Multimodal and Multi-Tissue Measures of Connectivity Revealed by Joint Independent Component Analysis

Abstract

The human brain functions as an efficient system where signals arising from gray matter are transported via white matter tracts to other regions of the brain to facilitate human behavior. However, with a few exceptions, functional and structural neuroimaging data are typically optimized to maximize the quantification of signals arising from a single source. For example, functional magnetic resonance imaging (FMRI) is typically used as an index of gray matter functioning whereas diffusion tensor imaging (DTI) is typically used to determine white matter properties. While it is likely that these signals arising from different tissue sources contain complementary information, the signal processing algorithms necessary for the fusion of neuroimaging data across imaging modalities are still in a nascent stage. In the current paper we present a data-driven method for combining measures of functional connectivity arising from gray matter sources (FMRI resting state data) with different measures of white matter connectivity (DTI). Specifically, a joint independent component analysis (J-ICA) was used to combine these measures of functional connectivity following intensive signal processing and feature extraction within each of the individual modalities. Our results indicate that one of the most predominantly used measures of functional connectivity (activity in the default mode network) is highly dependent on the integrity of white matter connections between the two hemispheres (corpus callosum) and within the cingulate bundles. Importantly, the discovery of this complex relationship of connectivity was entirely facilitated by the signal processing and fusion techniques presented herein and could not have been revealed through separate analyses of both data types as is typically performed in the majority of neuroimaging experiments. We conclude by discussing future applications of this technique to other areas of neuroimaging and examining potential limitations of the methods.

Constrained Source-Based Morphometry Identifies Structural Networks Associated with Default Mode Network

“…Application of a purely data-driven method (joint ICA [jICA]) to combine information from both the functional connectivity and WM connectivity has demonstrated that the functional connectivity in the DMN is highly dependent on the WM integrity of the corpus callosum connecting the two hemispheres. (Franco et al, 2008). The genu of the corpus callosum was reported to play an important role interconnecting different regions of the DMN in a recent study reconstructing the WM pathways between functionally connected brain regions forming resting-state networks (van den Heuvel et al, 2009a).…”

“…An integrated analysis of the sMRI GM data and DTI WM data will enrich our understanding of the brain structural networks. It has been increasingly reported in several studies that combining multimodal imaging provides a richer representation of the brain connectivity (Franco et al, 2008;Greicius et al, 2009;Skudlarski et al, 2008;Teipel et al, 2010). With the aid of the newly introduced concept of the DTI connectivity, a significant overall agreement was achieved by comparing the quantified anatomical connectivity with resting-state functional connectivity (Skudlarski et al, 2008), which provides a powerful validation for the representation of neuronal connectivity by resting-state correlations.…”

“…With the aid of the newly introduced concept of the DTI connectivity, a significant overall agreement was achieved by comparing the quantified anatomical connectivity with resting-state functional connectivity (Skudlarski et al, 2008), which provides a powerful validation for the representation of neuronal connectivity by resting-state correlations. A jICA approach has been used to combine fMRI resting-state data with DTI, which demonstrated that resting-state functional connectivity is highly dependent on the integrity of WM connections both between (corpus callosum) and within (cingulate bundles) the two hemispheres (Franco et al, 2008). The shared variance between the DMN and the FA values has also been discovered using the jICA approach, which would have been missed in the standard analysis of each individual modality separately (Franco et al, 2008).…”

“…A jICA approach has been used to combine fMRI resting-state data with DTI, which demonstrated that resting-state functional connectivity is highly dependent on the integrity of WM connections both between (corpus callosum) and within (cingulate bundles) the two hemispheres (Franco et al, 2008). The shared variance between the DMN and the FA values has also been discovered using the jICA approach, which would have been missed in the standard analysis of each individual modality separately (Franco et al, 2008). …”

A Comparison of Joint ICA and Parallel ICA Multimodal Fusion Methods in Schizophrenia

“…Usually these maps are coupled together by a shared loading parameter. Joint ICA have been used in fusing couples of the modalities (fMRI-sMRI (GM) [16][17][18], fMRI-EEG [19][20][21][22], fMRI-DTI (FA) [23,24], GM-WM [25]). In this paper Joint ICA was applied on two modalities fMRI and EEG of both healthy and Schizophrenia patients to jointly compare the features of each of them together to find some features that can describe the nature of the Schizophrenia.…”