Parcel-Based Connectivity Analysis of fMRI Data for the Study of Epileptic Seizure Propagation

Abstract

The aim of this work is to improve fMRI Granger Causality Analysis (GCA) by proposing and comparing two strategies for defining the topology of the networks among which cerebral connectivity is measured and to apply fMRI GCA for studying epileptic seizure propagation. The first proposed method is based on information derived from anatomical atlas only; the other one is based on functional information and employs an algorithm of hierarchical clustering applied to fMRI data directly. Both methods were applied to signals recorded during seizures on a group of epileptic subjects and two connectivity matrices were obtained for each patient. The performances of the different parcellation strategies were evaluated in terms of their capability to recover information about the source and the sink of the network (i.e., the starting and the ending point of the seizure propagation). The first method allows to clearly identify the seizure onset in all patients, whereas the network sources are not so immediately recognizable when the second method was used. Nevertheless, results obtained using functional clustering do not contradict those obtained with the anatomical atlas and are able to individuate the main pattern of propagation. In conclusion, the way nodes are defined can influence the easiness of identification of the epileptogenic focus but does not produce contradictory results showing the effectiveness of proposed approach to formulate hypothesis about seizure propagation at least in the early phase of investigation.