Graduation Semester and Year
Spring 2025
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Biomedical Engineering
Department
Bioengineering
First Advisor
Dr. Christos Papadelis
Abstract
Epilepsy surgery stands out as the most effective treatment for patients dealing with focal drug-resistant epilepsy (DRE). Its effectiveness depends on successfully removing or disconnecting the epileptogenic zone (EZ), which is the brain area crucial for seizure generation. The seizure onset zone (SOZ) serves as the best approximation of the EZ; this is the region where most seizures begin, identified through invasive electroencephalographic (iEEG) recordings. However, the unpredictable nature of seizures means they can take hours or even days to occur, consuming valuable human and financial resources. As a result, there is a pressing need for an interictal biomarker that can help identify the EZ from iEEG data without waiting for a seizure. Spikes are the most recognized interictal biomarkers of epilepsy, but they lack specificity, as they can appear in both the EZ and non-epileptogenic regions that should not be removed. High frequency oscillations (HFOs) are viewed as more specific indicators of the EZ compared to spikes. HFOs are categorized into ripples (80-250 Hz) and fast ripples (250-500 Hz). While fast ripples tend to correlate with the EZ, they are often less common in patients with DRE, and their lower sensitivity might complicate EZ localization. On the other hand, ripples are more prevalent in most DRE patients and occur more frequently than fast ripples. However, not all ripples are indicative of pathology, as they can also be found in normal brain areas, including the hippocampus, occipital cortex, and paracentral regions. Distinguishing between physiological and pathological ripples is crucial for their application as presurgical interictal biomarkers in epilepsy. Over the years, several investigations have attempted to achieve this goal; however, there is still no definitive method to clearly differentiate these two types. The focus on distinguishing physiological from pathological HFOs has largely relied on iEEG data from patients with DRE. This approach faces two significant challenges: first, the inability to gather control data from healthy individuals (those without epilepsy or any other neurological conditions) to effectively characterize physiological HFOs; and second, the limited brain coverage afforded by iEEG electrodes, which are primarily placed in regions considered potentially epileptogenic, therefore rarely capturing data from genuinely healthy areas of the brain. In conclusion, current evidence indicates that there is no singular interictal biomarker capable of definitively identifying the EZ.
Here, I present three research projects of my dissertation having the following aims: (i) to examine the temporal relationship between spikes, ripples, and fast ripples, and assess the ability of these biomarkers (and their combinations) to delineate the area to resect and predict surgical outcome in children with DRE; (ii) to differentiate physiological from pathological HFOs noninvasively by comparing data from children with DRE with typically developing children (TDC); and (iii) to study propagation features of HFOs in the brain of children with DRE as well as in the brain of TDC and assess their effective connectivity profiles.
Results from the first project showed that fast ripples are the best biomarker, but they can be seen in only half of patients with drug-resistant epilepsy. Spikes on ripples are a good alternative with more universal applicability since they can be seen in all patients while their resection predicts good outcome; their performance is improved in patients with frequent spikes. The second project showed that physiological high frequency oscillations, recorded from the healthy brain, have distinct temporal, morphological, spectral, and spatial features compared to those generated by the epileptic brain. Finally, the third project showed that ripple propagation occurs in both healthy controls and children with drug-resistant epilepsy, likely playing a key role in brain information transfer and that effective connectivity measures not only differs between children with DRE and TDC, but also within more and less epileptogenic areas of the DRE brain.
Overall, the dissertation proposes: (i) the temporal overlap of spikes and ripples as a more specific biomarker of the epileptogenic zone compared to each biomarker alone; (ii) a comparison of HFO features extracted from noninvasive data between patients with DRE and a cohort of healthy controls to refine the differentiation between physiological and pathological HFOs; and (iii) the combination of propagation and effective connectivity features to characterize brain networks in children with DRE and TDC.
Disciplines
Biomedical Engineering and Bioengineering
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Fabbri, Lorenzo, "High Frequency Oscillations as Biomarkers of the Epileptogenic Zone in Children with Drug Resistant Epilepsy" (2025). Bioengineering Dissertations. 200.
https://mavmatrix.uta.edu/bioengineering_dissertations/200