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See comment in PubMed Commons belowDynamic network participation of functional connectivity hubs assessed by resting-state fMRI.
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- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany.
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- Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany.
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- Department of High-field Magnetic Resonance, Max Planck Institute for Biological Cybernetics Tübingen, Germany.
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- Department of Psychology, The University of York Hesslington, UK.
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- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany ; Department of Neurology, Biomagnetic Center, University Clinics Jena Jena, Germany.
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- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany ; Berlin School of Mind and Brain, Mind and Brain Institute Berlin, Germany ; Department of Cognitive Neurology, University Hospital Leipzig Leipzig, Germany ; Center for Stroke Research, Charité - Universitätsmedizin Berlin, Germany.
Abstract
Network studies of large-scale brain connectivity have demonstrated that highly connected areas, or "hubs," are a key feature of human functional and structural brain organization. We use resting-state functional MRI data and connectivity clustering to identify multi-network hubs and show that while hubs can belong to multiple networks their degree of integration into these different networks varies dynamically over time. The extent of the network variation was related to the connectedness of the hub. In addition, we found that these network dynamics were inversely related to positive self-generated thoughts reported by individuals and were further decreased with older age. Moreover, the left caudate varied its degree of participation between a default mode subnetwork and a limbic network. This variation was predictive of individual differences in the reports of past-related thoughts. These results support an association between ongoing thought processes and network dynamics and offer a new approach to investigate the brain dynamics underlying mental experience.
KEYWORDS:
brain networks; graphs; mind wandering; self-generated thoughts
Figure 1
Connectivity clustering approach. (A) Example for two connections with low similarity. The common neighborhood of vertices i and j is only k therefore the similarity is S = 1/3. (B) An example for two connections with high similarity. As vertices i and j have the same neighborhood the similarity is 1. (C) Dendrogram (left) based on calculated similarities together with a plot of estimated partition densities (right). Red dotted line indicates the cutoff of the dendrogram estimated by maximizing partition density. (D) Zoomed view of the top of the dendrogram from (C) together with network numbers from Figure .
Front Hum Neurosci. 2014;8:195.
Figure 2
Illustration of research ideas. (A) One region as part of two connectivity clusters (red and green). (B) Green crosses mark connectivity change of connections within the green cluster, the red crosses of connections within the red cluster. The y-axis gives the amount of change of the respective connections. var1, var2 are the variances within the red and green cluster, while var3 gives the variance over all connections. (C) Four possible scenarios of changing connectivity: State A: reduction of both clusters, State B: reduction of red cluster and increase of green cluster, State C: increase of both clusters, and State D: reduction of green cluster and increase of red cluster.
Front Hum Neurosci. 2014;8:195.
Figure 3
Connectivity networks. All 33 connectivity networks found in the hierarchical cluster analysis of time and group averaged connectivity. Networks can also be inspected interactively and in three dimensions online (http://openscience.cbs.mpg.de/schaefer).
Front Hum Neurosci. 2014;8:195.
Figure 4
Whole brain regional overview. (A) Brain areas colored by their degree of connectivity. Areas with more connections are displayed in red, whereas regions with fewer connections are in green. (B) Brain areas colored by the number of networks they are part of. Regions participating in a higher number of networks are displayed in red. Regions which are part of fewer networks are depicted in green. (C) Brain areas colored by their variation of network participation. Areas with higher variation are colored in red, whereas regions with lower variation are shown in yellow and green. (D) Relation between number of connections and number of networks each brain region is part of. In the further analysis we only included regions which are part of at least two networks and for which the second largest belonging network consisted of at least two connections. These regions are colored in red. (E) Relation between number of connections and variation of network participation measured across brain areas included in analysis. (F) Relation between number of networks and variation of network participation measured across brain areas included in analysis.
Front Hum Neurosci. 2014;8:195.
Figure 5
Representative region for variation of network participation. (A) Anterior cingulate cortex (ACC) participating in two networks (networks 24 and 27). (B) Example plot of ACC participation in the two networks in a 50 s interval. Timepoint 2 and 7 are good examples of a variation in network participation (State B or State D, Figure ). (C) Permutation test shows that variation behavior measured by mean squared error is more pronounced in the found clustering than in a random clustering.
Front Hum Neurosci. 2014;8:195.
Figure 6
Whole Brain Variation of Participation. (A) Partial correlation between average variation of participation and age corrected for micro-movements. (B) Correlation between average variation and average micro-motion. (C) Partial correlation between average variation of participation and positive self-generated thoughts (SGT) corrected for micro-movements, age, gender, and the respective other four factors of SGT. (D) Correlation between age and positive self-generated thoughts (SGT) corrected for micro-movements. (E) Partial correlation between average variation of participation and age corrected for positive self-generated thoughts (SGT), micro-movements.
Front Hum Neurosci. 2014;8:195.
Figure 7
Left caudate region and self-generated thoughts about past events. (A) Left caudate region is part of two connectivity networks, a sub cortical network (network 10) and a subnetwork of the default mode network (network 31). Red arrow indicates the position of the left caudate region. (B) Increased correlation between variation of network participation of the left caudate region and self-generated thoughts about past events.
Front Hum Neurosci. 2014;8:195.
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