Qasim Bukhari^{1}, Aileen Schroeter^{1}, and Markus Rudin^{1,2}

^{1}Institute of Biomedical Engineering, ETH and University of Zurich, Zurich, Switzerland, ^{2}Institute of Pharmacology and Taxicology, University of Zurich, Zurich, Switzerland

### Synopsis

Dynamic functional
connectivity (dFC) analysis has gained considerable interest in the past years.
The goal of this technique is to estimate the temporal changes of resting state
functional connectivity networks and get insights into brain pathologies by analyzing
these dynamic patterns. dFC uses functional connectivity correlations as a
means to understand the brain functional principle. Dynamic Causal Modeling
(DCM) has been widely used in the neuroimaging community to estimate the
effective connectivity by fitting a neuronal model to the observed fMRI data.
Stochastic DCM together with Bayesian Model Comparison applied to resting state
fMRI data results in the selection of the most plausible neuronal model explaining
the observed data. The input to these model estimation methods are the full
length time series extracted from the regions of interest of mouse resting
state fMRI data, neglecting the temporal evolution of the model parameters. In
this work we combine the two approaches by estimating the temporal changes in
the effective connectivity as derived from DCM.

### Purpose

DCM estimates effective
connectivity[1; 2; 3] by fitting the neuronal
model convolved with the hemodynamic response function to the observed fMRI
data. Similar to dynamic functional connectivity [4; 5], the temporal variation
of effective connectivity i.e. dynamic effective connectivity can be estimated.
Knowing the temporal changes of the neuronal model
using DCM might allow us inferring the changes in factors underlying functional
connectivity, which we observe by correlating the time series extracted from
different regions in the brain [6]. Effective
connectivity estimates the interaction between the regions at the neuronal
level. However traditionally, DCMs have only been estimated for static case so
far. By estimating different DCMs at different time points of the time series,
we could potentially model the temporal evolution of effective connectivity. In
this study we applied a fully connected DCM model to the resting state fMRI
data of mice. The time series data were then splitted in two halves to evaluate
the effective connectivity for two time intervals.### Methods

Data acquisition: Resting-state
fMRI data sets of anesthetized mice were collected on a Bruker BioSpec 94/30 system
operating at 9.4T using a gradient-echo echo-planar imaging (GE-EPI) sequence
with 1s temporal resolution. Isoflurane dose of 1.1% was used in an air/oxygen
mixture (4:1). Animals were mechanically ventilated throughout the experiment.
Data analysis: After
preprocessing and realignment, concat-ICA was applied using the MELODIC toolbox
of FSL. These results were used for Dual Regression and Network Modeling [7] of the brain networks
using FSLNets. Based on the networks that were found significant, we identified
4 ICA components as shown in Figure 1. These ICs were then fed in to the
dynamic causal modeling (DCM) framework in the SPM toolbox. In order to
estimate the temporal evolution of effective connectivity networks, we divided
the time series of 360s (360 volumes with 1 volume/s) in two blocks (0-180s,
181-360s), and ran DCM framework together with Bayesian Model Selection
separately for each half of the time series for all subjects. We then applied a
fully connected stochastic DCM [6; 8] to the resting state fMRI
data, and compared it against a partially connected model. Connection strengths
were identified by inspecting the parameter estimates and posterior
expectations as stored in DCM.Ep. ### Result

We estimated temporal
variations of DCMs by estimating effective connectivity at different time
points. Figure 2 shows the final models selected under normal procedure (using
full time series length) as well as during estimating the temporal evolution of
DCMs. The changes in the estimated model parameters reflect the change in
dynamic effective connectivity over time. The results show differences in
models because of changes in posterior expectations as calculated by DCM.Ep. In
this case, the DCMs estimated from time point 181-360 seconds had a lower excitatory
influence from cingulate cortex (Cg) to motor cortex (M) as it was calculated
through a two sample t-test with p < 0.05. The final connection strengths
were then put into a DCM model and Bayesian Model Selection (BMS) was used to
verify the finally selected model. Figure 3 shows the results from BMS.### Conclusion

Time varying
DCMs may allow us to capture the effect of transient network patterns under different
brain states. In comparison to dynamic functional connectivity analysis,
dynamic effective connectivity yields information on the directionality of connections
within the network. Dynamic DCMs, as shown in this work, may help identifying
group differences based on the estimated time-varying neuronal networks.### Acknowledgements

No acknowledgement found.### References

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