Brain Connectivity Networks for the Study of Nonlinear Dynamics and Phase Synchrony in Epilepsy

Authors

Hoda Rajaei, Florida International UniversityFollow

Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Electrical Engineering

First Advisor's Name

Mercedes Cabrerizo

First Advisor's Committee Title

Committee chair

Second Advisor's Name

Malek Adjouadi

Second Advisor's Committee Title

Committee member

Third Advisor's Name

Armando Barreto

Third Advisor's Committee Title

Committee member

Fourth Advisor's Name

Jean Andrian

Fourth Advisor's Committee Title

Committee member

Fifth Advisor's Name

Naphtali Rishe

Fifth Advisor's Committee Title

Committee member

Keywords

Electroencephalography, Epilepsy, Functional Connectivity, Interictal Spike, Temporal Lobe Epilepsy, Classification, Graph theory, Nonlinear Recurrence-based method, Epileptogenic zone, epileptic focus

Date of Defense

10-9-2018

Abstract

Assessing complex brain activity as a function of the type of epilepsy and in the context of the 3D source of seizure onset remains a critical and challenging endeavor. In this dissertation, we tried to extract the attributes of the epileptic brain by looking at the modular interactions from scalp electroencephalography (EEG). A classification algorithm is proposed for the connectivity-based separation of interictal epileptic EEG from normal. Connectivity patterns of interictal epileptic discharges were investigated in different types of epilepsy, and the relation between patterns and the epileptogenic zone are also explored in focal epilepsy.

A nonlinear recurrence-based method is applied to scalp EEG recordings to obtain connectivity maps using phase synchronization attributes. The pairwise connectivity measure is obtained from time domain data without any conversion to the frequency domain. The phase coupling value, which indicates the broadband interdependence of input data, is utilized for the graph theory interpretation of local and global assessment of connectivity activities.

The method is applied to the population of pediatric individuals to delineate the epileptic cases from normal controls. A probabilistic approach proved a significant difference between the two groups by successfully separating the individuals with an accuracy of 92.8%. The investigation of connectivity patterns of the interictal epileptic discharges (IED), which were originated from focal and generalized seizures, was resulted in a significant difference ( ) in connectivity matrices. It was observed that the functional connectivity maps of focal IED showed local activities while generalized cases showed global activated areas. The investigation of connectivity maps that resulted from temporal lobe epilepsy individuals has shown the temporal and frontal areas as the most affected regions.

In general, functional connectivity measures are considered higher order attributes that helped the delineation of epileptic individuals in the classification process. The functional connectivity patterns of interictal activities can hence serve as indicators of the seizure type and also specify the irritated regions in focal epilepsy. These findings can indeed enhance the diagnosis process in context to the type of epilepsy and effects of relative location of the 3D source of seizure onset on other brain areas.

Identifier

FIDC007025

ORCID

https://orcid.org/0000-0002-0754-5586

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 License

Recommended Citation

Rajaei, Hoda, "Brain Connectivity Networks for the Study of Nonlinear Dynamics and Phase Synchrony in Epilepsy" (2018). FIU Electronic Theses and Dissertations. 3882.
https://digitalcommons.fiu.edu/etd/3882