Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3928-3932. doi: 10.1109/EMBC46164.2021.9631027.
In this study, we introduce a method to perform independent vector analysis (IVA) fusion to estimate linked independent sources and apply to a large multimodal dataset of over 3000 subjects in the UK Biobank study, including structural (gray matter), diffusion (fractional anisotropy), and functional (amplitude of low frequency fluctuations) magnetic resonance imaging data from each subject. The approach reveals a number of linked sources showing significant and meaningful covariation with subject phenotypes. One such mode shows significant linear association with age across all three modalities. Robust age-associated reductions in gray matter density were observed in thalamus, caudate, and insular regions, as well as visual and cingulate regions, with covarying reductions of fractional anisotropy in the periventricular region, in addition to reductions in amplitude of low frequency fluctuations in visual and parietal regions. Another mode identified multimodal patterns that differentiated subjects in their time-to-recall during a prospective memory test. In sum, the proposed IVA-based approach provides a flexible, interpretable, and powerful approach for revealing links between multimodal neuroimaging data.
在这项研究中，我们引入了一种独立向量分析（IVA）融合方法来估计关联独立源，并将其应用于英国生物库研究中超过 3000 名受试者的大型多模态数据集，包括每个受试者的结构（灰质）、扩散（各向异性分数）和功能（低频波动幅度）磁共振成像数据。该方法揭示了一些关联源，这些源与受试者表型存在显著且有意义的协变。其中一种模式在所有三种模态中均与年龄呈显著线性关联。在丘脑、尾状核和岛叶区域以及视觉和扣带区域观察到与年龄相关的灰质密度显著减少，此外在脑室周围区域的各向异性分数也随之减少，以及在视觉和顶叶区域的低频波动幅度减少。另一种模式则确定了可以区分前瞻性记忆测试中受试者回忆时间的多模态模式。总之，所提出的基于 IVA 的方法为揭示多模态神经影像学数据之间的联系提供了一种灵活、可解释和强大的方法。
