ORCID Identifier(s)

ORCID 0009-0001-4088-941X

Graduation Semester and Year

Fall 2025

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Biomedical Engineering

Department

Bioengineering

First Advisor

Dr. Christos Papadelis

Second Advisor

Dr. Georgios Alexandrakis

Third Advisor

Dr. M. Scott Perry

Fourth Advisor

Dr. Alexander Rotenberg

Sixth Advisor

Dr. Hanli Liu

Fifth Advisor

Dr. Eleonora Tamilia

Abstract

Pediatric epilepsies, particularly those that are drug-resistant or genetically driven, represent some of the most complex neurological disorders encountered in childhood. Central to their pathophysiology is a disruption in the delicate balance between cortical excitation and inhibition (E/I), often resulting from impaired GABAergic interneuron function. This imbalance manifests as aberrant network dynamics and altered neural oscillations, giving rise to seizures and long-term cognitive impairments. In this thesis, we developed a translational framework to identify electrophysiological biomarkers that (i) assess cortical E/I imbalance and (ii) map epileptogenic zones, with the aim of enhancing diagnosis, guiding surgical planning, and informing therapeutic monitoring in pediatric epilepsy.

Specifically, my dissertation included three aims. In the first aim, we utilized simultaneous recording of high-density electroencephalogram (HD-EEG) and magnetoencephalogram (MEG) during visual processing to identify cortical dysfunction in epilepsy patients. We compared visually evoked responses in children with epilepsy and neurotypical controls using these noninvasive modalities in a total of 97 participants (49 with epilepsy, 48 controls). Children with epilepsy displayed attenuated amplitudes and prolonged latencies in early visual components (P1, N2, M100, M150, M250), indicating disrupted sensory processing. Both evoked and induced beta and gamma oscillations originating from the visual cortex showed reduced power, lower peak frequencies, and delayed timing. These oscillatory changes reliably distinguished children with epilepsy from controls and showed promise as biomarkers of disease presence, severity, and treatment response, correlating with clinical measures such as age of onset, seizure duration, and use of antiseizure medications.

In the second aim, we assess the ability of functional connectivity (FC) measures to quantify the “epileptogenic status” of a brain area at a specific time point and predict the outcome in children with drug-resistant epilepsy (DRE) undergoing presurgical evaluation with intracranial EEG (iEEG). Specifically, we evaluated how FC vary across distinct temporal states (interictal without spikes, interictal with spikes, pre-ictal, ictal, and post-ictal) and different frequency bands. Nodal strength derived from these FC measures showed a state-dependent hierarchy: FC was comparatively low during interictal and pre-ictal phases but increased during ictal and post-ictal periods. Importantly, in patients with favorable surgical outcomes (Engel I), resected regions displayed significantly higher FC than non-resected tissue, a pattern that was not observed in poor-outcome cases (Engel II–IV). These results demonstrate that FC provides a dynamic, state-specific biomarker of epileptogenic networks, offering potential utility in guiding surgical planning and predicting postoperative seizure freedom.

In the third aim, we assessed whether gamma-band response after visual stimulation can serve as a functional biomarker of cortical network dysfunction in Dravet Syndrome (DS), a developmental epileptic encephalopathy caused by SCN1A+ mutations that impair Nav1.1 channels in GABAergic interneurons. Simultaneous HD-EEG and MEG recordings were obtained from neurotypical controls, children with non-DS epilepsy, and DS participants during visual stimulation. DS participants demonstrated attenuated and temporally disorganized gamma (30–100 Hz) oscillations, with reduced power, delayed timing, suggesting impaired cortical synchronization, whereas non-DS epilepsy participants showed intermediate deficits. Systemic neurotransmitter analysis revealed lower gamma-aminobutyric acid (GABA), glutamate, and serotonin, with elevated glycine in DS compared to controls. Additionally, in controls, systemic GABA strongly correlated with gamma power and amplitude, a relationship which was absent in DS, reflecting a loss of the normal relationship between systemic GABA and cortical gamma activity. These findings indicate that DS involves severe gamma-band disruption linked to GABAergic deficits and altered neurotransmitter profiles, positioning gamma oscillations as a promising noninvasive biomarker for monitoring disease progression and informing treatment strategies.

Collectively, this thesis demonstrates that electrophysiological biomarkers derived from both noninvasive (HD-EEG, MEG) and invasive recordings (iEEG) can capture cortical and brain network dysfunction in pediatric epilepsy. Distinct patterns of visual responses reliably differentiate children with epilepsy from neurotypical controls, while functional connectivity reveals state-dependent network signatures that identify epileptogenic regions and predict surgical outcomes in drug-resistant cases. In DS, disrupted gamma-band activity reflects impaired GABAergic interneuron function and abnormal systemic neurotransmitter profiles. Together, these findings establish a multidimensional framework for quantifying cortical E/I imbalance and defining clinically actionable biomarkers, with potential to improve diagnosis, guide surgical planning, and personalize treatment strategies across diverse pediatric epilepsy syndromes.

Keywords

antiseizure medications, broadband gamma, electroencephalography, epilepsy, excitatory–inhibitory balance, magnetoencephalography, narrowband gamma, support vector machine, functional connectivity, Intracranial Electroencephalogram, Dravet Syndrome, Pediatric Neuroimaging

Disciplines

Bioelectrical and Neuroengineering | Biological Engineering | Biomedical Engineering and Bioengineering | Biomedical Informatics | Signal Processing | Translational Medical Research

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