A Review of Multivariate Methods for Multimodal Fusion of Brain Imaging Data

By Jing Sui, T. Adalı, Qingbao Yu, Jiayu Chen, Vince D. Calhoun. Journal of Neuroscience Methods, 2011

411 citations recorded by the source corpus.

The development of various neuroimaging techniques is rapidly improving the measurements of brain function/structure. However, despite improvements in individual modalities, it is becoming increasingly clear that the most effective research approaches will utilize multi-modal fusion, which takes advantage of the fact that each modality provides a limited view of the brain. The goal of multimodal fusion is to capitalize on the strength of each modality in a joint analysis, rather than a separate analysis of each. This is a more complicated endeavor that must be approached more carefully and efficient methods should be developed to draw generalized and valid conclusions from high dimensional data with a limited number of subjects. Numerous research efforts have been reported in the field based on various statistical approaches, e.g. independent component analysis (ICA), canonical correlation analysis (CCA) and partial least squares (PLS). In this review paper, we survey a number of multivariate methods appearing in previous reports, which are performed with or without prior information and may have utility for identifying potential brain illness biomarkers. We also discuss the possible strengths and limitations of each method, and review their applications to brain imaging data.

Subfields: Analytical Chemistry, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/777d30cb997acfb28f162b779dde277b1ace0f90

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Citations in the corpus

A Review of Feature Reduction Techniques in Neuroimaging (2013, methodology): “A review of ICA applications in multi-modal feature reduction tasks is given elsewhere (Sui et al. 2012).”
Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation (2020, neutral): “Hypothesis-driven approaches, also called model-driven approaches, are usually in model-based setting, while data-driven approaches belong to blind source separation methods [227] .”
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects (2015, methodology): “astrophysics [59], biomedics [60], telecommunications [61], audio-vision [23], chemometrics [62], and more.”
Multi-subject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery (2012, positive): “ICA has also been applied successfully to both EEG and MEG [32], [33] and has been extensively used for multimodal data fusion [34], [35].”
A Survey on Deep Learning for Multimodal Data Fusion (2020, background): “In the past, some multimodal data fusion methods were presented to explore the complementary and cross-modality information between modalities (Sui, Adali, Yu, Chen, & Calhoun, 2012).”