatory agents in patients with relapsing-remitting MS. Similarly, MRI markers of neurodegeneration exhibit the potential as primary and secondary outcomes in clinical trials for progressive phenotypes. This review will summarize recent advances in brain neuroimaging in MS from the research setting to clinical applications.
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Affiliation(s)
- Rosa Cortese
- Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), London, UK
| | - Sara Collorone
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Russell Square, London WC1B 5EH, UK
| | - Olga Ciccarelli
- Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), London, UK
- National Institute for Health Research, UCL Hospitals, Biomedical Research Centre, London, UK
| | - Ahmed T. Toosy
- Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London (UCL), London, UK
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