The Organization of Local and Distant Functional Connectivity in the Human Brain

Howard Hughes Medical Institute, Cambridge, Massachusetts, United States of America.
PLoS Computational Biology (Impact Factor: 4.62). 06/2010; 6(6):e1000808. DOI: 10.1371/journal.pcbi.1000808
Source: PubMed


Information processing in the human brain arises from both interactions between adjacent areas and from distant projections that form distributed brain systems. Here we map interactions across different spatial scales by estimating the degree of intrinsic functional connectivity for the local (<or=14 mm) neighborhood directly surrounding brain regions as contrasted with distant (>14 mm) interactions. The balance between local and distant functional interactions measured at rest forms a map that separates sensorimotor cortices from heteromodal association areas and further identifies regions that possess both high local and distant cortical-cortical interactions. Map estimates of network measures demonstrate that high local connectivity is most often associated with a high clustering coefficient, long path length, and low physical cost. Task performance changed the balance between local and distant functional coupling in a subset of regions, particularly, increasing local functional coupling in regions engaged by the task. The observed properties suggest that the brain has evolved a balance that optimizes information-processing efficiency across different classes of specialized areas as well as mechanisms to modulate coupling in support of dynamically changing processing demands. We discuss the implications of these observations and applications of the present method for exploring normal and atypical brain function.

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Available from: B.T. Thomas Yeo, Oct 06, 2015
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    • "In stark contrast, unimodal networks including the visual and sensorimotor networks, along with the temporal and brainstem networks are balanced toward segregative processing (Fig. 5). These results are aligned with the notion that association networks participate in long-distance (Sepulcre et al., 2010), flexible (Cole et al., 2013), dynamic (Zalesky et al., 2014), and globally connected (Buckner et al., 2009; Cole et al., 2010; van den Heuvel and Sporns, 2013) information processing in the brain, and suggest that association networks may play a central role in facilitating the integration of information across distributed cortical regions (Yeo et al., 2013). Furthermore, our data provide novel evidence to suggest that subcortical structures such as the basal ganglia and the thalamus also support integration across large-scale networks of the brain (Figs. "
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    ABSTRACT: The study of resting-state networks provides an informative paradigm for understanding the functional architecture of the human brain. Although investigating specialized resting-state networks has led to significant advances in our understanding of brain organization, the manner in which information is integrated across these networks remains unclear. Here, we have developed and validated a data-driven methodology for describing the topography of resting-state network convergence in the human brain. Our results demonstrate the importance of an ensemble of cortical and subcortical regions in supporting the convergence of multiple resting-state networks, including the rostral anterior cingulate, precuneus, posterior cingulate cortex, bilateral posterior parietal cortex, bilateral dorsal prefrontal cortex, along with the caudate head, anterior claustrum and posterior thalamus. In addition, we have demonstrated a significant correlation between voxel-wise network convergence and global brain connectivity, emphasizing the importance of resting-state network convergence in facilitating global brain communication. Finally, we examined the convergence of systems within each of the individual resting-state networks in the brain, revealing the heterogeneity by which individual resting-state networks balance the competing demands of specialized processing against the integration of information. Together, our results suggest that the convergence of resting-state networks represents an important organizational principle underpinning systems-level integration in the human brain.
    Brain Connectivity 05/2015; DOI:10.1089/brain.2015.0348
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    • "Recent studies highlight the significance of the anatomical distance in shaping cortical connectivity patterns [Ercsey-Ravasz et al., 2013; Sepulcre et al., 2010]. Therefore , we further classified the connections into short-range and long-range connections (cut-off 5 75 mm) [Achard et al., 2006; He et al., 2007]. "
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    Human Brain Mapping 04/2015; 36(8). DOI:10.1002/hbm.22817 · 5.97 Impact Factor
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    • "f the pri - mary cerebral cortices are fixed , which is beneficial for dealing with continuingly recurring standard tasks ( Hellwig , 2002 ) . This characteristic was partially confirmed by results using BOLD fMRI that showed that the primary cerebral cortices exhibited high local functional connections based on an intrinsic activity correlation ( Sepulcre et al . , 2010 ) . Therefore , unlike the sub - cortical areas and the association cortex , the primary cerebral cortices may primarily exchange information that is constrained to local regions . Finally , the strength of the connectivity profiles obtained using tractography , as well as the certainty about the orientation measurements , decreases wit"
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    ABSTRACT: Primary cerebral cortices are of great importance for our understanding of the human brain. Although their functions are relatively monomodal, primary cerebral cortices have been suggested to compromise structurally and functionally distinct subregions from many evidences, for example, cytoarchitectionics, myeloarchitectonics and functional brain imaging. In recent years, structural connectivity-based parcellation using diffusion MRI has been extensively used to do parcellation of subcortical areas and association cortex. However, it has rarely been employed to primary cerebral cortices. In connectivity-based parcellation, connectivity profiles are very vital. Different researchers used different information of connectivity profiles, such as global connectivity profiles (the connectivity information from seed to the whole brain) and long connectivity profiles (the connectivity information from seed to other brain regions after excluding the seed). Given that primary cerebral cortices are rich of local hierarchical connections and possess high local functional connectivity profiles, we proposed that local connectivity profiles (the connectivity information in the seed region of interest (ROI)) might be used for parcellating primary cerebral cortices. Global, long and local connectivity profiles were compared on M1, A1, S1 and V1. We found that results using the three were all in good consistency with cytoarchitectonic results. More importantly, results using local connectivity profiles showed less inter-subject variability than results using the other two. This suggests that for parcellation of primary cerebral cortices local connectivity profiles are superior to global and long connectivity profiles. This also infers us that different connectivity profiles should be adopted according to the characteristics of the cerebral cortices.
    Frontiers in Neuroanatomy 04/2015; 9. DOI:10.3389/fnana.2015.00050 · 3.54 Impact Factor
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