Brain Functional Connectivity Analysis of Electroencephalograms (EEG) in Epilepsy

Authors

Elaheh Zarafshan, Florida International UniversityFollow

Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Electrical and Computer Engineering

First Advisor's Name

Dr. Malek Adjouadi

First Advisor's Committee Title

Committee chair

Second Advisor's Name

Dr. Mercedes Cabrerizo

Second Advisor's Committee Title

committee member

Third Advisor's Name

Dr. Jean Andrian

Third Advisor's Committee Title

committee member

Fourth Advisor's Name

Dr. Naphtali Rishe

Fourth Advisor's Committee Title

committee member

Fifth Advisor's Name

Dr. Armando Barreto

Fifth Advisor's Committee Title

committee member

Keywords

Electroencephalography (EEG), epileptogenic zone, functional connectivity, interictal epileptiform discharges (IED), linear regression, cross-correlation coefficient, nonlinear regression, nonlinear correlation coefficient, information theory, mutual information

Date of Defense

3-31-2023

Abstract

Epilepsy is a common chronic disease that is affecting nearly 3.5 million people in the U.S. and nearly 50 million people worldwide. This dissertation explores brain functional connectivity networks and assesses how they are disrupted due to the location of the seizure focus where the seizure may potentially emanate from. In the first study, we investigated and compared functional connectivity networks of three types of epileptiform discharges (ED) in 4 different brain regions. Our results showed different connectivity patterns among three ED types within and between brain regions. Our data-driven linear method resulted in negatively skewed connection distribution for only two types of ED; complex and repetitive, reflecting a higher number of strong connections due to spike activity in the EEG segments for these two types of ED which characterize generalized epilepsy.

In the second study, we aimed to use a nonlinear driven method based on mutual information (MI) to characterize scalp EEG recordings of pediatric epilepsy patients (PE: n=7) compared to pediatric control subjects (PC: n=7) in a clinical environment. A time-varying approach was used to construct functional connectivity networks (FCNs) for each subject. Our findings show a statistically significant difference in the mean FCNs between the PC and PE groups. Performance results showed an accuracy of 92.5% with a sensitivity of 90% and a specificity of 95.3%.

In the final study, FC maps of EEG electrodes taken from 20 PLWE diagnosed with focal epilepsy, were compared by applying three statistical measures involving mutual information (MI), linear correlation (r²), and nonlinear correlation (h²) measures to assess heterogeneity and unexpected variation in EEG. Our findings suggest commonalities and differences using these three statistical measures. Strong connections were observed between the affected area and the frontal and temporal lobes of the opposite hemisphere, which were particularly pronounced when using r² and h² measures.

The fully automated approach that was applied in our studies is a promising methodology that can provide clinicians with a validation tool to improve early and on-time diagnosis of pediatric and adult epileptic patients and enhance the quality of life of these vulnerable populations.

Identifier

FIDC011019

ORCID

0009-0001-0587-6082

Previously Published In

Zarafshan E, Rajaei H, Forouzannezhad P, Morar U, Cabrerizo M, & Adjouadi M. (2020, December). “Characterizing Focal and Generalized Epileptic Networks Using Interictal Functional Connectivity”, the 2020 International Conference on Computational Science and Computational Intelligence (CSCI), Dec. 16-18, 2020, Las Vegas, USA, pp. 1535-1540, DOI 10.1109/CSCI51800.2020.00285.

Zarafshan E, Forouzannezhad P, Mayrand P, Cabrerizo M, Pinzon A, & Adjouadi M, Mutual Information to Develop Functional Analysis on Interictal Epileptiform Activities. American Epilepsy Society Annual Meeting 2022, Abstract No: 3.213, Submission ID: 2205135, www.aesnet.org, Nov. 2022.

Zarafshan E, Ebrahimi Kalan M, Forouzannezhad P, Morar U, Cabrerizo M & Adjouadi M, Functional Brain Connectivity from Interictal EEG in Epilepsy. American Epilepsy Society Annual Meeting 2020, Epilepsia, Abstract No: 203, Submission ID: 2422550, www.aesnet.org, Nov. 2020.

Morar U, Martin H, Izquierdo W, Forouzannezhad P, Zarafshan E, Curiel R E, ... & Adjouadi M. (2020, December). A Deep-Learning Approach for the Prediction of Mini-Mental State Examination Scores in a Multimodal Longitudinal Study”, the 2020 International Conference on Computational Science and Computational Intelligence (CSCI), Dec. 16-18, 2020, Las Vegas, USA, pp. 761-766, DOI 10.1109/CSCI51800.2020.00285

Morar U, Izquierdo W, Martin H, Forouzannezhad P, Zarafshan E, Unger E, ... & Adjouadi M (2022). “A study of the longitudinal changes in multiple cerebrospinal fluid and volumetric magnetic resonance imaging biomarkers on converter and nonconverter Alzheimer’s disease subjects with consideration for their Amyloid-β status”, Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 2022 Feb 23;14(1): e12258. doi: 10.1002/dad2.12258.PMID: 35229014.

Ebrahimi Kalan M, Jebai R, Zarafshan E & Bursac Z, Distinction Between Two Statistical Terms: Multivariable and Multivariate Logistic Regression, Nicotine Tob Res, ntaa055, https://doi.org/10.1093/ntr/ntaa055

Khalilnejad A, Malekpour M, Zarafshan E & Sarwat A, "Long term reliability analysis of components of photovoltaic system based on Markov process," SoutheastCon 2016, Norfolk, VA, 2016, pp. 1-5, https://ieeexplore.ieee.org/document/7506762

Recommended Citation

Zarafshan, Elaheh, "Brain Functional Connectivity Analysis of Electroencephalograms (EEG) in Epilepsy" (2023). FIU Electronic Theses and Dissertations. 5304.
https://digitalcommons.fiu.edu/etd/5304