Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer’s Disease

By Randy L. Buckner, Jorge Sepulcre, Tanveer Talukdar, Fenna M. Krienen, Hesheng Liu, Trey Hedden, Jessica R. Andrews-Hanna, Reisa A. Sperling, Keith A. Johnson. 2009

2,863 citations recorded by the source corpus.

Recent evidence suggests that some brain areas act as hubs interconnecting distinct, functionally specialized systems. These nexuses are intriguing because of their potential role in integration and also because they may augment metabolic cascades relevant to brain disease. To identify regions of high connectivity in the human cerebral cortex, we applied a computationally efficient approach to map the degree of intrinsic functional connectivity across the brain. Analysis of two separate functional magnetic resonance imaging datasets (each n = 24) demonstrated hubs throughout heteromodal areas of association cortex. Prominent hubs were located within posterior cingulate, lateral temporal, lateral parietal, and medial/lateral prefrontal cortices. Network analysis revealed that many, but not all, hubs were located within regions previously implicated as components of the default network. A third dataset (n = 12) demonstrated that the locations of hubs were present across passive and active task states, suggesting that they reflect a stable property of cortical network architecture. To obtain an accurate reference map, data were combined across 127 participants to yield a consensus estimate of cortical hubs. Using this consensus estimate, we explored whether the topography of hubs could explain the pattern of vulnerability in Alzheimer's disease (AD) because some models suggest that regions of high activity and metabolism accelerate pathology. Positron emission tomography amyloid imaging in AD (n = 10) compared with older controls (n = 29) showed high amyloid-β deposition in the locations of cortical hubs consistent with the possibility that hubs, while acting as critical way stations for information processing, may also augment the underlying pathological cascade in AD.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Can structure predict function in the human brain? (2010, positive): “For example, structural hubs in the posterior medial cortex (Hagmann et al., 2008; Gong et al., 2009) correspond to hubs in resting-state fMRI FC networks (Buckner et al., 2009).”
Fast transient networks in spontaneous human brain activity (2014, background): “While there is strong evidence for hub-like behavior of the PCC/precuneus in terms of structural (Sporns et al., 2007; Hagmann et al., 2008) and functional (Buckner et al., 2009; Tomasi and Volkow, 2011) connectivity, evidence in MEG is more varied.”
Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks (2010, background): “Conversely, this could explain why such regions may be the first to be involved in degenerative processes (see infra, [49]).”
The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features (2018, positive): “To evaluate the influence of feature dimension, we also included rsFC strength (rsFCS) as an rsFC-extracted lower dimensional feature, which is simply defined as the sum of all linked rsFC values for each brain region and putatively can capture the global communication ability of brain regions (Beuc”
Exploring the brain network: a review on resting-state fMRI functional connectivity. (2010, background): “Graph analysis of resting-state fMRI data have revealed a number of highly connected hub-regions in the human brain and it has been hypothesized that these specialized hub nodes may be affected in Alzheimer's disease, resulting in decreased functional brain efficiency in these patients (Buckner et a”
The Influence of Head Motion on Intrinsic Functional Connectivity MRI (2011, methodology): “For the present paper we took the z-transformed local degree connectivity map (with values ranging from 0 to 1; Buckner et al., 2009) of 50 young healthy subjects as published previously (Sepulcre et al.”
Network hubs in the human brain. (2013, methodology): “The spatial locations of these functional cortical hubs suggest signiﬁcant overlap with subregions of the default mode network [80].”
Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks (2012, neutral): “…et al., 2009; Whitﬁeld-Gabrieli and Ford 2012), ADHD (Castellanos et al., 2008), autism (Weng et al., 2010), depression (Greicius et al., 2007; Hamilton et al., 2011), bipolar disorder (Chai et al., 2012), and Alzheimer’s disease (Buckner et al., 2009; Greicius et al., 2004; Wang et al., 2007).”
Comparison of Characteristics between Region- and Voxel-Based Network Analyses in Resting-State fMRI Data (2009, positive): “…analyses have consistently identified the posterior cingulate cortex (PCC) and the nearby precuneus (PCun) as highly connected nodes, or hubs (Buckner, et al. 2009; Hagmann, et al. 2008; van den Heuvel, et al. 2008), only voxel-based networks allow further localization of hub nodes within…”
BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics (2013, background): “modularity [14,15], highly connected hubs [8,16] and ‘rich-club’ configurations [17,18].”
GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks (2012, background): “These studies have illustrated an alteration of arrangements in structural and functional brain networks associated with normal aging, multiple sclerosis, Alzheimer’s disease, schizophrenia, depression, and epilepsy [4,5,9,12,14,15,20,22,26].”