Visualizing Brain Activity from fMRI Data

Classically, visualization the time-varying data acquired during fMRI experiments is performed using static activation maps obtained by testing voxels for the presence of significant activity, using parametric or non-parametric methods. The models used in these analysis methods have a number of tunable settings, which profoundly impact the detection of active brain areas. Also, studying the temporal dependencies and cascading effects of brain activation from these static maps is challenging. In this paper, we propose a methodology to visualize brain function in a time-varying fashion. This will allow neurologists to not only validate the static activation maps, but also to develop a better understanding of temporal characteristics of the activation patterns. The method is based on extracting the underlying BOLD signal from the data, de-noising and de-trending using a wavelet representation, timewindowed hierarchical clustering of voxels in the wavelet domain, followed by a spatio-temporal visualization of brain activity. We also demonstrate the utility of the method by presenting two use-cases on a real data-set.