Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach

Abstract

Objective Combined source imaging techniques and directional connectivity analysis can provide useful information about the underlying brain networks in a non-invasive fashion. Source imaging techniques have been used successfully to either determine the source of activity or to extract source time-courses for Granger causality analysis, previously. In this work, we utilize source imaging algorithms to both find the network nodes (regions of interest) and then extract the activation time series for further Granger causality analysis. The aim of this work is to find network nodes objectively from noninvasive electromagnetic signals, extract activation time-courses and apply Granger analysis on the extracted series to study brain networks under realistic conditions. Methods Source imaging methods are used to identify network nodes and extract time-courses and then Granger causality analysis is applied to delineate the directional functional connectivity of underlying brain networks. Computer simulations studies where the underlying network (nodes and connectivity pattern) is known were performed; additionally, this approach has been evaluated in partial epilepsy patients to study epilepsy networks from inter-ictal and ictal signals recorded by EEG and/or MEG. Results Localization errors of network nodes are less than 5 mm and normalized connectivity errors of ~20% in estimating underlying brain networks in simulation studies. Additionally, two focal epilepsy patients were studied and the identified nodes driving the epileptic network were concordant with clinical findings from intracranial recordings or surgical resection. Conclusion Our study indicates that combined source imaging algorithms with Granger causality analysis can identify underlying networks precisely (both in terms of network nodes location and internodal connectivity). Significance The combined source imaging and Granger analysis technique is an effective tool for studying normal or pathological brain conditions.

A framework for seizure detection using effective connectivity, graph theory, and multi-level modular network

“…Most seizure activities are in the frequency range 0.5 Hz to 30 Hz [12]. In [61,26,33], connectivity is calculated at different frequencies, and eventually the connectivity are averaged over all frequencies and used as the final connectivity. In [19], the frequency that includes the highest power associated with seizure activity is selected using the Morlet wavelet transform.…”

“…In addition to the importance of frequency, there are several effective connectivity to investigate the relationship between different channels. In [26,33,61] DTF, in [27,33] PDC, and in [33] DC were used to seizure detection. In some studies, these effective connectivity were compared with each other, for example, in [61], the performance of the DTF and PDC is compared with each other, and it has been clearly shown that the PDC is more accurate than the DTF.…”

“…In [26,33,61] DTF, in [27,33] PDC, and in [33] DC were used to seizure detection. In some studies, these effective connectivity were compared with each other, for example, in [61], the performance of the DTF and PDC is compared with each other, and it has been clearly shown that the PDC is more accurate than the DTF. In view of the fact that our main aim is to provide a framework that is highly accurate for a large number of patients, the results of the classification of the three different effective connectivity are combined with the MENN method ( Fig.…”

Functional connectivity analysis in EEG source space: The choice of method

“…In particular, high-amplitude uncorrelated noise dominating the signal from synchronized sources would result in the underestimation of FC as discussed in [8]. Recently similar effects of noise on source localization and effective connectivity have been shown [41, 51]. …”

Measuring the Non-linear Directed Information Flow in Schizophrenia by Multivariate Transfer Entropy

“…The GCA is initially formulated for linear models and later extended to nonlinear systems by applying to local linear models. Despite its success in detecting the direction of interactions in the brain, it either makes assumptions about the structure of the interacting systems or the nature of their interactions and as such, it may suffer from the shortcomings of modeling systems/signals of unknown structure (Lainscsek et al, 2013;Sohrabpour et al, 2016;Bonmati, 2018). Even though much has been achieved with the GCA, a different data-driven approach which involves information theoretic measures like Transfer entropy (TE) may play a critical role in elucidating the effective connectivity of non-linear complex systems that the GCA may fail to unearth (Schreiber, 2006;Madulara et al, 2012;Dejman et al, 2017).…”