IEEE Trans Med Imaging. 2021 Mar;40(3):951-962. doi: 10.1109/TMI.2020.3042786. Epub 2021 Mar 2.
With the development of neuroimaging techniques, a growing amount of multi-modal brain imaging data are collected, facilitating comprehensive study of the brain. In this paper, we jointly analyzed functional magnetic resonance imaging (fMRI) collected under different paradigms in order to understand cognitive behaviors of an individual. To this end, we proposed a novel multi-view learning algorithm called structure-enforced collaborative regression (SCoRe) to extract co-expressed discriminative brain regions under the guidance of anatomical structure of the brain. An advantage of SCoRe over its predecessor collaborative regression (CoRe) lies in its incorporation of group structures in the brain imaging data, which makes the model biologically more meaningful. Results from real data analysis has confirmed that by incorporating prior knowledge of brain structure, SCoRe can deliver better prediction performance and is less sensitive to hyper-parameters than CoRe. After validation with simulation experiments, we applied SCoRe to fMRI data collected from the Philadelphia Neurodevelopmental Cohort and adopted the scores from the wide range achievement test (WRAT) to evaluate an individual's cognitive skills. We located 14 relevant brain regions that can efficiently predict WRAT scores and these brain regions were further confirmed by other independent studies.
随着神经影像学技术的发展，越来越多的多模态脑成像数据被收集，这有助于对大脑进行全面研究。在本文中，我们联合分析了在不同范式下采集的功能磁共振成像（fMRI）数据，以便了解个体的认知行为。为此，我们提出了一种名为结构增强协同回归（SCoRe）的新型多视图学习算法，该算法在大脑解剖结构的指导下提取共同表达的有判别力的脑区。SCoRe 相较于其前身协同回归（CoRe）的优势在于，它将大脑成像数据中的组结构纳入其中，这使得模型在生物学上更有意义。来自真实数据分析的结果证实，通过结合大脑结构的先验知识，SCoRe 可以提供更好的预测性能，并且比 CoRe 对超参数的敏感性更低。在通过模拟实验验证后，我们将 SCoRe 应用于费城神经发育队列的 fMRI 数据，并采用广泛成就测验（WRAT）的分数来评估个体的认知技能。我们定位到了 14 个与 WRAT 分数相关的有效预测脑区，这些脑区也得到了其他独立研究的进一步验证。
