Sui Jing, Huster Rene, Yu Qingbao, Segall Judith M, Calhoun Vince D
The Mind Research Network, Albuquerque, NM 87106, USA; Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Experimental Psychology Lab, Carl von Ossietzky University, Oldenburg, Germany.
Neuroimage. 2014 Nov 15;102 Pt 1:11-23. doi: 10.1016/j.neuroimage.2013.09.044. Epub 2013 Sep 29.
Despite significant advances in multimodal imaging techniques and analysis approaches, unimodal studies are still the predominant way to investigate brain changes or group differences, including structural magnetic resonance imaging (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI) and electroencephalography (EEG). Multimodal brain studies can be used to understand the complex interplay of anatomical, functional and physiological brain alterations or development, and to better comprehend the biological significance of multiple imaging measures. To examine the function-structure associations of the brain in a more comprehensive and integrated manner, we reviewed a number of multimodal studies that combined two or more functional (fMRI and/or EEG) and structural (sMRI and/or DTI) modalities. In this review paper, we specifically focused on multimodal neuroimaging studies on cognition, aging, disease and behavior. We also compared multiple analysis approaches, including univariate and multivariate methods. The possible strengths and limitations of each method are highlighted, which can guide readers when selecting a method based on a given research question. In particular, we believe that multimodal fusion approaches will shed further light on the neuronal mechanisms underlying the major structural and functional pathophysiological features of both the healthy brain (e.g. development) or the diseased brain (e.g. mental illness) and, in the latter case, may provide a more sensitive measure than unimodal imaging for disease classification, e.g. multimodal biomarkers, which potentially can be used to support clinical diagnosis based on neuroimaging techniques.
尽管多模态成像技术和分析方法取得了显著进展，但单模态研究仍然是调查大脑变化或组间差异的主要方式，包括结构磁共振成像（sMRI）、功能磁共振成像（fMRI）、扩散张量成像（DTI）和脑电图（EEG）。多模态脑研究可用于理解大脑解剖、功能和生理改变或发育的复杂相互作用，并更好地理解多种成像测量的生物学意义。为了以更全面和综合的方式研究大脑的功能-结构关联，我们回顾了一些结合了两种或更多种功能（fMRI和/或EEG）和结构（sMRI和/或DTI）模态的多模态研究。在这篇综述论文中，我们特别关注了关于认知、衰老、疾病和行为的多模态神经影像学研究。我们还比较了多种分析方法，包括单变量和多变量方法。突出了每种方法可能的优点和局限性，这可以在读者根据给定的研究问题选择方法时提供指导。特别是，我们认为多模态融合方法将进一步揭示健康大脑（如发育）或患病大脑（如精神疾病）主要结构和功能病理生理特征背后的神经元机制，在后一种情况下，对于疾病分类可能提供比单模态成像更敏感的测量方法，例如多模态生物标志物，其有可能用于支持基于神经影像学技术的临床诊断。
