Abstract
A key area of research in epilepsy neurological disorder is the characterization of epileptic networks as they form and evolve during seizure events. In this paper, we describe the development and application of an integrative workflow to analyze functional and structural connectivity measures during seizure events using stereotactic electroencephalogram (SEEG) and diffusion weighted imaging data (DWI). We computed structural connectivity measures using electrode locations involved in recording SEEG signal data as reference points to filter fiber tracts. We used a new workflow-based tool to compute functional connectivity measures based on non-linear correlation coefficient, which allows the derivation of directed graph structures to represent coupling between signal data. We applied a hierarchical clustering based network analysis method over the functional connectivity data to characterize the organization of brain network into modules using data from 27 events across 8 seizures in a patient with refractory left insula epilepsy. The visualization of hierarchical clustering values as dendrograms shows the formation of connected clusters first within each insulae followed by merging of clusters across the two insula; however, there are clear differences between the network structures and clusters formed across the 8 seizures of the patient. The analysis of structural connectivity measures showed strong connections between contacts of certain electrodes within the same brain hemisphere with higher prevalence in the perisylvian/opercular areas. The combination of imaging and signal modalities for connectivity analysis provides information about a patient-specific dynamical functional network and examines the underlying structural connections that potentially influences the properties of the epileptic network. We also performed statistical analysis of the absolute changes in correlation values across all 8 seizures during a baseline normative time period and different seizure events, which showed decreased correlation values during seizure onset; however, the changes during ictal phases were varied.
Highlights
Brain connectivity measures are widely used to study and characterize both normal brain functions and changes that occur in serious neurological disorders such as Alzheimer’s disease and epilepsy (Bettus et al, 2010; van Diessen et al, 2012; Burggren and Brown, 2014; Bartolomei et al, 2017)
We describe the development of an integrative approach to characterize the dynamics of network motifs formed during epileptic seizures using functional connectivity and correlate the results with structural connectivity measures derived from pre-surgical diffusion weighted imaging (DWI)
We described the development and application of an integrative analysis technique to study epileptic seizure networks using functional as well as structural connectivity measures
Summary
Brain connectivity measures are widely used to study and characterize both normal brain functions and changes that occur in serious neurological disorders such as Alzheimer’s disease and epilepsy (Bettus et al, 2010; van Diessen et al, 2012; Burggren and Brown, 2014; Bartolomei et al, 2017). Structural connectivity measures derived from diffusion weighted imaging (DWI) data represent stable white matter tracts. Functional connectivity measures derived from functional imaging data or electrophysiological signal data represent coupling between brain activity recorded from different regions (Friston, 2011). Structural and functional networks represent complementary views of brain connectivity, accurate characterization of the interactions between these two types of networks is difficult. Certain brain regions are highly connected by white matter tracts and have stable long-term functional connections between them. A better understanding of the interaction between structural and functional networks, especially in severe neurological disorders, can provide important insights into the progression and evolution of these diseases
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