Sui Jing, Zhi Dongmei, Calhoun Vince D
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.
Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, GA 30303, United States.
Psychoradiology. 2023 Nov 22;3:kkad026. doi: 10.1093/psyrad/kkad026. eCollection 2023.
In the era of big data, where vast amounts of information are being generated and collected at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal fusion methods. These methods aim to integrate diverse neuroimaging perspectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders. However, analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data. This is where data-driven multi-modal fusion techniques come into play. By combining information from multiple modalities in a synergistic manner, these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed. In this paper, we present an extensive overview of data-driven multimodal fusion approaches with or without prior information, with specific emphasis on canonical correlation analysis and independent component analysis. The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics, environment, cognition, and treatment outcomes across various brain disorders. After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications, we further discuss the emerging neuroimaging analyzing trends in big data, such as N-way multimodal fusion, deep learning approaches, and clinical translation. Overall, multimodal fusion emerges as an imperative approach providing valuable insights into the underlying neural basis of mental disorders, which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.
在大数据时代，海量信息以前所未有的速度被生成和收集，对创新的数据驱动多模态融合方法有着迫切需求。这些方法旨在整合不同的神经影像学视角，以提取有意义的见解，并更全面地理解复杂的精神疾病。然而，单独分析每种模态可能只会揭示部分见解，或者错过不同类型数据之间的重要关联。数据驱动的多模态融合技术正是在这种情况下发挥作用。通过以协同方式组合来自多种模态的信息，这些方法使我们能够发现否则将仍然未被注意到的隐藏模式和关系。在本文中，我们对有无先验信息的数据驱动多模态融合方法进行了广泛概述，特别强调了典型相关分析和独立成分分析。此类融合方法的应用范围广泛，使我们能够纳入多种因素，如遗传学、环境、认知和各种脑部疾病的治疗结果。在总结了多样的神经精神磁共振成像融合应用后，我们进一步讨论了大数据中新兴的神经影像学分析趋势，如N路多模态融合、深度学习方法和临床转化。总体而言，多模态融合成为一种至关重要的方法，为精神障碍的潜在神经基础提供有价值的见解，它可以揭示可能有益于靶向治疗和个性化医疗干预的细微异常或潜在生物标志物。
