Trò Rosella, Roascio Monica, Tortora Domenico, Severino Mariasavina, Rossi Andrea, Garyfallidis Eleftherios, Arnulfo Gabriele, Fato Marco Massimo, Fadnavis Shreyas
Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy.
Neuroradiology Unit, IRCCS Istituto Giannina Gaslini, Genoa, Italy.
Front Neurosci. 2024 Dec 20;18:1480735. doi: 10.3389/fnins.2024.1480735. eCollection 2024.
High 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.
Rather 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).
The 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.
Our 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.
高角分辨率扩散成像（HARDI）模型已成为一种有价值的工具，可用于比标准扩散磁共振成像（dMRI）更详细地研究微观结构。在本研究中，我们探索了多种先进的微观结构扩散模型在研究早产方面的潜力，以识别白质发育改变的非侵入性标志物。
我们并非专注于单一的MRI模态，而是研究了46名在足月等效年龄时在3T扫描仪上进行研究的早产婴儿以及23名足月出生的对照新生儿的HARDI技术组合。此外，我们使用了多种分析方法来研究早产的判别模式，这些方法源自两个看似不同的建模目标，即推理和预测。因此，我们采用了：（i）一种传统的单变量体素级推理方法，即基于束的空间统计（TBSS）方法；（ii）一种单变量预测方法，即支持向量机（SVM）分类；以及（iii）一种多变量预测典型相关分析（CCA）。
TBSS分析显示，在多个HARDI特征方面，早产和足月队列在几个白质区域存在显著差异。对骨架化HARDI测量值进行SVM分类产生了令人满意的准确性，特别是对于关于纤维方向性的高信息量参数。评估两种方法在投票选出最具判别力特征时的重叠程度，显示出良好的一致性，尽管该一致性依赖于参数。最后，CCA精确地识别出了那些在TBSS和SVM之间对应性较低的测量值的联合变化。
我们的结果表明，数据驱动的模态内成像方法对于收集深入且互补的信息至关重要。这种方法学概述的主要贡献在于通过不同的探究重点，全面研究与早产相关的白质变化，以期解决这一问题，既着眼于机制性洞察，又优化预测准确性。
