Predicting human resting-state functional correlation from structural correlation

Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 03/2009; 106(6):2035-40. DOI: 10.1073/pnas.0811168106
Source: PubMed

ABSTRACT In the cerebral cortex, the activity levels of neuronal populations are continuously fluctuating. When neuronal activity, as measured using functional MRI (fMRI), is temporally coherent across 2 populations, those populations are said to be functionally connected. Functional connectivity has previously been shown to correlate with structural (anatomical) connectivity patterns at an aggregate level. In the present study we investigate, with the aid of computational modeling, whether systems-level properties of functional networks--including their spatial statistics and their persistence across time--can be accounted for by properties of the underlying anatomical network. We measured resting state functional connectivity (using fMRI) and structural connectivity (using diffusion spectrum imaging tractography) in the same individuals at high resolution. Structural connectivity then provided the couplings for a model of macroscopic cortical dynamics. In both model and data, we observed (i) that strong functional connections commonly exist between regions with no direct structural connection, rendering the inference of structural connectivity from functional connectivity impractical; (ii) that indirect connections and interregional distance accounted for some of the variance in functional connectivity that was unexplained by direct structural connectivity; and (iii) that resting-state functional connectivity exhibits variability within and across both scanning sessions and model runs. These empirical and modeling results demonstrate that although resting state functional connectivity is variable and is frequently present between regions without direct structural linkage, its strength, persistence, and spatial statistics are nevertheless constrained by the large-scale anatomical structure of the human cerebral cortex.

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Available from: Olaf Sporns, Sep 28, 2015
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    • "At long time scales (i.e., 10-min), test–retest reliability is moderate (r = 0.39 to r = 0.61) (Honey et al., 2009), and is lower for higher-order associative regions that comprise the CCN than for lower-order sensory regions. Honey and colleagues (2009) also noted that reliability is lower than would be expected within a single scan run, even when considering sample size, acquisition noise, or registration artifacts. Variability at short time scales (<1-min) exhibits substantial power in very low frequencies, is lowest between regions with direct structural connections, and is observed in both empirical and simulated resting-state time series. "
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    ABSTRACT: Cognitive control is a process that unfolds over time and regulates thought and action in the service of achieving goals and managing unanticipated challenges. Prevailing accounts attribute the protracted development of this mental process to incremental changes in the functional organization of a cognitive control network. Here, we challenge the notion that cognitive control is linked to a topologically static network, and argue that the capacity to manage unanticipated challenges and its development should instead be characterized in terms of inter-regional functional coupling dynamics. Ongoing changes in temporal coupling have long represented a fundamental pillar in both empirical and theoretical-based accounts of brain function, but have been largely ignored by traditional neuroimaging methods that assume a fixed functional architecture. There is, however, a growing recognition of the importance of temporal coupling dynamics for brain function, and this has led to rapid innovations in analytic methods. Results in this new frontier of neuroimaging suggest that time-varying changes in connectivity strength and direction exist at the large scale and further, that network patterns, like cognitive control process themselves, are transient and dynamic.
    09/2015; DOI:10.1016/j.dcn.2015.08.006
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    • "The most common approach is to perform dMRI based fiber tracking between a set of pre-defined areas of interest in the brain (Fig. 1, top) to gain insights into their structural connectivity. However, results from connectomic analyses have shown that modeling whole brain structural connectivity may lead to more integrated results when studying the brain's structural architecture (Bullmore and Sporns, 2009; Hagmann et al., 2008) or its structure–function relationship (Honey et al., 2009; Horn et al., 2013; Skudlarski et al., 2008). Thus, it became apparent that dMRI-based fiber-tractography experiments needed to be extended from a local point-to-point to a global whole brain approach. "
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    ABSTRACT: The analysis of the structural architecture of the human brain in terms of connectivity between its sub-regions has provided profound insights into its underlying functional organization and has coined the concept of the "connectome", a structural description of the elements forming the human brain and the connections among them. Here, as a proof of concept, we introduce a novel group connectome in standard space based on a large sample of 169 subjects from the Enhanced Nathan Kline Institute - Rockland Sample (eNKI-RS). Whole brain structural connectomes of each subject were estimated with a global tracking approach, and the resulting fiber tracts were warped into standard stereotactic (MNI) space using DARTEL. Employing this group connectome, the results of published tracking studies (i.e., the JHU white matter and Oxford thalamic connectivity atlas) could be largely reproduced directly within MNI space. As a second experiment, a study that examined structural connectivity between regions of a functional network, namely the default mode network, was reproduced. Voxel-wise structural centrality was then calculated and compared to prior literature findings. Furthermore, including additional resting-state fMRI data from the same subjects, structural and functional connectivity matrices between approximately forty thousand nodes of the brain were calculated. This was done to estimate structure-function agreement indices of voxel-wise whole brain connectivity. Taken together, the combination of a novel whole brain fiber tracking approach and an advanced normalization method led to a group connectome that allowed (at least heuristically) to perform fiber tracking directly within MNI space. Hence, it may be used for various purposes such as the analysis of structural connectivity and modeling experiments that aim at studying the structure-function relationship of the human connectome. Moreover, it may even represent a first step towards a standard DTI template of the human brain in stereotactic space. The standardized group connectome might thus be a promising new resource to better understand and further analyze the anatomical architecture of the human brain on a population level.
    NeuroImage 08/2015; DOI:10.1016/j.neuroimage.2015.08.048 · 6.36 Impact Factor
    • "in the lateral temporal network, which includes the perirhinal/entorhinal and anterior lateral temporal cortices (Kahn et al. 2008; Libby et al. 2012; Poppenk and Moscovitch 2011), subserving memory processes including familiarity or the sense of prior occurrence in the absence of recall of other qualitative details (e.g., Yonelinas et al. 2005; Ranganath 2010). Distinct structural connectivity profiles of the antHC and postHC could play an important role in supporting the distinct functional engagement of these regions that underlie different memory processes (Honey et al. 2009). Indeed, the antHC has direct connections with the amygdala (Duvernoy 2005), and to the temporal pole, insula and ventromedial PFC via the uncinate fasciculus (Kier et al. 2004; Catenoix et al. 2011), while the postHC is connected to frontal and parietal neo-cortical regions via a polysynaptic pathway involving the fornix projections to mammillary bodies, anterior nucleus of the thalamus, and anterior cingulum (Duvernoy 2005; for review see Poppenk et al. 2013). "
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    ABSTRACT: Recent research suggests the anterior and posterior hippocampus form part of two distinct functional neural networks. Here we investigate the structural underpinnings of this functional connectivity difference using diffusion-weighted imaging-based parcellation. Using this technique, we substantiated that the hippocampus can be parcellated into distinct anterior and posterior segments. These structurally defined segments did indeed show different patterns of resting state functional connectivity, in that the anterior segment showed greater connectivity with temporal and orbitofrontal cortex, whereas the posterior segment was more highly connected to medial and lateral parietal cortex. Furthermore, we showed that the posterior hippocampal connectivity to memory processing regions, including the dorsolateral prefrontal cortex, parahippocampal, inferior temporal and fusiform gyri and the precuneus, predicted interindividual relational memory performance. These findings provide important support for the integration of structural and functional connectivity in understanding the brain networks underlying episodic memory.
    Brain Structure and Function 07/2015; DOI:10.1007/s00429-015-1084-x · 5.62 Impact Factor
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