Statistical Learning for Epilepsy

Overseen By Taylor J Abel, MD

Age: < 65

Sex: Any

Trial Phase: Academic

Sponsor: University of Pittsburgh

Disqualifiers: Intellectual disabilities, Abnormal epileptiform activity, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 2

25 Participants Needed

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. ^{1 2 3 4 5}

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. ^{5 6 7 8 9}

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

You have normal or corrected-to-normal vision.

My hearing is normal in both ears.

I am between 15 and 25 years old.

Exclusion Criteria

You do not speak or understand English well.

You have serious problems with understanding language or hearing.

You have been diagnosed with autism or ADHD in the past.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

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.

Up to 3 hours per session

1 session

Behavioral Response Collection

Behavioral responses are collected as participants provide category judgments based on perceived phonemes during acoustic stimuli presentation.

Up to 3 hours per session

1 session

Follow-up

Participants are monitored for any adverse effects or changes in neural response post-recording sessions.

4 weeks

Treatment Details

Interventions

• Dimension-Based Statistical Learning

Trial Overview The study explores how the brain processes speech by examining responses to voice onset time (VOT) and fundamental frequency (F0) during routine clinical sEEG monitoring in patients with epilepsy.

Participant Groups

Group I: Patient Participants Experimental Treatment 1 Intervention

This single-group study will recruit patients through the PI's clinical practice who are undergoing invasive neurophysiological monitoring (sEEG) with clinically necessary placement of electrodes in the supratemporal plane. All participants will complete the same behavioral response paradigms.

Who Is Running the Clinical Trial?

University of Pittsburgh

Lead Sponsor

Trials

1,820

Recruited

16,360,000+

National Institutes of Health (NIH)

Collaborator

Trials

2,896

Recruited

8,053,000+

Carnegie Mellon University

Collaborator

Trials

80

Recruited

540,000+

National Institute on Deafness and Other Communication Disorders (NIDCD)

Collaborator

Trials

377

Recruited

190,000+

Findings from Research

A study of 462 epilepsy patients identified three distinct clusters based on psychosocial health, revealing that those with poor psychosocial health had significant predictors such as failure to achieve seizure freedom and social determinants like income support needs.

The findings suggest that targeted interventions focusing on social support and medication management could improve health outcomes for patients in the 'poor psychosocial health' cluster, highlighting the importance of addressing both medical and social factors in epilepsy care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Psychosocial profiles and their predictors in epilepsy using patient-reported outcomes and machine learning. Josephson, CB., Engbers, JDT., Wang, M., et al.[2021]

A machine learning model using multi-modality features from 103 children with tuberous sclerosis complex (TSC) achieved a strong predictive accuracy (AUC of 0.812) for epilepsy drug treatment outcomes, highlighting the potential of advanced analytics in clinical settings.

Key factors influencing treatment outcomes included specific EEG characteristics, age of onset, and MRI lesion types, indicating that detailed clinical and imaging data can significantly inform treatment strategies for epilepsy in TSC patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Machine learning and statistic analysis to predict drug treatment outcome in pediatric epilepsy patients with tuberous sclerosis complex. Zhao, X., Jiang, D., Hu, Z., et al.[2023]

The EpiBioS4Rx project aims to identify biomarkers for epileptogenesis after traumatic brain injury (TBI) by analyzing extensive data from both human subjects and animal models, which is crucial for developing antiepileptogenic therapies.

Using advanced data analysis techniques, the project seeks to standardize and interpret complex data from multiple sources, enabling the design of effective clinical trials to prevent or cure epilepsy.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Analytic Tools for Post-traumatic Epileptogenesis Biomarker Search in Multimodal Dataset of an Animal Model and Human Patients. Duncan, D., Barisano, G., Cabeen, R., et al.[2020]

A predictive model developed from MRI scans of 53 subjects (12 left TLE, 21 right TLE, and 20 healthy controls) identified that longer durations of temporal lobe epilepsy (TLE) are linked to reduced functional connectivity and structural atrophy in the ipsilateral temporal lobe, particularly the hippocampus and pallidum.

The study found that significant changes in brain structure and function occur over 10 to 35 years of TLE, indicating a gradual functional isolation of the affected temporal lobe and some impact on the contralateral hemisphere.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Increasing structural atrophy and functional isolation of the temporal lobe with duration of disease in temporal lobe epilepsy. Morgan, VL., Conrad, BN., Abou-Khalil, B., et al.[2022]

In a study of 91 patients undergoing epilepsy monitoring, 61.5% experienced seizure clustering, which is defined as three or more seizures within 24 hours, with significant risk factors including a history of clustering at home and the presence of mesial temporal sclerosis (MTS) on MRI.

Seizure clustering did not affect the localization of the epileptogenic zone, as the mean interseizure intervals were similar for both clustered and non-clustered seizures, indicating that both types of seizures are equally important for determining the source of epilepsy.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Seizure clustering during epilepsy monitoring. Haut, SR., Swick, C., Freeman, K., et al.[2023]

The developed algorithm effectively detects seizure onset and identifies seizure-generating brain regions using only one or two seizure examples from intracranial electroencephalography (iEEG) recordings of 16 drug-resistant epilepsy patients, demonstrating a high generalization rate of 100% for most patients.

This algorithm outperforms existing methods, including deep learning approaches, by achieving higher specificity (94.84%) and accuracy (95.42%) while maintaining a significantly lower memory footprint, making it a promising tool for clinical decision-making in epilepsy management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Hyperdimensional Computing With Local Binary Patterns: One-Shot Learning of Seizure Onset and Identification of Ictogenic Brain Regions Using Short-Time iEEG Recordings. Burrello, A., Schindler, K., Benini, L., et al.[2020]

The patient-specific algorithm for predicting epileptic seizures, tested on 216 patients with a total of 16,729.80 hours of EEG data, achieved a sensitivity of 38.47% and a low false positive rate of 0.20 per hour.

The method showed statistically significant performance in only 11% of the patients, highlighting the need for further refinement in feature selection to improve the distinction between pre-ictal and nonpre-ictal states.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Realistic Seizure Prediction Study Based on Multiclass SVM. Direito, B., Teixeira, CA., Sales, F., et al.[2019]

The study introduces new methods to reduce high-dimensional recordings of epileptic activity into low-dimensional (2D or 3D) representations, making it easier to visualize and analyze seizure prediction data.

Clustering algorithms were evaluated to automatically identify patterns within these low-dimensional descriptors, and the methods were adapted to effectively handle long-term datasets that can span several days or weeks.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Uncovering low-dimensional structure in high-dimensional representations of long-term recordings in people with epilepsy. Rapela, J., Proix, T., Todorov, D., et al.[2021]

The improved manifold adaptive Farahmand-Szepesvári-Audibert (FSA) dimension estimator provides a more accurate measure of intrinsic dimensionality in data analysis, outperforming previous methods like the mode and mean.

Using the corrected median-FSA estimator, researchers were able to identify brain areas with lower-dimensional dynamics that may be causal sources for epileptic seizures, highlighting its potential application in understanding neural dynamics during seizures.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Manifold-adaptive dimension estimation revisited. Benkő, Z., Stippinger, M., Rehus, R., et al.[2022]

References

1. United States pubmed.ncbi.nlm.nih.gov

Psychosocial profiles and their predictors in epilepsy using patient-reported outcomes and machine learning. [2021]

2. Netherlands pubmed.ncbi.nlm.nih.gov

Machine learning and statistic analysis to predict drug treatment outcome in pediatric epilepsy patients with tuberous sclerosis complex. [2023]

3. Switzerland pubmed.ncbi.nlm.nih.gov

Analytic Tools for Post-traumatic Epileptogenesis Biomarker Search in Multimodal Dataset of an Animal Model and Human Patients. [2020]

4. Netherlands pubmed.ncbi.nlm.nih.gov

Increasing structural atrophy and functional isolation of the temporal lobe with duration of disease in temporal lobe epilepsy. [2022]

5. United States pubmed.ncbi.nlm.nih.gov

Seizure clustering during epilepsy monitoring. [2023]

6. United States pubmed.ncbi.nlm.nih.gov

Hyperdimensional Computing With Local Binary Patterns: One-Shot Learning of Seizure Onset and Identification of Ictogenic Brain Regions Using Short-Time iEEG Recordings. [2020]

7. Singapore pubmed.ncbi.nlm.nih.gov

A Realistic Seizure Prediction Study Based on Multiclass SVM. [2019]

8. United States pubmed.ncbi.nlm.nih.gov

Uncovering low-dimensional structure in high-dimensional representations of long-term recordings in people with epilepsy. [2021]

9. United States pubmed.ncbi.nlm.nih.gov

Manifold-adaptive dimension estimation revisited. [2022]