Inter-individual differences in resting-state functional connectivity predict task-induced BOLD activity

Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience at the NYU Child Study Center, 215 Lexington Avenue 14th Floor, New York, NY 10016, USA.
NeuroImage (Impact Factor: 6.36). 05/2010; 50(4):1690-701. DOI: 10.1016/j.neuroimage.2010.01.002
Source: PubMed


The resting brain exhibits coherent patterns of spontaneous low-frequency BOLD fluctuations. These so-called resting-state functional connectivity (RSFC) networks are posited to reflect intrinsic representations of functional systems commonly implicated in cognitive function. Yet, the direct relationship between RSFC and the BOLD response induced by task performance remains unclear. Here we examine the relationship between a region's pattern of RSFC across participants and that same region's level of BOLD activation during an Eriksen Flanker task. To achieve this goal we employed a voxel-matched regression method, which assessed whether the magnitude of task-induced activity at each brain voxel could be predicted by measures of RSFC strength for the same voxel, across 26 healthy adults. We examined relationships between task-induced activation and RSFC strength for six different seed regions [Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Van Essen, D.C., Raichle, M.E., 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. U. S. A. 102, 9673-9678.], as well as the "default mode" and "task-positive" resting-state networks in their entirety. Our results indicate that, for a number of brain regions, inter-individual differences in task-induced BOLD activity were predicted by one of two resting-state properties: (1) the region's positive connectivity strength with the task-positive network, or (2) its negative connectivity with the default mode network. Strikingly, most of the regions exhibiting a significant relationship between their RSFC properties and task-induced BOLD activity were located in transition zones between the default mode and task-positive networks. These results suggest that a common mechanism governs many brain regions' neural activity during rest and its neural activity during task performance.

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Available from: Bharat Biswal, Mar 30, 2014
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    • "For instance, is functional connectivity present in the absence of structural connectivity, or is one predictive of the other (Honey et al., 2009)? While many studies have investigated this in the past (Honey et al., 2009; van den Heuvel et al., 2009; Mennes et al., 2010; Zhang et al., 2010; Várkuti et al., 2011; Bowman et al., 2012; Hermundstad et al., 2013; Sporns, 2013; Whittingstall et al., 2013; Goni et al., 2014; Ward et al., 2014; Zhu et al., 2014), surprisingly few studies have investigated how subtle changes in the analysis pipeline may alter structure-function relationships (Bastiani et al., 2012). For example, functional connectivity between highly vascularized areas may be artificially large due to increased signal-to-noise-ratio (SNR) (Vigneau-Roy et al., 2014). "
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    ABSTRACT: In the past decade, the fusion between diffusion magnetic resonance imaging (dMRI) and functional magnetic resonance imaging (fMRI) has opened the way for exploring structure-function relationships in vivo. As it stands, the common approach usually consists of analysing fMRI and dMRI datasets separately or using one to inform the other, such as using fMRI activation sites to reconstruct dMRI streamlines that interconnect them. Moreover, given the large inter-individual variability of the healthy human brain, it is possible that valuable information is lost when a fixed set of dMRI/fMRI analysis parameters such as threshold values are assumed constant across subjects. By allowing one to modify such parameters while viewing the results in real-time, one can begin to fully explore the sensitivity of structure-function relations and how they differ across brain areas and individuals. This is especially important when interpreting how structure-function relationships are altered in patients with neurological disorders, such as the presence of a tumor. In this study, we present and validate a novel approach to achieve this: First, we present an interactive method to generate and visualize tractography-driven resting-state functional connectivity, which reduces the bias introduced by seed size, shape and position. Next, we demonstrate that structural and functional reconstruction parameters explain a significant portion of intra-and inter-subject variability. Finally, we demonstrate how our proposed approach can be used in a neurosurgical planning context. We believe this approach will promote the exploration of structure-function relationships in a subject-specific aspect and will open new opportunities for connectomics.
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    • "Functional connectivity can be analyzed using task-free (or resting-state) and task-based fMRI protocols. Previous research on functional connectivity has shown that task-related co-activation patterns correspond well with brain systems that are measured at rest (Smith et al., 2009; Mennes et al., 2010). However, there is also evidence that task-based acquisitions may capture specific dynamic neural responses in regions with a key role in task processing (Buckner et al., 2009; Mennes et al., 2013). "
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    ABSTRACT: Obesity is associated with structural and functional alterations in brain areas that are often functionally distinct and anatomically distant. This suggests that obesity is associated with differences in functional connectivity of regions distributed across the brain. However, studies addressing whole brain functional connectivity in obesity remain scarce. Here, we compared voxel-wise degree centrality and eigenvector centrality between participants with obesity (n=20) and normal-weight controls (n=21). We analyzed resting state and task-related fMRI data acquired from the same individuals. Relative to normal-weight controls, participants with obesity exhibited reduced degree centrality in the right middle frontal gyrus in the resting-state condition. During the task fMRI condition, obese participants exhibited less degree centrality in the left middle frontal gyrus and the lateral occipital cortex along with reduced eigenvector centrality in the lateral occipital cortex and occipital pole. Our results highlight the central role of the middle frontal gyrus in the pathophysiology of obesity, a structure involved in several brain circuits signaling attention, executive functions and motor functions. Additionally, our analysis suggests the existence of task-dependent reduced centrality in occipital areas; regions with a role in perceptual processes and that are profoundly modulated by attention. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
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    • "The brain at rest consistently yields activity in the default mode network (DMN), which includes areas in the posterior cingulate cortex (PCC), precuneus, medial prefrontal areas, and the medial temporal lobes (Raichle et al., 2001; Greicius et al., 2003). The DMN was initially considered to represent neural baseline activity until further investigations showed that activity within the DMN is functionally related to internally driven mental states, such as self-referential processing, long-term memory, and mentalizing, and that its deactivation plays a functional role during externally directed tasks (Buckner et al., 2008; Kelly et al., 2008; Burianova et al., 2010; Mennes et al., 2010; Sambataro et al., 2010; Anticevic et al., 2012). In addition, an emerging view suggests that cognitive performance in general might rely on the dynamic interaction between the DMN and two other large-scale neural networks: the fronto-parietal task-positive network (FPN), which is associated with attention and cognitive control, and the salience network (SN) in anterior cingulate and fronto-insular cortex, which is involved in the selection of emotionally and motivationally relevant stimuli (Fox et al., 2005; Seeley et al., 2007; Sridharan et al., 2008; Chen et al., 2013; Spreng et al., 2013; Andrews-Hanna et al., 2014). "
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