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Churchill NW, Roudaia E, Chen JJ, Sekuler A, Gao F, Masellis M, Lam B, Cheng I, Heyn C, Black SE, MacIntosh BJ, Graham SJ, Schweizer TA. Persistent fatigue in post-acute COVID syndrome is associated with altered T1 MRI texture in subcortical structures: a preliminary investigation. Behav Brain Res 2024; 469:115045. [PMID: 38734034 DOI: 10.1016/j.bbr.2024.115045] [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: 02/20/2024] [Revised: 05/03/2024] [Accepted: 05/06/2024] [Indexed: 05/13/2024]
Abstract
Post-acute COVID syndrome (PACS) is a global health concern and is often associated with debilitating symptoms. Post-COVID fatigue is a particularly frequent and troubling issue, and its underlying mechanisms remain incompletely understood. One potential contributor is micropathological injury of subcortical and brainstem structures, as has been identified in other patient populations. Texture-based analysis (TA) may be used to measure such changes in anatomical MRI data. The present study develops a methodology of voxel-wise TA mapping in subcortical and brainstem regions, which is then applied to T1-weighted MRI data from a cohort of 48 individuals who had PACS (32 with and 16 without ongoing fatigue symptoms) and 15 controls who had cold and flu-like symptoms but tested negative for COVID-19. Both groups were assessed an average of 4-5 months post-infection. There were no significant differences between PACS and control groups, but significant differences were observed within the PACS groups, between those with and without fatigue symptoms. This included reduced texture energy and increased entropy, along with reduced texture correlation, cluster shade and profile in the putamen, pallidum, thalamus and brainstem. These findings provide new insights into the neurophysiological mechanisms that underlie PACS, with altered tissue texture as a potential biomarker of this debilitating condition.
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Affiliation(s)
- Nathan W Churchill
- Brain Health and Wellness Research Program, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada; Keenan Research Centre for Biomedical Science of St. Michael's Hospital, Unity Health Toronto, Canada; Physics Department, Toronto Metropolitan University, Canada.
| | - Eugenie Roudaia
- Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, Ontario, Canada
| | - J Jean Chen
- Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, Ontario, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Allison Sekuler
- Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, Ontario, Canada; Department of Psychology, University of Toronto, Toronto, Ontario, Canada; Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, Ontario, Canada
| | - Fuqiang Gao
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Mario Masellis
- Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, Ontario, Canada; Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada; Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Benjamin Lam
- Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, Ontario, Canada; Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada; Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Ivy Cheng
- Evaluative Clinical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada; Integrated Community Program, Sunnybrook Research Institute, Toronto, Ontario, Canada; Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Chris Heyn
- Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada; Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - Sandra E Black
- Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, Ontario, Canada; Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada; Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Bradley J MacIntosh
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada; Computational Radiology & Artificial Intelligence Unit, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Simon J Graham
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, Ontario, Canada; Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Tom A Schweizer
- Brain Health and Wellness Research Program, St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada; Keenan Research Centre for Biomedical Science of St. Michael's Hospital, Unity Health Toronto, Canada; Faculty of Medicine (Neurosurgery), University of Toronto, Canada
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Zhang Y, Liu WQ, Hosseinpour Z, Pike GB, Cerchiaro G, Greenfield J, Yong VW, Metz LM. Feasibility study to assess lesion repair in relapsing-remitting multiple sclerosis: A randomized controlled pilot clinical trial of domperidone add-on treatment. Mult Scler Relat Disord 2024; 85:105525. [PMID: 38461731 DOI: 10.1016/j.msard.2024.105525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 02/01/2024] [Accepted: 02/26/2024] [Indexed: 03/12/2024]
Abstract
BACKGROUND Identification of therapies to promote repair in multiple sclerosis is challenged by the lack of an accepted trial model and associated outcome measures. The goal of this study was to determine the feasibility of a new trial model that enrolls disease modifying therapy (DMT)-treated relapsing-remitting multiple sclerosis (RRMS) participants who have enhancing lesions on clinically indicated brain MRI, and to explore estimates of lesion repair using MRI. METHODS This was a single site randomized controlled clinical trial. Recruitment took place between November 2015 and January 2019, with final follow-up in February 2019. DMT-treated RRMS participants aged 18-60 years with at least one gadolinium-enhancing lesion on clinically indicated brain MRI were included. Participants were randomized 2:1 to oral domperidone add-on 10-mg three times daily for 16 weeks or no add-on treatment (control). The primary outcomes were feasibility of the model pre-defined as recruitment of 24 participants within 36 months with a 79 % completion rate, and MRI outcomes of lesion repair measured at 16 and 32 weeks using texture analysis, magnetization transfer imaging (MTI), and diffusion tensor imaging (DTI). The impact of domperidone on serum prolactin at 6 and 16 weeks was also evaluated. RESULTS Of 237 RRMS participants screened, 17 (14 women) were randomized: 12 to domperidone add-on and 5 to control. All completed the study. Median (range) age was 38.9 (26.7-55.9) years; EDSS was 1.5 (1.0-3.5); and disease duration was 12.9 (2.9-23.3) years. Both groups showed improvement in MRI texture and diffusion fractional anisotropy (FA) at 32 weeks, and the domperidone group demonstrated additional recovery at 16 weeks in both texture and FA. There was no significant group difference in any MRI outcome. Of the 12 domperidone participants, 7 had ≥4x higher serum prolactin than normal. There were no serious adverse events. CONCLUSION The recruitment target was not met and therefore the trial model was not feasible despite a full completion rate. The imaging techniques performed well, especially MRI texture analysis, suggesting the sample size being sufficient for estimating lesion repair. The main challenge of this trial model may be recruiting gadolinium-enhancing lesions in DMT-treated RRMS participants. Prolactin is safe and may hold promise as a remyelination therapy. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT02493049.
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Affiliation(s)
- Yunyan Zhang
- Department of Radiology, University of Calgary, AB, Canada; Department of Clinical Neurosciences, University of Calgary, AB, Canada; Hotchkiss Brain Insitute, University of Calgary, AB, Canada.
| | - Wei-Qiao Liu
- Department of Clinical Neurosciences, University of Calgary, AB, Canada; Hotchkiss Brain Insitute, University of Calgary, AB, Canada
| | - Zahra Hosseinpour
- Department of Biomedical Engineering, University of Calgary, AB, Canada
| | - G Bruce Pike
- Department of Radiology, University of Calgary, AB, Canada; Department of Clinical Neurosciences, University of Calgary, AB, Canada; Hotchkiss Brain Insitute, University of Calgary, AB, Canada
| | | | - Jamie Greenfield
- Department of Clinical Neurosciences, University of Calgary, AB, Canada
| | - V Wee Yong
- Department of Clinical Neurosciences, University of Calgary, AB, Canada; Hotchkiss Brain Insitute, University of Calgary, AB, Canada
| | - Luanne M Metz
- Department of Clinical Neurosciences, University of Calgary, AB, Canada; Hotchkiss Brain Insitute, University of Calgary, AB, Canada
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Nabizadeh F, Zafari R, Mohamadi M, Maleki T, Fallahi MS, Rafiei N. MRI features and disability in multiple sclerosis: A systematic review and meta-analysis. J Neuroradiol 2024; 51:24-37. [PMID: 38172026 DOI: 10.1016/j.neurad.2023.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 11/28/2023] [Accepted: 11/28/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND In this systematic review and meta-analysis, we aimed to investigate the correlation between disability in patients with Multiple sclerosis (MS) measured by the Expanded Disability Status Scale (EDSS) and brain Magnetic Resonance Imaging (MRI) features to provide reliable results on which characteristics in the MRI can predict disability and prognosis of the disease. METHODS A systematic literature search was performed using three databases including PubMed, Scopus, and Web of Science. The selected peer-reviewed studies must report a correlation between EDSS scores and MRI features. The correlation coefficients of included studies were converted to the Fisher's z scale, and the results were pooled. RESULTS Overall, 105 studies A total of 16,613 patients with MS entered our study. We found no significant correlation between total brain volume and EDSS assessment (95 % CI: -0.37 to 0.08; z-score: -0.15). We examined the potential correlation between the volume of T1 and T2 lesions and the level of disability. A positive significant correlation was found (95 % CI: 0.19 to 0.43; z-score: 0.31), (95 % CI: 0.17 to 0.33; z-score: 0.25). We observed a significant correlation between white matter volume and EDSS score in patients with MS (95 % CI: -0.37 to -0.03; z-score: -0.21). Moreover, there was a significant negative correlation between gray matter volume and disability (95 % CI: -0.025 to -0.07; z-score: -0.16). CONCLUSION In conclusion, this systematic review and meta-analysis revealed that disability in patients with MS is linked to extensive changes in different brain regions, encompassing gray and white matter, as well as T1 and T2 weighted MRI lesions.
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Affiliation(s)
- Fardin Nabizadeh
- School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
| | - Rasa Zafari
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mobin Mohamadi
- School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Tahereh Maleki
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Nazanin Rafiei
- School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
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da Silveira RV, Li LM, Castellano G. Texture-based brain networks for characterization of healthy subjects from MRI. Sci Rep 2023; 13:16421. [PMID: 37775531 PMCID: PMC10541866 DOI: 10.1038/s41598-023-43544-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 09/25/2023] [Indexed: 10/01/2023] Open
Abstract
Brain networks have been widely used to study the relationships between brain regions based on their dynamics using, e.g. fMRI or EEG, and to characterize their real physical connections using DTI. However, few studies have investigated brain networks derived from structural properties; and those have been based on cortical thickness or gray matter volume. The main objective of this work was to investigate the feasibility of obtaining useful information from brain networks derived from structural MRI, using texture features. We also wanted to verify if texture brain networks had any relation with established functional networks. T1-MR images were segmented using AAL and texture parameters from the gray-level co-occurrence matrix were computed for each region, for 760 subjects. Individual texture networks were used to evaluate the structural connections between regions of well-established functional networks; assess possible gender differences; investigate the dependence of texture network measures with age; and single out brain regions with different texture-network characteristics. Although around 70% of texture connections between regions belonging to the default mode, attention, and visual network were greater than the mean connection value, this effect was small (only between 7 and 15% of these connections were larger than one standard deviation), implying that texture-based morphology does not seem to subside function. This differs from cortical thickness-based morphology, which has been shown to relate to functional networks. Seventy-five out of 86 evaluated regions showed significant (ANCOVA, p < 0.05) differences between genders. Forty-four out of 86 regions showed significant (ANCOVA, p < 0.05) dependence with age; however, the R2 indicates that this is not a linear relation. Thalamus and putamen showed a very unique texture-wise structure compared to other analyzed regions. Texture networks were able to provide useful information regarding gender and age-related differences, as well as for singling out specific brain regions. We did not find a morphological texture-based subsidy for the evaluated functional brain networks. In the future, this approach will be extended to neurological patients to investigate the possibility of extracting biomarkers to help monitor disease evolution or treatment effectiveness.
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Affiliation(s)
- Rafael Vinícius da Silveira
- Department of Cosmic Rays and Chronology, Gleb Wataghin Physics Institute, University of Campinas - UNICAMP, R. Sérgio Buarque de Holanda, 777, Cidade Universitária Zeferino Vaz, Campinas, SP, 13083-859, Brazil.
- Brazilian Institute of Neuroscience and Neurotechnology - BRAINN, Campinas, SP, 13083-887, Brazil.
| | - Li Min Li
- Department of Neurology, School of Medical Sciences, University of Campinas - UNICAMP, R. Tessália Vieira de Camargo, 126, Cidade Universitária Zeferino Vaz, Campinas, SP, 13083-887, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology - BRAINN, Campinas, SP, 13083-887, Brazil
| | - Gabriela Castellano
- Department of Cosmic Rays and Chronology, Gleb Wataghin Physics Institute, University of Campinas - UNICAMP, R. Sérgio Buarque de Holanda, 777, Cidade Universitária Zeferino Vaz, Campinas, SP, 13083-859, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology - BRAINN, Campinas, SP, 13083-887, Brazil
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Hosseinpour Z, Oladosu O, Liu WQ, Pike GB, Yong VW, Metz LM, Zhang Y. Distinct characteristics and severity of brain magnetic resonance imaging lesions in women and men with multiple sclerosis assessed using verified texture analysis measures. Front Neurol 2023; 14:1213377. [PMID: 37638198 PMCID: PMC10449451 DOI: 10.3389/fneur.2023.1213377] [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: 04/27/2023] [Accepted: 07/25/2023] [Indexed: 08/29/2023] Open
Abstract
Background and goal In vivo characterization of brain lesion types in multiple sclerosis (MS) has been an ongoing challenge. Based on verified texture analysis measures from clinical magnetic resonance imaging (MRI), this study aimed to develop a method to identify two extremes of brain MS lesions that were approximately severely demyelinated (sDEM) and highly remyelinated (hREM), and compare them in terms of common clinical variables. Method Texture analysis used an optimized gray-level co-occurrence matrix (GLCM) method based on FLAIR MRI from 200 relapsing-remitting MS participants. Two top-performing metrics were calculated: texture contrast and dissimilarity. Lesion identification applied a percentile approach according to texture values calculated: ≤ 25 percentile for hREM and ≥75 percentile for sDEM. Results The sDEM had a greater total normalized volume yet smaller average size, and worse MRI texture than hREM. In lesion distribution mapping, the two lesion types appeared to overlap largely in location and were present the most in the corpus callosum and periventricular regions. Further, in sDEM, the normalized volume was greater and in hREM, the average size was smaller in men than women. There were no other significant results in clinical variable-associated analyses. Conclusion Percentile statistics of competitive MRI texture measures may be a promising method for probing select types of brain MS lesion pathology. Associated findings can provide another useful dimension for improved measurement and monitoring of disease activity in MS. The different characteristics of sDEM and hREM between men and women likely adds new information to the literature, deserving further confirmation.
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Affiliation(s)
- Zahra Hosseinpour
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Olayinka Oladosu
- Department of Neuroscience, Faculty of Graduate Studies, University of Calgary, Calgary, AB, Canada
| | - Wei-qiao Liu
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - G. Bruce Pike
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - V. Wee Yong
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Luanne M. Metz
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Yunyan Zhang
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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Murray C, Oladosu O, Joshi M, Kolind S, Oh J, Zhang Y. Neural network algorithms predict new diffusion MRI data for multi-compartmental analysis of brain microstructure in a clinical setting. Magn Reson Imaging 2023; 102:9-19. [PMID: 37031880 DOI: 10.1016/j.mri.2023.03.023] [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: 10/18/2022] [Revised: 03/14/2023] [Accepted: 03/31/2023] [Indexed: 04/11/2023]
Abstract
High angular resolution diffusion imaging (HARDI) is a promising method for advanced analysis of brain microstructure. However, comprehensive HARDI analysis requires multiple acquisitions of diffusion images (multi-shell HARDI), which is time consuming and often impractical in clinical settings. This study aimed to establish neural network models that can predict new diffusion datasets from clinically feasible brain diffusion MRI for multi-shell HARDI. The development included 2 algorithms: multi-layer perceptron (MLP) and convolutional neural network (CNN). Both followed a voxel-based approach for model training (70%), validation (15%), and testing (15%). The investigations involved 2 multi-shell HARDI datasets: 1) 11 healthy subjects from the Human Connectome Project (HCP); and 2) 10 local subjects with multiple sclerosis (MS). To assess outcomes, we conducted neurite orientation dispersion and density imaging using both predicted and original data and compared their orientation dispersion index (ODI) and neurite density index (NDI) in different brain tissues with 2 measures: peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Results showed that both models achieved robust predictions, which provided competitive ODI and NDI, especially in brain white matter. The CNN outperformed MLP with the HCP data on both PSNR (p < 0.001) and SSIM (p < 0.01). With the MS data, the models performed similarly. Overall, the optimized neural networks can help generate non-acquired brain diffusion MRI, which will make advanced HARDI analysis possible in clinical practice following further validation. Enabling detailed characterization of brain microstructure will allow enhanced understanding of brain function in both health and disease.
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Affiliation(s)
- Cayden Murray
- Department of Neuroscience, University of Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, AB, Canada
| | - Olayinka Oladosu
- Department of Neuroscience, University of Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, AB, Canada
| | - Manish Joshi
- Departments of Radiology, University of Calgary, AB, Canada; Clinical Neurosciences, University of Calgary, AB, Canada
| | - Shannon Kolind
- Department of Medicine (Neurology), University of British Columbia, BC, Canada
| | - Jiwon Oh
- Division of Neurology, Department of Medicine, St. Michael's Hospital, University of Toronto, Canada
| | - Yunyan Zhang
- Hotchkiss Brain Institute, University of Calgary, AB, Canada; Departments of Radiology, University of Calgary, AB, Canada; Clinical Neurosciences, University of Calgary, AB, Canada.
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