Multi-view fusion of diffusion MRI microstructural models: a preterm birth study
ObjectiveHigh Angular Resolution Diffusion Imaging (HARDI) models have emerged as a valuable tool for investigating microstructure with a higher degree of detail than standard diffusion Magnetic Resonance Imaging (dMRI). In this study, we explored the potential of multiple advanced microstructural d...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2024.1480735/full |
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| author | Rosella Trò Monica Roascio Domenico Tortora Mariasavina Severino Andrea Rossi Andrea Rossi Eleftherios Garyfallidis Gabriele Arnulfo Gabriele Arnulfo Marco Massimo Fato Shreyas Fadnavis |
| author_facet | Rosella Trò Monica Roascio Domenico Tortora Mariasavina Severino Andrea Rossi Andrea Rossi Eleftherios Garyfallidis Gabriele Arnulfo Gabriele Arnulfo Marco Massimo Fato Shreyas Fadnavis |
| author_sort | Rosella Trò |
| collection | DOAJ |
| description | ObjectiveHigh Angular Resolution Diffusion Imaging (HARDI) models have emerged as a valuable tool for investigating microstructure with a higher degree of detail than standard diffusion Magnetic Resonance Imaging (dMRI). In this study, we explored the potential of multiple advanced microstructural diffusion models for investigating preterm birth in order to identify non-invasive markers of altered white matter development.ApproachRather than focusing on a single MRI modality, we studied on a compound of HARDI techniques in 46 preterm babies studied on a 3T scanner at term-equivalent age and in 23 control neonates born at term. Furthermore, we investigated discriminative patterns of preterm birth using multiple analysis methods, drawn from two only seemingly divergent modeling goals, namely inference and prediction. We thus resorted to (i) a traditional univariate voxel-wise inferential method, as the Tract-Based Spatial Statistics (TBSS) approach; (ii) a univariate predictive approach, as the Support Vector Machine (SVM) classification; and (iii) a multivariate predictive Canonical Correlation Analysis (CCA).Main resultsThe TBSS analysis revealed significant differences between preterm and term cohorts in several white matter areas for multiple HARDI features. SVM classification on skeletonized HARDI measures yielded satisfactory accuracy, particularly for highly informative parameters about fiber directionality. Assessment of the degree of overlap between the two methods in voting for the most discriminating features exhibited a good, though parameter-dependent, rate of agreement. Finally, CCA identified joint changes precisely for those measures exhibiting less correspondence between TBSS and SVM.SignificanceOur results suggest that a data-driven intramodal imaging approach is crucial for gathering deep and complementary information. The main contribution of this methodological outline is to thoroughly investigate prematurity-related white matter changes through different inquiry focuses, with a view to addressing this issue, both aiming toward mechanistic insight and optimizing predictive accuracy. |
| format | Article |
| id | doaj-art-55c25fadeff54b18a53f17286b9ecc50 |
| institution | Kabale University |
| issn | 1662-453X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj-art-55c25fadeff54b18a53f17286b9ecc502024-12-20T06:29:00ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2024-12-011810.3389/fnins.2024.14807351480735Multi-view fusion of diffusion MRI microstructural models: a preterm birth studyRosella Trò0Monica Roascio1Domenico Tortora2Mariasavina Severino3Andrea Rossi4Andrea Rossi5Eleftherios Garyfallidis6Gabriele Arnulfo7Gabriele Arnulfo8Marco Massimo Fato9Shreyas Fadnavis10Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, ItalyDepartment of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, ItalyNeuroradiology Unit, IRCCS Istituto Giannina Gaslini, Genoa, ItalyNeuroradiology Unit, IRCCS Istituto Giannina Gaslini, Genoa, ItalyNeuroradiology Unit, IRCCS Istituto Giannina Gaslini, Genoa, ItalyDepartment of Health Sciences (DISSAL), University of Genoa, Genoa, ItalyIntelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN, United StatesDepartment of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, ItalyNeuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, FinlandDepartment of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, ItalyMassachusetts General Hospital, Harvard Medical School, Boston, MA, United StatesObjectiveHigh Angular Resolution Diffusion Imaging (HARDI) models have emerged as a valuable tool for investigating microstructure with a higher degree of detail than standard diffusion Magnetic Resonance Imaging (dMRI). In this study, we explored the potential of multiple advanced microstructural diffusion models for investigating preterm birth in order to identify non-invasive markers of altered white matter development.ApproachRather than focusing on a single MRI modality, we studied on a compound of HARDI techniques in 46 preterm babies studied on a 3T scanner at term-equivalent age and in 23 control neonates born at term. Furthermore, we investigated discriminative patterns of preterm birth using multiple analysis methods, drawn from two only seemingly divergent modeling goals, namely inference and prediction. We thus resorted to (i) a traditional univariate voxel-wise inferential method, as the Tract-Based Spatial Statistics (TBSS) approach; (ii) a univariate predictive approach, as the Support Vector Machine (SVM) classification; and (iii) a multivariate predictive Canonical Correlation Analysis (CCA).Main resultsThe TBSS analysis revealed significant differences between preterm and term cohorts in several white matter areas for multiple HARDI features. SVM classification on skeletonized HARDI measures yielded satisfactory accuracy, particularly for highly informative parameters about fiber directionality. Assessment of the degree of overlap between the two methods in voting for the most discriminating features exhibited a good, though parameter-dependent, rate of agreement. Finally, CCA identified joint changes precisely for those measures exhibiting less correspondence between TBSS and SVM.SignificanceOur results suggest that a data-driven intramodal imaging approach is crucial for gathering deep and complementary information. The main contribution of this methodological outline is to thoroughly investigate prematurity-related white matter changes through different inquiry focuses, with a view to addressing this issue, both aiming toward mechanistic insight and optimizing predictive accuracy.https://www.frontiersin.org/articles/10.3389/fnins.2024.1480735/fulldiffusion Magnetic Resonance Imagingpreterm birthintramodal imaging approachinferenceprediction |
| spellingShingle | Rosella Trò Monica Roascio Domenico Tortora Mariasavina Severino Andrea Rossi Andrea Rossi Eleftherios Garyfallidis Gabriele Arnulfo Gabriele Arnulfo Marco Massimo Fato Shreyas Fadnavis Multi-view fusion of diffusion MRI microstructural models: a preterm birth study Frontiers in Neuroscience diffusion Magnetic Resonance Imaging preterm birth intramodal imaging approach inference prediction |
| title | Multi-view fusion of diffusion MRI microstructural models: a preterm birth study |
| title_full | Multi-view fusion of diffusion MRI microstructural models: a preterm birth study |
| title_fullStr | Multi-view fusion of diffusion MRI microstructural models: a preterm birth study |
| title_full_unstemmed | Multi-view fusion of diffusion MRI microstructural models: a preterm birth study |
| title_short | Multi-view fusion of diffusion MRI microstructural models: a preterm birth study |
| title_sort | multi view fusion of diffusion mri microstructural models a preterm birth study |
| topic | diffusion Magnetic Resonance Imaging preterm birth intramodal imaging approach inference prediction |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2024.1480735/full |
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