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Quantitative methods and neurostimulation mapping to guide precision epilepsy therapies William Karl Selboe Ojemann
- Format:
- Book
- Thesis/Dissertation
- Author/Creator:
- Ojemann, William Karl Selboe, author.
- Language:
- English
- Subjects (All):
- Bioengineering.
- Biomedical engineering.
- Neurosciences.
- Therapy.
- 0202.
- 0541.
- 0317.
- 0212.
- Local Subjects:
- Bioengineering.
- Biomedical engineering.
- Neurosciences.
- Therapy.
- 0202.
- 0541.
- 0317.
- 0212.
- Genre:
- Academic theses
- Physical Description:
- 1 online resource (204 pages)
- Contained In:
- Dissertations Abstracts International 87-12A
- Place of Publication:
- Ann Arbor : ProQuest Dissertations and Theses, 2026
- Language Note:
- English
- Summary:
- Epilepsy-defined by recurring seizures driven by hypersynchronous neuronal activity-is among the most prevalent neurological disorders worldwide, and imposes serious health, social, economic, and psychological burdens. Despite advances in epilepsy care, roughly one in three patients continue to have seizures after failing medications, and nearly half experience seizure recurrence after surgical intervention. For these patients, accurately mapping the epileptogenic zone from intracranial EEG (iEEG) is the cornerstone of surgical planning, yet this process relies on expert visual review that is time-consuming, poorly scalable, and yields only moderate inter-clinician agreement. Further, the location and dynamics of seizure-generating tissue fluctuate on timescales of days to months in ways that current approaches largely fail to capture. Machine learning and quantitative methods offer the potential to automate and improve the interpretation of iEEG for localizing epileptic foci and guiding therapy. In this dissertation, I collate and facilitate expert review of data across multi-center cohorts to address three specific aims. First, I quantify the variability of expert seizure annotations and develop an unsupervised algorithm that detects seizure onset and spread by measuring deviation from patient-specific baseline neural dynamics. Second, I show that stimulation-induced seizures can rapidly map seizure-generating tissue, including secondary epileptogenic regions missed by spontaneous seizure analysis alone. Third, I demonstrate that multi-day fluctuations in seizure onset zone network dynamics associate with seizure risk and may be modulated by neurostimulation therapy. Overall, this dissertation advances quantitative iEEG analysis for mapping the spatio-temporal dynamics of epileptic networks, contributing open datasets, validated tools, and scientific insights toward precision surgical and neuromodulation therapies for drug-resistant epilepsy
- Notes:
- Source: Dissertations Abstracts International, Volume: 87-12, Section: A.
- Advisors: Litt, Brian Committee members: Beauchamp, Michael; Shinohara, Russell T.; Conrad, Erin C.
- Ph.D. University of Pennsylvania 2026
- Vendor supplied data
- Local Notes:
- School code: 0175
- ISBN:
- 9798247982180
- Access Restriction:
- Restricted for use by site license
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