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Bhagavatula S, Cabeen R, Harris NG, Gröhn O, Wright DK, Garner R, Bennett A, Alba C, Martinez A, Ndode-Ekane XE, Andrade P, Paananen T, Ciszek R, Immonen R, Manninen E, Puhakka N, Tohka J, Heiskanen M, Ali I, Shultz SR, Casillas-Espinosa PM, Yamakawa GR, Jones NC, Hudson MR, Silva JC, Braine EL, Brady RD, Santana-Gomez CE, Smith GD, Staba R, O'Brien TJ, Pitkänen A, Duncan D. Image data harmonization tools for the analysis of post-traumatic epilepsy development in preclinical multisite MRI studies. Epilepsy Res 2023; 195:107201. [PMID: 37562146 PMCID: PMC10528111 DOI: 10.1016/j.eplepsyres.2023.107201] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 07/04/2023] [Accepted: 07/31/2023] [Indexed: 08/12/2023]
Abstract
Preclinical MRI studies have been utilized for the discovery of biomarkers that predict post-traumatic epilepsy (PTE). However, these single site studies often lack statistical power due to limited and homogeneous datasets. Therefore, multisite studies, such as the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx), are developed to create large, heterogeneous datasets that can lead to more statistically significant results. EpiBioS4Rx collects preclinical data internationally across sites, including the United States, Finland, and Australia. However, in doing so, there are robust normalization and harmonization processes that are required to obtain statistically significant and generalizable results. This work describes the tools and procedures used to harmonize multisite, multimodal preclinical imaging data acquired by EpiBioS4Rx. There were four main harmonization processes that were utilized, including file format harmonization, naming convention harmonization, image coordinate system harmonization, and diffusion tensor imaging (DTI) metrics harmonization. By using Python tools and bash scripts, the file formats, file names, and image coordinate systems are harmonized across all the sites. To harmonize DTI metrics, values are estimated for each voxel in an image to generate a histogram representing the whole image. Then, the Quantitative Imaging Toolkit (QIT) modules are utilized to scale the mode to a value of one and depict the subsequent harmonized histogram. The standardization of file formats, naming conventions, coordinate systems, and DTI metrics are qualitatively assessed. The histograms of the DTI metrics were generated for all the individual rodents per site. For inter-site analysis, an average of the individual scans was calculated to create a histogram that represents each site. In order to ensure the analysis can be run at the level of individual animals, the sham and TBI cohort were analyzed separately, which depicted the same harmonization factor. The results demonstrate that these processes qualitatively standardize the file formats, naming conventions, coordinate systems, and DTI metrics of the data. This assists in the ability to share data across the study, as well as disseminate tools that can help other researchers to strengthen the statistical power of their studies and analyze data more cohesively.
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Affiliation(s)
- Sweta Bhagavatula
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
| | - Ryan Cabeen
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Neil G Harris
- Department of Neurology, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA, USA
| | - Olli Gröhn
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - David K Wright
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Rachael Garner
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Alexis Bennett
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Celina Alba
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Aubrey Martinez
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - Pedro Andrade
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Tomi Paananen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Robert Ciszek
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Riikka Immonen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Eppu Manninen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Noora Puhakka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Jussi Tohka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Mette Heiskanen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Idrish Ali
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Sandy R Shultz
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Pablo M Casillas-Espinosa
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Glenn R Yamakawa
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Nigel C Jones
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Matthew R Hudson
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Juliana C Silva
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Emma L Braine
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Rhys D Brady
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Cesar E Santana-Gomez
- Department of Neurology, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA, USA
| | - Gregory D Smith
- Department of Neurology, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA, USA
| | - Richard Staba
- Department of Neurology, David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, CA, USA
| | - Terence J O'Brien
- Departments of Neuroscience and Neurology, Central Clinical School, Alfred Health, Monash University, Melbourne, Victoria, Australia
| | - Asla Pitkänen
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Dominique Duncan
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
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Klimecki OM, Liebscher M, Gaubert M, Hayek D, Zarucha A, Dyrba M, Bartels C, Buerger K, Butryn M, Dechent P, Dobisch L, Ewers M, Fliessbach K, Freiesleben SD, Glanz W, Hetzer S, Janowitz D, Kilimann I, Kleineidam L, Laske C, Maier F, Munk MH, Perneczky R, Peters O, Priller J, Rauchmann BS, Roy N, Scheffler K, Schneider A, Spruth EJ, Spottke A, Teipel SJ, Wiltfang J, Wolfsgruber S, Yakupov R, Düzel E, Jessen F, Wagner M, Roeske S, Wirth M. Long-term environmental enrichment is associated with better fornix microstructure in older adults. Front Aging Neurosci 2023; 15:1170879. [PMID: 37711996 PMCID: PMC10498282 DOI: 10.3389/fnagi.2023.1170879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 08/04/2023] [Indexed: 09/16/2023] Open
Abstract
Background Sustained environmental enrichment (EE) through a variety of leisure activities may decrease the risk of developing Alzheimer's disease. This cross-sectional cohort study investigated the association between long-term EE in young adulthood through middle life and microstructure of fiber tracts associated with the memory system in older adults. Methods N = 201 cognitively unimpaired participants (≥ 60 years of age) from the DZNE-Longitudinal Cognitive Impairment and Dementia Study (DELCODE) baseline cohort were included. Two groups of participants with higher (n = 104) or lower (n = 97) long-term EE were identified, using the self-reported frequency of diverse physical, intellectual, and social leisure activities between the ages 13 to 65. White matter (WM) microstructure was measured by fractional anisotropy (FA) and mean diffusivity (MD) in the fornix, uncinate fasciculus, and parahippocampal cingulum using diffusion tensor imaging. Long-term EE groups (lower/higher) were compared with adjustment for potential confounders, such as education, crystallized intelligence, and socio-economic status. Results Reported participation in higher long-term EE was associated with greater fornix microstructure, as indicated by higher FA (standardized β = 0.117, p = 0.033) and lower MD (β = -0.147, p = 0.015). Greater fornix microstructure was indirectly associated (FA: unstandardized B = 0.619, p = 0.038; MD: B = -0.035, p = 0.026) with better memory function through higher long-term EE. No significant effects were found for the other WM tracts. Conclusion Our findings suggest that sustained participation in a greater variety of leisure activities relates to preserved WM microstructure in the memory system in older adults. This could be facilitated by the multimodal stimulation associated with the engagement in a physically, intellectually, and socially enriched lifestyle. Longitudinal studies will be needed to support this assumption.
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Affiliation(s)
- Olga M Klimecki
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Maxie Liebscher
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Malo Gaubert
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
- Department of Neuroradiology, Rennes University Hospital Centre Hospitalier Universitaire (CHU), Rennes, France
| | - Dayana Hayek
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Alexis Zarucha
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Claudia Bartels
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Göttingen, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Michaela Butryn
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Peter Dechent
- Magnetic Resonance (MR)-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University Goettingen, Göttingen, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Silka Dawn Freiesleben
- Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Stefan Hetzer
- Berlin Center for Advanced Neuroimaging, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Luca Kleineidam
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Section for Dementia Research, Department of Psychiatry and Psychotherapy, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Franziska Maier
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Robert Perneczky
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, United Kingdom
| | - Oliver Peters
- Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
- Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany
- University of Edinburgh and United Kingdom Dementia Research Institute (UK DRI), Edinburgh, United Kingdom
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-Universität (LMU) Munich, Munich, Germany
| | - Nina Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Eike Jakob Spruth
- Department of Psychiatry and Neurosciences, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, University of Bonn, Bonn, Germany
| | - Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Jens Wiltfang
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen, University of Goettingen, Göttingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
- Neurosciences and Signaling Group, Department of Medical Sciences, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | - Steffen Wolfsgruber
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases, University of Cologne, Cologne, Germany
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurodegenerative Disease and Geriatric Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Sandra Roeske
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Miranka Wirth
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
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Skorska MN, Thurston LT, Biasin JM, Devenyi GA, Zucker KJ, Chakravarty MM, Lai MC, VanderLaan DP. Cortical Structure Differences in Relation to Age, Sexual Attractions, and Gender Dysphoria in Adolescents: An Examination of Mean Diffusivity and T1 Relaxation Time. Brain Sci 2023; 13:963. [PMID: 37371441 PMCID: PMC10296103 DOI: 10.3390/brainsci13060963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 06/08/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023] Open
Abstract
Recent research found that the combination of masculine gender identity and gynephilia was associated with cortical T1 relaxation time, which is considered to reflect gray matter density. We hypothesized that mean diffusivity (MD), a diffusion tensor imaging metric that reflects the degree to which water movement is free versus constrained, in combination with T1 relaxation time would provide further insight regarding cortical tissue characteristics. MD and T1 relaxation time were measured in 76 cortical regions in 15 adolescents assigned female at birth who experience gender dysphoria (GD AFAB) and were not receiving hormone therapy, 17 cisgender girls, and 14 cisgender boys (ages 12-17 years). Sexual orientation was represented by the degree of androphilia-gynephilia and the strength of sexual attraction. In multivariate analyses, cortical T1 relaxation time showed a weak but statistically significant positive association with MD across the cortex, suggesting that macromolecule-rich cortical tissue also tends to show water movement that is somewhat more constrained. In further multivariate analyses, in several left frontal, parietal, and temporal regions, the combination of shorter T1 relaxation time and faster MD was associated with older age and greater gynephilia in GD AFAB individuals and cisgender boys and with stronger attractions in cisgender boys only. Thus, for these cortical regions in these groups, older age, gynephilia, and stronger attractions (cisgender boys only) were associated with macromolecule-rich tissue in which water movement was freer-a pattern that some prior research suggests is associated with greater cell density and size. Overall, this study indicates that investigating T1 relaxation time and MD together can further inform how cortical gray matter tissue characteristics relate to age and psychosexuality.
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Affiliation(s)
- Malvina N. Skorska
- Child & Youth Psychiatry, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada; (M.N.S.)
| | - Lindsey T. Thurston
- Department of Psychology, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada
| | - Jessica M. Biasin
- Department of Psychology, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada
| | - Gabriel A. Devenyi
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, QC H4H 1R3, Canada (M.M.C.)
- Department of Psychiatry, McGill University, Montreal, QC H3A 1A1, Canada
| | - Kenneth J. Zucker
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
| | - M. Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, QC H4H 1R3, Canada (M.M.C.)
- Department of Psychiatry, McGill University, Montreal, QC H3A 1A1, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, QC H3A 2B4, Canada
| | - Meng-Chuan Lai
- Child & Youth Psychiatry, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada; (M.N.S.)
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health, Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Department of Psychiatry and Autism Research Unit, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei 100229, Taiwan
| | - Doug P. VanderLaan
- Child & Youth Psychiatry, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada; (M.N.S.)
- Department of Psychology, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada
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Classification Algorithms for Brain Magnetic Resonance Imaging Images of Patients with End-Stage Renal Disease and Depression. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:4795307. [PMID: 35854766 PMCID: PMC9279039 DOI: 10.1155/2022/4795307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 06/13/2022] [Indexed: 11/30/2022]
Abstract
This study was aimed to explore the relationship between depression and brain function in patients with end-stage renal disease (ESRD) complicated with depression based on brain magnetic resonance imaging (MRI) image classification algorithms. 30 people in the healthy control group and 70 people in the observation group were selected as the research objects. First, the preprocessing algorithms were applied on MRI images. With the depression classification algorithm based on deep learning, the features were extracted from the capsule network to construct a classification network, and the network structure was compared to obtain the difference in the distribution of brain lesions. Different classifiers and degree centrality, functional connection, low-frequency amplitude ratio, and low-frequency amplitude were selected to analyze the effectiveness of features. In the deep learning method, the neural network model was constructed, and feature extraction and classification network were carried out. The classification layer was based on the capsule network. The results showed that the correct rate of the deep learning feature extraction network structure combined with the capsule network classification was 82.47%, the recall rate was 83.69%, and the accuracy was 88.79%, showing that the capsule network can improve the heterogeneity of depression. The combination of fractional amplitude of low-frequency fluctuation (fALFF), DC, and amplitude of low-frequency fluctuation (ALFF) can achieve the accuracy of 100%. In summary, MRI images showed that patients with depression may have neurological abnormalities in the white matter area. In this study, the classification algorithm based on brain MRI images can effectively improve the classification performance.
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Asfuroğlu BB, Topkan TA, Kaydu NE, Sakai K, Öner AY, Karaman Y, Yamada K, Tali ET. DWI-based MR thermometry: could it discriminate Alzheimer's disease from mild cognitive impairment and healthy subjects? Neuroradiology 2022; 64:1979-1987. [PMID: 35536331 DOI: 10.1007/s00234-022-02969-y] [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: 01/31/2022] [Accepted: 04/27/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE The aim of this study is to compare lateral ventricular cerebrospinal fluid (CSF) temperature of the patients with Alzheimer's disease (AD), mild cognitive impairment (MCI), and healthy subjects (HS) using diffusion-weighted imaging (DWI)-based magnetic resonance (MR) thermometry. METHODS Seventy-two patients (37 AD, 19 MCI, 16 HS) who underwent 3-T MR examination from September 2018 to August 2019 were included in this study. Smoking habits, education level, disease duration, and comorbidity status were recorded. Patients were assessed using Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating (CDR) score. Brain temperatures were measured using DWI-based MR thermometry. Group comparisons of brain temperature were performed using the Pearson chi-square, Mann-Whitney, and Kruskal-Wallis tests. Further analysis was performed using the post hoc Bonferroni test. Receiver operating characteristic (ROC) analysis was also used. RESULTS A CDR score of 0.5, 1, and 2 was 2 (5.4%), 14 (37.8%), and 21 (56.8%) in AD, respectively. The median MMSE score had significant differences among groups and also in pairwise comparisons. The median CSF temperature (°C) values showed statistically significant difference among groups (HS: 38.5 °C, MCI: 38.17 °C, AD: 38.0 °C). The post hoc Mann-Whitney U test indicated a significant difference between AD patients and HS (p = 0.009). There were no significant CSF temperature differences in other pairwise comparisons. CONCLUSION Lower CSF temperatures were observed in AD patients than in HS, probably due to decreased brain metabolism in AD. DWI-based MR thermometry as a noninvasive imaging method enabling the measurement of CSF temperatures may contribute to the diagnosis of AD.
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Affiliation(s)
- Berrak Barutcu Asfuroğlu
- Department of Radiology, Faculty of Medicine, School of Medicine, Gazi University, 06500, Besevler, Ankara, Turkey.
| | - Tuğberk Andaç Topkan
- Department of Neurology, Faculty of Medicine, School of Medicine, Gazi University, Ankara, Turkey
| | - Nesrin Erdoğan Kaydu
- Department of Radiology, Faculty of Medicine, School of Medicine, Gazi University, 06500, Besevler, Ankara, Turkey
| | - Koji Sakai
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Ali Yusuf Öner
- Department of Radiology, Faculty of Medicine, School of Medicine, Gazi University, 06500, Besevler, Ankara, Turkey
| | - Yahya Karaman
- Department of Neurology, Faculty of Medicine, School of Medicine, Gazi University, Ankara, Turkey
| | - Kei Yamada
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - E Turgut Tali
- Department of Radiology, Faculty of Medicine, School of Medicine, Gazi University, 06500, Besevler, Ankara, Turkey.,Department of Radiology, School of Medicine, Lokman Hekim University, Ankara, Turkey
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Tazoe J, Lu CF, Hsieh BY, Chen CY, Kao YCJ. Altered diffusivity of the subarachnoid cisterns in the rat brain following neurological disorders. Biomed J 2022; 46:134-143. [PMID: 35066210 PMCID: PMC10104961 DOI: 10.1016/j.bj.2022.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 12/20/2021] [Accepted: 01/10/2022] [Indexed: 11/02/2022] Open
Abstract
BACKGROUND Although changes in diffusion characteristics of the brain parenchyma in neurological disorders are widely studied and used in clinical practice, the change in diffusivity in the cerebrospinal fluid (CSF) system is rarely reported. In this study, free water diffusion in the subarachnoid cisterns and ventricles of the rat brain was examined using diffusion magnetic resonance imaging (MRI), and the effects of neurological disorders on diffusivity in CSF system were investigated. METHODS Diffusion MRI and T2-weighted images were obtained in the intact rats, 24 h after ischemic stroke, and 50 days after mild traumatic brain injury (mTBI). We conducted the assessment of diffusivity in the rat brain in the subarachnoid cisterns around the midbrain, as well as the lateral ventricles. One-way ANOVA and Kruskal-Wallis test were used to evaluate the change in mean diffusivity (MD) and MD histogram, respectively, in CSF system following different neurological disease. RESULTS A significant decrease in the mean MD value of the subarachnoid cisterns was observed in the stroke rats compared with the intact and mTBI rats (p < 0.005). In addition, the skewness (p < 0.002), maximum MD (p < 0.002), and MD percentiles (p < 0.002) in the stroke rats differed significantly from those in the intact and mTBI rats. By contrast, no difference was observed in the mean MD value of the lateral ventricles among three groups of rats. We proposed that the assessment of the subarachnoid cisterns, rather than the lateral ventricles, in the rat brain would be useful in providing diffusion information in the CSF system. CONCLUSIONS Alterations in MD parameters of the subarachnoid cisterns after stroke provide evidence that brain injury may alter the characteristics of free water diffusion not only in the brain parenchyma but also in the CSF system.
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7
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Optimal strategy for measuring intraventricular temperature using acceleration motion compensation diffusion-weighted imaging. Radiol Phys Technol 2020; 13:136-143. [DOI: 10.1007/s12194-020-00560-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 03/03/2020] [Accepted: 03/05/2020] [Indexed: 10/24/2022]
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Stieb S, Klarhoefer M, Finkenstaedt T, Wurnig MC, Becker AS, Ciritsis A, Rossi C. Correction for fast pseudo-diffusive fluid motion contaminations in diffusion tensor imaging. Magn Reson Imaging 2019; 66:50-56. [PMID: 31655141 DOI: 10.1016/j.mri.2019.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 07/18/2019] [Accepted: 09/15/2019] [Indexed: 11/26/2022]
Abstract
In this prospective study, we quantified the fast pseudo-diffusion contamination by blood perfusion or cerebrospinal fluid (CSF) intravoxel incoherent movements on the measurement of the diffusion tensor metrics in healthy brain tissue. Diffusion-weighted imaging (TR/TE = 4100 ms/90 ms; b-values: 0, 5, 10, 20, 35, 55, 80, 110, 150, 200, 300, 500, 750, 1000, 1300 s/mm2, 20 diffusion-encoding directions) was performed on a cohort of five healthy volunteers at 3 Tesla. The projections of the diffusion tensor along each diffusion-encoding direction were computed using a two b-value approach (2b), by fitting the signal to a monoexponential curve (mono), and by correcting for fast pseudo-diffusion compartments using the biexponential intravoxel incoherent motion model (IVIM) (bi). Fractional anisotropy (FA) and mean diffusivity (MD) of the diffusion tensor were quantified in regions of interest drawn over white matter areas, gray matter areas, and the ventricles. A significant dependence of the MD from the evaluation method was found in all selected regions. A lower MD was computed when accounting for the fast-diffusion compartments. A larger dependence was found in the nucleus caudatus (bi: median 0.86 10-3 mm2/s, Δ2b: -11.2%, Δmono: -14.4%; p = 0.007), in the anterior horn (bi: median 2.04 10-3 mm2/s, Δ2b: -9.4%, Δmono: -11.5%, p = 0.007) and in the posterior horn of the lateral ventricles (bi: median 2.47 10-3 mm2/s, Δ2b: -5.5%, Δmono: -11.7%; p = 0.007). Also for the FA, the signal modeling affected the computation of the anisotropy metrics. The deviation depended on the evaluated region with significant differences mainly in the nucleus caudatus (bi: median 0.15, Δ2b: +39.3%, Δmono: +14.7%; p = 0.022) and putamen (bi: median 0.19, Δ2b: +3.1%, Δmono: +17.3%; p = 0.015). Fast pseudo-diffusive regimes locally affect diffusion tensor imaging (DTI) metrics in the brain. Here, we propose the use of an IVIM-based method for correction of signal contaminations through CSF or perfusion.
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Affiliation(s)
- Sonja Stieb
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland.
| | | | - Tim Finkenstaedt
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
| | - Moritz C Wurnig
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
| | - Anton S Becker
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
| | - Alexander Ciritsis
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
| | - Cristina Rossi
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland
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Lundervold AJ, Vik A, Lundervold A. Lateral ventricle volume trajectories predict response inhibition in older age-A longitudinal brain imaging and machine learning approach. PLoS One 2019; 14:e0207967. [PMID: 30939173 PMCID: PMC6445521 DOI: 10.1371/journal.pone.0207967] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Accepted: 03/04/2019] [Indexed: 01/06/2023] Open
Abstract
Objective In a three-wave 6 yrs longitudinal study we investigated if the expansion of lateral ventricle (LV) volumes (regarded as a proxy for brain tissue loss) predicts third wave performance on a test of response inhibition (RI). Participants and methods Trajectories of left and right lateral ventricle volumes across the three waves were quantified using the longitudinal stream in Freesurfer. All participants (N = 74;48 females;mean age 66.0 yrs at the third wave) performed the Color-Word Interference Test (CWIT). Response time on the third condition of CWIT, divided into fast, medium and slow, was used as outcome measure in a machine learning framework. Initially, we performed a linear mixed-effect (LME) analysis to describe subject-specific trajectories of the left and right LV volumes (LVV). These features were input to a multinomial logistic regression classification procedure, predicting individual belongings to one of the three RI classes. To obtain results that might generalize, we evaluated the significance of a k-fold cross-validated f1-score with a permutation test, providing a p-value that approximates the probability that the score would be obtained by chance. We also calculated a corresponding confusion matrix. Results The LME-model showed an annual ∼ 3.0% LVV increase. Evaluation of a cross-validated score using 500 permutations gave an f1-score of 0.462 that was above chance level (p = 0.014). 56% of the fast performers were successfully classified. All these were females, and typically older than 65 yrs at inclusion. For the true slow performers, those being correctly classified had higher LVVs than those being misclassified, and their ages at inclusion were also higher. Conclusion Major contributions were: (i) a longitudinal design, (ii) advanced brain imaging and segmentation procedures with longitudinal data analysis, and (iii) a data driven machine learning approach including cross-validation and permutation testing to predict behaviour, solely from the individual’s brain “signatures” (LVV trajectories).
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Affiliation(s)
- Astri J. Lundervold
- Department of Biological and Medical Psychology University of Bergen, Norway
| | - Alexandra Vik
- Department of Biological and Medical Psychology University of Bergen, Norway
| | - Arvid Lundervold
- Mohn Medical Imaging and Visualization Centre, Department of Biomedicine, University of Bergen, Norway
- * E-mail:
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10
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Riascos RF, Kamali A, Hakimelahi R, Mwangi B, Rabiei P, Seidler RD, Behzad BB, Keser Z, Kramer LA, Hasan KM. Longitudinal Analysis of Quantitative Brain MRI in Astronauts Following Microgravity Exposure. J Neuroimaging 2019; 29:323-330. [DOI: 10.1111/jon.12609] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2018] [Revised: 02/06/2019] [Accepted: 02/08/2019] [Indexed: 01/22/2023] Open
Affiliation(s)
- Roy F. Riascos
- Department of Diagnostic and Intervention ImagingUniversity of Texas Health Science Center Houston TX
| | - Arash Kamali
- Department of Diagnostic and Intervention ImagingUniversity of Texas Health Science Center Houston TX
| | | | - Benson Mwangi
- Department of Psychiatry & Behavioral SciencesUniversity of Texas Health Science Center Houston TX
| | - Pejman Rabiei
- Department of Diagnostic and Intervention ImagingUniversity of Texas Health Science Center Houston TX
| | - Rachael D. Seidler
- Department of Applied Physiology & KinesiologyUniversity of Florida Gainesville FL
| | - Barzin B. Behzad
- Department of RadiologyTexas Tech University Health Sciences Center El Paso TX
| | - Zafer Keser
- Department of NeurologyUniversity of Texas Health Science Center Houston TX
| | - Larry A. Kramer
- Department of Diagnostic and Intervention ImagingUniversity of Texas Health Science Center Houston TX
| | - Khader M. Hasan
- Department of Diagnostic and Intervention ImagingUniversity of Texas Health Science Center Houston TX
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11
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Genome-wide association study of 23,500 individuals identifies 7 loci associated with brain ventricular volume. Nat Commun 2018; 9:3945. [PMID: 30258056 PMCID: PMC6158214 DOI: 10.1038/s41467-018-06234-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Accepted: 08/08/2018] [Indexed: 01/28/2023] Open
Abstract
The volume of the lateral ventricles (LV) increases with age and their abnormal enlargement is a key feature of several neurological and psychiatric diseases. Although lateral ventricular volume is heritable, a comprehensive investigation of its genetic determinants is lacking. In this meta-analysis of genome-wide association studies of 23,533 healthy middle-aged to elderly individuals from 26 population-based cohorts, we identify 7 genetic loci associated with LV volume. These loci map to chromosomes 3q28, 7p22.3, 10p12.31, 11q23.1, 12q23.3, 16q24.2, and 22q13.1 and implicate pathways related to tau pathology, S1P signaling, and cytoskeleton organization. We also report a significant genetic overlap between the thalamus and LV volumes (ρgenetic = −0.59, p-value = 3.14 × 10−6), suggesting that these brain structures may share a common biology. These genetic associations of LV volume provide insights into brain morphology. An increase in the volume of the brain lateral ventricles is a sign of normal aging, but can also be associated with neurological and psychiatric disorders. Here, Vojinovic et al. identify seven genetic loci in a GWA study for ventricular volume in 23,500 individuals and find correlation with thalamus volume.
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12
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Sparacia G, Cannella R, Lo Re V, Mamone G, Sakai K, Yamada K, Miraglia R. Brain-core temperature of patients before and after orthotopic liver transplantation assessed by DWI thermometry. Jpn J Radiol 2018; 36:324-330. [DOI: 10.1007/s11604-018-0729-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Accepted: 03/15/2018] [Indexed: 10/17/2022]
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13
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Hasan KM, Mwangi B, Keser Z, Riascos R, Sargsyan AE, Kramer LA. Brain Quantitative MRI Metrics in Astronauts as a Unique Professional Group. J Neuroimaging 2018; 28:256-268. [DOI: 10.1111/jon.12501] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2017] [Revised: 01/04/2018] [Accepted: 01/16/2018] [Indexed: 11/28/2022] Open
Affiliation(s)
- Khader M. Hasan
- Medical SchoolDepartment of Diagnostic and Interventional ImagingThe University of Texas Health Science Center Houston TX
| | - Benson Mwangi
- Medical SchoolDepartment of PsychiatryThe University of Texas Health Science Center Houston TX
| | - Zafer Keser
- Medical SchoolDepartment of NeurologyThe University of Texas Health Science Center Houston TX
| | - Roy Riascos
- Medical SchoolDepartment of Diagnostic and Interventional ImagingThe University of Texas Health Science Center Houston TX
| | | | - Larry A. Kramer
- Medical SchoolDepartment of Diagnostic and Interventional ImagingThe University of Texas Health Science Center Houston TX
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14
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Schmitz JM, Green CE, Hasan KM, Vincent J, Suchting R, Weaver MF, Moeller FG, Narayana PA, Cunningham KA, Dineley KT, Lane SD. PPAR-gamma agonist pioglitazone modifies craving intensity and brain white matter integrity in patients with primary cocaine use disorder: a double-blind randomized controlled pilot trial. Addiction 2017; 112:1861-1868. [PMID: 28498501 PMCID: PMC5593771 DOI: 10.1111/add.13868] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Revised: 02/14/2017] [Accepted: 05/05/2017] [Indexed: 01/31/2023]
Abstract
BACKGROUND AND AIMS Pioglitazone (PIO), a potent agonist of PPAR-gamma, is a promising candidate treatment for cocaine use disorder (CUD). We tested the effects of PIO on targeted mechanisms relevant to CUD: cocaine craving and brain white matter (WM) integrity. Feasibility, medication compliance and tolerability were evaluated. DESIGN Two-arm double-blind randomized controlled proof-of-concept pilot trial of PIO or placebo (PLC). SETTING Single-site out-patient treatment research clinic in Houston, TX, USA. PARTICIPANTS Thirty treatment-seeking adults, 18 to 60 years old, with CUD. Eighteen participants (8 = PIO; 10 = PLC) completed diffusion tensor imaging (DTI) of WM integrity at pre-/post-treatment. INTERVENTION Study medication was dispensed at thrice weekly visits along with once-weekly cognitive behavioral therapy for 12 weeks. MEASUREMENTS Measures of target engagement mechanisms of interest included cocaine craving assessed by the Brief Substance Craving Scale (BSCS), the Obsessive Compulsive Drug Use Scale (OCDUS), a visual analog scale (VAS) and change in WM integrity. Feasibility measures included number completing treatment, medication compliance (riboflavin detection) and tolerability (side effects, serious adverse events). FINDINGS Target engagement change in mechanisms of interest, defined as a ≥ 0.75 Bayesian posterior probability of an interaction existing favoring PIO over PLC, was demonstrated on measures of craving (BSCS, VAS) and WM integrity indexed by fractional anisotropy (FA) values. Outcomes indicated greater decrease in craving and greater increase in FA values in the PIO group. Feasibility was demonstrated by high completion rates among those starting treatment (21/26 = 80%) and medication compliance (≥ 80%). There were no reported serious adverse events for PIO. CONCLUSIONS Compared with placebo, patients receiving pioglitazone show a higher likelihood of reduced cocaine craving and improved brain white matter integrity as a function of time in treatment. Pioglitazone shows good feasibility as a treatment for cocaine use disorder.
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Affiliation(s)
- Joy M Schmitz
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Charles E Green
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
- UT-Houston Center for Clinical Research and Evidence-Based Medicine, Houston, TX, USA
| | - Khader M Hasan
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jessica Vincent
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Robert Suchting
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Michael F Weaver
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | | | - Ponnada A Narayana
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Kathryn A Cunningham
- Center for Addiction Research, University of Texas Medical Branch, Galveston, TX, USA
| | - Kelly T Dineley
- Center for Addiction Research, University of Texas Medical Branch, Galveston, TX, USA
| | - Scott D Lane
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
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15
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Kramer LA, Hasan KM, Sargsyan AE, Marshall-Goebel K, Rittweger J, Donoviel D, Higashi S, Mwangi B, Gerlach DA, Bershad EM. Quantitative MRI volumetry, diffusivity, cerebrovascular flow, and cranial hydrodynamics during head-down tilt and hypercapnia: the SPACECOT study. J Appl Physiol (1985) 2017; 122:1155-1166. [DOI: 10.1152/japplphysiol.00887.2016] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Revised: 01/24/2017] [Accepted: 02/11/2017] [Indexed: 01/17/2023] Open
Abstract
To improve the pathophysiological understanding of visual changes observed in astronauts, we aimed to use quantitative MRI to measure anatomic and physiological responses during a ground-based spaceflight analog (head-down tilt, HDT) combined with increased ambient carbon dioxide (CO2). Six healthy, male subjects participated in the double-blinded, randomized crossover design study with two conditions: 26.5 h of −12° HDT with ambient air and with 0.5% CO2, both followed by 2.5-h exposure to 3% CO2. Volume and mean diffusivity quantification of the lateral ventricle and phase-contrast flow sequences of the internal carotid arteries and cerebral aqueduct were acquired at 3 T. Compared with supine baseline, HDT (ambient air) resulted in an increase in lateral ventricular volume ( P = 0.03). Cerebral blood flow, however, decreased with HDT in the presence of either ambient air or 0.5% CO2( P = 0.002 and P = 0.01, respectively); this was partially reversed by acute 3% CO2exposure. Following HDT (ambient air), exposure to 3% CO2increased aqueductal cerebral spinal fluid velocity amplitude ( P = 0.01) and lateral ventricle cerebrospinal fluid (CSF) mean diffusivity ( P = 0.001). We concluded that HDT causes alterations in cranial anatomy and physiology that are associated with decreased craniospinal compliance. Brief exposure to 3% CO2augments CSF pulsatility within the cerebral aqueduct and lateral ventricles.NEW & NOTEWORTHY Head-down tilt causes increased lateral ventricular volume and decreased cerebrovascular flow after 26.5 h. Additional short exposure to 3% ambient carbon dioxide levels causes increased cerebrovascular flow associated with increased cerebrospinal fluid pulsatility at the cerebral aqueduct. Head-down tilt with chronically elevated 0.5% ambient carbon dioxide and acutely elevated 3% ambient carbon dioxide causes increased mean diffusivity of cerebral spinal fluid within the lateral ventricles.
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Affiliation(s)
- Larry A. Kramer
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, Texas
| | - Khader M. Hasan
- Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, Texas
| | | | - Karina Marshall-Goebel
- Division of Space Physiology, Institute of Aerospace Medicine, German Aerospace Center (DLR), Cologne, Germany
- Department of Medicine, University of Cologne, Cologne, Germany
| | - Jörn Rittweger
- Division of Space Physiology, Institute of Aerospace Medicine, German Aerospace Center (DLR), Cologne, Germany
- Department of Neurology, University of Cologne, Cologne, Germany
| | - Dorit Donoviel
- Department of Pharmacology and Space Medicine, Baylor College of Medicine, Houston, Texas
| | - Saki Higashi
- Tokushima University Medical School, Tokushima, Japan
| | - Benson Mwangi
- Department of Behavioral Sciences, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, Texas; and
| | - Darius A. Gerlach
- Division of Space Physiology, Institute of Aerospace Medicine, German Aerospace Center (DLR), Cologne, Germany
| | - Eric M. Bershad
- Neurology and Space Medicine, Baylor College of Medicine, Houston, Texas
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16
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Sparacia G, Sakai K, Yamada K, Giordano G, Coppola R, Midiri M, Grimaldi LM. Assessment of brain core temperature using MR DWI-thermometry in Alzheimer disease patients compared to healthy subjects. Jpn J Radiol 2017; 35:168-171. [DOI: 10.1007/s11604-017-0616-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2016] [Accepted: 01/23/2017] [Indexed: 01/24/2023]
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17
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Feng C, Zhao D, Huang M. Image segmentation and bias correction using local inhomogeneous iNtensity clustering (LINC): A region-based level set method. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.09.008] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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18
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Keser Z, Hasan KM, Mwangi B, Gabr RE, Nelson FM. Diffusion Tensor Imaging-Defined Sulcal Enlargement Is Related to Cognitive Impairment in Multiple Sclerosis. J Neuroimaging 2016; 27:312-317. [PMID: 27862549 DOI: 10.1111/jon.12406] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2016] [Revised: 10/03/2016] [Accepted: 10/12/2016] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND AND PURPOSE Cerebrospinal fluid (CSF) in the brain can be compartmentalized into two main divisions: ventricular CSF and subarachnoid space (sulcal CSF). Changes in CSF volumetry are seen in many neurological conditions including multiple sclerosis (MS) and found to correlate with clinical outcomes. We aimed to test the relation between the volumetry of sulcal and ventricular CSF and cognitive impairment (CI) based on the minimal assessment of cognitive function in MS (MACFIMS) in patients with MS. MATERIAL AND METHODS Forty-six patients with MS underwent the MACFIMS battery and classified as nonimpaired (MSNI) (n = 10) and cognitively impaired (MSCI) (n = 30) and borderline (MSBD) MS patients (n = 6). Volumes of sulcal and ventricular CSF along with global gray and white matter volumes and cortical thickness were obtained by diffusion tensor imaging (DTI) and T1-weighted (T1w)-based segmentation. These measures were statistically analyzed for associations with CI after adjusting for the age, education in years, lesion load, and disease duration. RESULTS Sulcal CSF showed the strongest correlation with CI (r = .51, P = .001) in our cohort, whereas ventricular CSF (P = .28, P = .19) along with cortical thickness and gray matter volume failed to show a significant correlation. Group analyses unadjusted for multiple comparisons showed significant difference in volumes of sulcal CSF and ventricular CSF between MSNI and MSCI groups (P < .05). CONCLUSION Sulcal CSF correlates with CI in patients with MS, possibly explained by cortical atrophy. DTI/T1w-based sulcal CSF segmentation method might be used as an indirect and simple neuroimaging marker to monitor CI in MS patients.
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Affiliation(s)
- Zafer Keser
- Department of Neurology, The University of Texas Health Science Center McGovern Medical School, Houston, TX
| | - Khader M Hasan
- Department of Interventional and Diagnostic Radiology, The University of Texas Health Science Center McGovern Medical School, Houston, TX
| | - Benson Mwangi
- UT Center of Excellence on Mood Disorders, The University of Texas Health Science Center McGovern Medical School, Houston, TX
| | - Refaat E Gabr
- Department of Interventional and Diagnostic Radiology, The University of Texas Health Science Center McGovern Medical School, Houston, TX
| | - Flavia M Nelson
- Department of Neurology, The University of Texas Health Science Center McGovern Medical School, Houston, TX
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Oliver J, Abbas K, Lightfoot JT, Baskin K, Collins B, Wier D, Bramhall JP, Huang J, Puschett JB. Comparison of Neurocognitive Testing and the Measurement of Marinobufagenin in Mild Traumatic Brain Injury: A Preliminary Report. J Exp Neurosci 2015; 9:67-72. [PMID: 26351409 PMCID: PMC4517832 DOI: 10.4137/jen.s27921] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2015] [Revised: 06/01/2015] [Accepted: 06/04/2015] [Indexed: 11/24/2022] Open
Abstract
The evaluation of concussed athletes, including testing to determine if and when they may return to play, has become an important task of athletic trainers and team physicians. Currently, concussion protocols are in place, which depend largely upon assessments based upon neurocognitive testing (NCT). The authors have evaluated the use of a biomarker of brain trauma, marinobufagenin (MBG), and compared its application in concussed athletes with the performance of NTC. We found a disparity between these two testing procedures. In this communication, the findings of these comparative data are presented. We noted that athletes whose NCT evaluations had returned to baseline and who were allowed to again participate in play then showed a recurrence of elevated urinary MBG excretion. These observations raise concern as to the processes currently in effect with regard to the decision as to returning athletes to the full activity. They suggest a need for further evaluation.
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Affiliation(s)
- Joel Oliver
- Department of Pathobiology, College of Veterinary Medicine and Biosciences, Texas A&M University, College Station, TX, USA
| | - Kamran Abbas
- Department of Pathobiology, College of Veterinary Medicine and Biosciences, Texas A&M University, College Station, TX, USA
| | - J Timothy Lightfoot
- Department of Health and Kinesiology, Texas A&M University, College Station, TX, USA
| | - Kelly Baskin
- Department of Health and Kinesiology, Texas A&M University, College Station, TX, USA
| | - Blaise Collins
- Department of Health and Kinesiology, Texas A&M University, College Station, TX, USA
| | - David Wier
- The Department of Athletics, Texas A&M University, College Station, TX, USA
| | - Joe P Bramhall
- The Department of Athletics, Texas A&M University, College Station, TX, USA
| | - Jason Huang
- The Department of Neurosurgery, Baylor Scott & White Healthcare and Texas A&M Health Science Center College of Medicine, Temple, TX, USA
| | - Jules B Puschett
- Department of Pathobiology, College of Veterinary Medicine and Biosciences, Texas A&M University, College Station, TX, USA
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20
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Oh J, Sotirchos ES, Saidha S, Whetstone A, Chen M, Newsome SD, Zackowski K, Balcer LJ, Frohman E, Prince J, Diener-West M, Reich DS, Calabresi PA. Relationships between quantitative spinal cord MRI and retinal layers in multiple sclerosis. Neurology 2015; 84:720-8. [PMID: 25609766 DOI: 10.1212/wnl.0000000000001257] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
OBJECTIVE To assess relationships between spinal cord MRI (SC-MRI) and retinal measures, and to evaluate whether these measures independently relate to clinical disability in multiple sclerosis (MS). METHODS One hundred two patients with MS and 11 healthy controls underwent 3-tesla brain and cervical SC-MRI, which included standard T1- and T2-based sequences and diffusion-tensor and magnetization-transfer imaging, and optical coherence tomography with automated segmentation. Clinical assessments included visual acuity (VA), Expanded Disability Status Scale, MS functional composite, vibration sensation threshold, and hip-flexion strength. Regions of interest circumscribing SC cross-sections at C3-4 were used to obtain cross-sectional area (CSA), fractional anisotropy (FA), perpendicular diffusivity (λ⊥), and magnetization transfer ratio. Multivariable regression assessed group differences and SC, retinal, and clinical relationships. RESULTS In MS, there were correlations between SC-CSA, SC-FA, SC-λ⊥, and peripapillary retinal nerve fiber layer (pRNFL) (p = 0.01, p = 0.002, p = 0.001, respectively) after adjusting for age, sex, prior optic neuritis, and brain atrophy. In multivariable clinical models, when SC-CSA, pRNFL, and brain atrophy were included simultaneously, SC-CSA and pRNFL retained independent relationships with low-contrast VA (p = 0.04, p = 0.002, respectively), high-contrast VA (p = 0.06, p = 0.008), and vibration sensation threshold (p = 0.01, p = 0.05). SC-CSA alone retained independent relationships with Expanded Disability Status Scale (p = 0.001), hip-flexion strength (p = 0.001), and MS functional composite (p = 0.004). CONCLUSIONS In this cross-sectional study of patients with MS, correlations exist between SC-MRI and retinal layers, and both exhibit independent relationships with clinical dysfunction. These findings suggest that the SC and optic nerve reflect ongoing global pathologic processes that supplement measures of whole-brain atrophy, highlighting the importance of combining measures from unique compartments to facilitate a thorough examination of regional and global disease processes that contribute to clinical disability in MS.
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Affiliation(s)
- Jiwon Oh
- From the Departments of Neurology (J.O., E.S.S., S.S., A.W., S.D.N., K.Z., D.S.R., P.A.C.), Electrical and Computer Engineering (M.C., J.P.), Computer Science (J.P.), Physical Medicine and Rehabilitation (K.Z.), Biostatistics (M.C., M.D.-W., D.S.R.), and Radiology and Radiological Science (D.S.R.), Johns Hopkins University, Baltimore, MD; Division of Neurology (J.O.), Department of Medicine, St. Michael's Hospital, University of Toronto, Canada; Motion Analysis Laboratory (K.Z.), Kennedy Krieger Institute, Baltimore, MD; Department of Neurology (L.J.B.), University of Pennsylvania School of Medicine, Philadelphia; Departments of Neurology and Ophthalmology (E.F.), University of Texas Southwestern Medical Center at Dallas; and Translational Neuroradiology Unit (D.S.R.), National Institute of Neurological Disorders and Stroke, Bethesda, MD.
| | - Elias S Sotirchos
- From the Departments of Neurology (J.O., E.S.S., S.S., A.W., S.D.N., K.Z., D.S.R., P.A.C.), Electrical and Computer Engineering (M.C., J.P.), Computer Science (J.P.), Physical Medicine and Rehabilitation (K.Z.), Biostatistics (M.C., M.D.-W., D.S.R.), and Radiology and Radiological Science (D.S.R.), Johns Hopkins University, Baltimore, MD; Division of Neurology (J.O.), Department of Medicine, St. Michael's Hospital, University of Toronto, Canada; Motion Analysis Laboratory (K.Z.), Kennedy Krieger Institute, Baltimore, MD; Department of Neurology (L.J.B.), University of Pennsylvania School of Medicine, Philadelphia; Departments of Neurology and Ophthalmology (E.F.), University of Texas Southwestern Medical Center at Dallas; and Translational Neuroradiology Unit (D.S.R.), National Institute of Neurological Disorders and Stroke, Bethesda, MD
| | - Shiv Saidha
- From the Departments of Neurology (J.O., E.S.S., S.S., A.W., S.D.N., K.Z., D.S.R., P.A.C.), Electrical and Computer Engineering (M.C., J.P.), Computer Science (J.P.), Physical Medicine and Rehabilitation (K.Z.), Biostatistics (M.C., M.D.-W., D.S.R.), and Radiology and Radiological Science (D.S.R.), Johns Hopkins University, Baltimore, MD; Division of Neurology (J.O.), Department of Medicine, St. Michael's Hospital, University of Toronto, Canada; Motion Analysis Laboratory (K.Z.), Kennedy Krieger Institute, Baltimore, MD; Department of Neurology (L.J.B.), University of Pennsylvania School of Medicine, Philadelphia; Departments of Neurology and Ophthalmology (E.F.), University of Texas Southwestern Medical Center at Dallas; and Translational Neuroradiology Unit (D.S.R.), National Institute of Neurological Disorders and Stroke, Bethesda, MD
| | - Anna Whetstone
- From the Departments of Neurology (J.O., E.S.S., S.S., A.W., S.D.N., K.Z., D.S.R., P.A.C.), Electrical and Computer Engineering (M.C., J.P.), Computer Science (J.P.), Physical Medicine and Rehabilitation (K.Z.), Biostatistics (M.C., M.D.-W., D.S.R.), and Radiology and Radiological Science (D.S.R.), Johns Hopkins University, Baltimore, MD; Division of Neurology (J.O.), Department of Medicine, St. Michael's Hospital, University of Toronto, Canada; Motion Analysis Laboratory (K.Z.), Kennedy Krieger Institute, Baltimore, MD; Department of Neurology (L.J.B.), University of Pennsylvania School of Medicine, Philadelphia; Departments of Neurology and Ophthalmology (E.F.), University of Texas Southwestern Medical Center at Dallas; and Translational Neuroradiology Unit (D.S.R.), National Institute of Neurological Disorders and Stroke, Bethesda, MD
| | - Min Chen
- From the Departments of Neurology (J.O., E.S.S., S.S., A.W., S.D.N., K.Z., D.S.R., P.A.C.), Electrical and Computer Engineering (M.C., J.P.), Computer Science (J.P.), Physical Medicine and Rehabilitation (K.Z.), Biostatistics (M.C., M.D.-W., D.S.R.), and Radiology and Radiological Science (D.S.R.), Johns Hopkins University, Baltimore, MD; Division of Neurology (J.O.), Department of Medicine, St. Michael's Hospital, University of Toronto, Canada; Motion Analysis Laboratory (K.Z.), Kennedy Krieger Institute, Baltimore, MD; Department of Neurology (L.J.B.), University of Pennsylvania School of Medicine, Philadelphia; Departments of Neurology and Ophthalmology (E.F.), University of Texas Southwestern Medical Center at Dallas; and Translational Neuroradiology Unit (D.S.R.), National Institute of Neurological Disorders and Stroke, Bethesda, MD
| | - Scott D Newsome
- From the Departments of Neurology (J.O., E.S.S., S.S., A.W., S.D.N., K.Z., D.S.R., P.A.C.), Electrical and Computer Engineering (M.C., J.P.), Computer Science (J.P.), Physical Medicine and Rehabilitation (K.Z.), Biostatistics (M.C., M.D.-W., D.S.R.), and Radiology and Radiological Science (D.S.R.), Johns Hopkins University, Baltimore, MD; Division of Neurology (J.O.), Department of Medicine, St. Michael's Hospital, University of Toronto, Canada; Motion Analysis Laboratory (K.Z.), Kennedy Krieger Institute, Baltimore, MD; Department of Neurology (L.J.B.), University of Pennsylvania School of Medicine, Philadelphia; Departments of Neurology and Ophthalmology (E.F.), University of Texas Southwestern Medical Center at Dallas; and Translational Neuroradiology Unit (D.S.R.), National Institute of Neurological Disorders and Stroke, Bethesda, MD
| | - Kathy Zackowski
- From the Departments of Neurology (J.O., E.S.S., S.S., A.W., S.D.N., K.Z., D.S.R., P.A.C.), Electrical and Computer Engineering (M.C., J.P.), Computer Science (J.P.), Physical Medicine and Rehabilitation (K.Z.), Biostatistics (M.C., M.D.-W., D.S.R.), and Radiology and Radiological Science (D.S.R.), Johns Hopkins University, Baltimore, MD; Division of Neurology (J.O.), Department of Medicine, St. Michael's Hospital, University of Toronto, Canada; Motion Analysis Laboratory (K.Z.), Kennedy Krieger Institute, Baltimore, MD; Department of Neurology (L.J.B.), University of Pennsylvania School of Medicine, Philadelphia; Departments of Neurology and Ophthalmology (E.F.), University of Texas Southwestern Medical Center at Dallas; and Translational Neuroradiology Unit (D.S.R.), National Institute of Neurological Disorders and Stroke, Bethesda, MD
| | - Laura J Balcer
- From the Departments of Neurology (J.O., E.S.S., S.S., A.W., S.D.N., K.Z., D.S.R., P.A.C.), Electrical and Computer Engineering (M.C., J.P.), Computer Science (J.P.), Physical Medicine and Rehabilitation (K.Z.), Biostatistics (M.C., M.D.-W., D.S.R.), and Radiology and Radiological Science (D.S.R.), Johns Hopkins University, Baltimore, MD; Division of Neurology (J.O.), Department of Medicine, St. Michael's Hospital, University of Toronto, Canada; Motion Analysis Laboratory (K.Z.), Kennedy Krieger Institute, Baltimore, MD; Department of Neurology (L.J.B.), University of Pennsylvania School of Medicine, Philadelphia; Departments of Neurology and Ophthalmology (E.F.), University of Texas Southwestern Medical Center at Dallas; and Translational Neuroradiology Unit (D.S.R.), National Institute of Neurological Disorders and Stroke, Bethesda, MD
| | - Elliot Frohman
- From the Departments of Neurology (J.O., E.S.S., S.S., A.W., S.D.N., K.Z., D.S.R., P.A.C.), Electrical and Computer Engineering (M.C., J.P.), Computer Science (J.P.), Physical Medicine and Rehabilitation (K.Z.), Biostatistics (M.C., M.D.-W., D.S.R.), and Radiology and Radiological Science (D.S.R.), Johns Hopkins University, Baltimore, MD; Division of Neurology (J.O.), Department of Medicine, St. Michael's Hospital, University of Toronto, Canada; Motion Analysis Laboratory (K.Z.), Kennedy Krieger Institute, Baltimore, MD; Department of Neurology (L.J.B.), University of Pennsylvania School of Medicine, Philadelphia; Departments of Neurology and Ophthalmology (E.F.), University of Texas Southwestern Medical Center at Dallas; and Translational Neuroradiology Unit (D.S.R.), National Institute of Neurological Disorders and Stroke, Bethesda, MD
| | - Jerry Prince
- From the Departments of Neurology (J.O., E.S.S., S.S., A.W., S.D.N., K.Z., D.S.R., P.A.C.), Electrical and Computer Engineering (M.C., J.P.), Computer Science (J.P.), Physical Medicine and Rehabilitation (K.Z.), Biostatistics (M.C., M.D.-W., D.S.R.), and Radiology and Radiological Science (D.S.R.), Johns Hopkins University, Baltimore, MD; Division of Neurology (J.O.), Department of Medicine, St. Michael's Hospital, University of Toronto, Canada; Motion Analysis Laboratory (K.Z.), Kennedy Krieger Institute, Baltimore, MD; Department of Neurology (L.J.B.), University of Pennsylvania School of Medicine, Philadelphia; Departments of Neurology and Ophthalmology (E.F.), University of Texas Southwestern Medical Center at Dallas; and Translational Neuroradiology Unit (D.S.R.), National Institute of Neurological Disorders and Stroke, Bethesda, MD
| | - Marie Diener-West
- From the Departments of Neurology (J.O., E.S.S., S.S., A.W., S.D.N., K.Z., D.S.R., P.A.C.), Electrical and Computer Engineering (M.C., J.P.), Computer Science (J.P.), Physical Medicine and Rehabilitation (K.Z.), Biostatistics (M.C., M.D.-W., D.S.R.), and Radiology and Radiological Science (D.S.R.), Johns Hopkins University, Baltimore, MD; Division of Neurology (J.O.), Department of Medicine, St. Michael's Hospital, University of Toronto, Canada; Motion Analysis Laboratory (K.Z.), Kennedy Krieger Institute, Baltimore, MD; Department of Neurology (L.J.B.), University of Pennsylvania School of Medicine, Philadelphia; Departments of Neurology and Ophthalmology (E.F.), University of Texas Southwestern Medical Center at Dallas; and Translational Neuroradiology Unit (D.S.R.), National Institute of Neurological Disorders and Stroke, Bethesda, MD
| | - Daniel S Reich
- From the Departments of Neurology (J.O., E.S.S., S.S., A.W., S.D.N., K.Z., D.S.R., P.A.C.), Electrical and Computer Engineering (M.C., J.P.), Computer Science (J.P.), Physical Medicine and Rehabilitation (K.Z.), Biostatistics (M.C., M.D.-W., D.S.R.), and Radiology and Radiological Science (D.S.R.), Johns Hopkins University, Baltimore, MD; Division of Neurology (J.O.), Department of Medicine, St. Michael's Hospital, University of Toronto, Canada; Motion Analysis Laboratory (K.Z.), Kennedy Krieger Institute, Baltimore, MD; Department of Neurology (L.J.B.), University of Pennsylvania School of Medicine, Philadelphia; Departments of Neurology and Ophthalmology (E.F.), University of Texas Southwestern Medical Center at Dallas; and Translational Neuroradiology Unit (D.S.R.), National Institute of Neurological Disorders and Stroke, Bethesda, MD
| | - Peter A Calabresi
- From the Departments of Neurology (J.O., E.S.S., S.S., A.W., S.D.N., K.Z., D.S.R., P.A.C.), Electrical and Computer Engineering (M.C., J.P.), Computer Science (J.P.), Physical Medicine and Rehabilitation (K.Z.), Biostatistics (M.C., M.D.-W., D.S.R.), and Radiology and Radiological Science (D.S.R.), Johns Hopkins University, Baltimore, MD; Division of Neurology (J.O.), Department of Medicine, St. Michael's Hospital, University of Toronto, Canada; Motion Analysis Laboratory (K.Z.), Kennedy Krieger Institute, Baltimore, MD; Department of Neurology (L.J.B.), University of Pennsylvania School of Medicine, Philadelphia; Departments of Neurology and Ophthalmology (E.F.), University of Texas Southwestern Medical Center at Dallas; and Translational Neuroradiology Unit (D.S.R.), National Institute of Neurological Disorders and Stroke, Bethesda, MD.
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Hasan KM, Lincoln JA, Nelson FM, Wolinsky JS, Narayana PA. Lateral ventricular cerebrospinal fluid diffusivity as a potential neuroimaging marker of brain temperature in multiple sclerosis: a hypothesis and implications. Magn Reson Imaging 2014; 33:262-9. [PMID: 25485790 DOI: 10.1016/j.mri.2014.11.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2014] [Revised: 11/24/2014] [Accepted: 11/24/2014] [Indexed: 01/03/2023]
Abstract
In this retrospective study we tested the hypothesis that the net effect of impaired electrical conduction and therefore increased heat dissipation in multiple sclerosis (MS) results in elevated lateral ventricular (LV) cerebrospinal fluid (CSF) diffusivity as a measure of brain temperature estimated in vivo using diffusion tensor imaging (DTI). We used validated DTI-based segmentation methods to obtain normalized LV-CSF volume and its corresponding CSF diffusivity in 108 MS patients and 103 healthy controls in the age range of 21-63 years. The LV CSF diffusivity was ~2% higher in MS compared to controls that correspond to a temperature rise of ~1°C that could not be explained by changes in the CSF viscosity due to altered CSF protein content in MS. The LV diffusivity decreased with age in healthy controls (r=-0.29; p=0.003), but not in MS (r=0.15; p=0.11), possibly related to MS pathology. Age-adjusted LV diffusivity increased with lesion load (r=0.518; p=1×10(-8)). Our data suggest that the total brain lesion load is the primary contributor to the increase in LV CSF diffusivity in MS. These findings suggest that LV diffusivity is a potential in vivo biomarker of the mismatch between heat generation and dissipation in MS. We also discuss limitations and possible confounders.
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Affiliation(s)
- Khader M Hasan
- The University of Texas Health Science Center at Houston, Department of Diagnostic & Interventional Imaging, 6431 Fannin Street, Houston, Texas 77030.
| | - John A Lincoln
- The University of Texas Health Science Center at Houston, Department of Neurology, 6431 Fannin Street, Houston, Texas 77030
| | - Flavia M Nelson
- The University of Texas Health Science Center at Houston, Department of Neurology, 6431 Fannin Street, Houston, Texas 77030
| | - Jerry S Wolinsky
- The University of Texas Health Science Center at Houston, Department of Neurology, 6431 Fannin Street, Houston, Texas 77030
| | - Ponnada A Narayana
- The University of Texas Health Science Center at Houston, Department of Diagnostic & Interventional Imaging, 6431 Fannin Street, Houston, Texas 77030
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Tazoe J, Yamada K, Sakai K, Akazawa K, Mineura K. Brain core temperature of patients with mild traumatic brain injury as assessed by DWI-thermometry. Neuroradiology 2014; 56:809-15. [PMID: 25015424 PMCID: PMC4180914 DOI: 10.1007/s00234-014-1384-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2014] [Accepted: 05/16/2014] [Indexed: 11/06/2022]
Abstract
Introduction The aim of this study was to assess the brain core temperature of patients with mild traumatic brain injury (mTBI) using a noninvasive temperature measurement technique based on the diffusion coefficient of the cerebrospinal fluid. Methods This retrospective study used the data collected from April 2008 to June 2011. The patient group comprised 20 patients with a Glasgow Coma Scale score of 14 or 15 who underwent magnetic resonance imaging within 30 days after head trauma. The normal control group comprised 14 subjects who volunteered for a brain checkup (known in Japan as “brain dock”). We compared lateral ventricular (LV) temperature between patient and control groups. Follow-up studies were performed for four patients. Results LV temperature measurements were successfully performed for both patients and controls. Mean (±standard deviation) measured LV temperature was 36.9 ± 1.5 °C in patients, 38.7 ± 1.8 °C in follow-ups, and 37.9 ± 1.2 °C in controls, showing a significant difference between patients and controls (P = 0.017). However, no significant difference was evident between patients and follow-ups (P = 0.595) or between follow-ups and controls (P = 0.465). Conclusions A reduction in brain core temperature was observed in patients with mTBI, possibly due to a global decrease in metabolism.
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Affiliation(s)
- Jun Tazoe
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kajii-cho, Kawaramachi Hirokoji Agaru, Kamigyo-ku, Kyoto City, Kyoto, 602-8566, Japan,
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