Effective connectivity during working memory and resting states: A DCM study

Jung, Kyesam,Friston, Karl J.,Pae, Chongwon,Choi, Hanseul H.,Tak, Sungho,Choi, Yoon Kyoung,Park, Bumhee,Park, Chan-A,Cheong, Chaejoon,Park, Hae-Jeong Elsevier 2018 NeuroImage Vol.169 No.-

<P><B>Abstract</B></P> <P>Although the relationship between resting-state <I>functional</I> connectivity and task-related activity has been addressed, the relationship between task and resting-state directed or <I>effective</I> connectivity – and its behavioral concomitants – remains elusive. We evaluated effective connectivity under an N-back working memory task in 24 participants using stochastic dynamic causal modelling (DCM) of 7 T fMRI data. We repeated the analysis using resting-state data, from the same subjects, to model connectivity among the same brain regions engaged by the N-back task. This allowed us to: (i) examine the relationship between intrinsic (task-independent) effective connectivity during resting (A<SUB>rest</SUB>) and task states (A<SUB>task</SUB>), (ii) cluster phenotypes of task-related changes in effective connectivity (B<SUB>task</SUB>) across participants, (iii) identify edges (B<SUB>task</SUB>) showing high inter-individual effective connectivity differences and (iv) associate reaction times with the similarity between B<SUB>task</SUB> and A<SUB>rest</SUB> in these edges. We found a strong correlation between A<SUB>rest</SUB> and A<SUB>task</SUB> over subjects but a marked difference between B<SUB>task</SUB> and A<SUB>rest</SUB>. We further observed a strong clustering of individuals in terms of B<SUB>task</SUB>, which was not apparent in A<SUB>rest</SUB>. The task-related effective connectivity B<SUB>task</SUB> varied highly in the edges from the parietal to the frontal lobes across individuals, so the three groups were clustered mainly by the effective connectivity within these networks. The similarity between B<SUB>task</SUB> and A<SUB>rest</SUB> at the edges from the parietal to the frontal lobes was positively correlated with 2-back reaction times. This result implies that a greater change in context-sensitive coupling – from resting-state connectivity – is associated with faster reaction times. In summary, task-dependent connectivity endows resting-state connectivity with a context sensitivity, which predicts the speed of information processing during the N-back task.</P>

Dynamic effective connectivity in resting state fMRI

Park, Hae-Jeong,Friston, Karl J.,Pae, Chongwon,Park, Bumhee,Razi, Adeel ACADEMIC PRESS 2018 NEUROIMAGE Vol.180 No.2

<▼1><P>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.</P></▼1><▼2><P><B>Highlights</B></P><P>•<P>We describe efficient estimation of dynamics in resting state effective connectivity.</P>•<P>Spectral DCM and PEB are used to model fluctuations in neuronal coupling over time.</P>•<P>Dynamics in responses are explained in terms of its causes (effective connectivity).</P>•<P>Baseline and dynamic components of the default mode connectivity are identified.</P></P></▼2>

Structural and Functional Brain Networks: From Connections to Cognition

Park, Hae-Jeong,Friston, Karl American Association for the Advancement of Scienc 2013 Science Vol.342 No.6158

<P>How rich functionality emerges from the invariant structural architecture of the brain remains a major mystery in neuroscience. Recent applications of network theory and theoretical neuroscience to large-scale brain networks have started to dissolve this mystery. Network analyses suggest that hierarchical modular brain networks are particularly suited to facilitate local (segregated) neuronal operations and the global integration of segregated functions. Although functional networks are constrained by structural connections, context-sensitive integration during cognition tasks necessarily entails a divergence between structural and functional networks. This degenerate (many-to-one) function-structure mapping is crucial for understanding the nature of brain networks. The emergence of dynamic functional networks from static structural connections calls for a formal (computational) approach to neuronal information processing that may resolve this dialectic between structure and function.</P>

A validation of dynamic causal modelling for 7T fMRI

Tak, S.,Noh, J.,Cheong, C.,Zeidman, P.,Razi, A.,Penny, W.D.,Friston, K.J. Elsevier 2018 Journal of neuroscience methods Vol.305 No.-

<P><B>Abstract</B></P> <P><B>Background</B></P> <P>There is growing interest in ultra-high field magnetic resonance imaging (MRI) in cognitive and clinical neuroscience studies. However, the benefits offered by higher field strength have not been evaluated in terms of effective connectivity and dynamic causal modelling (DCM).</P> <P><B>New method</B></P> <P>In this study, we address the validity of DCM for 7T functional MRI data at two levels. First, we evaluate the predictive validity of DCM estimates based upon 3T and 7T in terms of reproducibility. Second, we assess improvements in the efficiency of DCM estimates at 7T, in terms of the entropy of the posterior distribution over model parameters (i.e., information gain).</P> <P><B>Results</B></P> <P>Using empirical data recorded during fist-closing movements with 3T and 7T fMRI, we found a high reproducibility of average connectivity and condition-specific changes in connectivity – as quantified by the intra-class correlation coefficient (ICC = 0.862 and 0.936, respectively). Furthermore, we found that the posterior entropy of 7T parameter estimates was substantially less than that of 3T parameter estimates; suggesting the 7T data are more informative – and furnish more efficient estimates.</P> <P><B>Compared with existing methods</B></P> <P>In the framework of DCM, we treated field-dependent parameters for the BOLD signal model as free parameters, to accommodate fMRI data at 3T and 7T. In addition, we made the resting blood volume fraction a free parameter, because different brain regions can differ in their vascularization.</P> <P><B>Conclusions</B></P> <P>In this paper, we showed DCM enables one to infer changes in effective connectivity from 7T data reliably and efficiently.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We address the validity of Dynamic Causal Modelling (DCM) for 7T fMRI at two levels. </LI> <LI> We evaluate reproducibility and efficiency of DCM estimates across field strengths. </LI> <LI> High reproducibility of effective connectivity between 3T and 7T was observed. </LI> <LI> Posterior entropy of 7T parameter estimates was less than that of 3T estimates. </LI> <LI> DCM enables inference about effective connectivity from 7T reliably and efficiently. </LI> </UL> </P>