A Machine Learning Model to Predict Seizure Susceptibility from Resting-State fMRI Connectivity
Open accessAbstract
Traumatic brain injury (TBI) is a leading cause of disability globally. Many patients develop post-traumatic epilepsy, or recurrent seizures following TBI. In recent years, significant efforts have been made to identify biomarkers of epileptogenesis that may assist in preventing seizure occurrence by identifying high-risk patients. We present a novel method of assessing seizure susceptibility using data from 49 patients enrolled in the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx). We employ a machine learning paradigm that utilizes a Random Forest classifier trained with resting-state functional magnetic resonance imaging (fMRI) data to predict seizure outcomes. Following 100 rounds of stratified cross-validation with 70% of resting state fMRI scans as the training set and 30% as the testing set, our model was found to assess seizure outcome in the testing set with 69% accuracy. To validate the method, we compared our results with classification by Support Vector Machines and Neural Network classifiers.
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Garner et al. [162] achieved 69% accuracy in predicting epilepsy outcomes.
“The model achieved a 69% accuracy rate in predicting epilepsy outcomes on the test set after 100 stratified cross-validation rounds, using 70% of resting-state fMRI scans for training and 30% for testing.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [162]
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A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
Xu, X., Li, J., Zhu, Z., Zhao, L., Wang, H., Song, C., Chen, Y., Zhao, Q., Yang, J., & Pei, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11030219 · Cited by 242 · see citing works
- Authors
- Garner, R., Rocca, M. L., Barisano, G., Toga, A. W., Duncan, D., & Vespa, P.
- Affiliations
- University of Southern California (US)
- University of California, Los Angeles (US)
- Venue
- 2019 Spring Simulation Conference (SpringSim)
- Publisher
- IEEE
- Published
- 2019-04
- Volume
- 2019
- Pages
- 1-11
- PMC
- PMC9760283
- OpenAlex
- W2952091391
- Cited by
- 22
- References
- 32
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 3 · 2023: 1 · 2022: 1 · 2021: 4 · 2020: 7 · 2019: 1
- FWCI
- 5.61
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Spring Simulation Multiconference (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) (submittedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (U54NS100064)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Epidemiology › Traumatic Brain Injury Research
- Health Sciences › Medicine › Psychiatry and Mental health › Epilepsy research and treatment
- Keywords
- seizure susceptibility, post-traumatic epilepsy, traumatic brain injury, epileptogenesis, resting-state functional connectivity, antiepileptogenic therapy, biomarkers, seizure outcome prediction, random forest
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