Beschreibung:
Abstract: The brain is a huge network with outstanding computational capabilities. Understanding the brain network and its principle of operation is of high interest. Therefore, brain activity can be measured to retrieve information about the brains functioning using functional magnetic resonance imaging, which is a great non-invasive tool to measure the neuronal activity. However, with this method the neuronal activity is measured only indirectly as the desired information is distorted by a specific low-pass filter, which differs in its form in different brain regions. <br>In this thesis a methodology is presented to estimate this low pass filter for every brain region. Thereby, a high variability of this filter function throughout the brain is found. With the knowledge about the filter the data can be corrected for relative timing offsets between brain regions to yield a good estimate of the underlying neuronal activity. Using the corrected data, further a novel methodology is presented to estimate the sparse directed brain connectivity, which is applied to resting-state fMRI data of healthy subjects, where it proves superior to conventional undirected connectivity analysis