제1저자 겸 교신저자 박해정(핵의학교실)
Neuroimage. 2017 Nov 17. pii: S1053-8119(17)30960-6. doi: 10.1016/j.neuroimage.2017.11.033. [Epub ahead of print]
Dynamic effective connectivity in resting state fMRI.
Park HJ1, Friston K2, Pae C3, Park B4, Razi A5.
1Department of Nuclear Medicine, Radiology and Psychiatry, Yonsei University College of Medicine, Seoul, Republic of Korea; Department of Cognitive Science, Center for Systems and Translational Brain Sciences, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, Republic of Korea; BK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea. Electronic address: firstname.lastname@example.orgThe Wellcome Trust Centre for Neuroimaging, University College London, London, UK.3BK21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea.4Department of Statistics, Hankuk University of Foreign Studies, Yong-In, Republic of Korea.5The Wellcome Trust Centre for Neuroimaging, University College London, London, UK; Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan; Monash Biomedical Imaging and Monash Institute of Cognitive & Clinical Neurosciences, Monash University, Clayton, Australia.
Context-sensitive and activity-dependent fluctuations in connectivity underlie functional integration in the brain and have been studied widely in terms of synaptic plasticity, learning and condition-specific (e.g., attentional) modulations of synaptic efficacy. This dynamic aspect of brain connectivity has recently attracted a lot of attention in the resting state fMRI community. To explain dynamic functional connectivity in terms of directed effective connectivity among brain regions, we introduce a novel method to identify dynamic effective connectivity using spectral dynamic causal modelling (spDCM). We used parametric empirical Bayes (PEB) to model fluctuations in directed coupling over consecutive windows of resting state fMRI time series. Hierarchical PEB can model random effects on connectivity parameters at the second (between-window) level given connectivity estimates from the first (within-window) level. In this work, we used a discrete cosine transform basis set or eigenvariates (i.e., expression of principal components) to model fluctuations in effective connectivity over windows. We evaluated the ensuing dynamic effective connectivity in terms of the consistency of baseline connectivity within default mode network (DMN), using the resting state fMRI from Human Connectome Project (HCP). To model group-level baseline and dynamic effective connectivity for DMN, we extended the PEB approach by conducting a multilevel PEB analysis of between-session and between-subject group effects. Model comparison clearly spoke to dynamic fluctuations in effective connectivity - and the dynamic functional connectivity these changes explain. Furthermore, baseline effective connectivity was consistent across independent sessions - and notably more consistent than estimates based upon conventional models. This work illustrates the advantage of hierarchical modelling with spDCM, in characterizing the dynamics of effective connectivity.