Author: Schaefer A1, Margulies DS2, Lohmann G3, Gorgolewski KJ2, Smallwood J4, Kiebel SJ5, Villringer A6.
Affiliation: 1Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany. 2Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany. 3Department of High-field Magnetic Resonance, Max Planck Institute for Biological Cybernetics Tübingen, Germany. 4Department of Psychology, The University of York Hesslington, UK. 5Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences Leipzig, Germany ; Department of Neurology, Biomagnetic Center, University Clinics Jena Jena, Germany. 6Department 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.
Conference/Journal: Front Hum Neurosci.
Date published: 2014 May 6
Other:
Volume ID: 8 , Pages: 195 , Special Notes: doi: 10.3389/fnhum.2014.00195. , Word Count: 164
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
PMID: 24860458