Journal article
Dynamic functional connectivity using state-based dynamic community structure: method and application to opioid analgesia
NeuroImage (Orlando, Fla.), v 108
Mar 2015
PMID: 25534114
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We present a new method, State-based Dynamic Community Structure, that detects time-dependent community structure in networks of brain regions. Most analyses of functional connectivity assume that network behavior is static in time, or differs between task conditions with known timing. Our goal is to determine whether brain network topology remains stationary over time, or if changes in network organization occur at unknown time points. Changes in network organization may be related to shifts in neurological state, such as those associated with learning, drug uptake or experimental conditions. Using a hidden Markov stochastic blockmodel, we define a time-dependent community structure. We apply this approach to data from a functional magnetic resonance imaging experiment examining how contextual factors influence drug-induced analgesia. Results reveal that networks involved in pain, working memory, and emotion show distinct profiles of time-varying connectivity.
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Details
- Title
- Dynamic functional connectivity using state-based dynamic community structure: method and application to opioid analgesia
- Creators
- Lucy F Robinson - Drexel UniversityLauren Y Atlas - New York UniversityTor D Wager - University of Colorado Boulder
- Publication Details
- NeuroImage (Orlando, Fla.), v 108
- Publisher
- Elsevier
- Grant note
- R01DA027794 / NIDA NIH HHS R01MH076136 / NIMH NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000349618600029
- Scopus ID
- 2-s2.0-84921314136
- Other Identifier
- 991019168660004721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Neuroimaging
- Neurosciences
- Radiology, Nuclear Medicine & Medical Imaging