The brain is the most complex organ in the human body, which, despite extensive research, is still relatively poorly understood. Brain function is usually investigated by analysing various signals generated by the brain. One of the most common ways to measure these signals is with functional magnetic resonance imaging. By analyzing functional connectivity, we want to find out which brain regions are mutually dependent when firing the neurons and, as a result, find out how they are functionally connected. The existing literature provides us with many metrics for calculating functional connectivity, but their use is inconsistent. As part this work, we implemented, tested and compared widely used and established functional connectivity metrics. The metrics were compared by execution time, noise resistance, lag resistance and correctness of detected causality. Our results confirm the fact that different metrics are suitable for different problems, however, we found that the combination of Pearson's coefficient, inverse covariance and cross-correlation achieved the best results.
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