Brain Effective Connectivity Pattern Modulation by Repeating Blocks of an fMRI Task

Arash Zare Sadeghi, Amirhomayoun Jafari, Seyed AmirHosein Batouli, Mohammad Ali Oghabian


Effective connectivity is an active type of association between brain regions, and its resulting network is observed to change with time. The change of links’ strength in effective connectivity networks has been studied before using Granger Causality method but as far as we know, the change in the structure of the network has not yet been tested. We used a simulated time-variable data including three regions and one input to validate our method. In addition we used a real fMRI data in order to evaluate the time-variability of brain effective connectivity between four brain regions using Dynamic Causal Modeling. For this data the model space contained 38 models, all including the four regions of ventromedial prefrontal cortex, dorsolateral prefrontal cortex, amygdala, and ventral striatum. In both data a proper moving window algorithm was used to find the changes during time. The results of simulated data showed good compliance to the input pattern change during time. The results of real data initially showed time-dependent changes in the strength of some of the connections between brain regions. The most valid changes happened in the input and non-linear modulatory links. The input links’ strength increased and the nonlinear links’ strength decreased exponentially during time. These results show that the pattern of effective connectivity network changes during time and so reporting a single network for the whole data acquisition period is not meaningful. In this study, we have used a method to find the time-dependent pattern change during an fMRI task. We have shown the links’ strength change during time and accordingly the structure of the network changes.


Dynamic Causal Modeling, fMRI, Sliding Window, Time Variability