Function-structure associations of the brain: evidence from multimodal connectivity and covariance studies - PubMed (original) (raw)

Review

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

Jing Sui et al. Neuroimage. 2014.

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.

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Figures

Figure 1

Figure 1. Functional and anatomical connectivity that differs between healthy controls and schizophrenia

(a) Connections between brain regions that was lower in schizophrenia patients than controls (p<0.01) in measure of anatomical connectivity (DTI) (b)Connections between brain regions that differ between SZ and HC in functional connectivity (FMRI). Red lines represent connections for which the functional connectivity was higher in patients, whereas for green line, connectivity was higher in control subjects (p<0.02). IFG, inferior frontal gyrus; IPL, inferior parietal lobule; MTG, middle temporal gyrus; STG, superior temporal gyrus. Ant., anterior;

Figure 2

Figure 2. An example of functional-structural connectivity study by (Segall et al., 2012)

The structural (sMRI) components (red) and corresponding rs-fMRI components (blue). The spatial correlation between component pairs is indicated adjacent to the functional component number. Both sMRI and fMRI aggregate components were converted to z-scores and thresholded at Z > 2. Structural components are displayed at the slices with peak activation, indicated as (x, y, z) coordinates in MNI space. Functional components are displayed at different coordinates that best represent their activation.

Figure 3

Figure 3. fMRI-sMRI-DTI fusion by mCCA+jICA

Summary of joint and modal specific group discriminative ICs(p<0.05). Joint ICs is significantly group-discriminative in more than 2 modalities, such as IC1, IC2 and IC9. In addition, fMRI_IC4, DTI_IC3 and DTI_IC7 only show significance in a single modality, they are called modal-specific discriminative ICs(pink framed). Hence the modal MCCA+jICA enable people to capture components of interest that are either common or distinct across modalities.

Figure 4

Figure 4. Frequency of published multimodal fusion studies using brain imaging data

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