Estimation of Time-Varying Connectivity Patterns Through the Use of an Adaptive Directed Transfer Function

Frequency-derived identification of the propagation of information between brain regions has quickly become a popular area in the neurosciences. Of the various techniques used to study the propagation of activation within the central nervous system, the directed transfer function (DTF) has been well used to explore the functional connectivity during a variety of brain states and pathological conditions. However, the DTF method assumes the stationarity of the neural electrical signals and the time invariance of the connectivity among different channels over the investigated time window. Such assumptions may not be valid in the abnormal brain signals such as seizures and interictal spikes in epilepsy patients. In the present study, we have developed an adaptive DTF (ADTF) method through the use of a multivariate adaptive autoregressive model to study the time-variant propagation of seizures and interictal spikes in simulated electrocorticogram (ECoG) networks. The time-variant connectivity reconstruction is achieved by the Kalman filter algorithm, which can incorporate time-varying state equations. We study the performance of the proposed method through simulations with various propagation models using either sample seizures or interictal spikes as the source waveform. The present results suggest that the new ADTF method correctly captures the temporal dynamics of the propagation models, while the DTF method cannot, and even returns erroneous results in some cases. The present ADTF method was tested in real epileptiform ECoG data from an epilepsy patient, and the ADTF results are consistent with the clinical assessments performed by neurologists.