  Measuring functional connectivity from fMRI is important in understanding
processing in cortical networks. However, because brain's connection pattern is
complex, currently used methods are prone to produce false connections. We
introduce here a new method that uses derivative for estimating functional
connectivity. Using simulations, we benchmarked our method with other commonly
used methods. Our method achieves better results in complex network
simulations. This new method provides an alternative way to estimate functional
connectivity.
