Statistical Learning for Epilepsy
Trial Summary
What is the purpose of this trial?
The overarching goal of this exploratory research is to understand the dynamic and flexible nature of speech processing in the human supratemporal plane. The temporal lobe has long been established as a region of interest in the speech perception and processing literature because it contains the auditory cortex. More recently, research has localized the supratemporal plane as an area that exhibits response specificity to acoustic properties of complex auditory signals like speech. The supratemporal plane, comprised of Heschl's gyrus, the planum polare, and the planum temporale, is capable of the rapid spectrotemporal analysis required to map acoustic information to linguistic representation. Neural activity in this area, however, is rarely studied directly because it is difficult to access with non-invasive measures like scalp electroencephalography (EEG). Capitalizing on the unique opportunity to access these areas via routine clinical stereoelectroencephalography (sEEG) in a patient population, this study seeks to understand how cortical responses reflect the diagnosticity of two acoustic-phonetic dimensions of interest and how responses rapidly and flexibly adapt to changes in listening demands. Examining how neural response to voice onset time (VOT) and fundamental frequency (F0) modulates as a function of perceptual weight carried in signaling phoneme categories, and identifying how changes in listening context shift perceptual weight, will provide invaluable data that indicates how speech processing flexibly adapts to short-term acoustic patterns.
Will I have to stop taking my current medications?
The trial information does not specify whether you need to stop taking your current medications.
What data supports the effectiveness of the treatment Dimension-Based Statistical Learning for epilepsy?
The research on machine learning models for predicting epilepsy drug treatment outcomes suggests that advanced data analysis techniques can help tailor treatments to individual patients, potentially improving outcomes. Additionally, the use of machine learning to identify unique patient profiles in epilepsy indicates that personalized approaches, like Dimension-Based Statistical Learning, could be effective in managing the condition.12345
How does the treatment in the 'Statistical Learning for Epilepsy' trial differ from other epilepsy treatments?
This treatment is unique because it uses advanced statistical learning techniques to analyze brain activity data, aiming to identify seizure onset zones and predict seizures more accurately than traditional methods. It leverages high-dimensional data analysis and machine learning algorithms to provide personalized insights into epilepsy management.56789
Research Team
Taylor J Abel, MD
Principal Investigator
University of Pittsburgh
Eligibility Criteria
This trial is for individuals aged 15-25 with epilepsy who are undergoing sEEG in the supratemporal plane and have normal hearing, vision, cognitive, and speech-language skills. They must be fluent English speakers without a history of autism or ADHD.Inclusion Criteria
Exclusion Criteria
Trial Timeline
Screening
Participants are screened for eligibility to participate in the trial
sEEG-EEG Recording
Neural activity is measured via simultaneous EEG-sEEG monitoring in the supratemporal plane and other cortical regions as participants listen to acoustic stimuli with manipulated acoustic dimensions and in different listening contexts.
Behavioral Response Collection
Behavioral responses are collected as participants provide category judgments based on perceived phonemes during acoustic stimuli presentation.
Follow-up
Participants are monitored for any adverse effects or changes in neural response post-recording sessions.
Treatment Details
Interventions
- Dimension-Based Statistical Learning (Behavioral Intervention)
Find a Clinic Near You
Who Is Running the Clinical Trial?
University of Pittsburgh
Lead Sponsor
National Institutes of Health (NIH)
Collaborator
Carnegie Mellon University
Collaborator
National Institute on Deafness and Other Communication Disorders (NIDCD)
Collaborator