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393 results on '"Guttmann, Charles R. G."'

2. Diagnostic performance of computed tomography features in detecting oropharyngeal squamous cell carcinoma extranodal extension

Author

Tran, Ngoc-Anh, Palotai, Miklos, Hanna, Glenn J., Schoenfeld, Jonathan D., Bay, Camden P., Rettig, Eleni M., Bunch, Paul M., Juliano, Amy F., Kelly, Hillary R., Suh, Chong Hyun, Zander, David A., Morales Pinzon, Alfredo, Kann, Benjamin H., Huang, Raymond Y., Haddad, Robert I., Guttmann, Charles R. G., and Guenette, Jeffrey P.

Published
2023
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5. Boutiques: a flexible framework for automated application integration in computing platforms

Author

Glatard, Tristan, Kiar, Gregory, Aumentado-Armstrong, Tristan, Beck, Natacha, Bellec, Pierre, Bernard, Rémi, Bonnet, Axel, Camarasu-Pop, Sorina, Cervenansky, Frédéric, Das, Samir, da Silva, Rafael Ferreira, Flandin, Guillaume, Girard, Pascal, Gorgolewski, Krzysztof J., Guttmann, Charles R. G., Hayot-Sasson, Valérie, Quirion, Pierre-Olivier, Rioux, Pierre, Rousseau, Marc-Eienne, and Evans, Alan C.

Subjects
Computer Science - Software Engineering, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

We present Boutiques, a system to automatically publish, integrate and execute applications across computational platforms. Boutiques applications are installed through software containers described in a rich and flexible JSON language. A set of core tools facilitate the construction, validation, import, execution, and publishing of applications. Boutiques is currently supported by several distinct virtual research platforms, and it has been used to describe dozens of applications in the neuroinformatics domain. We expect Boutiques to improve the quality of application integration in computational platforms, to reduce redundancy of effort, to contribute to computational reproducibility, and to foster Open Science., Comment: 10 pages

Published
2017

6. Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation

Author

Ghafoorian, Mohsen, Mehrtash, Alireza, Kapur, Tina, Karssemeijer, Nico, Marchiori, Elena, Pesteie, Mehran, Guttmann, Charles R. G., de Leeuw, Frank-Erik, Tempany, Clare M., van Ginneken, Bram, Fedorov, Andriy, Abolmaesumi, Purang, Platel, Bram, and Wells III, William M.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?; and, 2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset.The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch., Comment: 8 pages, 3 figures

Published
2017
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11. Incidence and severity of pulmonary embolism in COVID-19 infection: Ancestral, Alpha, Delta, and Omicron variants

Author

Wada, Noriaki, primary, Li, Yi, additional, Gagne, Staci, additional, Hino, Takuya, additional, Valtchinov, Vladimir I., additional, Gay, Elizabeth, additional, Nishino, Mizuki, additional, Hammer, Mark M., additional, Madore, Bruno, additional, Guttmann, Charles R. G., additional, Ishigami, Kousei, additional, Hunninghake, Gary M., additional, Levy, Bruce D., additional, Kaye, Kenneth M., additional, Christiani, David C., additional, and Hatabu, Hiroto, additional

Published
2023
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12. New Enhancing MRI Lesions Associate with IL-17, Neutrophil Degranulation and Integrin Microparticles: Multi-Omics Combined with Frequent MRI in Multiple Sclerosis

Author

Illes, Zsolt, primary, Jørgensen, Malene Møller, additional, Bæk, Rikke, additional, Bente, Lisa-Marie, additional, Lauridsen, Jørgen T., additional, Hyrlov, Kirsten H., additional, Aboo, Christopher, additional, Baumbach, Jan, additional, Kacprowski, Tim, additional, Cotton, Francois, additional, Guttmann, Charles R. G., additional, and Stensballe, Allan, additional

Published
2023
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13. Multicenter Evaluation of AI-generated DIR and PSIR for Cortical and Juxtacortical Multiple Sclerosis Lesion Detection

Author

Bouman, Piet M., primary, Noteboom, Samantha, additional, Nobrega Santos, Fernando A., additional, Beck, Erin S., additional, Bliault, Gregory, additional, Castellaro, Marco, additional, Calabrese, Massimiliano, additional, Chard, Declan T., additional, Eichinger, Paul, additional, Filippi, Massimo, additional, Inglese, Matilde, additional, Lapucci, Caterina, additional, Marciniak, Andrzej, additional, Moraal, Bastiaan, additional, Morales Pinzon, Alfredo, additional, Mühlau, Mark, additional, Preziosa, Paolo, additional, Reich, Daniel S., additional, Rocca, Maria A., additional, Schoonheim, Menno M., additional, Twisk, Jos W. R., additional, Wiestler, Benedict, additional, Jonkman, Laura E., additional, Guttmann, Charles R. G., additional, Geurts, Jeroen J. G., additional, and Steenwijk, Martijn D., additional

Published
2023
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16. Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation

Author

Ghafoorian, Mohsen, primary, Mehrtash, Alireza, additional, Kapur, Tina, additional, Karssemeijer, Nico, additional, Marchiori, Elena, additional, Pesteie, Mehran, additional, Guttmann, Charles R. G., additional, de Leeuw, Frank-Erik, additional, Tempany, Clare M., additional, van Ginneken, Bram, additional, Fedorov, Andriy, additional, Abolmaesumi, Purang, additional, Platel, Bram, additional, and Wells, William M., additional

Published
2017
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17. Diffusion Tensor and Functional MRI Fusion with Anatomical MRI for Image-Guided Neurosurgery

Author

Talos, Ion-Florin, O’Donnell, Lauren, Westin, Carl-Fredrick, Warfield, Simon K., Wells, William, III, Yoo, Seung-Schik, Panych, Lawrence P., Golby, Alexandra, Mamata, Hatsuho, Maier, Stefan S., Ratiu, Peter, Guttmann, Charles R. G., Black, Peter M., Jolesz, Ferenc A., Kikinis, Ron, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Ellis, Randy E., editor, and Peters, Terry M., editor

Published
2003
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22. Fractional Segmentation of White Matter

Author

Warfield, Simon K., Westin, Carl-Fredrik, Guttmann, Charles R. G., Albert, Marilyn, Jolesz, Ferenc A., Kikinis, Ron, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Taylor, Chris, editor, and Colchester, Alain, editor

Published
1999
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25. Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence

Author

Vrenken, Hugo, Jenkinson, Mark, Pham, Dzung L, Guttmann, Charles R G, Pareto, Deborah, Paardekooper, Michel, de Sitter, Alexandra, Rocca, Maria A, Wottschel, Viktor, Cardoso, M Jorge, Barkhof, Frederik, MAGNIMS Study, Group, Filippi, M, Vrenken, Hugo, Jenkinson, Mark, Pham, Dzung L, Guttmann, Charles R G, Pareto, Deborah, Paardekooper, Michel, de Sitter, Alexandra, Rocca, Maria A, Wottschel, Viktor, Cardoso, M Jorge, Barkhof, Frederik, MAGNIMS Study, Group, and Filippi, M

Subjects
Big Data, Multiple Sclerosis, Information Dissemination, Computer science, business.industry, Reviews, Cognition, Large scale data, Crowdsourcing, Magnetic Resonance Imaging, Functional networks, Data sharing, Reward, Artificial Intelligence, Benchmark (computing), Data Protection Act 1998, Leverage (statistics), Humans, Neurology (clinical), Artificial intelligence, business, Algorithms
Abstract

Patients with multiple sclerosis (MS) have heterogeneous clinical presentations, symptoms, and progression over time, making MS difficult to assess and comprehend in vivo. The combination of large-scale data sharing and artificial intelligence creates new opportunities for monitoring and understanding MS using MRI. First, development of validated MS-specific image analysis methods can be boosted by verified reference, test, and benchmark imaging data. Using detailed expert annotations, artificial intelligence algorithms can be trained on such MS-specific data. Second, understanding disease processes could be greatly advanced through shared data of large MS cohorts with clinical, demographic, and treatment information. Relevant patterns in such data that may be imperceptible to a human observer could be detected through artificial intelligence techniques. This applies from image analysis (lesions, atrophy, or functional network changes) to large multidomain datasets (imaging, cognition, clinical disability, genetics). After reviewing data sharing and artificial intelligence, we highlight 3 areas that offer strong opportunities for making advances in the next few years: crowdsourcing, personal data protection, and organized analysis challenges. Difficulties as well as specific recommendations to overcome them are discussed, in order to best leverage data sharing and artificial intelligence to improve image analysis, imaging, and the understanding of MS.

Published
2021
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27. Abstracts from the ASENT 2010 annual meeting March 4–6, 2010

Author

Apter, Jeffrey T., Fox, Robert J., Cronin, Thomas, Lin, Jian, Sakaie, Ken, Ontaneda, Daniel, Mahmoud, Shamseldeen Y., Lowe, Mark J., Phillips, Michael D., Wang, Xiaofeng, Goldberg-Zimring, Daniel, Chavarro-Nieto, Christian D., Healy, Brian C., Achiron, Anat, Warfield, Simon K., Neema, Mohit, Arora, Ashish, Tauhid, Shahamat, Stankiewicz, James M., Chavarro-Nieto, Christian, Ceccarelli, Antonia, Dell’Oglio, Elisa, Guttmann, Charles R. G., Atassi, Nazem, Ratai, Eva Maria, Greenblatt, David, Cudkowicz, Merit, DiBernardo, Allitia, Tauhid, Shahamat S., Moodie, Jenniffer, Buckle, Guy J., Gauthier, Susan A., Glanz, Bonnie I., Chitnis, Tanuja, Khoury, Samia J., Guttmann, Charles R. G., Weiner, Howard L., Ceccarelli, Antonia, Jackson, Jonathan S., Tauhid, Shahamat S., Chavarro-Nieto, Christian, Dell’Oglio, Elisa, Bakshi, Anshika, Bakshi, Rohit, Cramer, Joyce, Dlugos, Dennis, Wiegand, Frank, Novak, Gerald, Olson, William, Grinspan, Augusto, Li, Shihong, Loane, David J., Stoica, Bogdan A., Faden, Alan I., Pajoohesh-Ganji, Ahdeah, Faden, Alan, Wesnes, Keith Andrew, Brooker, Helen, Edgar, Chris, Haubenberger, Dietrich, Kalowitz, Daniel, Nahab, Fatta B., Toro, Camilo, Ippolito, Daniel, Wittevrongel, Loretta, Hallett, Mark, Guttuso, Thomas, Salins, Naomi, Lichter, David, Alexander, Elaine, Pfadenhauer, E. H., von Gunten, Charles, Helton, David, Gopal, Srihari, Berwaerts, Joris, Nuamah, Isaac, Akhras, Kasem, Coppola, Danielle, Daly, Ella, Hough, David, Palumbo, Joseph, Bermel, Robert, Imrey, Peter, and Cohen, Jeffrey

Published
2010
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28. Predicting personalised risk of disability worsening in multiple sclerosis with machine learning

Author

Eshaghi, Arman, primary, Wijeratne, Peter A, additional, Oxtoby, Neil P, additional, Arnold, Douglas L, additional, Collins, Louis, additional, Narayanan, Sridar, additional, Guttmann, Charles R. G., additional, Thompson, Alan J, additional, Alexander, Daniel C, additional, Barkhof, Frederik, additional, Chard, Declan, additional, and Ciccarelli, Olga, additional

Published
2022
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31. Reduced accuracy of MRI deep grey matter segmentation in multiple sclerosis : an evaluation of four automated methods against manual reference segmentations in a multi-center cohort

Author

de Sitter, Alexandra, Verhoeven, Tom, Burggraaff, Jessica, Liu, Yaou, Simoes, Jorge, Ruggieri, Serena, Palotai, Miklos, Brouwer, Iman, Versteeg, Adriaan, Wottschel, Viktor, Ropele, Stefan, Rocca, Mara A., Gasperini, Claudio, Gallo, Antonio, Yiannakas, Marios C., Rovira, Alex, Enzinger, Christian, Filippi, Massimo, De Stefano, Nicola, Kappos, Ludwig, Frederiksen, Jette L., Uitdehaag, Bernard M. J., Barkhof, Frederik, Guttmann, Charles R. G., Vrenken, Hugo, Universitat Autònoma de Barcelona, de Sitter, Alexandra, Verhoeven, Tom, Burggraaff, Jessica, Liu, Yaou, Simoes, Jorge, Ruggieri, Serena, Palotai, Miklo, Brouwer, Iman, Versteeg, Adriaan, Wottschel, Viktor, Ropele, Stefan, Rocca, Mara A, Gasperini, Claudio, Gallo, Antonio, Yiannakas, Marios C, Rovira, Alex, Enzinger, Christian, Filippi, Massimo, De Stefano, Nicola, Kappos, Ludwig, Frederiksen, Jette L, Uitdehaag, Bernard M J, Barkhof, Frederik, Guttmann, Charles R G, Vrenken, Hugo, de Sitter, A., Verhoeven, T., Burggraaff, J., Liu, Y., Simoes, J., Ruggieri, S., Palotai, M., Brouwer, I., Versteeg, A., Wottschel, V., Ropele, S., Rocca, M. A., Gasperini, C., Gallo, A., Yiannakas, M. C., Rovira, A., Enzinger, C., Filippi, M., De Stefano, N., Kappos, L., Frederiksen, J. L., Uitdehaag, B. M. J., Barkhof, F., Guttmann, C. R. G., Vrenken, H., Radiology and nuclear medicine, Neurology, and Amsterdam Neuroscience - Brain Imaging

Subjects
Correlation coefficient, Caudate nucleus, Grey matter, 030218 nuclear medicine & medical imaging, Multiple sclerosis, 03 medical and health sciences, 0302 clinical medicine, Atrophy, Automated segmentation methods, Deep grey matter, Medicine, Humans, Segmentation, Multiple sclerosi, Gray Matter, Neuroradiology, Original Communication, business.industry, Putamen, Brain, medicine.disease, Magnetic Resonance Imaging, medicine.anatomical_structure, Neurology, Brain size, Automated segmentation method, Neurology (clinical), business, Nuclear medicine, 030217 neurology & neurosurgery, Human
Abstract

Background Deep grey matter (DGM) atrophy in multiple sclerosis (MS) and its relation to cognitive and clinical decline requires accurate measurements. MS pathology may deteriorate the performance of automated segmentation methods. Accuracy of DGM segmentation methods is compared between MS and controls, and the relation of performance with lesions and atrophy is studied. Methods On images of 21 MS subjects and 11 controls, three raters manually outlined caudate nucleus, putamen and thalamus; outlines were combined by majority voting. FSL-FIRST, FreeSurfer, Geodesic Information Flow and volBrain were evaluated. Performance was evaluated volumetrically (intra-class correlation coefficient (ICC)) and spatially (Dice similarity coefficient (DSC)). Spearman's correlations of DSC with global and local lesion volume, structure of interest volume (ROIV), and normalized brain volume (NBV) were assessed. Results ICC with manual volumes was mostly good and spatial agreement was high. MS exhibited significantly lower DSC than controls for thalamus and putamen. For some combinations of structure and method, DSC correlated negatively with lesion volume or positively with NBV or ROIV. Lesion-filling did not substantially change segmentations. Conclusions Automated methods have impaired performance in patients. Performance generally deteriorated with higher lesion volume and lower NBV and ROIV, suggesting that these may contribute to the impaired performance.

Published
2020
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32. Younger age at multiple sclerosis onset is associated with worse outcomes at age 50.

Author

Bose, Gauruv, Healy, Brian C., Barro, Christian, Glanz, Bonnie I., Lokhande, Hrishikesh A., Polgar-Turcsanyi, Mariann, Guttmann, Charles R. G., Bakshi, Rohit, Weiner, Howard L., Chitnis, Tanuja, and Guttmann, Charles Rg

Abstract

Objective: Older age at multiple sclerosis (MS) onset has been associated with worse 10-year outcomes. However, disease duration often exceeds 10 years and age-related comorbidities may also contribute to disability. We investigated patients with>10 years disease duration to determine how age at MS onset is associated with clinical, MRI and occupational outcomes at age 50.Methods: We included patients enrolled in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital with disease duration>10 years. Outcomes at age 50 included the Expanded Disability Status Scale (EDSS), development of secondary-progressive multiple sclerosis (SPMS), brain T2-lesion volume (T2LV) and brain parenchymal fraction (BPF), and occupational status. We assessed how onset age was independently associated with each outcome when adjusting for the date of visit closest to age 50, sex, time to first treatment, number of treatments by age 50 and exposure to high-efficacy treatments by age 50.Results: We included 661 patients with median onset at 31.4 years. The outcomes at age 50 were worse the younger first symptoms developed: for every 5 years earlier, the EDSS was 0.22 points worse (95% CI: 0.04 to 0.40; p=0.015), odds of SPMS 1.33 times higher (95% CI: 1.08 to 1.64; p=0.008), T2LV 1.86 mL higher (95% CI: 1.02 to 2.70; p<0.001), BPF 0.97% worse (95% CI: 0.52 to 1.42; p<0.001) and odds of unemployment from MS 1.24 times higher (95% CI: 1.01 to 1.53; p=0.037).Conclusions: All outcomes at age 50 were worse in patients with younger age at onset. Decisions to provide high-efficacy treatments should consider younger age at onset, equating to a longer expected disease duration, as a poor prognostic factor. [ABSTRACT FROM AUTHOR]

Published
2022
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33. Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in multiple sclerosis: A multicenter study

Author

Burggraaff, Jessica, Liu, Yao, Prieto, Juan C., Simoes, Jorge, de Sitter, Alexandra, Ruggieri, Serena, Brouwer, Iman, Lissenberg-Witte, Birgit I., Rocca, Mara A., Valsasina, Paola, Ropele, Stefan, Gasperini, Claudio, Gallo, Antonio, Pareto, Deborah, Sastre-Garriga, Jaume, Enzinger, Christian, Filippi, Massimo, De Stefano, Nicola, Ciccarelli, Olga, Hulst, Hanneke E., Wattjes, Mike P., Barkhof, Frederik, Uitdehaag, Bernard M. J., Vrenken, Hugo, Guttmann, Charles R. G., Universitat Autònoma de Barcelona, Neurology, Amsterdam Neuroscience - Neuroinfection & -inflammation, Radiology and nuclear medicine, Amsterdam Neuroscience - Brain Imaging, Epidemiology and Data Science, Anatomy and neurosciences, Other Research, APH - Methodology, UCL - (SLuc) Centre du cancer, UCL - (SLuc) Service de chirurgie et transplantation abdominale, UCL - SSS/IREC/CHEX - Pôle de chirgurgie expérimentale et transplantation, Institut Català de la Salut, [Burggraaff J, Simoes J] Department of Neurology, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1118, 1081 HV Amsterdam, The Netherlands. [Liu Y, de Sitter A] Department of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, Location VUmc, De Boelelaan 1117, 1118, 1081 HV Amsterdam, The Netherlands. [Prieto JC] Center for Neurological Imaging, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, 1249 Boylston Street, Boston, MA 02215, USA. [Ruggieri S] Department of Human Neurosciences, 'Sapienza' University of Rome, Piazzale Aldo Moro, 5, 00185 Roma RM, Italy. Department of Neurosciences, San Camillo Forlanini Hospital, Circonvallazione Gianicolense, 87, 00152 Roma RM, Italy. [Pareto D] Secció de Neuroradiologia, Unitat de Ressonància Magnètica, Departament de Radiologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Universitat Autònoma de Barcelona, Bellaterra, Spain. [Sastre-Garriga J] Servei de Neurologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain. Universitat Autònoma de Barcelona, Bellaterra, Spain, Vall d'Hebron Barcelona Hospital Campus, Burggraaff, J., Liu, Y., Prieto, J. C., Simoes, J., de Sitter, A., Ruggieri, S., Brouwer, I., Lissenberg-Witte, B. I., Rocca, M. A., Valsasina, P., Ropele, S., Gasperini, C., Gallo, A., Pareto, D., Sastre-Garriga, J., Enzinger, C., Filippi, M., De Stefano, N., Ciccarelli, O., Hulst, H. E., Wattjes, M. P., Barkhof, F., Uitdehaag, B. M. J., Vrenken, H., and Guttmann, C. R. G.

Subjects
WLG, Word List Generation, SPM12, Statistical Parametric Mapping 12, Audiology, Tàlem - Imatgeria, Etiv, estimated total intracranial volume, lcsh:RC346-429, Otros calificadores::Otros calificadores::/complicaciones [Otros calificadores], GIF, Geodesic Information Flows, 0302 clinical medicine, Cognition, Segmentation, Thalamus, CP, cognitively preserved, NBV, Normalized brain volume, Multiple Sclerosi, Other subheadings::/diagnosis [Other subheadings], Neuropsychological assessment, Cognitive decline, Generalized estimating equation, WMV, white matter volume, ICC, intraclass correlation coefficient, medicine.diagnostic_test, 05 social sciences, Nervous System Diseases::Autoimmune Diseases of the Nervous System::Demyelinating Autoimmune Diseases, CNS::Multiple Sclerosis [DISEASES], sistema nervioso::sistema nervioso central::encéfalo::prosencéfalo::diencéfalo::tálamo [ANATOMÍA], Regular Article, Neuropsychological test, HC, healthy control, SDMT, Symbol Digit Modalities Test, Magnetic Resonance Imaging, Neurology, PASAT, Paced Auditory Serial Addition Test, enfermedades del sistema nervioso::enfermedades autoinmunitarias del sistema nervioso::enfermedades autoinmunes desmielinizantes del SNC::esclerosis múltiple [ENFERMEDADES], lcsh:R858-859.7, VolBrain, MRI Brain Volumetry System, SRT, Selective Reminding Test, Human, MRI, Esclerosi múltiple - Complicacions, medicine.medical_specialty, CNR, contrast-to-noise ratio, Multiple Sclerosis, Cognitive Neuroscience, RRMS, Relapsing-Remitting Multiple Sclerosis, Otros calificadores::/diagnóstico [Otros calificadores], Nervous System::Central Nervous System::Brain::Prosencephalon::Diencephalon::Thalamus [ANATOMY], 10/36 SRT, 10/36 Spatial Recall Test, lcsh:Computer applications to medicine. Medical informatics, NGMV, Normalized grey matter volume, 050105 experimental psychology, SD, standard deviations, CII, cognitive impairment index, EDSS, Expanded Disability Status Scale, WCST, Wisconsin Card Sorting Test, 03 medical and health sciences, Atrophy, medicine, Humans, 0501 psychology and cognitive sciences, Radiology, Nuclear Medicine and imaging, WM, white matter, lcsh:Neurology. Diseases of the nervous system, Thalamu, CI, cognitively impaired and preserved (CP), BRB-N, Brief Repeatable Battery of Neuropsychological Tests, business.industry, Multiple sclerosis, FSL-FIRST, FMRIB Integrated Registration and Segmentation Tool, eTIV, estimated total intracranial volume, CAT12, Computational Anatomy Toolbox for Statistical Parametric Mapping 12, GM, grey matter, medicine.disease, Deep grey matter, NWMV, Normalized white matter volume, MS, Multiple Sclerosis, GMV, grey matter volume, IPS, information processing speed, Neurology (clinical), business, 030217 neurology & neurosurgery, Other subheadings::Other subheadings::/complications [Other subheadings]
Abstract

Highlights • Thalamus atrophy is associated with cognitive impairment in multiple sclerosis. • This was confirmed by automated and manual segmentations, but effect sizes varied. • The algorithms work in a multi-center setting. • Automated techniques exhibit proportional bias with respect to thalamus size. • Differences between vendors can affect the robustness of these associations., Background and rationale Thalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods. We investigated the consistency of the association between thalamus volume and cognition in MS for two common automated segmentation approaches, as well as fully manual outlining. Methods Standardized neuropsychological assessment and 3-Tesla 3D-T1-weighted brain MRI were collected (multi-center) from 57 MS patients and 17 healthy controls. Thalamus segmentations were generated manually and using five automated methods. Agreement between the algorithms and manual outlines was assessed with Bland-Altman plots; linear regression assessed the presence of proportional bias. The effect of segmentation method on the separation of cognitively impaired (CI) and preserved (CP) patients was investigated through Generalized Estimating Equations; associations with cognitive measures were investigated using linear mixed models, for each method and vendor. Results In smaller thalami, automated methods systematically overestimated volumes compared to manual segmentations [ρ=(-0.42)-(-0.76); p-values

Published
2021
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34. Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data.

Author

UCL - (SLuc) Centre du cancer, UCL - (SLuc) Service de chirurgie et transplantation abdominale, UCL - SSS/IREC - Institut de recherche expérimentale et clinique, Eshaghi, Arman, Young, Alexandra L, Wijeratne, Peter A, Prados, Ferran, Arnold, Douglas L, Narayanan, Sridar, Guttmann, Charles R G, Barkhof, Frederik, Alexander, Daniel C, Thompson, Alan J, Chard, Declan, Ciccarelli, Olga, UCL - (SLuc) Centre du cancer, UCL - (SLuc) Service de chirurgie et transplantation abdominale, UCL - SSS/IREC - Institut de recherche expérimentale et clinique, Eshaghi, Arman, Young, Alexandra L, Wijeratne, Peter A, Prados, Ferran, Arnold, Douglas L, Narayanan, Sridar, Guttmann, Charles R G, Barkhof, Frederik, Alexander, Daniel C, Thompson, Alan J, Chard, Declan, and Ciccarelli, Olga

Abstract

Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously published studies. We use a training dataset from 6322 MS patients to define MRI-based subtypes and an independent cohort of 3068 patients for validation. Based on the earliest abnormalities, we define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression (CDP) and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. Our findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials.

Published
2021

36. Regional areas and widths of the midsagittal corpus callosum among HIV-infected patients on stable antiretroviral therapies

Author

Tate, David F., Sampat, Mehul, Harezlak, Jaroslaw, Fiecas, Mark, Hogan, Joseph, Dewey, Jeffrey, McCaffrey, Daniel, Branson, Daniel, Russell, Troy, Conley, Jared, Taylor, Michael, Schifitto, Giavoni, Zhong, J., Daar, Eric S., Alger, Jeffrey, Brown, Mark, Singer, Elyse, Campbell, T., McMahon, D., Tso, Y., Matesan, Janetta, Letendre, Scott, Paulose, S., Gaugh, Michelle, Tripoli, C., Yiannoutsos, Constantine, Bigler, Erin D., Cohen, Ronald A., Guttmann, Charles R. G., Navia, Bradford, and for the HIV Neuroimaging Consortium

Published
2011
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38. Abstracts from the ASENT 2008 Annual Meeting March 6–8, 2008

Author

VanDenburgh, Amanda, Shah, Manasee V., Hua, S., Abu-Shakra, S., Wagg, Jon, Khariton, Tatiana, Beddingfield, F. C., Brin, M. F., Herr, Barbara E., Hart, Kimberly A., McDermott, Michael, Griggs, Robert C., Herbelin, Laura, Barohn, Richard, Loane, David J., Byrnes, Kimberly R., Stoica, Bogdan A., Pajoohesh-Ganji, Ahdeah, Faden, Alan I., Nahab, Fatta, Wittevrongel, Loretta, Hallett, Mark, Li, Ingrid, Brady, Linda, Driscoll, Jamie, Guyer, Mark, Yao, Yong, Almeida, Luis, Maia, Joana, Soares-da-Silva, Patricio, Rocha, Jose Francisco, Lopes, Carlos, Nunes, Teresa, Machado, Rita, Costa, Raquel, Torrão, Leonel, Loureiro, Ana-Isabel, Vaz-da-Silva, Manuel, Wright, Lyndon, Falcão, Amilcar, Coelho, Rui, Albino-Teixeira, Antonio, Cusack, Noel, Hurtt, Mark, Katner, Simon, Torchia, John, Solomon, Bruce, Guinn, Robyn, Rohde, Candace, Thorne, Christine, Cusack, Noel J., Aungst, Bruce, Venkatarangan, Lata, Behrens, Davette, Brown, Foster, Meltzer, Peter, Bakshi, Rohit, Neema, Mohit, Healy, Brian C., Liptak, Zsuzsanna, Betensky, Rebecca A., Buckle, Guy J., Gauthier, Susan A., Stankiewicz, James, Meier, Dominik, Egorova, Svetlana, Arora, Ashish, Guss, Zachary D., Glanz, Bonnie I., Khoury, Samia J., Guttmann, Charles R. G., Weiner, Howard L., Guss, Jason G., and CINCH Study Group

Published
2008
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39. Targeted Blood Brain Barrier Opening With Focused Ultrasound Induces Focal Macrophage/Microglial Activation in Experimental Autoimmune Encephalomyelitis

Author

Schregel, Katharina, primary, Baufeld, Caroline, additional, Palotai, Miklos, additional, Meroni, Roberta, additional, Fiorina, Paolo, additional, Wuerfel, Jens, additional, Sinkus, Ralph, additional, Zhang, Yong-Zhi, additional, McDannold, Nathan, additional, White, P. Jason, additional, and Guttmann, Charles R. G., additional

Published
2021
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41. Microstructural Changes in the Left Mesocorticolimbic Pathway are Associated with the Comorbid Development of Fatigue and Depression in Multiple Sclerosis

Author

Palotai, Miklos, primary, Small, Catherine, additional, Makris, Nikolaos, additional, Somes, Nathaniel G., additional, Pinzon, Alfredo Morales, additional, Rathi, Yogesh, additional, Marzullo, Aldo, additional, Levitt, James J., additional, Bakshi, Rohit, additional, Chitnis, Tanuja, additional, and Guttmann, Charles R. G., additional

Published
2021
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46. Corrigendum: Perivascular Unit: This Must Be the Place. The Anatomical Crossroad Between the Immune, Vascular and Nervous System

Author

Troili, Fernanda, primary, Cipollini, Virginia, additional, Moci, Marco, additional, Morena, Emanuele, additional, Palotai, Miklos, additional, Rinaldi, Virginia, additional, Romano, Carmela, additional, Ristori, Giovanni, additional, Giubilei, Franco, additional, Salvetti, Marco, additional, Orzi, Francesco, additional, Guttmann, Charles R. G., additional, and Cavallari, Michele, additional

Published
2020
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47. Perivascular Unit: This Must Be the Place. The Anatomical Crossroad Between the Immune, Vascular and Nervous System

Author

Troili, Fernanda, primary, Cipollini, Virginia, additional, Moci, Marco, additional, Morena, Emanuele, additional, Palotai, Miklos, additional, Rinaldi, Virginia, additional, Romano, Carmela, additional, Ristori, Giovanni, additional, Giubilei, Franco, additional, Salvetti, Marco, additional, Orzi, Francesco, additional, Guttmann, Charles R. G., additional, and Cavallari, Michele, additional

Published
2020
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48. Smoking and Disease Progression in Multiple Sclerosis

Author

Healy, Brian C., Ali, Eman N., Guttmann, Charles R. G., Chitnis, Tanuja, Glanz, Bonnie I., Buckle, Guy, Houtchens, Maria, Stazzone, Lynn, Moodie, Jennifer, Berger, Annika M., Duan, Yang, Bakshi, Rohit, Khoury, Samia, Weiner, Howard, and Ascherio, Alberto

Published
2009
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