TY - JOUR
T1 - Function-structure associations of the brain
T2 - Evidence from multimodal connectivity and covariance studies
AU - Sui, Jing
AU - Huster, Rene
AU - Yu, Qingbao
AU - Segall, Judith M.
AU - Calhoun, Vince D.
N1 - Funding Information:
This work was partially supported by the “100 Talents Plan” of Chinese Academy of Sciences (to Sui J), the National Institutes of Health grants R01EB 006841 and R01EB005846 (to Calhoun VD), the National Sciences Foundation grants 1016619 , USA (to Calhoun VD), the National Key Basic Research and Development Program of China (973) (Grant No. 2011CB707800 ), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB02030300 ), and the German Research Foundation: DFG grant number HU 1729/2-1 (to Huster RJ).
Publisher Copyright:
© 2013 Elsevier Inc.
PY - 2014/11/5
Y1 - 2014/11/5
N2 - 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.
AB - 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.
KW - Brain connectivity
KW - Diffusion MRI
KW - EEG
KW - FMRI
KW - Multimodal fusion
KW - SMRI
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U2 - 10.1016/j.neuroimage.2013.09.044
DO - 10.1016/j.neuroimage.2013.09.044
M3 - Review article
C2 - 24084066
AN - SCOPUS:84908324846
SN - 1053-8119
VL - 102
SP - 11
EP - 23
JO - NeuroImage
JF - NeuroImage
IS - P1
ER -