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Neuroimage. 2014 Nov 15;102 Pt 1:11-23. doi: 10.1016/j.neuroimage.2013.09.044. Epub 2013 Sep 29.

Function-structure associations of the brain: evidence from multimodal connectivity and covariance studies.

Author information

1
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. Electronic address: kittysj@gmail.com.
2
Experimental Psychology Lab, Carl von Ossietzky University, Oldenburg, Germany.
3
The Mind Research Network, Albuquerque, NM 87106, USA.
4
The Mind Research Network, Albuquerque, NM 87106, USA; Dept. of ECE, University of New Mexico, Albuquerque, NM 87131, USA. Electronic address: vcalhoun@unm.edu.

Abstract

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.

KEYWORDS:

Brain connectivity; Diffusion MRI; EEG; Multimodal fusion; fMRI; sMRI

PMID:
24084066
PMCID:
PMC3969780
DOI:
10.1016/j.neuroimage.2013.09.044
[Indexed for MEDLINE]
Free PMC Article

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