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

• Main Authors: Arash Zare Sadeghi, Amirhomayoun Jafari, Seyed AmirHosein Batouli, Mohammad Ali Oghabian
• Format: Article
• Language: English
• Published: Tehran University of Medical Sciences 2016-12-01
• Series: Frontiers in Biomedical Technologies
• Subjects: Dynamic Causal Modeling; fMRI; Sliding Window; Time Variability
• Online Access: https://fbt.tums.ac.ir/index.php/fbt/article/view/81

Purpose: Effective connectivity is an active time-variable type of association between brain regions. The change of links’ strength in effective connectivity networks has been studied before but as far as we know, the change in the structure of the network has not yet been tested. Procedures: We simulated a 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. The model space contained 38 models, all including the four regions of ventromedial prefrontal cortex, dor-solateral prefrontal cortex, amygdala, and ventral striatum. In both data, a proper moving window algorithm was used to find the changes over time. Results: The results of simulated data matched the simulated pattern change over 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. These results show that the pattern of effective connectivity network changes and so reporting a single network for the whole data acquisition period is not meaningful. Conclusion: In this study, we have used a method to find the time-dependent pattern changes during an fMRI task. We have shown the links’ strength change over time and accordingly the structure of the network changes.