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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] [What about the content of this article? (0)] [Affiliation(s)] [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] [What about the content of this article? (0)] [Affiliation(s)] [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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Oladosu O, Liu WQ, Brown L, Pike BG, Metz LM, Zhang Y. Advanced diffusion MRI and image texture analysis detect widespread brain structural differences between relapsing-remitting and secondary progressive multiple sclerosis. Front Hum Neurosci 2022; 16:944908. [PMID: 36034111 PMCID: PMC9413838 DOI: 10.3389/fnhum.2022.944908] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 07/18/2022] [Indexed: 11/29/2022] Open
Abstract
Introduction Disease development in multiple sclerosis (MS) causes dramatic structural changes, but the exact changing patterns are unclear. Our objective is to investigate the differences in brain structure locally and spatially between relapsing-remitting MS (RRMS) and its advanced form, secondary progressive MS (SPMS), through advanced analysis of diffusion magnetic resonance imaging (MRI) and image texture. Methods A total of 20 patients with RRMS and nine patients with SPMS from two datasets underwent 3T anatomical and diffusion tensor imaging (DTI). The DTI was harmonized, augmented, and then modeled, which generated six voxel- and sub-voxel-scale measures. Texture analysis focused on T2 and FLAIR MRI, which produced two phase-based measures, namely, phase congruency and weighted mean phase. Data analysis was 3-fold, i.e., histogram analysis of whole-brain normal appearing white matter (NAWM); region of interest (ROI) analysis of NAWM and lesions within three critical white matter tracts, namely, corpus callosum, corticospinal tract, and optic radiation; and along-tract statistics. Furthermore, by calculating the z-score of core-rim pathology within lesions based on diffusion measures, we developed a novel method to define chronic active lesions and compared them between cohorts. Results Histogram features from diffusion and all but one texture measure differentiated between RRMS and SPMS. Within-tract ROI analysis detected cohort differences in both NAWM and lesions of the corpus callosum body in three measures of neurite orientation and anisotropy. Along-tract statistics detected cohort differences from multiple measures, particularly lesion extent, which increased significantly in SPMS in posterior corpus callosum and optic radiations. The number of chronic active lesions were also significantly higher (by 5–20% over z-scores 0.5 and 1.0) in SPMS than RRMS based on diffusion anisotropy, neurite content, and diameter. Conclusion Advanced diffusion MRI and texture analysis may be promising approaches for thorough understanding of brain structural changes from RRMS to SPMS, thereby providing new insight into disease development mechanisms in MS.
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Affiliation(s)
- Olayinka Oladosu
- Department of Neuroscience, Faculty of Graduate Studies, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Wei-Qiao Liu
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Lenora Brown
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Bruce G. Pike
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Luanne M. Metz
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Yunyan Zhang
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- *Correspondence: Yunyan Zhang
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Hosseinpour Z, Jonkman L, Oladosu O, Pridham G, Pike GB, Inglese M, Geurts JJ, Zhang Y. Texture analysis in brain T2 and diffusion MRI differentiates histology-verified grey and white matter pathology types in multiple sclerosis. J Neurosci Methods 2022; 379:109671. [PMID: 35820450 DOI: 10.1016/j.jneumeth.2022.109671] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 06/19/2022] [Accepted: 07/07/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND Multiple sclerosis (MS) is a co mplex disease of the central nervous system involving several types of brain pathology that are difficult to characterize using conventional imaging methods. NEW METHOD We originated novel texture analysis and machine learning approaches for classifying MS pathology subtypes as compared with 2 common advanced MRI measures: magnetization transfer ratio (MTR) and fractional anisotropy (FA). Texture analysis used an optimized grey level co-occurrence matrix method with histology-informed 7T T2-weighted magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) from 15 MS and 12 control brain specimens. DTI analysis took an innovative approach that assessed the texture across diffusion directions upsampled from 30 to 90. Tissue types included de- and re-myelinated lesions and normal-appearing areas in both grey and white matter, and diffusely abnormal white matter. Data analyses were stepwise, including: (1) group-wise classification using random forest algorithms based on all or individual imaging parameters; (2) parameter importance ranking; and (3) pairwise analysis using top-ranked features. RESULTS Texture analysis performed better than MTR and FA, with T2 texture performed the best. T2 texture measures ranked the highest in classifying most grey and white matter tissue types, including de- versus re-myelinated lesions and among grey matter lesion subtypes (accuracy=0.86-0.59; kappa=0.60-0.41). Diffusion texture best differentiated normal appearing and control white matter. COMPARISON WITH EXISTING METHODS There is no established method in imaging for differentiating MS pathology subtypes. In combined texture analysis and machine learning studies, there is also no direct evidence comparing conventional with advanced MRI measures for assessing MS pathology. Further, this study is unique in conducting innovative texture analysis with DTI following data-augmentation using robust methods. CONCLUSIONS T2 and diffusion MRI texture analysis integrated with machine learning may be valuable approaches for characterizing MS pathology.
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Affiliation(s)
- Zahra Hosseinpour
- Biomedical Engineering Graduate Program, University of Calgary, Alberta T2N 4N, Canada; Hotchkiss Brain Institute, University of Calgary, Alberta T2N 4N1, Canada
| | - Laura Jonkman
- Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Olayinka Oladosu
- Department of Neuroscience, University of Calgary, Alberta T2N 4N1, Canada; Hotchkiss Brain Institute, University of Calgary, Alberta T2N 4N1, Canada
| | - Glen Pridham
- Department of Clinical Neurosciences, University of Calgary, Alberta T2N 4N1, Canada; Hotchkiss Brain Institute, University of Calgary, Alberta T2N 4N1, Canada
| | - G Bruce Pike
- Department of Clinical Neurosciences, University of Calgary, Alberta T2N 4N1, Canada; Department of Radiology, University of Calgary, Alberta T2N 4N1, Canada; Hotchkiss Brain Institute, University of Calgary, Alberta T2N 4N1, Canada
| | - Matilde Inglese
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA 10029; Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DiNOGMI) and Center of Excellence for Biomedical Research (CEBR), University of Genoa, Genoa, Italy
| | - Jeroen J Geurts
- Department of Anatomy & Neuroscience, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Yunyan Zhang
- Department of Clinical Neurosciences, University of Calgary, Alberta T2N 4N1, Canada; Department of Radiology, University of Calgary, Alberta T2N 4N1, Canada; Hotchkiss Brain Institute, University of Calgary, Alberta T2N 4N1, Canada.
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Oladosu O, Liu WQ, Pike BG, Koch M, Metz LM, Zhang Y. Advanced Analysis of Diffusion Tensor Imaging Along With Machine Learning Provides New Sensitive Measures of Tissue Pathology and Intra-Lesion Activity in Multiple Sclerosis. Front Neurosci 2021; 15:634063. [PMID: 34025338 PMCID: PMC8138061 DOI: 10.3389/fnins.2021.634063] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 04/15/2021] [Indexed: 12/02/2022] Open
Abstract
Tissue pathology in multiple sclerosis (MS) is highly complex, requiring multi-dimensional analysis. In this study, our goal was to test the feasibility of obtaining high angular resolution diffusion imaging (HARDI) metrics through single-shell modeling of diffusion tensor imaging (DTI) data, and investigate how advanced measures from single-shell HARDI and DTI tractography perform relative to classical DTI metrics in assessing MS pathology. We examined 52 relapsing-remitting MS patients who had 3T anatomical brain MRI and DTI. Single-shell HARDI modeling yielded 5 sub-voxel-based metrics, totalling 11 diffusion measures including 4 DTI and 2 tractography metrics. Based on machine learning of 3-dimensional regions of interest, we evaluated the importance of the measures through several tissue classification tasks. These included two within-subject comparisons: lesion versus normal appearing white matter (NAWM); and lesion core versus shell. Further, by stratifying patients as having high (above 75%ile) and low (below 25%ile) number of MS lesions, we also performed 2 classifications between subjects for lesions and NAWM respectively. Results showed that in lesion-NAWM analysis, HARDI orientation distribution function (ODF) energy, DTI fractional anisotropy (FA), and HARDI orientation dispersion index were the top three metrics, which together achieved 65.2% accuracy and 0.71 area under the receiver operating characteristic curve (AUROC). In core-shell analysis, DTI mean diffusivity (MD), radial diffusivity, and FA were the top three metrics, and MD dominated the classification, which achieved 59.3% accuracy and 0.59 AUROC alone. Between patients, FA was the leading feature in lesion comparisons, while ODF energy was the best in NAWM separation. Collectively, single-shell modeling of common diffusion data can provide robust orientation measures of lesion and NAWM pathology, and DTI metrics are most sensitive to intra-lesion abnormality. Combined analysis of both advanced and classical diffusion measures may be critical for improved understanding of MS pathology.
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Affiliation(s)
- Olayinka Oladosu
- Department of Neuroscience, Faculty of Graduate Studies, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Wei-Qiao Liu
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Bruce G Pike
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Marcus Koch
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Luanne M Metz
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Yunyan Zhang
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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Zhang Y, Hong D, McClement D, Oladosu O, Pridham G, Slaney G. Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging. J Neurosci Methods 2021; 353:109098. [PMID: 33582174 DOI: 10.1016/j.jneumeth.2021.109098] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 02/04/2021] [Accepted: 02/07/2021] [Indexed: 12/20/2022]
Abstract
BACKGROUND Deep learning using convolutional neural networks (CNNs) has shown great promise in advancing neuroscience research. However, the ability to interpret the CNNs lags far behind, confounding their clinical translation. NEW METHOD We interrogated 3 heatmap-generating techniques that have increasing generalizability for CNN interpretation: class activation mapping (CAM), gradient (Grad)-CAM, and Grad-CAM++. To investigate the impact of CNNs on heatmap generation, we also examined 6 different models trained to classify brain magnetic resonance imaging into 3 types: relapsing-remitting multiple sclerosis (RRMS), secondary progressive MS (SPMS), and control. Further, we designed novel methods to visualize and quantify the heatmaps to improve interpretability. RESULTS Grad-CAM showed the best heatmap localizing ability, and CNNs with a global average pooling layer and pretrained weights had the best classification performance. Based on the best-performing CNN model, called VGG19, the 95th percentile values of Grad-CAM in SPMS were significantly higher than RRMS, indicating greater heterogeneity. Further, voxel-wise analysis of the thresholded Grad-CAM confirmed the difference identified visually between RRMS and SPMS in discriminative brain regions: occipital versus frontal and occipital, or temporal/parietal. COMPARISON WITH EXISTING METHODS No study has examined the CAM methods together using clinical images. There is also lack of study on the impact of CNN architecture on heatmap outcomes, and of technologies to quantify heatmap patterns in clinical settings. CONCLUSIONS Grad-CAM outperforms CAM and Grad-CAM++. Integrating Grad-CAM, novel heatmap quantification approaches, and robust CNN models may be an effective strategy in identifying the most crucial brain areas underlying disease development in MS.
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Affiliation(s)
- Yunyan Zhang
- Radiology, University of Calgary, Alberta, T2N 4N1, Canada; Department of Clinical Neurosciences, University of Calgary, Alberta, T2N 4N1, Canada; Hotchkiss Brain Institute, University of Calgary, Alberta, T2N 4N1, Canada.
| | - Daphne Hong
- Departments of Schulich School of Engineering, University of Calgary, Alberta, T2N 4N1, Canada
| | - Daniel McClement
- Hotchkiss Brain Institute, University of Calgary, Alberta, T2N 4N1, Canada
| | - Olayinka Oladosu
- Hotchkiss Brain Institute, University of Calgary, Alberta, T2N 4N1, Canada; Department of Neuroscience, University of Calgary, Alberta, T2N 4N1, Canada
| | - Glen Pridham
- Hotchkiss Brain Institute, University of Calgary, Alberta, T2N 4N1, Canada
| | - Garth Slaney
- Departments of Schulich School of Engineering, University of Calgary, Alberta, T2N 4N1, Canada
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Luo T, Oladosu O, Rawji KS, Zhai P, Pridham G, Hossain S, Zhang Y. Characterizing Structural Changes With Devolving Remyelination Following Experimental Demyelination Using High Angular Resolution Diffusion MRI and Texture Analysis. J Magn Reson Imaging 2018; 49:1750-1759. [PMID: 30230112 DOI: 10.1002/jmri.26328] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 08/19/2018] [Accepted: 08/20/2018] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Changes in myelin integrity are associated with the pathophysiology of many neurological diseases, including multiple sclerosis. However, noninvasive measurement of myelin injury and repair remains challenging. Advanced MRI techniques including diffusion tensor imaging (DTI), neurite orientation dispersion and density index (NODDI), and texture analysis have shown promise in quantifying subtle abnormalities in white matter structure. PURPOSE To determine whether and how these advanced imaging methods help understand remyelination changes after demyelination using a mouse model. STUDY TYPE Prospective, longitudinal. ANIMAL MODEL Demyelination was induced in the thoracic spinal cord of 21 mice using the chemical toxin lysolecithin. FIELD STRENGTH/SEQUENCES 9.4T ASSESSMENT: Imaging was done at day 7 (demyelination) and days 14 to 35 (ongoing remyelination) postsurgery, followed by histology. Image analysis focused on both lesions and peri-lesional areas where remyelination began. In histology, we quantified the complexity of tissue alignment using angular entropy, in addition to staining area. STATISTICAL ANALYSIS Two-way analysis of variance was performed for assessing differences between tissue types and across timepoints, followed by post-hoc analysis to correct for multiple comparisons (P < 0.05). RESULTS All diffusion and texture parameters were worse in lesions than the control tissue (P < 0.05) except orientation dispersion index (ODI) and neurite density index (NDI) over late remyelination. Longitudinally, ODI decreased and NDI increased persistently in both lesions and peri-lesion regions (P < 0.05). Fractional anisotropy showed a mild decrease at day 35 after increase, when lesion texture heterogeneity showed a trend to decrease (P > 0.05). Both lesion size and angular entropy decreased over time, and no change in any measure in the control tissue. DATA CONCLUSION Diffusion and MRI texture metrics may provide compensatory information on myelin repair and ODI and NDI could be sensitive measures of evolving remyelination, deserving further validation. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:1750-1759.
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Affiliation(s)
- Tim Luo
- Bachelor of Health Sciences Program, University of Calgary, AB, Canada
| | | | - Khalil S Rawji
- Department of Neuroscience, University of Calgary, AB, Canada
| | - Peng Zhai
- Department of Radiology, University of Calgary, AB, Canada.,Department of Clinical Neurosciences, University of Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, AB, Canada
| | - Glen Pridham
- Department of Radiology, University of Calgary, AB, Canada.,Department of Clinical Neurosciences, University of Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, AB, Canada
| | | | - Yunyan Zhang
- Department of Radiology, University of Calgary, AB, Canada.,Department of Clinical Neurosciences, University of Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, AB, Canada
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