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


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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    • "However, recent evidence increasingly shows that inter-regional signal associations are dynamic over time, and are highly modulated by attention, medications, and cognitive state (Chang and Glover, 2010). In addition, (Honey et al., 2009) have found that resting state functional connectivity exhibits a large degree of variability both within and across scanning sessions. (Ma et al., 2014) have also demonstrated that functional connectivity fluctuates over time within scans, furthermore finding that first-order temporal dynamics may approximate these dynamics . "
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    ABSTRACT: Brain graphs provide a useful way to computationally model the network structure of the connectome, and this has led to increasing interest in the use of graph theory to quantitate and investigate the topological characteristics of the healthy brain and brain disorders on the network level. The majority of graph theory investigations of functional connectivity have relied on the assumption of temporal stationarity. However, recent evidence increasingly suggests that functional connectivity fluctuates over the length of the scan. In this study, we investigate the stationarity of brain network topology using a Bayesian hidden Markov model (HMM) approach that estimates the dynamic structure of graph theoretical measures of whole-brain functional connectivity. In addition to extracting the stationary distribution and transition probabilities of commonly employed graph theory measures, we propose two estimators of temporal stationarity: the S-index and N-index. These indexes can be used to quantify different aspects of the temporal stationarity of graph theory measures. We apply the method and proposed estimators to resting-state functional MRI data from healthy controls and patients with temporal lobe epilepsy. Our analysis shows that several graph theory measures, including small-world index, global integration measures, and betweenness centrality, may exhibit greater stationarity over time and therefore be more robust. Additionally, we demonstrate that accounting for subject-level differences in the level of temporal stationarity of network topology may increase discriminatory power in discriminating between disease states. Our results confirm and extend findings from other studies regarding the dynamic nature of functional connectivity, and suggest that using statistical models which explicitly account for the dynamic nature of functional connectivity in graph theory analyses may improve the sensitivity of investigations and consistency across investigations.
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    • "These refinements, which are region-specific and include changes in volume and myelination status, contribute to defining the brain's anatomical network. A substantial amount of variation in the magnitude of functional connectivity can be explained by the pattern in which anatomical connections, reflecting white matter fascicles, are configured (Honey et al., 2009; Hermundstad et al., 2013; Goñi et al., 2014; Miši´c et al., 2015), and there is evidence that the strength of this relationship varies with age (Hagmann et al., 2008). Thus, by influencing functional connectivity patterns, it is possible that age-related changes in anatomical connectivity ultimately underpin the observed variation in functional communities. "
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    DESCRIPTION: The human brain is a complex network of interconnected brain regions organized into functional modules with distinct roles in cognition and behavior. An important question concerns the persistence and stability of these modules over the human lifespan. Here we use graph-theoretic analysis to algorithmically uncover the brain's intrinsic modular organization across multiple spatial scales ranging from small communities comprised of only a few brain regions to large communities made up of many regions. We find that at coarse scales modules become progressively more segregated, while at finer scales segregation decreases. Module composition also exhibits scale-specific and age-dependent changes. At coarse scales, the module assignments of regions normally associated with control, default mode, attention, and visual networks are highly flexible. At fine scales the most flexible regions are associated with the default mode network. Finally, we show that, with age, some regions in the default mode network, specifically retrosplenial cortex, maintain a greater proportion of functional connections to their own module, while regions associated with somatomotor and saliency/ventral attention networks distribute their links more evenly across modules.
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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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