Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data
Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data
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DOI：
10.1109/jproc.2015.2425807
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发表时间：
2015-09-01
影响因子：
20.6
通讯作者：
Muller, Klaus-Robert
中科院分区：
文献类型：
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作者：
Daehne, Sven;Biessmann, Felix;Muller, Klaus-Robert
多模态数据在工程、通信、机器人、计算机视觉，或者更普遍地说，在工业和科学中无处不在。所有学科都开发了各自的分析工具集，以融合所有测量模式中可用的信息。在本文中，我们回顾了经典的以及最近的机器学习方法（特别是因子模型），用于融合来自功能神经成像技术的信息，如：LFP， EEG， MEG， fNIRS和fMRI。区分了早期和晚期融合场景，并针对各自场景提出了适当的因子模型，以及来自选定的多模态神经影像学研究的示例应用。进一步强调了结果模型参数的可解释性，特别是通过强调因素模型如何与源定位所需的物理模型相关联。我们讨论的方法允许从神经数据中提取信息，这最终有助于1)更好地理解神经科学；2)提高诊断效能；3)发现与给定认知范式最大程度相关的感兴趣的神经信号。虽然我们清楚地研究了多模态功能神经成像挑战，但所讨论的机器学习技术具有广泛的适用性，即在一般数据融合中，因此可能对一般感兴趣的读者提供信息。
Multimodal data are ubiquitous in engineering, communications, robotics, computer vision, or more generally speaking in industry and the sciences. All disciplines have developed their respective sets of analytic tools to fuse the information that is available in all measured modalities. In this paper, we provide a review of classical as well as recent machine learning methods (specifically factor models) for fusing information from functional neuroimaging techniques such as: LFP, EEG, MEG, fNIRS, and fMRI. Early and late fusion scenarios are distinguished, and appropriate factor models for the respective scenarios are presented along with example applications from selected multimodal neuroimaging studies. Further emphasis is given to the interpretability of the resulting model parameters, in particular by highlighting how factor models relate to physical models needed for source localization. The methods we discuss allow for the extraction of information from neural data, which ultimately contributes to 1) better neuroscientific understanding; 2) enhance diagnostic performance; and 3) discover neural signals of interest that correlate maximally with a given cognitive paradigm. While we clearly study the multimodal functional neuroimaging challenge, the discussed machine learning techniques have a wide applicability, i.e., in general data fusion, and may thus be informative to the general interested reader.